(Ebook pdf) olap

Contents
Contents and services
OLAP fundamentals
Using OLAP to make better decisions
How to choose the right OLAP tool/s
The anatomy of an OLAP tool
Guide to the evaluations
Briefing papers
Growth, transition and change: trends in business intelligence and
implications for the OLAP market
Summary of the evaluations
Summary of the evaluations 1999
Evaluations
Applix TM1
Brio Technology Brio Enterprise
Business Objects BusinessObjects
Cognos PowerPlay
Gentia Software Gentia Millenium Applications Platform
Hummingbird BI/Suite
Hyperion Solutions Hyperion Essbase
Information Advantage DecisionSuite
Microsoft SQL Server 7.0 OLAP Services
Microstrategy DSS Product Suite
Oracle Oracle Express Development Suite
Pilot Software Pilot Decision Support Suite
SAP AG SAP Business Information Warehouse
Seagate Seagate Holos
Sterling Software Eureka:Suite
WhiteLight Systems Whitelight Analytic Application Server

Using OLAP to
make better decisions
Overview ............................................................................................................................. 2
What does an OLAP tool do? ............................................................................................. 3
Ovum definition of OLAP .................................................................................................... 5
The uses of OLAP............................................................................................................... 6

Overview
Decision making is at the heart of running a business. Whatever the depart-
mental function or level of management, there are decisions to be made.
Decisions range from operational issues requiring immediate resolution to
longer term strategic issues. At the heart of decision making is access to
quality information, meaning that it is correct, complete, timely and consist-
ent. It is generally implied (rather than explicitly stated) that this informa-
tion must also be in an accessible form.
Online application processing (OLAP) is an important technology for organi-
sations looking for better ways to access and analyse information. OLAP can
enable organisations to improve their analysis of performance indicators,
manage their customer relationships more efficiently and support critical
parts of the manufacturing process.
Given the current corporate climate, all decision makers need to know about,
and exploit, this technology. Improving decision making with tool support is
not an option, it is an imperative.

What does an OLAP tool do?
Online application processing (OLAP) is the interactive analysis of business
information. End users can explore important business measures (such as
profits, sales and costs) along many different ‘dimensions’. With an OLAP
tool, the user moves seamlessly from one perspective on the business (‘an-
nual sales for all stores’) to another (‘the most profitable stores over the last
three months’) and drills between different levels of detail (sales by day,
week or quarter). This interactive exploration of information is commonly
referred to as multidimensional analysis. The common factor defining all
OLAP tools – and there are many different implementations of the core
functionality – is an analytical engine that turns corporate data into multidi-
mensional information for online analysis.
Complex decision support and tailored, easy-to-use applications with limited
functionality can be built with OLAP tools. However, OLAP tools also sup-
port applications that match the needs of a much wider range of users.
These applications are characterised by the flexibility offered to the user not
merely in terms of navigation through a multidimensional model, but also in
terms of the definition of reports and applications.
OLAP applications are characterised by a lack of fixed structure. An OLAP
tool provides an analytical environment for the power user or specialist
knowledge worker, which enables them to use a range of functions to explore
the information available. As well as core multidimensional operations such
as drilling and rotation, users can quickly define new reports and may even
have access to advanced features such as forecasting algorithms, data
mining tools or software agents.
The overlapping relationship between reporting, OLAP and data mining
tools is shown in Figure 1.
Reporting tools are aimed at a general audience, and the results are dissemi-
nated throughout the organisation. OLAP tools are specialised for the
interactive exploration of multidimensional information and are used at all
levels of the organisation. The division between the two types of tool is not
totally clear-cut however, because some reporting tools have limited facilities
to allow users to explore data, and OLAP tools benefit from some of the
features of reporting tools.
Figure 1 Tool relationships
Increasingly specialised
Reporting tools Data mining toolsOLAP tools

At the other end of the spectrum, data mining tools allow users to find
patterns and explore data using less structured hypotheses. Some OLAP
tools offer limited data mining features, although the current trend is more
towards integration with data mining tools than an expansion of the func-
tionality of the OLAP tool.

Ovum definition of OLAP
The Ovum definition
Ovum defines online analytical processing as:
the interactive, multidimensional analysis of business information on an
enterprise scale.
Interactive, multidimensional analysis
Multidimensionality frees the users to examine their key business measures
(sales revenues, costs, profits and so on) from different perspectives (for
example, by time, by products or by region). It is the interactive exploration
of information along these different ‘dimensions’ that distinguishes OLAP
from simple query and reporting tools.
Business information
OLAP allows users to focus on business concepts (such as sales, profits,
customers and products). The users work intuitively with the data they wish
to analyse, framing questions that answer their business needs. The user is
unaware and unconcerned about technical issues such as the physical data
formats and client-server architectures. OLAP does not force users to con-
form to the requirements of the technology.
Enterprise scale
Users are able to work with corporate data sources and share information
across the enterprise. OLAP is a strategic technology: it must be robust and
scalable enough to meet the information analysis needs of an organisation as
a whole.

The uses of OLAP
Information is a resource
Thirty years of automating manual and administrative processes has gener-
ated unimaginable amounts of data. The amount of data collected has
further escalated with the widespread use of bar codes and EPOS systems
and dramatic reduction in the price/performance ratio for collecting, storing
and analysing that data. Now more than ever, exploitation of this resource is
seen as a crucial element in the armoury of any competitive strategy.
There are three major driving forces behind the desire to make better use of
information within all organisations.
The complexity of the market
The increasing volume of data is only one aspect of an increasingly complex
commercial environment. Deregulation of markets, new competitors, new
forms of relationship (with both customers and suppliers), and external
technological, social and economic changes all help to further complicate the
forecasting and planning process.
Customer focus
Competitive advantage is no longer seen in terms of price and quality alone.
Companies must be able to innovate to survive. They must understand their
customers needs and be able to meet them in an increasingly personalised
fashion. The move from mass marketing to individual marketing requires
great resources to be devoted to information collection and analysis.
Organisational change
The 1990s have seen major organisational changes on a worldwide scale.
Business process re-engineering has led to a drastic thinning of middle
management ranks and a new emphasis on flatter, more flexible organisa-
tion structures. The re-engineered organisation requires information to be
available to those who need it to make the most effective decisions at the
most effective time.
How OLAP can help
OLAP has an important role to play in helping organisations deal with
several challenges:
• it helps establish, monitor and communicate the performance measures
that allow managers to understand how the business is performing in a
complex competitive environment
• it enables a better understanding of markets and customers
• it can make manufacturing more efficient
• it allows quality information to be shared and disseminated within the
organisation.

Measuring performance
A core function of OLAP is the monitoring of important performance meas-
ures. OLAP provides a flexible environment for the definition, analysis and
sharing of information on performance, including:
• the interactive analysis and exploration of performance measurement
data
• exception reporting, which provides an automatic check of important
performance indicators
• flexibility in the definition and redefinition of applications to monitor
performance measures – making it easier to redefine those measures in
line with changing business needs
• an environment for the definition and communication of the values to be
measured, helping to build agreement and support for the underlying
goals.
OLAP has a well-established role in budgeting and planning, cost monitor-
ing and other standard management functions, but it also has much to offer
in support of other performance measures.
New measures for performance
Traditionally, finance has provided the only measures of corporate perform-
ance, and fiscal indicators remain critical to any business. Nonetheless, there
is an increasing movement to bring in other factors, such as quality meas-
ures (fed by production systems), capital-investment projects, business and
operational efficiency, and human-resource measures.
Summary financial measures provide a single, ‘rear-view mirror’ perspective,
and many organisations are beginning to realise that they need a wider
range of measures in order to understand their performance in a highly
competitive and fast moving market.
One means of developing a common set of values for measuring company
performance is the balanced scorecard. The balanced scorecard method adds
three additional perspectives – customers, internal business processes and
learning & growth – to traditional financial measures. The devisors of this
approach have suggested that it can be further extended to monitor strategic
management systems: linking strategic goals with the results of short-term
actions such as quality improvement or new training initiatives. They
highlight the role of information systems in helping managers disaggregate
summary measures in order to investigate the underlying causes for any
unexpected signals.
OLAP has a role to play that extends beyond the monitoring of important
performance measures. The definition of such measures should be a process
of articulating shared values and goals. In many cases, this process is as
important as the results from the measurement exercise itself. OLAP tools
can provide a collaborative environment in which goals can be defined and
disseminated.

OLAP and other financial tools
As well as the analysis of financial performance, OLAP is commonly used in
finance departments for budgeting, cost allocation and financial modelling.
There is a clear overlap in the area of financial reporting, planning and
analysis between OLAP and the use of spreadsheets and corporate account-
ing packages.
Most OLAP tools provide links to Excel and other spreadsheets, which
allows the robust data access, multi-user support and server-based architec-
ture of OLAP to be used in conjunction with familiar front-end tools.
Many users also want direct links between their transaction-based financial
applications and the powerful query-and-analysis facilities of OLAP. This
market opportunity is being filled by OLAP vendors producing specialised
financial applications and by vendors specialising in financial packages
incorporating OLAP functionality into their products.
Customer relationship management
The need for a customer-centric approach to business is now a truism of
commercial life. Mass marketing is being replaced by targeted marketing
that focuses on the needs of specific customer segments. In its most radical
guise, ‘segment-of-one’ marketing focuses on the personalisation of the
products and services offered to customers.
In order to win customers, and even more importantly, to retain them, an
organisation must understand who its customers are and what their needs
are, and be able to monitor their satisfaction with the services and products
with which they are provided. It is equally important to know who your most
important customers are. Typically, 20–25% of customers will generate 80%
of profit. These are the customers that offer the best prospect for cross-
selling new products and services.
Organisations involved in large-scale retailing – large telecommunication
companies, airlines, utilities, financial services organisations, supermarket
chains and fast-moving consumer goods suppliers – are leading the way in
the implementation of OLAP and data warehousing solutions. It is not
simply a question of collecting data about customers, but of maximising the
use that can be made of that information for commercial advantage.
Typical OLAP applications in the customer relationship management area
include:
• sales forecasting
• promotions analysis
• customer analysis
• market and customer segmentation
• market share analysis.

Informing the manufacturing process
Many parts of the manufacturing process can benefit from the better under-
standing of information that OLAP provides. The industry sectors that most
rapidly adopted OLAP were finance, retailing and pharmaceuticals. Manu-
facturing is increasingly starting to use the technology; for instance:
• purchasing staff can more easily negotiate discounts and pricing
structures if they have better access to information about supplier
delivery performance, material costs and expenditure
• inventory levels can be more accurately controlled if staff are better
informed about factors influencing stock volumes and movement
• quality control staff can test hypotheses and explore correlations to
understand causal factors
• supply chain management involves the interaction of complex data about
inventory levels, finished stock, logistics, distribution and payment. All of
these factors interact, and managers need to be able to explore how they
relate to each other.
Support for collaboration
Along with analytical power, OLAP provides an environment in which users
can collaborate in the analysis process and in which results can be dissemi-
nated throughout the organisation. It does this by providing:
• a consistent view on corporate data and a shared framework for
information analysis
• a means of sharing information directly between users
• techniques that allow the automatic dissemination of information among
a large number of users
• support for analysis over intranets and the Web, thus increasing the
number of users with access to this information.
There has been a radical downsizing of the management layers in many
organisations. This trend has increased the need for better information
systems to support more flexible working practices and more fluid job
specifications. If the aim is to make everyone a decision maker, then every-
one must have access to the information needed to make the relevant deci-
sions.
Many organisations now find themselves bereft of the embedded knowledge
of experienced practitioners. The greater scarcity of managerial experience
and the fact that businesses are invented and re-invented at a much greater
rate – the software industry being a perfect example – means that the lack
of personally acquired knowledge has to be compensated for by better infor-
mation systems.
However, the drive for better information should not be a purely negative
requirement in response to cost-cutting exercises. The better use of informa-
tion requires a common framework for understanding what information is
important and the opportunities and risks that it discloses. Information
rarely comes in self-contained ‘nuggets’, and even when it does its value is
enhanced within a context of shared goals and values.

The sharing of information obviously has many ramifications in terms of the
culture and politics of an organisation. The move to a more horizontal
structure does not come without challenges to all involved. However, al-
though organisational issues must be given their due weight, technology has
an important role to play in providing more flexible mechanisms for making
information available to all who need it.

How to choose
the right OLAP tool/s
Overview ............................................................................................................................. 2
The Ovum evaluation framework for OLAP ........................................................................ 3
Important considerations when constructing a profile of OLAP requirements .................... 4
Questions that need to be answered .................................................................................. 6

Overview
Unfortunately there is no universally ‘best’ tool that magically adjusts to the
needs of your users, connects to the available data sources, requires zero
maintenance, scales without limit and came free with Cornflakes. While we
wait for this to happen, we need to acknowledge that tools and organisa-
tional needs are very diverse. Effective use of OLAP results from getting the
best fit between the two.
Choosing an OLAP solution is a multi-faceted decision and the starting point
is to consider your requirements. In this section, we outline the important
issues that influence this. One consequence of this diversity is that you may
require several tools to meet the interactive decision support requirements
in your organisation. You may also require several tools if you adopt a ‘best
of breed’ approach to creating an OLAP system. We describe seven questions
that need to be addressed to establish your requirements profile.
To enable you to use your profile of needs with our evaluations, we summa-
rise the evaluation framework and show in a summary chart how your
needs can be matched to the right tool using our evaluation framework.

The Ovum evaluation framework for OLAP
The aim of the Ovum evaluation framework is to provide a comprehensive
means of describing OLAP tools. The framework covers the totality of OLAP
functionality, so none of the tools we have evaluated offer all the features in
all the categories. Indeed, even if a tool did, it would not necessarily make
the ideal tool for every user. Many users would be paying for unused func-
tionality.
In our evaluations, we describe the components of each toolset, the architec-
tural configurations supported and describe the support provided for all
aspects of OLAP use. A full description is given in the Guide to the Evalua-
tions; here, the eight perspectives of OLAP functionality are briefly summa-
rised.
End-user functionality
How easy is it for casual users to find and use a previously created model?
We also consider support for report distribution and subscription.
Building the business model
Does the tool enable the model builder to build a complex multidimensional
business model?
Advanced analytical power
What in-built support does the tool provide for complex analytics?
Web support
Can the tool be used to access and create models via the Web?
Management
How easy is it to manage the models, persistent data and users?
Adaptability
How does the tool ensure that the data sources, models, reports derived from
these and metadata are all synchronised?
Performance tunability
What are the tuning options?
Customisation
What support is available to customise and develop applications?
In the following pages, we describe the major issues that need to be
considered when choosing an OLAP tool, and how these relate to these
perspectives.

Important considerations when constructing a profile of OLAP
requirements
Overview
Building a profile of OLAP requirements is neither simple nor a ‘one-off’
exercise. Requirements change and your tool needs to be flexible enough to
accommodate these. In this section, we describe the major considerations
that need to be addressed when considering which tool/s to use, and we show
how to use our evaluations to pick the best fit between your requirements
and the tools available. Bear in mind that different tools can be used to
support different clusters of needs, and that requirements change over time.
Figure 1 shows the main questions that need to be addressed to decide your
organisational requirements for OLAP. Each of these points is explored in
more detail in the following pages. In the summary below, we show how the
answers to these questions relate to the information given in the
evaluations.
How to use the evaluations
When you have decided which of the considerations described are important
in your organisational setting, you can use this information when reading
our evaluations. Figure 2 gives an indication of how this can be done. The
figure shows which aspects of our evaluation framework are important in
gauging tool support for these seven business requirements. These are the
aspects you need to pay particular attention to when reading the evalua-
tions. So, for instance, if the considerations that are most important to you
require a high score in End-user functionality, Building the business model,
Adaptability and Customisation, then this profile can be used with our
scores for the tools.
Matching your profile of needs with our scores is the first step to choosing a
tool that will meet your needs. The details in the evaluations will enable you
to refine your decisions.
Figure 1 Choosing an OLAP tool
The OLAP
tool decision
What type
of analysis?
Integration
with other tools?
What is the size
and nature of the
data to be analysed?
How will OLAP be
delivered to the user?
Which roles
need most support?
Need for
customisation?
How many
business models?

Figure 2 Using the evaluations
Complex and specialised analysis
Support for power users
Support for casual users
Support for the designer
Support for the administrator
Support for the application developer
Managing a large number of volatile models
Support for customisation
Web access to explore models/reports
Web access to create models
Use of the Internet/Web to disseminate
OLAP reports
An architecture appropriate to the nature of
the data sources and models
Integration with other tools
End-userfunctionality
Buildingthebusinessmodel
Advancedanalyticalpower
Websupport
Management
Performancetunability
Adaptability
Customisation
See descriptions of architectural configurations in the
evaluations
Information is given in Adaptability and Deployment
It is desirable to have a good score if this is required
It is essential to have a good score if this is required

Questions that need to be answered
Here, we examine in detail the issues, briefly introduced above, that need to
be considered when working out your organisational requirements for
OLAP.
What type of analysis is required?
There are three main types of analysis:
• general
• ad hoc
• specialised.
General analysis
This is the least complex type of analysis. This type of analysis was once
delivered via hard copy scheduled business reports. Each report was a single
perspective. With OLAP, the data can now be interactively explored. In
general, this type of analysis uses what is provided and does not create extra
information. The models are pre-defined and the analysis is conducted using
the basic OLAP functionality of drill-down, pivoting and slice-and-dice.
Ad hoc analysis
This type of analysis demands more functionality than the general analysis
described above. It extends the previous requirements by requiring that the
user can enhance the model provided with the addition of extra dimensions
or new information derived from what is currently available.
Specialised business analysis
The major feature of specialised business analysis is that it models a com-
plex business problem, often using specialist financial, statistical, math-
ematical or forecasting functions. Such specialised complexity cannot be
bought off-the-shelf, but is built to individual requirements.
The OLAP tools we have evaluated offer broadly similar functionality to
support general and ad hoc analysis; the principal differentiator in this field
is the support offered for specialised business analysis.
Figure 2 indicates which parts of the evaluation framework show the level of
support that tools provide for this requirement.
What is the size and nature of the data to be analysed?
A relational OLAP (ROLAP) architecture is necessary when multidimen-
sional OLAP (MOLAP) cannot scale up to the data requirements. In ROLAP,
the data is stored in a relational database and retrieved as required; in
MOLAP the data for the model is stored in a multidimensional database
(MDDB). These concepts and terms are further described in The anatomy of
an OLAP tool.
However, there are many criteria that can be used to determine when a
ROLAP architecture is the best fit. Apart from the fact that the MOLAP
vendors will put forward one set of criteria and the ROLAP vendors another,

the issue is made more complex because of the interactive nature of the
determinants. For instance, there is general agreement that some of the
factors necessitating a ROLAP architecture are:
• the size of the source data for the analysis (hundreds of gigabytes of raw
data suggests a ROLAP system)
• when there is a large number of unique members (generally more than
one million in a single dimension)
• when there is a large number of dimensions (simply adding an extra
dimension with no increase in the amount of input data can double the
size of a consolidated database).
However, in some circumstances it is more difficult to choose (for example, if
there is a small database with a large number of dimensions and 500,000
unique members, or a terabyte-sized database with only a few thousand
members). Unfortunately there is no absolute formula. This means that –
apart from circumstances where all these factors indicate that a ROLAP
solution is most adequate – other issues need to be taken into account when
choosing an OLAP tool.
Within the evaluation framework, we describe the architectural configura-
tions that the tools support.
Which OLAP roles need most support?
When accessing the suitability of tools, prospective purchasers need to
consider the intended use and users of the system. Tools offer different
support for different roles. In this section, we first describe the four key roles
involved in using OLAP and then indicate which aspects of the evaluation
framework are most relevant to each type of user.
The four main OLAP roles are:
• end user
• designer
• administrator
• application developer.
Of course, one person may have more than one role.
The end user
End users are not a homogeneous group. The unifying feature of this role is
that all the users have a business rather than an IT perspective. OLAP is a
means to an end, and the end is making better business decisions as a result
of interactively exploring quality information. From an end-user perspective,
an OLAP tool is as good as the support it provides for business functions. It
delivers important functionality, but how it does it is of less concern.
End users are divided into two main groups, which have different levels of
familiarity with the tool and the extent to which they expect to push the
tool.

Casual user
The casual user requires ease-of-use as a primary feature. The use of the tool
should be largely intuitive, so it can be used intermittently without refer-
ence to manuals or mentors. The casual user is greatly supported if the tool
can provide a simplified EIS-style interface or a selection of reports provide
a multidimensional business model with some text to give it a context.
It is tempting, but wrong, to assume that the casual user only wants to carry
out what we defined earlier as a ‘general’ type of analysis on the data. What
is important to the casual user is the presentation; it is perfectly possible
that ‘under the bonnet’ there are highly specialised algorithms manipulating
the data. If this is an important requirement, it is essential that the tool
supports customisation so that the complexity can be hidden.
Power user
The power user asks questions that make extensive demands on the analyti-
cal capabilities of the tool. They want to add complex new measures on an ad
hoc basis and to extrapolate from given information using a variety of
assumptions. Power users want to apply financial, statistical or specialised
knowledge to the historical data. They require good support for sophisticated
analytical capabilities.
The designer
The designer is responsible for creating the mapping layer (if one exists)
between the data sources and business view of the data, the multidimen-
sional business models and the reports derived from these. In some cases,
the creation of reports is also carried out by end users.
The designer role requires technological skills allied with an understanding
of business needs. This is the role most likely to be undertaken by the IT
department. Although the most visible output of this role is the creation of
models and reports, the most demanding part is likely to be the organisation
of connections to the data sources. This involves negotiating access, deter-
mining schedules and ensuring that the structure of the available data
supports the requirements of the models.
The designer needs good model building support from an OLAP tool, com-
bined with the option of providing advanced analytics for those models that
require them.
The administrator
The administrator, sometimes known as the manager, has the responsibility
for maintaining the system. One of the regular tasks is the scheduling and
maintenance of stored data. This is of particular importance if the data is
stored in a multidimensional database, but also relevant if data is cached by
ROLAP tools. In all cases, when data is uploaded an administrator must
ensure that the operation is completed satisfactorily, and deal with it if this
is not the case.
The administrator also needs to set up and manage user and model security.
If the OLAP system was static, this would be the main focus of the adminis-
trator’s role, but there is inevitably a high degree of volatility in the system
and the administrator must deal with this. Part of this is the need to tune

the system for performance gain. OLAP tools offer a range of support for
this, some to the extent of enabling the administrator to choose between
MOLAP and ROLAP type access. In general terms, the administrator seeks
to tune performance by maximising the speed of response of the system
while minimising the load time for any stored data.
The OLAP system requires that the data sources, the models, the metadata,
and reports based on them, are all kept synchronised. There will be pres-
sures from the business users to change the models and reports, and the
data sources are unlikely to stay static. The administrator of an OLAP
system has to have good organisational skills as well as tool support.
A final responsibility, although one which is outside the scope of most sys-
tems, is that of cleansing and organising the data for the multidimensional
business models. In many systems this is entirely delegated to the data
warehouse, but in some cases further data manipulation is carried out
within the OLAP tool.
The application developer
There are different contexts for application development, such as in-house
customisation, in-house application development and software development
by ISVs. The needs are similar, with ISVs requiring additional support such
as a clearer separation between development and run-time versions and a
need to completely hide the origins of the engine.
The main division is between:
• application developers that want to simplify the OLAP tool so it presents
an EIS dashboard-type interface whose main characterisation is ease-of-
use
• those wanting to produce more complex applications containing
multidimensional models tailored to a particular purpose.
There is an increasing trend towards the development by ISVs of vertical
and horizontal applications containing embedded OLAP functionality.
Multidimensional business model maintenance – are they volatile?
In some large companies that make extensive use of OLAP, there may be
more than 100 different business models to build and maintain. Each of
these will be in response to a particular set of departmental or line of busi-
ness needs. A further issue is the volatility of the models. Are the user
requirements constantly changing, requiring administrative effort to ensure
synchronisation of the data sources, metadata and models? If there are a
large number of models and, particularly, if these are constantly being
adapted, the tool must provide strong management support.
Is there a requirement for customisation?
Customisation enables the tool to be tailored to a particular group of users
or business function. It might be important to:

• present the user with the standard company ‘look-and-feel’
• enable OLAP functionality to be embedded within an application focused
on a particular business function
• include highly sophisticated analysis methods without the user being
aware of the complexities involved. For instance, a credit-scoring
application might involve a neural network application to predict a credit
score without the user knowing this
• enable ISVs to develop new products.
Alternatively, if the need is for a tool that requires no development effort
and can be deployed quickly, then a shrink-wrapped, out-of-the-box tool is
more suitable. Out-of-the-box tools have the further advantage of producing
quickly tangible results, which might be necessary to convince prospective
users of the benefits or to get management buy-in for further or extended
projects.
How will OLAP be delivered to the user?
The desktop was once the primary delivery mechanism. Now, all the tools we
have evaluated also offer web access. The attractions of a web interface are
that:
• it frees the user from having to use a machine with the OLAP tool
installed
• it provides platform portability
• it is easier to manage if there are a large number of users and/or they are
very geographically dispersed
• it is cheaper to manage (the term ‘zero maintenance’ implies an unlikely
degree of stability)
• for some users it is the preferred and familiar interface.
The primary use of the Web is to access models and reports and allow users
to interactively explore these. This is generally well supported by the tools
we have evaluated, although in most cases the functionality offered by the
web interface is slightly less than that offered by the desktop one. Less well
supported is the ability to build models via the Web.
A different use of the Web and the Internet is as a distribution medium. If
collaboration and sharing are important, then this is an important
consideration.
Is there a need for integration with other OLAP and data warehousing tools?
If you wish to wish to adopt a best-of-breed approach to your OLAP solution,
you need to ensure that all the components work together. Ideally, they
should do more than this, and have a degree of integration that enables

metadata generated by one part of the process to be used by products from
other vendors.
Within the OLAP part of the data warehousing process, the main split is
between server and client components. This has long been possible, but its
use has been limited by the lack of a standard interface between the server
and the end-user tool. All the multidimensional databases had proprietary
interfaces, and thus end-user tool vendors had to make choices about which
of these to support. The first attempt to produce a standard API was the
OLAP Council’s MD-API, but this has not been widely adopted. More re-
cently, in February 1998, Microsoft released the OLE DB for OLAP specifica-
tion. While this is still proprietary, it is rapidly becoming the de facto stand-
ard because all the major OLAP vendors, apart from Oracle, have expressed
support for it.
The emergence of this de facto standard makes it much easier for users to
plan for an integrated best-of-breed solution. In the Deployment section of
the evaluation framework, we describe the interface standards that the tool
supports.
Another aspect to integrating components is the support given for metadata
exchange between components from different vendors. While both Microsoft
and Oracle have plans to provide a metadata repository that can be used as
the basis for sharing metadata, neither of them has yet been able to deliver
this. It is therefore left to individual vendors to develop technical integration
between their products to facilitate metadata exchange.
In the evaluation framework, in Adaptability, we describe any support the
tool offers to access metadata generated in earlier stages of the data ware-
housing process by third-party tools.

The anatomy
of an OLAP tool
Overview ............................................................................................................................. 2
Why is multidimensionality so important? ........................................................................... 4
MOLAP, ROLAP and HOLAP .............................................................................................. 9
Multidimensional OLAP ....................................................................................................... 9
OLAP architectures ........................................................................................................... 14

Overview
In this section we describe the functionality that an OLAP system provides,
and the terms and concepts that are important in understanding this. We
explain industry terms such as ‘multidimensionality’, ‘dimensions’ and
‘business measures’.
There has been much controversy about the relative merits of MOLAP,
ROLAP and HOLAP methods of storing and accessing the data required for
analysis. These terms are explained and a comparison made between the
two main alternatives of MOLAP and ROLAP. HOLAP is a combination of
both; it promises (but has not yet delivered) the best of both worlds.
There are four main OLAP architectures, differentiated by the data storage
method used and whether the processing takes place on the client, the mid-
tier server or in the relational database. In this section, the architectures
and their advantages and limitations are described. In the evaluations we
describe if (and how) each of these configurations is supported.
The functionality of OLAP systems
The functionality of an OLAP system is provided by three main components:
• a multidimensional, business-level model for interactive analysis
• an OLAP engine that processes multidimensional queries against the
target data
• a storage mechanism for holding the data to be analysed (this might be
external to the tool; for example, an RDBMS)
Figure 1 shows this in a simplified form. In practice, OLAP tools vary consid-
erably in how they implement these functions. All three functions, for exam-
ple, may be carried out on a single server, or the model and engine may be
PC-based with data extracted from a RDBMS on-the-fly.
This core functionality of any OLAP tool is enhanced by three other
functions:
• end-user access
• application development
• the sharing and distribution of the results of analysis.
Each of these functions may be integrated within the OLAP tool or be
provided in conjunction with other products. For example, application devel-
opment might be supported by links to third-party development tools.
OLAP is also associated with two other technologies that aid analysis and
dissemination of information: data mining and the Web.

Figure 1 A simplified OLAP architecture
OLAP engine
Data store (MDDB, data warehouse)
OLAP client OLAP clientOLAP client
Multidimensional
business model

Why is multidimensionality so important?
A multidimensional view of data offers important advantages. Interactive
analysis against a multidimensional model:
• enables you to view your business measures from various perspectives
• is an intuitive way of working with aggregated or summarised data
• makes it easier to identify patterns and trends in that data
• makes the user an explorer of the information presented to them, rather
than a passive recipient.
Many query tools are now available that hide the physical characteristics of
data from end users, allowing them to frame their queries without needing
to use SQL or to know obscure technical names for tables and columns in the
database. Multidimensional analysis provides much more. It allows users to
execute complex queries and analytical functions involving many facets of
their business, without assistance from the IT department.
Multidimensionality explained
A simple database query that lists all cars sold in February may have a use
in an operational context, but it does not a give a view of how well the
business is performing. Decision makers rely on summarised data to give
them a picture of the business at a relevant level of detail. A manager does
not base next year’s budget on a list of products sold, but rather on a sum-
mary of sales of products over the year in different categories and different
markets. A more useful view is that shown in Figure 2, with sales aggre-
gated for each model.
Figure 2 A typical management report
Month Model Region Number
of sales
.......
February Blackbird North 30
February Blackbird South 30
February Griffin North 11
February Griffin South 10
February Robin North 14
February Robin South 11
.........
March Blackbird North 55
March Blackbird South 35
......

Dimensions
Even in a report such as that shown in Figure 2, it is still difficult to identify
any pattern or trends in the data, particularly if the report runs to many
pages. The problem arises from the fact that there are several dimensions to
our information, but these are hidden in this single dimension report. By
simply cross-referencing this information we obtain a multidimensional
table that provides much more information about our sales. For example, the
question ‘how many cars of different models have we sold over the last three
months?’ can be answered by a table such as that shown in Figure 3. We now
have two dimensions to our sales figures, product and time, represented by
the row and column headings in our table.
Typically, however, users want to ask queries that cover more than two
dimensions. For example, a sales manager may want to know how many cars
of each model were sold in the different sales regions over the last three
months. We need to add another dimension to our table in order to answer
this question. We now have three dimensions to our sales figures: product,
time and geography. Figure 4 represents this three-dimensional view.
Figure 3 A two-dimensional table
January February March
Robin 20 25 22
Griffin 30 21 15
Bluebird 20 60 90
Figure 4 A three-dimensional table
South
Jan Feb Mar Jan Feb Mar
Robin 11 14 17 9 11 5
Griffin 18 11 8 12 10 7
Bluebird 0 30 55 20 30 35
North

A three-dimensional view is easily envisaged as a cube, as shown in Figure
5. It is more difficult to envisage a four- or five-dimensional model. This can
be thought of a series of inter-related cubes. Six dimensions are not uncom-
mon in a business model, although most people have problems working with
more than nine.
Business measures
Reviewing the data in Figure 4, a sales manager may want to add sales by
revenue to the query, or be able to compare planned with actual sales side-
by-side. Quantities such as ‘sales revenue’ or ‘units sold’ are called business
‘measures’. Each measure is understood in terms of the dimensions to which
it is related in a query. Geography, time and product are the three most
common dimensions in a multidimensional business model, but the specific
dimensions used vary from business to business and from department to
department. Other common dimensions include customers, departments,
promotions and suppliers. Indeed any factor that you need to track in rela-
tion to your business measures may be considered a dimension.
Aggregation hierarchies
Our data is now summarised by a number of cross-referenced dimensions.
However, these dimensional categories themselves need to be grouped and
summarised to provide clear information at a suitable level. Our sales
manager may want to break the information down to a finer level of detail
(for example, to see sales by city). Similarly, a higher level view may be
required (for example, annual or quarterly sales).
Figure 5 A three-dimensional cube
North South East West
Falcon
Bluebird
Griffin
Robin
Q1
Q2
Q3
Q4
Geography
Product
Time

Each level of summarised detail can be imagined (and implemented) as a
separate dimension, but this quickly becomes overly complex. It is more
common to see each dimension as having a number of different summary
levels which are relevant to the business. Together these summary levels
build up a ‘hierarchy’ through which a user can navigate. Figure 6 shows a
simple hierarchy for geography. Other hierarchies may be much more com-
plex than this. Product management, for example, requires products to be
classified under a number of different hierarchies as shown in Figure 7. This
means that, for example, sales revenues for a product can be aggregated (or
‘rolled up’) by category or by supplier, depending on the query.
Together, dimensions, measures and aggregation hierarchies form the
constituent elements of any multidimensional model.
Of course, the multidimensional table need not include summarised informa-
tion. It might, for example, include budgeted quarterly expenses for each
department. However, even here the principal benefit of multidimensionality
is the ease with which these figure are ‘rolled up’ to provide information on
budgeted expenses for the whole company over the year. In general,
multidimensionality is most relevant to data that needs to be summarised in
one form or another. A list of staff salaries is an example of data that is not
suited to multidimensional view, because there is a one-to-one correspond-
ence between each employee and their salary. Multidimensional analysis,
however, becomes useful once salaries are aggregated as wage costs for each
department.
Figure 6 A simple aggregation hierarchy
Country
CentralNorth South
Store HStore G Store IStore BStore A Store C
Figure 7 A multiple hierarchy
Product category Supplier
Production
Roll-up

Navigating a multidimensional model
Dimensions, measures and aggregation hierarchies are the constituent
elements of any multidimensional model. As a model becomes more complex
– involving many dimensions, several measures and various hierarchies – it
becomes more difficult to conceptualise and navigate through. OLAP tools
must make working with complex multidimensional models an intuitive and
efficient process.
Three basic operations – drilling, rotation and slicing & dicing – are required
to simplify the task of working with a multidimensional model. To enable
intuitive, interactive analysis, each operation must be simple to define and
be implemented without significant delay.
Drilling
Drilling is the ability to move up and down between different aggregation
levels. You drill-down, for example, from annual to quarterly sales figures or
drill-up from stores to regions. Drilling is usually invoked by double clicking
at the relevant point in a multidimensional table or chart.
Rotation
Rotation (also known as pivoting) allows dimensions to be viewed from any
perspective. For example, Figure 8 shows how we can rotate a three-dimen-
sional cube to show different aspects of our data. Rotation is much easier to
use in practice than it is to envisage in the mind. The user of an OLAP tool
does not need to think about cubes or rotation, they simply indicate that the
wish to see sales by quarter for the northern region. This is usually achieved
by dragging and dropping a dimension to a new position: the OLAP tool
rotates the perspective automatically.
Slicing and dicing
A user may only want to see sales figures for January, or for regions where
sales were below $100,000. The process of selecting the required data is
referred to as ‘slicing and dicing’, in reference to the necessary operations on
a multidimensional cube to pick out the required information. As well as
simple selections, OLAP tools should allow users to select specific items from
a dimension, select items by ranking (for example, the top five selling prod-
ucts) and combine selection criteria to build complex queries.
Figure 8 Rotating a multidimensional cube
North South East West
D
C
B
A
Q1
Q2
Q3
Q4
Sales for Q1 by region
North
South
East
West
D
C
B
A
Q1 Q2 Q3 Q4
Rotate to view sales by
quarter for the North region
North
South
East
West
D
C
B
A
Q1 Q2 Q3 Q4
Rotate to see sales for product
A by region and quarter

MOLAP, ROLAP and HOLAP
The core data for multidimensional analysis has to be stored in a structure
that provides high performance querying, scalability and multi-user access.
Relational databases are optimised for frequent, simple queries. They are
less suited to supporting complex, multidimensional queries. Many queries
cannot be handled by a single SQL query (for example, the selection of the
top five products by market share). Multidimensional queries require more
table joins and full table scans, both of which drastically reduce
performance.
Three data storage strategies can be employed that overcome the limitations
of the relational model for multidimensional analysis:
• the use of a specialised multidimensional databases (MDDBs), which
provide optimised storage and retrieval of data for OLAP queries
• the use of a data warehouse, built using relational technology but
optimised for decision support rather than transactional operations
• a combination of these approaches.
OLAP tools that provide multidimensional storage are often referred to as
MOLAP tools, while those that access data stored in relational databases are
referred to as relational OLAP or ROLAP tools. Tools that combine the two
approaches and work with data stored in a multidimensional structure as
well as data retrieved from a RDBMS are known as Hybrid OLAP tools
(HOLAP).
Multidimensional OLAP
A multidimensional database (MDDB) stores data in a series of array struc-
tures, indexed to provide optimal access time to any element in the array.
These structures can be envisaged as multidimensional cubes similar to that
shown in Figure 5. MOLAP vendors originally fell into two groups – those
using a hyper-cube architecture and those using multi-cubes. Increasingly,
the distinction is being blurred as hyper-cubes can be partitioned (effectively
becoming multi-cubes) and multi-cubes can be joined to form a virtual
hyper-cube.
Hyper-cubes
A hyper-cube architecture provides a single ‘cube’ in which each measure is cross-
referenced against all dimensions. The advocates of this approach emphasise its
simplicity and the consistency of performance, whatever dimensions or parts of a
dimension are selected in a query. However, it uses more disk space and requires
good sparsity handling if the cube’s size is not to become unmanageable.
Multi-cubes
A multi-cube architecture allows measures to be cross-referenced against selected
dimensions only. One cube may include sales revenues dimensioned by time, geogra-
phy and product. Another cube may dimension costs by department and time. This
approach uses less disk space and provides better performance for each cube;
therefore, it tends to be more scalable. However, performance may not be consistent
for queries that require access to more than one cube, and more complex processing
is required to ensure consistent results across multiple cubes.

Pre-consolidation
One of the advantages of an MDDB is that it can pre-consolidate the an-
swers to many queries. Whereas a relational database will normally need to
search all relevant records and aggregate data in order to answer a question
such as ‘how many packets of soap powder did we sell last quarter?’, an
MDDB can calculate such totals quickly as it only has to add up the cells in
the relevant rows and columns of a multidimensional array. Once calculated,
totals can be stored in the array structure. Most MDDBs have strong array
handling functions, which further speed up calculations.
MDDBs can pre-consolidate all data; so, for example, totals for each level in
each hierarchy are calculated when the database is loaded. This approach
gives very fast response times to most queries, but it requires considerable
disk space and makes the load time longer. An alternative is to consolidate
only commonly used totals and to calculate others on-the-fly. An important
optimisation decision for the MDDB administrator is which data should be
pre-consolidated and which calculated at runtime by the OLAP engine.
Retrieval of detail information
Most MDDBs also provide SQL access to data in an attached RDBMS for
retrieving rows of detail information (for example, ‘all orders that contribute
to an aggregation’). This information can only be displayed and not used for
further analysis.
Sparsity
MDDBs store data collected from a series of detailed transaction records.
Sparsity is a by-product of cross-referencing those records to provide a
multidimensional model.
This is best explained through an example. A large retailer sells thousands
of products in its stores across the country. It wishes to analyse those sales
on a daily basis by store. Not all products, however, will be sold in all stores
every day. A cube created by dimensioning sales numbers by store by prod-
uct by day will, therefore, have many empty cells where no products were
sold. If only 20% of the cells in a cube are populated, then the data is said to
be 80% sparse. It is not uncommon in some application areas for data to be
more than 90% sparse.
An MDDB must have a way of dealing with sparse data to prevent storage
space being swamped by null or zero values. Each vendor has its own mecha-
nism, but in general they compress the database so that null values do not
need to be stored. Although there are some performance costs when decom-
pressing the data, in recompense access times for very sparse data are
improved because of more efficient indexing.
An MDDB must have optimisation strategies for dealing with both sparse
and dense datasets and, ideally, be able to combine these in the most effec-
tive manner. Some applications tend to generate very sparse data. Product
management applications, for example, often require analysis of many
attributes of a product (such as size, price, colour or package size). A multidi-
mensional cube that cross-references such dimensions against other dimen-
sions (such as geography and time) will tend to have a high degree of spar-
sity. Other applications, however, will not produce sparse datasets (financial
applications for budgeting and planning, for example, tend to create dense
datasets).

Sparsity also tends to prevail at certain levels of aggregation (when analys-
ing product sales by region there will be fewer ‘gaps’).
Relational OLAP
Although relational databases are not optimised for multidimensional
analysis, they do have advantages over MDDBs in other areas. In particular,
they scale to larger datasets and include support for replication, rollback and
recovery. Moreover, most organisations already have in-house skills and
significant experience with their strategic RDBMS.
With large data warehouses (in the hundreds of gigabytes or terabyte
range), the advantages of an RDBMS over an MDDB becomes clear.
Some OLAP tools can provide multidimensional analysis of data stored in a
relational database. These relational OLAP (ROLAP) tools, provide a busi-
ness model and an OLAP engine that sits above the data warehouse. A
metadata layer is used to map the warehouse structure onto a multidimen-
sional model. The tool then generates the SQL necessary to retrieve data to
satisfy the user queries.
ROLAP tools work with the RDBMS in significantly different ways:
• some tools use the RDBMS to do all the data processing. To do this they
generate multi-pass SQL statements and create temporary tables in the
DBMS where necessary to process complex queries (this is the approach
adopted by MicroStrategy)
• some tools provide calculation functionality outside the RDBMS. SQL is
still generated to retrieve data, but calculations (including some joins or
aggregations) will be carried out by the ROLAP tool (this is the approach
adopted by Information Advantage).
Relational OLAP tends to provide strong support for applications that access
very large datasets, such as product or customer management applications
for large retailers. Such applications require analysis of many items (thou-
sands of products and possibly millions of customers) by a large number of
dimensions and therefore push the capacity limits of MDDBs.
Although ROLAP tools do not need to be concerned with sparsity directly,
aggregation values tend to be stored in the data warehouse for better per-
formance, and so sparsity remains an issue albeit one dealt with by the data
warehouse designer rather than the OLAP vendor.
Hybrid OLAP
Hybrid OLAP is most easily defined by saying that it is not pure MOLAP or
pure ROLAP. The reason for defining it in this way is that there are several
variants of HOLAP. In summary, the main ones are:
• an MDDB that can retrieve and analyse detail information
• a multidimensional store, optionally with some pre-consolidation.
Increasingly, the term is associated with the first definition and the wish to
combine the advantages of MOLAP and ROLAP.

An MDDB that can retrieve and analyse detail information
This definition of HOLAP is likely to become the generally accepted one, as
it is the term used in Microsoft SQL Server 7.0 OLAP Services. In the
Microsoft tool, HOLAP is defined as storing the summary data in the MDDB
and the detail data in the RDBMS. The user works with one model, which
transparently accesses two types of storage.
The significant aspect of the data storage option is its ease of use, rather
than the approach. Other vendors have supported analysis against data
retrieved from the RDBMS using ‘reach through’ SQL. When the data is
needed it is dynamically retrieved and processed by the MDDB engine.
A multidimensional store
Some client-based OLAP tools extract a selection of data from an RDBMS
and then construct a multidimensional cube (sometimes called a micro-cube)
on the client.
The functional difference between a multidimensional store and an MDDB
is that the latter provides a database manager layer that shields the data-
base users from the technical implementation. MDDBs thus provide a data
manipulation language (DML) that is used to access the data. Each of the
MDDBs offers a proprietary query language. There are also architectural
differences between MDDBs and multidimensional stores in terms of how
the data is stored.
Some vendors using this approach give the option of including pre-consoli-
dated values within the store (for example, Cognos), while others just store
the data and do consolidations as required. (for example, Business Objects).
Some vendors (such as Seagate) offer a wide variety of storage options for
these multidimensional stores.
Relational or multidimensional?
There has been a great deal of debate about the respective merits of the two
architectures. As outlined in Figure 9, each technology has its advantages
and disadvantages. While a few years ago there was an implicit assumption
that one approach was superior and would ‘win’, there is now an acknowl-
edgement of the strengths of both approaches. The focus of the debate is now
shifting towards defining the criteria to use in choosing one or the other, and
evaluating the extent to which HOLAP does, in practice, combine the best of
both worlds.
The most important issue is to understand your business requirements and
the implications for the type and size of the data you need to access now and
in the foreseeable future. It is important to consider how this specific goal
fits into the wider strategy for decision support in your organisation. Is this
a departmental application or the first step towards a larger enterprise-wide
information delivery system? Is the aim to provide information to a wider
user base or to provide strong analytical functionality to a much smaller set?
These are not contradictory aims, but it is important to know which has
priority. Armed with this information it is possible to look for an OLAP tool
that suits your needs. No single approach is sufficient for all situations and
needs will increasingly be met by a combination of tools from a variety of
vendors.

Figure 9 reflects the generic advantages and disadvantage of MOLAP and
ROLAP technology. Individual OLAP tools, of course, vary in the extent to
which they match the relevant profile.
Figure 9 A comparison of MOLAP and ROLAP
MOLAP advantages MOLAP disadvantages
Optimised storage for multi Generally limited to the analysis
dimensional analysis of summary data
Fast and consistent performance Scalability limited by the capacity
for interactive queries of the MDDB
Can act as a departmental datamart Lack of support for parallel platforms,
data replication, systems management
software
Easy to set up and manage Time for data loading acts as limit
on scalability
Strong support for advanced Multidimensional model can be
analytical functions inflexible to rapidly changing
business needs
Support for what-if analysis requiring Requires an additional data
write access to the database management layer
Support for sales and marketing and A proprietary storage format (although
budgeting applications OLE DB for OLAP is emerging as the
de facto standard for data access)
ROLAP advantages ROLAP disadvantages
Close integration with the data Query performance is not as fast
warehouse as MDDBs
Exploits existing DBMS skills Requires a data warehouse
Able to analyse data along many Limited range of modelling and
dimensions forecasting functions
Capable of handling very large Not suitable for departmental
datasets (up to terabyte range) applications
Analysis is possible to the level of Needs work-around for what-if type
transactional data analyses
Particularly suited to product and Not suited for budgeting and financial
customer management applications planning applications

OLAP architectures
The components of the OLAP system are implemented in various ways, so
there are several possible architectural configurations. The main
differentiators are:
• where the data is stored in a multidimensional store that is part of the
OLAP tool or in a relational database (or similar) that is outside the
scope of the tool
• where the processing takes place on the client, in the mid-tier server or
on the relational database.
The OLAP engine does the processing
At the heart of any OLAP tool is a software ‘engine’ that turns corporate
data into consistent and widely-available business information for interac-
tive analysis.
The OLAP engine takes a user’s requests for information, executes them
against a database, and returns the results. Users’ requests are defined
through a GUI-based client and are framed in terms of a multidimensional
model as operations on business entities such as products, sales, stores,
profits and costs.
An OLAP engine is itself composed of two important elements:
• the metadata that maps the multidimensional model to the source data.
The metadata provides the user with a business-level view of that data. It
also provides the information the OLAP engine needs to formulate
queries.
• a calculation and query engine which retrieves and processes the
requested data for viewing by the client.
The engine uses the metadata information to formulate the data access
commands and execute the request. As well as selecting data, the engine
may need to do further processing to satisfy the request, such as additional
aggregations, comparisons of different items (in order to select the top five
products by sales revenue, for example) or the application of forecasting
algorithms.
The main OLAP architectures
There is no ideal architecture: the right choice depends on your require-
ments. Here we describe the main options and the strengths and limitations
of each configuration.
Figure 10 shows the main architectures.

Figure 10 Four OLAP architectures
DW/operational
sources
DW/operational
sources
DW/operational
sources
DW/operational
sources
Client interface Client interface Client interface Client interface
Metadata Metadata
OLAP engine OLAP engine
Data
Web server Web server
Metadata Metadata Metadata Metadata
OLAP engine
MDDB
Full mid-tier architecture Light mid-tier architecture Desktop architecture Mobile architecture
Database
server
Mid-tierserverClient
OLAP engine
SQL SQL SQL SQLData Data Data Data
Full mid-tier architecture
This is the architecture used by MOLAP tools, and the main feature is the
use of an MDDB. The mid-tier server holds the metadata describing the
models, the OLAP engine and the data for the models. Data is regularly
loaded into the MDDB from a data warehouse or operational sources. Que-
ries from the user are processed by the OLAP engine using metadata about
the models, and the results are then passed back to the client for presenta-
tion. A web server can be added to the mid-tier to enable end-user access via
a desktop or web browser interface.
Few tools offer load sharing between distributed OLAP servers.
The advantage of this architecture is that the server offers a central point of
control and it is thus easier to manage. As metadata about the models is
held centrally, there is no issue about synchronising multiple copies. Finally,
as the OLAP client merely has to deal with presentational issues, this
architectural model easily supports the thin client architecture required for
the Web.
The limitations of this architecture are those of MOLAP, as described earlier.

Light mid-tier architecture
ROLAP tools use either the light mid-tier or the desktop architecture. In the
light mid-tier architecture, the OLAP engine is on the mid-tier server, but
the data source is not an MDDB. It is usually an RDBMS or it may be a
multidimensional store (for example, Cognos’s PowerCube).
In contrast to MDDBs, which all have their own data access language,
ROLAP tools use SQL to access data in relational tables. The OLAP engine
has to find a way of compensating for the limitations of SQL for multidimen-
sional analysis. The tools address this in one of two ways:
• creating multi-pass SQL statements and creating temporary tables in the
database in order to process complex queries such as those involving
inter-row comparisons
• using SQL for the basic manipulation of data, but completing any further
calculations in the engine itself.
The first approach reduces the network traffic but makes demands on the
database, while the second has the opposite effect.
This architecture is server-based, so it can support web access by the addi-
tion of a web server on the mid-tier. As with the full mid-tier server architec-
ture, few tools offer load sharing between distributed OLAP servers.
One of the main limitations of this architecture is that queries take longer
than if they access the optimised storage of the MDDB. Also, if the query is
complex and processing is done on the source RDBMS, the creation of many
temporary tables can slow it down. One widely-used solution to this is to
cache retrieved data either on the mid-tier server (where it can be shared) or
locally on the desktop PC. This introduces a range of management issues.
The strength of this approach includes the ease of handling large and com-
plex datasets, as described in the section on ROLAP data storage.
Desktop architecture
The principal difference between this and the light mid-tier architecture is
that the OLAP engine is on the desktop. The main consequence of this ‘fat
client’ architecture is that it cannot support web access. Vendors that origi-
nally had only desktop architectures have had to add a mid-tier server so
that the processing of queries can be moved off the desktop to support a thin
client.
As with the light mid-tier architecture, access time is reduced if the data for
queries is stored either locally or on a mid-tier server. Most tools support
this.
This architectural configuration requires good management support to
ensure that models on the desktop are synchronised to reflect one version of
the truth. The main limitation is the overhead of managing a large number
of fat clients, rather than centralising control on a mid-tier server.
The appeal of this architecture is that it is usually quick to deploy, with
minimum dependence on IT staff to configure the mid tier. This speed of
deployment is, however, predicated on the data for the models being in a
ready-to-use state.

Mobile architecture
The mobile architecture is similar to the desktop one, except that it must be
possible for the end user to download a useful subset of the data, sever the
link to the main data source, and still have the same functionality as if the
link has been maintained. This is less easy to implement using tools with
ROLAP architecture because, by definition, the data is stored in a relational
database. The crucial questions for these tools are whether they support a
caching mechanism that is independent of the mid-tier server, and whether
there is any reduced functionality when the cached data is being accessed
without the use of the mid-tier server.
A requirement of this architecture is the need to ensure that downloaded
models are synchronised with the model from which they were derived when
the mobile user next logs in. The user must be able to reconcile any changes
they have made to the structure of the model – such as the addition of new
calculated measures – to the latest version of the data. If write-back is
supported there is a further level of complexity to ensure synchronisation.
The advantages of this architecture are, as its name suggests, that it can be
used by a mobile workforce and thus extends the user base. The limitations
are the dangers of using out-of-date data and the need for sophisticated
mechanisms to ensure co-ordination with ‘the mothership’.

The evaluations – an overview
End-user functionality.............................................................................. 3
Building the business model.................................................................... 4
Advanced analytical power...................................................................... 5
Web support ............................................................................................ 6
Management ........................................................................................... 7
Adaptability .............................................................................................. 8
Performance tunability............................................................................. 9
Customisation ....................................................................................... 10
Format of the evaluations
At a glance ............................................................................................ 11
Terminology of the vendor ..................................................................... 11
Ovum’s verdict....................................................................................... 11
Product overview ................................................................................... 11
Future enhancements
Company background ........................................................................... 13
Customer support ................................................................................. 13
Distribution ............................................................................................ 13
Product evaluation................................................................................. 13
Deployment ........................................................................................... 13
The evaluations in detail
End-user functionality............................................................................ 15
Building the business model.................................................................. 17
Advanced analytical power.................................................................... 19
Web support .......................................................................................... 22
Management ......................................................................................... 23
Adaptability ............................................................................................ 26
Performance tunability........................................................................... 29
Customisation ....................................................................................... 31

Evaluation methodology
In this section, we describe the framework used in evaluating the products
and the assumptions underpinning it. It is this structure that makes the
product evaluations consistent and comparable.
We have built an evaluation framework, populated it with detailed criteria
and then applied it rigorously to each of the products we evaluated. The
individual criteria are not mere checklists of surface-level features; they are
questions about the core capabilities of the product – capabilities that affect
its business benefit and applicability.
For each evaluation we work closely with the vendor’s technical and market-
ing personnel and wish to thank them for the time and effort they have
taken in ensuring we understand the whole picture. However, it is important
to note that this extensive research programme is funded entirely by Ovum.
We do not write to please vendors, nor do we rely on them buying our re-
ports. We write to inform prospective buyers. We give our frank opinion,
backed up with ascertainable facts; we question; we challenge. Our aim is to
help you decide which OLAP product is most appropriate for your business.

The evaluations – an overview
In this section we describe each of the eight perspectives used to evaluate
the products and give the rationale for its inclusion. Not all of these will be
of equal importance in every setting. We indicate the features that differenti-
ate products, including why and when they are important.
The tools under evaluation have come from different backgrounds and have
been developed to meet different needs. This is not a homogeneous set of
products, nor is there a standard OLAP application. The strengths and
weaknesses reflect both the genesis of the product and the differing cus-
tomer bases of the vendors. Therefore, we caution you not to add up all the
scores of a product and deem the product with the highest total ‘best’. Pro-
spective purchasers are advised to aim for a ‘best fit’ between their require-
ments and the strengths of the tool. (In How to choose the right OLAP tool/s
in the OLAP fundamentals section, we describe how to use the evaluations
to achieve this.)
You should decide:
• which perspectives are the ‘least’ and ‘most’ important
• what functionality is critical within these perspectives.
The product scores indicate the impact of choosing the product as a strategic
OLAP solution within a typical IT user organisation. If you have specialised
needs, or are choosing the product for a specific decision support project,
then we give enough detail for you to decide whether your particular re-
quirements are met.
End users, particularly those that do not use the system on a regular basis, need
to be able to easily find and use a previously created multidimensional business
model.
A high score is important if the tool is to be used by occasional users and those
with minimal IT skills. While power users will always get to grips with the tool,
casual users require more support. The score in this dimension will be of less
importance if the intended users of the tools are an elite group and there is little
concern with distributing resulting information to a wider audience.
Ease of use is a particularly important issue in OLAP because:
• the main users are business workers, not IT staff
• compared to order entry systems, where there is no choice, the use of
OLAP tools may be optional, because there are several routes to business
decision making
• the front-end user tool is the most visible part of a data warehouse, and
the choice of tool will greatly increase the perceived usefulness of the
data warehouse.

There is a logical separation between creation and use of the business model;
while the same user may carry out both processes, the skill sets are differ-
ent. Support for occasional users requires that the skill set necessary to find
and use the model is minimal.
Having identified a relevant model, the user needs to be able to interactively
explore it. We briefly summarise the tool’s ability to offer core OLAP features
such as drill down/up/across, table pivoting, charting and changing the
dimensions using point-and-click. This functionality is essential in an OLAP
tool and we deduct credit if the tool does not provide it.
Over and above this basic functionality there are a number of optional
features that enable users to make more use of the models. For instance,
tools are enhanced by support for the generation and distribution of interac-
tive reports based on the business model. The term ‘report’ is used to mean a
subset of the multidimensional business model. The report will always
contain a table or chart that the user can interactively explore, and may be
supplemented by text and objects such as bitmaps, sound or video.
Examples of advanced report distribution features are the generation of
dynamic address lists (for example, the top ten sales people), time and event-
driven scheduling, and parameterisation of the content. Allied to report
distribution is the need for users to be able to see the range of reports and
subscribe to them on a conditional basis.
Underpinning this section is the belief that models are useful to the indi-
vidual and a wider audience, and that tools should provide support for end
users to communicate their findings and, conversely, to access work created
by others.
Designers of the multidimensional business model need tools that offer enough
flexibility for the model to be built to fit the business needs.
A high score here is important if you want to ‘fine tune’ a complex business model
using the OLAP tool. A high score indicates that the tool offers more support for
tailoring dimensions and measures. A low score will not be of concern if the
intended data model is simple and largely a reflection of the data structures in
the data warehouse or data sources.
In contrast to End-user functionality, a more specialised skill set is assumed,
so while ease of use is a consideration, there is more concern about what is
possible. What are the limits of sophistication that the tool can support?
The model designer may be an IT person with a business understanding or a
business analyst with some technical skills. In some cases, the tool lends
itself to IT personnel creating the mapping layer, and end users creating
models from these.
The first process in building a business model is defining the data from the
data sources in familiar business terms. Sometimes this is on a per-model
basis, but a better alternative is the creation of a universally available

mapping layer. The advantages of the second approach are that it ensures
consistency of terms. The layered approach reduces duplication of effort and
is easier to manage, because many changes in the source system can be dealt
with in the mapping layer and no change is required to the business models.
Once the data sources have been defined in business terms, the dimensions
and measures of the model are defined.
A business model is based on a combination of data that is used ‘as is’ from
the data sources (although possibly with some cleansing) and data that is
derived. To use data sources effectively, the designer must be able to access
the schema of these sources and metadata, generated either by extraction
tools or database design tools. The developer should also be able to access a
sample of the data to aid understanding.
When building the business model there are separate requirements for
defining dimensions and measures.
Defining dimensions
The lowest level of a dimension hierarchy will almost always be mapped
onto an actual data source. This is a field or column in a relational data
source, or its equivalent in non-relational sources (for example, comma
delimited text files). We expect levels above to offer more options, so that
they can be directly mapped onto data sources or be defined by the designer.
The tool should offer support for alternative drill-down patterns in the
dimension hierarchy.
Defining measures
The major issue with regard to measures is the support the tool gives for
specifying calculated measures. Most tools will allow data to be defined
using arithmetic, relational and logical operators. Minimal support is pro-
vided if the complexity of derived data is limited to what can be specified in
SQL. It is preferable if the tool offers a range of functions that enable more
complex calculations to be defined.
Specialised users need a ‘ready to use’ selection of statistical, financial and
forecasting functions, as well as the ability to write additional ones.
A high score is essential if your users intend to use the OLAP tool for complex
analytical work, business modelling or forecasting, probably as a business
analyst or a power user. A low score will be acceptable if your analysis consists
of manipulating and presenting historical data, rather than applying complex
formulae or algorithms to the data.
Some end users require specialised support, beyond the simple manipulation
and presentation of multidimensional data. Spreadsheets (such as Microsoft
Excel and Lotus 1-2-3) already provide much of this analytical functionality,
and we expect the tools to provide tight integration with spreadsheet inter-
faces. Integration with other tools offering specialised analysis is highly
beneficial to specialised users.

Data mining capabilities, either inherently or through integration with other
tools, give additional support to the user.
In forecasting and budgetary applications, a critical requirement is the need
to be able to write back data to the business model so that new values,
dependent on this, can be calculated. This is equivalent to ‘what-if’ analysis
using a spreadsheet, but with a more complex set of interacting factors.
Web support
To fully exploit the Web, tools should support web publishing and the exploration
and creation of models via a web browser.
A high score is crucial if the intention is to empower a large number of users with
OLAP at minimal cost, or if users require ‘access from any desktop’, but will be
less important if the intention is to constrain the use to a small group of power
users equipped with standard PCs.
There are many factors contributing to the growing importance of web
access to OLAP, including:
• the cost benefits of using browsers requiring minimal maintenance (over-
optimistically dubbed ‘the zero administration option’)
• the ease of providing OLAP functionality to a large number of users via
the Web
• the increasing expectation that intranets as well as the Internet will be
used for information dissemination.
Providing OLAP via the Web is now an intrinsic requirement. However,
while all vendors offer web access, there are important differences in the
web solutions.
An important aspect is the comparison of end-user functionality delivered
via the desktop and web interfaces. Web access should deliver the same
functionality to the user as the desktop access, but this is now shifting
towards recognising the different strengths of the two access routes. It is not
necessary for the web interface to be a cut down version of the desktop
interface. In some areas, the web interface can offer facilities not easily
available to the desktop user (for instance, the incorporation of data from
URLs and the use of Internet-based search engines). We give extra credit for
a web interface that exploits web features and adds value over the desktop
access.
Being able to develop models via the Web is useful, but less important than
providing interactive access to pre-built models, and thus receives only a
small amount of additional credit.

Management
Tools should offer support for the management of models, data and users that is
easy to use and reduces the workload of the administrator.
A high score is essential if the management of the OLAP system is to be under-
taken by a general administrator rather than a DBA specialist. The importance of
the score here is also a function of the number of users and the complexity of
models being maintained. A low score will not cause too much concern if there is
little data to manage and security is not an important issue.
Management of models
The main issues in managing models are security and query monitoring.
Information from monitoring the use of models enables the administrator to
respond to changing demands, and supports the process of tuning both the
models and the data sources that feed them.
Management of data
The management of persistent data is more complicated. In MOLAP, by
definition, the data used in the multidimensional business model is stored in
a multidimensional database. In ROLAP, while the main data source is a
relational database, all the tools evaluated also make use of a persistent
data cache outside the RDBMS. This is required both to reduce query times
and to minimise the number of calls to the source data. Regardless of the
storage mechanism, all data organised for multidimensional analysis will
explode in size if a large proportion of the potential consolidations are pre-
calculated, and will therefore require support for size management. In the
case of ROLAP tools, this is the responsibility of the data warehouse and
outside the scope of the tool. With a MOLAP-type store, it is managed within
the tool.
The detailed management tasks associated with MOLAP, ROLAP and
HOLAP are different, but the main issue in all cases is the quality of support
the tool gives for ease of management.
Some of the issues common to all types of persistent storage are scheduling,
distributing the data and informing the user of the currency of the data.
Management of users
The tool should allow the administrator to define individual and group
security profiles. Query governance should be provided to ensure that users
are at least warned about, if not prevented from, making resource-expensive
requests.

Adaptability
Tools need to provide support to keep the data sources, multidimensional
business models (and the reports derived from them) and the metadata about all
of these synchronised.
A high score is important if the data sources or user requirements are likely to be
volatile and the implementation is medium to large. A low score will be accept-
able in the unlikely situation that both user requirements and data sources are
considered to be comprehensive and stable.
The adaptability of the system to cope with changes at both ends of the
process (that is, the data sources used and the business requirements) is
important for all OLAP systems.
Another aspect of adaptability is the extent to which the tool can mix-and-
match data storage options; for instance, can data for a business model be
stored in an MDDB and/or a relational database, and can these options be
modified once the business model has been created? This flexibility gives the
designer choices about how to optimise the system.
An OLAP environment combines a number of potentially volatile elements.
These include the requirements of the end user. Although it is essential that
a requirement specification be drawn up before the models are built, new
needs will emerge as users make use of the system. If the requirements
change, it is a sign that the system is being used, not that it was poorly
defined.
Further changes will be required as different types of users are added to the
system. It is therefore essential that the tool provides mechanisms to make
it as straightforward as possible to adapt to changing requirements.
One of the important aids to ensuring ease of adaptability as requirements
change is comprehensive and well managed metadata. Most OLAP tools
interpret metadata as meaning source table schemata and the business
names given to columns. We regard this as a minimal requirement. The next
step up, in terms of the quality of metadata, is to enable the designer to add
descriptive text to some of the objects (for example, models, dimensions and
measures). Ideally, the metadata should be much richer and with structures
to capture author details, versions and changes, dates, data derivation and
end-user annotations, and then give the user facilities to search the
metadata. In general, few tools offer this quality of support.
The ideal situation is that rich contextual metadata is available in a control-
led repository, so that access and visibility can be managed from within the
toolkit. The metadata should be searchable to allow users and designers to
easily find relevant models and model components.
You need to be wary of how this section is interpreted, because one benefit of
limited metadata (for instance, if it merely captures the schematic details) is
that it makes it much easier to keep it synchronised with the models. Thus, a
high score in this perspective coupled with a low score for the business
model perspective could indicate that the model is easy to synchronise
because it is basic.

The administrator needs tool support to enhance performance by tuning the data
extraction and the data manipulation processes.
A high score is essential whatever the scope or nature of the OLAP operation. A
low score is only acceptable in the short term if the users have either not been
getting the information at all or waiting for requests to be processed by the IT
department. In the longer term, a low score is unacceptable for all tools.
Users’ performance expectations are always shaped by their previous experi-
ence. If static reports previously took a week, and the richer, more interac-
tive OLAP model takes ten minutes to respond, the user will initially be
delighted. However, this will not last and all users, ultimately, aspire to an
instantly responsive system.
There are some aspects of performance tunability that are applicable regard-
less of the architecture, but some that are particular to MOLAP and ROLAP
tools.
Shared requirements, regardless of architecture, are the need to:
• extract data as speedily as possible from relational sources
• distribute the processing of the users’ queries
• speed up the processing by taking advantage of symmetrical
multiprocessing (SMP).
Tools with different architectures offer different challenges to performance,
and different tuning options:
• MOLAP
how to trade off size and load time against speed of response and the
support of multiple users
• ROLAP
query response time is the main performance issue.
We give maximum credit when the tool offers the administrator a compre-
hensive range of methods for balancing performance against factors such as
database size, number of users, load time and storage architectures.

Customisation
We consider the support the tool provides to develop applications that include
multidimensional data in tabular and chart format that the user can interactively
explore.
A high score is essential if the organisation wants to develop in-house applica-
tions for OLAP or if the tool is to be used as the basis of decision support
applications by an ISV or a VAR. There are situations in which a low score will not
be of great concern; for instance, if the intended user group can handle an ‘out of
the box’, non-customised environment and the functionality provided meets the
users’ requirements.
The strongest support is provided by tools with a complete development
environment, which includes the provision of components and a 4GL that
can directly help build multidimensional applications. Although this re-
quires developers to master a new environment, it reduces the build time.
In contrast to this, there are tools that provide an open API through which
data can be extracted from models and then manipulated in applications
built using familiar application development environments (such as C++ or
Visual Basic). However, with these tools more work is required to give the
user the same facilities to dynamically explore the data. Some, but less,
credit is given to tools that offer this facility.
Multidimensional analysis may be the focal point of the application, or it
may be an embedded part of a package whose main focus is the management
of sales, financial or other types of information. In this case, the OLAP
features are supplementary. In both cases, the advantages of building such
OLAP applications in-house include the tighter fit with requirements, the
greater complexity supported and a potential reduction in cost.
We also look for support for a simpler form of customisation, which enables
the user to interact with a model through a simplified front end with a
reduced number of options. These are generally known as EIS-type
applications.

Format of the evaluations
At a glance
This one-page overview summarises the product and its principal features.
It includes:
• the name and principal location of the product vendor
• the name/s and version number/s of the product/s evaluated and their
release date/s
• three key facts about the product, generally the type of OLAP tool (for
example, MDDB, relational OLAP or desktop), the platforms it runs on
and an interesting fact about the product or company that might affect a
purchasing decision
• three strengths of the product
• three points to watch, or aspects that may be weaknesses of the product
in some circumstances
• the ratings chart – a tabular summary of the product’s scores on the
evaluation perspectives.
Terminology of the vendor
The main vendor-specific terms that need explanation. OLAP, more than
many areas of IT, is particularly prone to each vendor using different terms
for similar things. To minimise the confusion, our policy is to use Ovum
terminology throughout the evaluations to give consistency to the descrip-
tions where possible, and explain how these relate to the vendors’ own
terms.
Ovum’s verdict
What we think
A summary of Ovum’s opinion of the product (good, bad and neutral), with
the reasons.
When to use
A description of the circumstances in which you should shortlist the product
and those in which it is less suitable.
Product overview
Components
The main components of the product and their version numbers are listed
and described.

Figure 1 Four OLAP architectures
DW/operational
sources
DW/operational
sources
DW/operational
sources
DW/operational
sources
Client interface Client interface Client interface Client interface
Metadata Metadata
OLAP engine OLAP engine
Data
Web server Web server
Metadata Metadata Metadata Metadata
OLAP engine
MDDB
Full mid-tier architecture Light mid-tier architecture Desktop architecture Mobile architecture
Database
server
Mid-tierserverClient
OLAP engine
SQL SQL SQL SQLData Data Data Data
Architectural options
We describe whether the toolset can be configured in each of four
architectures:
• full mid-tier architecture
• light mid-tier architecture
• desktop architecture
• mobile architecture.
Figure 1 summarises the main storage and processing features of the
architectures, which are described in more detail in The anatomy of an
OLAP tool in the OLAP fundamentals section.
Using the toolset
We describe features that differentiate the product from others and include
screenshots to give a feel for the tool’s interface and style of use.

Future enhancements
The vendor’s stated plans for enhancements to the product. Planned devel-
opments for the product can often also be used as a guide to what the vendor
thinks is missing from the current product.
Company background
History and commercial
The company’s history, other product lines, revenue and profitability.
Character and direction
The main business of the company, its customers and distribution channels.
The current marketing and product strategies and the way in which the
company plans to change. We describe the vendor’s view of how OLAP
should be implemented and the philosophy behind the company’s product
set.
Customer support
The support, training and consultancy available to purchasers of the
product.
Distribution
The name, address and telephone numbers for the company’s main contact
in the US, Europe and Asia-Pacific. Includes the web address of the vendor.
Product evaluation
Each of the vendor’s OLAP toolsets is evaluated along eight perspectives:
• end-user functionality
• building the business model
• advanced analytical power
• web support
• management
• adaptability
• performance tunability
• customisation.
These are described in detail in the next section.
Deployment
Platforms
The platforms that the server and client component/s run on.

Data access
The data sources that the tool can access; for example, relational databases,
comma delimited files, personal productivity tools (such as Excel), third-
party MDDBs and data from ERP applications.
Standards
Whereas the relational database world has standardised on SQL, there are a
various competing standards in the MDDB world. The OLAP Council offers
a variety of standards including version 1 and 2 of the OLAP Council specifi-
cation, Microsoft’s OLE DB for OLAP (both as a consumer and a provider)
and, finally, there are proprietary but published standards for accessing
MDDBs. We state which standard/s the product supports.
The importance of this information is that it determines compatibility
between products from different vendors.
Published benchmarks
We describe any published benchmarks for the product, but advise caution
in attributing too much importance to these measures because:
• the methods used are open to considerable interpretation and debate
• the leadership pattern is unstable in an area such as OLAP, which is new
to performance measurement.
Price structure
The pricing structure, as supplied by the vendor, is described. However, we
advise prospective purchasers to contact vendors directly for details concern-
ing site licences and volume discount deals.

The evaluations in detail
Summary
A brief discussion of the product score in this dimension.
Finding and understanding the model
Finding and loading a multidimensional model
We give credit for tool support for logical groupings of models. Extra credit is
given if the user can search for models using metadata. For example, a
search for all the models to do with ‘customer returns’ or ‘designed by Joe
Smith’.
Metadata for end users
End users that are unfamiliar with the model are helped by explanatory
metadata about the model as a whole and the individual components of it.
Some credit is given for a non-structured description, but more is given if
the metadata also includes details such as the author, date of design, contact
details and keywords.
We give additional credit if the tool gives end users access to metadata
generated upstream when the data is extracted from operational sources, as
well as metadata entered by the developer.
Annotation by the end user
There are several potential sources of metadata. One generally ignored
source is the end user, who might wish to annotate the model or its compo-
nents either for their own future use or for selected others. We give credit if
the end user can annotate in this way and additional credit if the visibility of
such comments can be controlled.
Using the model
Basic OLAP functionality
We confirm that the tool offers the expected facilities to drill-up and -down,
change dimensions and measures using drag-and-drop, nest the measures
and display the data in cross-tab or graphical format. We deduct credit if it
does not. Additional credit is given if alerts can be defined using point-and-
click.
Changing the position of members in a dimension level
When data is shown in a chart format, it shows the members of the dimen-
sion in the same order as in the table. For visual clarity, the user might want
to re-order the members within a level so that the larger values are at the
back of a three-dimensional chart. We give credit if the members within a
dimension level can be repositioned.
Visualising the drill-down hierarchies
Some tools inform the user that they can drill-down on a dimension by
changing the shape of the cursor. An additional optional feature is to be able
to see your position within the whole hierarchy, so that the user instantly

knows what is possible. Credit is given for ease of seeing the whole hierarchy
and the current position within this.
Drilling down to detailed data
It is the nature of multidimensional tools to show summary data. However,
users will sometimes want to drill-down to the detailed data. With a ROLAP
tool, this requires an additional SQL query and some query governance to
prevent ‘queries from hell’ impeding the performance of the source database.
In a MOLAP architecture, where the detailed data is held in an RDBMS, the
connection to the data source has to be established as well as the query
generation.
We give credit if this can be done, but reduce this if substantial preparatory
work is necessary (for MOLAP architectures) or there are no controls (for
ROLAP architectures).
Range of front-end user tools
Users should be able to visualise the business model using specialised and
familiar tools. The de facto end-user OLAP tool is Microsoft Excel, and we
describe and give credit for the use of this application and any additional
front-end tools that can be used to view the model.
Visualising the results
All OLAP tools provide a wide range of charts, but not all provide maps that
can be used to display geographic data. (We do not expect support for the
display of GIS data.) We describe any distinctive ways in which data can be
displayed and whether maps and charts can be displayed on the same page
as tables.
We give credit for such support depending on its usefulness.
Saving and sharing results
Designing a report
The term ‘report’ in the context of these evaluations is used to mean an
electronic document containing a table or chart that the user can interac-
tively explore, which may be supplemented by text and objects such as
bitmaps, sound and video. Note that this is different from the more typical
use of the term meaning the presentation of static data without drill-down
and pivot facilities.
Credit is given for the ability to support nested cross-tabs and the ease of
adding text and other objects (such as bitmaps, sound and video) to the
model to create an informative and visually attractive report. Generally, the
addition of objects is implemented through the use of Microsoft’s OLE, and
full credit is given for this despite its limitation to the Windows platform.
Another feature we expect is that the end user should be able to add locally-
defined calculations (in addition to those created when the model is de-
signed).
Publishing a report
OLAP is important in both individual and group decision making. If col-
leagues are to be involved, then it must be easy to embed a model in a report
and make it available, either globally or within the organisation. This may
be done in a variety of ways; the most usual being through the generation of
HTML pages for the Web or through an FTP-based application.

Regardless of the technology, the user requires ease-of-use and the option of
scheduling the publication of the report. Credit is given for support for these
functions that is suitable for use by end users as opposed to administrators.
Extra credit is given for any other form of support for group work.
Targeted distribution via e-mail
As well as publishing reports for general availability, there is a need to
target information at particular users. If the users have the necessary
software installed, they will be able to make use of models or reports con-
taining them. If not, they merely need a static picture of the data, either in
tabular or graphical form.
We give credit if the tool supports e-mail distribution of models, reports and
static views from within the end-user tool. Extra credit is given if the tool
supports dynamic address lists; for instance, ‘send the report to the top ten
sales people’.
Subscribing to reports
The other side of the distribution coin is that users should be able to see
what reports are available, ideally with some explanatory metadata, and
then elect to subscribe to particular ones. Credit is given if this is supported
and additional points are awarded if the subscriber can choose conditional
receipt. For example, ‘only send the report containing the sales figures to
individuals or groups of users if one or more of the targets is not met’.
Other end-user features
We describe and give credit for any other functionality that helps the user
find and use models and reports.
Summary
Basic design
Design interface
We describe the design interface and give credit for an easy-to-use GUI
interface.
Visualising the data source
We give credit if the tool enables the developer to see a sample of source
data as well as the schema, because this informs decisions about mapping
fields onto dimensions and measures.
Universally available mapping layer
The dimensions and measures in the multidimensional business model have
to be mapped onto fields in data sources. This may be done directly or there
may be a ‘mapping layer’ between the logical business model and the
physical data. The mapping layer acts as a catalogue of the data sources,
with the replacement of any cryptic column names with meaningful business
names, sometimes with the addition of metadata above data transformation.
The developer of the multidimensional business model then works from the
data definitions in the mapping layer, rather than directly with the data

sources. The advantages of this are that it is easier to build the cubes as the
meaning of the data is clearer, and it can also be used to insulate the model
user from changes to the source data. If the name or location of a source
data field changes, the administrator only has to change the reference in the
mapping layer, and all models using this field will continue to work without
further modification.
Credit is given for support for this facility.
Prompts for metadata
While metadata about structural details can be captured automatically,
contextual details such as a description, author, contact details and rationale
have to be entered manually. We give credit if the tool automatically prompts
the developer for such details.
Building the dimensions
Selecting columns for the dimensions
Credit is given if, as is almost always the case, columns for the dimensions
can be selected using point-and-click.
Selecting the members shown in a dimension level
While almost all tools enable the selection of members in a dimension level,
some do it with point-and-click and others with SQL. The former approach
gets a higher score.
Defining a dimension hierarchy
We expect the levels in the dimension hierarchy to be defined using point-
and-click and for the user to easily insert user-defined levels in the dimen-
sions (for example, a regional level above the stores level). Extra credit is
given if the developer can define different drill-down patterns (sometimes
called unbalanced dimensions or ragged dimensions).
Time dimension
We describe the support given to define the time dimension and if a range of
time hierarchies are provided. Credit is given if new time units can be
defined. Extra credit is given if these can be defined dynamically (for exam-
ple, for the last six months).
Annotating the dimensions
We expect the tool to enable the designer to provide both short and long
names for the dimensions and to allow them to choose which is used for
charts and visual displays. Credit is given for the support provided and the
ease with which it can be implemented.
Default level of a dimension hierarchy
Credit is given if the developer can easily specify the dimensions and levels
shown when the model is opened and if users can tailor this to their own
requirements.
Defining the measures
Calculated measures
As well as using measures that map directly to data sources, tools should be
able to create new calculated measures. A very simple example is:

‘profit=revenue–cost’, but the expression defined may be much more com-
plex.
Credit is given if arithmetic such as this is done using a point-and-click
environment, but reduced if an expression has to be typed in because this
offers less support to the designer. Additional credit is given if there is an
extensive range of functions available to use within the definition of the new
measures.
Support for multiple measures with a set of dimensions
It is expected that all tools support multiple measures with a set of dimen-
sions, and credit is deducted if tools do not support this.
Multiple designers
Multiple designers
We describe the support the tool gives to prevent multiple users overwriting
each other’s work. Credit is given for appropriate mechanisms.
Support for versioning
Versioning support is extremely useful whether one or several designers are
working on a model. It is described and credit given for any versioning
support directly provided by the tool.
Other ‘building the business model’ features
We describe and give credit for any other functionality that assists in build-
ing the business model.
Summary
Third-party tool integration
The tools should provide an add-in to spreadsheets (Excel or Lotus 1-2-3)
that allows end users to directly exploit the analytical functions provided by
these packages against multidimensional data.
Extra credit is given to tools that integrate closely with specialised statisti-
cal and econometric modelling packages that provide their own comprehen-
sive range of functions (for example, SPSS and SAS ETS).
Defining specialised models
Ranking and sorting
Most tools offer the facility to rank and sort the members of a dimension on
a particular measure. If there are any unusual features, these are described.
We give extra credit for more sophisticated classification and sorting tech-
niques supported by the tool, such as Pareto analysis.
Mathematical methods
The tool’s support for mathematical functions underpins the power of its
analytical capabilities. We expect all the tools to support basic arithmetic
functions and logical operators. However, more complex analysis is possible

if the tool supports more complex and specialised mathematical techniques
such as matrix algebra, linear and quadratic functions and polynomials.
We give credit for specialised functions and their ease of application in
analyses.
Financial functions
The performance of a business is principally assessed in terms of its finan-
cial standing. The world of finance has its own set of conventions, concepts
and functions, including compound interest, depreciation, net present value,
fixed interest schedules, discounted cash flow and internal rates of return.
Some tools that are geared specifically for financial consolidation applica-
tions, and which have a substantial customer base in the financial world,
offer a range of specialised financial functions. Any such functions provided
by the tool are briefly described and evaluated for their usefulness.
Statistical models
While many OLAP tools provide basic statistical inference of data such as
‘max’, ‘min’, ‘mean’, ‘median’ and ‘mode’, some offer more specialised support
for statistical modelling. These include standard deviation, variance, moving
averages, correlation, frequency distribution, probability, confidence and
significance testing. We give credit for specialised support provided by the
tool or for close links to statistical modelling packages.
Trend analysis
Credit is given for the inclusion of functions to assist with identifying trends
in historical data. Most tools offer some basic graphical support for straight-
line (linear) trend analysis through an Excel plug-in, and a small amount of
credit is given for this.
However, we give extra credit for more sophisticated trending methods,
including moving averages, exponential smoothing functions and specialised
curve fitting techniques (exponential, geometric, hyperbolic and quadratic).
Simple regression
Users look to their OLAP tools to help them forecast future trends as well as
to analyse past ones. We identify the forecasting algorithms that are sup-
ported by the tool and how easy they are to use.
As a minimum, the tool should be able to support simple regression
techniques for forecasting future events on the basis of historical data.
Various regression algorithms that can be used, including auto regression,
linear/non-linear regression and univariate/multi-variate regression.
Time series forecasting
A frequent use of forecasting is to use time-series data to make comparisons
of values in one time period with another. We describe and give credit for
including a time-series analysis in forecasts and supporting; for example,
simple averages of the past, smoothing functions, seasonal adjustment,
cyclical variation and random (residual) factors.
Extra credit is provided if the tool supports time series forecasting methods
such as Box Jenkins and Holt-Winters extrapolation functions.

User-definable extensions
Experienced analyst users may wish to develop their own analytical func-
tions or modify and extend existing functions provided by the OLAP tool
using procedural logic. We give credit if either is possible through a friendly
graphical user interface, and if the new functions can be documented and
stored in a universally accessible library for sharing and re-use.
Write back for ‘what-if’ analysis
Just as spreadsheets enable users to write new values back into a cell,
enabling the user to explore ‘what-if’ scenarios by examining how this new
value affects other cells, similarly in a more complex multidimensional
model users may wish to write back values to explore the consequences. This
is sometimes called sensitivity analysis.
Write back is a not a feature that is likely to be appropriate for a large
community of users, and is primarily of interest to the power user, particu-
larly in the analysis of budgets. Credit is given only if permission to use the
write back facility is well controlled or if the changes are made in a local and
temporary version of the model.
Incorporating non-numerical data
Although OLAP tools are geared towards the analysis of numerical data,
such data is not the only source of information. Credit is given if the tool can
support the incorporation and analysis of non-numerical data (for example,
using Logit and Probit analysis) and other information sources such as free
format text documents, online news feeds and Internet pages.
Data mining
OLAP tools adopt one of three positions with regard to data mining:
• they ignore it
• they provide some built-in data mining functionality
• they integrate with another tool.
The built-in data mining functionality may be based on decision tree algo-
rithms, inductive reasoning, pattern matching, cluster analysis or neural
network technology. If the support is provided by integration with another
tool, credit is given regardless of whether the tool originates from the OLAP
vendor or a third party; the main issue is the need for ease of integration. No
credit is given for joint marketing ventures with no technological substance
behind them.
We describe the nature of the support and give credit for both provision
within the tool and close integration.
Data mining tools and methods are evaluated in greater detail in Ovum
Evaluates: Data Mining.
Other analytical functionality
We describe and give credit for any other complex analytical functionality
provided by the tool that is particularly useful for general business perform-
ance measurement and assessment (such as Monte Carlo goal seeking

functions) or geared towards horizontal applications (sales and marketing)
or specific vertical sectors.
We expect that users of the specialised facilities described in this section will
be familiar with the functions on offer, but if a wizard facility for guiding the
user through the use of a specialised feature is provided, we give extra credit
because this makes it more accessible to the non-specialist.
Web support
Summary
End-user functionality via the Web
Functionality of Web access to explore models
Most OLAP tools were developed for use on the desktop, and web access was
developed as an additional feature. As a result of this background, most
products tend to offer inferior versions of the desktop client via the Web,
rather than having a ‘made for the Web’ appearance. Credit is given in this
section for web functionality that most closely mimics the desktop access
and is reduced if this is less than that of desktop access. Extra credit is given
for web-specific features that exploit the differences of web access.
Supports both registered and unregistered Web access
It should be possible for known users and guests to access business models
via the web interface. Known users should have the same access rights as
when they use the desktop, and new users should be able to log on as guests
with a restricted set of rights. We describe and give credit for support for
both types of user.
In some cases, there is no technological restriction on giving open access to
the model, but the licensing agreement with the vendor may prevent it. In
this case, we score the tool as if unregistered access is not possible.
Range of users supported by the web interface
While the preceding two points are concerned with web access to the busi-
ness model, here we consider the extent to which the tool supports the
production of EIS-style reports for viewing with a web browser; these are
characterised by simplicity and ease of use. We consider how easy it is to
provide such pages in which the available options are restricted.
The provision of EIS-style pages enables a wider audience to be supported
by the web interface.
Creating models via the Web
Editing the mapping layer
There are far more users than developers, so access to the business model
via the web interface is generally more important than the means of creat-
ing it. However, there are situations in which the creation of the mapping
layer and models via a web browser is important; for instance, if a large
number of power users are supported or if the designer works by sitting next
to users at their machine.

We describe and give credit for support to edit the mapping layer.
Building and editing models
This complements the point above and covers the support provided for
building and editing models via a web browser.
Distributing via the Internet and the Web
Generate HTML and Java
Publishing on the Web requires the tool to generate HTML and/or Java code.
The drawback of HTML is that it does not offer good support for tables and
graphical displays and can result in a visual display that is substantially less
elegant than the desktop one. However, its advantage is that it is accessible
to the large number of corporate users that have not upgraded to a 32-bit
operating system (usually Windows 95 or 98), which is required to support
Java. Ideally, the tool should be able to offer users the option of generating
HTML or Java to support web publication. Full credit is given if the tool
supports both of these options.
Corporately organised distribution via the Internet
We consider whether there is support for distributing models and reports to
assist core business processes. Whereas in the section on End-user function-
ality we considered how personal productivity could be enhanced by targeted
distribution, here we are considering the support for centrally organised
targeted distribution using either the Internet (e-mails) or web pages. For
instance, when a user logs in using a browser they might be directed to-
wards a collection of reports targeted at them, as well as being able to access
a corporate repository. Alternatively, reports might be e-mailed to them.
We give credit for support for distribution within a corporate context and
additional points if the recipients can be defined dynamically.
Include URLs in a report
Credit is given if URLs can be incorporated in reports so that, for instance,
users can be directed to related information
Distribution of Web server processing
The OLAP Web server can be a bottleneck for queries coming from Web
servers. Here we describe and give credit for any means of distributing the
Web server processing, such as integration with a middleware product.
Other Web support features
We describe and give credit for any other features that support Web usage.
Management
Summary

Separate management interface
There are separate roles with different skill sets necessary to support OLAP.
For ease of administration it is preferable that the management of the
system is done via an interface designed for this purpose. Here we give
credit for a graphical interface designed for the management function.
Security of models
OLAP information requires the same level of security as database informa-
tion. While it is self-evident that data stored in MDDBs requires access
control, it is also the case with ROLAP tools as all of them store data persist-
ently to enhance performance. We do not give credit if the tool relies on the
security of the databases supplying the data, but only if there is a separate
and convincing security mechanism within the OLAP tool.
Query monitoring
Query monitoring is required both to tune the system for performance and
to tailor its content. The most popular queries may need to be optimised in
various ways, such as the provision of pre-calculated aggregate tables or
caching the data locally. Query monitoring also assists in ensuring user
satisfaction by helping the developer tailor the content of the business
models according to usage.
Query monitoring should provide the administrator with details about the
use of reports (for example, which reports are run when and by whom) as
well as processing details (for example, average, mean and mode times for
processing, number of records processed and so on).
Credit is given for support for these functions, but the score is reduced if
there is poor integration with the rest of the toolkit.
Management of data
How persistent data is stored (not scored)
Here we describe the storage mechanism used by the tool; this is not scored.
Scheduling of loads/updates
As all the tools we have evaluated have some form of persistent data store,
they require scheduling support to control the update process. We give credit
for an easy-to-use interface offering a wide range of options. We do not give
credit if the tool relies on the scheduling facilities of the operating system or
third-party tools, unless these are very well integrated with the rest of the
tool’s management facility.
Rather than having to individually define schedules for each business model
it should be possible to name a specification and then re-use it as required.
Extra credit is given if there is support for this.
Event-driven scheduling
Being able to define scheduling as contingent on events, such as the comple-
tion of a data load process in the data warehouse, gives extra flexibility to
the tool. Credit is given for an easy to use means of doing this.

Failed loads/updates
If an update fails the administrator needs to know this, needsto know why it
has happened and should ideally be able to specify that the failed update is
automatically resubmitted a set number of times. Comprehensive error
reporting is extremely important to assist in resolving the problem.
Credit is given for the breadth and depth of scheduling support.
Distribution of stored data
The administrator should be able to specify whether the stored data is held
on a local client, a central server or anywhere on the network. We give credit
if the administrator has these options.
Sparsity (only for persistent models)
We expect tools that include consolidated aggregates to have a method for
handling sparsity and thus minimising the data explosion that results from
the storage of aggregates of sparse data. Here we describe the way in which
the tool handles sparsity and deduct credit if the tool does not combine ease
of use with effectiveness.
(ROLAP tools, for which the management of this within the OLAP tool is not
an issue, get full credit.)
Methods for managing size
This is less of an issue in ROLAP, where the decisions about aggregates and
indexing are the responsibility of the data warehouse or data source admin-
istrator and outside of the scope of the OLAP tool. In MOLAP, the issue of
how to deal with the explosion in size resulting from pre-computed data has
to be dealt with by the OLAP tool.
The final size of a multidimensional structure is primarily a function of the
number of stored pre-calculated aggregates, which is made more acute if the
data is sparse.
Credit is given if you can select which aggregates are pre-calculated and
additional points are awardedif there is wizard support for this.
In memory caching options
Credit is given for support analogous to that provided in mature RDBMS
products, which allow the DBA to configure the size and use of the cache to
optimise performance; for example, for particular users or tables. We give
credit if there is some support to enable the administrator to adjust the size
of the cache, and extra credit if there is wizard assistance to reduce the skill
set necessary to make these adjustments.
Informing the user when stored data was last uploaded
The user should be able to find out the currency of the data; for instance,
‘when was customer credit rating last updated?’. This may require the
system to reach back and retrieve upstream metadata from the data ware-
house. Here we describe any facilities the tool offers to support this and give
credit if it is easy for the user to ascertain when the data in the model was
last refreshed. Additional credit is given if this can be supplemented with
information from load processes further upstream.

Management of users
Multiple users of models with write facilities
Relational databases generally offer facilities to prevent update errors when
multiple users access the same data. If an MDDB allows users to write
values back, it must provide similar locking mechanisms to prevent lost
updates.
We describe the mechanism used by the tool and give credit if it locks for
writing but allows read-only access.
User security profiles
We describe the way in which individual and group profiles are defined.
Credit is given for a system that supports a heterogeneous user community
with a granularity which allows visibility, read and write permissions to be
controlled at an individual level.
Query governance
If it is possible for users to issue ‘the query from hell’ that monopolises the
processing capabilities of the system, then it is necessary to have some form
of query governance to prevent inexperienced or overly demanding users
from bringing the system to its knees.
(MOLAP tools, for which this is not a problem, receive full credit.)
Effective query governance has several levels, from the ability to inform
users of the time a query will take, to the prevention of queries above a
defined threshold. Credit is given depending on the range and sophistication
of available options.
Restricting queries to specified times
A feature that can be useful to allow for maintenance work or to control the
usage pattern is to be able to restrict users to certain days or times of the
day. Here we describe and give credit for the available options.
Management of metadata
Controlling visibility of the ‘road map’
There is a need to be able to hide politically sensitive data and credit is
given if this is supported. So the general manager may be able to ‘see’ a
dimension relating to personal productivity, but the other employees cannot.
Other management features
We describe and give extra credit for other functionality, that assists in the
management of the system.
Adaptability
Summary

Change in business requirements
Adding new dimensions to a model
The nature of OLAP is that end users will request additions to the model, no
matter how thorough the requirements phase. This is a sign that the system
is being used, not an indication of a poor requirements spec. Here we give
credit for the ease with which new dimensions can be added to the business
model and any change management facilities to support this.
We give extra credit for a system that incorporates a mapping layer, which
defines the data in the data source in business terms. When the business
model is created, the developer uses the resources defined in the mapping
layer rather than the original source data. The advantages of this approach
are that it ensures consistency of terms, it reduces duplication of effort and
the layered approach is easier to manage.
Re-use of dimension definition
Adaptability is facilitated if dimension definitions can be re-used. Credit is
given if the newly created dimension can be named, described, stored and
easily retrieved.
Adding new measures to a model
Just as end users will request the addition of new dimensions, they will also
want to incorporate new measures into the model. This is credited as in Re-
use of dimension definition.
Re-use of calculated measure definition
Adaptability is facilitated if calculated measures can be reused. For maxi-
mum flexibility, these should allow the base measures to be referenced by
either a name or an index.
Credit is given if the newly created measure can be named, described, stored
and easily retrieved.
Changing the architecture to reflect business needs
A high level distinction between MOLAP and ROLAP architectures is that
the first is optimised for speedy retrieval but has limited scalability, whereas
the latter can deal with datasets with large numbers of dimension members
but is slower. If user requirements always clearly fell into one camp or the
other, the choice of tool could be heavily influenced by its mode of use. The
reality is that users’ needs do not always point clearly to one mode of stor-
age.
For instance, a system that initially seems to require a MOLAP-type solution
may then incorporate data sources that put pressures on the scalability of
the MDDB. Conversely, end users may, in practice, only use parts of what
originally appeared to be data sources with many millions of dimension
members and could benefit from conversion to a MOLAP-type solution.
Another solution is a HOLAP one, in which summary data is held in a
MDDB or similar and the detailed data is held in a relational database and
retrieved as required. The user should be unaware of the source of the data
being viewed.

Here we describe and give credit for the ease of changing the architecture to
align it with new user requirements.
Changes to data sources
Keeping the data source and model schema synchronised
The business model the end user works with is a logical entity mapped onto
data sources in various ways. In any mapping arrangement such as this
there is the potential for divergence between the actual data sources and the
representation of them. Here we describe how the two are synchronised and
give credit if, when opening a model or report, the user is informed when
opening a model or report if some data for the model is unavailable.
If there is no possibility of these getting out of synchronisation (for example,
in a MDDB) then full credit is given.
Automatic updating of members in a dimension
While the members of some dimensions are unlikely to change (for example,
there will always be 12 months in the time dimension) many will be much
more volatile (for example, new stores and products will be added). Develop-
ers need to be able to specify how such new members are dealt with. The
basic requirement is that members are automatically updated in this situa-
tion. However, in some situations the developer may want to lock levels so
that new members are not added and it should be evident to the end user
that this is the case. The score here reflects the flexibility of the system.
We have not considered the support for slowly changing dimensions (for
example, change of marital status in customers), as this is the responsibility
of the data warehouse rather than the OLAP tool.
Metadata
Synchronising model and model metadata
The model and information about the model need to be synchronised. Some
parts of this process can be automated (for instance, if the description of the
dimension includes the number of members it contained), but inevitably
much of the metadata is manually entered. The simplest, and probably most
effective, way of ensuring synchronisation is if the system automatically
prompts for new metadata when edits are made.
Credit is given for the effectiveness of ensuring synchronisation. In cases
where there is no metadata to synchronise full credit is given.
Impact analysis
Changing the data sources affects on the business models and this, in turn,
affects on any reports based on these. Credit is given for tools that support
impact analysis so that the consequences of changes can be anticipated and
dealt with in advance.
Metadata audit trail (technical and end users)
If the history of the metadata is stored then end users and technical devel-
opers can use this to get an understanding of how the models have changed
over time. Additional credit is given here if end users can easily carry out
such an audit.

Access to upstream metadata
Adapting the system is easier if the designer has access to full information
about the data. Here we describe any integration with third-party tools that
gives access to metadata generated during the extraction part of the process.
Ideally this metadata will capture details about the sources, upload details,
transformations and the quality checks carried out on the data, as well as
descriptive text.
Other adaptability features
We describe and give extra credit for other functionality that assists
adaptability.
Summary
ROLAP
Multipass SQL
ROLAP tools issue SQL queries against relational databases to retrieve the
data required to build the business model. The data can either be retrieved
by a single SQL query or using multipass SQL. With multipass SQL, as its
name suggests, multiple SQL queries are generated and processed, stored in
temporary files and finally combined after processing is complete. The
advantage of multipass SQL is that more complex queries can be supported.
For example, calculations requiring aggregation at different levels within a
dimension; so to show sales at regional level as a percentage of sales at
country level requires two passes, one to get the sum of sales at regional
level and the second to get the sum of sales at country level. These are then
combined to get the percentage.
Credit is given if the tool supports multipass SQL.
Options for SQL processing
SQL processing, such as sorting and ranking, can either be carried out on
the database server or the OLAP server. The advantage of using the data-
base server, particularly for operations such as filtering for the top ten, is
that the network traffic can be reduced. However, the drawback is that
complex processing requires the creation of many temporary tables which
can cause a bottleneck.
We give credit if the developer has choices about whether the data process-
ing takes place on the database server or the OLAP server, or if the system
intelligently balances the processing.
Speeding up end-user data access
Retrieval time is an issue for ROLAP tools. There are two parts to the
process: the retrieval of the data and the calculation of the cross-tab results
from it. Data access can be speeded up by the storage of data in relational
tables once it has been retrieved, or storing it once it has been further
processed for cross-tab display, that is, in a more optimised form. However it

is stored, the end user needs to be aware that they are using stored rather
than freshly retrieved data, and should be informed about the currency of it.
Credit is given if data can be stored for re-use and the user is always aware
that stored data is being accessed.
Aggregate navigator
Aggregate navigators process SQL queries so that they automatically make
use of summary tables and thus speed up retrieval time by minimising the
processing. Credit is given if the tool offers integration with an aggregate
navigator or equivalent built-in functionality.
MOLAP
Trading off load time/size and performance
Load time, rather than end-user performance, is a particular issue for
MDDBs. A major contributing factor is the re-calculation of stored aggre-
gates. Credit is given if the tool offers support so the administrator can
decide how to trade off the poorer performance resulting from an incomplete
set of precalculated aggregates against the faster load time and reduced size
resulting from this. Extra credit is given if the tool minimises the effect of
adding new data by making use of metadata about the dimension and
recalculating only those values that are affected. (This is sometimes called
incremental roll-up.)
Support for multiple users
We describe the limits on end-user numbers and ways in which these can be
overcome. Credit is given in proportion to the size of user community the
tool can support.
Processing
Use of native SQL to speed up data extraction
From an OLAP tool vendor’s point of view, OLE DB or ODBC is the simplest
way to connect to data sources for the extraction of data, as it means that
only one set of SQL commands has to be produced regardless of the type of
database being accessed. However, if the OLAP engine can generate native
SQL for data extraction, the extraction process can frequently be speeded
up.
Credit is given if native SQL can be used to extract data from the major
RDBMSs.
Distribution of processing
The OLAP engine is responsible for data extraction, the calculation of
aggregations and the creation of cross-tabular data. There is the danger that
as the number of end-user queries increases it creates a bottleneck. The
most obvious way of avoiding this is to distribute the processing with
automatic load balancing. Here we describe and give credit for such
facilities.

SMP support
Parallelism speeds up processing. Here we give credit if the server compo-
nent of the tool is based upon a multi-threaded architecture that can take
advantage of symmetric multiprocessing.
Other performance tunability features
We describe and give extra credit for other functionality that assists in
performance tunability.
Customisation
Summary
Customisation
Option of using a restricted interface
Although most of the tools described in these evaluations are easy to use, the
range of options available means that there is always a learning curve for
the new or occasional user. What is needed is a means of producing a suit-
able interface for such users.
There are two approaches to the problem:
• for the tool to offer a restricted interface option
• for the developer to be able to produce, essentially using point-and-click
rather than programming, a simple-to-use front end to selected models.
The option of a restricted interface is evaluated here, and the second option
in the next section.
Ease of producing EIS style-reports
We describe how a simple-to-use front end is created within the tool and give
credit if it is straightforward.
Applications
Simple Web applications
The problem this section addresses is identical to the one described above
under Ease of producing EIS-style reports, except that we are now consider-
ing the production of such an EIS-style report that can be viewed with a Web
browser.
Credit is given for ease of producing such an application.
Development environment
The development of applications is greatly facilitated by the provision of an
OLAP-specific development environment that includes components such as
tables supporting drill-down, and linked chart and visual display options.
General application development languages such as Visual Basic and C++ do
not provide such components: they are only provided in the specialist OLAP
application development environments.
Credit is given for the nature and quality of such specialist support.

Use of third-party development tools
The drawback of the specialist development environment is that it requires
the programmer to learn another language. Here we describe whether
applications can be developed in a familiar programming environment such
as Visual Basic, C++ and/or Java. Credit is given for the number of such
environments supported.
Other customisation features
We describe and give extra credit for other functionality that assists in
customisation, including support for localisation.

Growth, transition and change
Trends in business intelligence and implications for the OLAP market
Summary............................................................................................................................. 2
Growth of the business intelligence market........................................................................ 4
Trends in business intelligence ......................................................................................... 10
Key messages for the market ........................................................................................... 14

2
Ovum Evaluates: OLAP
© 1999 Ovum Ltd. Unauthorised reproduction prohibited.
Article: Market analysis and forecast
Summary
Growth and uncertainty
The OLAP software tools market is worth $1.5 billion in 1999 and will grow
steadily to a $4 billion-plus industry by 2003. The market is complex and is
characterised by a lack of clear leaders, a large number of vendors and a
complex web of alliances and partnerships. Accompanying this growth is
uncertainty. New entrants, such as Microsoft and SAP, add a new dimen-
sion to the market and have radically changed its structure.
The OLAP market cannot be viewed in isolation – it overlaps with a wider
‘market’ defined as business intelligence. Business intelligence is a high
growth sector which Ovum predicts will have an overall spend (including
software, hardware and services) of over $20 billion at the start of the new
millennium (see Figure 1).
Underlying the steady growth are radical changes in the way that business
intelligence is packaged and delivered. There are significant trends in:
• how business intelligence systems are built
• what is being built.
The most significant change is the transition from a ‘build’ to a ‘buy’
paradigm.
Figure 1 Overall business intelligence spend ($ billion)
$billion
0
20
40
60
1999 2000 2001 2002 2003

3
Implications for the OLAP market
The emerging trends in business intelligence have important implications
for the OLAP market. The successful vendors and IT users will be those
that can use the new trends to implement business intelligence systems.
For OLAP vendors, the maturation of the market, combined with the entry
of influential new players has spurred a great deal of movement. These
vendors are increasingly having to reconsider their existing market strate-
gies. In order to remain competitive, they need to supply solutions rather
than ‘point’ tools. They also need to exploit new channels and target new
markets (vertical applications and CRM) to gain market share.
For IT users, the growing number of strategy options for implementing
business intelligence systems has increased the complexity of buying
decisions. They are faced with a wide choice of strategies, ranging from
traditional data warehouse development using best-of-breed point tools,
end-to-end infrastructures and packaged analytical applications purchased
from speciality solution providers. They must assess the value of buying
solutions from various vendors and steer a careful course between ‘buy’ and
‘build’ solutions, reconciling the benefits and pitfalls of each approach.
Systems integrators are benefiting from the increasing trend to outsource
the implementation of business intelligence systems. They need to be aware
of the changing trends and tailor their existing service offerings accord-
ingly. Most of the large consultancies have already invested heavily in one
or more of the competing high-level strategies, and will be anxious not to be
caught out.

4
Growth of the business intelligence market
OLAP complements a larger ‘market’, defined as business intelligence. In
this section we define the scope of this market, examine its growth, and
assess the impact of changes to the future development of the OLAP
market.
Business intelligence
Business intelligence is a wide term that requires definition of both
processes and technology. It includes:
• technology-related processes, such as extracting data from a company’s
operational systems and databases and integrating that data into a
coherent whole in order to make it suitable for analysis
• business-related processes, such as determining the appropriate forms of
analysis required to support decision making, disseminating and
interpreting the results of analysis, and determining how best to feed
back the results into a company’s operations.
These processes are supported by a wide range of closely related tools and
technologies that make up the business intelligence market:
• OLAP – including multidimensional databases, relational OLAP engines
and front-end OLAP clients
• query and reporting
• data mining
• data extract, transform and load (ETL)
• relational DBMSs.
Typically, a large business intelligence implementation will use many, if not
all, of these tools, as well as a significant amount of consulting.
Market growth
Ovum’s estimate of the industry spend on business intelligence software
(excluding services) over the next five years is shown in Figure 2. We expect
business intelligence to be a more popular area for new development in
1999 than OLTP. But there are still a significant number of user organisa-
tions that are holding off development until after 2000, when the market
will experience rapid growth.
Services are growing faster
Services are the fastest growing segments of the business intelligence
market (as shown in Figure 3), representing a significant portion of overall
spend on business intelligence.

5
Figure 2 Growth of the business intelligence software market ($ billion)
$billion
0
10
20
30
40
1999 2000 2001 2002 2003
Figure 3 Growth of the services market for business intelligence ($ billion)
0
5
10
15
1999 2000 2001 2002 2003
$billion
Business intelligence is now seen as a major growth area by the IT service
industry. All the large systems integrators and global consultancies are
targeting business intelligence as one of two key growth areas (along with
e-commerce). Most have made significant investments in the expertise to
deliver advice and implementation services in generic data warehousing,
specialised decision support (for example, customer churn and risk analy-
sis), and packaged analytical applications. They are also acquiring special-
ised business intelligence consultancies to strengthen their business intelli-
gence services.
Data warehousing
Business intelligence is often closely associated with data warehousing.
While there is considerable overlap in terms of definition, data warehousing
mainly focuses on the technical process of building and maintaining a store
of data that is specifically intended to be used for decision support.
There are back-end processes for loading data into the data warehouse
using ETL tools, and front-end processes for accessing and analysing data
using OLAP and data-mining tools.

6
For some years now, OLAP vendors have benefited from the surge of inter-
est in data warehousing (and datamart) implementation. Most medium to
large organisations have a data warehouse strategy in place, and most of
those data warehouses are feeding data to one or more OLAP tools.
OLAP software licences
Market growth
The OLAP tools business has been flourishing for several years and will
continue to do so for the next five years at least. Revenue from the sale of
end-user OLAP software licences (not including implementation and con-
sultancy services) is shown in Figure 4.
As with the overall business intelligence market, there is steady, higher
than average growth. However, the market is mature; many organisations
will have already built data warehouses and will, in most cases, continue to
maintain and develop them. For many, this will involve either buying new
OLAP tools, replacing old tools or choosing which OLAP tool to consolidate
on where there are many in use in an organisation.
A superficial analysis of this consistent market growth suggests ‘more of
the same’. However, accompanying growth is a radical shift in the way in
which OLAP tools are being packaged by vendors and delivered to
customers.
Market composition
A survey of the OLAP market shows that it is very fragmented. There are
more than 30 vendors providing a wide spectrum of OLAP products, though
not all are direct competitors. Despite the consolidation that might have
been expected, and the number of mergers and acquisitions that have
recently occurred, the number of major players in the market has remained
more or less the same. New entrants into the OLAP market are Microsoft,
SAP and the merchant database vendors, and their impact is already being
felt.
Figure 4 Growth of the OLAP software market ($ billion)
$billion
0
1
2
3
4
5
1999 2000 2001 2002 2003

7
Most OLAP vendors are increasing their revenues, even though they may
be losing market share. There is no clear-cut leader; the largest player is
Hyperion Solutions (formed by the merger between Hyperion Software and
Arbor Software in August 1998), which has just over 20% of market share.
Market segment analysis
OLAP suppliers can be grouped into four principal categories, although
many span multiple categories with different products or by unbundling
and bundling components.
Multidimensional databases (MDDBs)
This segment has existed since the late 1980s and consists of unbundled
multidimensional or hybrid databases. Most MDDBs are sold at a premium
price by specialist vendors that do nothing else, leaving partners to supply
tools and application components. The three leading vendors in this seg-
ment are Hyperion Essbase, Applix TM1 and Oracle Express. In late-1998,
they were joined by Microsoft with its OLAP Services module bundled into
SQL Server 7.0.
Vendors in this segment are competing head-on as MDDB vendors, and it is
unlikely that more than two or three vendors can survive with this strat-
egy. MDDBs are generally evolving to support more flexible ‘hybrid’ OLAP
architectures, thereby taking the heat out of the MOLAP versus ROLAP
argument that polarised the market in the early 1990s.
Relational OLAP (ROLAP)
Relational OLAP (ROLAP) represents the smallest sector. The two leading
proponents in this field are MicroStrategy and Information Advantage.
ROLAP tools have generated a great deal of publicity thanks to some astute
marketing from these vendors, and increased interest from merchant
database vendors such as Oracle, Sybase and Informix.
The two leading ROLAP vendors show high revenue growth, and the
market will continue to be sustained by the rising popularity of customer-
focused applications, such as customer churn analysis and customer rela-
tionship management (CRM), which require analysis of large volumes of
data. However, ROLAP will remain a niche market, characterised by fewer
‘big-ticket’ sales. Typical implementations tend to be large and expensive
and almost always come with a significant amount of consulting either from
the vendor or an external services organisation.
Desktop OLAP
Desktop OLAP clients have existed since 1990, but it has only recently
become recognised as a distinct segment of the OLAP market. This segment
is especially crowded and the leading vendors – such as Business Objects,
Brio Technology and Cognos – are all direct competitors. The leading ven-
dors have established a large and complex web of alliances and reseller
agreements; the bulk of sales are made via OEMs and VARs, which bundle
low-cost desktop OLAP tools as part of a complete business intelligence
solution.
Once the overall OLAP market growth slows down, a major shake out
seems inevitable in this segment. The leaders will be those that can estab-
lish thousands of user seats in an organisation – but only performing
simple OLAP analyses.

8
Analytical applications
Analytical applications are sold either as packaged ‘ready to go’ applications
(conceptually a ‘datamart-in-a-box’) or complete toolkits. There are several
specialist vendors that provide these types of products: Hyperion Solutions,
Comshare, Kenan, Gentia Software, SAS and Seagate Software. ERP ven-
dors such as SAP and PeopleSoft are also entering this segement by selling
packaged vertical datamarts, which include specialised OLAP applications
that analyse data from the OLTP systems.
As many of these analytical applications are aimed at vertical or horizontal
markets, there is room for many vendors to happily co-exist. But the suc-
cessful vendors will be those that can identify a lucrative niche market and
gain significant market share within it. In the long term there may only be
room for two or three major players in each narrow niche.
Packaged analytical applications represent the newest market segment in
business intelligence. We expect the number of applications to increase
dramatically over the coming years.
Market development
Over the next five years, the market will be shaped by large, influential
players, notably Microsoft and SAP. Microsoft’s SQL Server 7.0 OLAP
Services, and SAP’s Business Information Warehouse (SAP BW) add a new
dimension to the market:
• Microsoft’s entry will serve to raise the profile of OLAP considerably and
accelerate its adoption beyond its traditional niche (namely large
corporate finance departments)
• Microsoft’s competitive marketing strategy of bundling OLAP
capabilities into SQL Server 7.0 will expand OLAP at the low- to mid-
end of the market
• the backing of a large influential vendor also has the potential to
address the interoperability issues that have dogged OLAP for some
years. The OLE DB for OLAP API standard, spearheaded by Microsoft,
is rapidly becoming an industry standard that is enabling OLAP servers
and clients from different vendors to work together, and is stimulating
ISVs to produce a new generation of tools, applications and clients
• SAP BW raises the stakes by providing a low-cost, high return on
investment packaged data warehouses with integrated OLAP analytical
applications. This is a direct challenge to vendors (both of OLAP and
ETL origin) that previously managed by selling point tools to access and
analyse SAP data.
Microsoft’s entry drives OLAP closer to a commodity software market.
Using a combination of simplicity, pricing and bundling, Microsoft is aiming
to make OLAP servers almost as widely used as relational databases. OLAP
servers will be increasingly treated as commodity components that can be
bought, configured and embedded without a great deal of time and effort.
SAP is also positioning its BW product as a solution that can be used out of
the box, where OLAP is a value-added component.

9
However, Microsoft’s product is not a complete business intelligence
solution in its own right. Although it has forced a sharp drop in the average
cost of OLAP, the market will continue to be sustained by volume growth
through its wider use. Users are also starting to recognise the limitations of
SAP’s BW product for providing a tightly focused point analytical solution.
The overall market boundaries will continue to expand. Internet
technologies (which dominated all aspects of the OLAP market in 1999) and
emerging markets, such as customer relationship management (CRM) and
e-commerce, have encouraged wider deployment.
Vendors will have to face some difficult choices over the next year to cope
with the new market dynamics. A major shake out in the OLAP market is
likely to happen and we expect to see a great deal of changes in the for-
tunes of OLAP vendors over the next five years. Vendors will either
‘reinvent’ themselves as specialist solution providers; merge; acquire one
another or simply disappear.

10
Trends in business intelligence
Since 1998 there have been significant changes in how business intelligence
solutions have been built and maintained. The pace of evolution remains as
fast as ever; the only thing that has changed is the nature of the trends.
This section examines the changing ways in which business intelligence
systems, including data warehouses, are being built and delivered.
The overall trend from build to buy
A number of trends in business intelligence are having a profound affect on
the OLAP market. The most significant is the transition from best-of-breed
point tool solutions to packaged ‘ready to go’ business intelligence solutions.
Underlying this trend is the emphasis on a ‘buy’, rather than ‘build’,
paradigm as shown in Figure 5.
IT user organisations embarking on new business intelligence projects are
now expecting to concentrate more on data analysis, while more of the data
plumbing work is to be done for them via:
• the increased use of integrated toolsets
• the use of external service organisations to implement solutions
• an out-of-the-box solution, bought from either specialised analytical
application vendors or packaged datamart and application vendors (such
as SAP).
Underlying this shift are six individual trends that affect how business
intelligence solutions are built and what type of system is built.
Figure 5 ‘Buy’ not ‘build’
% of projects
requiring tool
selection
% of projects
which are ‘buy’
rather than build
Time

11
How solutions are built
The emergence of tools
Before the data warehousing/OLAP boom of the mid-1990s, business intelli-
gence solutions were based on hand-coded solutions (or on pioneering
OLAP tools such as IRI’s Express) at the front-end. At the back-end, hand
coding was inescapable since there were no commercially available tools to
help developers.
The introduction of a wide selection of OLAP tools for the front end and
ETL (data extract, transform and load) tools for the back end, and the slow,
but steady integration between the two, has increased the proportion of
business intelligence solutions assembled in-house from best-of-breed point
products – traditional data warehousing – to decrease initial development
and subsequent maintenance costs.
The shift towards ‘end-to-end’ infrastructure
A logical continuation of the increasing use of tools is the trend to selecting
integrated sets of tools that cover both the front- and back-end processes,
rather than using best-of-breed point tools that only cover subsets of the
required infrastructure.
IT users wishing to implement a new business intelligence/data warehous-
ing project are increasingly expecting to be offered ‘end-to-end’ solutions
rather than a ‘grab-bag’ of loosely integrated tools that are often sourced
from different vendors.
The range of vendors claiming to offer complete end-to-end business intelli-
gence solutions has only recently become significant. Most of the major
database software vendors (IBM, Informix, Sybase and Oracle) have created
their own end-to-end offerings by including OLAP (as an OEM component)
with their DBMS engines, where it becomes a standard database function.
A further manifestation of the end-to-end trend is the growth in the
number of systems integrators offering data warehousing and business
intelligence systems expertise.
But each solution offered has varying levels of success and there is still
considerable scope for improvement in terms of integration, usability and
service provision.
The increasing use of systems integrators for implementation
At the same time as increasing tool use and expecting increased end-to-end
integration, IT user organisations have increasingly been outsourcing
implementation. In some cases, this includes the whole business analysis
function, including infrastructure and analysis expertise.
In the mid-1990s, there were hardly any consultants with any useful busi-
ness intelligence implementation experience, and companies wishing to
implement a data warehouse had little choice but to use in-house skills to
build it. This has changed. The large-scale IT user organisations with the
confidence and in-house resources to undertake data warehousing them-
selves have largely done so. But new implementers are increasingly turning
to systems integrators, and business intelligence is now seen as a major
growth area by all the IT services industry, as shown in Figure 3.

12
What type of system is built
The swing from specific to enterprise wide solutions … and back again
Prior to the mid-1990s, business intelligence projects were treated as ‘con-
ventional’ IT projects, driven by specific line-of-business requirements (for
example, to enable interactive drill-down on sales figures) and implemented
as ‘one-off’ solutions to a specific business problem.
The data warehousing movement in the early to mid-1990s encouraged
companies to take an enterprise-wide approach to business intelligence
infrastructure, to improve data quality and consistency and to increase the
business value of analytical information. A substantial proportion of large
companies have taken this route, but many still continue to develop line-of-
business systems parallel to their data warehousing projects and/or have
returned to this paradigm as a reaction to a general dissatisfaction with the
centralised warehouse. The move back to specific solutions is best illus-
trated by the current wave of interest of customer-focused applications
under the banner of CRM.
The emergence of packaged analytical applications
Starting in the 1970s, a community of vendors started to offer packaged off-
the-shelf solutions to common business intelligence problems – for example,
risk/fraud analysis in the financial market and, more recently, customer
churn analysis for telcos. These vendors’ products competed with the in-
house development of business intelligence solutions. Several of these
companies still exist, and the market for problem-specific packaged analyti-
cal applications (or packaged datamarts) has remained healthy into the late
1990s.
Packaged analytical applications (sometimes called packaged datamarts)
claim to expedite the process of building a fully functional data warehouse.
Though limited in functionality in comparison, they have proved particu-
larly popular in budgeting, forecasting, sales and marketing, customer
analysis (churn and CRM) and ‘balanced scorecard’ – a method of aggregat-
ing multiple performance indicators into a composite index of business
performance measures.
The problem in implementing such applications has always been in provid-
ing suitable data for analysis – the ‘back-end problem’. Some packaged
application companies have actually disappeared as a result of demanding
specialised data for analysis without adequately helping their customers
with the back-end problem.
Nevertheless, partially as a result of a backlash against unrealistic expecta-
tions for data warehousing, there has been an increasing demand for
packaged analytical applications throughout the late 1990s. This has been
acknowledged by data warehouse infrastructure vendors, both of OLAP and
ETL (data extract, transform and load) origin, in:
• their sales and marketing positioning – they are now offering solutions
rather than infrastructure
• acquisition of/by, or very close partnership with, specialist packaged
analytical application vendors. A prime example is the merger between
Hyperion and Arbor.

13
The emergence of analytical applications from operational application vendors
Packaged analytical applications have traditionally been developed by, and
purchased from, specialised vendors in the business intelligence sector. But
operational ERP (Enterprise Resource Planning) application vendors are
picking up on this trend and are quietly moving into this sector. In 1997,
SAP took the lead among packaged OLTP application vendors in promising
to provide packaged analytical functionality via SAP BW. Similarly,
PeopleSoft announced its ‘Enterprise Warehouse’ in 1998, and Oracle is also
developing a similar product as part of its Strategic Enterprise Manage-
ment Suite.
For IT users, this seems an attractive proposition as it provides:
• a ‘one-stop shop’ for analytical functionality as well as OLTP –
surpassing even the perceived benefits of a one-stop shop for business
intelligence solutions
• an apparent solution to the ‘back-end problem’ if the information to be
analysed by SAP BW is sourced mainly from SAP R/3 systems – in
practice, things are not so simple.

14
Key messages for the market
The emerging trends we have outlined point to a general reduction of
business intelligence projects based on best-of-breed tool selection and an
increase in the number of bought solutions – though there is still only a
limited number of ‘off-the-shelf’ solutions and many of these are early
versions.
The trend to ‘buy’ rather than ‘build’ business intelligence provides a new
set of opportunities and challenges. In this section we examine the implica-
tions of these for IT users, OLAP vendors and systems integrators.
Implications for IT users
IT users are faced with an increased number of high-level strategic choices
– some new, some resuscitated from the 1970s, some genuine and some
partially illusionary – for their business intelligence needs. These include:
• ‘classical’ bespoke data warehousing using best-of-breed point tools
• in-house assembly using end-to-end product suites and infrastructures
• the use of packaged analytical applications supplied by speciality
solution providers – without intervening data warehouses
• the use of packaged analytical solutions combined with packaged
operational systems – implicitly from the same vendor
• the dependence on any level of service provider, consultancy or systems
integrator involvement for any of the above options.
However, the transition to buy rather than build is much less straightfor-
ward than some vendors would like potential customers to believe. Two key
issues arising from these changes are how to:
• assess the amount of added value from ‘one-stop shop’ solutions
• steer a careful course between the advantages of point analytical
solutions and the dangers of unintegrated ‘islands’ of information.
Whatever option is chosen, and whatever level of service provider involve-
ment, outsourcing investment decisions frequently run into millions of
dollars. The pressure on IT user organisations to make the ‘right’ decision
has never been higher and depends on several factors, including the type
and structure of the organisation – especially its IT department.
Assess the added value of ‘one-stop shop’ solutions
The added value of a ‘one-stop shop’ solution over an infrastructure based
on best-of-breed components depends on whether:
• the individual components of the one-stop shop solution are a good
technical fit for user requirements – in almost all cases, the OLAP and
ETL components will be the most critical
• the one-stop shop provider offers any value-added integration between
the components of its solution. Often the OLAP and ETL components
will be OEM’d from other vendors. Even if all the components are
‘owned’ by a single supplier, they are likely to have been developed in
isolation by different R&D groups, and users should be very cautious
about assuming that they will be well integrated

15
• the vendor has the ability to provide a one-stop shop for the ‘soft’
components of the system – licence negotiation, customer support and
services, stability and longevity – and the value of these to the IT user
organisation. Some vendors are much more capable than others in these
areas. Moreover, the very wide range of functionality and maturity of
the components of one-stop shop solutions makes it very important for
users to check the technical suitability of the solution before considering
the added value of ‘soft’ factors.
There are several vendors offering to relieve IT users of the pain of OLAP
tool selection and the subsequent integration. We assess the different sets of
players and their offerings, and assess their worth to user organisations as
well as the pitfalls of each approach that you should be aware of.
ISVs
A number of independent software companies position themselves as offer-
ing a complete ‘turnkey’ business intelligence solution using their own
products. These range from the relatively small start-up companies (gener-
ally associated with datamarts) such as Sagent and Broadbase, to the
larger, well established software producers, such as Platinum Technology
and SAS Institute. While it is not easy to generalise about the added value
of these infrastructures, three observations can be made:
• in a small scale business environment, with limited access to IT support,
there is undoubtedly added value over an unintegrated set of tools
• not all components are equally strong, so a best-of-breed infrastructure
would not necessarily deliver greater functionality
• the benefits of integration, particularly metadata, have not been fully
exploited.
Merchant database vendors
A number of database companies have assembled ‘end-to-end’ solutions by
OEM’ing components from point tool vendors. Examples include:
• Sybase, which includes WhiteLight Systems’ WhiteLight ROLAP server
as an OEM extension to its Warehouse Studio (under the brand name
‘Power Dimensions’)
• IBM, which licenses Hyperion Solutions’ Essbase multidimensional
server for its IBM DB2 OLAP product and is a core element in its Visual
Warehouse offering.
These vendors claim to offer above and beyond what OLAP vendors are
offering. The real question is the extent to which there is real integration of
the components or whether the integration is primarily in the presentation
layer. In most cases, adopting the database vendor’s package is effectively
adopting the database vendor’s own best-of-breed selection. The primary
benefit is the reduction in decision-making effort, but seldom does the value
of the whole much exceed the sum of the value of the parts in technical
terms.
Systems integrators
Increasingly, systems integrators are developing business intelligence (and
data warehousing) solutions as one of their key service offerings. Often the

16
systems integrators are the delivery mechanism for one of the business
intelligence solutions described above. Where implementation is done by a
systems integrator it is often difficult for the customer to know just how
‘out of the box’ the solution really is.
The added value of using a systems integrator to provide a one-stop shop
solution is that it insulates the user from tool choice, implementation and
integration issues. Again, the degree of added value depends on the amount
of integration needed. When, and if, packaged analytical solutions deliver
on their promises, the amount of added value will diminish for integration,
and service providers will concentrate more on providing assistance with
data analysis and using the results to improve business performance.
Hardware vendors
The ‘one-stop shop’ offerings of hardware manufacturers are typically a
collection of components from third-party vendors; the added value is
limited to the benefits of dealing with one party and non-technological
issues such as licensing and pricing.
Many of these hardware vendors act mainly as systems integrators –
though this is not clear from their marketing.
Steer a careful course between build and buy
Vendors are offering packaged analytical applications that appear to be ‘out
of the box’ (for example, customer churn analysis) and do not seem to relate
to data warehousing at all. Packaged analytical applications promise a
great deal that appeals to line-of-business managers.
But as the number of packaged analytical applications – bought from either
OLAP vendors or packaged operational application vendors such as SAP –
increases, there will be a greater temptation for users to adopt them as
short-term point analytical solutions, without full consideration of the long-
term consequences.
There are a number of potential pitfalls associated with the packaged
approach:
• users need to reconcile the advantages of point analytical solutions with
the dangers of unintegrated ‘islands’ of information that may result from
tightly focused analytical applications. These point analytical solutions
threaten to re-issue all the warnings about unintegrated ‘stove pipe’
datamarts, with the substitution of ‘packaged analytical applications’ for
the term ‘independent datamart’
• users need to recognise the limitations of the packaged approach. Only a
limited number of applications are available out-of-the-box – mainly
targeted at sales & marketing and financial applications. If a more
specialised application is needed, such as outcome analysis for
healthcare or seat utilisation for the airline industry, then users may
have to build it themselves
• the packaged approach assumes that the data required for the
application is in a single, static and ready-to-use form. It further
assumes that competitive advantage can be gained without a great deal
of further customisation. If your information needs exceed the
capabilities of the packaged applications, then a cross-functional data
warehouse or datamart would be a more flexible option.

17
We would expect, in the short term at least, the lure of packaged analytical
solutions to override such concerns for many user organisations. But in the
longer term, IT users will need to ensure that vendors of packaged analyti-
cal solutions have a convincing story about integrating point solutions with
an enterprise-wide business intelligence strategy. Otherwise, many users
who buy the packaged analytical strategies will find themselves building
data warehouses as well, to address the inflexibility of this approach.
Implications for OLAP vendors
OLAP vendors will have to face difficult choices over the next few years as
they adapt their strategies to effectively compete in the newly defined
market. Many have already positioned themselves to exploit new opportuni-
ties. But the successful OLAP vendors will be those that manage the impli-
cations of the trends in the business intelligence market in their favour.
They will need to:
• rethink their existing market strategies and pricing models – in light of
Microsoft’s entry into the market
• offer more complete business intelligence solutions – not just point tools
• take advantage of new opportunities in packaged analytical applications
• exploit new channels for delivering OLAP
• embrace the emerging CRM market for OLAP.
Learn to live with Microsoft ... whether you like it or not
Few vendors would want to compete directly with Microsoft’s aggressive
marketing. OLAP vendors are now faced with a number of difficult choices
to take advantage of, or simply survive, Microsoft’s entry into the market.
They can:
• specialise and try to dominate a profitable vertical, or niche, market with
analytical applications. OLAP server vendors such as Gentia Software,
Pilot Software and possibly Arbor (following its recent merger with
Hyperion) have shifted their focus towards specialised analytical
applications rather than selling point OLAP tools. They are now building
OLAP functionality into business applications as a value-added
component. Hyperion Solutions is one vendor that focuses on this
application-oriented approach. It offers solutions that integrate with
major packages from JD Edwards, PeopleSoft and i2 Technologies
• endorse the Microsoft OLE DB for OLAP standard and fill niches that
Microsoft does not want; Applix’s TMI MDDB server was the first OLAP
offering to support OLE DB for OLAP as a data provider and targets
specialised financial users; WhiteLight and SAS have also committed to
support the standard. Over 50 OLAP vendors – mostly of desktop origin
– have also announced support as consumers

18
• exploit the new market created by Microsoft’s OLAP Services product;
while Microsoft may have washed away most of the OLAP server
market, it has left the door wide open for ISVs on the client side to meet
the demand for new tools, applications and clients. A number of start-up
software companies are riding on the back of SQL Server 7.0 and
focusing on developing value-added modules. Portola Dimensional
Systems, Maximal, Decisionism and PowerPlan are also developing SQL
Server-exploitative client tools – these companies are betting that users
can do a better job from scratch rather than by adapting. Established
OLAP vendors have also responded: Knosys has discontinued its OLAP
server and focuses on adding value to Microsoft’s product; Cognos has
developed NovaView, a client tool specially designed to access OLAP
Services cubes
• find the next frontier: ROLAP vendors such as IA and MicroStrategy are
now targeting their products at Internet business intelligence portals
and information broadcasting business respectively. In March 1999,
Hummingbird acquired PCDOCS, a document management company,
and plans to integrate its OLAP and document management technology
within a knowledge management framework.
Rethink your pricing strategy – the cost of OLAP is tumbling
The greatest impact of Microsoft’s entry into the market is on the price of
OLAP, particularly for the low- to mid-end of the market. The high cost of
OLAP technology has been under pressure for some years now, and
Microsoft’s SQL Server 7.0 product will accelerate the trend towards lower
pricing.
The tumbling cost of OLAP is undoubtedly good news for users. But it has
significant implications for OLAP vendors:
• the most direct challenge is to MDDB vendors. Given that the entry
level price for existing MDDBs has been in excess of $40,000, Microsoft
SQL Server will have a radical impact from a pricing standpoint alone.
These vendors will now struggle to justify their premium-priced
products
• ROLAP tool vendors are less directly affected. They are still benefiting
from the growth of data warehousing and most implementation comes
with a significant services and consulting overhead. However, they too
cannot afford to ignore Microsoft in the long term
• desktop OLAP vendors will be short-term beneficiaries because of the
increased demand for OLAP clients. But they will face increasing
competition from Microsoft in upcoming versions of Office 2000 which
promises stronger OLAP capabilities
• packaged application vendors that can or already use OLAP should be
long-term beneficiaries; they now have access to low-cost OLAP
technology on a profitable OEM basis. This increases the possibility of
more applications becoming ‘OLAP-enabled’.

19
Provide complete business intelligence solutions – not just point tools
IT users are no longer buying point OLAP tools. They are increasingly
looking for a more complete solution. Only a handful of database and
software vendors can justifiably claim to provide a complete end-to-end
business intelligence solution.
OLAP vendors should be seeking to expand their solutions offerings either
by partnering closely with vendors and systems integrators offering com-
plementary products or services, or by buying-in complementary technology
to provide a suite of business intelligence products. Two examples of ven-
dors that have opted for the latter route are:
• Information Advantage, which acquired IQ Software and its enterprise
query, analysis and reporting tools to complement its ROLAP system
• Seagate Software, which has acquired or developed a number of business
intelligence products, including the Holos development system, Seagate
(Crystal) Reports, Seagate Info and Seagate Worksheet Web OLAP
client.
Take advantage of new opportunities in packaged applications
The growing popularity of packaged applications that include OLAP as a
value-added component provides immense opportunities for OLAP vendors
to:
• OEM their core OLAP products to specialist applications vendors –
depending on the level of integration, users may not even be aware that
a key component of the application is an embedded OLAP server engine
or client
• partner with systems integrators that are installing business
intelligence solutions built using packaged analytical applications.
Be prepared to exploit new channels
In the early days of tool support for building data warehouses there was a
direct channel between the tool providers and the IT users building the
system.
With the ‘build’ to ‘buy’ shift, this channel has now been supplemented by
the indirect channels via systems integrators and software and hardware
vendors providing end-to-end solutions. The implications of this are
straightforward: OLAP vendors that create partnerships to exploit these
new channels will gain market share.
Embrace CRM for OLAP
CRM (customer relationship management) represents a significant market
opportunity for OLAP tool vendors. There are a wealth of definitions for
CRM, but the generally agreed rationale is that knowing about your cus-
tomers enables you to acquire them at less cost, service them more effi-
ciently, cross-sell more effectively and retain them.

20
We differentiate between tactical and strategic CRM:
• ‘tactical CRM’ is associated with customer-facing systems such as call
centres and other solutions that directly address the customers’ needs.
Such tactical CRM systems are often self-contained and rely on a
datastore fed by the CRM application which is not integrated with other
corporate data sources
• ‘strategic CRM’ is associated with finding out about the customer and
often uses OLAP and data-mining tools; for instance, to identify
customer segmentation. Strategic CRM does not usually have its own
data sources, but draws from multiple data stores from within the
company.
OLAP (and related technologies such as data mining) will undoubtedly ride
on the recent wave of interest created by CRM, as there is a hidden market
within it for CRM data analysis and presentation. Customers can benefit
from integrated applications, and OLAP and CRM vendors can benefit from
a wider market for their products.
All the ROLAP vendors already have, or plan to provide, CRM solutions
based on their technology. Turnkey datamart vendors, such as Broadbase,
are also unveiling a number of packaged analytical applications designed
specifically for CRM.
CRM is also bringing data access, analysis and management companies into
close partnerships. We expect to see significant mergers and acquisitions
and alliances between CRM software applications vendors and data ware-
housing tool (OLAP and ETL) providers in the future.
Implications for systems integrators
The trend in business intelligence is increasingly towards outsourcing
implementation. This is of great interest to systems integrators, who have
made significant investments in one or more of the competing high-level
strategies to provide expertise and deliver advice and implementation
services in generic data warehousing, specialised decision support (for
example, customer churn and risk analysis), and packaged applications
(SAP R/3, for example).
Systems integrators need to give advice about the conflicting choices facing
their clients. But with many large systems integrators the ‘buy from vendor
X’ and ‘build a data warehouse’ options are handled by completely separate
business units – each of which may only be able to implement one of the
options. Therefore, they need to resolve how they should be sizing their
investments in generic data warehousing advice and implementation
services for SAP’s BW for example – a directly competing type of solution
probably handled by a completely different business unit. Furthermore, a
consultant’s business unit may only be able to implement one of the options.

Summary of the evaluations
Criteria
End-user functionality.............................................................................. 2
Building the business model.................................................................... 5
Advanced analytical power...................................................................... 8
Web support .......................................................................................... 11
Management ......................................................................................... 14
Adaptability ............................................................................................ 17
Customisation ....................................................................................... 23

BusinessObjects
DecisionSuite
DSS Product Suite
Essbase
Oracle Express Development Tools
PowerPlay
Seagate Holos
Gentia Platform for Analytical Applications
SQL Server 7.0 OLAP Services
1 2 3 4 5 6 7 8 9 10
Business Objects – BusinessObjects
BusinessObjects’ main strength is its ease-of-use; it provides excellent ‘out-
of-the-box’ functionality with little or no need for prior adaptation. It shields
users from the complexities of SQL, providing access to data through an
easy-to-use mapping layer. All OLAP functions are available through point-
and-click, regardless of whether a user wants to analyse models or format a
report. Reports can be shared and distributed by using the Document Agent
Server, or via integration with e-mail systems.
Cognos – PowerPlay
PowerPlay is an extremely easy-to-use tool with a very intuitive interface.
The metadata support to aid end-user understanding of the models includes
a description of dimensions and measures.
However, there is no support from within the tool to enable end users to
schedule the distribution of reports to colleagues, or to subscribe to such
reports.
Gentia – Gentia Platform for Analytical Applications
Gentia is primarily an application development environment, so the end-
user functionality largely depends on what the developer builds into the
application. Here, we have considered the functionality that is easily incor-
porated into applications (that is, there is component support) and that
provided by the Microsoft Excel Add-in.
The Gentia tools provide all the usual OLAP functions of drill-down and
pivot. Distribution is supported by publishing reports, contained in ‘books’,
‘chapters’ and ‘pages’ to users and workgroups.

The product would be enhanced by a wider range of front-end user tools,
direct support for distribution via e-mail and the addition of a subscription
service.
Hyperion Solutions – Essbase
The range of end-user analysis, presentation and reporting functions depend
largely on the interface used. The Essbase spreadsheet interface can be used
‘out-of-the-box’, but does not offer the same flexibility as other OLAP front-
end tools. Wired for OLAP provides a more graphical presentation interface
for OLAP analysis and reporting. However, there is no direct support for
sharing and distributing reports. Generally, this is provided by integrating
with third-party tools, such as Seagate Info.
Information Advantage – DecisionSuite
DecisionSuite supports an extremely intuitive notebook-style interface that
is consistent across all its client interfaces. Most OLAP functions are easily
available from reports. Reports and calculations are easily defined by end
users, without IS involvement. However, DecisionSuite’s real strength is its
support for flexible group-working. It provides a number of useful features
to promote the sharing and intelligent distribution of reports. Particularly
impressive is the tool’s collaborative working environment, which allows end
users to access shared reports via e-mail, customise them and store them in
their own personalised workspace.
Microsoft – SQL Server 7.0 OLAP Services
The end-user functionality is almost entirely dependent on the end-user
client tool used. The features described here are found in entry level tools
based on Microsoft’s PivotTable Service, a COM component. More extensive
features, such as subscription and distribution services, are outside of the
scope of this component, but are provided by more powerful tools that can
access OLAP Services models using the OLE DB for OLAP interface (such as
Cognos’s PowerPlay and Business Object’s BusinessObjects, both of which
are evaluated elsewhere in this report).
Microstrategy – DSS Product Suite
The initial/casual end user can access the multidimensional model via a
desktop or web interface. Ease of access via the desktop interface is facili-
tated by the ability to select the ‘high-level user’ interface, which does not
include development functionality. In general, the end-user interface pro-
vides the range of features users have come to expect. DSS Broadcaster adds
some very useful distribution facilities.
The toolkit is prevented from getting a higher score due to its inability to
search the metadata for relevant reports and to add text or OLE objects to
reports. This latter point results in reports that are perfectly functional, but
lack many of the presentational features that other products provide.
Oracle – Oracle Express Development Suite
The end-user functionality depends on the tool used to access the model or
application. Here, we mainly assess the functionality offered by Analyzer
when accessing a multidimensional business model. Although the model is
easy to use and offers the expected range of drill-down and pivot features,
the product is prevented from getting a higher score for this criteria by the

lack of support for cataloguing, publishing and distributing models and
reports. The tool could benefit from absorbing some of the features of Oracle
Reports. (There is no integration between the two tools.)
Seagate Software – Seagate Holos
Core OLAP analysis is well supported via the Seagate Worksheet or custom
application interfaces. The Worksheet is well suited to power users wanting
to take full advantage of Holos’s advanced forecasting and modelling capa-
bilities from a ‘no-nonsense’ spreadsheet-like interface. However, Holos is a
tool for IS developers building applications for diverse user requirements.
Holos supports report distribution via the Web or by integrating with report-
ing tools such as Seagate Info. Holos data can also be exported to Lotus
Notes databases as a shared resource, for distribution to group working
environments. Report subscription services are not provided.

BusinessObjects
Essbase
PowerPlay
Seagate Holos
DecisionSuite
DSS Product Suite
1 2 3 4 5 6 7 8 9 10
The process of building a business model is split between DBAs (who create
the mapping layer) and end users (who create reports by querying the
database using the mapping layer). Easy-to-use graphical tools are provided
for both types of user. The design tools provide extensive wizard support,
and DBAs can readily exploit existing database schemas. Multi-designer
environments are also well supported by concurrency and versioning con-
trols.
The strength of the tool lies in its ease-of-use in defining the structure of a
multidimensional model and populating it with data. There is automatic
support for defining the time dimension and quickly producing a prototype if
the data source is appropriately structured. Using Impromptu to access SQL
data prevents a sample of data being available to the designer.
In the Gentia toolkit, the multidimensional business model is built in
GentiaDB, but because of the lack of end-user tools to directly access it, will
almost always be embedded in a Gentia application. The multidimensional
business model in the MDDB can be accessed through its API, but the
company claims this is not often requested or required. Although much of
the specification is done using point-and-click, it is less easy-to-use than
other MDDBs we have seen. Other areas that could be strengthened include
support for more specialised calculated measures and metadata.

Essbase provides a very intuitive interface for business modelling. The
graphical representation of the dimensional hierarchy in the database
outline view works very well, and other graphical tools are provided to make
the definition of measures and data load rules easy. This allows users to
build quite complex business models without the need for specialist DBA
skills. Models can be shared to support multi-designer environments, though
there is no direct support for versioning control.
In DecisionSuite, the report is just one perspective on the business model.
Much of the work is done beforehand when defining a business-oriented map
of the underlying database table structure (metadata tables). This allows
developers to build a logical business model to simplify end-user construc-
tion of reports. The business model is flexible, and the use of filters and
calculations allow for considerable adaptability. A wizard-driven interface
guides designers through the process of describing complex drilling hierar-
chies and aggregation table information. However, a diagrammatic editor
would ease the task of setting up and managing the metadata tables.
The distinguishing feature of building a multidimensional business model in
OLAP Services is the extent to which the designer is assisted by wizards.
While the product cannot be faulted on ease of use, it is prevented from
getting a higher score in this criteria by its limited support for customising
the dimensions and measures. Additionally, and possibly because the model
so closely maps onto the columns in the data warehouse, there is no support
to collect metadata about the model or its components. A further weakness,
if the tool is to be used in a corporate setting, is the lack of support for
multiple designers.
Using the Microstrategy toolkit, much of the work that results in the multi-
dimensional model is done upstream when designing the tables in the data
warehouse and defining the mapping layer in DSS Architect. This makes the
building of models easy, but limits the flexibility. It is, for instance, not
possible to introduce user-defined levels in the dimensions. Nor is there any
tool support for defining the time dimension; it is limited to what is available
in the data warehouse. One of the reasons for the low score on this criteria is
the dependency on the data warehouse for part of the model-building proc-
ess.
The tool provides a flexible and powerful interface to define the business
model, and prototypes can be quickly built using the database wizard. It
offers a full range of features for building the multidimensional business
model. The main ways in which the tool could be enhanced are through more
structured support for the collection of metadata (to collect richer informa-
tion) and by the provision of version control.

Seagate – Seagate Holos
In Holos, business models are built exclusively by IS or developers. These
users are well supported by the Holos language and a number of graphical
tools for defining structures, hierarchies and adding calculations to models.
The combination of structures, models and rules provides tremendous
flexibility for designing a business model, and quite complex business mod-
els can be built by overlaying different types of structures. But a single view
of the various types of structures and dimensions available would ease the
process considerably.

Seagate Holos
Essbase
PowerPlay
DecisionSuite
DSS Product Suite
BusinessObjects
1 2 3 4 5 6 7 8 9 10
BusinessObjects is limited in its support for complex analytical functions.
Generally, the range of native functions provided is geared towards general
business analysis. Advanced functions such as statistical modelling and
forecasting rely entirely on integration with third-party tools. A bonus is the
integrated and easy-to-use data mining facilities provided by the
BusinessMiner tool.
PowerPlay provides multidimensional support, but for more specialised
analytics the user would have to make an additional purchase of Scenario
(although this is included if the business intelligence suite is purchased) for
data mining, and 4Thought for business modelling and forecasting. While
PowerPlay is promoted by Cognos as an easy-to-use tool and has few in-built
functions supporting specialised analytics, the tool includes a scripting
language that enables users to extend the capabilities of the product.
The score reflects the facilities within PowerPlay, but the text indicates how
the other two products could significantly enhance this.
Analytical functions can be built into the multidimensional business model
when it is defined in the GentiaDB and others added when the model is used
within an application created in the Gentia VDE. Within the MDDB and the
application development environment a few analytical functions are pro-
vided. The company’s philosophy behind the product is that if complex
analytics are required by users they could be created as re-usable compo-
nents using the GDL.
The toolkit would be enhanced by a greater range of ready-to-use analytical
functions.

With Essbase, analytical functionality is built into the server model during
design. The main advantage of this approach is to ensure a consistent ana-
lytical environment. Essbase is quite limited in the range of pre-built ana-
lytical functions it provides, which affects the score in this section. Although
Essbase does not provide any advanced ad hoc analysis functions and mod-
els, it does support write back for ‘what-if’ analysis and budgeting applica-
tions. Users that require more sophisticated functions must integrate with
specialist third-party tools, or build their own calculations using calc scripts.
DecisionSuite provides limited support for advanced analytics, although a
number of specialised functions geared towards customer-centric analysis
are provided. Calculation templates could feasibly be used to add more
powerful analytical capabilities to the product, but these need to be built
into the model during the design phase. Users that wish to apply statistical
analysis and sophisticated forecasting algorithms directly to model data will
need to use specialist tools. There is no Excel add-in facility.
OLAP Services’ limited support for advanced analytical power emphasises
that this is a tool for ease and speed of implementation and should not, by
itself, be used for powerful analytical work. It is possible to extend the
analytical functionality by incorporating COM components; this requires an
additional sophisticated skill set.
When writing applications that access data stored in OLAP Services, the
user can use some multidimensional expression (MDX) functions. However,
the functions available within the tool are those that reference a member
rather than those that process sets. It thus provides very limited assistance
in defining complex analytical functions using OLAP Services’ Calculated
Member Builder. End-user tools accessing OLAP Services could make use of
the MDXs mentioned below to provide users with some specialised analyti-
cal support.
There is support for the core analytical functions (such as ranking and
sorting), but Microstrategy’s products have limited support for complex and
specialised analytics over and above that provided by the data warehouse.
The designer can use functions available in the data warehouse database,
but there is no scope within the OLAP tool for extending the range of func-
tions available. This is primarily a consequence of its ROLAP architecture,
which is geared towards using specialised analytics created and stored in
the data warehouse, rather than creating them within the OLAP tool.
Support for advanced analytics is one of Oracle Express’s strengths. It has a
rich collection of ready-to-use functions supporting financial, time series and
forecast analysis. Users, with appropriate permissions, can change the
property of cells and write values back so that values in the model can be re-
calculated. If required, additional functions can be written in the stored
procedure language. The product would be enhanced by the addition of data
mining functionality.

Holos provides an extensible set of advanced analytical functions for statisti-
cal analysis, trend analysis and time series forecasting. Write-back is also
supported for ‘what-if’ analysis. End users have the flexibility to apply a
number of specialised pre-built functions to model data directly from a
custom application or the worksheet interface. The Holos language also
supports advanced data mining capabilities, though the integration of this
technology requires considerable programming effort. Holos does not help
analysts to interpret results of analyses.

Web support
BusinessObjects
DSS Product Suite
DecisionSuite
Essbase
PowerPlay
Seagate Holos
1 2 3 4 5 6 7 8 9 10
WebIntelligence II provides strong support for web access. It supports most
of the OLAP query, analysis and reporting functions as its desktop counter-
part, including the ability to create new models and define format report
formats. WebIntelligence II also benefits from the distribution of processing
across multiple servers for increased scalability and load balancing. The web
tools share the same metadata layer and security as the client-server tools,
allowing for integrated management.
Web support is provided by PowerPlay Server Web Edition, which is
purchased separately from PowerPlay. It allows browser access to
PowerCubes. The functionality provided by browser access to PowerCubes is
getting closer to that offered via the desktop. However, it does not yet offer
facilities to edit or create models via the browser. While there are facilities
for individuals to initiate publication and distribution of models and reports,
the product would be enhanced by additional features to support centrally
organised distribution.
Web access in Gentia is supported by Gentia WebSuite, which uses a CGI
gateway between the usual web server and the Gentia server. It thus ena-
bles a web browser to access Gentia applications and base models. Applica-
tions written especially for the Web can offer excellent functionality. (There
is a web version of Gentia’s flagship product, the Renaissance Balanced
Scorecard.) However, when accessing base models via the Web, the user has
less functionality than with desktop client access. One very useful feature is
that write back via the browser interface is supported.

A current limitation of the system is that if applications are to be accessed
via the Web, designers need to modify them in anticipation of this. There is
little exploitation of the Internet as a distribution mechanism.
The Web Gateway provides the standard interactive analysis and HTML
web publishing capabilities expected from an HTML-based implementation.
Wired for OLAP uses Java applets for a more interactive experience, includ-
ing reporting and charting options. Both clients offer write-back capabilities
from the web browser. However, Essbase’s web capabilities have not been
designed for data modelling. Web access is restricted to registered users and
there is little support for utilising the Internet for dynamic distribution of
reports. There is no support for balancing processing across multiple web
servers.
WebOLAP provides strong web access for accessing and analysing
predefined reports. However, web users cannot define new reports or add
new filters or calculations to the report definitions. Reports can be easily
published and distributed to a wide range of users over the Web using
Internet-based search engines, hyperlinks and e-mail.
OLAP Services, by itself, does not provide web support. As with several of
the features considered in this evaluation, if they are required, they have to
be added on by the use of an appropriate third-party and/or end-user tool.
Some of the entry level tools offer web access using a COM component or
Active Server Pages on IIS. This enables users to explore models using
browser access, but there is no support for the creation of models, nor for
using the Web and the Internet as distribution mechanisms.
Microstrategy offers good web support via two products, DSS Web and DSS
Broadcaster. DSS Web has several modes of action; it provides a develop-
ment environment, is a server enabling thin client creation and access of
reports, and provides administrative control of web usage.
DSS Broadcaster is designed, as its name suggests, to distribute reports to
users. A unique feature is the ability to distribute these to a range of differ-
ent devices simply using a pull-down menu.
The only significant omission in this criteria is the inability to generate
HTML pages directly from within DSS Agent when creating or running
reports.
The Oracle Express development tools enable users to access models using
Web Agent (which is bundled with Express Server) and to publish pages
using Web Publisher. As with most OLAP tools, there is support for web
access but not for the creation of new models.

Holos supports both HTML and Java-based web interfaces. The HTML
implementation is far from elegant, but does provide web users with a
simple and effective means of accessing and navigating through reports,
albeit in a restricted manner. The Java-implementation provides a more
flexible interface for OLAP analysis. The web tools are aimed at end users;
there is no support for designing models or developing applications.

Management
BusinessObjects
PowerPlay
DecisionSuite
DSS Product Suite
Essbase
Seagate Holos
1 2 3 4 5 6 7 8 9 10
The BusinessObjects Supervisor provides excellent graphical management
tools that ease the administration of reports, metadata and end users. The
tool provides strong query governance mechanisms and supports a sophisti-
cated security model to restrict user access to models and other application
objects. But query monitoring is only available with WebIntelligence II and
there is no support provided for tuning the BusinessObjects client cache.
There are two main editions of PowerPlay, (client- and server-based). In both
editions, user and PowerCube securities can be defined. The management
features of the server edition are designed to support a large number of
users. The score here reflects the good support given to the management of
users. In version 6, the features for performance tunability of the data have
been substantially enhanced, but to fully exploit these the administrator has
to manually control the partitioning features.
There is scope for confusion in this section as there are two locations in
which data and users have to be managed: the GentiaDB and applications
developed in the Visual Development Environment (VDE). The main focus
here is on the management facilities within the application development
environment as this has a wider scope.
In the VDE there is a good range of management facilities for controlling
access rights. Through the use of Agents the management of large systems
can be largely automated.

Essbase provides facilities for deploying multidimensional databases to end
users. It provides strong security facilities for both models and users, and
the tool’s sparse data handling capabilities and intelligent calculation op-
tions facilitate efficient data storage and retrieval. But data loading sched-
ules are only supported through a scripting language; there is no point-and-
click support. Event-driven scheduling is not supported. Essbase does not
provide facilities for query monitoring or governance – though this is less of
an issue for optimised MDDBs.
All DecisionSuite application, metadata and user management is defined
and maintained on the server through graphical interfaces. The security of
reports relies entirely on the Unix and RDBMS security. Agents are used for
scheduling report updates and can be based on times and events in the data
warehouse. As expected from a ROLAP tool, DecisionSuite provides strong
support for query monitoring and governance, and produces detailed usage
statistics.
OLAP Services has good support for managing size and partitioning the
data, but poor support for scheduling uploads and providing user security
and controls.
OLAP Services provides a published API, Decision Support Objects (DSO), to
control the management aspects of the tool. This is used, for instance, by the
cube-building wizards. Microsoft has not yet produced easy-to-use function-
ality to support the scheduling of loads and updates, so users have to either
do this manually or write their own applications using the DSO.
The most obvious enhancement needed in this area is the provision of
wizard support for the management of data.
Management of models and users is supported in several of the components
of the toolkit and is easy to use. The toolkit focuses on query management
and there is good support for monitoring the usage of the system.
The score is reduced because some features, such as user security, are
largely delegated to the data warehouse and not provided by the toolset.
In ROLAP, persistent models are a pragmatic convenience rather than a
fundamental aspect of the system. However, they still require management
and the product would be enhanced by more information on failed refresh
schedules.

The Oracle Express development tools offer developers a large number of
options and configurations, and managing these is unlikely to be a simple
task. While support for most of the important management functions is
available, it assumes a DBA mind and skill set. Some tasks (for example,
defining securities) require the administrator to write stored procedures in
the Express language, rather than doing most of the work in a GUI environ-
ment with the occasional need to script. Thus most of the expected features
are available, but the task of the administrator could be eased with wizard
support and more intuitive interfaces.
Holos lacks a separate graphical management console for administering
Holos models, data and end users. A command-line interface is provided to
define scheduling and user security. The security of models relies heavily on
the underlying operating system or database, though stricter access can be
programmed. It will be up to developers to define and maintain these
controls.

Adaptability
Essbase
Seagate Holos
BusinessObjects
DecisionSuite
PowerPlay
DSS Product Suite
1 2 3 4 5 6 7 8 9 10
In BusinessObjects, adaptability is generally a case of incorporating new
members automatically and being able to modify the mapping layer to meet
changing business requirements. All of this is well supported. Access up-
stream metadata from data warehousing tools can be used to synchronise
the mapping layer with data sources. However, there are no facilities to
inform end users of the updates or impact analysis for existing models.
In a client-based configuration, with minimum metadata, adaptability is
generally a case of incorporating new members automatically and being able
to modify the model to meet changing business requirements. All of this is
well supported.
Adaptability is more of an issue in a large-scale environment, where it is
likely that a server-centric model would be used. Adaptability could be
extended to support some of the features described below by the use of MDL,
a fourth generation language, and C shell scripts. There is thus power and
flexibility, but a requirement for additional skills.
It is easy to adapt the application in the light of changing business require-
ments. It is possible, using Agents, to provide very sophisticated support to
ensure that the data sources, models and applications using these are
synchronised. This does, however, require some development effort. Gentia
does not have ‘out-of-the-box’ adaptability, but it does have the tools to
develop powerful mechanisms to support large installations.
The tool does not provide support for impact analysis and change
management.

Essbase models can adapt to change, but there is limited support for the
management of change. Users can take advantage of the drag-and-drop
method for adding new dimensions and measures in models. Changes in
underlying data sources can be automatically uploaded to the multidimen-
sional database as part of a standard batch update process. But there are no
facilities for ensuring that metadata remains synchronised with changes to
models and/or data sources. Essbase does not provide any facilities for
impact analysis and there is no integration with upstream metadata.
DecisionSuite’s metadata layer allows for an adaptable business model. New
dimensions and measures can easily be defined and re-used across models.
All additions are automatically time-stamped. Model metadata can be
referenced to ensure that reports are kept synchronised at all times, but
there are no facilities for keeping data sources and models in line. There is
no possibility to change the architecture from ROLAP to MOLAP mode.
The most notable strength of the tool with regard to adaptability is the ease
of changing the storage architecture. It is also easy to add dimensions and
measures. There is effectively no metadata to synchronise, which – although
it is a limitation in other areas – does at least make implementing the
changes straightforward. The tool is prevented from getting a higher score
by the lack of support to track and predict the impact of changes.
There is support to add new dimensions and measures to a model and re-use
these definitions. The low score reflects the lack of facilities for keeping the
data sources, multidimensional business models and the metadata all syn-
chronised. There is also no possibility of adapting the architecture should
the business needs suggest this.
The most useful feature in Oracle Express supporting adaptability is the
ability to change the data storage architecture. The system can be configured
so that all data is held in the multidimensional database or, using Express
Relational Access Administrator, data stored in a relational database can be
used in the model. Finally, the system can be configured using Express
Administrator, so that some (usually the summary) data is held in the
MDDB and the rest is retrieved using SQL commands (HOLAP). Although
the toolset offers a great deal of flexibility, it does not offer wizard support
and requires a competent DBA to manage it.
Adding new dimensions and measures is straightforward, but the tool lacks
facilities to re-use and track these changes. The simplicity of the metadata,
while in other contexts a negative feature, does simplify the process of
adapting the model.

Within Holos, it is easy to adapt a model to support changing business
requirements; all model design operations produce Holos language scripts
that can be stored and re-used. Holos can also incorporate different types of
structures in a model and easily adapt from ROLAP to MOLAP modes and
vice versa. However, there is no direct support to ensure that data sources
are automatically synchronised with Holos structures and applications. Nor
does Holos provide any support for impact analysis.

Seagate Holos
Essbase
PowerPlay
DecisionSuite
BusinessObjects
DSS Product Suite
1 2 3 4 5 6 7 8 9 10
BusinessObjects’ desktop architecture has potential restrictions on perform-
ance and scalability. To overcome the size and performance issues DBAs can
tune BusinessObjects to exploit native SQL access, multipass SQL and
aggregation tables. Special third-party technology can also be licensed to
handle large and complex data sets. However, performance would be en-
hanced by a more scalable, server-based OLAP engine.
The original design of the PowerCube gave little scope for performance
tunability, but this has changed in recent versions. The combination of
breaking the model into separate cubes that are linked so users can drill
through from one to another, and partitioning, give significant tuning op-
tions. Partitioning gives the administrator the option of trading off build
time against execution time. The incremental update enables new data to be
appended to the PowerCube rather than recreating the whole model.
In the commonly used client-centric configuration, the PowerCube is loaded
down to the desktop; the speed of the local processing is a function of the
desktop hardware and the settings for the in-memory cache.
The distributed client-server architecture of the Visual Development Envi-
ronment offers excellent support for flexibly allocating processing.
Within GentiaDB, performance tunability is largely dependent upon good
design decisions, and the tool would be enhanced by some wizard support for
this process.

One of the strengths of Essbase is its fast multidimensional database engine.
Therefore, query performance is taken for granted. For data loading, per-
formance can be tuned by specifying incremental data loads and dynamic
calculations at runtime. Processing times can also be enhanced using a
combination of the partitioning capabilities and SMP. However, there is no
native SQL access to RDBMSs, which affects the score in this section.
DecisionSuite utilises the strengths of relational database technology, while
ensuring that processing is optimised between the DecisionSuite Server and
the database. It also provides a number of performance-tuning services
aimed primarily at minimising access times, such as multipass SQL, native
SQL access and SMP parallelism.
The administrator has most scope for performance tuning when the tool is
used in MOLAP mode. The most useful feature is the visualisation of the
relationship between database size and performance. In ROLAP and
HOLAP mode there is limited scope for performance tuning.
In a ROLAP model such as Microstrategy’s, the main performance issues are
how to minimise access time, particularly with complex queries. This is
achieved by providing options to cache data and using multipass SQL,
enabling queries of greater complexity to be handled. The Microstrategy
approach is for all the processing to be done on the database server so that
the minimum data is retrieved which reduces the network load, although
the load on the database server is increased.
The product is prevented from getting a higher score in this dimension by its
inability to tune for speed of access through the pre-calculation of aggre-
gates (a consequence of its ‘ROLAP-only’ architecture), distribute the
processing of the SQL queries and the lack of support for load balancing.
Express Server can operate in both MOLAP and ROLAP mode, so it could
potentially be finely tuned for both approaches. As expected, its tunability
strengths are as a MOLAP engine.
The appropriate design of multi-cubes can enhance performance, but there is
no automatic support for this. The tool supports SMP.
One weakness of the tool, when used in ROLAP mode, is that the users have
no direct way of knowing how long the data they are viewing has been
cached.

Holos provides strong tunabaility features for both MOLAP and ROLAP
operation. For ROLAP mode, Holos supports the generation of multipass
SQL and native SQL access to all the major relational databases. For
MOLAP configurations, multidimensional structures can be loaded incre-
mentally. The loading and precalucation of data can also be distributed
across multiple processors simultaneously using SMP technology.

Customisation
Seagate Holos
DSS Product Suite
Essbase
DecisionSuite
PowerPlay
BusinessObjects
1 2 3 4 5 6 7 8 9 10
BusinessObjects is positioned as a ‘ready-to-use’ tool for end users – the
client modules do not require any customisation or adaptation. Support for
specialised application development is limited to a procedural Visual Basic-
like scripting language, for customising aspects of the tool’s interface and
behaviour, or links to Windows development tools via OLE automation.
PowerPlay is designed as an ‘out-of-the-box’ toolkit and thus has few fea-
tures for building standalone executable applications with multidimensional
features. PowerPlay supports OLE automation and thus applications that
make use of PowerPlay components can be developed in third-party lan-
guages.
The usual division of responsibilities is that IT-based staff create and main-
tain the multidimensional models from which PowerCubes are generated,
and business operators use these to produce customised interactive reports.
These reports can be considered as applications, but they do not disguise
their PowerPlay genesis.
Gentia is primarily a platform for analytical application development and
offers a comprehensive range of services to support this. It enables special-
ised OLAP applications to be quickly developed in an easy-to-use GUI
environment. As a result of its distributed architecture, the applications can
run on both Unix and Windows using data stored on a variety of platforms.
While it is possible for the developer to produce sophisticated applications
for the Web, the development environment is still more geared to the produc-
tion of applications for desktop access. It is the responsibility of the devel-
oper to note the differences between developing for the desktop and the Web.

The toolkit would be enhanced by more direct support for the development
of web applications.
Application development is provided by a set of Active X controls and via
Essbase’s published API. The controls can be assembled to build custom EIS-
type applications. They can also be integrated with third-party development
tools. The API is functionally rich, and is extensively used by third-party tool
vendors and VARs to integrate with Essbase.
DecisionSuite provides limited support for application development. Add-ins
and a procedural scripting language are available to customise applications
and reports. Application development relies on the tool’s API, and using
external development tools that can use the same DLL that links the
DecisionSuite client modules to the server.
The low score in this criteria reflects the absence of support provided by the
tool to develop customised interactive applications. However, if viewed as a
component within an application, then the ubiquity of the API and the low
cost makes it attractive to developers. It cannot be used to customise, but is
itself customisable.
OLAP Services is a component, with an open API, that can be used within a
customised application. The development of the application can be carried
out in any COM-compliant environment such as Visual Basic or C++, but
OLAP Services itself does not provide an environment for developing these.
The toolkit provides three development environments:
• an API to support application development in OLE-enabled application
development languages such as Visual Basic, Visual C++, VBA and
Delphi
• development for the Web using DSS Web
• the production of EIS reports using DSS Executive.
None of these environments provide ‘OLAP-aware’ components (such as
cross-tab and graphical objects), which limits the type of application that can
be easily produced.
Oracle Express and its associated products provide excellent support for
application development. Reports with multidimensional features can be
developed using Oracle Express Analyzer’s visual development environment.
Using Oracle Express Objects, fully featured applications can be developed
combining ease of use with the power of a procedural language. Finally, Web
Publisher enables ‘browser aware’ applications to be created.

Holos is a powerful 4GL application development tool. The Holos language is
purpose-designed for building business intelligence applications, and is
supported by an integrated and easy-to-use set of graphical development
tools. The range of development options should satisfy most development
needs, from simple EIS reporting systems to advanced analytical
applications.

Applix TM1
Summary
At a glance .............................................................................................. 2
Terminology of the vendor ....................................................................... 3
Ovum’s verdict......................................................................................... 4
Product overview ..................................................................................... 6
Future enhancements ........................................................................... 14
Commercial background
Distribution ............................................................................................ 16
Product evaluation
Web support .......................................................................................... 23
Management ......................................................................................... 23
Adaptability ............................................................................................ 25
Customisation ....................................................................................... 27
Deployment
Platforms ............................................................................................... 29
Data access .......................................................................................... 29
Standards .............................................................................................. 29
Published benchmarks .......................................................................... 29
Price structure ....................................................................................... 29

2
Evaluation: Applix – Applix TM1
At a glance
Developer
Applix, Westboro, Massachusetts, USA
Versions evaluated
Applix TM1 version 7.1
Key facts
• A MDDB that runs in memory and works with standard spreadsheets or
OLE DB for OLAP clients
• Server runs on Windows 95, Windows 98, Windows NT and Unix; clients
run on Windows 3.1, Windows 95, Windows 98 and Windows NT
• TM1 was the first OLAP vendor to support Microsoft’s OLE DB for OLAP
as a data provider
Strengths
• Extremely quick response times provide fast OLAP analysis for small
datasets
• No time-consuming precalculation or batch loading of the MDDB
• TM1 architecture is well suited for remote and mobile computing
Points to watch
• Does not yet support its own web client – web access relies on third-party
tools
• Performance can be adversely affected by large concurrent user loads
when accessing models with high volumes of data and complex OLAP
calculations
• Simple modelling tools that are best suited to small data volumes
Ratings
End user functionality
Web support
Management
Adaptability
Customisation
1 2 3 4 5 6 7 8 9 10

3
Cube
This is constructed from two or more multidimensional structures (dimen-
sions). A cube is defined by combining a series of dimensions into a matrix of
cells, where each cell is a unique intersection of dimension elements. In TM1,
a collection of cubes forms a multidimensional database.
Database
This contains one or more multidimensional cubes and associated dimen-
sions, security assignments and other metadata.
Data dictionary
A set of Windows folders that contains TM1 cube, dimension, rules and other
system information that gets loaded into memory when TM1 runs.
Data point
A unique data value held in a cell. Data points can be sourced from a
RDBMS, a flat file, by a manual input, or they can be derived from a rule.
Dimensions
Dimensions define the structure of cubes. They are simply used to organise
how data is stored in cubes and to generate consolidations. Each dimension
contains a hierarchy of elements.
Elements
These are individual items within each dimension. The intersection of
element values for two or more dimensions identifies the location of a cell in
a cube. In TM1, there are two types of elements: base-level elements (such as
sales and receivables) or calculated elements (such as net income or gross
margin).
Level
This refers to the position of an element in the dimension hierarchy and is
used to specify what data is requested in a query. Base-level elements
contain input data only. Higher level elements are aggregations of lower
level elements.
Real-time OLAP
An Applix term used to describe a multidimensional database that runs in
memory and performs calculations on demand, rather than according to
precalculation.
Rules
Rules are formulas that perform specific calculations on data. Rules calcu-
late values for cells in the cube when they are requested by a query or are
required to create values for a query; the values are stored (temporarily) in
memory.

4
Ovum’s verdict
What we think
TM1 takes to heart the concept of OLAP as ‘a spreadsheet on steroids’. In
this respect, it will be most appreciated by financial users wishing to com-
bine the flexible display and ad hoc calculations of spreadsheets with the
performance of a multidimensional database.
TM1 scores modestly according to our evaluation criteria, but in some
specialised applications it has definite strengths. Its best feature is its
memory-resident OLAP engine, which is unique on the OLAP market and
provides exceptional performance. TM1 multidimensional databases can be
put in memory and calculated quickly in real time. TM1 only stores the
lowest level of model data in the OLAP engine and calculates aggregations
on demand, so it avoids the ‘data explosion’ problems associated with
MDDBs. It also removes the need for batch recalculations each time fresh
data is uploaded. TM1’s small data footprint is ideal for mobile computing
environments and strong data replication and synchronisation capabilities
are provided for disconnected analysis.
TM1 performs best as a ‘local’ desktop OLAP solution; its exclusive calcula-
tion-on-demand approach is optimised for small data volumes and small
numbers of users. Query performance can be affected by large numbers of
concurrent read-write users working with models that contain high volumes
of data with deep hierarchies and complex calculations. However, new
features in version 7.1 make the product more credible as a scalable server
product. The spreadsheet interface provides a simple and flexible OLAP
front end, but TM1 would benefit from more robust modelling tools.
TM1 exploits the OLE DB for OLAP interface to provide web access and
advanced analysis and reporting functions through third-party tools. Hence,
the level of functionality is dependent on the front-end tool used. There is
little support for developing custom analytical applications, although TM1
can easily be embedded into third-party systems. Overall, TM1 is a mature
OLAP tool, with many reference sites, but it has suffered from poor market-
ing in recent years. Applix will need to shout loud and clear to get its ‘real-
time OLAP’ message across in an increasingly competitive OLAP server
market.
When to use
TM1 is suitable if you:
• want to support financial planning, budgeting and forecasting
applications that require relatively small data volumes
• want to build large business models of five of more dimensions, where
traditional MDDBs would ‘explode’ the data
• have dynamic applications that require frequent input of data,
recalculation, or analysis of ‘what-if’ scenarios
• have existing spreadsheet skills you want to exploit
• want to support mobile users for offline analysis.

5
It is less suitable if you want:
• to develop ‘ROLAP-style’ applications running against large volumes of
data
• to rollout OLAP to large numbers of casual business users with simple
OLAP needs – TM1 is primarily aimed at analysts
• integrated development tools for building highly customised analytical
applications.

6
Product overview
Components
TM1 version 7.1 consists of the following components:
• TM1 Server
• TM1 Perspectives
• TM1 Client
• TM1 Architect
• TM1 Data Control
• TM1 Process Objects
• TM1 API.
Figure 1 shows the primary functions of the components and how they relate
to client-server systems.
TM1 Server
A multidimensional database and OLAP engine that stores and provides
access to multidimensional models (cubes) managed in local or remote TM1
Servers.
TM1 Server works with memory-based cubes and its most significant fea-
ture is its memory-resident calculation engine; all OLAP consolidations and
calculations are performed on-the-fly, rather than working with
precalculated cubes stored on disk. The engine can be run as a multi-user
remote server, or from within the TM1 Perspectives and TM1 Architect
clients in local, ‘standalone’ mode.
TM1 Perspectives
A 32-bit client interface, for spreadsheet users, to the TM1 Server. TM1
Perspectives is provided as an add-on to Microsoft Excel and Lotus 1-2-3
spreadsheets. It has three main components:
• TM1 multidimensional database engine, which stores cubes in memory
and performs OLAP calculations on demand
Figure 1 Component functions
Data loading OLAP analysis Administration Customisation
Client TM1 Data Control TM1 Perspectives TM1 Architect
TM1 Client
Server TM1 Process Objects TM1 Server TM1 API

7
• Server Explorer, which provides DBA-like tools for creating dimensions
and cubes and administering TM1 Server, data replication processes and
end-user security. It includes a data acquisition module for creating
dimensions, cubes and loading data
• Spreadsheet Integration, which adds a TM1 menu item to the Excel and
Lotus 1-2-3 programs for analysing cubes. It provides dialogues for
filtering data and performing OLAP analysis. It also supports an
alternative worksheet-oriented environment for developing cubes from
flat files.
TM1 Perspectives can run against a multi-user remote TM1 Server, or in
‘standalone’ mode against a local TM1 Server.
TM1 Client
TM1 Client is an independent version of the Spreadsheet Integration compo-
nent found in TM1 Perspectives. The TM1 Client is an exclusive interface for
spreadsheet users that want to access predefined TM1 cubes and does not
provide the development or administration capabilities of TM1 Perspectives.
TM1 Client can also run against a multi-user remote TM1 Server, or in a
‘standalone’ mode against a local TM1 Server.
TM1 Classic is a 16-bit version that integrates with Excel 5 and Lotus 5
spreadsheets, but does not support a local TM1 Server engine.
TM1 Architect
TM1’s standalone development, deployment and administration tool. TM1
Architect provides the same functionality as TM1 Perspectives, with the
exception of Spreadsheet Integration. It supports the same Server Explorer
GUI, including a functional Cube Viewer and Dimension Subset dialogues
that are designed to aid development. TM1 Architect is also used to manage
applications deployed through third-party clients (OLE DB for OLAP and
Java).
TM1 Data Control
An optional front-end tool for automating and scheduling TM1 back-end
processes for building and maintaining dimensional hierarchies, creating
cubes and importing data.
Data Control combines TM1 Server’s OLAP engine and ODBC access to
source data and is hosted by the spreadsheet. Spreadsheet users can define
and execute ODBC queries that load or update data into TM1, create rudi-
mentary transformation processes to map source data to TM1 cubes, and
also export data from TM1. Individual processing tasks can be grouped
together in a single Data Control ‘job’ and run on demand or at a scheduled
time.
Data Control was developed by Revelwood, a New York-based consulting
firm that specialises in TM1 application development. The product is written
in Visual Basic, and is marketed and resold exclusively by Applix.

8
Process Objects
Version 7.1 provides new server-based Process Objects. These objects eclipse
the client-based data import and update facilities by providing server-side
capabilities for handling complex event-driven scheduling. Process Objects
can be linked to the TM1 rules language for conditional trigger of back-end
TM1 Server processes, such as mapping, transformations and creating and
updating TM1 cubes and dimension hierarchies.
TM1 API
The TM1 API is the element that allows native TM1 clients or third-party
applications to interact with the TM1 Server. TM1 supports four APIs:
• API 7.0, which provides public documented access to TM1’s own
language, and all the calls necessary to develop, manage and use TM1
applications. It is available in C++ and Visual Basic
• JavaBean API, which allows third-parties to develop Java-based OLAP
applications. The TM1 Java API contains all the functionality of the C++
API
• OLE DB for OLAP as a data provider. This opens TM1 for access from
any OLE DB for OLAP consumer tool
• API 1.5, which translates applications written to TM1 Server 6.0’s 1.5
API specification into the appropriate 7.0 API call.
This is the ‘natural’ architecture for TM1, consisting of TM1 Servers and
clients running TM1 Perspectives, TM1 Architect and/or TM1 Client. The
TM1 Server loads data in memory on the mid-tier server and services
requests for data from TM1 Client programs. If the client is an OLE DB for
OLAP consumer, it uses a MDX parser facility to interpret requests and
return data.
A web server can be introduced to provide access to the TM1 Server using
supported OLE DB for OLAP clients that offer web access.
The OLAP engine and MDDB store run in memory on a mid-tier server,
where all OLAP calculations are carried out. Cubes can be stored persist-
ently on disk, in either a proprietary disk-based structure or any ODBC-
compliant database. This provides the option of storing cubes in relational
tables and working with these tables through relational query tools.
Depending on the configuration, TM1 clients may have exclusive access to a
single local TM1 Server, which acts as a repository for their private data, or
shared access to one or more remote TM1 Servers; the level of access de-
pends on the security group assigned by administrators. TM1 supports
multiple cubes that can be distributed across several servers. Data can also
be replicated from/to another remote satellite server, and updates can be
synchronised back to the source server.

9
A ‘light’ mid-tier architecture is not supported.
Desktop and mobile architectures
TM1 Perspectives supports a self-contained OLAP engine that can run as a
separate thread or as an in-process server on a laptop computer. Asynchro-
nous replication of data from a remote TM1 Server into the local OLAP
engine allows users to perform complex analyses while detached from the
mid-tier server. Updates made locally can also be synchronised back to the
remote server.
Using TM1
Real-time analytical processing
TM1’s most distinctive feature is its support for ‘real-time analytical process-
ing’ (RAP). This is made possible by its RAM-based OLAP engine (for which
Applix has a patent), which loads and runs the MDDB entirely in memory.
The MDDB can be loaded into memory because all derived values in TM1
are calculated on-demand. This avoids both ‘database explosion’ and the
lengthy pre/recalculation times when loading or updating the database. The
downside is that the derived values take time to calculate – generally, the
time taken is a function of the complexity of the calculations and the depth
of the dimensional hierarchies in the model. Additionally, TM1 uses a com-
pression algorithm to minimise the use of memory. Generally, the source
database stores one number per record that will be input into the MDDB
and requires up to 50–100 bytes per record. TM1 on the other hand stores
one number (plus indices) in approximately 12 bytes. Hence, a TM1 multidi-
mensional database is typically 10–25% the size of the data source.
Performance, in terms of response time, is also enhanced by retaining calcu-
lation results in memory (as long as they are still valid) to support future
requests. This prevents the same calculation from being repeatedly executed
for a cell and can greatly increase performance. TM1 flags calculations as
invalid when values in the cube are modified. The next time a value is
requested, a fresh calculation is performed.
The anatomy of a TM1 model
It is easier to understand the TM1 approach to OLAP if key concepts under-
lying a TM1 model are clarified first.
In a TM1 model, all data is stored on the server as cubes. A cube represents
a combination of two or more multidimensional structures, and a collection
of cubes forms a TM1 multidimensional database. Dimensions are the raw
building blocks of TM1 cubes and are used to define the hierarchically
structured axes of cubes. Dimensions contain no data and are stored inde-
pendently of cubes. Dimensions consist of one or more elements that may be
arranged in a sub-hierarchy. There are two main types of elements: base-
level elements, which represent the lowest level of input data in a model,
and calculated values, which are derived from formulas called rules.

10
Building a TM1 model
TM1 supports two different approaches for creating dimensional hierarchies
and cubes, mapping them to source data and loading in data.
Spreadsheet-based approach
The first approach uses a spreadsheet-based development interface and is
supported by TM1 Perspectives. It is restricted to importing data in comma-
delimited flat files (in ASCII format).
The process of creating dimensions and cubes is driven directly from the
spreadsheet through a series of special worksheets. These worksheets look
similar to standard Microsoft Excel and Lotus 1-2-3 spreadsheets, except
that they contain special TM1 functions for defining dimension structures
and are linked to TM1 multidimensional databases.
Dimension worksheets are used to layout dimension structures using stand-
ard spreadsheet columns. Cubes can then be created by selecting appropri-
ate dimensions. Wizards are available to automate both the dimension- and
cube-building processes. Calculations and inter-cube rules can be defined in
a rules worksheet. TM1 supports a non-procedural language for evaluating
rules.
When a cube is created, it is still an empty matrix and data can be entered
either manually from a Cube Viewer worksheet, or by creating a processing
worksheet that reads records into spreadsheet rows and inputs field values
into separate cells. ‘Database send’ formulas in the process worksheet map
the cell values to an appropriate address in the TM1 cube. The processing
worksheet can also be used for the transformation of the data because it is
loaded into the cube. TM1’s Data Control facility can be used with the proc-
ess worksheet to add a layer of automation and scheduling to the whole
process; it can also be used to specify relational tables as data sources.
Data Loader
The second approach uses a dialogue-driven interface and is supported by
TM1 Architect. It provides a more graphical method of defining dimensions
and cubes and loading data directly from relational database tables or flat
files. The process is supported by the following dialogues:
• Dimension Editor, for manually defining small dimensional hierarchies.
This approach assumes some knowledge of the structure of the source
data
• Dimension Loader, for importing dimension and element names from
delimited flat files and RDBMS tables. It is used to identify one or more
input columns that supply elements for a single dimension. It represents
a faster approach for building a long list of elements
• Data Loader, for creating dimensions during the process that imports
data into a new, or updates an existing, cube. A dialogue box is provided
to allow end users to selectively map dimensions to input columns and
identify the value to be stored. However, input rows must contain
element names that exactly match the spelling of elements in the cube’s
dimensions; simple and complex transformations are not supported.

11
The new Process Objects in version 7.1 remove much of the manual effort,
by adding a layer of automation and management for the data integration
and loading process.
Spreadsheet-based analysis
TM1 Perspectives and TM1 Client are both designed to exploit existing
spreadsheet skills. TM1 is tightly integrated with standard Microsoft Excel
and Lotus 1-2-3 spreadsheets via special add-ons; TM1 commands are
available from a single pull-down menu from within the spreadsheet.
Two main navigational and analysis tools are provided: the Cube Viewer
dialogue and the Dimension dialogue.
Cube Viewer
The Cube Viewer dialogue, shown in Figure 2, allows end users to navigate
through TM1 models from the worksheet.
Figure 2 Cube Viewer

12
The Cube Viewer facility represents the structure of the cube and shows the
dimensions that make up the model. Each dimension is presented as a
button; the arrangement of buttons determines a particular ‘perspective’ of
model data that can be sliced into the spreadsheet. Version 7.1 also supports
OLAP functions from an OLE object directly embedded in the spreadsheet.
Dimension dialogue
Double-clicking on any dimension in the Cube Viewer brings up the Dimen-
sion dialogue shown in Figure 3, which allows end users to refine the subset
of what appears in the Cube Viewer by selecting and filtering Dimension’s
members. The advanced settings in the Dimension dialogue provide access
to Dimension edit and query functions, and OLAP functions such as drill-
down and roll-up and query data in the cube.
By using the advanced browser features in TM1, a subset of the data can be
defined that represents a useful ‘perspective’ of the data you might want to
use in the future. This perspective can be saved as either:
• a worksheet, called a ‘slice worksheet’
• a subset of the model data, called a ‘view’.
A view provides slice-and-dice capabilities, but does not support charting or
spreadsheet formatting options.
Slice worksheets are similar to standard worksheets: end users can format
them and add rows, columns or new formulas. Although the slice worksheet
loses its pivot capability, it does allow users to place any cell from any cube
within the worksheet. Slice worksheets remain linked to the TM1 Server.
Therefore, if a number changes in the multidimensional database, that
change is reflected in the worksheet when it is recalculated. Similarly, if the
worksheet user changes a value, it is also changed in the corresponding cell
in the TM1 database, provided they have the correct access privileges.

13
Figure 3 Dimension

14
Future enhancements
Version 7.2 of TM1 is expected in the summer of 1999. It will contain two
major enhancements:
• scenario cubes, which support the creation of cubes that are ‘variants’
(scenarios) of other cubes. The scenario cube overlays the source cube and
include changes made to the scenario cube by the cube user. This will
allow users to make changes to the cube without affecting other users.
Any changes can subsequently be committed into the source cube if
desired. The same approach can be used to import large sets of data that
are held in ‘suspense’ and incorporated on demand
• dynamic subsets, which allow dimension subsets to be defined by an
expression, rather than as a list of members. The subsets are dynamic
and automatically synchronise with changes to its underlying data or
metadata. A subset editor will be provided to define expressions and store
them as objects in the server that get re-evaluated when data/metadata
changes.
As part of its web strategy, Applix is working with established partners to
develop web-based analytical applications. Integration with Revelwood’s
SmartSite development environment will be available in the second quarter
of 1999. This will include integration with Microsoft FrontPage for web page
development.
The development of a Java version of Architect is under review.

15
Company background
Applix is a US company founded in 1983 to develop and market software
applications for the Unix market. In 1986, it introduced Alis, its first office
automation product. Alis was replaced by the Aster*x product, which pro-
vided the technology for Applixware, a suite of real-time decision support
tools. In 1995, Applix acquired Target Systems, a developer of customer
interaction software, and two major business lines were subsequently
formed: Decision Support Systems (DSS) and Customer Interaction Software
(CIS).
All Applix’s products are based on the concept of real-time decision support:
• Applixware, an integrated family of desktop and development tools for
real-time decision support applications
• TM1, a multidimensional OLAP server that runs in memory
• Applix Enterprise, a suite of customer-interaction management systems
including Applix Service (for customer support), Applix HelpDesk (for
internal IT support) and Applix Sales (for sales and marketing support)
• Applix Anyware, software that exploits Java and thin-client computing
for deploying Applixware and Enterprise applications over the Internet.
The original developer of TM1 was TM1 Software (founded in 1984 as
Sinper) – a privately-owned venture specialising in OLAP products. Applix
acquired TM1 Software in 1996 for $11 million. TM1 Software is now an
operating unit of Applix based in Warren, New Jersey, US. TM1 was first
released in 1984 as a single-user, DOS-based multidimensional engine. The
product was completely re-architected for client-server systems in 1989.
Applix is a public company quoted on Nasdaq. Revenues for 1997 dropped
5% to $48.5 million, and the company recorded a net loss of $0.4 million.
Applix employs more than 300 people and is based in Westboro, Massachu-
setts, US, with seven regional offices in North America and subsidiaries in
Europe and Asia-Pacific.
TM1 has been available since 1984 and has amassed more than 3,000 cus-
tomers spread evenly throughout the US and Europe. Therefore, it is sur-
prising that TM1 has failed to gain any significant mindshare in the OLAP
market. Two reasons are that TM1 Software was historically an engineering-
led company, and TM1 was primarily seen as a desktop OLAP solution.
However, Applix has boosted the marketing of version 7.1 of TM1 and is now
starting to capitalise on ‘real-time OLAP’, an Applix term used to describe a
MDDB that runs in memory and performs all calculations on demand. Its
TM1 business is growing rapidly; Applix claims that TM1-related revenues
more than doubled in 1997, fuelled by a number of enterprise deployments.

16
TM1 is sold directly and via channel partners, including systems integrators,
ISVs, consultants, OEMs and more than 100 VARs. Historically, around 95%
of sales were through partners. TM1 has a particularly strong presence in
markets such as banking and telecommunications.
Applix has bundling deals with a number of OLAP vendors. However, a long-
standing licensing agreement with Hyperion Software has ended after
Hyperion’s merger with Arbor Software in September 1998 – Essbase will
now be the server of choice for Hyperion’s analytical applications. Other
major TM1 partners include Comshare, Information Advantage, IBI,
Platinum Technology and JBA.
Customer support
Support
Applix can provide around-the-clock worldwide support for TM1, via
telephone, e-mail, fax and the Web. The company sponsors local, national
and international user groups.
Training
Applix offers public training courses held at local sites or at its headquarters
in Westboro, Massachusetts, US. Onsite training is also available.
Consultancy services
Applix offers performance tuning and site-specific implementation services
for TM1. However, most implementations are done by partners. Consulting
operations are based in the US, France, Germany and the UK, as well as
those supplied by partners worldwide.
Distribution
US
Applix
112 Turnpike Road
Westboro, MA 01581
USA
Tel: +1 508 870 0300
Fax: +1 508 366 0995
Europe
Applix (UK)
114 Middlesex Street
London E1 7HY
UK
Tel: +44 171 426 0915
Fax: +44 171 426 0916

17
Asia-Pacific
Applix
9 Raffles Place #27-01
Republic Plaza
Singapore 048619
Tel: +65 435 0490
Fax: +65 536 4315
E-mail: applixinfo@applix.com
http://www.applix.com

18
Product evaluation
Summary
1 2 3 4 5 6 7 8 9 10
TM1 uses either Microsoft Excel or Lotus 1-2-3 as a front end. It will there-
fore appeal to experienced spreadsheet users, since it allows for the easy
browsing of models in spreadsheets that can also become reports. OLAP
functions are set up via a menu-driven interface and not directly from the
spreadsheet. The spreadsheet lacks the flexibility to support large distributed
environments and does not support advanced OLAP reporting and data
visualisation functions provided by other dedicated OLAP front ends. How-
ever, integration with a wide range of OLE DB for OLAP tools compensates
for this.
Models are accessed using the graphical Server Explorer interface. Models
can be grouped into standard hierarchical directories. Related model files,
views and subsets are grouped into a set of Windows folders called data
dictionaries. However, there are no search facilities.
Most of the metadata provided by TM1 is structural and relates to dimen-
sion names, member categories and their co-ordinates in the model.
Annotation by end user
Models cannot be annotated by the end user. However, once data has been
loaded into worksheets, footnotes and explanations can be added using
standard spreadsheet functions.
Using the model
Standard OLAP functions such as drill-down and slice-and-dice are graphi-
cally supported via the Cube Viewer and Dimension dialogue windows; users
simply click on a spreadsheet cell, which brings up the appropriate Dimen-
sion dialogue window for OLAP manipulation and then switches back to the
native spreadsheet view. Version 7.1 of TM1 provides an alternative to the
Cube Viewer and Dimension dialogue windows by offering OLAP functions
directly from an OLE object embedded in the worksheet.
It is not possible to change the position of members in a dimension directly
from a worksheet. This is only possible using the model design dialogues or
by editing the dimension worksheet.

19
Icons in the worksheet model provide pointers on drill-down paths. The
overall structure of the dimensional hierarchy can be displayed graphically
in tree-form in the Dimension dialogues.
End users can drill through TM1 cubes and access transaction-level data in
a RDBMS (provided that the TM1 architecture is configured to relational
DBMS data storage).
All TM1 clients are add-ons to the Microsoft Excel and Lotus 1-2-3
spreadsheet environments. Additionally, TM1 exploits the OLE DB for OLAP
interface to extend the range of front-end options to more than ten OLE DB
for OLAP consumers, including Cognos PowerPlay, Knosys ProClarity, Brio
Enterprise and Seagate Worksheet. However, most integrations are read-
only. The OLE DB for OLAP support also provides links to custom front-end
interfaces developed by other tools, including Gentia Software’s Holos,
Comshare’s Decision and ArcPlan’s Insight development environments.
The visualisation of results depends on the front-end tool employed. The
TM1 clients rely on the charting and graphing conventions provided by
Excel and Lotus 1-2-3. Data from several models may be included in a single
worksheet view.
Designing a report
TM1’s report design capabilities depend on the front-end tool employed. TM1
client programs rely on the reporting facilities provided by the spreadsheet.
Support is provided for stacked (nested) cross-tab spreadsheet views. End
users can also exploit the macro functions of Microsoft Excel and Lotus 1-2-3
to add their own locally-defined calculations. Similarly, objects can easily be
embedded into worksheets via OLE.
Publishing a report
There is no direct support for scheduling the publication of models across
the enterprise.
There are no additional facilities provided for end users to e-mail ‘live’ TM1
models from the spreadsheet client interface.
TM1 does not provide any report subscription services.

20
Summary
1 2 3 4 5 6 7 8 9 10
TM1’s spreadsheet-based approach to modelling adds simplicity to the data
mapping and transformation process. Although it sounds slow and limited, it
is adequate for small data volumes – as typically found in the application
areas that TM1 targets. An alternative (and much quicker) approach relies on
a straightforward graphical mapping to relational and flat file sources, but
is only suitable for basic cases, where the source data and metadata require
no transformations. Server-based Process Objects add a much needed layer of
automation and management to the model building process.
Basic design
Design interface
TM1 supports two design interfaces for building models:
• TM1 Architect, which provides a number of graphical dialogues for point-
and-click development
• TM1 Perspectives, which uses the spreadsheet as the development
interface.
It is possible to view source tables. However, it is not possible to view the
database schema or bring up a sample of data on screen.
TM1 does not support a universal mapping layer.
TM1 does not provide any automatic prompts for including metadata during
the model-building process.
Columns for dimensions can be selectively included (or excluded) via point-
and-click. Each TM1 model can have a maximum of 16 dimensions.
Members can be selected and hidden in a dimension level using point-and-
click.
Multiple hierarchies and consolidation paths can be built using graphical
dialogues. TM1 Perspectives can edit the dimension worksheet to layout the
dimension structure.

21
Time dimension
TM1 does not require a cube to have a special time dimension, but can
recognise time dimensions if defined. Aggregations and other OLAP calcula-
tions over time must be built-in to the model manually.
There is no support for the annotation of model dimensions.
When a model is created, the system default shows only the highest level of
consolidated elements along the title dimensions. However, end users can
create a subset of data for a specific dimension level. This can be saved as a
‘view’ for future access.
Calculated measures
A graphical Rules Editor dialogue can be used to define formulas for calcu-
lated measures (or any other dimension elements) in a model via point-and-
click. Typically, a rules formula is an expression of arithmetic operators and
parentheses, numeric constraints, numeric and string functions, conditional
logic and cube references. The rules language allows for rules to inter-relate
with multiple cubes.
A model can store data against one or more measures.
Multiple designers
Multiple designers
Other than simple locking mechanisms, there are no special check-out/in
facilities to support multiple designer environments.
There is no support for versioning files created by TM1.
Summary
1 2 3 4 5 6 7 8 9 10
TM1 provides limited support for advanced prebuilt analytical functions.
There are basic ranking, arithmetic and financial aggregation functions.
Beyond this, it is expected that end users will either build their own custom
functions using the TM1 rules language, or exploit native spreadsheet func-
tions to extend the analytical capability.

22
All the TM1 clients are offered as Excel add-ons and therefore benefit from
the analytical functions provided by the spreadsheet tool. There is no
integration with specialised statistical packages.
Ranking and sorting
Simple sorting and ranking functionality is supported via the OLE DB for
OLAP interface.
TM1 only supports basic arithmetic operators such as addition, subtraction,
multiplication, division and exponentiation.
Financial functions
TM1 supports standard financial aggregation and currency conversion
functions.
Statistical models
There is no support for advanced statistical modelling.
Trend analysis
Through its integration with Microsoft Excel and Lotus 1-2-3, TM1 supports
basic graphical support for straight-line (linear) trend analysis.
Simple regression
TM1’s forecasting functions rely entirely on the regression functions
provided by Microsoft Excel or Lotus 1-2-3.
Time-series forecasting
There are no special functions for advanced time-series forecasting.
TM1 supports a non-procedural rules language for defining custom multidi-
mensional and inter-cube calculations.
TM1 supports multi-user write back to the database for what-if analysis
scenarios. At a local level, spreadsheet users can write-back new values in a
worksheet for uncalculated values in a cube and immediately see the effect
of changes in cell values across the current view. Changed data in a shared
server is available immediately to all users of the server.
TM1 can store textual information as attributes or as cell values. These
values can be used in simple counts or rules calculations.
Data mining
There is no support for data mining in TM1.

23
Web support
Summary
1 2 3 4 5 6 7 8 9 10
TM1 does not support its own web client for accessing TM1 Server. The TM1
web strategy is based upon providing a range of capabilities for TM1 applica-
tion deployment and development across the Web from third-party tools using
the OLE DB for OLAP interface. The level of OLAP functionality provided
varies considerably from tool to tool, and most integrations are read-only.
Management
Summary
1 2 3 4 5 6 7 8 9 10
Most model administration tasks are achieved graphically using the Server
Explorer interface. Strong data replication and synchronisation facilities are
provided to manage distributed environments. The introduction of Process
Objects greatly improves the back-end processing and scheduling capabilities
of the tool. Because of its concentration on financial applications, TM1
security goes further than most OLAP products. Access controls can be de-
fined on a per model-, dimension- or cell-level for users, groups or servers.
TM1’s monitoring facilities log all OLAP transactions, but the presentation of
metadata could be improved.
The Server Explorer provides a graphical interface for managing models and
administering local and remote TM1 Servers. Common model administration
tasks are achieved through two windows:
• one presents hierarchical lists of models and dimensions and other
related server objects that are accessible via the TM1 Server
• the other references the properties of the TM1 Server objects.
Security of models
Security controls can be defined for servers, cubes, dimensions and elements
to restrict access to models:
• cube-level security governs overall user access to models; privilege levels
include read/write access, reserve access (provides exclusive rights to the
model until it is released), lock access (means that other users cannot
modify the model, but can access it as read-only)
• element-level security governs access to cells identified by certain
elements
• dimension-level security governs the ability to add, remove and re-order
the elements in a dimension.

24
Query monitoring
TM1 Server tracks the transactions made by client OLAP requests in an
ASCII log file. It provides information about who made the change, what
model it was made to, when it was made and how certain cell values were
affected.
Management of data
TM1 stores data persistently on disk as compressed proprietary files. It also
offers the option of storing data in relational tables.
Only the lowest-level detail (base-level) data for a TM1 multidimensional
model is stored persistently, and is loaded into memory on the server when
requested by end users. All consolidations and calculations are done on-the-
fly and are also stored in memory.
Process Objects provides an activity scheduler for controlling tasks such as
defining and executing ODBC queries for loading and updating cubes from
relational databases or flat file systems.
Process Objects supports external event-driven scheduling functions.
Data Control provides a ‘key error report’ that provides a list of the key
errors that occurred during the data load/update process and other back-
ground processing tasks.
TM1 Server supports server-to-server replication of data. Replication is bi-
directional and the ability to see or change data in replicated model data is
managed through security assignments. Model metadata (dimensions and
rules) is only replicated from the replication server to the planet servers,
and cannot be changed on the planet servers.
Sparsity
For calculations across sparse dimensions, TM1 uses a sparse consolidation
algorithm that skips over areas of the model that are zero or undefined.
TM1 only stores the lowest input elements in the model and does not
precalculate any data. This serves to shrink the size of the MDDB consider-
ably. Additionally, TM1 uses compression technology and algorithms for both
disk and memory storage.
In-memory caching options
TM1 relies on the caching configuration options provided by the Windows
operating system.

25
There are no facilities for automatically informing end users of the currency
of data accessed in the TM1 Server.
Management of users
TM1 supports concurrent multi-user read/write concurrent access.
User security is maintained by groups. Users can belong to multiple groups.
User profiles are built upon six access levels and can be easily set-up and
maintained graphically.
Query governance
There is no concept of query governance within TM1.
There is no support for restricting queries to specified times of the day.
Controlling visibility of the ‘roadmap’
TM1’s security schemes control overall access to models and metadata.
Adaptability
Summary
1 2 3 4 5 6 7 8 9 10
Adaptability in TM1 is generally a case of adding new cubes, dimensions and
rules to TM1 models. All this is well supported using the dialogues of the
worksheet development interfaces. There are limited facilities to ensure that
the data sources, models and metadata remain synchronised at all times.
TM1 has a rigid MOLAP-only architecture.
New dimensions can easily be added to a model. Users are limited to 16
dimensions per cube. All changes are logged in an audit file.
Dimensions can be shared between multiple TM1 cubes, databases and
applications.
New rules can be written to calculate model data or to integrate additional
data in inter-model calculations.

26
Rules defined in TM1 can be saved in a file and re-used across different
models.
TM1 is a MOLAP tool. A ‘relational storage’ option permits the storage of
multidimensional data in relational tables. However, there is no support for
configuring TM1 to operate in ROLAP mode.
TM1 implements a MDDB datastore. As such, there is no possibility of the
model getting out of synch with the data.
New members can be added to dimensions during scheduled incremental
loads, without taking the TM1 Server offline.
Metadata
There is very little model metadata to synchronise in TM1.
Impact analysis
There is no support for analysing the impact of changes in the data source
on TM1 models.
TM1 automatically logs database changes, which can be viewed in a detailed
log file. The information logged includes the date and time the transaction
was made, the name of the client, the value changes before and after the
transaction, and elements that identify the cells that have changed.
There is no access to external metadata sources or repositories.
Summary
1 2 3 4 5 6 7 8 9 10
TM1 performs exceptionally well against small data volumes. Business
models can be processed in memory, which allows for fast calculations of
models in real-time. For large-scale deployment, TM1 supports SMP, multi-
threading, load balancing and server clustering. However, performance can
be adversely affected by large concurrent user loads, particularly where large
numbers of users are writing back to the database and performing complex
OLAP calculations. Adding RAM to the server running TM1 has a direct
effect on capacity rather than performance.

27
ROLAP
TM1 is a MOLAP-only tool.
MOLAP
TM1 loads only the lowest level input elements of a model into the OLAP
engine and does not precalculate and store data during batch loads. All
calculations are done in real time as users request data. Model data is stored
in a very efficient manner, allowing it to be easily loaded into memory.
Subsequent calculations are also stored in memory for enhanced
performance.
Typically, a single TM1 Server can support around 100 end users. However,
in large-scale environments, performance is highly dependent on the
number of concurrent users reading and writing back to the database, as
well as the complexity of the OLAP calculations performed.
Processing
TM1 uses ODBC to extract data from relational databases.
TM1 supports a multi-cube architecture and is able to distribute processing
at multiple ‘regional’ cubes that feed a higher-level ‘consolidation’ cube. The
cubes can be stored and processed across clusters of TM1 Servers.
SMP support
TM1 supports multi-threading and SMP parallelism.
Customisation
Summary
1 2 3 4 5 6 7 8 9 10
TM1 is an out-of-the-box OLAP solution, and there is limited scope for devel-
oping custom applications or interfaces. TM1 does not support a visual
development environment, but an API is provided that allows third-party
tools to access TM1 Server functions. Integration with third-party OLAP
development environments is supported via OLE DB for OLAP.
Customisation
Option of a restricted interface
The different versions of the TM1 client tools (Perspectives, Architect and
Client) naturally lend themselves to providing restricted functionality.
However, it is not possible to turn off specific functions for different users.

28
Ease of producing EIS-style reports
There is no direct support in TM1 to produce EIS-style reporting interfaces.
Applications
Simple web applications
TM1 supports a JavaBean API, which allows OEM-type development of
bespoke Java applications that access TM1 Server.
TM1 does not support a visual development environment.
The TM1 API allows for the development of custom front ends using Visual
Basic, PowerBuilder, Delphi and C++.
Localisation
TM1 is available in English, German, French, Hebrew and Japanese
language versions.

29
Deployment
Platforms
Client
TM1 clients (Perspectives, Architect, Client and Classic) run on Windows 95,
Windows 98 and Windows NT. The TM1 Classic client also runs on Windows
3.1.
TM1 Perspective and TM1 Client require Microsoft Excel (7 and 97) and
Lotus 1-2-3 97 software. TM1 Classic supports Excel 5 and Lotus 5 software.
Server
TM1 Server runs on Windows 95, Windows 98, Windows NT, Unix (AIX,
Solaris and HP-UX) and Linux.
Data access
TM1 can access data from any ODBC-compliant relational database. It can
also access Microsoft SQL Server OLAP Services MDDB.
Standards
TM1 supports Microsoft’s OLE DB for OLAP API as a data provider. Applix
has established a third-party certification programme for its OLE DB for
OLAP partners.
In December 1997, Applix published results of the OLAP Council’s APB-1
benchmark for TM1.
Price structure
Pricing for TM1 Server ranges between $28,000 for five concurrent users, to
$110,000 for 100 concurrent users.

30

Brio Enterprise
Summary
At a glance ............................................................................................. 2
Terminology of the vendor ...................................................................... 3
Ovum’s verdict ....................................................................................... 4
Product overview .................................................................................... 6
Future enhancements .......................................................................... 16
Company background .......................................................................... 17
Distribution ............................................................................................ 19
Product evaluation
End-user functionality ........................................................................... 20
Building the business model ................................................................. 22
Advanced analytical power ................................................................... 24
Web support ......................................................................................... 26
Management ........................................................................................ 27
Adaptability ........................................................................................... 30
Performance tunability .......................................................................... 31
Customisation ....................................................................................... 33
Deployment
Platforms .............................................................................................. 35
Data access .......................................................................................... 35
Standards ............................................................................................. 35
Published benchmarks ......................................................................... 35
Price structure ...................................................................................... 35

2
Evaluation: Brio Technology – Brio Enterprise
At a glance
Developer
Brio Technology, Palo Alto, CA, USA
Version evaluated
Brio Enterprise, version 6.0
Key facts
• A desktop business intelligence tool that provides query, OLAP analysis
and reporting
• Servers run on Windows NT and Unix; clients run on Windows 3.1,
Windows 95, Windows 98, Windows NT, Macintosh and Unix Motif. Web
access is also provided
• In June 1999, Brio acquired Sqribe Technologies, a provider of web-based
enterprise query and reporting tools; it plans tight integration between
the two product sets
Strengths
• A tightly integrated OLAP suite that is easy to use and deploy
• Sophisticated report distribution via ‘push’ and ‘pull’ web servers
• Strong metadata integration with a range of data warehousing tools
Points to watch
• Not the strongest product set for complex OLAP analysis
• Inconsistent server administration facilities
• Desktop architecture has scalability issues for analysing large datasets
Ratings
Web support
Management
Adaptability
Customisation
1 2 3 4 5 6 7 8 9 10

Ovum Evaluates: OLAP Evaluation: Brio Technology – Brio Enterprise
© 1999 Ovum Ltd. Unauthorised reproduction prohibited. 3
Analytic application
Brio defines an ‘analytic application’ as a customised solution that addresses
a specific set of analysis requirements for either a horizontal or vertical
market and/or a specific set of business users.
DataCube
A dynamic view of multidimensional data that is fed directly from the
Desktop DataCache. Each Brio report has a DataCube as its basis.
Data model
A visual representation of database tables that provides end users with a
business-oriented view of the data. Data models are saved as part of a
document file and stored in a central repository (typically in a relational
database).
Desktop DataCache
This stores a slice of relational data extracted from a database. The
DataCache is stored in a compressed format on the client and provides the
source data for analysis and reports. The DataCache also supports a local
OLAP engine.
Document
A Brio file that stores data model and query specifications for retrieving data
from a database, as well as the results set and reports created from the
queried data. Documents can be stored locally on the client or remotely in a
file server.
Items
Items are discrete informational attributes of topics (such as customer ID)
and represent the column fields of data in database tables. Items are
organised within topics and are used to query data. Computed items
calculate a fresh value for each original value based on a computation; for
example, revenue calculated from price and units.
Repository
A special set of relational tables that centrally stores data models and
document security settings. The repository information is referenced each
time a document is requested by end users.
Topics
Topics provide a visual representation of tables in the database and are an
element of data models. Topics are organised in logical groupings, which
reflect a particular aspect of the business, such as customers or sales. Each
topic contains a list of items. Meta-topics are custom topics created from
items in other topics. They are used to simplify views of the underlying data
by creating ‘virtual’ tables that are independent of the underlying database.

4
Ovum’s verdict
What we think
Brio Enterprise is a strong client tool for business intelligence that is both
easy to use and quick to deploy, although its administration facilities could be
improved. It offers a tightly integrated suite of tools that is best suited to
large, geographically dispersed organisations that need to provide users with
easy access to a wide range of data sources from a PC or web browser.
Cross-platform support in conjunction with ease of use makes Brio
Enterprise particularly suitable for deployment across large enterprises. It is
designed to exploit the Internet or corporate intranets. Business intelligence
can easily be deployed across the enterprise via a flexible ‘publish-and-
subscribe’ model using ‘push-and-pull’ servers for report processing and
distribution – although these capabilities come with a substantial price tag.
Brio’s Adaptive Reports capability allows administrators to easily adjust the
functionality of reports to match the diverse needs of users. End users are
provided with an equally flexible set of tools that is suitable for general
business use, rather than complex analytical applications. Visual
development capabilities have also been integrated into the core query, OLAP
and reporting environment to build analytical front-ends. The development
tools adequately support simple application needs, but lack the sophistication
of a fully-fledged development environment.
Brio Enterprise easily connects to a range of multidimensional and relational
data sources via its ‘snap-in’ APIs. It also sets the standard for metadata
integration with data warehouses. This removes the need for Brio to
maintain proprietary semantic layers, and ensures that views of data models
are based on consistent metadata that is shared across the enterprise.
Brio Enterprise’s server administration facilities are mediocre and dilute the
product suite’s overall strength. The product’s client-centric architecture
easily lends itself to the analysis of small parts of the database, which are
automatically loaded onto the desktop for immediate OLAP analysis. While
this is ideal for ease of use and quick deployment, scalability is limited by the
size of the datasets being analysed and the complexity of the OLAP
calculations; optimal performance is gained when working with results sets
of less than 50,000 rows. However, the effective use of metadata means that
users can query large data warehouses and often still work satisfactorily
with results that fit within these criteria.
When to use
Brio Enterprise is suitable if you:
• want an integrated out-of-the-box query, OLAP and reporting solution
that is easy to use and quick to rollout
• are a large, geographically dispersed enterprise that wants to provide
general business users with easy access to data and the ability to develop
simple analytic applications without IS involvement
• want to exploit your corporate intranet for distributing corporate data
• want easy connection to a wide range of data sources
• already have a data warehouse or OLAP server in place and are looking
for a flexible front-end tool that easily connects to it.

It is less suitable if you:
• have applications that require the analysis of large datasets with more
than 50,000 rows of data
• want to support OLAP analysis based on dimensionally complex business
models
• require advanced financial budgeting and forecasting functions
• want to develop analytical applications with highly specialised OLAP
functions from scratch.

6
Product overview
Components
Brio Enterprise, version 6.0, consists of the following components:
• Brio Enterprise servers – OnDemand Server and Broadcast Server
• BrioQuery end-user tools – Designer, Explorer and Navigator
• web clients – Brio.Insight and Brio.Quickview.
Figure 1 shows the primary functions of the components and whether they
run on the client or the server.
Brio Enterprise Server Solution
The Brio Enterprise Server Solution consists of two ‘push’ and ‘pull’ servers –
Broadcast Server and OnDemand Server – and a unified administration tool.
Data models and document security settings are stored in the Brio
repository: typically, a group of specialised tables stored in a RDBMS server.
Broadcast Server
This is a query server that schedules and automates query processing and
report distribution. Broadcast Server ‘pushes’ precomputed documents and
reports in a highly compressed format out to web, client-server and mobile
clients via FTP, e-mail, web servers, document file servers and network
printers. The processing and delivery of documents can be based on time or
event criteria.
Typically, Broadcast Server is used to reduce the load on OnDemand Server
by following the 80/20 rule – distributing weekly reports that 80% of users
need.
OnDemand Server
This is a web application server that enables ad hoc querying via the Web. It
is a ‘pull’ server for executing database queries, compressing results and
transmitting data to Brio web clients (Brio.Insight and Brio.Quickview) for
further analysis. Output documents created by Broadcast Server can also be
accessed (on-demand) by web clients from a document file server. The web
environment benefits from Brio’s Adaptive Reports technology, which ‘adapts’
query, analysis and reporting functionality based on user privileges.
OLAP
Analysis
Web access Model design, application
development and administration
Distribution
Client BrioQuery
Explorer
BrioQuery
Navigator
Brio.Insight
Brio.Quickview
BrioQuery Designer
Server OnDemand
Server
Brio Server Administrator Broadcast Server

The OnDemand Server requires a Java runtime environment and supports
CGI, ISAPI and NSAPI Internet APIs to connect to web servers. It works
against relational data warehouses and third-party OLAP servers.
BrioQuery end-user tools
These are sets of client-server desktop tools that integrate ad hoc query,
OLAP and EIS reporting in a single interface. They also provide
administrators with access to security and DBA-like services. The BrioQuery
tools differ from the Brio web clients in that they access databases directly,
using a combination of native and standard database connectivity APIs
(Microsoft’s OLE DB and ODBC).
The BrioQuery tools are ‘fat’ desktop clients and are available in three
editions. Each edition incorporates core query, analysis and EIS reporting
capabilities, but adds increasing levels of data modelling functionality.
BrioQuery Navigator
This provides business users with access to a repository of predefined data
models and reports created by an administrator. The functionality is
weighted towards ‘information consumers’ that do not have the technical
ability (or the need) to access database tables to independently build their
data models. Navigators typically use data models stored in the repository to
create ad hoc queries, perform OLAP analysis and generate reports.
BrioQuery Explorer
This adds data modelling capabilities to the Navigator tool. It is designed for
power users that need direct access to database tables to build their own data
models and reports, on top of easy access to repositories of predefined data
models and reports.
BrioQuery Explorer also has graphical development tools to connect to data
sources and map database tables and columns to Brio data models. The
product also provides access to Brio’s Open Metadata Interpreter (OMI)
functions, in order to access upstream metadata from a variety of data
warehousing tools at runtime, including Informatica’s PowerMart Suite, BM
Visual Warehouse, Ardent DataStage and Prism Warehouse Manager.
BrioQuery Designer
This is a graphical tool, similar to DBA, for administering the Brio
environment. It adds repository creation, repository management, security
and auditing capabilities to the Explorer feature set in order to build,
maintain and distribute data models that can be accessed by Explorer users
via the repository.
The EIS section in Designer provides application development tools for
assembling analytic front-ends. The environment uses a combination of drag-
and-drop and a scripting language (JavaScript) for building simple analytic
front-ends. The development capabilities are explored in detail in the Using
Brio Enterprise section.
Brio web clients
The web clients provide similar functionality to the BrioQuery Navigator and
Explorer tools, but do not offer any data modelling or application
development capabilities.

8
The web clients also use Brio’s Adaptive Reporting technology; both clients
can ‘adapt’ their capabilities based on a combination of the content of each
report and the user’s security profile. These capabilities can be restricted to
simple report browsing or can provide users with full query and analysis
functions.
Brio.Insight
A web-based query, OLAP analysis and reporting tool that is offered as a
plug-in to existing web browsers. It provides a similar level of functionality to
the BrioQuery Navigator tool, including ad hoc query and OLAP analysis.
Brio.Insight works with both of the Brio Enterprise servers; it can
manipulate documents posted by the Broadcast Server and allows ad hoc
querying when used with OnDemand Server.
Brio.Quickview
This is a web-based report browser extension, which allows business users to
access and view portfolios of precomputed and formatted Brio reports; it only
supports the ‘view’ and ‘view and process’ capabilities provided by
Brio.Insight. End users can navigate multiple reports by going through a
series of tabs at the bottom of a Brio document.
When used in conjunction with OnDemand Server, administrators can also
grant end users the right to refresh the data on-demand on a report-by-report
basis, or limit the view based on a set of criteria. Scripts and EIS-tabs can
also be used to guide novice users through a series of reports.
Brio Enterprise does not support a full mid-tier OLAP engine and MDDB
store. However, it can easily connect to most third-party OLAP servers
natively or through OLE DB for OLAP. Connection wizards are provided to
facilitate the process of creating connection files (OCEs).
Two variants of the ‘light’ mid-tier architecture are supported by the
introduction of OnDemand Server or Broadcast Server (or a combination of
both).
OnDemand Server
The OnDemand Server is the ‘thin-client’ variant. It operates behind a web
server. Communication is facilitated by three web broker components –
NSAPI, ISAPI and CGI. OnDemand Server processes queries against a range
of relational and OLTP data sources and MDDBs. SQL generation and
processing is performed on the server, which then transmits compressed
results data back to Brio’s web clients (Brio.Insight and Brio.Quickview) for
further analysis.
Broadcast Server
The ‘fat-client’ variant uses Broadcast Server as a mid-tier processing engine
for immediate or scheduled delivery of reports. The results are processed on
the server and published to specified users or groups. Broadcast Server can
be used in client-server environments or web-based intranets.

The typical client-server configuration for Brio Enterprise is based on a local
desktop OLAP engine, although most implementations are now via the Web,
using the Brio web clients on the desktop and the Brio Enterprise Servers for
database connectivity.
When the BrioQuery client runs a query, the raw data is returned to the
desktop in a highly compressed format called a DataCache. For OLAP
analysis and reporting of the data, end users create temporary
multidimensional structures (called DataCubes) on-the-fly in the local client
machine’s memory. These DataCubes are ‘dynamic’ in the sense that they
allow end users to perform OLAP functions (such as pivot and drill-down) to
the data in order to gain different business views. There is seldom a need to
re-query the database server, because all the data is held locally on the client
machine.
The Brio architecture naturally lends itself to mobile configurations. Data
can be downloaded and saved locally on a laptop for offline analysis.
Similarly, a ‘snapshot’ slice of a cube retrieved directly from an MDDB into a
Pivot structure can be exported to, and retained in, an independent results
grid within a Brio document for disconnected analysis.
Using Brio Enterprise
An integrated OLAP environment
Brio Enterprise’s greatest strength is its intuitive and easy-to-use interface,
which Microsoft 2000 users will feel comfortable with. Data modelling, query,
OLAP and EIS reporting functions are seamlessly integrated, and end users
can navigate through these modes in a non-procedural manner using the six
report sections – Query, Results, Pivot, Chart, Detail and EIS – that are
provided by the document map.
Building the data model
The BrioQuery Explorer and BrioQuery Designer tools provide a Query
section and tools for creating the ‘data model’. The model provides a
simplified representation of database tables, which allows end users to
graphically query data (as opposed to using SQL).
End users create data models by mapping tables and columns as topics and
fields as items, and making their own custom joins. Connection to a database
is via the source content area that provides a tree-like display of all the
tables in the database. Selected tables can be dragged from the source
content area into the Query section to build a topic, as shown in Figure 2.
Users can view the topic as a simple list of data items or get a more detailed
view by providing a sample of the underlying data.
Data models are stored in a central repository and assigned access rights for
controlled distribution to end users. Data models are referenced by
‘documents’ – physical Brio files that persistently store references to a data
model, query specifications, results data and one or more reports. Documents
can be stored locally on the client or remotely in a file server.

10
Figure 2 Building a data model
Query and analysis
Once a data model is created in the Query section, it can be used to query the
database and download results to the client desktop. A query is simply
constructed by dragging topic items listed in the source content area to a
‘request’ line. End users can also drag topics to the ‘limit line’ to further
restrict the information returned. Similarly, requested topics can also be
dropped into the ‘sort’ line for a range of nested ‘sort’ functions. A single Brio
document can contain multiple query sections and these may be any of the
data access query types (relational, OLAP, imported datasets or local-join
queries).
The results data is downloaded to the client desktop and cached in memory in
a relational data structure called a DataCache. End users can also apply data
functions to aggregate and calculate totals, as well as derive new columns of
data based on the results data returned. A results set can be used as the
basis of one or more Brio reports. Four report sections are available – Pivot,
Chart, Detail and EIS.

Figure 3 Pivot section
OLAP capabilities are provided by the Pivot section shown in Figure 3,
through which drill-down, pivot and ‘slice-and-dice’ operations can be
defined. Dimensions can be dragged from the source content area to an
outliner section, to change the nested levels of dimensions. It is also possible
to add a number of surface-level calculations to the tabular data. Another
report section provides support for charting and detail reports. Each has its
own outliner section for creating drillable business charts and designing
professional reports. Tools are also provided to build briefing-book style EIS
interfaces for casual end users.
Working directly against MDDBs
When connecting directly to an MDDB, the document immediately opens the
Query section. Rather than working with a data model, representing tables
and columns, the Query section displays the structure of the MDDB as a
hierarchical tree. In this instance, the query building process introduces
dimension members and measures directly from the tree to the Pivot outliner
tool. This query method bypasses Brio’s previous method of ‘flattening’ the
cube. Instead, a slice of the cube requested from the MDDB is loaded directly
into a Brio Pivot structure – each additional OLAP query to the Pivot report
re-queries the MDDB for a new slice.
When working in ‘hardwire’ mode, the database is re-queried after every user
change to the outliner. A ‘process query’ mode allows users to introduce
multiple items into the outliner, before the request is sent to the database to
retrieve the cube slice – which is useful for building large OLAP reports
without the need to continuously burst traffic to the server.
A Slicer tool is provided to limit the scope of the server cube. It defines a
logical slice of the cube, by instructing the server to ignore all values that are

12
not part of the slice. For example, if users want to limit the view of a cube to
data only for 1998, the member value 1998 from the year dimension is simply
dragged into the Slicer tool.
A Filter Box is also provided for defining limits once levels have been
introduced in reports. The Filter Box allows for the setting of comparison
operators that act on the values for that member (similar in concept to
member selection). Additional server-specific functions are available in the
Filter Box to be included as part of the limit. Each MDDB supports its own
list of functions – representative functions include top N and top N%.
Advanced OLAP reporting
At the core of OnDemand Server is its unique Adaptive Reports capability,
which is designed to meet the diverse needs of many business users across
the enterprise – ranging from simple report browsing to advanced query and
analysis. It is a useful tool for managing user access and system resources.
Adaptive Reports is only supported in a web environment. On a report-by-
report basis, Brio.Insight and Brio.Quickview can ‘adapt’ their capabilities,
based on the intersection of the content of each report and the end-user’s
security profile. Five different modes can be enabled, which cascade towards
higher levels of functionality. These range from the simple viewing of – and
navigation through – predefined reports, the refreshing of results by
reprocessing the query over the Web, ad hoc analysis (create and edit Pivot,
Chart and Detail reports) of retrieved documents, allowing new data to be
queried, and changing existing queries upon which reports are based.
Metadata integration
A key feature of Brio Enterprise is its ability to directly access upstream
metadata from data warehousing tools and a number of third-party metadata
repositories from Informatica, Ardent and others (an exhaustive list is
provided on Brio’s website).
Access to third-party metadata eliminates the need to define and maintain
proprietary metadata within the Brio environment. As the tools source
metadata dynamically at runtime from the underlying data sources, any
changes to the associated metadata are automatically updated to the query
tool at runtime.
If a metadata source is available and stored in relational tables, Brio
Explorer and Brio Designer can use the Open Metadata Interpreter (OMI)
facility to link it to data models and apply metadata information
automatically. The OMI is a feature of Brio’s Open Catalogue, which manages
database connectivity through a graphical connection interface, as shown in
Figure 4. The interface provides several tabs for adjusting connection
preferences and accessing table, column, join, lookups and remarks
metadata.
Snap-in metadata templates are included and available to users via the Meta
Connection wizard. These templates provide the definitions required for the
‘Remarks’ interface and are fully customisable.
A ‘Remarks’ dialog, as shown in Figure 5, on a topic or item may have
separate tabs displaying the definition of a column, the last update of the
table, the number of rows in the table, transformation rules applied to the
data and the source of the column. Administrators can define as many
different elements of metadata to display as required.

Figure 4 Graphical connection interface
Figure 5 Remarks interface
Access to structural metadata about the data warehouse streamlines the
model-building process, by automatically applying metadata table- and
column-naming conventions and descriptive information to data models.
Metadata can also help to build models that produce faster results – for
example, finding distinct values for a column from a 50 million row table can
be achieved quickly, because the SQL query is run against a look-up table
instead of a fact table. Furthermore, changes in the metadata definitions can
be synchronised with data models stored in the repository for increased
adaptability and maintenance.

14
From an end-user perspective, contextual metadata (such as special database
remarks inputted by the DBA, which describe the origin, derivation or detail
about data) provide a business-oriented description of model components.
Metadata can also be accessed to show from where the data was sourced.
Building analytic applications
Brio Enterprise supports the development of custom analytic applications.
The term ‘analytic’ in this context refers to the delivery of pre-packaged
business intelligence information that is encapsulated within a graphical
interface similar to EIS. The tools are aimed at two audiences: users that
wish to extend or assemble their own applications to support specific
business needs, and ISVs or VARs that want to create domain-specific
applications.
Application development is carried out in the EIS section (in design mode).
The EIS section provides a number of interfaces and tools to support
application development: embedded report components, EIS controls and
scripting. Typically, a Brio developer combines embedded report sections with
user interface controls using a visual layout tool, and then scripts
interactivity between these controls and the native Brio application.
Embedded sections
Brio report components (Pivots, Charts and Tables) can be embedded directly
into the EIS workspace for display. All embedded report components are ‘live’
objects and are automatically updated with fresh data.
Embedded reports carry a property defining them as either:
• view-only – a static image of the report with no interaction capabilities
• active – allowing the end user to interact with the report data, providing
drill-down, pivoting and other analysis functions via point-and-click
• hyperlink – follows the browser model of a single-click, jumping directly to
the original section. For Brio reports, this means switching the display to
the native report section in the document.
Extended EIS controls
This is a set of user interface controls, which is also provided for inclusion in
EIS applications. The controls include buttons, radio buttons, check boxes,
text (edit) boxes, list boxes and drop-down list boxes. The controls can be
populated with values at design time (or dynamically at runtime with values)
by the scripting language.
Each of these controls can have scripts attached to them to trigger events in
the document. Layout tools, such as rulers, snap-to-grid and design guides,
are provided for positioning controls onto the EIS screen.
Object model and scripting
JavaScript is the underlying scripting language for controlling Brio
applications – Brio Enterprise has the Netscape JavaScript (version 1.4)
built-in to the application. In addition, an object model is provided for
manipulation by the internal and external script languages.

A BrioQuery application is composed of a series of objects, each with methods
and properties. A set of application objects has been exposed to the
programmability system, with constructs to query and set properties and
invoke methods. The object model will typically be manipulated by
JavaScript from inside an EIS section. The object model can also be
accessible from external applications capable of making OLE automation
calls.
Several tools are available to assist in writing JavaScript and to manipulate
the Brio object model. These include:
a built-in (but somewhat basic) script editor window used to attach
JavaScript to an EIS object
• an object browser, which provides a tree-like display of control objects,
methods and properties that can be included in a script via point-and-
click. The browser is dynamic and displays the active state of the objects
available in the application at any point in time
• execution and console windows, for testing commands and debugging
scripts for syntax. A console window is provided to display error messages
and alert values generated by the JavaScript interpreter; application
designers can write messages to the console window to track the state of
variables and the progress of the script.

16
Future enhancements
The next release of Brio Enterprise will focus on integrating the software
tools acquired from Sqribe. The release of Brio Enterprise version 7.0 is
planned for the first half of 2000 and is expected to deliver a full integration
between the two product sets. This will include a central user repository and
enhanced and fully integrated administration tools for the servers. In
particular, the transaction-oriented capabilities of Sqribe’s ReportMart
enterprise information portal architecture will be integrated within Brio
Enterprise’s web environment.
For the core Brio Enterprise suite, OLE support, both as a consumer and
provider (server), will also be provided. Connection to Oracle Express is
expected in the second half of 1999 – after version 2.0 of the OLAP Council’s
MDAPI is made publicly available.
Brio is also planning more sophisticated data visualisation tools and will
move the product to a much thinner client architecture. It will also provide
snap-in metadata capabilities to the Microsoft and Platinum metadata
repositories.
Brio aims to deliver a number of vertically focused analytic applications; its
subsidiary company, MerlinSoft (which Brio acquired in 1998), is considering
vertical niche opportunities.
As part of the company’s Private Label Partner programme, Brio expects an
increasing number of vertical analytic applications ‘powered’ by Brio
technology to be developed by partners, such as Broadbase.

Company background
Brio Technology is a US company, which was founded in 1984. The company
spent its first six years as a small decision-support consulting firm. Brio
introduced DataPrism, a graphical end-user query tool, in 1990. This was
followed a year later by DataPivot, a desktop-based OLAP analysis tool. In
1991, the consulting component of the company was terminated and it moved
to a more traditional software product vendor model. The existing tools were
later merged into BrioQuery in 1994.
In March 1995, Swedish founder (and current CEO), Yourgen Edholm
embarked on an expansion programme, bringing in new investors and
building up sales & marketing, recruitment and product development. Since
then, the company has consistently recorded year-on-year growth of more
than 100%. Brio’s latest product suite, grouped under the ‘Enterprise’
banner, was first released in November 1997. This represented a significant
shift away from the company’s initial desktop focus to enterprise-wide
deployment.
As a privately held company, Brio received significant investment from
several venture capitalist firms. The company had an IPO in May 1998,
which raised approximately $30 million and led the way for rapid growth.
Brio’s revenues grew 74% to $46.5 million for fiscal 1999. Significantly, the
company also achieved profitability in 1999, with net income standing at
$776,000, compared to a loss of $6.7 million in fiscal 1998.
In June 1999, the company acquired Sqribe Technologies (www.sqribe.com), a
provider of web-based enterprise query, reporting and information portal
software, in a transaction valued at $270 million, whereby Brio shareholders
own approximately 55% of the combined company and Sqribe 45%. Sqribe is
a venture-backed firm that has revenues of $39 million and 7,500 customers
worldwide. Its main revenue-generating product is SQR, an enterprise
reporting tool. The combined company, which retains the Brio name, will
have revenues in excess of $80 million and employ over 500 people
worldwide. The completion of the acquisition is planned for mid-1999.
Edholm will retain his position as president and CEO, with Sqribe’s CEO,
Ofir Kedar, serving as chairman.
Brio Enterprise is aimed at the enterprise level and its functionality is
designed to support the needs of a large number of general business users
(that only use OLAP perhaps 10% of the time), rather than an elite group of
analysts (that use OLAP 100% of the time). The tools are designed to be
easily and quickly implemented, to scale from departmental deployment to
enterprise solutions and to minimise the dependence on IS for business
intelligence needs.
While Brio initially positioned its tools as the ‘universal’ front-end for
business intelligence, the company is reinventing itself to move beyond its
desktop OLAP technology base, to become a provider of ‘enterprise-class’
business intelligence solutions. The recent acquisition of Sqribe brings
together two growing and complementary players in the business

18
intelligence market; previously, Brio had forged a partnership with Sqribe’s
chief rival, Actuate Software. However, Brio believes that integrating
Sqribe’s server-based enterprise reporting capabilities into the core of Brio
Enterprise suite gives it a better chance of gaining market leadership in the
broadening market of (increasingly web-based) business intelligence
solutions. The addition of Sqribe’s portal technology also gives Brio a
headstart on the emerging business intelligence portal segment, which has
few players. From a commercial standpoint, the deal triples Brio’s client base
and provides significant opportunities to cross-sell its OLAP tools to Sqribe’s
existing customers (around 7,500 sites) and vice versa.
With version 6.0 of the product, Brio is also making a serious play for the
analytic applications market. The company is positioning its tools as a
development platform for delivering pre-packaged and custom business-
intelligence applications to mass audiences. Although not an analytic
application itself, VARs can build programs with Brio Enterprise 6.0 and sell
them to vertical or other markets. As a result, Brio is preparing to sell its
technology to integrators, ISVs and VARs, and is seeking partners with
vertical sector expertise.
Brio principally sells to large Global 2000 companies through a direct sales
organisation in the US, Canada, the UK, France and Australia. Indirect
channels (VARs, distributors, OEMs and systems integrators) account for
20% of revenues. Brio Enterprise has a strong presence in the high-tech,
manufacturing/publishing, education and Federal US government sectors.
Notable customers include IBM (67,000 users), several US Federal
government agencies (50,000 users), Delta Airlines, Dell Computers, the
Dutch Police Agency and the US armed forces.
Brio’s BI Partnership initiative has forged partnerships with all the leading
database and data warehousing tools vendors. Several partnerships have led
to technical integration, such as Brio’s numerous metadata links, as well as
collaborative marketing and bundling agreements. The company has signed
up around 40 software vendors as part of its Private Label Partner
programme, which encourages ISVs to embed Brio’s tools in vertical
applications. Brio has also signed major reseller agreements with
PricewaterhouseCoopers and has a close relationship with IBM (as part of
IBM’s DecisionEdge solution for sales analysis, and eWarehouse, a program
for delivering business intelligence applications for the Internet). It also has
a partnership with Informatica, to integrate Brio Enterprise with
Informatica’s Business Components and PowerConnect software products.
Customer support
Support
International around-the-clock support (GlobalPlus) is available via
telephone, e-mail or the Web. Web support is particularly strong and includes
the ability to monitor and control support requests internally and from
distributors. Support centres are located in the US, the UK, France and
Australia. Annual maintenance and support contracts range from 15–25% of
the licence fee.
Training
Brio offers a range of public and private on-site courses for all its products;
one or two-day classes are available for casual end users, power users and
administrators. Brio has a number of certified training partners and also
provides computer-based training packages.

Consulting services are provided by Brio’s Expert to Expert group. A typical
engagement involves working closely with data warehousing projects to
provide advice on model design, metadata integration, connectivity and
implementation. Brio also maintains numerous referral partnerships with
external consultancies.
Distribution
US
Brio Technology
3460 West Bayshore Road
Palo Alto
CA 94303
USA
Tel: +1 650 856 8000
Fax: +1 650 856 8020
Europe, Middle East and Africa
Brio Technology Sarl
Immeuble Le Cristal
2, rue Hélène Boucher
78280 Guyancourt
France
Tel: +33 1 39 44 73 73
Fax: +33 1 39 44 21 00
Asia-Pacific
Brio Technology
Suite A, Level 10
121 Walker Street
North Sydney, NSW 2060
Australia
Tel: +61 2 9964 9533
Fax: +61 2 9964 9755
E-mail: info@brio.com
http://www.brio.com

20
Product evaluation
Summary
All the end-user tools have functional and extremely user-friendly interfaces to
support general business analysis. The tools offer varying levels of
sophistication and users can easily navigate between query, reporting and
analysis using report ‘tabs’. The optional semantic layer speeds up queries by
shielding users from the complexities of SQL and the database schema. All the
tools support advanced WYSIWYG report construction and sophisticated
groupware facilities, using scheduled agents and ‘push-and-pull’ servers to
enable easy distribution across the enterprise. Wizards are provided to help
users through complex tasks.
BrioQuery is a capable tool, which suits the needs of small departments.
However, there is little reason to use Explorer or Navigator, given the
additional benefits provided by the server-based web tools.
The OnDemand Server provides an HTML page interface for easy navigation
and location of documents. Available documents are organised in a folder
structure and presented as a ‘document map’ in a pop-up window. Keyword
search facilities are also provided.
In a client-server environment, data models (and associated documents) can
be organised in logical groupings in a file system folder and stored in the
repository. End users can easily browse the tree structure and access data
models, but there are no search facilities.
End users can only view metadata such as name, author, date created and a
textual description of the data model. But power users designing models can
access structural metadata using the Open Metadata Interpreter (OMI)
functions in the query section. Wizards are provided for easy connection to
metadata sources. Changes to metadata are automatically propagated to the
query tool at runtime.
End users cannot annotate the data model directly. However, they can easily
annotate any reports that are created from the model.
Using the model
OLAP functionality is provided in the Pivot section. Standard OLAP
functions, such as pivot, slice-and-dice and drill-anywhere and drill-out (to a
1 2 3 4 5 6 7 8 9 10

linked data model), can easily be performed on data using drag-and-drop.
The outliner panel is used to change the nesting of dimensions. Colour-coded
‘spotlighting’ can also be defined for exception reporting.
For flexible OLAP query, Brio supports two unique features:
• member selection – another way to limit data retrieved from the server
cube. Member selection is similar to the Slicer, with the key distinction
that it introduces the member value in a report and multiple members
may be selected from a single hierarchy
level skipping – BrioQuery allows an OLAP query to be built that skips one
or more levels in the dimensional hierarchy; other OLAP query tools usually
require queries to include all levels of a dimension from the top down to the
desired level of granularity.
A menu is available to sort data in ascending and descending order according
to different calculations (for example, count, average or sum).
Drill-down icons in the Outliner panel show which dimensions have drill-
down operations. An overall representation of the hierarchies is provided,
but there is no support to show the user’s position within it.
The ‘drill-out’ option allows end users to drill to detail-level data. A useful
feature is incremental drilling, which combines predefined drills and drill-to-
detail, so that end users can navigate relational databases in a similar way to
navigating through a multidimensional cube. However, users can only drill
within a single database – not across multiple databases.
Brio’s range of end-user tools provides cascading levels of functionality for
different types of end user. An Excel add-in facility is provided, but only to
access documents and load results into worksheets. Custom EIS front-ends
can also be developed using the integrated development tools.
The client tools support standard cross-tabular and band-style reports.
Additionally, more than ten graphical charting options are provided,
including pie charts, dual Y axis, scatter and line-bar combo charts. Charts
include drill-down, rotation and zoom functions. Compound reports
containing charts alongside tabular data can be created. An optional EIS tab
allows for the controlled display of data in a ‘briefing book’-style interface.
There is no support for displaying data in maps.
Designing a report
The Brio reporting tools are easy to use and support advanced report
construction. The ‘Reporter’ tool supports free-form WYSIWYG report design
and allows multiple results sets from different data sources to be included in
a single report. But there is no support for embedding OLE objects in reports.
The EIS tab allows for the creation of EIS-style reporting interfaces.

22
Publishing a report
The ‘report bursting’ option in Broadcast Server allows users to publish a
single query and have the result tailored for, and delivered to, different
people or divisions within the enterprise. Results data can be managed and
distributed according to different rules and criteria. End users can also
publish HTML reports for web access, using an HTML wizard and report
templates. Web users also benefit from adaptive reports.
Broadcast Server integrates with Microsoft Exchange and other MAPI or
SMTP-based e-mail systems. E-mail can be used to distribute documents or
for notification purposes (for example, to include a URL reference for an
HTML report), but dynamic distribution lists are not supported.
Brio Enterprise does not provide any direct support for report subscription.
Summary
The process of building a business model is split between creating the data
model by mapping dimensions to database tables and columns, and then
using the model to query and analyse data. Easy-to-use graphical tools are
provided for both processes. However, model designers are expected to have a
good understanding of the underlying table and join structures – here the tools
would benefit from some wizard support.
The functions provided are geared towards general business modelling, rather
than building highly complex models – a single Brio query can only access
data from a single database. Designers are also restricted by the lack of
complex calculated measures that can be defined in models.
Basic design
Design interface
BrioQuery Explorer and Designer support a graphical workspace for building
data models. Modelling tasks, such as mapping topics and items to database
tables, columns and defining joins, are achieved by point-and-click.
The table catalogue provides a graphical display of the source tables, columns
and data types. When tables are moved to the design workspace, the
relationships (joins) between them are graphically shown. It is also possible
to bring up a detail view of sample data.
A ‘master’ data model can be developed to provide an initial mapping layer,
upon which subsequent query and analysis is based. The master data model
provides a visual representation of the database, using familiar business
terms that cannot be changed by users. Any query section that refers to the
1 2 3 4 5 6 7 8 9 10

‘master’ data model will automatically inherit changes made to the main
data model.
When loading a data model into the repository, end users are prompted to
include metadata information, such as model type, author and a textual
description.
Columns can be selected via point-and-click.
A list of member values can be selected using point-and-click. If the source is
a relational database, the list will be filled by a Select SQL query. If the
source is a MDDB, then the native APIs are used to show the member values.
For OLE DB for OLAP-compliant OLAP servers, MDX is used.
By default, there are no predefined hierarchies; end users are free to drill
however they wish. But optional support for loading predefined hierarchies is
available for more restrictive drill-paths. A check-box interface is available
for marking a topic as a dimension and arranging the order of columns (top
to bottom) to create a drill hierarchy. The Pivot tab supports a dialogue for
defining custom levels.
The definition of unbalanced dimension hierarchies is supported via point-
and-click.
Time dimension
Time dimensions are not automatically recognised. However, designers can
create month, quarter and year dimensions from a date field. Custom time
periods can also be defined.
It is possible to substitute descriptive names for arcane database table and
column names when creating dimensions. Simple data transformations (such
as replacing underscores with spaces and displaying dimension names in
mixed upper/lower case) can be automatically performed. However, it is not
possible to provide short and long names for dimensions for different display
purposes.
Predefined hierarchy levels are supported, but there are no facilities for
specifying a default level when opening a report.
Calculated measures
Complicated measures, such as cost of sales or profit margin, rely heavily on
DBAs anticipating these requirements when designing the database
structure, so that the database makes calculations as the raw data is
queried.
It is also possible to supplement calculations already stored in the database
by creating computed items (calculated measures), which can be included as

24
part of a data model. The computed item is a value, variable, logic statement
or formula that instructs the Brio client or the RDBMS to perform a certain
calculation. Standard arithmetic and logical operators can be used to create
computed items, either by typing into a formula panel or via point-and-click.
Scalar functions, which calculate and substitute a new data value for each
value associated with a data item, are also supported, although some are
provided by the RDBMS.
Up to 20 measures can be associated with a set of dimensions.
Multiple designers
Multiple designers
There is no special support for multi-designer environments.
The repository provides a central, version-controlled database store of data
models.
Summary
Brio Enterprise provides only basic mathematical and statistical functions.
For advanced analytics, it relies on exposing and exploiting native database
functions (for example, RISQL functions in Red Brick) and builds analytical
complexity into the SQL query itself. When connecting to third-party OLAP
servers, the range of analytics available is determined solely by the MDDB.
Custom functions can be built using a scripting language, but there is limited
scope for re-use. Simple links to spreadsheets are provided for importing
results data, but data cannot be queried directly from specialist analysis tools.
There is no integration with third-party statistical, forecasting and analysis
tools or spreadsheets. However, a scripting language can be used to pass data
back and forth between Brio clients and third-party applications.
Ranking and sorting
Simple descending/ascending ranking functions are provided. More complex
ranking can be implemented in reports by adding custom measures using ‘if,
then, else’ logic. Additionally, native database ranking functions found in Red
Brick and Oracle can also be exploited.
A range of functions is provided, including standard arithmetic operators and
absolute value, cosine, sine, exponential power and hyperbolic sine.
1 2 3 4 5 6 7 8 9 10

Financial functions
There are no special financial functions.
Statistical models
The Brio client tools support basic statistical functions, such as median,
mode, percentile, standard deviation and variance. More advanced statistical
functions are only available through the Oracle RDBMS.
Trend analysis
There are no special functions for analysing trends.
Simple regression
Regression functions for forecasting are not available.
There is no support for advanced time-series forecasting methods.
Users can build or extend functions by using the existing arithmetic, logical
and scalar functions and by exploiting native database functions through the
SQL. JavaScript has been included as a function language within documents
– JavaScript expressions can be written to define more complex computed
columns in reports. But these functions can only be re-used within a single
document – there is no scope for storing them in a central repository for
wider re-use.
Write back for ‘what if?’ analysis
Direct write-access to the database is not supported. A ‘process-to-table’
feature can be used to run a query against the RDBMS, and instead of
pulling the results set to the Brio client, it will create (or append to) a table.
This can be used as a simple transform or for staging results for complex
reporting.
It is possible to add alpha-numeric text fields as measures. By changing the
calculation type to count-mode, end users can analyse non-numerical data;
for example, how many people answered ‘red’ or ‘blue’. Non-numeric
functions include non-null average, count distinct, null-count and non-null
count.
Data mining
Brio Enterprise does not support data mining.

26
Web support
Summary
Brio.Insight and Brio.Quickview come very close to reaching functional parity
with the client-server tools. Both sets of tools share the same drag-and-drop
interface for query, OLAP and reporting, and access the same document
repository. Modelling functions are not available via the Web. However, the
web configuration introduces a mid-tier server for ad hoc querying and
benefits from the Adaptive Reports feature for flexible reporting and
distribution. Generally, the dynamic web publishing (via plug-ins) is more
suited to intranet environments rather than extranets.
Functionality of web access to explore models
The OnDemand Server and web clients fully support OLAP query (including
dimensions and measure tree navigation), all query features and the ability
to define local calculations in reports. The only difference is that query
processing is in ‘process’ mode – a live ‘hardwire’ mode is not available.
There are two types of web client, each offering different functionality:
Brio.Insight, which provides web users with access to a repository of
predefined data models and reports created by an administrator. The
functionality is weighted towards ‘information consumers’, but through an
HTML interface. It uses a drag-and-drop interface and includes the ability to
access OMI-sourced metadata, create new queries, reports and charts and
schedule documents. The only restriction is that it does not support drill-out
Brio.Quickview, which provides a similar level of functionality as the
BrioQuery Navigator tool, but through an HTML interface.
Supports both registered and unregistered web access
All Brio web users must be pre-registered.
The Brio.Insight interface provides capabilities for interactive query, OLAP
and reporting. The Brio.Quickview provides an EIS-type interface for users
that only wish to access and view predefined reports.
There is no support for editing data models via the Web.
Brio.Insight and Brio.Quickview can use, but not create, data models.
1 2 3 4 5 6 7 8 9 10

Brio clients can publish a report in HTML format. Charts can also be
exported as HTML with links to JPEGs. An HTML wizard is provided to
guide users through the process using custom web templates.
Broadcast Server can be used to schedule and distribute ‘weekly’-style
reports to individuals or groups of users via the Web. The HTML wizard can
be used to create personalised web pages that allow end users to access a
defined set of reports, each of which can be assigned its own analysis and
formatting privileges for security.
E-mail notifications can include multiple URLs, which can reference other
Brio reports. But URLs are not supported in Brio reports.
Distribution of web server processing
Processing can be distributed across multiple web and OnDemand servers.
However, intelligent load balancing is not supported.
Other web support features
Common security layer
The web access levels are secured in the same way as they are handled in the
client-server version. Brio supports a user-defined Java Bean authentication
feature that enables integration of existing security with the OnDemand
Server, which allows administrators to maintain a single source of log-in
authentication.
SSL support
Brio web clients support SSL for all OnDemand Server communications with
SSL-enabled web servers, including reprocessing a query to retrieve new
results sets.
Management
Summary
Brio Enterprise provides flexible scheduling capabilities, but has an
inconsistent server administration utility that is limited and cumbersome to
use. Brio clients can register documents for web access via the OnDemand
Server, but can only schedule them using the Broadcast Server or BrioQuery
Designer administration tools. Also, when a report has been modified, it needs
to be re-registered in order to see the modifications. Model security relies on
the databases supplying the data, although levels of security can be applied
via group, user and report metadata definitions. Strong monitoring and
governance facilities are supported for analysing user activity, the utilisation
of models and query duration and volume.
1 2 3 4 5 6 7 8 9 10

28
The concept of a ‘master’ data model lends itself to the centralised
deployment and management for data models.
The management of data models in the repository is carried out graphically
using the BrioQuery Designer interface. The OnDemand and Broadcast
Servers share a graphical administrator interface for defining end-user
security and managing system-level settings.
Security of models
The security of models relies on the underlying database security schemes.
Brio Enterprise focuses security on the document repository and the
distribution layer.
Query monitoring
A SQL log monitors all queries, including all SQL statements generated and
usage activity (such as the number of rows returned). The SQL shown can be
edited to optimise the performance of a frequently requested query.
Additionally, an auditing feature allows administrators to collect usage
statistics about data models stored in the repository. The information can
include how long queries take to process and which tables and columns are
used most often.
Management of data
Persistent data is stored in document files either in the repository (which can
be a relational database), in a remote file server or locally on the client.
The loading of data into the data warehouse is beyond the scope of Brio
Enterprise. Broadcast Server provides graphical scheduling tools for periodic
and batch-style processing and data refreshes. Schedules can be based on
timed intervals (ASAP, daily, weekly, monthly, quarterly or custom) or user-
defined events, and are supported by e-mail notifications.
Event-based scheduling is supported through the polling of data sources. The
Broadcast Server can be triggered to refresh reports based on external
events, such as the completion of an update to a data warehouse or a business
exception rule.
All report processing activity, including errors and failed refreshes, is
monitored and logged in a job repository. Administrators and end users can
be notified (via e-mail or pager) upon the completion or failure of a report
processing activity. However, there are no automatic retry functions.
OnDemand Server does support failover. If processing fails on one server, it is
automatically re-submitted to another server in the cluster.
Documents can be stored centrally in a repository (database server), on a
remote file server or locally (on the client machine).

Sparsity handling is the responsibility of the data warehouse or MDDB that
Brio Enterprise connects to for data.
The decisions about aggregates and indexing are the responsibility of the
data warehouse. Data returned to the client (DataCache) and documents are
stored in a compressed format, typically between one-third and one-tenth of
the original size. The typical size of a DataCache is around 100,000 rows of
data.
The DataCache automatically manages in-memory caching.
The results data of a query is time-stamped to show when the data was last
refreshed. Additionally, when querying data, end users can view metadata
that shows the last time the data warehouse was updated.
Management of users
Brio Enterprise does not support direct multi-user write-back facilities to the
database.
Defining user and group-level security is maintained through a graphical
tree-like interface. Interaction levels and group assignments can be easily
administered via point-and-click.
Query governance
Query governors are available to set controls on the time a query takes to
perform and the number of (unique) rows returned to the client.
There is no support for restricting end-user queries to particular times of the
day, but a query-sizing feature is provided to query the database and to show
how many records a query will retrieve – this is useful for testing a
questionable query and postponing processing of large results sets during
peak network periods.
Brio Enterprise relies on model and user security assignments to control the
visibility of metadata.

30
Adaptability
Summary
Brio data models are quite adaptable to change. New dimensions and
measures can easily be added to data models. Adaptability is greatly
enhanced by the OMI feature, which dynamically maps existing metadata
from a range of back-end data sources. This ensures that the data source, data
models and metadata are kept synchronised. Data models stored in the
central repository are automatically updated to reflect changes in the source
database. However, support for impact analysis and metadata audit trails is
not provided.
New dimensions can easily be added to a data model at any time via drag-
and-drop. If a model is updated in the repository, documents based upon it
are automatically updated. However, users are not provided with details of
the changes.
Dimension definitions are contained in the data model and can be re-used.
New measures can easily be added to data models.
Measure definitions are contained in the data model and can be re-used.
There is no direct support for changing the architecture to a full MOLAP or
ROLAP mode. However, Brio Enterprise can implicitly link into both these
environments. Mobile architectures are well supported.
A database synchronisation feature is available to keep data models stored in
the repository, in line with changes to the source database. An itemised list of
the changes made is provided.
Dimension members can automatically be updated when there is a change in
the data source. Changes to master data models are automatically reflected
in dependent reports.
1 2 3 4 5 6 7 8 9 10

Metadata
The OMI feature ensures that changes in upstream metadata remain
synchronised with data models. However, descriptive metadata input when
loading a data model remains unaffected and must be manually updated.
Impact analysis
There is no support to inform the administrator of the effect on documents
and reports when there is a change in the structure of the data warehouse.
An audit trail showing changes to the history of the metadata is not
available. Generally, the auditing capabilities provided relate to usage of
data models.
Brio’s Open Metadata Interpreter (OMI) reads and interprets existing
metadata from most of the leading data warehousing tools. The OMI link
enables model designers to view extraction and transformation metadata, as
well as descriptive information and naming conventions about tables and
columns in the data warehouse to help them build data models. Metadata is
propagated so that Brio reports that run against old metadata can update
themselves (although some user intervention is usually required).
Integration is provided with a variety of data warehousing tools, including
Informatica’s PowerMart Suite, HP Intelligent Warehouse, IBM Visual
Warehouse, Ardent DataStage, Broadbase, Logic Works Universal Directory,
Carleton Passport, Pine Cone and Prism Warehouse Manager. Snap-in and
customisable metadata templates are also provided for several leading
metadata vendors.
Summary
As Brio Enterprise effectively operates as an MDDB cache to a relational
database, it can be difficult to understand how to optimise such a system,
since both the underlying database access and the access into the cache can be
optimised. For example, dimensionality can be handled in the database
schema or an alternative dimensionality can be layered in the desktop
multidimensional cache.
For the analysis of small datasets’ performance, this is not an issue. However,
Brio Enterprise’s desktop architecture does impose limitations on the size and
complexity of models it can analyse on the client. The web configuration
supports a more scalable three-tier architecture – the OnDemand Server
supports SMP and can easily be scaled across a cluster of multiprocessor
servers for simple load balancing and failover support. Administrators should
give particular consideration to how best to use Broadcast Server alongside
OnDemand Server to load balance queries and improve overall performance
of the system.
1 2 3 4 5 6 7 8 9 10

32
ROLAP
Multipass SQL
Multipass SQL is not supported.
Processing can be carried out on the database server or the Brio server,
depending on where the calculations are defined. For example, measures
added to the query section will be processed by the RDBMS and measures
added to the results section will be performed by the Brio server.
Brio Enterprise also supports native DB2, Teradata and Red Brick functions
for more sophisticated processing on database servers.
The DataCache can be stored in a document and subsequently retrieved for
optimal query performance. The DataCache is time-stamped to show the
currency of the data.
Aggregate navigator
Brio Enterprise relies on aggregate awareness implemented in the target
database. There is no native aggregate navigation provided by the Brio tools.
MOLAP
Brio Enterprise is not a MOLAP tool.
Typically, OnDemand Server supports 200–300 users. Performance can be
significantly enhanced if dedicated hardware is used for the OnDemand
Server and the repository resides on a separate database server.
Processing
Native access is supported for Oracle, Sybase (Adaptive Server), Red Brick
and Informix (Dynamic Server and MetaCube). Brio Enterprise uses ODBC
to access other relational data sources.
A cluster of OnDemand Servers can be configured and multiple queries can
be routed across the servers for simple load balancing. Each cluster is
comprised of a manager and one or more nodes – the distribution is ‘round
robin’ across all the active nodes in the cluster. Load balancing can also be
achieved for Broadcast Servers, although this requires significant
programming.
SMP support
The OnDemand Server is multi-threaded and supports SMP parallelism.

Customisation
Summary
Brio Enterprise is primarily an out-of-the-box solution, but graphical
development tools are also provided to simplify the construction of simple
analytic applications. The development environment uses a combination of
drag-and-drop and scripting (JavaScript). Third-party development tools can
be used via OLE automation.
Development is object-based, rather than object-oriented and geared towards
the construction of analytic EIS-style interfaces, rather than complex OLAP
applications that require specialised functionality.
Customisation
The Brio clients are essentially the same program, but with different
features disabled, based on their intended target audience. The Adaptive
Reports feature offers users five levels of interactivity, depending on the
user’s profile and the document’s profile.
The EIS tab section, included in BrioQuery Designer, supports a
development environment for building graphical front-ends and electronic
dashboards using a combination of drag-and-drop user interface controls and
JavaScript. Layout tools are provided for embedding objects such as bar
charts, hot spots, graphics or ‘top seller’ lists on a screen to be viewed by
high-level users. Data on the screen is live and updated regularly.
Applications
Simple reporting applications can be developed for the web clients by using
the development and JavaScript scripting facilities provided.
The EIS tab section provides graphical and scripting tools for development.
Report components and user application objects can easily be assembled on
screen using drag-and-drop. A number of standard user interface controls
(radio buttons, check boxes, list boxes and so on) are provided. A scripting
language (JavaScript) is available to manipulate Brio application objects and
build functionality into the application. The scripting editor supports basic
debugging and testing facilities.
The development environment is object-based rather than object-oriented –
which lends itself to simplicity and ease of maintenance.
1 2 3 4 5 6 7 8 9 10

34
The Brio object model is accessible via OLE automation allowing the
BrioQuery application to be controlled by third-party development tools such
as Visual Basic, C++, Delphi and PowerBuilder.
Proprietary scripting
A proprietary scripting language can also be used to script Brio client
applications from external applications; special add-ons are provided for
Microsoft Excel and Macintosh HyperCard applications. Generally, scripting
from other applications is simply a matter of establishing communication
(via DDE or AppleEvent) between the client application and the Brio client,
which acts as a ‘server’. Scripts can also be sent directly to the Brio client
running in a Unix Motif environment, using a command line parameter.
Localisation
Brio Enterprise is available in five different language versions and includes
support for double-byte character sets (DBCS).

Deployment
Platforms
Client
BrioQuery clients (Designer, Navigator and Explorer) run on Windows 3.1,
95, 98 and NT workstation, Apple Macintosh and Unix Motif.
Brio.Insight and Brio.Quickview run on Microsoft and Netscape web
browsers using their plug-in APIs.
Server
The Brio Enterprise servers (Broadcast Server and OnDemand Server) run
on Windows NT and Unix (HP-UX, Solaris and AIX). OnDemand Server
works with a variety of web servers (including Apache web servers on Unix)
via ISAPI, NSAPI and CGI.
Data access
Brio Enterprise provides native access to Oracle, Sybase, Red Brick and
Informix database servers. ODBC access is provided for other relational
databases, including IBM DB2, Teradata, Microsoft SQL Server,
QueryObjects (CrossZ) and White Cross.
It can also connect to third-party OLAP servers. Native access is provided for
Hyperion Essbase and IBM DB2 OLAP Server (both accessed via the
GridAPI), Informix MetaCube and SAP BW. OLE DB for OLAP access is also
provided to connect to Microsoft SQL Server 7.0, OLAP Services, NCR
TeraCubes, SAS, WhiteLight and Applix TM1.
Additionally, Brio Enterprise can also be accessed from knowledge
repositories, such as Sqribe (ReportMart) and VIT (MetaWarehouse). Access
to SAP R/3 is provided by Acta Technology’s RapidMarts for SAP. Integration
with other ERP systems is planned.
Standards
Brio Enterprise supports Microsoft’s OLE DB for OLAP as a consumer.
Brio Enterprise has conducted its own internal benchmarking tests for
OnDemand Server – the results will be published by the end of 1999.
Price structure
Pricing for Brio Enterprise Server is $32,495 for Windows NT and $44,995
for Unix systems. Both editions include OnDemand Server and Broadcast
Server, Brio Enterprise Administrator and ten named-user licences for the
Brio.Quickview web client.
All server and client components are also separately available; OnDemand
Server costs $19,995 for Windows NT and $29,995 for Unix; Broadcast
Server costs $14,995 for Windows NT and $19,995 for Unix. Pricing for
individual Brio client tools ranges from $50 for Brio.Quickview, up to $3,995
for BrioQuery Designer.

36

BusinessObjects
Summary
At a glance .............................................................................................. 2
Ovum’s verdict......................................................................................... 4
Distribution ............................................................................................ 13
Product evaluation
Web support .......................................................................................... 18
Management ......................................................................................... 20
Adaptability ............................................................................................ 22
Customisation ....................................................................................... 24
Deployment
Platforms ............................................................................................... 26
Data access .......................................................................................... 26
Standards .............................................................................................. 26
Price structure ....................................................................................... 26

2
Evaluation: Business Objects – BusinessObjects
At a glance
Developer
Business Objects, twin headquarters in Paris, France and San Jose, USA
Versions evaluated
BusinessObjects version 4.0, comprising of the BusinessObjects user module,
BusinessObjects Designer, BusinessObjects Supervisor, Document Agent
Server, BusinessQuery and BusinessMiner; and WebIntelligence II version
2.0
Key facts
• A client-based tool that provides OLAP query, analysis and reporting
• Runs as a Windows 3.1. Windows 95, Windows NT, Unix Motif or Java
client; servers run on Windows NT and Unix
• Business Objects has re-architected its product lines to support a 32-bit
component environment, and has made significant enhancements for web
access
Strengths
• An easy-to-use ‘out-of-the-box’ tool
• Flexible OLAP query, analysis and reporting available via the Web
• Graphical set of IS tools for creating the mapping layer and deploying
OLAP across the enterprise
Points to watch
• The mid-tier Document Agent Sever component lacks the capabilities of a
full OLAP engine
• Potential scalability issues for large result sets and complex OLAP
calculations
• Little support for custom application development
Ratings
Web support
Management
Adaptability
Customisation
1 2 3 4 5 6 7 8 9 10

3
Note the difference between the company name and the product name.
Business Objects denotes the company, whereas BusinessObjects is the
name of its product.
Business objects
Equivalent to dimensions and measures in OLAP terms. A combination of
business objects is used to create a multidimensional business model.
Document
A container for a multidimensional model that can be stored persistently on
a server for scheduling and distribution.
Microcube
A local multidimensional data structure that allows users to perform OLAP
analysis. A microcube is created ‘on the fly’ from a query and is viewed in a
report. It corresponds to Ovum’s definition of a multidimensional business
model.
Semantic layer
A mapping layer that provides a representation of the database using famil-
iar business terms (such as ‘customer’ or ‘sales’).
Report
Provides a graphical interface that can be used directly as a starting point
for multidimensional analysis because it is connected to the microcube that
holds the data.
Universe
Hosts ‘classes’ of business objects. Each universe corresponds to the needs of
a particular group of users, an application or a department.

4
Ovum’s verdict
What we think
Building on its established strength of providing flexible access to corporate
data, the most distinguishing feature of BusinessObjects is its ease of use.
The tool provides ‘out-of-the-box’ functionality for the masses of general
business users, rather than analysts seeking high analytical functionality, or
customers seeking to develop specialised OLAP applications.
The architecture supports a well designed mapping layer that shields end
users from the complexities of the underlying data sources. This provides
easy and flexible access to corporate data using familiar business terms. The
‘dynamic’ nature of BusinessObjects’ multidimensional models means that
users can easily direct queries to additional data during analysis in an ad
hoc way. The BusinessObjects graphical designer and administration tools
have also been implemented with an emphasis on usability, and specifically
address the problems of rolling out BusinessObjects to large numbers of
users. Business Objects has made significant strides in the area of web
enablement. Its WebIntelligence II product provides one of the strongest web
interfaces in the OLAP market, and is closely integrated with the
BusinessObjects tools in terms of end-user functionality and infrastructure.
The main challenge for Business Objects is to build on its early success in
the low-end business intelligence market to capture share in an ‘enterprise’
space that demands high performance and scalability. The BusinessObjects’
mid-tier component provides a range of report processing services for sched-
uling and distribution. But it does not constitute a full mid-tier OLAP server.
Customers with queries that return large data sets, and require complex
OLAP calculations must therefore integrate with third-party OLAP engines,
or license specialist technology to support this capability.
When to use
BusinessObjects is suitable if you:
• want ‘out-of-the-box’ functionality
• want to empower hundreds of general business users with query,
analysis, reporting and data mining via an integrated interface
• want to support ad hoc queries to a range of data sources via a web
browser
• require quick and easy deployment across the enterprise.
• want to provide advanced analysis and forecasting functions, without
having to integrate with third-party technology
• want to build highly customised OLAP applications
• intend to build large, dimensionally complex business models that
require extensive OLAP calculations.

5
Product overview
Components
BusinessObjects is comprised of the following end-user tools and IS tools:
• BusinessObjects user module version 4.0
• Designer version 4.0
• Supervisor version 4.0
• Document Agent Server version 2.0
• WebIntelligence II version 2.0
• BusinessQuery version 4.0
• BusinessMiner version 4.0.
BusinessObjects is a client-based tool that provides ad hoc OLAP query,
analysis and reporting capabilities from a PC. A web configuration pushes
most of the processing to a mid-tier application server. BusinessObjects
works directly against relational data warehouses or datamarts, and data
providers enable access to non-SQL sources such as ERP applications and
third-party multidimensional databases. BusinessObjects provides a central
repository for metadata definitions, models and reports. The tool also sup-
ports a Visual Basic-like scripting language called ReportScript for
customisation.
BusinessObjects user module
The user module consists of the Reporter and Explorer components. It
provides a graphical drag-and-drop user interface to access corporate data
via an integrated querying, reporting and OLAP environment.
The BusinessObjects Reporter component allows users to dynamically query
the database and define their own reports. Queries are expressed via a
query panel, and use a mapping layer to represent the data stored in the
database. End users simply combine business objects to create native SQL or
ODBC calls to the database server to retrieve data.
OLAP OLAP Web access Data mining Distribution Management
design analysis & security
Client Designer BusinessObjects WebIntelligence II BusinessMiner Supervisor
user modules
BusinessQuery
Server WebIntelligence II Document
Agent Server

6
When data is downloaded into a report, it is stored locally on the client as a
multidimensional data file, called a ‘microcube’. The BusinessObjects Ex-
plorer components provide multidimensional analysis capabilities directly
from the report. If a further level of detail is requested for drill-down to data
(for example, for it to be displayed along another dimension), the microcube
can be expanded or moved dynamically.
Designer
A graphical DBA tool for defining classes of business objects (equivalent to
dimensions of a business model) that map to the source database; Business
Objects refers to this mapping as its ‘semantic layer’ (for which it holds a
patent). The semantic layer allows end users to view data using familiar
business terms.
Every BusinessObjects client holds a local copy of the mapping layer. How-
ever, to simplify distribution and administration it is also possible to store it
centrally in a repository within the database, from where it is available to all
end users.
Supervisor
A graphical administration tool for managing end users and system re-
sources. An object-based security model lets administrators assign and
modify the rights granted to groups of end users. User profiles include access
privileges to the mapping layer, reports and individual menu functions.
Additionally, the size of the result of a query, or the query execution time,
can be limited for end users.
Document Agent Server
A mid-tier server component that provides scheduling and batch processing
facilities for distributing microcubes and report definitions as stored ‘docu-
ments’. When an end user submits a document for processing, it is stored as
a pending job in the BusinessObjects repository. Document Agent Server
checks the pending jobs, either periodically or conditionally, and retrieves
them for processing as scheduled. When it has finished it places them back
in the repository for retrieval via the BusinessObjects user module or a web
browser.
Document Agent Server can also perform tasks such as sending documents
via e-mail or to a web server in HTML format. The server supports its own
administrator interface for modifying the scheduling, priority settings and
distribution of processed documents.
WebIntelligence II
Enables query, OLAP analysis and reporting functions from a Java-enabled
web browser. WebIntelligence II includes an object request broker for the
server and a Java applet for the browser.
A web query panel is used to create new reports on-the-fly; this is
downloaded to the web browser as a Java applet, and includes a copy of the
mapping layer. Web users are provided with a range of report formatting
options and can also add their own simple calculations to report data. The
interface is based on a personal user homepage, from which end users can
access personal and shared documents, or targeted reports can be sent to an
‘inbox’ via the Document Agent Server.

7
BusinessQuery (optional)
A Microsoft Excel spreadsheet add-in that lets end users pull data directly
into Excel spreadsheets using the terminology from the BusinessObjects
semantic layer.
BusinessMiner (optional)
An end-user tool that provides data mining facilities. It uses decision-tree
algorithms to graphically depict hierarchical relationships in data.
WebIntelligence II is a thin client implementation of BusinessObjects that
extends the architecture to incorporate a more powerful application server
that comes closer to the functions expected of a ‘full’ mid-tier OLAP server.
Significantly, this configuration moves most of the BusinessObjects code
from the client to the WebIntelligence II application server.
WebIntelligence II is based on the use of Java applets for ad hoc queries. By
downloading a web panel and a local copy of the mapping layer onto the web
client, users are provided with a similar level of functionality to the
BusinessObjects desktop client. WebIntelligence II runs as a set of centrally
managed software components. It has a distributed component architecture
(DCA) that supports multiple copies of the server components across differ-
ent web servers. DCA is implemented using Corba technology licensed from
Visigenic Software.
This architecture includes the addition of a mid-tier report server compo-
nent called Document Agent Server. The configuration allows users to run
queries in real-time, or schedule the batch processing of documents on the
Document Agent Server; the server can run on either a separate application
server or on the database server.
Document Agent Server is a ‘light’ mid-tier server that provides report
processing services. It does not support a static MDDB, nor does it offer the
powerful processing functions expected of a full OLAP server engine. End
users do not communicate directly with the Document Agent Server, but the
BusinessObjects repository, which stores all the processed documents. There
is no caching or storage of model data on the Document Agent Server itself.
A web server can be added to provide web publishing capabilities. The web
server uses a CGI interface to communicate with the Document Agent
Server to distribute standard BusinessObjects reports published in HTML
format. Selected reports can be downloaded with an associated microcube,
which makes further analysis on the desktop possible if the BusinessObjects
client is also installed locally on the recipients’ client.
The ‘natural’ BusinessObjects client-server architecture is a two-tier desktop
architecture, based on a full BusinessObjects client that interfaces with one
or more relational databases. BusinessObjects supports a number of differ-

8
ent schemata, including normalised tables, star, snowflake, multistar and
data warehouses with aggregates.
The desktop architecture uses a local copy of the mapping layer stored on
the client. Using this mapping layer, an end-user formulates a query and
sends it to the desktop query generator, which automatically generates SQL
(joins, group byte, multipass SQL statements and other SQL clauses) and
sends it to the database server. After processing the request, the database
server returns the results data back to the client where it is cached as a
‘dynamic’ microcube. Microcubes are local multidimensional data files
created ‘on-the-fly’, and contain a slice of data needed for a particular query.
If the user drills down into the microcube to obtain more detailed levels of
data, a new query is sent back to the database server. BusinessObjects can
pull data from several sources into a single microcube. Multiple microcubes
can be linked to allow queries to span across more than one microcube.
Mobile architecture
The BusinessObjects desktop architecture ‘naturally’ lends itself to a mobile
architecture. While disconnected, users are restricted to the data stored in
the downloaded microcube.
Using BusinessObjects
Main concepts
It is easier to understand the BusinessObjects approach to OLAP if three
principal concepts – semantic layer, business objects and universes – are
clarified first.
The idea of providing users with a way to refer to corporate data in business
terms lies at the heart of BusinessObjects. It is achieved by using the seman-
tic layer, a centrally defined and controlled model of the underlying data-
base. This provides a mapping layer that allows end users to view data using
familiar business terms called business objects (approximately equivalent to
dimensions or measures in OLAP terms).
There are no restrictions on the way users combine business objects to
create their queries. Business objects are ‘semantically dynamic’, which
means that they retain their meaning in whatever combination they are
used. For example, the object ‘sales revenue’ will report the correct amount
whether used in conjunction with customer or product. The SQL statements
needed to retrieve the data are automatically generated by BusinessObjects,
with no awareness required on the part of the user.
Business objects are organised in classes; for example, the class ‘customer’
might consist of different business objects, such as age, group or sex. Uni-
verses consist of different classes and objects. Typically, different users use
different universes for different purposes. Multiple universes, such as sales,
personnel or inventory, can be created to meet the needs of different groups
of users.

9
Figure 2 Designer graphical interface
Page 1
BusinessObjects DesignerBusinessObjects Designer
Designing the mapping layer
The mapping layer is typically created by DBA or IS staff using the
BusinessObjects Designer tool.
As illustrated in Figure 2, Designer employs a highly graphical interface to
create a schema which defines how users see the database. As well as draw-
ing and printing the database schema, the tool also includes routines for
automatic design checking, including loop detection and resolution. A Quick
Design Wizard is also provided for the rapid creation and maintenance of
new schemata.
Flexible modelling and analysis
Users either work with predefined models, or build their own ‘on-the-fly’ by
directly querying the database.
Users start by selecting an appropriate universe that contains dimensions
and measures specific to their business problem. Users simply drag across
appropriate dimensions and measures in a query panel. This is shown in
Figure 3. When a user issues a query, data is delivered to the client in the
form of a ‘dynamic’ microcube (a model, in Ovum’s terms) that can be ana-
lysed using standard OLAP functions such as drill-down and slice-and-dice.

10
Figure 3 Query panel
Page 1
Query PanelQuery Panel
Available
Information
Requested information
Conditions
The model is dynamic and is typically stored as part of a document. Models
can be expanded dynamically when a user requests to view data along
another dimension or wants to drill-down. By navigating through the model
in this way, users can view all the data to which they have access. The whole
process of creating, moving or changing a microcube is completely transpar-
ent to the user, who only works with the business representation of corpo-
rate data.
Users can enhance the model with additional calculations, and create re-
ports on the data contained within it. Users can also link and incorporate
related data from other data sources into the model.

11
Future enhancements
The next major release of BusinessObjects is due in early 1999. The new
version will provide a number of enhancements, including:
• a toolkit for writing custom data providers
• a set of Active X controls for custom application development
• integration with a wider range of metadata sources, including the
Microsoft repository.

12
Company background
Business Objects was founded in 1990 by two former Oracle employees in
Paris, France. The company was modelled on the venture capital funded
technology start-ups in the US. It attracted investments from venture
capitalists in Silicon Valley and Europe, including the founding shareholders
of National Semiconductor and Oracle.
BusinessObjects was first released in 1991, and has since sold more than
870,000 licences worldwide. In 1994, 25% of the company was floated on the
Nasdaq. The flotation raised more than $30 million in capital. In 1996,
Business Objects decided to re-architect its product to support a 32-bit
component-based architecture. However, delays in the introduction of a
stable version 4.0 of the product caused a financial loss in the third quarter
of 1996. Business Objects has now recovered from this hiccup and, with a
major product transition now behind it, has returned to financial growth and
stability. Business Objects’ revenues for fiscal 1997 grew 34% to $114.3
million.
Business Objects has joint headquarters in Paris, France and San Jose,
California, US. The company employs 800 people and has additional offices
in North America, Europe and Asia-Pacific.
Business Objects has been a player in the reporting and analysis tools
market for some years and has achieved great success in this market. Its
OLAP products are positioned at the client tool end of the OLAP market,
and are targeted at general business intelligence requirements. The idea of
providing large user communities with a way to query, analyse and report on
corporate data in familiar business terms lies at the heart of Business
Objects’ philosophy.
BusinessObjects is sold directly and through VARs and OEMs. It is posi-
tioned as a ‘neutral’ front-end component, providing an open back-end to
access a range of data sources including third-party OLAP server engines
(Microsoft SQL Server OLAP Services, Hyperion Solutions Essbase, Oracle
Express and Informix Metacube). Partnerships are key to its OLAP strategy.
The company has close relationships with all the major RDBMS vendors,
data warehousing vendors and ERP application software vendors. For
example, IBM bundles BusinessObjects as part of its Business Intelligence
initiative. Business Objects also resells Informatica’s PowerMart Suite.
Customer support
Support
Business Objects offers a multi-tiered help-desk support system at a corpo-
rate and a field level. Support is provided via telephone hot-line and the
Web.

13
Training
Education services are available in several languages both in-house and on-
site. Public courses are run frequently for end users, designers, administra-
tors and supervisors. Computer-based training is also available.
Business Objects’ consultants are mainly relational database specialists and
are available to provide advice and development support on all aspects of
product implementation. However, no significant portion of revenues is
attributed to consulting, except in the UK where it accounts for 18% of
revenue. Consulting projects include requirements for data access and the
analysis of the relational database schema. Business Objects also has nu-
merous consulting and referral partners.
Distribution
US
Business Objects
2870 Zanker Road
San Jose
CA 95134
USA
Tel: +(1) 408 953 6000
Fax: +(1) 408 973 1057
Europe
Business Objects
1, Square Chaptal
92300 Levallois-Perret
France
Tel: +(33) 1 41 25 21 21
Fax: +(33) 1 41 25 31 00
Asia-Pacific
Business Objects Australia
Suite 210
283 Alfred Street North
North Sydney
NSW 2060
Australia
Tel: +(61) 2 9922 3049
Fax: +(61) 2 9922 3069
http://www.businessobjects.com
E-mail: info@businessobjects.com

14
Product evaluation
Summary
1 2 3 4 5 6 7 8 9 10
BusinessObjects’ main strength is its ease-of-use; it provides excellent ‘out-of-
the-box’ functionality with little or no need for prior adaptation. It shields
users from the complexities of SQL, providing access to data through an
easy-to-use mapping layer. All OLAP functions are available through point-
and-click, regardless of whether a user wants to analyse models or format a
report. Reports can be shared and distributed by using the Document Agent
Server, or via integration with e-mail systems.
Generally, models are dynamic, and created on-the-fly. However, users can
search for predefined documents stored in the BusinessObjects repository.
Text string and keyword searches are supported for finding files.
In the context of BusinessObjects, most metadata is structural information
about the database, and therefore relevant only for developers. However,
developers have the option of including universe and object descriptions that
can be displayed in reports.
The end user cannot annotate the microcube directly, but can add textual
annotations as part of a report. These annotations are accessible to all users
of the report.
Using the model
BusinessObjects excels in its simple and intuitive graphical interface that
integrates query, analysis and reporting from a consistent interface. All
OLAP functions are easily achieved using either point-and-click or drag-and-
drop.
The position of members in a report can be changed using point-and-click.
An ‘explore’ option allows end users to navigate through the dimensional
hierarchy.
End users can drill-down to detail data held in relational databases directly
from the BusinessObjects client. The detail data is freshly retrieved and
viewed directly from a BusinessObjects report.

15
The BusinessObjects query panel is embedded in specialist third-party tools
such as Geoconcept, StatSoft and SPSS. This allows them to query and pull
in data using the BusinessObjects mapping layer.
Business Query is an add-in for Microsoft Excel that enables users to extract
information from databases and load it into Excel cells for further analysis.
Reports present data in tabular (standard and master/detail tables), graphi-
cal and cross-tab (matrix) formats. BusinessObjects supports a range of
business graphs and charts. Users can also drill-down directly from graphs.
Multiple charts and tabular data can co-exist on the same screen. Integra-
tion with the Geoconcept tool allows GIS-type mapping to be applied to data.
Designing a report
Free-form reports can be designed from scratch by end users. A Report
Wizard is provided. Alternatively, users can choose a predefined report
template. Full production-style report formatting capabilities are provided.
Reports can also contain data from different sources, including spreadsheets
and OLE objects from external applications.
Publishing a report
Reports can be published and sent to the Document Agent Server for sched-
uled delivery.
Reports can be sent via MAPI-compliant e-mail systems directly from the
BusinessObjects interface. Reports can be e-mailed on a scheduled basis to
groups of users by using a BusinessObjects script. But dynamically gener-
ated address lists are not supported.
The Document Agent Server supports the conditional delivery of reports
based on user-defined preferences.
Summary
1 2 3 4 5 6 7 8 9 10
The process of building a business model is split between DBAs (who create
the mapping layer) and end users (who create reports by querying the data-
base using the mapping layer). Easy-to-use graphical tools are provided for
both types of user. The design tools provide extensive wizard support, and
DBAs can readily exploit existing database schemas. Multi-designer environ-
ments are also well supported by concurrency and versioning controls.

16
Basic design
Design interface
The BusinessObjects Designer module provides a graphical interface for
designing the mapping layer (the meta-model) and creating universes (end-
user perspectives on the meta-model). The interface has a standard
Microsoft Office 97 look-and-feel. A Quick Design Wizard is provided to
guide developers through each step in the process, and includes facilities for
design checking.
Models are built using queries, and viewed and analysed in a report. Users
simply drag-and-dropping important business objects in a familiar Microsoft
Office-style interface to retrieve data. The query functions are also sup-
ported by wizards.
A graphical view of the database schema is provided. Sample data for a
particular table or column can also be viewed on-screen.
BusinessObjects supports a mapping layer, which it calls the ‘semantic
layer’. The universe defines a particular type of mapping for groups of end
users.
Developers and end users are not automatically prompted to provide addi-
tional metadata when creating the mapping layer or building models. All
metadata inputs are optional.
Columns can be included and excluded selectively via point-and-click using
the Table Browser facility.
Dimension members can be selected using point-and-click or via SQL.
Dimension hierarchies can be built using point-and-click. Hierarchies can be
built using the existing database schema or by custom definitions.
Time dimension
Time aggregations need to be explicitly defined. Custom time dimensions
can be built. But there is no direct support for dynamic period-to-date
dimensions.
Designers can annotate dimensions with long name descriptions. These
descriptions can be displayed in report and chart views.
It is possible to default a report to view specific dimensions and levels upon
opening.

17
Calculated measures
Calculations can be added to a model using a calculator-type interface.
BusinessObjects provides a range of standard numeric and arithmetic
functions and operators.
It is possible to include multiple measures within a dimension.
Multiple designers
Multiple designers
The Designer tool supports a centralised metadata repository with check-
out/check-in facilities and concurrency controls; it provides locks on the
mapping layer so that only one designer can modify a universe at a time.
The metadata repository uses delta versioning.
Summary
1 2 3 4 5 6 7 8 9 10
BusinessObjects is limited in its support for complex analytical functions.
Generally, the range of native functions provided is geared towards general
business analysis. Advanced functions such as statistical modelling and
forecasting rely entirely on integration with third-party tools. A bonus is the
integrated and easy-to-use data mining facilities provided by the
BusinessMiner tool.
Tight integration is provided with specialist third-party tools. Vendors have
licensed the BusinessObjects Query Panel to access BusinessObjects data.
These include SPSS, StatSoft and Geoconcept. A Microsoft Excel spreadsheet
add-in is also provided.
Ranking and sorting
Ranking and sorting functions are supported.
BusinessObjects supports standard arithmetic, trigonometric and
logarithmic functions.
Financial functions
BusinessObjects does not support any financial functions.

18
Statistical models
There are no statistical modelling facilities directly provided by
BusinessObjects; this is supported via integration with SPSS.
Trend analysis
BusinessObjects supports simple period-on-period analysis. Apart from this
there is no direct support for advanced trend analysis based on exponential
smoothing or curve-fitting techniques.
Simple regression
BusinessObjects does not support any regression forecasting functions.
Time series analysis forecasting
BusinessObjects does not support advanced time series forecasting
algorithms.
A variable and formula editor is available for creating simple user-defined
analytical functions. Scripting is also available.
BusinessObjects does not support write back to the database.
Reports can contain textual data. But there is no direct support for the
analysis of textual data sources.
Data mining
BusinessMiner is a data mining tool that can be fully integrated into
BusinessObjects; it uses the same semantic and security layer as
BusinessObjects and is accessed as an option on the BusinessObjects menu.
BusinessMiner is client-based and uses decision-tree technology developed
by Isoft to graphically depict relationships in data.
BusinessMiner is suitable for general business users; the emphasis of the
tool is clearly on ease-of-use, rather than on advanced data mining
algorithms.
Web support
Summary
1 2 3 4 5 6 7 8 9 10
WebIntelligence II provides strong support for web access. It supports most of
the OLAP query, analysis and reporting functions as its desktop counterpart,
including the ability to create new models and define format report formats.
WebIntelligence II also benefits from the distribution of processing across
multiple servers for increased scalability and load balancing. The web tools
share the same metadata layer and security as the client-server tools, allow-
ing for integrated management.

19
WebIntelligence II provides strong facilities to explore and modify models
from a web browser. It provides the same level of OLAP functionality (in-
cluding drill-down, slice-and-dice and pivot) as the desktop client through
the web panel. The Web Panel is implemented as a Java applet that com-
bines the functions of BusinessObjects’ query panel and slice-and-dice panel.
WebIntelligence II supports standard functions (sum, count, average, min,
max and percentage) that enable users to define and add their own calcula-
tions in web reports. Charting and report formatting is also available via the
Web.
WebIntelligence II supports both registered and unregistered (guest) access.
The HTML and Java-based web interfaces support both casual users that
need browse access to static HTML reports, and more advanced users that
require a more interactive experience.
It is not possible to edit the mapping layer using the web tools.
WebIntelligence II users can access the mapping layer to create new models
and reports. The web panel is a Java applet that provides similar query and
model building functions as the desktop tool’s query panel. Web users can
therefore easily add additional data not stored in the microcube by initiating
another ad hoc query via the Web.
Reports can be saved in HTML format using point-and-click.
Reports can be e-mailed over the Internet to corporate/workgroup folders or
personal in-boxes. These reports can be accessed by registered
WebIntelligence II via their personal homepage. Document Agent Server can
also schedule delivery of reports via the Internet.
Reports can include multiple URLs.
WebIntelligence II supports a distributed component architecture that uses
CORBA-compliant technology from Visigenic Software. This allows for the
distribution of processing across multiple web servers for load balancing.

20
Other web support features
Multimedia objects can be embedded in web reports. Hyperdrill allows
report cells to be hyperlinks that can drill out of a report into any Internet-
based data sources.
Management
Summary
1 2 3 4 5 6 7 8 9 10
The BusinessObjects Supervisor provides excellent graphical management
tools that ease the administration of reports, metadata and end users. The
tool provides strong query governance mechanisms and supports a sophisti-
cated security model to restrict user access to models and other application
objects. But query monitoring is only available with WebIntelligence II and
there is no support provided for tuning the BusinessObjects client cache.
The management of reports and users is via the Supervisor interface. The
mapping layer is managed using the Designer module. Both tools share a
similar graphical interface, and most tasks are achieved using point-and-
click.
Security of models
Administrators can set multi-level security controls for reports. Read-edit
access can be specified on individual reports.
Query monitoring
The BusinessObjects client-server tools do not provide any graphical facili-
ties to monitor queries; though this can be set up using event-driven scripts.
WebIntelligence II, however, does provide an audit trail facility for tracking
queries.
Management of data
Data is stored in the document domain of the repository as a document file.
The file contains the report definition and one or more microcubes (models).
Data can also be stored locally on the client or a separate file server.
With Document Agent Server, users can schedule time-specific updates on
an hourly, daily, monthly or custom interval basis.
Event-driven scheduling in Document Agent Server can be achieved through
the use of scripts that check a specified environment variable before execut-
ing a schedule.

21
Failed loads automatically produce an error message and log file. Scripts can
be set up to e-mail error messages to DBAs. Document Agent Server can
reprocess failed updates, and administrators can specify the number of re-
submission attempts.
Data can be stored on the client, in the repository or on a file server.
Because only the lowest level of detail needed is stored in a microcube, there
is no requirement for sparsity handling in BusinessObjects.
No limits are imposed on the size of the target model. The size of the
microcube is restricted only by the time taken to download it to the client.
Facilities are provided for tuning the cache.
All reports specify when the model was last refreshed.
Management of users
BusinessObjects does not support a write-back capability.
An object-based security model is supported for users. Administrators can
grant access rights to individuals or groups of users (including nested
groups). Several user profiles are provided, and custom profiles can also be
set up. Complex security hierarchies can be set up and displayed graphically;
users ‘inherit’ security attributes and access rights from ascendant groups.
Query governance
Controls can be set that restrict the size of the result of a query; for example,
the maximum number of rows returned by a query, and limit the maximum
‘fetch’ time for a query. Additional controls can be set on:
• elements of SQL syntax generated by queries (such as nested ‘select’
commands and operator functions)
• the use of certain business objects in a query
• access to specific rows in a database table.
Users and certain types of queries can be restricted to certain times of the
day.

22
BusinessObjects’ ‘universe domains’ are used to restrict the visibility of the
metadata model for specific users or groups of users. These domains provide
users with a controlled view of the mapping layer and data operations they
can access.
Adaptability
Summary
1 2 3 4 5 6 7 8 9 10
In BusinessObjects, adaptability is generally a case of incorporating new
members automatically and being able to modify the mapping layer to meet
changing business requirements. All of this is well supported. Accessing
upstream metadata from data warehousing tools can be used to synchronise
the mapping layer with data sources. However, there are no facilities to
inform end users of the updates or impact analysis for existing models.
New dimensions can easily be added to the mapping layer and subsequently
be incorporated in reports. This is easily achieved using point-and-click and
re-running the query. All changes and additions are automatically time-
stamped.
Re-use a dimension definition
New dimensions added to the universe can be re-used across reports.
New measures can easily be added to the mapping layer and subsequently
used in new or existing reports.
Reuse of calculated measure definition
New measures added to the mapping layer can be re-used across reports.
There is no direct support for changing the architecture to a full MOLAP or
ROLAP mode. But BusinessObjects can implicitly link into both these
environments.
The Design Wizard can read database schema or accessing metadata about
the data source from external metadata tools. This enables the mapping
layer to respond to changes in the database and synchronise any reports
built using it. But users are not automatically informed of any changes to
source data.

23
Changes to the structure of the database, such as the inclusion of a new
dimension member, will require updates to the mapping layer; these changes
are automatically picked up.
Metadata
There are facilities to keep reports and the mapping layer synchronised.
Impact analysis
There is no support for impact analysis.
WebIntelligence II provides audit trail facilities for administrators. But
these facilities are not yet supported by the client-server tools.
BusinessObjects can access metadata created by data warehousing tools
such as Informatica, Prism Solutions and Carleton Apertus. This metadata
can be mapped to a universe schema.
Summary
1 2 3 4 5 6 7 8 9 10
BusinessObjects’ desktop architecture has potential restrictions on perform-
ance and scalability. To overcome the size and performance issues DBAs can
tune BusinessObjects to exploit native SQL access, multipass SQL and
aggregation tables. Special third-party technology can also be licensed to
handle large and complex data sets. However, performance would be en-
hanced by a more scalable, server-based OLAP engine.
ROLAP
Multipass SQL
BusinessObjects automatically generates multipass SQL.
SQL processing is done in the database server.
Microcubes are cached on the server in an optimised format for queries.
These microcubes can be directly accessed to speed up end-user access times.
Aggregate navigator
BusinessObjects can use aggregate tables in the database. If DBAs aggre-
gate data in the target database at multiple levels (day, week and month),
BusinessObjects automatically selects the highest level of aggregation that
satisfies the query.

24
MOLAP
BusinessObjects does not provide its own persistent MDDB store.
Microcubes are created ‘on-the-fly’ as small multidimensional data struc-
tures and do store pre-calculated aggregate data. However, BusinessObjects
can integrate with third-party MDDBs.
BusinessObjects supports large-scale deployment through its ease of set-up
and extensive user administration features.
Processing
The BusinessObjects query engine supports native SQL and ODBC access to
databases.
Depending on the architecture implemented, processing can be done on the
client (desktop architecture) or the server (Document Agent Server or
WebIntelligence II).
SMP support
If the target database supports SMP, Windows 95 and Windows NT,
BusinessObjects clients can take advantage of this. The WebIntelligence II
application server supports shared processing.
AnswerSets can be used to enhance the handling of large data sets.
AnswerSets is a graphical set-based data segmentation and sampling tool
that integrates tightly with BusinessObjects and other desktop OLAP tools.
It can be used to parameterise large queries by selecting, grouping and
banding data using ‘set-theory’ in advance of running a query. Complex
queries can thus be broken down into a series of small, simple queries using
optimised multipass SQL.
AnswerSets is developed by Next Action Technology (www.answersets.com)
and is licensed as a separate product. Business Objects exclusively resells
AnswerSets in the UK.

25
Customisation
Summary
1 2 3 4 5 6 7 8 9 10
BusinessObjects is positioned as a ‘ready-to-use’ tool for end users – the client
modules do not require any customisation or adaptation. Support for special-
ised application development is limited to a procedural Visual Basic-like
scripting language, for customising aspects of the tool’s interface and behav-
iour, or links to Windows development tools via OLE automation.
Customisation
Certain aspects of the BusinessObjects user interface can be modified to
restrict functionality.
A scripting language (called ReportScript) can be used to create custom EIS
reporting systems. Scripting is a two-part process: creating a visual inter-
face; and defining the actions to be taken from the interface.
Applications
There are no tools provided to develop web applications.
BusinessObjects does not support a graphical development environment. An
internal scripting language, called ReportScript, does allow developers to
design screen layouts and define program logic to launch custom
BusinessObjects reports and queries, as well as other desktop applications.
ReportScript is based on Visual Basic, and includes standard editor, compiler
and debugging facilities.
BusinessObjects supports OLE automation (client and server). This allows
BusinessObjects functions to be called from Windows development tools,
such as Microsoft Visual Basic and Visual C++.
BusinessObjects is available in English, French, German, Spanish, Hebrew
and Japanese language versions.

26
Deployment
Platforms
Client
BusinessObjects clients run on Windows 3.1, Windows 95, Windows NT and
Unix Motif. WebIntelligence II supports web browser which supports Java.
Server
Document Agent Server runs on Windows 95, Windows NT and Unix.
WebIntelligence II application server runs on Windows NT.
Data access
BusinessObjects has native drivers for all the major relational databases.
ODBC access is also provided. It also provides pre-packaged data providers
for a number of non-SQL sources, including spreadsheet data, multidimen-
sional databases (Microsoft SQL Server OLAP Services, Hyperion Solutions’
Essbase, Oracle Express and Informix MetaCube) and external applications;
rapid deployment templates are provided for SAP, Oracle, PeopleSoft and
Baan applications.
Standards
BusinessObjects provides a published API. It also supports Microsoft’s OLE
DB for OLAP (as a consumer) Oracle Express’ SNAPI and Hyperion Solu-
tions’ Essbase API.
BusinessObjects does not have any published benchmarks.
Price structure
The standard BusinessObjects query and reporting modules are priced at
$595 each. BusinessObjects Explorer and Analyzer modules cost $695 each.
The BusinessObjects Supervisor and Designer tools each cost $1,995.
WebIntelligence II is priced at $595 per user; the WebIntelligence II Ex-
plorer modules cost an additional $395 per user. Document Agent Server is
priced at $7,995 for the Windows NT version and $15,995 for the Unix
version. BuisnessMiner is priced at $495 per user.

PowerPlay
Summary
At a glance .............................................................................................. 2
Ovum’s verdict......................................................................................... 4
Distribution ............................................................................................ 13
Product evaluation
Web support .......................................................................................... 20
Management ......................................................................................... 21
Adaptability ............................................................................................ 23
Customisation ....................................................................................... 26
Deployment
Platforms ............................................................................................... 28
Data access .......................................................................................... 28
Standards .............................................................................................. 28
Price structure ....................................................................................... 28

At a glance
Developer
Cognos, Ottawa, Canada
Versions evaluated
PowerPlay and PowerPlay Web version 6
Key facts
• A desktop tool for multidimensional analysis
• Runs on Windows 95 and NT. Optionally, processing can be carried out on
a Unix server (AIX, HP-UX or Sun Solaris)
• Cognos also produces Aristotle, a client tool specifically designed for
access to Microsoft SQL Server 7 OLAP
Strengths
• An easy-to-use ‘out-of-the-box’ tool with no programming requirements
• Analytical and data mining functionality can be added via other Cognos
tools
• Good performance tuning for a desktop tool
Points to watch
• Accessing data from relational databases to build the model requires the
use of Impromptu, Cognos’s report writing tool
• Little support for specialised analytical analysis
• No web access to detailed data
Ratings
Web support
Management
Adaptability
Customisation
1 2 3 4 5 6 7 8 9 10

Categories
The Cognos term for members of a dimension.
Impromptu
Impromptu is Cognos’s report and query tool. Although it is possible to
import data from RDBMSs without the use of Impromptu, in practice it is
usually used for this.
Impromptu is also used to display the detailed data.
Impromptu catalogue
The business view of the data that is produced in Impromptu Administrator.
This is available to developers via the Impromptu icon within Transformer.
Impromptu queries
PowerPlay can access relational data sources, but the definition of the data
to be retrieved is done in Impromptu and saved as a ‘query’. A query defini-
tion is thus the SQL definition and metadata to enable Transformer to
execute the query.
Model
The model is the specification from which PowerCubes are generated. The
model is usually designed by IT staff.
The end user works with PowerCubes (data stored in a multidimensional
format) rather than the model (a design specification).
Portfolio
This organises PowerPlay reports into briefing books for EIS use.
PowerCube
Cognos’s storage format for multidimensional data. PowerCubes can be
stored as files or in a relational database if extra management and security
features are required.

Ovum’s verdict
What we think
PowerPlay used to be described as ‘an easy-to-use desktop tool’, but this is
now only one of its several aspects. When used in this mode, it is differenti-
ated from its competitors primarily by its integration with other modules
from Cognos – thereby potentially offering an impressive range of data
mining and forecasting features.
While it is most frequently used in a client-centric configuration, it also
provides a mid-tier engine.
PowerPlay now offers a compromise between an end-user tool and a MDDB.
It offers better datastore facilities than most desktop OLAP products, and
better end-user facilities than most OLAP servers. It is an ideal tool if you do
not know how large or specialised your requirements will become, because it
allows you to introduce a specialised OLAP server if one is needed.
The increasing functionality provided by PowerCubes – particularly the
partitioning options – offers some of the performance benefits of a MDDB.
Cognos will have to decide whether to continue to enhance the datastore
features and compete directly with the OLAP servers offered by Arbor,
Oracle and Microsoft, or whether to concentrate on strengthening the end-
user functionality and assume that the tool will be used to access a third-
party OLAP database.
When to use
Cognos’s PowerPlay should be on your shortlist if you:
• want something that runs out-of-the-box
• want to separate the roles of model builder and model user
• already use Impromptu for reports
• want integration with desktop data mining and forecasting tools
• want to access ERP (particularly SAP) data.
It is less suitable if you wish:
• to build models with more than 500,000 unique members
• to develop customised applications
• to avoid buying two tools, because Impromptu is essential to efficiently-
built models based on relational data
• to use complex analytics.

Product overview
Components
The main components from Cognos that support OLAP are:
• PowerPlay version 6.0
• Impromptu version 5.0
• PowerPlay Server Web Edition version 6.0
• Scenario version 2.0
• 4Thought version 4.0.
The main focus in this evaluation is on PowerPlay and PowerPlay Server
Web Edition.
Figure 1 shows whether the component runs on a client or a mid-tier server,
the stage at which it is typically used and its primary function.
PowerPlay
PowerPlay provides support for the OLAP aspect of the business intelligence
spectrum. It is used to build multidimensional models and uses these and
third-party OLAP models built using Arbor’s Essbase, Oracle Express, DB2
OLAP and OLE DB for OLAP providers.
This component is well integrated with Impromptu and, in practice, even if
the main requirement is for OLAP, both tools would be used. There are two
reasons for this: when drilling down to the detail, Impromptu is required
and, if data in relational databases is required in the PowerCube, it is im-
ported via an Impromptu query.
Other components that are installed as part of PowerPlay, but have separate
icons on the desktop are listed below.
Transformer
Transformer mode is used to build multidimensional models from Im-
promptu queries (this is the means of accessing relational databases as
described above), ASCII files, Excel, Lotus, dBase, Paradox and FoxPro.
Figure 1 Component details
Main purpose
Extract data Build and Web access Desktop data Forecasting
from RDBMS use OLAP mining
Client Impromptu PowerPlay Scenario 4Thought
Mid-tier PowerPlay Server
server Web Edition

Portfolio
This organises PowerPlay reports into briefing books for EIS use.
Scheduler
For scheduling the update of the reports defined in Impromptu or multidi-
mensional models defined in PowerPlay, and automatically publishing
reports on the Web.
Authenticator
This is used to set up user privileges and access controls on the multidimen-
sional models.
Impromptu
Impromptu is primarily a report writing tool, but reports specified in it can
also be used as data sources in PowerPlay. This indirect method is used to
incorporate data from relational databases and ERP sources such as SAP,
Baan, PeopleSoft and Oracle Financials into multidimensional models
defined in PowerPlay.
Impromptu comes in two editions:
• the Administration edition for creating a catalogue – giving a business
view of the data sources (that is, metadata)
• the Enterprise User edition for creating reports.
PowerPlay Server Web Edition
This enables web access to pre-built PowerCubes, or third-party OLAP
databases, but does not allow the user to build new models. Users identify
themselves when logging on and only have access to certain PowerCubes.
Users can drill across and down within the PowerCube and can drill-down to
detailed data using Impromptu Web Query or Impromptu Web Reports if
they have access to these tools.
Scenario (not included in this evaluation)
Scenario is a data mining tool designed to help non-experts understand the
contribution of factors in determining an outcome. The user chooses a meas-
ure to investigate and the columns that may be influential. The contribution
of each of these factors is displayed with the sample size. The user can drill-
down and use the Impromptu component to display detailed data. It is
entirely driven by point-and-click. PowerCubes and Impromptu reports are
just two of the input sources.

4Thought (not included in this evaluation)
4Thought was acquired in 1997 from Right Information Systems. It is a
business modelling and forecasting tool with time series or profile analysis;
it uses neural network technology. PowerCubes and Impromptu reports are
just two of the input sources.
PowerPlay is usually described as a desktop OLAP tool, but it can also be
configured so that the processing to build and manipulate the multidimen-
sional model is carried out on a mid-tier Unix server (HP/UX, IBM/AIX or
Sun Solaris).
Full mid-tier architecture, in which the OLAP engine, an MDDB and
metadata are held on a mid-tier server, is not supported by PowerPlay.
Light mid-tier
PowerPlay supports two forms of light mid-tier architecture (in which the
processing is done on a mid-tier server, but there is no MDDB):
• using a Unix server for the generation and scheduling of PowerCubes
• using the PowerPlay Server Web Edition.
When a Unix mid-tier server is used, the design of the model is done on the
desktop using the Transformer component of PowerPlay and then passed to
the mid-tier server for building. The model can then be accessed in the usual
way using PowerPlay on the client.
PowerPlay Server Web Edition is used to access, but not build, models. As
with most OLAP tools, it uses a four-tier architecture in which the
PowerPlay Web Server uses CGI to communicate with the web server and
thus enables the generation of HTML pages.
This is the ‘natural’ architectural configuration for PowerPlay. The multidi-
mensional model is designed and built on the desktop PC. The PowerCube
can be stored locally or centrally and can, optionally, be stored in an RDBMS
if database management features are required.
Mobile architecture
PowerPlay supports a mobile architecture, in which OLAP processing can
continue when the links to external data and processing sources are severed.
It is simply achieved by copying the PowerCube onto a PC with PowerPlay
installed.
Using PowerPlay
Need for Impromptu to access data to build the models
One feature that is unusual in PowerPlay is the way in which some data for
the model is accessed. To use data from an RDBMS or a specialised data
source (such as SAP or Oracle Financials) in a model, a ‘query definition’ has

to be defined in Impromptu to extract the data. This query definition then
appears as a data source in the PowerPlay interface. Thus, Impromptu must
be installed and used when the multidimensional model is built.
The data is then stored within the PowerCube, so it is not necessary to have
Impromptu when the model is being accessed.
Multiple opportunities to add calculated measures
More opportunities bring the benefit of greater flexibility. The use of Im-
promptu to define queries to extract data means that there are three points
in the OLAP process at which the designer or end user can add calculated
measures:
• when defining the query in Impromptu
• when building the multidimensional model in PowerPlay
• when running a report.
The Expression Editor in Impromptu is shown in Figure 2. Impromptu is
also able to incorporate functions provided by the database being accessed.
Figure 3 shows the Expression Editor in Transformer that is used when the
model is being built.
Out-of-the-box ease-of-use
PowerPlay is an easy-to-use, out-of-the-box tool. Version 6 makes use of
multiple frames, as shown in Figure 4. This shows how the navigation frame
on the left enables the user to get an overall picture of the model.
Figure 2 Impromptu Expression Editor

Figure 3 Transformer Expression Editor
Figure 4 PowerPlay frames

Division of responsibility
The tool lends itself to a division of responsibility between designer and user.
The multidimensional business model is designed, usually by an IT person,
with the Transformer interface in PowerPlay. This is accessed by PowerPlay
in Explorer or Reporter mode.
Support for multidimensional databases
Models built in multidimensional databases (such as Arbor Essbase, Oracle
Express, DB2 OLAP and OLE DB for OLAP providers) are directly accessed
using PowerPlay Connect, an easy-to-use interface in which the path to a
model is specified and given an alias with an ‘.mdc’ extension. It then be-
haves like a PowerCube.
Partitioning options
In early versions of PowerPlay only the detailed data was held in the
PowerCube and all aggregates were calculated on-the-fly. Versions 5 and 6
offer partitioning (manual or automatic). Partitioning is a process in which a
potentially large model is divided into a number of partitions, or nested sub-
cubes. The partitions contain pre-calculated aggregates on some dimensions.
The effect of partitioning is to increase the size of the cube but potentially to
improve performance. Load time is traded off against end-user access time
by the design decisions made when setting up partitions.
PowerCubes can be designed so that the user can navigate easily from one to
another if they share dimensions.

Future enhancements
Version 6.5 of PowerPlay is scheduled for release in the first half of 1999.
Cognos intends to include the following enhancements:
• the provision of a multi-server back end. This is expected to support load
balancing, write-back from 4Thought to PowerCubes and greater support
for PowerPlay Web Reports
• support for the remote installation of the client version of PowerPlay
• support for WAN deployment of the client version of PowerPlay
• the addition of extra features to the PowerPlay Server Web Edition to
support Java clients, drill through to Impromptu Web Reports and a
common log-in for all web products.

Company background
Cognos was established in 1969 and is based in Ottawa, Canada. It was
originally a consulting company and developed into a single product com-
pany selling PowerHouse, its 4GL for mid-range systems. In the late 1980s,
it broadened its portfolio from straight application development tools into
data analysis and reporting, launching PowerPlay in 1990 and Impromptu a
year later.
In the early 1990s, the company lost momentum when the switch towards
client-server systems reduced demand for PowerHouse and the emphasis
within the company moved from its 4GL product to desktop business intelli-
gence tools.
It has extended the range of its business intelligence desktop tools through
acquisitions. In 1995, it licensed data mining software from Angoss Software
in Toronto, which emerged as Cognos’s data mining product, Scenario, in
1997. Also in 1997, the company acquired 4Thought, a forecasting tool using
neural networks, from Right Information Systems, a UK-based company, for
$8 million. Another 1997 acquisition, Interweave Software, provided the
basis for the web versions of Impromptu. In early 1998, Cognos licensed an
end-user tool, code named Aristotle, from Panorama, the Israeli company
that originally developed Microsoft SQL Server 7 OLAP Services. Microsoft
claims that this does not give Cognos any advantage over other ISVs be-
cause the Aristotle team at Panorama post-dated the OLAP Services devel-
opments.
Cognos has reported good revenue growth and profitability for several years.
Revenue for the financial year ending February 1998 was $244 million, with
a profit of $33 million (or $50 million excluding the cost of acquisitions). In
the previous year revenue was $198 million and profit was $36 million.
While Cognos’s 4GL product, PowerHouse, still brings in revenue, the energy
and focus of the company in the business intelligence market. It is posi-
tioned at the end-user tool end of the market, although several of the im-
provements in version 6 relate to the multidimensional storage capabilities
of the PowerPlay cube.
Through the acquisition of Scenario and 4Thought, Cognos now has a com-
prehensive range of business intelligence end-user tools. In response to the
challenge of the low-entry cost associated with Microsoft SQL Server 7
OLAP Services, the company acquired Aristotle, described by Cognos as a
‘little brother’ to PowerPlay. Cognos has also responded to the growing
awareness of the need to harness ERP data with its ‘Accelerator for SAP’
product, which is an extraction and reporting tool for SAP data.
The Cognos products are generally targeted at generic business intelligence
requirements. Cognos seeks to provide an integrated set of decision support
tools and believes that this enables customers to choose the tools they
require.

The philosophy behind PowerPlay is to provide a range of support for a
range of users, without any need for programming.
The company has licensed NCR to include its products in data warehouse
products for financial institutions. Other significant partnerships include an
agreement with IBM to bundle Cognos Impromptu and PowerPlay with
Visual Warehouse. IBM also supports the use of Cognos with IBM’s DB2
OLAP server.
Cognos uses direct and indirect channels to sell its business intelligence
products. The majority of revenue is generated by its direct sales force,
which is organised around vertical sectors including food, drink, retail,
telecommunications, finance, transportation and manufacturing. Indirect
sales, in which partners either distribute the products or incorporate them
in their own solutions, tend to be for lower volumes. It is likely that sales of
Aristotle will be through partners.
Cognos intends to continue to sell its products through a combination of
direct sales and partnerships.
Customer support
Support
Hotline support is available, typically at a cost of 20–25% of the licence fee.
Training
Training is offered at Cognos sites worldwide as well as on-site.
Cognos offers consultancy directly and via its partners. The company runs
the Cognos Certified Professional Programme for product specialists.
Distribution
Headquarters
Cognos
3755 Riverside Drive
PO Box 9707, Station T
Ottawa, ON
Canada K1G 4K9
Tel: +1 613 738 1440
Fax: +1 613 738 0002
US
One Burlington Business Centre
67 South Bedford St
Suite 200W
Burlington, MA
USA
Tel: +1 781 229 6600
Fax: +1 781 229 9844

Europe
Cognos
Westerly Point
Market Street
Bracknell
Berkshire
RG12 1QB
UK
Tel: +44 1344 486668
Fax: +44 1344 485124
Asia-Pacific
Cognos
110 Pacific Highway
Third Floor
St Leonards
NSW 2065
Australia
Tel: +61 2 9437 6655
Fax: +61 2 9438 1641
http://www.cognos.com

Product evaluation
Summary
1 2 3 4 5 6 7 8 9 10
PowerPlay is an extremely easy-to-use tool with a very intuitive interface. The
metadata support to aid end-user understanding of the models includes a
description of dimensions and measures.
However, there is no support from within the tool to enable end users to
schedule the distribution of reports to colleagues, or to subscribe to such
reports.
In PowerPlay, business models are grouped into folders. Alternatively,
Impromptu and PowerPlay reports can be grouped in an EIS-style interac-
tive briefing book, called a ‘portfolio’.
Metadata, in the form of descriptive text, can be entered for measures,
dimensions and members. It is available to the end user via an ‘explain’
dialogue box.
The currency of the data in the PowerCube can be built into the title and, if
the ‘preferences’ options are set appropriately, will always appear.
The end user cannot annotate the PowerCube, but can add text when view-
ing it in Explorer and save this as a report. It is thus accessible to users of
the report.
Using the model
All the usual OLAP functions, including exception reporting, are accessed
using point-and-click. A more restricted (easier to use) environment can be
created in an EIS-style portfolio.
Values in a dimension can be repositioned using point-and-click.
The levels of a dimension can be shown in a navigation window.
If an Impromptu query is used to build the model, the user can drill-down to
detailed data if Impromptu is available. The detailed data is freshly re-
trieved.

From PowerPlay, the report can be saved in Excel format, comma delimited,
ASCII or as a sub-cube.
There is a range of charts and graphs, but no maps. Detailed data and charts
can be seen on the same page.
Designing a report
Reports prepared in Explorer can incorporate bitmaps and sounds.
Publishing a report
There is a ‘publish as HTML’ menu option to create a series of web pages
with navigation buttons showing a series of pages, each containing a ‘slice’ of
information. That is, the pages are not interactive, but contain snapshots of
the data.
There is no direct point-and-click support for scheduling the publication of a
report to an individual or group, although it could be executed dynamically
or scheduled as a one-off or recurring task using the macro scripting lan-
guage.
Users can also publish a PowerPlay report as a page within a portfolio
briefing book for EIS use.
There is a menu option to mail a link to a PowerCube. A static view of a
report can be mailed by copying it and then pasting it into the e-mail, ob-
tainable via the ‘send’ menu option.
There is no direct support for users to see and subscribe to available reports.
Summary
1 2 3 4 5 6 7 8 9 10
The strength of the tool lies in its ease-of-use in defining the structure of a
multidimensional model and populating it with data. There is automatic
support for defining the time dimension and quickly producing a prototype if
the data source is appropriately structured. Using Impromptu to access SQL
data prevents a sample of data being available to the designer.

Basic design
Design interface
The design interface is easy-to-use and exclusively point-and-click.
It is not possible to display a sample of data from the data source/s to be
used in the model.
There is no direct support for a universally available mapping layer. Some
support is provided by the use of re-usable Impromptu queries.
The developer is not automatically prompted but can, optionally, provide a
description for dimensions, measures and members in the model. This is
accessible to end users.
There is no integration to access metadata generated by extraction or data-
base tools in the preparation of the data.
Columns for the levels in the dimensions can be selected using point-and-
click.
Members can be hidden using point-and-click.
A dimension hierarchy can be built automatically from a data source using
the Autodesign feature or point-and-click. The tool supports unbalanced
dimensions, the specification of alternative drill-down paths and the inser-
tion of user-defined levels.
Time dimension
PowerPlay has a set of date dimension dialogue boxes to assist with the
creation of a hierarchy in this dimension. New date units can be defined in
this, and automatically allocates the members, including calculations such as
year-to-date change or growth.
Dimensions can be annotated using the properties sheet in which there is a
description tab. Information entered here can be viewed by the end user by
right clicking on the dimension and choosing ‘explain’ from the menu. It is
also possible to define both long and short names for the members.
The default levels in a dimension are the top ones. However, having drilled
down and changed dimensions, the user can save this view which then
becomes the starting point when the view is re-opened.

Calculated measures
Calculated measures are defined in a calculator-type interface in Trans-
former. It allows ‘if, then, else’ constructions and a range of functions.
A set of dimensions can have multiple measures attached to it.
Multiple designers
Multiple designers
Support for multiple designers is only an issue if a mid-tier server is used, in
which case only one client is allowed to access models on the server. This
ensures that there are no lost updates.
There is no direct support for versioning.
Using a ‘count’ button on the toolbar, the developer can see the number of
members in each dimension.
Prototype models can be quickly built using the Autodesign feature from one
or multiple data sources if the data is appropriately structured.
Using a currency table query, the user can format the measures in the
appropriate currency.
Summary
1 2 3 4 5 6 7 8 9 10
PowerPlay provides multidimensional support, but for more specialised
analytics the user would have to make an additional purchase of Scenario
(although this is included if the business intelligence suite is purchased) for
data mining, and 4Thought for business modelling and forecasting. While
PowerPlay is promoted by Cognos as an easy-to-use tool and has few in-built
functions supporting specialised analytics, the tool includes a scripting
language that enables users to extend the capabilities of the product.
The score reflects the facilities within PowerPlay, but the text indicates how
the other two products could significantly enhance this.
From Explorer, reports can be saved in Excel format and then loaded into
Excel to use its analytical functions. The amount of integration with
Cognos’s other products, which offer more sophisticated analytical support,
is continually increasing. From Scenario, the desktop mining tool, users can
now drill-down to display detailed data in Impromptu. Scenario and
4Thought (for business modelling and forecasting) can both read
PowerCubes. Scenario can generate a PowerCube for further exploration.

Ranking and sorting
This is provided in Explorer mode in PowerPlay.
Not provided by PowerPlay.
Financial functions
Not provided by PowerPlay.
Statistical models
In PowerPlay, the user can add standard deviation and regression lines to
graphs, but more support is available in 4Thought and Scenario.
Trend analysis
There is no direct support for trend functions, such as moving averages and
smoothing functions.
Simple regression
There is no support for regression algorithms.
Dynamic time series, such as ‘the last 12 months’, can be defined in
PowerPlay. Forecasting is the main focus of 4Thought.
There are facilities for users to extend the analytical capabilities by writing
their own functions using CognosScript, a scripting language similar to
Visual Basic. PowerPlay provides a development environment for this, which
includes a debugger. The language exposes the dimensions, measures and
members, either by name or using an index.
There is no provision for write back, either in PowerPlay or 4Thought.
There is no support within PowerPlay, but 4Thought and Scenario can
process categorical data.
Data mining
This is not provided by PowerPlay, but is the main focus of Scenario.
Although the two additional products, Scenario and 4Thought, are acquired
products, the amount of integration between them is steadily increasing.
From Scenario, users can now drill-down to detail displayed in Impromptu.
Cognos users have a site on CompuServe (that is, not a Cognos-supported
website) from which macros for some of these functions (for example, Holt-
Winters and Pareto) can be downloaded.

Web support
Summary
1 2 3 4 5 6 7 8 9 10
Web support is provided by PowerPlay Server Web Edition, which is pur-
chased separately from PowerPlay. It allows browser access to PowerCubes.
The functionality provided by browser access to PowerCubes is getting closer
to that offered via the desktop. However, it does not yet offer facilities to edit
or create models via the browser. While there are facilities for individuals to
initiate publication and distribution of models and reports, the product
would be enhanced by additional features to support centrally organised
distribution.
Web access offers most of the functionality offered by desktop access, al-
though the interaction is via drop-down charts rather than drag-and-drop,
resulting from the use of Java Script rather than Java applets. The access is
comparable to using the desktop version in Explorer mode, but does not
allow the greater flexibility that users get with Reporter mode on the desk-
top, in which they can add information at different levels and dimensions as
well as perform calculations.
Only minimal detail data is available on the same screen as the graphical
display. Pivoting is supported via a toolbar button and a range of charts is
available.
There is no support to drill-down to detailed data using web access. Users
can only access data held in the PowerCube.
Technically, PowerPlay supports registered and unregistered users. However,
the standard licence agreement with Cognos does not allow access to unreg-
istered users. Internet access licensing is available by negotiation.
The web browser interface is primarily for interactive exploration of busi-
ness models. It is possible to save the result so that it can be imported into
Excel, but there are no facilities to build a report using the information
retrieved.
Creating models via Internet and the Web
There is no intervening mapping layer.
There is no support for creating models via the web interface. They have to
be created in the desktop version.

Distributing via the Web
HTML text can be generated when running PowerPlay in Reporter mode.
There is no option to generate Java code.
There is no support for the centrally organised distribution of models or
reports, although as described in the section on End-user functionality there
is support for individually organised distribution.
URLs cannot be included in web reports.
Using information provided by PowerPlay Web Administrator (as described
in Query monitoring), the administrator can manually balance web process-
ing between multiple servers.
Management
Summary
1 2 3 4 5 6 7 8 9 10
There are two main editions of PowerPlay, (client- and server-based). In both
editions, user and PowerCube securities can be defined. The management
features of the server edition are designed to support a large number of users.
The score here reflects the good support given to the management of users. In
version 6, the features for performance tunability of the data have been
substantially enhanced, but to fully exploit these the administrator has to
manually control the partitioning features.
The administrator uses the Transformer interface of PowerPlay running on
the client to manage the design and build process.
Security of models
Security is controlled through the Authenticator document, which is usually
stored on the mid-tier server. The location of this is specified within each
PowerCube. It enables the administrator to define privileges for users and
PowerCubes.
Query monitoring
Query monitoring is not supported in the client version. However, PowerPlay
Web Administrator provides status and performance information that can be
used to balance web processing over multiple servers. Information given
includes the number of requests received in the last minute and the average
time to process requests.

Management of data
Persistent data is stored in PowerCubes. PowerCubes can be stored locally,
on a mid-tier server or in an RDBMS (for additional security and manage-
ment features).
In early versions of PowerPlay, only the detailed data was held in the
PowerCube, and all aggregates were calculated on-the-fly. Versions 5 and 6
offer partitioning (manual or automatic), which adds partitions to the cube
that generates pre-calculated aggregates on some dimensions. The effect of
partitioning is to increase the size of the cube but improve performance. The
size of cubes is reduced by compression.
PowerCubes can be designed so that the user can navigate easily from one to
another if they share dimensions.
Scheduling can be controlled either from the desktop or from the mid-tier
server. If this is done from the desktop, a separate Scheduler module is
available to schedule refreshing of the PowerCube. With this module, a
schedule can be defined using point-and-click. If scheduling is centralised on
the server, it uses the Unix scheduling utilities of cron and crontab.
Using the scripting language, multiple cubes can share a refresh schedule.
Event-driven scheduling can be defined using the scripting language from
within the scheduler in the client version.
When a PowerCube is built, information is created in a log file, including the
time taken, number of records processed and whether the processing was
successful or not. If a load is partially successful, a checkpoint is created so
the load job can start from this point rather than the beginning. If a job fails,
the user is informed next time Transformer is opened.
PowerCubes can be stored wherever the user wishes.
Transformer, by default, assumes that the combinations of dimensions in a
model are sparse. Sparsity only becomes an issue when pre-calculated
consolidated aggregates are also stored and is not a concern unless partition-
ing is used to do this (that is, the basic PowerCube does not include consoli-
dations). The handling of sparsity is therefore dependent upon the design of
the partitions.
A basic PowerCube contains only detailed data and no aggregates. In
PowerPlay, size is a function of the amount and design of partitioning.
Without partitioning, records are stored in the PowerCube and rolled up at
runtime by the PowerPlay client to provide summary values. Potentially,
millions of records could be involved in this. Partitioning reduces the

summarisation required at runtime by writing consolidated records to a
partition, and leaving lower level detail records in a separate partition.
In general, the effect of partitioning is to trade-off end-user access time
against increased build time. Cognos claims that partitioning may increase
size by 50–100%, but speeds up retrieval time between ten and 100 times.
The cache size used in the client when building and executing models can be
set through the ‘Cognos.ini’ file. It is a manual process, requiring adjustment
if there appears to be heavy disk activity or the building of PowerCubes
takes longer than expected.
As described in the End-user criteria, reports can (optionally) include the
refresh date in their title. PowerCubes viewed in Explorer do inform the
user when the stored data was last uploaded.
Management of users
Not applicable.
There are a number of ways of controlling security, including password
access to PowerCubes, definition of ‘user classes’ (that is, group), security
profiles, and the use of the RDBMS (if the PowerCubes are saved in a data-
base) and network security. The file containing the user class definition
information is encrypted.
Query governance
There are no query governance features, apart from the time restriction.
Users can be restricted to particular times for accessing PowerCubes.
Users are not aware of PowerCubes to which they have no access rights.
Different users can have different views on the same PowerCube.
Adaptability
Summary
1 2 3 4 5 6 7 8 9 10
In a client-based configuration, with minimum metadata, adaptability is
generally a case of incorporating new members automatically and being able
to modify the model to meet changing business requirements. All of this is
well supported.

Adaptability is more of an issue in a large-scale environment, where it is
likely that a server-centric model would be used. Adaptability could be ex-
tended to support some of the features described below by the use of MDL, a
fourth generation language, and C shell scripts. There is thus power and
flexibility, but a requirement for additional skills.
A new dimension can be added to a model using point-and-click and a new
PowerCube generated using a button on the toolbar.
In a client-server configuration there are straightforward mechanisms to
ensure that server and client models are synchronised.
There are no change management facilities.
There is no support for the re-use of a dimension definition.
As with dimensions, a new measure can be added to a model and a new
PowerCube generated.
Measure definitions cannot be saved and re-used in other models.
The architecture of the system can be changed from being client-based to
server-based if required. Previously defined PowerCubes can still be used.
Some of the benefits of HOLAP architecture are achieved by storing sum-
mary data in PowerCubes and using Impromptu query definitions to re-
trieve the detail data. Impromptu query definitions always retrieve data as
required, rather than accessing a pre-stored source.
The data and model are both held in the PowerCube, so they are always
synchronised.
When a PowerCube is refreshed, new members are automatically included.
There is support for level locking so that new categories in that level are not
added. This is indicated to users by an icon of a lock on the level.
Metadata
There is no mechanism to ensure that when changes are made to the dimen-
sions and measures that the descriptive metadata is synchronised.
Impact analysis

There is little metadata to audit.
There is no support for access to upstream metadata.
Summary
1 2 3 4 5 6 7 8 9 10
The original design of the PowerCube gave little scope for performance
tunability, but this has changed in recent versions. The combination of break-
ing the model into separate cubes that are linked so users can drill through
from one to another, and partitioning, give significant tuning options. Parti-
tioning gives the administrator the option of trading off build time against
execution time. The incremental update enables new data to be appended to
the PowerCube rather than recreating the whole model.
In the commonly used client-centric configuration, the PowerCube is loaded
down to the desktop; the speed of the local processing is a function of the
desktop hardware and the settings for the in-memory cache.
ROLAP
Not applicable, because the data for the model is retrieved from a pre-built
multidimensional PowerCube.
MOLAP
This is achieved using partitioning. Without partitioning, records are stored
in the PowerCube and rolled up at runtime by the PowerPlay client to
provide summary values. Potentially, millions of records might be involved
in this, leading to reduced end-user performance. Partitioning reduces the
summarisation required at runtime by writing consolidated records to a
partition, and leaving lower level detail records in a separate partition.
In general, the effect of partitioning is to trade off end-user access time
against increased build time. Partitioning may increases the size by 50–
100%, but speeds up retrieval time between ten and 100 times.
There is no wizard support to help in deciding what to consolidate; this is a
manual design decision.
Users do not generally download a copy of the appropriate PowerCube to
their desktop, but they can do if necessary. They can also define a sub-cube
and download that. If the PowerCubes are shared, the number of simultane-
ous users that can be supported can be increased by partitioning (as this
speeds up access time).

The number of simultaneous users is limited by the network traffic that can
be supported.
Processing
Data for the PowerCubes is retrieved using Impromptu query definitions.
Impromptu uses native SQL for all the major databases including Oracle,
Informix, Microsoft SQL Server, Sybase, CA-Ingres and MDI DB2 gateway.
There is no support for the distribution of processing.
SMP support
The application uses multi-threading and can thus take advantage of SMP.
Customisation
Summary
1 2 3 4 5 6 7 8 9 10
PowerPlay is designed as an ‘out-of-the-box’ toolkit and thus has few features
for building standalone executable applications with multidimensional
features. PowerPlay supports OLE automation and thus applications that
make use of PowerPlay components can be developed in third-party lan-
guages.
The usual division of responsibilities is that IT-based staff create and main-
tain the multidimensional models from which PowerCubes are generated,
and business operators use these to produce customised interactive reports.
These reports can be considered as applications, but they do not disguise
their PowerPlay genesis.
Customisation
There is no option for the user to access the model via a restricted interface.
This is provided by the portfolio, a module provided with both Impromptu
and PowerPlay, which provides an EIS environment to give the simplicity of
a ‘button-driven’ application.
Applications
There is no direct support to develop applications specifically for the Web.
The web browser is used to access PowerCubes developed via the desktop or
server.

There is no OLAP-specific development environment.
Use of third party development tools
PowerPlay offers OLE automation and thus reports, briefing books and other
PowerPlay objects can be embedded in applications written using an OLE-
compliant language.

Deployment
Platforms
If used in a client-server configuration with PowerCube generation and
scheduling carried out on the server, the available server platforms are HP-
UX 9.04 and 10.x, AIX version 4.1, and Sun Solaris 2.4 (SunOS 5.4).
Data access
The sources that can be used to build a model include Impromptu files
(which gives access to RDBMS and ERP data), comma delimited files, and
personal data sources such as dBase, Excel and Foxpro. PowerPlay can
directly access Microsoft Access databases.
Impromptu uses native SQL for all the major databases, including Oracle,
Informix, Microsoft SQL Server, Sybase, CA-Ingres and the MDI DB2 gate-
way.
Standards
PowerPlay has an OLE DB for OLAP consumer interface.
Price structure
If purchased separately, Impromptu (End User Edition), PowerPlay Client
and Scenario cost $700 per user. When purchased as a bundle, the cost is
$1,300 per user.
Impromptu Web Query and PowerPlay Server Web Edition cost $500 per
user (when purchased for 100 users) and $255 per user (when purchased for
1,000 users). Internet access licensing is also available. Contact Cognos for
details.
PowerPlay Administrator costs $2,000 (only one is required).
Impromptu Administrators Edition costs $900 per user.
Published Benchmarks
There are no published benchmarks for PowerPlay.

Gentia Millenium
Applications Platform
Summary
At a glance ............................................................................................. 2
Ovum’s verdict ....................................................................................... 4
Distribution ............................................................................................ 17
Product evaluation
Web support ......................................................................................... 23
Management ........................................................................................ 24
Adaptability ........................................................................................... 26
Customisation ....................................................................................... 29
Deployment
Platforms .............................................................................................. 31
Data access .......................................................................................... 31
Standards ............................................................................................. 31
Price structure ...................................................................................... 31

2
Evaluation: Gentia Software – Gentia Millenium Applications Platform
At a glance
Developer
Gentia Software, London (UK)
Versions evaluated
Gentia Millennium Applications Platform (G-MAP), version 5.0.2
Key facts
• A development environment for analytical OLAP applications with an MDDB
• Server runs on Windows NT and leading Unix flavours; clients run on
Windows 95, 98, NT, Macintosh (PPC) and Sun Solaris. Web support is
also provided
• Gentia’s main analytical application is the Renaissance Balanced
Scorecard for implementing and tracking organisational strategies
Strengths
• Powerful development environment with specialist OLAP objects and
functions
• Supports a wide range of distributed architectures
• Gentia also provides packaged analytical applications for enterprise
performance management
Points to watch
• Limited ‘out-of-the-box’ OLAP functionality
• Developers may find learning and using the core visual development
environment difficult initially
• The Gentia MDDB cannot be accessed by third-party front-end tools
Ratings
Web support
Management
Adaptability
Customisation
1 2 3 4 5 6 7 8 9 10

Ovum Evaluates: OLAP Evaluation: Gentia Software – Gentia Millenium Applications Platform
Base models
These are physical multidimensional cubes that contain dimensions and
measures.
Book
A Gentia application is a number of pages organised into chapters and placed
in a book. A page is effectively a screen of information that is likely to contain
a set of visual objects for screen building, database access and analytical
functionality, and a multidimensional business model, which can be
interactively explored.
GDL
Gentia Development Language. A scripting language with procedural
control, used to define management tasks and to extend the flexibility of the
application development environment.
Item
The term used for measures.
Multicube architecture
GentiaDB uses a multicube architecture to minimise the size explosion
caused by combining sparse dimensions. In effect, the logical
multidimensional business model is built using a number of separate cubes,
each of which has dimensions that are dense with regard to each other.
Scenario
A virtual multidimensional cube that can be created from SQL tables and
other data stores. A scenario can be stored locally for personal offline use or
on a server for shared workgroup access.
Smart Agent
Manages the automation and delegation of common and complex processing
tasks within a Gentia application.
Warehouse
An organisational element used to group application details together and set
security restrictions on them. Each warehouse can contain one or more
books. Also known as an object store.

4
Ovum’s verdict
What we think
Gentia’s core competency is providing a complete development platform for
building and deploying analytical applications in a heterogeneous and
distributed environment. The Gentia Visual Development Environment
(VDE) is well equipped to cope with a range of application development needs
for medium-to-large sized organisations. The new Application Framework
extends application development to end users, but complex development will
require significant programming skills and IT involvement.
Gentia G-MAP is, however, a less out-of-the-box OLAP application than those
offered by other MDDB vendors – principally because of the lack of front-end
tools to access the database. While, in theory, the GentiaDB
multidimensional database could be used directly by end users, the original
decision of the company to base the API on the OLAP Council’s first
specification means that the MDDB cannot be accessed by third-party OLAP
tools. Gentia is well aware of this limitation and has already adopted the
OLE DB for OLAP standard as a consumer provider support is planned for
version 6.0.
The core application development product is sound, but users that require
greater sophistication and complexity in their applications may find learning
and using the VDE difficult initially. A sophisticated development
environment, such as Gentia will require significant training. We are also
concerned about the long-term viability of a small company offering a product
that needs a significant amount of customisation, when the entry of Microsoft
will hasten the move towards a commodity market. To the company’s credit,
it recognises the threat and has repositioned itself as a solutions-oriented
company, with the delivery of applications such as the Renaissance Balanced
Scorecard and the Impact range, designed to implement organisational
strategies and track performance.
When to use
The Gentia suite of products should be considered if you:
• have a distributed and heterogeneous IT environment
• require strategic enterprise management applications, such as balanced
scorecard, performance measurement and activity-based costing
• want to build highly specialised OLAP applications and have the
necessary development skills in-house to create them.
It is unsuitable if you:
• simply require an out-of-the-box OLAP business intelligence application
with minimal customisation
• want to use a range of third-party OLAP clients, as well as front-end tools
developed in-house
• intend to build large models with more than one million unique members
and flexible time dimensionality built-in.

Product overview
Components
The main components of the Gentia Millennium Applications Platform
(G-MAP), version 5.0.2 are:
• Visual Development Environment (VDE)
• Application Framework
• Open Network Architecture
• GentiaDB
• Gentia WebSuite
• Gentia Excel Add-in.
Figure 1 shows whether the component runs on a client or a mid-tier server,
the stage at which it is typically used and its primary function.
Visual Development Environment
The Visual Development Environment (VDE) component of the Gentia
platform can access a wide range of data sources (including MDDBs, ROLAP
systems and relational databases) and does not have to be used with the
GentiaDB, but they are almost always used together. There is a server and
client component to the VDE.
The Gentia VDE is an object-oriented development environment for client-
and server-based applications. Although termed ‘client-server’, the division of
responsibilities is more flexible than the term suggests, because the ‘client’
has the same engine as the server, with the addition of interface services.
Much of the development is done using drag-and-drop. Procedural control is
through the Gentia Development Language (GDL), which is a semi-
interpreted 4GL-type language. All applications developed in the VDE can
allow access from both web or client-server environments, without specific
programming.
OLAP analysis Application
development
Support a distributed
architecture
Web access
Client Gentia Excel
Add-in
Visual Development
Environment
Application
Framework
Open Network
Architecture
Server GentiaDB Gentia Web
Suite

6
Application Designer
Application Designer is a ‘personalised’ extension to the core Gentia VDE,
which allows business end users to build their own OLAP applications. It
provides a template-driven approach for building applications and
multidimensional models and designing reports using Excel. Users have
access to a number of specialised business templates or can choose to build
them from scratch.
The templates contain dimensions with business rules and users can select
and use predefined templates via Application Designer’s drag-and-drop
environment. Model structures can be imported from simple external file
structures and updates scheduled.
Application Framework
The Application Framework is a layer on top of the core Gentia VDE and
Application Designer components, which provides a further set of re-usable
components to speed up the development of applications. The components
include templates, pages, menus, toolbars, status information,
administration, navigation, online help and user preferences. The
Application Framework also includes an Object Library that contains
illustrative samples of code.
A Gentia User View facility is also provided, to allow all the components to be
customised.
Open Network Architecture
This is a proprietary Object Request Broker (ORB), based closely on the
CORBA standard that lets users access information from any data source,
regardless of the platform they are working on. This support for a
distributed, heterogeneous environment is one of Gentia’s distinguishing
features.
GentiaDB
The GentiaDB component of the Gentia Platform for Analytical Applications
is a multidimensional database; it uses a multicube approach to cope with
the sparsity issue. Each cube is made up of dimensions that are densely
related to each other. If there is a sparse relationship (for example, sale price
is not related to customer or location), then it is put in a separate cube. Thus,
the sparse, large model is a view and joins are performed on-the-fly to
produce slices of the view as required.
To minimise the time taken to load fresh data into the database, the process
of consolidation is carried out with minimal re-calculations. When a new
value is added, rather than re-calculate all its derivatives, using the
metadata, the system calculates which values will change as a result of this
and re-calculates this minimal set.
The GentiaDB also provides a central metadata store for dimension and
measure definitions. Base models (the equivalent of physical OLAP cubes)
are created using these definitions. In addition, virtual cubes can be created
dynamically by joining base models (called join models) or can be created
from SQL tables and other data stores (called scenarios).

Gentia WebSuite
This is a web extension to the Gentia environment that provides full
application functionality over the Internet. Using the Gentia WebSuite,
messages from the browser are relayed to an Internet server (for example,
Microsoft’s IIS), which then passes them to the WebSuite, which in turn acts
as an interface to the Gentia application server and interprets the message.
WebSuite offers three interfaces:
• View, for carrying out ad hoc query and OLAP-type operations, such as
drill-down, and then submits them to the web server to generate new
pages
• Report, which provides report formatting and distribution facilities
• Data Entry, for writing back to the database for budgeting applications.
Gentia Excel Add-in
The Microsoft Excel Add-in enables spreadsheet users to access Gentia
business models. It supports drill-down and pivoting, and reports created in
Excel can be saved or e-mailed to other users, using a predefined mailing list.
Creating the reports in ‘free format’ mode enables cells to be moved to any
part of the spreadsheet and thus gives the user more options than the usual
one of presenting the data in standard rows and columns. Reports, including
tables and charts, can also be published in HTML format for distribution
over the Internet.
A data entry mode provides realtime update and consolidate capabilities to
the GentiaDB.
The Gentia toolset is unique among the tools we have evaluated in that it can
support all four architectural options. The Object Request Broker allows for
location transparency and, when designing the application, the user allocates
processing to the server or the client.
This is the ‘natural’ architectural configuration for Gentia.
The full mid-tier architecture – in which the OLAP engine, data for the
models and metadata are held on the mid-tier server – is supported through
the use of the Gentia server, which gives access to the services provided by
GentiaDB. Access to third-party MDDB servers is also supported via OLE
DB for OLAP.
The client interface can be an application written using the Gentia Platform
for Analytical Applications, the Excel Add-in or a web browser.
In Gentia, the processing of OLAP queries can be performed by the client or
the server; both have an engine, but only the client has interface services.
The data being accessed by the application engine can be stored in an MDDB,
a RDBMS or several other formats. Access to the RDBMS does not give
ROLAP support, because the SQL queries are not produced dynamically as a
result of the user interacting with the model: they are predefined by the

8
application designer. These queries can be parameterised to support drill-
down to detailed data.
The flexibility is further enhanced as applications can transparently (using
the ORB) access data and services stored on a variety of platforms.
A light mid-tier architecture (where the processing is done on the mid-tier,
but there is no MDDB store) can therefore be supported.
There is the same range of client interfaces as with the full mid-tier
architecture.
As described for the light mid-tier architecture, there is flexibility about
where the processing takes place. By configuring the system so that
processing is carried out on the PC, a desktop architecture is supported,
although restrictions will apply on the size of the dataset that can be
analysed.
Mobile architecture
The small footprint of the client (approximately 8Mb) means that it is
possible to run an application on a laptop computer. Mobile users can
download predefined or ad hoc sections of data, by selecting pages from
books, and then analyse them offline. Changes made to the data are
automatically published to other workgroup members upon reconnection to
the network server.
Using G-MAP
Gentia Platform is for building analytical applications
Gentia is not primarily designed to be used out-of-the-box by end users,
although there is a definite move in more recent versions of the product to
provide more immediate ad hoc OLAP functionality, by extending application
development to business end users.
Development and deployment of analytical applications is at the heart of the
Gentia approach to business intelligence. An application consists of a number
of pages; a page will usually contain multiple views of a multidimensional
business model that have been created separately, using GentiaDB or a third-
party OLAP source.
The principal method of building Gentia applications is through the Visual
Development Environment (VDE), which uses a book-chapter-page metaphor.
The structure of a Gentia application is based on pages, which are collected
into chapters and then become books.
Development uses base-level objects together with higher-level application
objects that come as part of the Application Framework. There are three
categories of base-level objects: application objects (pages, text and SQL
scripts), page objects (tables, charts, hot spots and widgets) and connector
objects (MDDB data source, calculator and sorter). Once an object is defined,
it is held in the Object Store, where it can be viewed using the Object
Inspector.
The application developer works in ‘author’ mode with a Builder Palette,
from which objects are dragged onto the page, as shown in Figure 2. Widgets
from the palette are used to create tables, charts, list bars, slider bars,
buttons, bitmaps, text areas, split views, help text and so on. A default set of

attributes is inherited from a base builder object, but these may be modified
through the Object Inspector.
As in a Visual Basic development environment, the graphical interface is
designed and then code is attached to it.
Objects are linked to data using the Connections Mapper, which defines the
locations of data sources and associates the objects with a business model in
the GentiaDB.
In most cases, the base-level and Application Framework objects provided
will satisfy most application needs. However, Gentia also provides the Gentia
Development Language (GDL) to provide greater sophistication in
applications. GDL supports additional features, such as event handling,
dynamic SQL and an interface to the Object Store.
Extending application development to end users
Gentia’s ‘user’ mode was previously restricted to viewing information using
an application built by a developer in ‘author’ mode. The introduction of the
Application Designer component has blurred this distinction by allowing end
users to ‘toggle’ between the two environments in order to build their own
applications quickly and with minimal IT involvement.
The Application Designer is fully integrated into Gentia’s Application
Framework environment and provides a friendly drag-and-drop environment
for the easy construction and deployment of applications.
Predefined application and reporting templates are provided by Gentia and
application partners. End users can customise these templates according to
their own specific application needs or they can choose to build new ones
from scratch.
A template designer is provided to select dimensions, measures and
hierarchies that will be used in the OLAP application. Users can publish the
template as another re-usable object in the centralised Object Store.
Figure 2 Builder Palette

10
Building the multidimensional model – two choices of interface
Creating the structure for large, complex multidimensional business models
is generally a task for the administrator or application designer, although
end users can use the Application Designer interface to create their own
simple structures. As with most tools, there is a point-and-click interface
offering ease of use. There is also a data definition language (DDL), which
tends to be used more in production environments.
Some of Gentia’s customers have over 100 business models to maintain and
prefer more of a script-based interface. This approach requires some
technical competence. For instance, the first stage is to manually create some
initialised files to hold the structure and the data. This requires making
adjustments to the configuration file so the paths to these files are known to
the application. These changes can be generated from the Services Manager
via a GUI. Parts of the procedure of building the business model in ‘author’
mode have a dated feel about them, which is not evident in the more
graphical Application Designer modelling interface.
The business model is a logical structure and acts as a mapping layer. Once
this has been closed and committed, ‘base models’ defined using the business
model are created and data is loaded into them. These base models can be
combined to form ‘join models’, which are effectively views. For each base
model, security levels have to be defined (carried out easily using a point-
and-click interface) and a consolidation strategy selected. Models can also be
created from SQL tables and other data stores (called scenario models).
Scenario models can be stored locally for personal offline use or on a server
for shared workgroup access.
Support for data cleansing and transformation
Gentia offers more support than most OLAP tools for data cleansing and
transformation. This can occur at two stages in the process:
• when loading data into GentiaDB
• when loading data into the application.
When loading data into GentiaDB
As with many extraction tools, GentiaDB uses a visual display of the data
flow showing the data splits, aggregations and transformations. ‘Tasks’ are
constructed to define and maintain dimensions and to load data into the
database. A task consists of a number of transformation objects called
‘transformers’. As well as a number of provided transformers, developers can
write their own in GDL and include them in the flow.
The main transformers provided by the system are:
• ‘converter’ – to perform currency conversions on data being imported
• ‘expander’ – to add a text string to a field, either before or after the
incoming value
• ‘fielder’ – to add fields to the incoming data records
• ‘logger’ – to view the output from another transformer. They can be placed
at any point in the task
• ‘qualifier’ – to validate details from an incoming data record against an
existing dimension.

When loading data into an application
Each page in an application requires at least one connector to link data to
widgets and to manipulate the format of the data prior to display. The
specification for where the data is to come from and how it is to be
manipulated is defined by dragging connectors from the mapper keypad,
shown in Figure 3, and connecting them to form a visualisation of the data
flow, as shown in Figure 4.
Connector types include source (to define an MDDB source), SQL (to define a
SQL source), selector (to define the dimensions), sorter (to order the data in
rows or columns), filter and calculator (to generate new values using the
Gentia development language). A hierarchy connector is used to define the
drill-down hierarchy. It will automatically identify the levels in the
dimension from the model, but allows these to be edited, added to and
deleted.
Making Smart Agents do the work
Gentia supports Smart Agents, which are programs that automatically carry
out certain tasks and can be scheduled or event-driven. They have data and
methods that act on that data, but also have ‘beliefs, commitments and
goals’. That is, a degree of reasoning is applied to the data available to guide
the gathering of extra information, in order to achieve the goals that have
been set. While Agents carry out the tasks assigned to them, this is
supported by the Gentia Agency System, a network-wide infrastructure that
co-ordinates the work of separate Agents. The Agents and Agency are
machine- and platform-independent.
Figure 3 The Mapper keyboard

12
Figure 4 Data flows
Agents are defined using a graphical interface, as shown in Figure 5.
Other types of Agent include:
• Extractor, which automates text searches
• Phone, which, as its name suggests, will initiate a dial-up connection
• Director, which can be used to monitor changes in the source data files
• Process, which can be used to trigger a GDL routine that might, for
instance, read the source files and import and consolidate the analytical
model.

Figure 5 Agency definition in the GUI

14
Future enhancements
Version 6.0.1 of G-MAP is scheduled for general release in the fourth quarter
of 1999. The major planned enhancements include:
• OLE DB for OLAP provider support − any OLAP end-user tools complying
with this standard will be able to access the GentiaDB. Gentia is
partnering Simba Technologies to embed the SimbaProvider OLE DB for
OLAP products within Gentia
• extended thin-client options − users will be able to access data via any
Microsoft and Java web browser and a range of Citrix-based devices
• new data administration components − for controlling and monitoring
back-end data loading and processing tasks in the Gentia environment
• consumer and provider support for OLE and ActiveX objects − allowing
developers to embed external objects (for example, mapping and ERP
transaction objects) within the Application Framework and to embed
Gentia objects into third-party applications
• a page-build API − allowing end users to customise application pages on-
the-fly. Links will also be provided to the Application Designer for access
to a range of predefined templates
• support for VBScript and JavaScript − this will be added to complement
the GDL scripting environment
• enhanced capabilities for the GentiaDB − including advanced calculations,
alternative and multiple time dimensions, selective disablement of
consolidation of hierarchies and loading of data at different levels
• a revised SQL architecture − Gentia will use Merant’s DataDirect and
SequeLink drivers and manager to provide enhanced access to relational
databases, using ODBC, OLE DB, JDBC and native SQL drivers. The
drivers will be extended to include non-SQL sources such as Lotus Notes
databases.
Gentia is currently evaluating plans to optionally embed the K.wiz
datamining (acquired from Compression Sciences) components into G-MAP
and existing analytical applications. K.wiz will also provide the focus for the
development of new applications for the e-commerce sector, specifically
basket analysis and fraud detection.
Gentia Software is also expanding its suite of packaged analytical
applications. Two additional applications will be delivered in the second half
of 1999:
• Budget Impact, for budgeting and forecasting analysis. Gentia is currently
seeking a domain partner for development
• Traffic Optimizer, a network traffic analysis application for telcos, which
is being developed in partnership with Bell Atlantic and Hewlett-Packard.
Additional reseller partnerships for the RBSC application are also expected.

Company background
Gentia Software, originally a UK company called Planning Sciences, was
founded in 1983. The company’s first product was EIS-EPIC, a LAN-based
EIS system. This was followed by Gentium, a Windows-based visual
development tool that was first launched in 1993. The original product line
focused on the areas of financial modelling and the production of EIS
applications. It included support for all the major features of object
orientation from its inception. Agent technology was introduced in 1994 and
the multidimensional database, GentiaDB, was released in 1996. In 1997,
the company changed its name to Gentia and launched new web-based and
Excel Add-in products. The various components are now integrated and
marketed under the brand Gentia Millennium Applications Platform (G-
MAP).
In March 1998, the company signalled a change of direction with the release
of the Renaissance Balanced Scorecard (RBSC), a packaged application for
implementing organisational strategies. It provides software support for the
Balanced Scorecard management technique developed by Harvard Business
School professor, Robert Kaplan, and Renaissance Worldwide Solutions
president, David Norton; Renaissance remains a major partner and reseller.
In May 1998, Gentia acquired a consultancy firm, Technical and Computer
Management Services (TCMS), and the acquisition of Compression Sciences
was completed in November 1998 for $3.1 million. Compression Sciences
develops K.wiz, a Java-based datamining tool.
Gentia is one of the smaller vendors in the OLAP market and went public in
1996. The company employs approximately 200 staff worldwide and has joint
headquarters in London (UK) and Boston (US) and a network of
international distributors. Core R&D is based in Ipswich (UK), although
application development is also conducted in the US, Switzerland and Israel.
Since the early 1990s, the company experienced steady growth, which has
slowed down in the last two years. Revenues for fiscal 1998 grew less than
10% to $29.5 million. More worryingly, however, was the net loss of $15.6
million in 1998 (compared to a loss of $4 million in 1997). Around 60% of
revenue comes from Europe.
Gentia is one of the earlier OLAP vendors selling ‘big-ticket’ software. Until
early 1998, Gentia Software was clearly defined as a company providing
development tools for the production of enterprise-wide decision support
systems. With the announcement in January 1998 of its strategic
partnership with Renaissance Worldwide to develop the Renaissance
Balanced Scorecard (RBSC) application, and the acquisition of TCMS later in
the same year, Gentia has firmly indicated a repositioning from a technology-
led approach to a solutions-oriented company.
While Gentia intends to continue with its development tools, the main thrust
is now to ‘sell high’ and ‘sell strategic’. The company is assembling a
comprehensive suite of Enterprise Performance Management (EPM)
applications aimed at medium to large-sized organisations. The RBSC

16
application, released in March 1998, uses a broad range of leading and
lagging indicators – customer perspective, internal/business processes,
learning, growth and financials – to evaluate whether a business is moving
towards its strategic goals. Gentia also provides a range of Impact-branded
applications for performance measurement, which are designed to run
underneath RBSC, including:
• Profit Impact, an activity-based management application for profitability
analysis, developed in conjunction with Arthur Andersen. This is
primarily a profitability analysis application, based on activity-based
costing analysis. The analysis provides users with a view of profit, cost
and revenue by product, customer and channel
• Performance Impact, a tactical application used to track key performance
indicators (KPIs) across an organisation. It integrates with RBSC to
provide a more detailed measure analysis
• Revenue Impact, for sales analysis, which has been developed in
partnership with Decision Systems, an Israeli consultancy.
All the applications have been developed jointly with partners that have
application domain knowledge and expertise. Product endorsements from
partners are key to successful market uptake and Gentia has established a
Select Partner Program for delivering EPM applications. The adoption of
these EPM applications is crucial to reviving Gentia’s flagging fortunes and
returning the company to profitability.
Gentia anticipates that once a customer has bought a packaged application,
they may seek to buy the Gentia platform to integrate and enhance the
packaged solution or to complement it with additional packaged applications.
Gentia has more than 500 customers worldwide, including JP Morgan, Volvo,
McDonald’s Restaurants and Sun Microsystems. There are currently around
45 RBSC customers, although this figure is expected to grow substantially in
1999. While Gentia’s OLAP business continues steadily, analytical
applications represent the greatest opportunity to accelerate sales growth
over the long term. In particular, Gentia is aggressively pursuing RBSC, by
supporting it on all the major RDBMS platforms and by widening its
strategic sales partnerships. Gentia also founded the Balanced Scorecard
Technology Council (which has 3,800 members), which promotes market
awareness and acceptance.
Customer support
Support
Global 24×7 telephone hotline support is available; this is based in Atlanta,
Georgia, US with second line support provided by Ipswich, UK. Support is
also available via the Web and online product enhancement and fault
reporting facilities are provided.
Support and maintenance is priced at 20% of the annual licence fee.
Training
Gentia runs a variety of public or on-site courses to support customers,
including a four-day introductory course, an advanced four-day course and
shorter courses on specialised topics such as Gentia Agents.

Almost all purchasers of Gentia products buy consultancy of some kind. Most
of it is provided by accredited global consultancies (Arthur Andersen, CAP
Gemini and PricewaterhouseCoopers) and Technical and Computer
Management Services (TCMS), which Gentia acquired in May 1998.
Services accounted for 45% of Gentia’s revenues in 1998.
Distribution
Gentia has headquarters located in London (UK) and Boston (US).
Europe
Gentia Software
Tuition House
St George’s Road
Wimbledon
London, SW19 4EU
UK
Tel:+44 181 971 4000
Fax:+44 181 944 1604
US
Gentia Software
201 Edgewater Drive, Suite 241
Boston, MA 01880
USA
Tel:+1 781 224 0750
Fax:+1 781 224 4340
Asia-Pacific
Gentia Software Singapore
89 Science Park Drive
#04-06/07 The Rutherford
Singapore Science Park
Singapore 11826
Tel:+65 778 1678
Fax:+65 778 6884
E-mail: info@gentia.com
http//:www.gentia.com

18
Product evaluation
Summary
Gentia is primarily an application development environment, so the end-user
functionality largely depends on what the developer builds into the
application. However, the Application Designer and Excel Add-in tools
provide a greater degree of out-of-the-box functionality for end users.
Regardless of the tool used, all the Gentia applications provide the usual
OLAP functions of drill-down and pivot. Distribution is supported by
publishing reports using a ‘book, chapter, page’ metaphor for users and
workgroups. However, the product would be enhanced by a wider range of
front-end user tools and better support for subscribing to reports.
Gentia uses a ‘book, chapter, page’ metaphor to present applications to users.
Books, or sections of books, are accessible depending on the user’s access
rights. If the user has the correct permission they will see it, otherwise they
will not. Search facilities and hypertext navigation allow rapid navigation
through the system.
End users can access descriptive metadata about dimensions and measures
by right-clicking when over the element. The metadata is non-structured
descriptive text that is entered when the business model is built. There is no
end-user access to metadata generated upstream by, for instance, extraction
tools.
There is no direct support, but it could be built into the application using the
Gentia Development Language (GDL). The Object Library (part of the
Application Framework) contains some sample code to illustrate this.
Using the model
Drill-down/up and anywhere, slice-and-dice and pivot are supported by point-
and-click. Within the application, it is straightforward to define alerts and
colour-coded exceptions and include nested dimensions. The end user can
locally define a new calculated measure.
Members can be repositioned using drag-and-drop functions.
1 2 3 4 5 6 7 8 9 10

It is possible to see the drill-down hierarchies using a pop-up window with a
collapsible menu hierarchy. These windows can be customised for user
presentation.
This could be achieved by combining a summary table and a detailed table in
a page in the application. It is likely that the summary table will draw data
from the GentiaDB and the detailed table from another source, such as a
relational database. Using Agents, a parameterised query could be generated
to populate the detailed table appropriately.
An alternative method of achieving this is for the application to read off the
dimension members from where the user clicked and to use these as selection
clauses in the SQL. This is then sent to the SQL database by the client or
passed to the Gentia Server for execution against the RDBMS.
Gentia applications can be accessed by the Gentia client, a web browser (if
WebSuite is installed) and Microsoft’s Excel, but none of the well known end-
user tools provided by third-party vendors, such as Brio, Business Objects
and Cognos. There is a published API, which is compliant with the OLAP
Council API, version 0.5, but it has not been widely supported.
A number of display functions are supported, including tables, chart formats,
3-D graphics, value filtering, exception flagging and hot spots. It is possible
to see multiple charts on the same page as tables from multiple sources.
Designing a report
A Report Builder allows for the easy creation of reports, which include SQL
query facilities. Bitmaps can be incorporated into a report, but there is no
support for embedding OLE objects. The Application Designer interface
relies entirely on the functions available in Excel for report layout and
design.
Publishing a report
Users can publish reports to other users through the use of Gentia’s ‘book,
chapter, page’ approach. Reports can be scheduled for publication using an
Agent. It is possible to dynamically define groups using GDL.
In the Excel Add-in interface, users can publish Excel templates via Gentia’s
object store. Additionally, reports can be dynamically run and published to a
web server in HTML.
A MAPI interface is supported for report distribution via e-mail. Reports can
be e-mailed directly from Gentia applications.
There is no direct support to enable users to see available reports and elect to
subscribe to them; the Gentia model for sharing information is for the user to
create a report and allow other colleagues in their group to access it.

20
Summary
In the Gentia toolkit, the multidimensional business model is built in
GentiaDB, but, because of the lack of end-user tools to directly access it, it will
almost always be embedded in a Gentia application. The multidimensional
business model in the MDDB can be accessed through its API, but the
company claims this is not often requested or required. Although much of the
specification is done using point-and-click, it is less easy to use than other
MDDBs we have seen. The Application Designer provides a simpler interface
for end users to define and populate models, although they are typically less
complex. Areas that could be strengthened include support for more
specialised calculated measures and flexible time dimensions, and the ability
to capture and use metadata during the design process.
Basic design
Design interface
The design interface for dimension, member and measure definitions is
largely via point-and-click.
The developer can see both the schema and a sample of data.
There is some support for this within GentiaDB, through the use of shared
metadata.
There are no prompts to add metadata.
Columns are selected using point-and-click.
Members in a dimension level are selected using point-and-click.
Hierarchies can be created and edited using drag-and-drop; multi-level
hierarchies are supported as well as the ability to include different
hierarchies within a single dimension. Developers can also define unbalanced
dimensions, and Gentia will automatically implement appropriate drill-down
paths for them.
1 2 3 4 5 6 7 8 9 10

Time dimension
There is support for defining time periods and spans, which can be defined
dynamically (for example, year-to-date). There is no support for alternative
time dimensions and limited support for defining multiple time dimensions
in a single model.
The use of ‘display sets’ allows designers to add descriptive comments to
dimensions.
This cannot be specified within GentiaDB; it has to be specified using
dynamically populated filters for each user or group.
Calculated measures
Calculated measures are created by typing in an expression made up of
arithmetic operators and functions. Some mathematical, statistical and time
functions are provided. More complex calculated measures could be added
when the multidimensional model is used within an application.
This is supported.
Multiple designers
Multiple designers
Once the model has been designed, it is ‘committed’, meaning that the data is
loaded. GentiaDB supports ‘all or nothing’ consolidation to ensure that
updates are not lost.
GentiaDB provides version control.
Summary
Analytical functions can be built into the multidimensional business model
when it is defined in the GentiaDB (or third-party OLAP source), and others
added when the model is used within an application created in the Gentia
VDE. A few analytical functions are provided within the MDDB and the
application development environment. The company’s philosophy behind the
product is that if complex analytics are required by users, they could be
created as re-usable components using the GDL.
The toolkit would be enhanced by a greater range of ready-to-use analytical
functions, particularly for time-based calculations, and access to external
functions.
1 2 3 4 5 6 7 8 9 10

22
An add-in enables Gentia business models to make use of the analytical
functions in Microsoft Excel. There is no integration with third-party
statistical tools.
Ranking and sorting
This is supported in GentiaDB and in applications built using the Gentia
platform. In the MDDB there is a transformer with this functionality and,
when developing an application, it can be incorporated using a connector.
Within the connector the developer can specify whether the processing of the
data takes place on the client or the server.
Gentia supports a range of standard functions, including integer, absolute
value, exponential, logarithms, cosine, tangent and factorial. But there is no
in-built support for complex mathematical techniques.
Financial functions
There is support for currency conversions (including the euro) in GentiaDB.
The application development environment provides functions for lagging or
leading co-ordinate values (shifting them back or forward one position),
obtaining the net present value of an expression at a specified percentage
rate and compound interest calculations.
Statistical models
There are statistical functions to obtain the mean value of a distribution,
average deviation and skewness. Other statistical functions include moving
average and kurtosis.
Trend analysis
There are functions for exponential smoothing.
Simple regression
There is support for single linear regression.
There is some support for defining values in a time span in the GentiaDB,
making seasonal comparisons possible.
Developers can create their own functions using the Gentia Development
Language (GDL). The new functions can be stored and re-used.
Write back for ‘what-if?’ analysis
Write back to GentiaDB is supported from client-server and web applications
and the Gentia Excel Add-in client.
Gentia supports a text object that allows text and narrative data to be stored,
updated and queried. This functionality is prebuilt into the RBSC and
Impact range of applications, but can also be incorporated into any Gentia
application.

Gentia also offers a text management option called Text Infobase, which can
be used for analysis of unstructured, text-based information. Agents can be
used to feed information from this back into the system.
Data mining
One of the sample applications provided with the product is an example of
datamining. Further datamining applications could be developed using the
GDL.
Gentia acquired datamining technology (K.wiz) from Compression Sciences
in May 1998. Gentia is currently investigating plans to integrate some K.wiz
components into the G-MAP platform or future Impact applications. (see
Future enhancements).
Web support
Summary
Web access in Gentia is supported by Gentia WebSuite, which uses a CGI
gateway between the usual web server and the Gentia server. It thus enables a
web browser to access Gentia applications and base models.
Version 5.0.2 has removed the need for developers to specifically design
applications for web access, making WebSuite a more integral part of the
product. Web applications offer excellent functionality; there is even a web
version of Gentia’s flagship application, the Renaissance Balanced Scorecard.
However, when accessing base models via the Web, the user interface lacks the
sophistication and some of the functionality provided by desktop client access.
One useful feature is that write-back via the browser interface is supported.
But there could be better exploitation of the Internet as a distribution
mechanism.
End user functionality via the Web
Functionality of web access to models
Tables and charts are displayed; tables using HTML support tables and
charts using Java applets. Neither of them is an active object, as in the
Gentia client, and drilling is carried out via hypertext links.
Unregistered users are not supported.
There is no special support for the production of EIS-style reports, but with
appropriate design, a wide range of users can be supported. A feature of
particular use to power users is the ability to write back to the MDDB via the
web interface.
1 2 3 4 5 6 7 8 9 10

24
Not applicable.
While it is technically possible to create models via the Web, there are no
prebuilt objects to make this a simple or practical process.
Distributing via the Internet and Web
The Report Manager module within the Gentia suite is primarily designed to
support paper-based reporting, but can be used to generate static HTML
pages for web publishing. There is no support for generating Java.
There is no support for centrally organised targeted distribution via the
Internet or the Web. However, ‘report packs’ can be scheduled for distribution
over the Web using the Gentia Excel Add-in.
URLs and custom Java applets can be included in an application.
Some distribution of processing can be achieved using Agents.
Management
Summary
There are two locations in which data and users have to be managed: the
GentiaDB and applications developed in the Visual Development
Environment (VDE). But singular administration is available for client-
server and web environments.
The Application Designer provides a single console to administer both
application and data-level security. There is a good range of management
facilities for controlling access rights, and security wizards provide access to
cell level, along with the selective use of pages and objects. The management of
large systems can be largely automated through the use of Agents.
However, the GentiaDB would benefit from tools that allow systems
administrators to easily control and monitor processing tasks and data loads,
and view the GentiaDB log. The MDDB would benefit greatly from a more
flexible method for consolidating hierarchies and tools that help define which
measures should be precalculated or calculated on-the-fly.
1 2 3 4 5 6 7 8 9 10

Management of applications and their associated objects is carried out in
author mode, through the Warehouse Manager or through the Application
Designer interface.
Security of models
In the application development environment, Gentia has full security
support to define access for authoring and using applications.
Full data-level security, down to cell level, is also provided through security
wizards.
Query monitoring
Facilities are available to track which users have accessed what application
pages and models and when this occurred. There is a workable sample
application provided in the Object Library to analyse this information.
Management of data
Multidimensional data is stored in the MDDB. Data retrieved by SQL calls to
relational databases is always freshly retrieved.
Updates and schedules are organised by Agents, which can be scheduled
using point-and-click.
This is well supported using Agents, and is specified mainly through the use
of point-and-click.
In GentiaDB there is a ‘logger’ transformer that can be used to collect details.
The developer can specify how failed updates are handled, using GDL script
within an Agent. Rollback is provided, and by dividing large updates into
smaller tasks the consequences of failed loads can then be minimised.
One of the great strengths of Gentia is that the data can be partitioned and
distributed anywhere on the network. The ORB enables the application to
access data with location transparency.
The GentiaDB solution to sparsity is a multicube architecture, in which each
cube is made up of dense dimensions.
Within GentiaDB, all intermediate summary levels are precalculated at
consolidation time (which may be defined as different from load time).
Calculated measures can be either precalculated or calculated on-the-fly.
This is a manual task and there is no wizard support.
The cache can be configured to optimise performance of the MDDB through
the cache parameter in the config file.

26
There is no direct support for this, although it could be achieved with Agents
or by writing each update to a text object, which can be presented to end
users in an application.
Management of users
GentiaDB uses the concept of ‘shadow’ pages for writing and consolidation.
Only when write and consolidate are complete is the new data made
available to others. Users reading the data always see a consistent dataset.
User security profiles can be set at an individual or workgroup level and are
defined in the Application Designer or Warehouse Manager. Both user and
author access are defined on a level between one and seven, allowing a fine
level of control. Users are grouped into workgroups to enable sharing of
information within a workgroup. Users can be in several workgroups.
User security can be applied across client-server and web communities and
can be dependent on access mechanisms. For example, administrators may
restrict user or group access to an application either via a locally connected
client or a web browser.
Query governance
This is not necessary for MDDB data and not available for SQL queries.
Gentia relies on the underlying RDBMS for SQL monitoring and governance.
While Gentia cannot stop an SQL query while it is running within an
RDBMS, there is an option for limiting the number of records returned to the
client.
There is no direct support for limiting queries to certain times of the day or
week, although they can be scheduled to run at specific times with Agents.
The visibility of applications is controlled by the Warehouse Manager, so that
users can only see those to which they have access.
Adaptability
Summary
Gentia does not have ‘out-of-the-box’ adaptability but, while requiring some
programming, Agents can provide a powerful means of developing
mechanisms that support adaptability of models and applications.
1 2 3 4 5 6 7 8 9 10

It is easy to adapt the application in the light of changing business
requirements. It is possible, using Agents, to provide very sophisticated
support to ensure that the data sources, models and applications remain
synchronised at all times. However, this does require significant development
effort.
The tool does not provide support for impact analysis and change
management.
Adding a new dimension to the model is a two-stage process. Firstly, it needs
to be incorporated into the GentiaDB multidimensional database and then
the application needs to be edited to include it in the appropriate page(s).
Alternatively, dynamically generated pages can be set up to use the current
number of dimensions available, although this is dependent on the
underlying application functionality and process that is being modelled. The
tracking of changes made to underlying models is supported by version
control in the GentiaDB.
With regard to applications, if a developer changes the definition of an object,
that change will immediately be reflected in all pages and applications that
use those objects. The book manager tracks who created what and when, but
there is no explicit version control.
Since the business model is a mapping layer, the dimension definitions can
be named, saved and re-used.
New measures can be added directly to the application or, as described above,
built into the base model in the MDDB and they will then, by default, be
visible to any application using that model.
Since the business model is a mapping layer, the calculated measure
definitions can be named, saved and re-used.
Gentia applications are primarily based on data stored in the MDDB,
although they can incorporate data retrieved using SQL queries. However,
these are not dynamically generated SQL queries and there is limited
support for using these for multidimensional analysis. They can be processed
into ‘scenarios’, which are essentially mini personal data cubes that can be
viewed and manipulated like any other MDDB source.
The Gentia platform does not support a ROLAP option, but can implicitly
link into third-party ROLAP systems.
There is no direct support to ensure that the schema of the data source(s) is
synchronised with the application schema. However, Agents could be used to
check this before the application is run.

28
Agents are extensively used to manage this. There are a number of options
open to the application designer, including ‘add’, ‘ignore’, ‘add but warn’ and
put into a special ‘unknown’ bucket.
Metadata
There is no end-user metadata to synchronise.
Impact analysis
There is no support for impact analysis to show the consequences of changing
a model.
This is not applicable, as there is no real metadata to audit.
There is no direct integration for enabling the designer to access metadata
generated by the extraction tools.
Summary
The distributed client-server architecture of the Visual Development
Environment offers excellent support for allocating processing flexibly at the
server level, on the client or through a combination of the two. Processing can
also be configured down to the application page level; for example, an
application page could be constructed that automatically switches to server-
based processing for web applications, improving access speed and reducing
network traffic.
Within GentiaDB, performance tunability is largely dependent upon good
design decisions, such as storing (caching) large dimension business
structures locally and using the Gentia server to update those models. The tool
would be enhanced by some wizard support for this process.
ROLAP
A ROLAP option is not directly supported. Although data from SQL sources
can be incorporated into any Gentia application, it will be displayed in
tabular, rather than cross-tabular, form. Gentia can create ‘scenarios’ (virtual
multidimensional cubes) dynamically from SQL data sources, but these are
limited to what can be held in memory.
MOLAP
In GentiaDB, all intermediate summary levels are precalculated at
consolidation time (which may be defined differently from load time).
Calculated measures can be precalculated or calculated on-the-fly to speed
1 2 3 4 5 6 7 8 9 10

up performance and load times. GentiaDB also supports local caching of
large dimension structures on the client, which can be updated by the Gentia
server whenever the cached data is affected by fresh data loads.
Keeping business model files in separate partitions or, better still, on
separate disks can reduce load time.
The flexible architecture in which processing can be carried out on the client
or the server, and the support that Agents can give to the management
infrastructure, enable thousands of users to be supported.
Part of the application specification is whether (when it is run) the
application is totally, partially or not at all cached on the local machine. If
caching is selected, the models in the application are dynamically updated
when re-used.
Processing
GentiaDB and the Gentia Visual Development Environment (VDE) use
ODBC to access relational databases. The company ships Merant’s
DataDirect and SequeLink drivers and manager. Native access to RDBMSs
is provided by Merant’s SequeLink server software, embedded in G-MAP.
Processing can be carried out on the client or server, and is specified when
the application is designed. A decision is made at page level. The underlying
Object Request Broker means that the processing can make use of objects
and services regardless of their location.
SMP support
GentiaDB (but not the Gentia Visual Development Environment) is based on
a multi-threaded architecture that can take advantage of SMP.
Customisation
Gentia is designed from the outset as a platform for analytical application
development, and offers a comprehensive range of services to support this. The
Visual Development Environment (VDE) enables specialised OLAP
applications to be developed quickly in a GUI environment, and supports a
high degree of re-usability. Gentia also provides a friendlier, but simpler,
environment for allowing business end users to develop their own OLAP
applications using a template-driven interface.
As a result of its distributed architecture, the applications can run on both
Unix and Windows, using data stored on a variety of platforms.
It is possible for the developer to produce sophisticated applications for the
Web and for conventional desktop access (RBSC and Performance Impact are
excellent examples of where equal functionality has been enabled for both
environments).
1 2 3 4 5 6 7 8 9 10

30
Customisation
As the end-user tool is nearly always an application developed within the
VDE, a restricted interface to enable ease of use for occasional users may be
required, which can be built into the application.
The VDE can be used to create both complex applications and simple EIS-
type programs. Additionally, end users can create simple reporting
applications from the predefined reporting templates provided by the
Application Designer.
Applications
VDE’s drag-and-drop and publish-and-subscribe development environment
can be used to build a simple EIS application to be run in a web browser. The
Gentia approach is to provide web access to all applications.
As the development environment is specialised, there is a rich collection of
features, such as tables with drill-down features and charts to speed up the
process of building applications for multidimensional analysis.
The development environment is supported by the Application Framework
layer, which supports an extensive library of objects. The GDL can also be
used to add greater functionality to applications.
The development environment is OLE-compliant and there is extensive
documentation, which is provided on a CD rather than as hard copy.
Gentia has a published API and it is possible to develop the applications in
C++, Visual Basic or Java. In practice, this is seldom used, as the VDE offers
specialised OLAP functionality, enabling faster development.
Localisation
Gentia has excellent support for internationalisation and supports more than
30 languages in development mode; its multi-language capabilities are a
consequence of its support for the Unicode UTF-8 translation standard. The
display of different character sets, both single- and double-byte fonts, is
supported regardless of platform. The localisation of Gentia is addressed
through the use of message tables and developers can produce their own
application message tables.

Deployment
Platforms
Client
The Gentia Visual Development Environment (VDE) client runs on Windows
95, 98, NT, Macintosh (PPC) and Sun Solaris.
The Gentia WebSuite is supported on all current Gentia server platforms and
supports the following browsers: Netscape Navigator, Microsoft Internet
Explorer and Hot Java Browser.
The Gentia Add-In for Excel runs on Windows 95, 98 or NT. It is supported
on all Gentia server platforms, excluding Netware NLM.
Server
The GentiaDB and Gentia VDE Servers run on Windows NT and Unix (HP-
UX, Sun Solaris, Unixware, AIX, Generic SVR4, Pyramid and NeXT).
Additionally, Gentia VDE supports Netware NLM.
Data access
GentiaDB can access and load data from any ODBC-compliant relational
database, including Oracle, DB2, Informix, Ingres, SQL Server, Sybase,
Dbase, Paradox, Interbase, FoxPro and Btrieve. It can also access data held
in ASCII flat files and Excel spreadsheets.
Gentia is an OLE DB for OLAP consumer and can access third-party MDDBs
that support this standard.
Standards
Gentia VDE supports the OLAP Council’s MDAPI version 0.5 specification. It
has no plans to support version 2 of the OLAP Council specification.
Gentia supports Microsoft’s OLE DB for OLAP API as a consumer, allowing
it to access third-party MDDBs; Gentia plans to provide support as a data
provider with version 6.0.1 due in the fourth quarter of 1999.
Gentia does not have any published benchmarks.
Price structure
Pricing is based on the number of named users with a built-in volume
discount; there are six pricing bands, each with a ‘per user’ price. A 50-user
licence costs approximately $150,000.
Pricing for RBSC and Impact applications is also based on named users. In
addition, a restricted user licence (RUL) is available to provide customers
with the ability to build or customise applications using G-MAP component
modules.
Gentia will incorporate training in pricing models for future releases.

32

Hummingbird – BI/Suite
Summary
At a glance .............................................................................................. 3
Ovum’s verdict......................................................................................... 5
Distribution ............................................................................................ 16
Product evaluation
Web support .......................................................................................... 22
Management ......................................................................................... 23
Adaptability ............................................................................................ 25
Customisation ....................................................................................... 27
Deployment
Platforms ............................................................................................... 29
Data access .......................................................................................... 29
Standards .............................................................................................. 29
Price structure ....................................................................................... 29

2
Evaluation: Hummingbird – BI/Suite

3
At a glance
Developer
Hummingbird Communications, North York, Ontario, Canada
Version evaluated
BI/Suite version 5.1, comprising BI/Query version 5.0.2, BI/Analyze version
5.1, BI/Web version 2.0 and BI/Broker version 2.0.
Key points
• An integrated desktop query, OLAP and reporting tool
• The server runs on Windows NT and Unix; clients run on Windows 95/98/
NT and Java 1.1-based web browsers
• BI/Suite is based on OLAP and query tools developed by Andyne
Computing, which Hummingbird bought in January 1998
Strengths
• A tightly-integrated suite of tools that is easy to use and manage across
client-server and web environments
• Supports a distributed architecture with load balancing across multiple
servers
• Client tools connect to a wide range of third-party relational and
multidimensional databases
Points to watch
• Accessing data from relational databases to build the model needs BI/
Query, Hummingbird’s query and reporting tool
• No support for complex analytical analysis
• Limited range of front-end tools – HyperCubes can only be analysed
using the BI/Suite client tools
Ratings
Web support
Management
Adaptability
Customisation
1 2 3 4 5 6 7 8 9 10

4
App Handlers
Server-based components that handle client connections to underlying data
sources for query, OLAP and report processing.
Crosstab
Similar to a dynamic spreadsheet, this tool is used to display and analyse
data in a multidimensional model.
Data model
A graphical representation of the contents of a relational database in famil-
iar business terms. The data model acts as a semantic layer that allows end
users to query and access data using point-and-click. A data model only
exists in BI/Query and should not be mistaken for a multidimensional data
model.
HyperCubes
These are small, multidimensional cubes that are stored in specially-format-
ted files and downloaded on to the BI/Analyze client for subsequent OLAP
analysis on the desktop. HyperCubes are processed on the BI/Broker when
accessed by BI/Web. HyperCubes are built with a query result set using the
BI/Query tool or a flat file.
Levels
Different levels of aggregation in a dimensional hierarchy.
Members
Individual components or sub-categories in non-numeric dimensions; for
example, ‘the US’, ‘Canada’, ‘Ohio’ and ‘Ontario’ are all members of a ‘region’
dimension. Members can be shared within a dimension.
Metric
Corresponds to Ovum’s definition of a measure. A metric can represent a
single column of numeric data or can be derived from a calculation.
Presentation
A report that contains crosstabs, charts and OLE objects. A presentation can
include ‘live’ data in the form of a HyperCube or any supported multidimen-
sional source accessed natively or via OLE DB for OLAP.

5
Ovum’s verdict
What we think
BI/Suite provides an easy-to-use set of tools; its greatest strength is its
simplicity. The client-server and web-based end-user tools provide immedi-
ate access to data – even to inexperienced users with minimal training.
Users also benefit from the ability to schedule reports, refresh HyperCubes
and share them through BI/Suite’s mid-tier broker server. Support for a
single-server architecture gives users the benefits of a shared report and
metadata repository, and load balancing. The payoff for administrators
comes with centralised management and administration. Both thin and fat
clients use the same content, security and user profiles from a single server,
thereby eliminating the hassle of managing multi-server set-ups.
While BI/Suite firmly establishes Hummingbird in the enterprise query and
reporting space further development is needed for its to qualify as an enter-
prise OLAP solution in its own right. The client-server (BI/Analyze) and
web-based (BI/Web) OLAP clients satisfactorily cope with general business
intelligence needs; however, they are not suited to highly complex and
specialised analysis. Users that need this level of functionality will have to
export data to third-party tools such as Excel. Native and OLE DB for OLAP
access is provided for a range of third-party MDDB servers; however, the
product’s client-centric architecture restricts the size of the HyperCubes
(generated from a direct query to the relational database) that can be effec-
tively downloaded and analysed on the BI/Analyze desktop environment.
Hummingbird is rapidly establishing a presence in the business intelligence
market, largely through acquisitions. BI/Suite is Hummingbird’s first prod-
uct in this market. The company’s future success will depend on:
• how well it can integrate BI/Suite and its recently-acquired data
transformation, financial software and knowledge management
technology
• whether it can market this portfolio in a coherent way.
When to use
BI/Suite is most suitable if you:
• want a simple out-of-the-box solution that can be easily rolled-out across
the enterprise
• want to provide integrated query, OLAP analysis and reporting
capabilities to general business users, with minimal training
requirements
• want to perform OLAP on small datasets sourced directly from a variety
of relational databases or flat files
• have to distribute small business models across the enterprise.

6
BI/Suite is less suitable if you:
• want to build complex models with large numbers of unique members
• require the analysis of data using advanced or specialised analytical
functions
• do not want to use two tools to access and analyse RDBMS data – BI/
Query is needed to efficiently build models based on relational sources
• want to build custom analytical applications.

7
Product overview
Components
The important components of BI/Suite version 5.1 are:
• BI/Query version 5.0.2
• BI/Analyze version 5.1
• BI/Broker version 2.0
• BI/Web version 2.0.
Although Ovum Evaluates: OLAP covers the entire suite of BI/Suite tools,
the main focus of this evaluation is the OLAP functionality provided by BI/
Analyze and BI/Web.
BI/Query
This is primarily an end-user tool for building ad hoc queries. It provides a
graphical interface for querying relational databases and generating reports.
BI/Query simplifies the process of data access by creating a semantic layer
(data model) that provides a graphical representation of the structure of a
relational database in familiar business terms.
Results sets can be used to:
• create standard reports (using the integrated report writer)
• act as data sources for creating multidimensional models (HyperCubes).
This indirect method is used to incorporate data from relational databases
and transaction sources into multidimensional data structures defined in BI/
Analyze.
BI/Query is based on an enhanced version of Andyne’s GQL (Graphical
Query Language).
Ad hoc query OLAP analysis Web support
Client BI/Query BI/Analyze BI/Web
Server BI/Broker BI/Broker BI/Broker

8
BI/Analyze
This is an end-user tool for desktop OLAP analysis. It includes a
CubeCreator facility for building HyperCubes from BI/Query results sets, as
well as other flat-file data sources. Native access is provided for Hyperion
Essbase and Informix MetaCube. BI/Analyze can also connect to other third-
party MDDBs that support the OLE DB for OLAP interface as a data provider.
BI/Analyze is offered as a standalone desktop OLAP client or as a component
of BI/Suite.
BI/Analyze is an enhanced version of Andyne’s Pablo analysis tool.
BI/Broker
This is an application server that provides shared services, security and
administration functions. It includes a central repository that stores all data
models, queries, results sets, reports and their associated metadata. Admin-
istrators can publish information for multidimensional data sources to the
repository (although data sources are not stored there). BI/Analyze clients
access data sources via the repository. BI/Web uses the App Handlers within
the server to handle connections to data sources.
BI/Broker is built on a CORBA architecture. Visigenic’s ORB technology is
used to deploy application services as distributed objects over the network.
BI/Broker provides three main interfaces for managing the query and OLAP
environment:
• Administrator – primarily addresses server variables such as repository
directories and mapping the HTML links for BI/Web
• User and Group Manager – for end-user management and security
setting
• Scheduler – to schedule queries and refresh reports based on time- or
event-driven criteria.
Other components of BI/Broker include a load-balancing utility and a Session
Manager tool for managing user connections.
BI/Web
A thin client interface that provides query, OLAP analysis and reporting
functions via a web browser. BI/Web uses three main Java applets to gain
access to BI/Broker services. XML is used to render all content – reports,
data, models and query results – to web users.
The BI/Web interface is based on Java-based OLAP technology licensed from
Internetivity.
BI/Suite does not support a full mid-tier MDDB architecture; however, BI/
Analyze can implicitly link to these environments as a front end.

9
A thin-client variant of a light mid-tier architecture is supported by the
inclusion of BI/Web and BI/Broker. In this configuration, BI/Broker acts as a
mid-tier application server and offloads processing from the client to the
server. It also provides a central repository for storing models, reports and
metadata.
In a client-server environment, BI/Broker provides mid-tier architecture for
security access, publishing, scheduling, notification and distribution. Its role
is not to handle processing, but to manage throughput and the flow of data
to clients.
This is the natural architecture for BI/Analyze; it creates multidimensional
cubes for desktop analysis. All OLAP processing is performed on the client.
Mobile architecture
BI/Analyze has a standalone OLAP engine to support a mobile architecture.
Mobile users can choose to work offline by packaging a report and ‘slicing
off’ data from a HyperCube or a third-party MDDB server. Data can be
refreshed when the user is reconnected to the data source.
Using BI/Suite
Query first, then analyse
To analyse data from a relational database or a transactional data source in
a multidimensional cube, a query definition has to be defined in BI/Query to
extract the data. The results set then appears as a data source in BI/
Analyze. The query and the cube-creation environments (CubeCreator) are
tightly integrated; a multidimensional structure is generated via point-and-
click.
BI/Query provides an easy-to-use graphical interface to define queries. As
shown in Figure 2, it uses a ‘data model’ to provide a graphical representa-
tion of the database. The data model acts as a mapping layer through which
end users can query the database and return a subset of data. Icons in the
data model can:
• relate to database tables
• be used as virtual tables that include joins from multiple tables or
calculated attributes.
BI/Query provides a number of design windows for creating data models.
These windows are used to specify data objects (which represent tables in
the database) and the relationships that tie them together. The data objects
are the starting point for building queries that retrieve information from a
database. Queries are built by selecting the attributes from tables. Users can
use more than one object in the data model to build a query. BI/Query also
provides a facility for incorporating prompts into queries that make users
enter a value into a qualification.

10
Figure 2 BI/Query data model
It is not possible to use more than one query or data source to create a
HyperCube in BI/Analyze; in BI/Query, however, it is possible to combine
multiple queries into a single query. This query consolidates data from
multiple databases into a single source file before it is imported into BI/
Analyze.
Using BI/Query results sets as data sources for BI/Analyze is the most
efficient way of accessing relational data. Users can also create their own
data sources directly from other processes that return query results in a
comma- or tab-delimited flat-file format; saved BI/Query results sets are also
delimited text files.
All the HyperCubes built this way are ‘local’ HyperCubes – that is, stored on
the hard drive or on a network server – and can be distributed to end users.
Designing and building HyperCubes using CubeCreator
Having created a results set, end users can use the CubeCreator to design
and build a HyperCube for OLAP analysis.
The first stage is to create a logical design for a HyperCube. CubeCreator
provides two graphical interfaces for designing a multidimensional cube –
the Designer and the Editor.
The Designer
The Designer is used to create HyperCubes from source data. Users can
modify the design by rearranging and renaming the columns and consolidat-
ing detail data into summarised information.
Designer’s AutoDesign feature reads the source data and designs the
HyperCube based on the relationships that it finds. It scans a specified
number of rows in the results set and – based on the correlation it finds –
groups the related columns into levels within dimensions. It also identifies
most date columns, breaking them down to yearly, quarterly and monthly

11
levels within a date dimension. It also understands that numeric columns
are metrics that belong in a metrics dimension. An ‘arrange columns’ dia-
logue box is provided to rearrange or rename the columns specified by the
AutoDesign function.
Designer’s ‘learn as you go’ feature automatically applies these changes the
next time a similar HyperCube is built. Developers can also specify templates
(created using the Editor tool) to generate a design automatically.
After designing a HyperCube, it can be physically built. Developers can
refresh the HyperCube with data whenever there is a change in the structure
or underlying data source; they can also modify the design, adding greater
complexity into the model.
The Editor
The Designer is useful for generating a ‘starter’ set of dimensions. As shown
in Figure 3, developers can fine-tune the multidimensional model, adding
greater complexity to its structure using the Editor interface.
The Editor allows developers to design more advanced HyperCubes by
working with the metadata. The metadata includes information about the
underlying data (such as its location or the query that generated it), including
attributes, prompts, variables and qualifications.
The Editor can:
• create calculated metrics
• group metrics into hierarchies
• create and delete dimensions, levels and members
• create shared members
• build custom asymmetric hierarchies.
Developers can also set up the HyperCubes to refresh incrementally and
design ‘template’ HyperCubes without data. Templates contain metadata
about how columns should be organised into dimensions, levels and metrics.
Using multidimensional data sources
BI/Analyze can be used to access predefined multidimensional data: natively
from Hyperion Essbase and Informix MetaCube or from third-party MDDB
servers that support OLE DB for OLAP as data providers. HyperCubes
based on larger multidimensional data sources are called ‘sliced’
HyperCubes.
A sliced HyperCube is similar to a local HyperCube. Developers can:
• specify the dimensions and members that they want to include
• modify the structure using the Designer/Editor interface
• send it directly to BI/Analyze to create reports.
It is possible to build sliced HyperCubes from other HyperCubes as well as
from views created in BI/Analyze.
Sliced HyperCubes are suitable for mobile users that need fast, easy access
to specific data in a large data source. They are not suitable, however, if the
data being accessed is volatile and users need up-to-date data for analysis.

12
Web access to data using BI/Web
BI/Web provides query, OLAP and reporting capabilities via a Java-capable
web browser. The main entry-point for web users is the Personal Portfolio, as
shown in Figure 4, which uses an Explorer-like interface for organising data
models, reports and HyperCubes into a folder-like system.
Figure 3 The Editor interface
Figure 4 Personal Portfolio

13
Future enhancements
A number of minor maintenance releases are planned for 1999. Humming-
bird will port BI/Broker to Solaris, HP-UX and AIX platforms in mid-1999.
The next major release is due in mid-1999. Major enhancements include:
• the ability to drill through to detail-level data
• additional OLE DB features, such as support for dimension member
properties, multiple hierarchies and writeback
• enhancements to security
• tighter integration with Excel
• the ability to automatically load metadata (such as business terms and
short descriptions) into BI/Query data models from the Informatica
metadata repository
• increased analytical functions in the client tools
• enhancing the upper limits on the amount of data that can be stored in a
HyperCube and moving more processing tasks (such as filtering and
ranking) to the server.
In the longer term, support for accessing Oracle Express multidimensional
models is also planned. Integration will also be provided with third-party
metadata repositories for streamlined development. A wizard facility will be
provided, which will guide users in building BI/Query data models based on
the existing metadata. The metadata will also be accessible by BI/Analyze
when building HyperCubes.
Hummingbird intends to integrate its newly-bought extract, transform and
load (ETL) technology with its other products in order to provide an end-to-
end datamart solution. Detailed product announcements will be made in late
1999.
The intention to buy PC Docs will spur the development of an ‘enterprise
knowledge portal’ strategy; this strategy will integrate BI/Suite with PC
Docs’ document and knowledge management framework and Financials and
Case Management systems.

14
Company background
Hummingbird Communications is a Canadian software company. The com-
pany was founded in 1984 as a consulting firm; it changed direction shortly
afterwards to become a developer of PC-to-host connectivity and terminal
emulation software. It has three main products in this area:
• Exceed – a PC X server that connects Windows PCs to legacy X and Unix
applications
• NFS Maestro – designed to connect PC networks and host computer
systems
• HostExplorer – terminal emulation and connectivity software that links
PCs to IBM mainframes, and the AS/400 and Unix systems.
Hummingbird achieved rapid growth in the core network connectivity
market – it holds just under a 70% share of the PC X server market. Connec-
tivity software remains a profitable business for the company. As the market
began to plateau, Hummingbird reviewed its business direction and markets.
In January 1998, the company diversified into the business intelligence
market by buying Andyne Computing for $60 million. Andyne developed two
products:
• GQL (Graphical Query Language) – a query and reporting tool
• Pablo – a desktop OLAP tool.
These two products were rebranded as BI/Query and BI/Analyze. They were
integrated into the BI/Suite, which was first released in July 1998.
In March 1999, Hummingbird announced that it intended to buy three other
companies:
• PC Docs Group International, a US company that develops document and
knowledge management software. If PC Docs is bought by Hummingbird,
it will be the first evidence of knowledge management and business
intelligence technology coming together
• Context, a New York-based company specialising in software and
consultancy services for the financial industry. Context’s main product is
Financial Frameworks, a packaged software solution aimed at the
financial services sector
• Leonard’s Logic, developers of Genio, a data extract, transform and load
(ETL) tool
Hummingbird completed its first IPO in 1993 in Canada; it later issued two
more public offerings in the US. The company is quoted on the Nasdaq and
Toronto stock exchanges.
Hummingbird employs more than 800 people; its headquarters are in North
York (Toronto), Canada, with offices and distributors worldwide. R&D is
based in Montreal, Quebec and Toronto and Kingston (the original head-
quarters of Andyne), Ontario. Revenues for the 1998 fiscal year (excluding

15
revenue from bought companies) grew by 10% to $130 million; net income
was $26.6 million. Around one-quarter of the revenues come from business
intelligence tools. The company’s biggest market is North America, which
accounts for approximately 60% of sales.
Although Hummingbird’s network connectivity business still provides
substantial revenues, the company is targeting the business intelligence
market to drive future growth. Hummingbird’s newly-formed Business
Intelligence Division can be regarded as a start-up within the company.
Hummingbird’s presence in this market is based largely on the query and
analysis tools that it bought from Andyne. Andyne’s business intelligence
products had not previously been marketed and promoted particularly well
– they were not widely recognised in the enterprise OLAP market. Hum-
mingbird has introduced a three-tier architecture and implemented a more
focused sales and marketing strategy to rectify this.
The acquisition of Leonard’s Logic and its ETL products, enables Humming-
bird to offer a turnkey solution for deploying datamarts integrated with a
range of business intelligence capabilities – reflecting the general business
intelligence trend towards users seeking end-to-end solutions.
Other planned acquisitions also open up wider markets for Hummingbird to
exploit:
• Context’s Financial Framework technology will enable Hummingbird to
enter the financial sector, providing analytical applications for credit risk
management, profitability analysis and sales prospecting
• PC Docs will strengthen Hummingbird’s strategy to encompass the
enterprise knowledge management market. Planned integration between
the two companies’ products will spur the development of integrated
business intelligence and knowledge management solutions, as described
in Future enhancements.
Hummingbird has the potential to become a significant player in all these
markets if it can:
• avoid the usual problems of mergers
• tightly integrate its bought technology
• devise a coherent marketing strategy.
Hummingbird claims that more than 3,500 user sites worldwide have in-
stalled its business intelligence tools. Major OLAP (BI/Analyze) customers
include the UK’s Inland Revenue (45,000 users), the Nationwide Building
Society, Compaq, Conrail Consolidated Rail, Lockheed Martin, Whirlpool and
Teachers Insurance. Hummingbird’s products are sold directly to large
Fortune 1,000 companies and indirectly through resellers, OEM partners,
VARs, systems integrators and 40 worldwide distributors. Hummingbird has
established close relationships with software, systems and applications
vendors through its accredited QuickStart programme.

16
Customer support
Support
Worldwide technical support is provided by Hummingbird and its distributors
and VARs via telephone, e-mail, fax, the Web and an electronic bulletin
board system. Support (including upgrades) is priced annually at 20% of the
overall licence fee.
Training
Training courses for all components of BI/Suite are available on-site or at
training centres in Canada, the US and Europe. Courses include one-day
introductory classes or two- and three-day intensive programmes for users
and administrators. Training for resellers and distributors is offered.
Hummingbird’s Professional Services Group provides services for implemen-
tation, and project and strategic consultancy.
In April 1998, Hummingbird bought Datenrevision, a specialist German
data warehousing consultancy.
Distribution
North America
Hummingbird Communications
1 Sparks Avenue
North York
Ontario M2H 2W1
Canada
Tel: +1 416 496 2200
Fax: +1 416 496 2207
Europe
66 rue Escudier
92774 Boulogne Cedex
France
Tel: +33 1 41 10 0505
Fax: +33 1 41 10 0500
Asia-Pacific
Level 19, AGL Centre
111 Pacific Highway
Sydney NSW 2060
Australia
Tel: +61 2 9929 4999
Fax: +61 2 9956 6442
http://www.hummingbird.com
E-mail: sales@hummingbird.com

17
Product evaluation
Summary
1 2 3 4 5 6 7 8 9 10
BI/Suite’s greatest strength is its easy-to-use graphical interface for ad hoc
query, analysis and reporting functions. The extensive use of wizards guides
end users through the query, analysis and reporting cycle. BI/Analyze provides
a number of sophisticated mechanisms for navigating through a HyperCube,
but the range of front-end tools is limited; there is no Excel add-in or scope
for using third-party OLAP clients, for example. BI/Broker enables reports to
be distributed across the enterprise via e-mail and other push channels.
Users are provided with a profile that shows which data models and
HyperCubes they can access in the repository. These can be grouped into
public or personal folders for access. There are no search facilities, however.
End users can view metadata about data models and HyperCubes (for
example, the topic, author, when it was created and when it was last re-
freshed).
Annotation by end users
HyperCubes cannot be annotated directly; however, end users can add
textual prompts to reports and change the description of data sources.
Using the model
Multidimensional data is presented to end users in the form of a crosstab
presentation. Support for standard OLAP (such as drill-up/drill-down and
slice-and-dice functions) is provided using point-and-click. Users can also
drill-down on individual data cells to analyse exceptions. Colour-coded traffic
lighting can also be set on data cells.
The position of members can be changed using drag-and-drop.
An Explorer-type dialogue box shows the drill-down hierarchies graphically;
however, there is no support to show the current position within it. As users
move the cursor over the elements of a crosstab, it changes to a plus or
minus sign to show the ability to drill-up or drill-down to data.

18
Drilling-down to detailed data
Drilling-down to detailed transactional data is not supported; however, users
can easily return to the original query results set for a HyperCube.
End users are limited to the end-user tools provided by Hummingbird. An
Excel add-in for directly accessing multidimensional data is not available.
Data can be presented in tables, crosstabs or a variety of chart formats
(including two- or three-dimensional line, bar, area and pie charts; scatter
histograms and spectral maps). An optional link to MapInfo, for the spatial
representation of data on a map, is also available.
Designing a report
Guided by presentation wizards, users can easily create reports that contain
tables, crosstabs, nested dimensions, charts, graphics and OLE objects.
Multiple charts and crosstabs can be viewed on the same screen. Drill-down
is supported on charts; a report can contain data from different sources.
Local calculations can also be defined in reports to create additional rows or
columns in a table. Standard arithmetical operator, string, aggregate, date/
time and logical operator functions are provided.
Publishing a report
Reports and queries can be published to specific users or groups of users.
Distribution channels include e-mail, FTP, the Web and third-party ‘push’
channels (for example, Pointcast or Microsoft’s ActiveDesktop (CDF) channel
formats). Push notifiers send messages to users when information from a
push channel becomes available.
HyperCubes and reports can be distributed via e-mail to other users. The
SMTP, VIM and MAPI mail standards are supported. MAPI allows users to
e-mail HyperCubes directly from CubeCreator’s Editor interface.
Subscription services are not supported.
Summary
1 2 3 4 5 6 7 8 9 10
The strength of the tool lies in its ease-of-use – notably, the ability to define
the structure of a model quickly. CubeCreator provides a user-friendly
graphical interface that makes extensive use of wizards and design templates.
An AutoDesign feature eliminates most of the initial work in designing
HyperCubes by creating a ‘starter’ set of dimensions, measures and hierarchies
that can be further refined.

19
However, the tools are geared towards general business modelling rather
than creating large, specialised models with complex calculations. Developers
can only access a single data source to build a model. Using BI/Query to
access SQL data prevents a sample of source data being available to the
model designer (although OLAP users can bring up a view of the query
results set).
Basic design
Design interface
CubeCreator provides an easy-to-use graphical interface for model design
and incorporates some sophisticated design features. The AutoDesign facility
automatically builds HyperCubes from a BI/Query results set. CubeCreator
automatically parses the results set and builds a ‘starter’ set of dimensions
that can be further refined using wizards, design templates and a drag-and-
drop Editor interface.
Standard design templates can be built and re-used for future design.
It is possible to view a sample of the query data on screen; however, the
underlying database tables cannot be viewed.
The data model acts as a mapping layer for query only.
When building HyperCubes using flat files or BI/Query results sets,
CubeCreator generates most of the metadata (including its location,
attributes, prompts, variables and qualifications). It also describes the
structure of the data in the HyperCubes and includes meaningful
descriptions for members, levels and dimensions.
Developers can include additional metadata when creating a HyperCube
(such as the author, details and a description), but they are not explicitly
prompted to do so.
CubeCreator’s AutoDesign feature automatically arranges the columns of a
BI/Query results set or a flat file into dimensions. Alternatively, columns can
be selected or excluded manually using point-and-click.
Designers can add dimension members by checking off the dimensions using
point-and-click.
Designers can selectively edit data branches to create asymmetrical hierar-
chies that include different levels of detail in the same dimension or leave
only summary data. Dimension members can also be placed in multiple
hierarchies; Cube Creator automatically adjusts the roll-up totals to avoid
double-counting.

20
Alternative drill-down hierarchies are not supported.
Time dimension
CubeCreator’s AutoDesign feature automatically identifies date columns,
building time dimension based on calendar or fiscal year time periods.
Custom time periods can also be defined, but only if they are based on the
corresponding date levels that exist in the source data. There is no support
for dynamically-defined time dimensions such as ‘year to date’.
Model designers can rename the descriptions of the columns that become
dimensions, levels and metrics in a HyperCube for increased readability.
These naming conventions can be filtered through to reports.
Default levels of a dimension hierarchy
These can be specified when saving a report. A ‘home’ key can return a user
to a default level in the hierarchy.
Calculated measures
Calculated measures (called calculated metrics) can be created in
CubeCreator’s Editor interface using point-and-click. The range of functions
is limited to standard arithmetical operators, constants and conditional
expressions.
Multiple measures can be associated with a dimension.
Multiple designers
Multiple designers
Security governs reading and editing access to HyperCubes. HyperCubes are
locked when they are being edited by a developer, but there are no check-in/
check-out facilities.
There is no support for versioning.
Summary
1 2 3 4 5 6 7 8 9 10
The calculation functions supported by BI/Analyze are limited to ranking
and sorting functions, and simple arithmetical and statistical functions.
Users will need to export data to third-party tools (such as Excel) for advanced
calculations and forecasting applications – direct integration with such tools
is not provided.

21
There is no direct integration with Excel or specialised third-party analysis
tools. Data can be exported to these tools in a variety of formats, however.
Ranking and sorting
Standard ranking (top or bottom by per cent) and sorting (ascending or
descending) functions are supported. More complex sorts on nested
dimensions are also supported. BI/Analyze supports server-side ranking
functionality provided by third-party MDDBs that can be connected to.
Standard arithmetical calculation functions are supported. The Calc-O-
Matic tool provides predefined calculations (such as sum, difference, percent-
age difference, count, minimum and maximum) that are easy to apply to
data.
Financial functions
There is no support for special financial functions.
Statistical models
Only simple statistical functions (such as average, minimum and maximum)
are supported.
Trend analysis
There is no support for trend analysis.
Simple regression
Regression-based forecasting algorithms are not supported.
There is no support for time-based forecasting algorithms.
A non-procedural language is not provided for defining complex functions.
Custom calculations can be built using nested rules.
Writeback for ‘what if?’ analysis
Writeback is not supported.
Analysis is restricted to numerical data only.
Data mining
There is no direct support for data mining. Hummingbird has a partnership
with Angoss for integrating data mining capabilities.

22
Web support
Summary
1 2 3 4 5 6 7 8 9 10
BI/Web provides the same intuitive interface as BI/Query, with the addition
of a Java-based navigation bar. The web interface provides the same drag-
and-drop OLAP functionality as the fat client, with some exceptions: the
HTML export option is basic, limited to BI/Query-generated reports and
graphs only; and charts created in BI/Analyze cannot be exported in HTML
format.
End user functionality via the Web
BI/Web provides full ad hoc query and OLAP analysis capabilities via the
web browser. Similar functionality to the fat-client tool is provided through a
drag-and-drop graphical interface. The ability to create local calculations
and full charting capabilities is also supported. The only major difference is
the inability to set exceptions in reports.
Each web user has a personal workspace (called a Personal Portfolio) that
can be customised in a Java applet file or folder.
All web users must be registered and named users.
BI/Web provides support for advanced users that want to access interactive
reports for ad hoc query and analysis of data, and casual users that simply
want to view ‘canned’ reports.
It is not possible to create new multidimensional models via the Web.
Distributing via the Internet and Web
Reports can be saved in HTML or Java for publishing over the Internet. The
HTML export option is basic and applies only to reports and graphs created
with BI/Query. BI/Analyze cannot export graphics in HTML; instead, it
converts chart formats into generic text tables. Text formatting is not pre-
served.
Distribution allows you to distribute queries, results sets and HyperCubes
via the Internet using web pages, e-mail, FTP and third-party ‘push’ channels.
It is not possible to include URLs in reports.

23
BI/Broker uses Visigenic’s object request broker to load-balance processing
over multiple servers.
Management
Summary
1 2 3 4 5 6 7 8 9 10
The simplicity of the toolset lends itself to easy management. BI/Suite’s
single-server architecture provides a central point of control for web and
client-server environments, and eliminates the burden of managing multi-
server set-ups. The scheduling tools and security scheme are flexible, and help
to manage large user environments. There are limited facilities for governing
queries and monitoring performance, however.
BI/Broker provides several graphical administration interfaces for managing
data, schedules, push channels and end users.
Security of models
Security can be set on database table attributes and rows, and elements of a
HyperCube.
Query monitoring
Simple query statistics can be traced, logged and analysed using the client
tools. There is a facility to log performance metrics based on queries.
Management of data
Data can be stored on the server – BI/Broker or a remote file server – or
locally, on the client. In a client-server configuration, HyperCubes are always
downloaded on to the client machine for processing. In a thin-client architec-
ture, processing occurs on the server.
BI/Analyze can launch BI/Query to load or update data. HyperCubes can be
scheduled in this way to refresh at specified time periods (hourly, daily,
weekly, monthly, annually or custom). BI/broker notifies users of refreshes
by sending an e-mail or a broadcast message, or distributing it via FTP.
HyperCubes can be scheduled to load or update data based on external
events; for example, an update to the data source.

24
Failed loads or updates
BI/Broker monitors all scheduled jobs using tracing and logging utilities.
A notification facility alerts administrators (via e-mail) about particular
events; for example, the completion of a scheduled job, or errors. There are
no automatic rollback or retry facilities.
Data can be stored on the client or across multiple servers.
HyperCubes use a hashing technique to handle sparsity; they do not store
sparse rows.
HyperCubes are subject to size restrictions; the maximum size of a
HyperCube is limited to 64 bits and 20 dimensions. Managing the size and
build time for HyperCubes is achieved by:
• consolidating data on-the-fly – calculating values when data is requested
rather than at build time. CubeCreator supports full and partial
consolidations
• incremental updates – for loading and updating large datasets on the
desktop. It is not possible to incrementally refresh a sliced HyperCube
• compression techniques.
In-memory caching options are not supported.
End users can see when the HyperCube was last edited and/or updated
when opening a report.
Management of users
Writeback to models is not supported.
Object-level security is managed via the Access Control Manager. BI/Broker’s
User and Group Manager interface provides a drag-and-drop interface for
creating and administering security objects. Security profiles can be created
to govern access to HyperCubes.
Once users and groups have been established, system permissions (which
determine what services users can access) and access privileges (which
determine what items in the repository users can see and use) can be
assigned. Permissions can be set on an individual, group or business role
level. The tool supports a flexible system of privileges that can be assigned
on a report-by-report basis. Adding single users and groups is a painless
task; security permissions are inheritable, allowing for easy application to
large user groups, and security information can also be imported from
Windows NT. The tool would benefit, however, from the ability to save these
security profiles and apply them to other users or domains.

25
It is also possible to import users and groups from existing Windows NT
security schemes.
Query governance
Query governance is not supported in BI/Analyze; if a drill operation will
bring a very large return, however, users will be given a warning message.
BI/Query can limit the number of rows that a user can request when con-
structing a query.
There are no mechanisms that restrict queries to a specified time.
The visibility of HyperCubes (dimensions and measures), data sources
(tables, rows and values) and associated metadata can be controlled using
the Access Control Manager tool. Additionally, the user profile determines
what system data and resources end users have access to.
Adaptability
Summary
1 2 3 4 5 6 7 8 9 10
Adaptability in BI/Suite is generally a matter of being able to add new
dimensions and measures to models, although there are no change manage-
ment facilities provided to support this. Editing existing dimensions and
measures, however, is a tedious process; if substantial changes are needed, it
will be quicker in many cases to build them from scratch. Overall, the tool
would benefit from greater integration between the query (BI/Query) and
OLAP (BI/Analyze) environments.
Adding new dimensions to a model is a simple point-and-click process. But
there are no facilities provided to track the changes.
Dimensions can be re-used. It can be a tedious process, however, to change
an existing HyperCube’s dimension definition; it is easier to create a new
HyperCube.
Adding new measures to a model is a simple process, but there are no facili-
ties provided to track the changes.
It is not possible to re-use measure definitions; however, calculated measures
can be used in other calculations within the confines of a single model.

26
BI/Suite supports a desktop architecture for OLAP. There is no scope to
support MOLAP or ROLAP configurations, although BI/Analyze can link
into these environments.
Log files are kept when information goes out of synch, but there is no support
to warn users that the data source and model schema are out of synch.
When a HyperCube is refreshed with data, new members are automatically
updated; however, members are not automatically removed from a
HyperCube.
Metadata
The structural model metadata remains synchronised with the HyperCube
at all times. Descriptive metadata about a model needs to be updated manu-
ally, however, and on a per-model basis.
Impact analysis
Impact analysis is not supported.
A metadata audit trail facility is not supported.
BI/Analyze does not support metadata integration with third-party tools,
although it can take the descriptive metadata names from BI/Query and
implement them in the HyperCube. Hummingbird provides PowerView, a
metadata query and reporting tool that has a prebuilt BI/Query data model
– this provides a simplified view of Informatica’s PowerMart repository
tables.
Summary
1 2 3 4 5 6 7 8 9 10
BI/Broker has a scalable architecture that is enabled through the use of load
balancing. Multiple support servers can easily be added for replicating
application services; however, BI/Analyze is a desktop tool and performance
depends on the size and complexity of HyperCubes loaded on to the client
machine.
ROLAP
BI/Analyze does not support ROLAP operations.

27
MOLAP
Typically, the size of HyperCubes makes performance and size trade-offs a
non-issue. CubeCreator can update cubes by adding only new data, so that
cubes do not have to be completely rebuilt during a fresh data load. The
extent of consolidation at build time can be customised, but there is no
support for recalculating only the affected values.
The addition of a mid-tier application server (BI/Broker) increases the
scalability of BI/Suite. The server supports concurrent and named server
ports to control multi-user access.
Processing
BI/Query provides native SQL access to most of the leading relational
databases; ODBC support is also provided. BI/Analyze does not access
relational data directly.
Processing can be distributed across multiple BI/Broker application servers.
Load balancing is supported by replicating application services across
support BI/Broker servers as needed.
SMP support
The BI/Broker server does not support SMP parallelism.
Customisation
Summary
1 2 3 4 5 6 7 8 9 10
BI/Suite is designed to be used out-of-the-box. The end-user tools offer some
degree of customisation for creating restricted EIS-like interfaces. Apart from
OLE links to third-party applications and tools, there is no support for
custom application development.
Customisation
The BI/Query data model can be configured on a per-user basis to provide
restrictive interfaces for subsequent query and OLAP analysis.
There is support for producing EIS-style reporting interfaces. This is done by
creating EIS data models using BI/Query, which links buttons to predefined
reports.

28
Applications
There is no support for developing web-based analytical applications.
BI/Suite does not support its own development environment.
Integration with third-party development tools is achieved via OLE support
(as both a client and a server).

29
Deployment
Platforms
Client
BI/Query and BI/Analyze run on Windows 95/98/NT workstations. BI/Web
runs on any Java 1.1-capable web browser.
Server
BI/Broker runs on Windows NT and Unix (Sun Solaris, HP-UX and IBM AIX).
Data access
BI/Query provides native support to access data from all major relational
databases, including Oracle, DB2, Microsoft SQL Server, Sybase, Informix,
Ingres, NonStop SQL, RedBrick Warehouse, Teradata and Unidata. It can
also use ODBC to access other relational sources.
BI/Analyze can connect (natively or via OLE DB for OLAP) to third-party
MDDBs and ROLAP servers, including Hyperion Solutions Essbase, IBM
DB2 OLAP Server, Applix TM1, Microsoft SQL Server OLAP Services, SAP
Business Information Warehouse (SAP BW), WhiteLight, SAS, Informix
MetaCube and NCR TeraCubes. It can also access data held in flat files.
Access to ERP applications is via third-party tools (Acta for SAP and Noetix
Views for Oracle).
Standards
BI/Analyze supports Microsoft’s OLE DB for OLAP as a consumer.
BI/Suite does not have any published OLAP benchmarks.
Price structure
Pricing for the Windows NT version of BI/Suite starts at $20,000 for BI/
Broker with core reporting, publishing, scheduling and BI/Web capabilities.
BI/Web’s ad hoc query functionality costs an additional $10,000 and BI/Web
OLAP functionality costs $20,000 for each central BI/Broker. Concurrent and
named user pricing schemes are available for end users: 20 concurrent users
cost $50,000 and named ports are priced at $295 for each user (regardless of
use with fat or thin clients). Standalone versions of BI/Query and BI/Analyze
cost $695 per user. The BI/Query administration tool costs $1,995. Additional
BI/Broker support servers are $4,000 each.
Unix pricing is approximately 50% higher for the server components.

30

Hyperion Solutions –
Hyperion Essbase
Summary
At a glance ............................................................................................. 2
Ovum’s verdict ........................................................................................ 4
Distribution ............................................................................................ 19
Product evaluation
Web support ......................................................................................... 25
Management ........................................................................................ 26
Adaptability ........................................................................................... 29
Customisation ....................................................................................... 31
Deployment
Platforms .............................................................................................. 33
Data access .......................................................................................... 33
Standards ............................................................................................. 33
Price structure ...................................................................................... 33

2
Evaluation: Hyperion Solutions – Hyperion Essbase
At a glance
Developer
Hyperion Solutions, Sunnyvale, California, USA
Versions evaluated
Hyperion Essbase Server version 6.0; Wired for OLAP version 4.1; Hyperion
Integration Server version 1.1; Hyperion Essbase Web Gateway version 2.1;
Hyperion Essbase Objects version 1.1
Key facts
• A multidimensional database server that can be accessed from
spreadsheets, a web browser or a variety of third-party front-end tools
• Server runs on Windows NT, OS/2, Unix and AS/400; clients run on
Windows 95, Windows NT and Macintosh or can use a web browser
• The Essbase MDDB is embedded in more than 60 vertically-oriented
analytical applications developed by Hyperion and its application
partners
Strengths
• Friendly graphical environment for designing and maintaining models
• Provides strong multi-user write-back to the multidimensional database
• Can be accessed by a range of front-end tools, including standard
spreadsheets
Points to watch
• Analysis, presentation and distribution functionality depends entirely on
the front-end tool used
• Presumes a clean and consistent data source – Essbase does not provide
any of its own ETL capabilities
• Questions still remain about the growth and stability of the newly formed
company
Ratings
Web support
Management
Adaptability
Customisation
1 2 3 4 5 6 7 8 9 10

Ovum Evaluates: OLAP Evaluation: Hyperion Solutions – Hyperion Essbase
Application
Any analytical application that runs on Essbase. It consists of a
multidimensional database, rule files for loading data and scripts to calculate
data.
Attributes
Detailed descriptive qualities of dimension members; for example, customer
demographics and product details. Attributes in Essbase look and act like
normal dimensions.
Calc script
A procedural script that calculates the multidimensional database or subsets
of the database.
Data block
A multidimensional array of cells. It is the primary storage unit within the
database and is defined during the initial system build.
Data load rules
These are used to import data into the database, and also to define the
hierarchies and relationships within the dimensions. They are used during
the initial build process and for ongoing systems maintenance.
Database
A physical multidimensional data structure that is stored persistently in the
Essbase Server.
Database outline
Defines the structure of an Essbase multidimensional data model, including
the definition of all hierarchies and other relationships, plus many
calculations. It corresponds to Ovum’s definition of a business model.
Dimensional attributes
Essbase provides attribute information in the form of ‘dimensional
attributes’ that are attached to dimensions in the database outline.
Dimensional attributes behave like dimensions; they have structure, can be
cross-tabulated and calculated dynamically in models.
Partitioning
Divides a database into separate parts that can be loaded and calculated in
parallel on multiple servers.
UDAs
User defined attributes (UDAs) are textual tags attached to dimensions that
are used for filtering data.

4
Ovum’s verdict
What we think
Essbase’s main strength is in providing a consistently fast and easy-to-use
multidimensional database. Its powerful server-based calculation engine
provides a good fit for the logical structure of dimensionally complex business
models.
Essbase scores consistently across most of our evaluation perspectives.
Although many of its features are not unique, users will be hard pressed to
find another OLAP product with a similar range of functionality. The latest
release (version 6.0) builds on Essbase’s hallmark capabilities – namely
performance and modelling simplicity. It excels in its graphical design tools,
which provide a rapid and consistent approach to business modelling without
requiring advanced IS skills. The GUI-based definition of data load rules
simplifies the task of designing highly complex multidimensional models.
Hyperion has worked hard to remove the stigma attached to
multidimensional databases – notably scalability and database explosion.
Version 6.0 extends Essbase’s capabilities to larger dimensional structures
and provides greater agility in supporting attribute-rich data. While
financial planning will remain a ‘bread and butter’ application, these new
features move the product out of its comfortable financial niche to new areas
such as customer-centric analysis. But as Essbase continues to be pushed
into larger and more scalable applications, Hyperion will need to look and act
more like a RDBMS, including the provision of better management facilities
for fault tolerance, rollback and up-time.
Essbase provides a number of prebuilt functions for ad hoc analysis; its
multi-user write-back access is well equipped to support advanced budgeting
and forecasting applications. However, users that require advanced analytic
functionality will have to build it into the server-based model, or integrate
with specialist third-party tools. Essbase supports a choice of third-party
front-end tools, including familiar spreadsheets. However, customers should
choose their front-end tool carefully, because the level of functionality will
vary considerably from tool to tool.
While Essbase has the capability to be used standalone, it is primarily
designed to be used as part of a best-of-breed data warehousing solution. It
therefore presumes a clean data source. Essbase provides rudimentary data
transformation against relational databases, but it relies entirely on third-
party ETL tools for fully-fledged data extraction and scheduling.
Perhaps the greatest concern is the stability of the company following its
merger with Arbor Software. Its problems with disappointing earnings and
stock price has culminated in a number of executive shake-ups. These issues
still need to be fully resolved to convince customers (and shareholders) of
long-term growth. One of the primary missions will be to befriend application
partners that have been alienated by Hyperion’s strategy of being both a
platform for partners’ analytic applications and an application provider itself.

When to use
Essbase is suitable if you:
• need rapid response times for complex multidimensional queries
• have a need for rapid deployment of OLAP to power users across the
enterprise
• are building complex analytical applications that require concurrent
multi-user write access to the database
• have existing spreadsheet skills to exploit.
• do not have a structured data warehouse or other cleansed data sources
• want an end-to-end business intelligence solution
• have highly specialised analytical requirements that require custom
development
• require a flexible approach to OLAP – Essbase is a MOLAP-only solution.

6
Product overview
Components
The main components of a Hyperion Essbase OLAP solution are:
• Essbase Server version 6.0
• Essbase Application Manager version 6.0
• Essbase Spreadsheet Client version 6.0
• Hyperion Wired for OLAP version 4.1
• Hyperion Integration Server version 1.1
• Essbase Web Gateway version 2.1
• Essbase Objects version 1.1.
Figure 1 shows how these components relate to client-server systems.
Hyperion develops a number of packaged analytical applications aimed at
the finance sector. However, the focus of this evaluation is on Essbase – a
multidimensional server designed specifically for OLAP. It consists of a
multidimensional database server and OLAP clients for analysis and
reporting. The server takes data out of back-end data sources (usually a
relational database and other source systems) and organises it in such a way
that it can be easily and quickly queried from a multidimensional
perspective. Essbase assumes that it will work with cleansed data (typically
done when the data is first loaded into the data warehouse).
Essbase Server
This is the central component of Essbase, providing a fast, server-based
OLAP engine and a multidimensional database. It also stores all Essbase
application components, including rules for loading data and scripts to
calculate data.
Figure 1 Essbase component framework
Extract data
from RDBMS
Build model OLAP analysis Web access Application
development
Client Essbase
Application
Manager
EssbaseSpreadsheet
Client
Wired for OLAP
Essbase
Web
Gateway
Essbase Objects
Server Essbase
Integration
Server
Essbase Server

The Essbase Server is responsible for all data loading, OLAP query
processing, calculations and security. Calculations can be precalculated or
done at query time. Essbase databases use patented technology (which
Hyperion calls ‘dynamic dimensionality’) to provide optimisation for the
handling of sparse and dense dimensions in a database for efficient storage
and performance.
A major feature of Essbase is support for concurrent access and update by
multiple users. Essbase provides transparent locking of data to allow multi-
user write access. New attribute handling features also extend its scalability
to cope with models that include large dimensions and deep hierarchies.
Essbase Application Manager
A graphical DBA-type utility that resides in the Essbase Server. It is used for
designing and maintaining models, defining data load rules and
administering security.
Essbase Spreadsheet Client
An end-user tool that provides access to multidimensional models directly
from Excel and Lotus 1-2-3 spreadsheets. The Essbase functions for data
retrieval and OLAP display and analysis are accessible from the spreadsheet
interface.
Hyperion Wired for OLAP
Wired for OLAP is an out-of-the-box tool for constructing queries against
multidimensional models. The tool, acquired from AppSource in 1997, was
designed specifically for Essbase, but can also integrate with OLEDB for
OLAP data providers. It provides a range of OLAP analysis and reporting
functions from a graphical interface. It also includes designer tools for
building EIS-like briefing books applications. Access is available through
either a desktop PC or a Java-enabled web browser.
Hyperion Integration Server
Hyperion Integration Server is an optional server component that integrates
Essbase more closely with a data warehousing strategy. It provides a suite of
graphical tools for creating and deploying Essbase models directly from
relational databases.
Using a metadata layer, Integration Server provides end users with
predefined (and re-usable) dimensions, hierarchies and measures that are
directly mapped to relational data sources. The architecture is not quite
ROLAP, because the relational data returned is still staged in a MDDB
format prior to analysis.
Integration Server consists of two main tools: one for the developer and one
for the end user:
• OLAP Architect is a graphical DBA tool used to map relational tables onto
logical dimensional structures. It organises these mappings into an OLAP
metadata catalogue. The tool works with a variety of database schemata –
from normalised and denormalised OLTP to star and snowflake – and
includes rudimentary data transformation and cleansing facilities

8
• OLAP Builder is a graphical end-user tool that uses the catalogue to
assemble the re-usable structures to create ‘meta outlines’ to define what
data is to be retrieved and built-in to a model.
A third component automatically generates the SQL code, builds the physical
structure for the data and loads it into the Essbase Server in a
multidimensional model.
Essbase Web Gateway
Essbase Web Gateway is a toolkit that provides an HTML interface that
users can employ to construct OLAP queries against the Essbase Server from
a web browser. The Gateway populates HTML templates held on a web
server with Essbase data as users request it. Output is in HTML format, and
as the web user interacts with the data and requests new data, additional
HTML commands are generated to the web server to reconnect to the
Essbase Server and pull out the data.
Essbase Objects
Essbase Objects is a development environment for building OLAP
applications using ActiveX controls. Essbase Objects consists of a family of
controls for data access, visual data display, data navigation, query and
report layout. A number of third-party ‘Essbase-aware’ controls are also
available.
Essbase application modules (optional)
These are add-in tools that extend Essbase’s functionality to include SQL
drill through to transactional data, partitioning, SQL access to relational
data and currency conversion.
Hyperion analytical applications and tools (not evaluated)
Hyperion develops several packaged analytical applications and end user
tools that integrate with Essbase:
• Hyperion Enterprise – a packaged analytical application for financial
consolidation, reporting and analysis
• Hyperion Pillar – a packaged analytical application for budgeting,
financial planning and forecasting. Pillar can integrate with Hyperion
Enterprise – the AutoPilot module provides common process automation
for both products
• Hyperion Spider-man – a web application that extends financial reporting
to the Internet
• Hyperion Reporting – a graphical client-server tool for creating
production-quality management and statutory reports from Hyperion’s
packaged analytical applications
• Hyperion Allocation Manager – for the creation of complex business
allocation models that can be used across a variety of analytic
applications, including those for budgeting and planning, financial
consolidation, and customer and product profitability. It provides a
graphical allocation environment and library allocation method templates

• Hyperion Application Link – a suite of back-end application integration
services for accessing ERP, transaction-processing and packaged
analytical applications. Application Link is based on Oberon’s Prospero
EAI technology, which provides a visual design environment with plug-
and-play components. A Translation Manager module allows users to
define conversion rules that govern the process of mapping source data
elements.
Essbase is typically configured in a full mid-tier OLAP architecture,
consisting of the Essbase Server and clients – typically the spreadsheet
client, Wired for OLAP or other supported front-end tools. In this
architecture, data is loaded into the Server and stored in a MDDB as a
persistent multidimensional business model. The client tools access the
model directly and use the Essbase Server’s OLAP engine to perform all
processing and analysis functions on the model.
There are two variations of the full mid-tier architecture:
• distributed mid-tier, consisting of partitioned multicubes residing on
multiple Essbase Servers
• web-enabled – a ‘thin-client’ implementation using the Essbase Web
Gateway for HTML-based analysis, or Wired for OLAP for a Java-based
approach.
Distributed mid-tier
With the distributed architecture, Essbase models are spread across multiple
Essbase Servers to provide a partitioned multicube architecture. The
partitions divide a model into separate logical and physical parts, but end
users analyse it as a single multidimensional model. The benefits of
partitioning are increased scalability and performance; data loading and
OLAP query processing can be split across multiple servers.
A partition wizard is provided to create three types of partition:
• a linked partition that connects models of varying dimensionality by
defining multiple drill paths between model dimensions. This lets users
drill outside a model to data in another model
• a transparent partition, which integrates multiple model’s cubes into a
single logical model; each partition can either be the entire model or a
portion of it
• a replicated partition, where models can be replicated and refreshed
incrementally.
Essbase automatically synchronises data across all partitions. However,
developers will need to carefully think through and set up additional security
mechanisms for a partitioned model.

10
Web enablement
Web access is managed through the introduction of a web server that
interfaces with the Essbase Server. Users are able to access the Essbase
databases via standard web browsers. There are two options:
• the Essbase Web Gateway uses CGI to link the Essbase Server with a web
server. It utilises HTML to provide interactive analysis and reporting
capabilities
• Wired for OLAP also provides a web interface for constructing ad hoc
queries against the Essbase Server. It is implemented using Java applets.
Wired for OLAP ‘applications’ can be developed via a standard desktop
and deployed for web access. The applications are stored on a mid-tier
server and accessed via the web browser.
Essbase also supports a light mid-tier architecture. Through a relationship
with IBM, the Essbase Server’s multidimensional datastore is replaced by a
relational storage interface for IBM’s DB2 database. This configuration still
uses the OLAP engine for processing, but the model is held in a relational
database in a star schema. The Essbase Server automatically creates and
maintains the schema, and populates it with calculated data.
The product, called IBM DB2 OLAP Server, is marketed and sold as a
separate product by IBM.
Essbase is a server-based tool. There is no support for a two-tier desktop
mode.
Mobile architecture
A Personal Essbase version supports a mobile architecture. It allows users to
store a model, or part of a model, on a client PC for offline analysis.
Synchronisation of data and structures between the Essbase Server and
remote clients is supported upon reconnection.
Using Essbase
Essbase supports a set of highly graphical tools for designing and using
multidimensional models. The modelling tools are aimed at power users, and
there is no clear division of responsibilities between model designer and end
user. However, model designers are expected to have some level of DBA-type
skills and a good understanding of the business to use the tools effectively.
The end user can also be the power user/business analyst, or simply an
information consumer. The latter group requires no knowledge of the
database architecture, just an understanding of the business model.
Typically, they access the results of the work done by the model designers.

Figure 2 Outline Editor
Creating a model
The Application Manager is the main interface for designing models. Within
Application Manager, the four principal model building tasks of model
definition, calculation, data loading and reporting are clearly defined as
separate functions with appropriate graphical user interfaces for each.
Define the database outline
The database outline determines the structure of the business model. This
outline is created using the Outline Editor, which provides a graphical
representation of the dimension hierarchy; each dimension and consolidated
level in the model is represented in Figure 2.
As dimensions are added to the structure, it is assumed (by default) that they
will be aggregated according to the hierarchy, but this can be easily changed
via a point-and-click interface. While some dimensional hierarchies are
typically created manually, larger ones are invariably loaded directly from

12
existing systems by importing data and then mapping this to the required
model. If the underlying data sources subsequently change, this process can
be re-run to ensure that the models are always synchronised with the data
source.
Add calculated measures
Calculations are usually defined once, and built directly into the server-based
model. Calculations can easily be applied to any level within a dimension in
the database outline by typing them into the outline or via point-and-click
using a calculator-style interface called the Formula Editor. Essbase provides
a set of mathematical functions and cross-dimensional operators for
constructing calculation formulas.
Multiple formulas and actions can also be placed in calc scripts for advanced
calculations that require a more procedural approach or where multiple
iterations through the data are required. A Calc Script Editor provides a text
editing panel, customised menus, a syntax checker and function, and macro
templates for a point-and-click development environment.
Load in the data
Specifying data load rules is the easiest and quickest way to load data into
the model. The Data Prep Editor provides a graphical way of defining these
rules. Data load rules are sets of operations that Essbase performs on data
from an external data source file as it is loaded or copied into the Essbase
model. They support simple transformations for the mapping of raw data into
the multidimensional model.
Using Hyperion Integration Server
Hyperion Integration Server provides a graphical suite of tools for creating
and deploying Essbase models directly from back-end relational data sources.
Significantly, it pushes Essbase closer to a data warehouse strategy and
increases the ability to quickly build database outlines in an ad hoc manner.
A central metadata ‘catalogue’ of common dimensions, hierarchies, structures
and business rules is created and managed by DBA-type users using the
graphical OLAP Architect tool.
End users are then able to reference this catalogue directly and select the
required components using drag-and-drop to build and populate a model ‘on-
demand’ using the OLAP Builder.
Support for attribute analysis
Scalability in terms of handling large dimensions has always been the
‘Achilles heel’ of multidimensional databases. Version 6.0 of Essbase directly
addresses this limitation by providing support for analysing dimension
attributes.
Previous versions of Essbase had relied on the use of user defined attributes
(UDAs) – effectively textual tags that are used to filter dimensions – which
were unwieldy and limited in usage. Attributes are now attached to
dimensions in the database outline and appear and behave like other
dimensions; that is they can have a structure (for example, a stacked ‘age’
attribute), can be cross-tabulated and can be calculated dynamically in
models.
Support for attribute handling is significant because it opens up new classes
of analytical applications that were previously closed to multidimensional
tools – largely due to scalability issues. These include the analysis of a large
product and customer mix that are commonplace in customer relationship
management (CRM) and sales & marketing applications.

Exploring models
Users can access the model via the Essbase spreadsheet client or Wired for
OLAP client; a variety of other third-party front-end tools can also be used.
Navigation around the model is governed by the hierarchies and structures
built-in to the multidimensional model, though the actual method of
navigation varies according to the chosen front-end tool.
At its simplest, the spreadsheet user logs-on and connects to the appropriate
database and double-clicks on any cell to begin a query. The required level
within a dimension can be found by either drilling-down, by typing in the
name of the dimension level, or by using a select option that opens up the
database outline for searching. Data is presented in a standard spreadsheet
from which drill-down and slice-and-dice functions are directly available.
This is shown in Figure 3.
The Spreadsheet Client uses the native Excel or Lotus 1-2-3 environment for
further analysis of data. A query wizard is also provided to help with the
entire process. Alternatively, the Query Designer tool provides a graphical
drag-and-drop method for selecting dimension members and filtering data.
Figure 3 Spreadsheet Client

14
Wired for OLAP offers a similar range of query tools, but in a more
graphically-oriented fashion. Reports can be specified and additional
calculations and sorting methods defined from within the interface. The
designer tools provided by Wired for OLAP also support the presentation of
data in briefing book-type applications and other EIS-style interfaces. The
same capabilities are available on the desktop and via the Web.
The Web Gateway can be used to access the Essbase database from a web
browser as HTML pages. It provides full OLAP functionality such as drill-
down and slice-and-dice, as shown in Figure 4. Write access is also supported
via the web client.
Figure 4 Web Gateway

Future enhancements
Version 6.5
The next release of Essbase Server (version 6.5) is planned for the end of
2000. Version 6.5 will focus on enhanced parallel processing capabilities for
the server, specifically for data loading, OLAP queries and OLAP
calculations.
Version 7.0
Version 7.0 (due at the end of 2001) will support a hybrid OLAP architecture,
allowing data to be stored and accessed from either multidimensional or
relational sources. This will allow metadata (essentially the Essbase Outline)
and data to be wholly, or partially, stored in relational database tables. Other
RDBMS-like facilities will also be provided, including security, rollback and
recovery, and fault tolerance. Essbase administrators will be able to mix and
match data storage options between the MDDB and relational tables for
optimal performance. Version 7.0 will also deliver a Java version of Essbase
Application Manager.
Platforms
A Linux version of Essbase is planned for the second quarter of 2000;
Hyperion is a Red Hat Software development partner. Hyperion is also
working closely with IBM to develop an OS/390 MVS mainframe port for
Essbase, which is scheduled for general availability in the first quarter of
2000.
Consolidation
Integration between Hyperion’s tools is also planned on a number of fronts.
Initial efforts will focus on rationalising the various client-server and web
front-end tools to provide a more holistic product offering. For example:
• a new client, called Hyperion Analytic Reporter will replace Hyperion
Reporting and Spider-Man
• a new spreadsheet add-in will combine the existing Essbase and
Enterprise ones (Analyst & Retrieve)
• version 5.0 of Wired for OLAP will also replace the Essbase Web Gateway
• the capabilities of Integration Server will be merged into the Essbase
Application Manager component.
Web-enablement
Hyperion Information Portal is currently under development, and will
provide a single web-based interface to access personalised reports and
underlying business intelligence systems. Hyperion also plans to deliver a
Java version of Essbase Application Manager.
Analytic applications
Hyperion will focus on the provision of ‘Essbase-powered’ analytical
applications for its suite of Enterprise Performance Measurement (EPM)
analytical applications. These will include:
• Hyperion Consolidation – for financial consolidation and management
reporting
• Hyperion Planning – for financial budgeting and planning

16
• Hyperion Profitability – for profitability and allocations management
• CRM and e-business – specifically targeting clickstream analysis, e-
commerce sales and web marketing planning.
The new applications are expected to be released throughout 2000.

Company background
Hyperion Solutions Corporation was formed in August 1998 when Arbor
Software and Hyperion Software announced the completion of their merger.
Arbor and Hyperion were both public companies, but the similarity ended
there; Arbor developed a client-server multidimensional database, whereas
Hyperion focused on analytical applications.
Arbor, founded in 1991, was the first vendor to challenge the relational model
for databases when it introduced Essbase in 1993. Its early start is reflected
in Essbase’s general maturity and wide recognition in the OLAP market.
Hyperion, founded as IMRS International, was a more established and larger
company. After an earlier foray into the world of financial software, it
refocused its activities on providing analytical applications for financial
reporting and consolidation, and enterprise budgeting.
In May 1999, Hyperion acquired Sapling Corporation and inherited two new
analytical applications: Hyperion Performance Measurement (previously
Sapling’s NetScore) and Hyperion Activity Based Management (Sapling’s
NetProfit). It also acquired KPI Technologies in November 1999 to provide a
new CRM application.
Revenues for fiscal 1999 grew a modest 13% to $425 million. Net income for
the same year was $8 million. Approximately 50% of revenue is generated
from Essbase licences. Hyperion has its headquarters in Sunnyvale,
California (the old Arbor Software headquarters), and employs more than
2,300 people in 26 countries.
The merger between Arbor Software and Hyperion Software seemed logical.
Both were both strong, aggressive companies with large customer bases and
powerful marketing. Both had established considerable mindshare in the
OLAP and analytical applications markets respectively. However, fitting
together two equals has proved difficult – the departure of key executives
and concerns from Arbor shareholders are testament to the initial teething
problems that have been encountered.
Hyperion seems to have ridden this storm, and is now concentrating its
efforts on product development and consolidation, and increasing market
share. It has more than 6,000 customers worldwide. Essbase accounts for
nearly half of these customers, with around 850 sites in the EMEA region.
The merger has also resulted in a more balanced sales model; Hyperion plans
to sell its financial analysis products via Arbor’s historic VAR channel, while
Arbor gains direct access to Hyperion’s blue-chip customer base.
On a product front, the merger has presented a number of opportunities for
product synchronisation. The Essbase Server Engine is now firmly
established as the back-end OLAP server for all the company’s current and
future financial analytical applications – Hyperion Software previously had
an OEM agreement with Applix’s TM1 for OLAP support and a full
migration programme is in place. Partnerships are key to Hyperion’ strategy.
Its partner model includes:
• analytic application partners that resell or integrate Essbase and other
Hyperion products within their application suites. Examples include:
Comshare, PeopleSoft, Prism, Accrue, Lawson Software, i2 and Paragren

18
• OEM partners, such as IBM and ShowCase, that offer operating system
and relational database solutions for Essbase
• data integration (ETL) partners that either publish data to Integration
Server or produce multidimensional cubes directly. Hyperion has alliances
with leading ETL vendors, such as Acta, Informatica, Sagent, Ardent/
Informix and Constellar
• consulting partners, for complementary systems integration,
implementation and business consulting services.
A key partner for Essbase is IBM, which develops a specialised version of
Essbase, called IBM DB2 OLAP Server, aimed at DB2 customers. The
product is bundled into IBM’s Warehouse Center data warehousing offering.
The combination of application partners and the new capabilities in Essbase
6.0 open up new markets for Hyperion. Financial applications remain a core
focus, but the company claims that approximately 35% of new applications
are being developed for sales and marketing applications. Hyperion has
already developed (in conjunction with its application partners) 12 CRM-
based applications – focused on analysing customer and product mix. It also
acquired KPI Technologies and has established partnerships with many of
the leading CRM vendors in order to bolster its presence in this market.
Hyperion has also set up an e-Business division to develop three analytical
applications for e-business data:
• e-Marketing Analysis – to gauge the effectiveness of web marketing
campaigns
• Web site Analysis – to analyse clickstream data for visitor behaviour
• e-Commerce Analysis – to access purchasing and other transactions.
Hyperion has also entered into technology and marketing partnerships with
companies that specialise in website monitoring, analysis and advertising
including DataSage, DoubleClick, NetGravity and net.Genesis.
Customer support
Support
247 hot-line telephone support as well as on-site support arrangements are
available worldwide. The support operations of Hyperion and Arbor will be
merged. Support is included along with software updates for an annual
maintenance fee of 18%.
Training
Three-day introductory training courses on Essbase are available for power
users. A two-day course is also provided for systems administrators. Casual
end users do not require much training beyond a familiarisation with the
business model.
Hyperion has a large consultancy organisation, consisting of 350 consultants
worldwide. However, it offers a limited range of consultancy services for
Essbase, and no significant revenues are generated from this.

Hyperion also has formal relationships with many of the global management
consultancies and systems integrators (such as EDS, Shell Services and
Perot).
Distribution
North America
Hyperion Solutions Corporation
1344 Crossman Avenue
Sunnyvale
CA 94089
USA
Tel: +1 408 744 9500
Fax: +1 408 744 0400
Europe
Hyperion Solutions UK
Arbor House
Old Bracknell Lane West
Bracknell
RG12 7DD
UK
Tel: +44 1344 664000
Fax: +44 1344 664001
http://www.hyperion.com
E-mail: info@hyperion.com

20
Product evaluation
Summary
The range of end-user analysis, presentation and reporting functions available
depends largely on the front-end tool used – there are more than 50 tools to
choose from. The Essbase spreadsheet interface can be used out-of-the-box,
and is ideal for financial analysts. However, it does not offer the same
flexibility or sophisticated reporting capabilities as other OLAP front-end
tools. Wired for OLAP provides a more graphical presentation interface for
OLAP analysis and reporting. There is no direct support for sharing and
distributing reports; this is provided by integrating with third-party tools,
such as Seagate Info.
Essbase presents the models that users can access in a standard directory
structure. There are no search facilities for finding models based on a
dimension name or other keywords.
Structural information about a model is provided in the database outline.
Descriptive metadata about a model (such as ownership and authorship) can
be input using the database outline comment window. These comments can
be selectively viewed by end users.
Descriptive information can be assigned to each dimension level within a
model, either as a comment or via use of an alias or an alternative reporting
label. Detailed information can be assigned to cells in the model by the
linking in of textual, graphical, video or audio data using the Linked
Reporting Objects facility.
Using the model
Using the spreadsheet client, an additional pull-down menu appears in the
interface through which Essbase data retrieval and control functions
(including write-back) are accessed. Drill-down, pivoting and moving
dimensions can be achieved easily using point-and-click operations. Wired for
OLAP offers a more graphical interface for analysis. A range of analysis and
presentation options are available through point-and-click, including
enhanced navigational aids such as traffic-lighting.
Values within a dimension can be moved via drag-and-drop.
1 2 3 4 5 6 7 8 9 10

Essbase’s Outline Editor provides a graphical view of the model as a
hierarchical tree structure. The tree view is also available through the
Spreadsheet Client’s ‘member select’ option and via the Wired for OLAP
‘navigator’ facility.
A SQL drill-through option allows users to drill-down to the underlying
detail data directly from the client interface. Integration Server enhances
this capability by providing a direct mapping between the relational and
multidimensional data.
Essbase can integrate with more than 50 Essbase Ready client-server and
web tools via the Essbase API, including OLAP query and reporting clients,
statistical analysis data mining tools and custom-built analytical front-
ends. However, not all the Essbase Server functionality is available through
all the tool interfaces. Examples of such partner tools that connect to Essbase
include Business Objects, Brio Enterprise, Cognos PowerPlay, CorVu,
Hummingbird BI/Suite, Seagate Analysis/Info, SPSS, AlphaBlox, Painted
Word and TrackObjects. A definitive list is provided at http://
www.hyperion.com/alliances.cfm
The visualisation of results depends on the front-end tool employed. The
Spreadsheet Client relies on the business charting and graphing conventions
provided by Excel and Lotus 1-2-3. Wired for OLAP users can have a mixed
reporting environment with a complete suite of tabular, textual and
graphical components within the reporting application.
Essbase provides links to a GIS tool from Environmental Research Institute
(ERI) for mapping capabilities.
Designing a report
Data is presented to the end user through the spreadsheet or other front-end
tools. Thereafter, the full range of product capabilities is available to the end
user. The ‘Linked Reporting’ option can be used to add information to
spreadsheets, including OLE, text, graphics, video and image objects. Data
from multiple models can be displayed on spreadsheet reports.
Publishing a report
The Spreadsheet Client does not provide support to schedule publication of
reports to individuals or groups of users. Third-party tools need to be used to
support this functionality.
There are no additional facilities provided for end users to e-mail
multidimensional tables from within the Spreadsheet Client. Wired for
OLAP does allow users to e-mail static reports. Distribution lists can be
maintained, but cannot be dynamically defined.
Essbase does not support any report subscription services.

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Summary
Essbase provides a very intuitive interface for business modelling – thereby
obviating the need for specialist DBA skills. The graphical representation of
the dimensional hierarchy in the database outline view works very well, and
tools are provided to make the definition of measures and data load rules easy.
The structure of calculations and modelling simplicity of Essbase is especially
suited to financial modelling (which usually includes profit and loss
dimension) and it provides a good overview of calculations and aggregations.
However, building complex functionality into models will often require a
rules-based scripting approach that can be quite intensive. Integration Server
provides a more drag-and-drop approach for general ad hoc business
modelling.
Essbase models can be shared to support multi-designer environments, though
there is no direct support for versioning control.
Basic design
Design interface
Business models are defined and maintained in database outlines using the
graphical and highly intuitive Outline Editor. Users (with appropriate
security rights) can easily drag-and-drop dimensions, and specify
relationships between dimensions from this interface.
The Data Prep Editor and the Integration Server allow model designers to
view the file/table layout of source data held in relational databases and text
files. A sample of the data can also be viewed through a simple graphical
interface.
Integration Server’s metadata ‘catalogue’ provides a mapping layer to access
data stored in a relational database. The mapping layer is universally
available to end users.
Designers are prompted to include names for dimensions when creating the
database outline. They are also given the option to create additional model
metadata about the status of a model, but are not explicitly prompted to do
so.
Columns in a source table can be selected for building a dimension via point-
and-click. Dimension hierarchies can also be built from source SQL files.
1 2 3 4 5 6 7 8 9 10

Essbase usually builds dimensional structures by reading data from ASCII
files or by direct access to relational tables – in the latter instance, the
product generates the SQL necessary to extract the chosen field and requires
no knowledge of SQL by the user. Essbase also supports a point-and-click
additive and replacement policy for selecting members.
Essbase prompts users to define dimension hierarchies graphically using
point-and-click. Unbalanced or asymmetrical hierarchies are supported.
Time dimension
The time dimension is built in the same way as any other dimension, with
aggregations built up from the base level (day, week, month and so on).
Essbase supports eight automatic time series aggregations, such as year to
date, week to date or season to date, which are calculated dynamically.
Explanatory notes can be added for any dimension level. For reporting
purposes, a number of aliases can also be added to each dimension and
selected according to reporting needs. Aliases can also be used to add
information for each dimension level (for example, as short and long names).
When a model is built, it can have a default set of dimension levels pre-
selected to minimise the amount of user navigation.
Calculated measures
Essbase models are designed to understand common business rules and
calculation logic – such as income versus expense accounts for variance
analysis. Calculations can easily be applied to any dimension in the database
outline by typing them into the outline or by using the Formula Editor. A
point-and-click approach is available for selection from a range of
mathematical, conditional, Boolean and cross-dimensional operators. For
more complex calculations, server-based calc scripts can be used to define
complex formulas using an integrated script editor.
Multiple measures can be stored with a set of dimensions. The measures can
also be arranged in a hierarchy. Non-aggregated hierarchies can also be
specified.
Multiple designers
Multiple designers
Database outlines can be shared; the outlines are locked when being edited.
However, there are no check-out/check-in facilities.
There is no support for versioning control.

24
Summary
Generally, analytical functionality is built-in to Essbase’s server model during
design. The main advantage of this approach is to ensure a consistent
analytical environment. But it requires users to build their own analytical
functions from scratch, using Essbase’s rules-based scripting language.
However, version 6.0 of Essbase provides new out-of-the-box functions that can
be used for standard financial analysis, forecasting and trending. Essbase
also supports multi-user write back for ‘what-if’ analysis and budgeting
applications. Its analytical capability can be expanded by integrating with
specialist analysis tools.
Essbase integrates with spreadsheets and works with a range of third-party
statistical analysis (SPSS), datamining (IBM and Data Mind) and data
visualisation tools.
Ranking and sorting
Simple ranking and sorting of data can be achieved by both the Spreadsheet
Client and Wired for OLAP. Essbase provides a special rank function.
Essbase supports standard mathematical functions. These functions can be
enhanced using calc scripts.
Financial functions
Essbase provides a standard set of financial (consolidation) functions,
including standard options for allocations, asset depreciation, compound
interest calculations and discount.
A Currency Conversion module is also provided to enable users to translate,
analyse and report on foreign currency data (including the euro).
Essbase can also integrate Hyperion Allocation Manager – for the
assignment of revenues and indirect costs (an allocation model). Pre-
packaged, re-usable allocation method templates serve as calculation
building blocks that can be combined to define revenue allocation processes.
Allocation process information is stored in an embeddable Microsoft Access
repository or Oracle8 RDBMS repository for shared, enterprise-wide access.
Statistical models
Essbase supports a range of statistical functions, such as median, mode,
correlations and standard deviation. Essbase also links to SPSS to support
advanced statistical models.
Trend analysis
Functions are available for the definition of time-related calculations and
trending. Single, double and triple exponential smoothing functions are also
provided.
1 2 3 4 5 6 7 8 9 10

Simple regression
Essbase relies on the Spreadsheet Client to provide regression analysis.
There is no support for time series forecasting methods.
Calc scripts can be defined to provide server-based analytical functions. Calc
scripts enable users to define complex formulas using procedural logic. More
than 200 server-based functions are supported by the scripting language.
Write back for ‘what if?’ analysis
Essbase supports concurrent multi-user write-back to the database for ‘what
if?’ analysis.
Essbase supports the ability to associate additional text and date
information, or ‘attributes’ with the core numeric data elements in models.
Essbase supports the ability to associate additional text, numeric, Boolean
and date information or ‘attributes’ with the core dimensional members.
Such examples define attributes (such as colour, flavour and size) or
characteristics to a dimension. Essbase provides attribute information in the
form of ‘dimensional attributes’ and user-defined attributes (UDAs).
Dimensional attributes behave like dimensions, have structure and can be
cross-tabulated and filtered. UDAs are a much simpler form of dimensional
attributes in that they are only used for filtering and can only be text.
Datamining
Essbase does not provide any support for datamining.
Web support
Summary
Essbase’s server-based architecture easily lends itself to web deployment and
distributed OLAP applications. The Essbase Web Gateway provides the
standard interactive analysis and HTML web publishing capabilities
expected from an HTML-based implementation. Wired for OLAP uses Java
applets for a more interactive experience, including reporting and charting
options. Both clients offer write-back capabilities from the web browser.
However, Essbase’s web capabilities have not been designed for data
modelling and there is little support for utilising the Internet for dynamic
distribution of reports.
1 2 3 4 5 6 7 8 9 10

26
Essbase models can be interactively explored with full drill-down, pivot and
slice-and-dice access to models. Web users can also write-back to the
database and drill-down detail data stored in relational databases. The only
restriction is that users cannot add new dimensions or calculations to models.
All web users must be pre-registered and require a valid ID and log-on.
Web users can be divided into those who need direct access to the database
for interactive navigation and analysis, and those who will use the Web
simply as a means of accessing ‘canned’ reports. For example, Wired for
OLAP’s Analyzer Web Viewer Edition is designed specifically to support the
simple reporting needs of casual information consumers.
There are no facilities for development over the Web.
It is not possible to create new models or edit existing models via the Web.
There is no point-and-click support for saving model results in HTML format
using the Spreadsheet Client. The Web Gateway can be used to define HTML
pages.
The Web Gateway can be used to distribute static reports via the Internet as
e-mails. However, there is no support for dynamically creating address lists.
Wired for OLAP lets end users embed multiple URLs in reports.
There is no integration with third-party middleware for distributed
processing.
Management
Summary
Essbase provides facilities for deploying multidimensional databases to end
users. It provides strong security facilities for both models and users, and the
tool’s sparse data handling capabilities and intelligent calculation options
facilitate efficient data storage and retrieval. But data loading schedules are
1 2 3 4 5 6 7 8 9 10

only supported through a scripting language; there is no point-and-click
support. Event-driven scheduling is not supported. Essbase does not provide
facilities for query monitoring or governance – though this is less of an issue
for optimised multidimensional databases.
Application Manager serves as Essbase’s main management interface. All
key administrative functions, including model building, data loading and
security access, are managed through pull-down menus and toolbars.
Security of models
The integrity of models is controlled through a combination of:
• user security profiles; individuals or groups of users are granted or denied
the ability to view, change or create a model
• a multi-layered approach for intra-model security; a filter layer defines
read/write access levels for dimension levels (down to cell level).
Query monitoring
Essbase does not provide any query monitoring facilities.
Management of data
The Essbase Server stores multidimensional data persistently. The data is
refreshed periodically from back-end data sources – there is no caching of
data on the server or the client. If application partitioning is used, a database
may either be stored across multiple servers or be divided into a number of
sub-models (or partitions).
The scheduling of data loads and updates is handled either through a batch
control facility supported by the Essbase scripting language or by using
third-party tools such as Seagate Info.
There is no support for event-driven scheduling.
Essbase informs administrators of complete, partial and failed loads. It
generates an error log file and provides a detailed list of records that did not
load.
The partitioning facilities in Essbase allow multidimensional models to be
designed in a variety of ways and stored across separate servers. A single
model may be partitioned across Essbase Servers, with a ‘virtual’ model for
central consolidation. Cross-model calculations are supported via the use of
location aliases – effectively a ‘join’ between models.

28
Essbase uses two types of internal structures to store and access data: data
blocks and the index system. Combinations of dense data are physically
stored in data blocks, with the combinations of sparse dimensions held within
the index system as a series of pointers. Data is only ever held for
combinations that have data values. Essbase provides guidance as to which
dimensions should be sparse and which should be dense.
Essbase’s dynamic calculation facility enables administrators to define that
certain summarised or derived data values in a database are to be calculated
only when a user requests it. Administrators can also specify whether or not
to store the result of the user-driven calculation in the database.
In-memory caching options are supported which allow an administrator to
define the amount of memory used to cache queries and dimensional
members in memory. These are done by cache settings in the Essbase
Application Manager. However, it does not provide any self-tuning or wizard-
style interfaces to define settings.
There are no facilities for alerting users when a model was last refreshed
with data. But planned integration with metadata from other tools (via the
Integration Server) will give far greater information as to data genealogy.
Management of users
Essbase employs a data block locking scheme for handling multiple users
writing back to the database. It issues exclusive write locks for data blocks
when they are being updated; other users are able to access the data blocks
in a read-only mode.
Users with ‘supervisor’ privileges have full access to all system components
and functions. Four other levels of security rights for model access can be
defined for individuals or groups of users. These are read, write, calculation
and database designer.
Query governance
Generally, there is no concept of query governance within Essbase. However,
dynamic attribute calculations can be restricted by user security.
There is no support for restricting queries to a particular time of day.
Essbase’s overall security mechanisms govern the visibility of the metadata
road map. Users are only able to see the metadata and data they have been
granted access to. Access to parts of the metadata catalogue in Integration
Server can similarly be restricted. Other than this, there are no facilities to
hide or show metadata selectively.

Adaptability
Summary
Essbase models can adapt to change, but there is limited support for the
management of change. Users can take advantage of the drag-and-drop
method for adding new dimensions and measures in models. Changes in
underlying data sources can be automatically uploaded to the
multidimensional database as part of a standard batch update process. There
are no facilities for ensuring that metadata remains synchronised with
changes to models and/or data sources. Essbase does not provide any
facilities for impact analysis and there is no direct integration with upstream
metadata.
Adding a new dimension to an Essbase database outline model is a
straightforward point-and-click task. Through the Integration Server, new
dimensions can easily be added to existing models by accessing the
predefined metadata catalogue.
Each dimension can be cut and pasted from one model to another. The
Integration Server also ensures that a permanent ‘catalogue’ of dimension
definitions are available to users via a drag-and-drop interface.
Adding a new measure to a model is straightforward and can be achieved
using a point-and-click interface. The process is not supported by change
management or versioning controls to track the additions or edits.
Re-use of a calculated measure definition
It is not possible to store and recall calculated measure definitions in a
library for later re-use across models. But calculations or rules can be stored
and re-used in the metadata catalogue supported by the Integration Server.
Through the partnership with IBM, Hyperion offers users the ability to store
data in a relational form. These users benefit from exactly the same
multidimensional functions and features of Essbase.
Essbase data is stored persistently in a MDDB. As such, there is no
possibility of the data source and the model schema getting out of synch.
If new members are added to dimensions these will automatically be picked
up by the front-end tools when a user navigates through a model. If
dimensions change, Essbase will track the changes to OLAP metadata and
automatically synchronise applications (even across multiple partitions).
1 2 3 4 5 6 7 8 9 10

30
Metadata
Metadata comments and descriptions added to the database outline are not
automatically synchronised.
Impact analysis
Essbase does not support impact analysis.
Essbase does not provide an audit trail facility for end users.
Essbase cannot access metadata in third-party tools – although it does have a
preference for Acta for ERP data integration. However, as part of the
Integration Server development programme, a number of partnerships have
been announced with vendors in the ETL arena. This will give Essbase the
capability to access the metadata of these products and the underlying data
for rapid model development and increased adaptability.
Summary
One of Essbase’s strengths is its fast multidimensional database engine.
Therefore, query performance is taken for granted. For data loading,
performance can be tuned by specifying incremental data loads and dynamic
calculations at runtime. Large-scale user and application concurrency is
supported by the partitioning capabilities, SMP and a new memory-based
data cache. Administrators can optimise calculation strategies using one of
two options: precalculation or dynamic calculation. Version 6.0 improves
Essbase’s ability to handle attribute-rich data. However, questions still remain
over its performance with very large, and highly granular datasets typical of
e-business and customer data.
ROLAP
Essbase is a MOLAP-oriented product. Although Integration Server executes
ROLAP-like SQL queries directly to relational databases, the returned data
is staged in a prebuilt multidimensional model prior to analysis.
MOLAP
An Essbase model can have a mixture of precalculated and dynamically
calculated variables to avoid database explosion. Essbase can load updates
incrementally and subsequently calculate only those parts of the database
that are affected by the changes. Parallel loading and recalculation of
partitions also improve load performance.
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Version 6.0 of Essbase also provides a new memory-based data cache for
increased performance. This provides the ability to load the index file
(dimension member combinations look-up for data blocks) in memory and set
user retrieval buffers; when a user queries a block of data it goes straight
into memory. Any dimension structure change will trigger a restructure that
is done in memory.
The data block architecture of Essbase allows multiple users to concurrently
read from, or write to, the multidimensional database. Since each query is
usually only ever accessing a tiny portion of the database, and will only be
moving a very small amount of data to the client, there is unlikely to be any
bottleneck in either the CPU or network access. This enables Essbase
applications to potentially support thousands of concurrent users.
Processing
Data access is via ODBC drivers only.
Essbase’s Application Partitioning function enables developers to
simultaneously load and calculate Essbase models across several Essbase
Servers (or multiple processes in a single server).
SMP support
Essbase Server is based on 32-bit multi-threaded software that takes full
advantage of SMP parallelism.
Customisation
Summary
Application development is provided by a set of ActiveX controls and via
Essbase’s published API. The controls can be assembled to build custom EIS-
type applications. They can also be integrated with third-party development
tools. The Essbase API is functionally rich, and is extensively used by third-
party tool vendors and VARs to integrate with Essbase Server. A toolkit is also
provided to build custom OLAP capabilities directly into the Essbase
Spreadsheet Client.
Customisation
The Essbase Spreadsheet Client relies on the customisation features of Excel
to provide restricted views. Wired for OLAP provides greater scope for
customisation.
1 2 3 4 5 6 7 8 9 10

32
Essbase Objects is a suite of ‘OLAP-aware’ ActiveX controls, and can be used
to develop EIS-type reporting interfaces. Wired for OLAP offers a simple
Designer tool for producing EIS reporting capabilities.
Additionally, application designers can use the Essbase Spreadsheet toolkit
to build ‘push button’ capabilities that support EIS functionality.
Applications
The Essbase Web Gateway can integrate with standard web authoring tools
to produce HTML applications. The Gateway can also integrate Java and
ActiveX components and generates dynamic HTML form controls that can
interact with standard JavaScript and VBScript to add greater functionality
to web applications. Essbase Objects can also be used to produce ActiveX
applications for web deployment.
Essbase does not provide its own visual development environment. Essbase
Objects can link into graphical development languages such as Visual Basic.
Developers (or VARs) can assemble the ActiveX controls to build EIS-style
interfaces to Essbase. A number of third-party ‘Essbase-aware’ controls are
also provided by partners such as SPSS and John Galt Solutions.
Development tools (including Visual Basic, Microsoft Visual C++, Delphi and
PowerBuilder) can be used to integrate with Essbase Objects or the
spreadsheet interface.
Essbase API
The Essbase API provides a comprehensive library of more than 300 Essbase
Server functions that can be accessed by developers to build OLAP
applications. The API offers a full range of database access functions
including model building, calculation, navigation and write-back capabilities.
It is fully documented and also available in HTML format.
The Essbase API includes a special OLAP-aware API, called the ‘grid API’,
that provides all the functions of the Essbase Spreadsheet Client –
developers simply populate a spreadsheet array with the desired data layout
and specify an OLAP operation. The API supports popular programming
languages including C, C++ and Visual Basic.
Essbase Spreadsheet Toolkit
The Toolkit provides a library of 30 predefined Essbase-specific spreadsheet
macros and 50 VBA functions to incorporate custom OLAP navigation and
analysis features into Microsoft Excel or Lotus 1-2-3 applications.
Additionally, all the Essbase spreadsheet add-in features can be managed
programmatically.
Localisation
Localised Essbase versions are available in English, French, German and
multibyte Kanji languages.

Deployment
Platforms
Client
Essbase Spreadsheet Client (Excel and Lotus 1-2-3) and Wired for OLAP run
on Windows 95, Windows NT and web browsers.
Personal Essbase and Hyperion Integration Server clients run on Windows
95.
Server
Essbase Server runs on Windows NT (Intel and Compaq Alpha), OS/2 and
Unix (HP-UX, RS6000/AIX and Solaris). Showcase Corporation has also
ported Essbase to the AS/400 environment.
Hyperion Integration Server runs on Windows NT and Unix (HP-UX and
Solaris).
Data access
Essbase can access data from the major relational databases (Oracle,
Informix, Sybase, IBM DB2 and Microsoft SQL Server) and any other ODBC-
compliant database. It can also access data in text files and spreadsheets.
Hyperion Application Link can be used to integrate with third-party business
applications. Hyperion currently has or is developing certified links to all the
major ERP and CRM transaction systems, including SAP, Oracle, Lawson,
JD Edwards and Siebel.
Standards
Essbase has a published API that has been adopted by more than 300 third-
party application, service and tools partners to provide integration with
Essbase.
Wired for OLAP supports OLEDB for OLAP as a consumer.
Hyperion published figures for the OLAP Council’s APB-1 OLAP benchmark.
Essbase 6.0 performance figures for the APB-2 benchmark is also planned.
Price structure
Essbase Server is priced at $60,000 for a ten concurrent user licence.
Integration Server is priced at $20,000 per Essbase Server.
Essbase Objects and Essbase Web Gateway are both priced at $10,000 per
Essbase Server, with an unlimited developer/user licence. Wired for OLAP
clients cost $600 per seat for Windows and from $100 per seat for Java. Other
Essbase tools and modules are licensed separately.

34

DecisionSuite
Summary
At a glance .............................................................................................. 2
Ovum’s verdict......................................................................................... 4
Distribution ............................................................................................ 14
Product evaluation
Web support .......................................................................................... 20
Management ......................................................................................... 21
Adaptability ............................................................................................ 24
Customisation ....................................................................................... 26
Deployment
Platforms ............................................................................................... 28
Data access .......................................................................................... 28
Standards .............................................................................................. 28
Price structure ....................................................................................... 28

At a glance
Developer
Information Advantage, Eden Prairie, Minnesota, USA
Versions evaluated
DecisionSuite version 5.7
Key facts
• A ROLAP tool with a server-based OLAP engine
• Server runs on Unix platforms; clients run on Windows 3.1, Windows 95,
Windows NT and web browsers
• Acquired IQ Software, an enterprise query and reporting tool vendor, in
September 1998
Strengths
• A scalable system – query processing is automatically optimised between
the server and the RDBMS
• Supports a friendly notebook-style interface across all the client tools
• Flexible scheduling, report sharing and messaging facilities are matched
by few tools
Points to watch
• Server runs only on Unix platforms
• Limited support for advanced analytical and forecasting functions
• An expensive solution for small projects – aimed at organisations with a
large-scale data warehouse strategy
Ratings
Web support
Management
Adaptability
Customisation
1 2 3 4 5 6 7 8 9 10

Agents
A background process that users define to automatically run reports at a
pre-determined date and time. When combined with event triggers, they also
provide a means of automating analysis and reporting tasks.
Alerts
Alerts are part of DecisionSuite’s messaging system. For example, an agent
can announce the availability of an automated report by sending an alert to
one or more DecisionSuite users.
Category
Limits user access to the metadata tables to provide a particular view of the
data warehouse. Facts and filters are associated with a category, and all
reports are defined in relation to a category.
Facts
A generic term for data included in reports. Facts can be either data items
stored in database tables or calculations that are derived from stored data
items and formulae. Facts are defined in the metadata tables and viewed in
reports.
Filter
A static or dynamic constraint on the data presented in a report. They are
used to define logical groups of dimension items for inclusion in reports.
Filters are re-usable components stored in the metadata tables.
Metadata tables
DecisionSuite uses relational databases to store two types of metadata. The
first type is used to map a data warehouse structure to a business model.
The second type of metadata concerns all the other elements of the
DecisionSuite environment and client applications, including reports, tem-
plates and filters.
Report
A presentation of data organised according to a report definition that speci-
fies a particular view of the business model and its layout. DecisionSuite
reports are interactive, and different perspectives of the model can be
achieved.
Report template
A report definition saved as a template that defines its layout, content and
properties. Unlike a report, it does not contain results of processing the
definition.

Ovum’s verdict
What we think
DecisionSuite is an attractive ROLAP solution for customers requiring
access to corporate data stored in large, finely-tuned data warehouses. The
tool’s scalability is underpinned by a well designed server-based architec-
ture, including an object request broker and a proven ROLAP engine that
maximises the use of RDBMS technology while addressing the limitations of
SQL.
The DecisionSuite designer and end-user tools are easy to use and well
integrated; they share the same friendly notebook-style interface that will be
appreciated by casual and power users alike. DecisionSuite’s flexible report
scheduling, sharing and distribution options are matched by few other OLAP
tools and provide strong support for group working environments. However,
it lacks the analytical complexity needed for advanced and specialised
analysis models.
DecisionSuite requires a strong commitment to the ROLAP approach. The
server component only runs on Unix and connects to Unix-based RDBMSs.
Information Advantage argues that it has focused on the quality of its data
access rather than the quantity. Hence, it concentrates on providing
optimised, native access to those RDBMSs it has chosen to support.
DecisionSuite, like most ROLAP tools, can be difficult to implement because
any purchase decision usually involves a wider data warehousing considera-
tion. Customers without a data warehousing strategy will probably need to
buy-in some consulting and migration assistance. This means that
DecisionSuite is an expensive OLAP solution; the tool’s pricing strategy is
aimed primarily at ‘big-ticket’ accounts.
When to use
DecisionSuite is suitable if you:
• are already committed to a large-scale data warehouse strategy, or are
preparing for one
• want to develop customer relationship management applications that
analyse large sets of data
• have a requirement to easily share reports between large numbers of
users
• have a strong commitment to Unix.
• want to perform OLAP against normalised data sources
• want to develop highly customised OLAP applications
• have a need for advanced or specialised analytical functions
• need a flexible business model – the business model is tied closely to the
structure of the data warehouse.

Product overview
Components
DecisionSuite consists of the following components:
• DecisionSuite Server version 5.7
• DecisionSuite Client version 5.7; includes Analysis, NewsLine Data
Workbench and Application Workbench
• WebOLAP version 5.7.
DecisionSuite is a ROLAP tool designed to run directly against relational
data stored in a data warehouse. It uses a server-based OLAP engine to
interpret end-user queries and dynamically generate SQL queries. The tool
works best against relational data (typically aggregate tables) that are
organised in either star, snowflake, federation or constellation schemata.
DecisionSuite assumes that it will work against a cleansed data source;
therefore data warehouse population, data cleansing and advanced data
transformations are beyond its scope.
DecisionSuite Server
A Unix-based ROLAP engine that processes client requests against a data
warehouse. The server carries out a significant amount of data processing
(joins, aggregations and calculations). The server also takes care of security
and manages predefined DecisionSuite Agents to perform various back-
ground processing tasks and services. An Active Report Server component
acts as a repository for storing reports and report definition templates.
Report scheduling and distribution capabilities are also supported.
The ROLAP engine uses an intermediary metadata layer to dynamically
generate SQL for a query, and delivers formatted content back to the presen-
tation tier. The metadata layer provides a business-oriented map of the
underlying database table structures, which automatically synchronises
applications with changes in the RDBMS. This information is stored in a
series of metadata tables, usually in the data warehouse. The metadata can
also map data stored in more than one RDBMS.
Model design OLAP analysis Web access Management
Client Data Workbench Analysis WebOLAP Application Workbench
NewsLine
Server DecisionSuite Server Web OLAP

DecisionSuite clients
DecisionSuite clients are ‘thin’ implementations and provide design, presen-
tation and management services. All processing of data and storage of
reports is done on the server; there is no caching of data on the client.
DecisionSuite clients come in three flavours, offering different levels of
functionality for different types of user.
Analysis
An advanced end-user interface for defining ROLAP queries and new re-
ports. Reports can be enhanced by creating custom calculations directly from
the Analysis interface. A range of visualisation techniques are also provided.
NewsLine
A more basic interface for casual users. NewsLine users can tailor reports
built with Analysis, or simply view predefined reports delivered by the
DecisionSuite Server as part of a schedule or agent process.
Workbenches
Workbenches are aimed at designers and administrators. There are two
types of Workbench interface:
• Data Workbench, a DBA tool environment for creating, validating and
maintaining the metadata tables
• Application Workbench, for administering and managing the
DecisionSuite environment.
WebOLAP
Enables reports to be accessed and analysed from a web browser. It provides
NewsLine-like facilities. WebOLAP is closely integrated with the
DecisionSuite Server (via CGI), with reports dynamically generated in
HTML.
DecisionSuite is a ROLAP tool and does not implement a full MDDB store
on the server.
The ‘natural’ configuration for DecisionSuite is a light mid-tier ROLAP
architecture. It uses a ROLAP engine that sits on a mid-tier Unix server.
Clients run under Windows. If WebOLAP is used, a web server can be added
to the architecture to provide web access.
The DecisionSuite Server is the central hub of the system that processes
client requests against a data warehouse. DecisionSuite is optimised for
variations of star, snowflake and federation and constellation database
schemata and multiple table aggregation and partitioning strategies. The
ROLAP engine processes queries and temporarily caches them on the server
at runtime; there is no loading of data into a persistent MDDB store. The
server is based on an object request broker (ORB) architecture, where

messages are passed between the different service objects, such as those
responsible for receiving client requests, connecting to the RDBMS or for-
matting reports.
An important feature of the architecture is scalability. Depending on the
nature of the query, data processing is carried out on the server, the rela-
tional database or a combination of both. DecisionSuite optimises how
processing is split between the database and the server. For example,
DecisionSuite Server can do simple aggregations or data filters within the
database; more complex procedures that are inefficient to perform in SQL,
particularly calculations, will be done on the server.
DecisionSuite is a server-based tool and does not support a two-tier desktop
architecture; all processing is done on the server.
Mobile architecture
This architecture is not directly supported by DecisionSuite. However, a
single-tier mode is possible via a partnership with Brio Technology. The two
companies have developed DecisionSuite Brio Connection, a facility that
supports the transfer of DecisionSuite data into Brio’s Brio.Insight client
tool for detached, offline analysis.
Using DecisionSuite
DecisionSuite provides a number of tools that distinguish clear responsibili-
ties for designers, end users and administrators.
The metadata tables used to map the data warehouse structure to business
dimensions are defined by experienced DBAs using the Data Workbench
tools. These users are expected to have a good understanding of the data
warehouse and SQL syntax.
Reports can be defined by DBAs, but can also be created and viewed by
business end users using the Analysis client. Experienced power users can
use this interface to enhance models by including their own custom meas-
ures and filters. The NewsLine interface provides a simple interface for
‘information consumers’ that only require easy viewing access to reports
scheduled by the DecisionSuite Server.
Administrators are provided with a separate Workbench interface for man-
aging end users. The Application Workbench provides a number of graphical
tools that enable managers to configure user security profiles and govern
database queries according to time and the size of results sets returned from
the RDBMS. The Workbench also provides the management interface for the
Active Report Server component, allowing distribution schedules to be built
and developing agents that ‘push’ results directly to end users via alerts,
e-mails or report attachments.
Building and using a business model
Building and using an OLAP application in DecisionSuite is a three phase
process.

Creating the metadata model
The first step is to map an existing data warehouse structure in metadata
tables so that it is made visible to DecisionSuite Server. The mapping de-
fines the logical elements of a business model, such as dimensions, measures
and hierarchies. DecisionSuite Server references the metadata tables to
build the SQL statements that it submits to the source database.
Metadata tables are defined by an experienced DBA using the Data Work-
bench, which gives a spreadsheet-style tabular view of the various metadata
tables. Alternatively, the tables can be defined with an external editor.
A graphical wizard provides a prompted interface to create and populate the
metadata. Initially, DBAs need to set up around ten metadata tables that
map the dimensions, attributes and facts, drill-down hierarchies and time
periods held in the data warehouse. Figure 2 shows the graphical interface
for defining drill-down hierarchies.
Figure 2 Data Workbench: defining drill-down hierarchies

Add calculations and filters
Designers (or power users) can elaborate the metadata by adding calcula-
tions and filters. Calculations are an important element of DecisionSuite
models, and two types can be defined using a one-off formula or a calculation
template:
• simple report calculations, such as averages and totals for a group of cells,
can be added to a report via the report calculations dialogue
• custom calculations are calculated measures that are stored on the server
and are available for use in all reports. Market share, for example, might
be defined as a calculation.
Calculation templates are skeleton definitions of calculations that use
variables rather than actual dimensions. A calculation template can be re-
used as the basis for different calculated measures; the user simply selects
the appropriate dimension values to be used. DecisionSuite includes a
number of standard calculation templates, but users can define their own
calculation templates or build calculated facts from scratch using the Calcu-
lation Builder facility.
Filters define usable selections or groupings of dimension members for
inclusion in reports. They are typically used to enhance the model without
the user having to access the metadata directly. A filter, for example, can
define a new group of dimension members based on an attribute value, such
as all items whose product code starts with ‘88’. A filter is either a static or
dynamic constraint on the data:
• a static filter may define a specific number of dates or a group of items
from a dimension: for example, all products beginning with ‘Diet’
• a dynamic constraint will change as the database changes: for example, it
may select data for the last six months, or all sales of more than $500.
Filters are themselves re-usable objects with DecisionSuite. They are de-
fined through a set of dialogues, using an expression builder if required. All
filters are stored in the metadata tables and can be re-used by anyone with
the correct access rights.
Creating a report
The DecisionSuite development philosophy is focused on the concept of
‘reports’ that users create by selecting dimensions from the metadata tables.
Reports are defined for or by users, sent to other users, scheduled by agents
or published through the Web. Each report is based on a report template
that defines its layout, content and properties. Report templates are created
in the Template Editor. This is shown in Figure 3.
Analysis and Workbench users can readily create templates, but NewsLine
users can modify them by changing the layout of dimensions or including
different dimension members. Typically, templates will be created for stand-
ard reporting requirements in an organisation, such as a market share
summary or product ranking.

Figure 3 Template Editor
Sharing and distributing reports
The notebook-style interface
The user interface for all end-user tools is based on the notebook metaphor
of a ‘portfolio’. A portfolio is made up of a number of tabbed pages.
The first page is always the ‘alert page’ and lists any alerts received, with a
short description of each one. An alert might notify a user that a scheduled
report has been completed, or it might have an attached report sent by
another user. Other pages in a user’s portfolio are used to organise reports in
an efficient manner; they can be set up according to each user’s preferred
way of working. A portfolio can include folders shared by a workgroup.
Distributing reports
Support for the sharing of information between large numbers of users is an
important element of the DecisionSuite architecture. A number of features
within the product promote the easy sharing of reports. For example, a
message icon is provided on a standard toolbar across all the client tool

interfaces to allow end users and administrators to easily define alert mes-
sages, attaching, if required, one or more reports. When the report has run,
an alert message appears in the portfolio of all the recipient users. This is
shown in Figure 4.
As well as sending reports to other users, developers and users can create
DecisionSuite Agents to run reports. An Agent runs one or more reports and
can deliver alerts to one or more users when the report is completed. Agents
can be scheduled to run at set times or can be fired off by a specific trigger
event, such as the loading of the data warehouse.
Figure 4 Alert messages

Future enhancements
Information Advantage aims to release a Windows NT version of
DecisionSuite Server in 1999. The company also plans to make
DecisionSuite an OLE DB for OLAP data provider.
Following the purchase of IQ Software, integration between the two compa-
nies’ products has started at several levels. Initial efforts will focus on
providing an Internet-based ‘Active Content Server’ to provide users with a
personalised web portal through which they can securely access
DecisionSuite tools and data. The new product is expected by the end of 1998
and will be called Eureka! Server.
Information Advantage is also seeking partners for developing specific
vertical applications based on DecisionSuite tools.

Company background
Information Advantage was formed in 1990, following IBM’s purchase of
Metaphor, the EIS/DSS vendor. The Metaphor product group was absorbed
into IBM, but the consulting arm set up a new company which became
Information Advantage. For the first years of its existence, the new company
concentrated on providing consultancy. It developed its first product, a Unix-
based decision support engine called Axsys, as a by-product of its consul-
tancy work. Axsys eventually evolved into DecisionSuite Server. The
DecisionSuite client tools were first released in August 1994.
In 1993, the company obtained venture capital funding in order to develop
the product side of the business. Information Advantage is now focused on
being a product vendor, and has been building up its direct sales force from
offices in the US and Europe. In December 1997, Information Advantage
completed its IPO. Revenue for fiscal 1998 grew 118% to $25.6 million.
In September 1998, Information Advantage acquired IQ Software, an
enterprise query and reporting tool vendor, for $65 million. Although the two
companies had radically different product lines and sales models,
Information Advantage is now working towards product synergy. The
combined company (which retains the Information Advantage name)
employs 420 people and has its corporate headquarters in Eden Prairie,
Minnesota, with 27 subsidiaries and a network of distributors worldwide. It
is valued at $75 million and has more than 1.7 million users of its products
worldwide.
Following the merger with IQ Software, Information Advantage has taken a
significant step away from its ‘dimensional relational’ stance, to encompass
multidimensional, transactional relational and unstructured content data
sources through IQ Software’s technology. The combined company is now re-
positioning itself to provide a comprehensive enterprise business intelli-
gence suite with a strong focus on customer relationship management and
other sales and marketing applications. Both companies have complemen-
tary, non-overlapping product lines that will be integrated to cover a range
of business intelligence requirements and attract vertical application devel-
opment partners.
Information Advantage sells primarily to the enterprise and has a strong
direct sales culture; more than 90% of sales are through direct channels.
DecisionSuite’s strength lies in analysing large data warehouses, and is
therefore particulary strong in the retail, consumer packaged goods, tel-
ecommunications and insurance sectors. Most of the company’s largest
customers are Global 100 enterprises such as 3M (30,000 users), MasterCard
and Nabisco. IQ Software sells principally to the departmental level, prima-
rily through a vast network of VARs and distributors. Information Advan-
tage claims that the two business models are complementary, and will
provide increased opportunities to sell smaller, more frequent deals to small-
to medium-sized enterprises looking to start small and scale up to an enter-
prise level.

Customer support
Support
Information Advantage provides telephone hot-line support. On-site support
arrangements are also available.
Training
A number of public and on-site training courses are provided. These include
a one-day introductory course for casual end users, a two-day course for
analyst-type users and a four-day technical course for IS developers and
DBAs. Computer-based training is also available.
Information Advantage’s Services division consists of experienced data
warehousing consultants and systems integrators that focus on specific
vertical sectors. Consultants use Information Advantage’s DecisionPath
methodology for implementation.
Distribution
US
Information Advantage
7905 Golden Triangle Drive
Eden Prairie
MN 55344
USA
Tel: +1 612 833 3700
Fax: +1 612 833 3701
Europe
Information Advantage International
3 Furzeground Way
Stockley Park
Uxbridge
Middlesex, UB11 1JF
UK
Tel: +44 181 867 4600
Fax: +44 181 867 4699
http://www.infoadvan.com
E-mail: marketing@infoadvan.com

Product evaluation
Summary
1 2 3 4 5 6 7 8 9 10
DecisionSuite supports an extremely intuitive notebook-style interface that is
consistent across all its client interfaces. Most OLAP functions are easily
available from reports. Reports and calculations are easily defined by end
users, without IS involvement. However, DecisionSuite’s real strength is its
support for flexible group-working. It provides a number of useful features to
promote the sharing and intelligent distribution of reports. Particularly
impressive is the tool’s collaborative working environment, which allows end
users to access shared reports via e-mail, customise them and store them in
their own personalised workspace.
The DecisionSuite interface is organised around a ‘portfolio’ containing
tabbed pages. Users can have as many tabs as they wish, although generally
there are three – alert information from agents, personal information for
users’ own reports, and workgroup information (shared reports).
Reports are organised in a directory structure. Each report in each tab also
displays summary metadata (such as report author, report type, when it was
created and when it was last executed) that can be browsed by report users.
Keyword or content search facilities are provided for finding reports.
A description of the model in terms of its elements (dimensions and calcula-
tions), its author and when it was last updated is stored in the metadata
tables. The descriptions are readily accessible to end users of the report.
Only users with write access to the metadata model may annotate reports.
Using the model
DecisionSuite provides a friendly notebook-style interface. Core OLAP
functions such as drill-down, slice-and-dice and pivot are easily accessible
via point-and-click.
End users can change the location of dimension members (including rows,
columns or blocks of data) in a report using drag-and-drop.

Users are provided with pop-up menus to show their position in the drill-
down hierarchy. They can also jump to specific levels in the dimensional
hierarchy.
Users can drill-down to access detailed transactional data directly from the
report interface. DecisionSuite does not differentiate between aggregated
and detailed data; the same user interfaces are used and the same process-
ing is performed. The data does not have to undergo special preparation to
be accessible at detail level.
DecisionSuite supports two native front-end user tools for the desktop PC:
Analysis, for business analysts that wish to design, view and transmit
reports to other DecisionSuite users; and NewsLine, for general business
users that want to view and customise reports. A web interface is also
provided.
Third-party tools, such as Brio.Insight, can also access report data, but there
is no spreadsheet add-in.
The content of a report can be visualised in multiple variations; a default
mode is provided to automate visualisation upon initial access of the report.
Users can easily select and chart data from within reports. The charting
tools support a range of business graphs, and a wizard facility is also pro-
vided. Users can simultaneously display multiple tables and charts in a
report. However, it is not possible to drill-down or rotate dimensions from
within a chart. There is no direct support for visualising data in maps,
although integration is provided with MapInfo.
Designing a report
Reports can easily be defined from scratch or using templates. It is not
possible to embed images, video, sound or OLE objects in reports displayed
through the Analysis or NewsLine clients. However, this is supported in
WebOLAP.
Publishing a report
Users can publish formatted report content to a report repository for access
and distribution to other users:
• the report caster function automatically publishes and distributes reports
to end users based on either an individual or workgroup basis; public and
dynamically defined distribution lists are supported
• a narrow casting function limits publication to specific users based on
their personal or workgroup exceptions.
Reports can be distributed via e-mail from the client tool interface.
DecisionSuite supports individual, project-based and corporate report in-

boxes. DecisionSuite uses the Unix mail system to distribute reports. Ad-
dress lists set up within Unix mail may be used, but these cannot be gener-
ated dynamically.
DecisionSuite does not support any report subscription services.
Summary
1 2 3 4 5 6 7 8 9 10
In DecisionSuite, the report is just one perspective on the business model.
Much of the work is done beforehand when defining a business-oriented map
of the underlying database table structure (metadata tables). This allows
developers to build a logical business model to simplify end-user construction
of reports. The business model is flexible, and the use of filters and calcula-
tions allow for considerable adaptability. A wizard-driven interface guides
designers through the process of describing complex drilling hierarchies and
aggregation table information. However, a diagrammatic editor would ease
the task of setting up and managing the metadata tables.
Basic design
Design interface
The Data Workbench provides a graphical interface for mapping the data
warehouse structure onto DecisionSuite metadata. This interface displays
the metadata in spreadsheet-style tables. The Data Workbench is adequate
for this task, but it would be better if there was an overview of the main
elements, rather than just a series of tables. It would also help if it included
dialogues and pick lists to help with the maintenance of the metadata. The
wizard provides dialogues and pick lists during the metadata creation.
Reports (sets of dimensions, calculations and filters) represent the business
model. The design interfaces for both metadata and reports share the same
general style of interface.
Designers can see a sample of data from a selected relational table. However,
they cannot view the overall database schema.
Metadata tables can be defined to map dimensions, measures and hierar-
chies to specific parts of the data warehouse. Categories provide end users
with a restricted view of the metadata tables.
Designers are not automatically prompted to add additional metadata when
creating the metadata tables or defining reports.

Columns for dimensions can be selected using point-and-click. A wizard
facility is provided to speed up the mapping process.
Filters can be used to select dimension members. Filters are created by
point-and-click. There are three types of filter: dynamic, static and level. The
differences are related to the type of SQL generated.
Developers can easily define drill-down hierarchies using point-and-click.
Multiple and split drill-down hierarchies may be defined. Unbalanced
hierarchies are also supported.
Time dimension
Time dimensions must be defined according to standard or custom time
periods in the business model. Multiple time dimensions are supported, and
filters can be used to define non-standard time periods, such as fiscal year
and lunar months.
Dimensions in the model can be assigned long and short name descriptions
by designers, which can subsequently be viewed in a DecisionSuite report by
end users. End users cannot edit these dimension descriptions.
Designers can define a default level for a dimension hierarchy when opening
a report.
Calculated measures
Designers and end users can add new calculated measures to the business
model at any time, either using a calculator-type interface or a calculation
template. A scripting language is available for defining complex calculations.
A library of mathematical, logical and relational operators is provided.
Multiple measures can be stored with a set of dimensions. The measures can
also be arranged in a hierarchy.
Multiple designers
Multiple designers
DecisionSuite does not provide any special support for multiple designers.
There is no direct support for versioning control.

DecisionSuite has links to Logic Works’ Erwin data modelling software,
which is able to create DecisionSuite metadata tables.
The ETL tool Syntagma from Relational Matters also integrates with
DecisionSuite. It is able to build and populate DecisionSuite metadata and
aggregate tables.
Summary
1 2 3 4 5 6 7 8 9 10
DecisionSuite provides limited support for advanced analytics, although a
number of specialised functions geared towards customer-centric analysis are
provided. Calculation templates could feasibly be used to add more powerful
analytical capabilities to the product, but these need to be built into the
model during the design phase. Users that wish to apply statistical analysis
and sophisticated forecasting algorithms directly to model data will need to
use specialist tools. There is no Excel add-in facility.
DecisionSuite does not provide any direct integration with specialised third-
party analysis tools. Nor does it provide an Excel add-in.
Ranking and sorting
Support is provided for simple definitions of top and bottom order ranking.
Support is provided for logarithmic, trigonometric, exponential and factorial
functions.
Financial functions
Financial functions are not supported.
Statistical models
Support is provided for a number of simple statistical functions including:
moving averages and rolling sums, share and cumulative totals.
Trend analysis
There are simple functions available for analysing trends based on year-on-
year percentage change.
Simple regression
DecisionSuite offers no support for forecasting. It relies entirely on exporting
data to Excel or external statistics packages for this function.

There is no support for advanced time series forecasting algorithms.
A scripting language can be used to create add-ins that integrate with third-
party products (such as SPSS) to access advanced analytical functions.
‘What-if’ or budgeting applications that need write access to the database
require special handling. In most circumstances, the data warehouse tables
will be read-only, so a separate set of tables will need to be created that
support write-access. These will then need to be integrated with warehouse
data via a custom-built application.
DecisionSuite supports character string functions for comparing textual
data. The results of counts for sub-strings and word patterns can be included
in analyses. For example, the calculation builder allows analysts to create
procedural if then else type functions that compare text strings held as
metrics. The calculation could return a text string for display or a number,
which may be summed or counted, for example.
Data mining
DecisionSuite does not provide support for data mining.
DecisionSuite offers a number of functions geared towards retail and sales
marketing analysis applications. These include market share, average
inventory, rate of sales, BDI (brand development index) and SDI (share
development index).
Web support
Summary
1 2 3 4 5 6 7 8 9 10
WebOLAP provides strong web access for accessing and analysing predefined
reports. However, web users cannot define new reports or add new filters or
calculations to the report definitions. Reports can be easily published and
distributed to a wide range of users over the Web using Internet-based search
engines, hyperlinks and e-mail.
The WebOLAP client provides the same level of OLAP functions and report-
ing as the client-server desktop tools. However, it is not possible to add new
filters or calculations to models.

WebOLAP users can register reports with Internet-based search engines,
granting access to reports via hyperlinks. It is also possible to include sound
and video objects in web reports.
All WebOLAP users must be pre-registered.
WebOLAP is well suited for general business users that require easy access
to predefined or scheduled reports. Users wishing to define new reports or
implement their own calculations are not supported.
It is not possible to edit the metadata tables via the web browser.
It is not possible to create new model or report definitions.
All HTML generated by WebOLAP is dynamic and is built up from tem-
plates and rules built into the user profile. No conversion is required, as
reports are held in a neutral format and automatically converted to HTML
on-the-fly when requested by a WebOLAP client.
The DecisionSuite Report Caster facilities can dynamically distribute
reports via e-mail over the Internet.
Users can include multiple URLs in DecisionSuite reports. The URLs can
reference other reports.
There is no integration with middleware to support distributed processing.
Management
Summary
1 2 3 4 5 6 7 8 9 10
All DecisionSuite application, metadata and user management is defined
and maintained on the server through graphical interfaces. The security of
reports relies entirely on the Unix and RDBMS security. Agents are used for
scheduling report updates and can be based on times and events in the data
warehouse. As expected from a ROLAP tool, DecisionSuite provides strong
support for query monitoring and governance, and produces detailed usage
statistics.

DecisionSuite provides two graphical Workbench interfaces that are similar
in design: the Data Workbench is used for maintaining the metadata tables;
the Application Workbench is used for administering application compo-
nents, report objects and end users.
Security of models
The security of models is governed from a multi-level security model based
on Unix, metadata and the RDBMS security systems. All models have
associated properties which govern read/modify access.
Query monitoring
An audit log is generated for each query and report generated, including the
author and the time it was run. Administrators can also bring up the SQL
generated, and re-run the query for audit trail or debugging purposes.
Management of data
DecisionSuite processes data directly from the RDBMS and creates multidi-
mensional models at runtime which are cached on the server. However, once
a report has been defined, the data can be stored persistently on the
DecisionSuite Server or any other application server, and can be periodically
refreshed for current data.
The loading of data into the data warehouse is outside the scope of
DecisionSuite. Once it has been loaded and stored as part of a report defini-
tion, a scheduler facility can be used to automate the refresh of reports.
Scheduling can be based on times, dates or events. Users can apply a refresh
schedule to a group of reports.
Event driven scheduling
Event triggers can be specified for updating existing reports or scheduling
new reports. Triggers can be based on events such as an update to the data
warehouse or events external to the OLAP environment.
An agent may be set up to look for the completion of a report update and
then alert users. All agent tasks are persistent, and therefore automatically
re-submitted if the update fails.
Data is stored persistently in the database or the DecisionSuite Server (as a
report). When a query is executed, the data is temporarily cached on the
server at runtime; there is no caching on the client.
DecisionSuite uses two analytic ‘workspaces’ to efficiently process dense and
sparse data returned from the RDBMS. DecisionSuite dynamically routes
data to the appropriate workspace based on its sparsity percentage. For

sparse data models, DecisionSuite automatically uses multidimensional
b-tree, while for dense data models, data is returned as a multidimensional
array.
The size of the server cache is subject to size restrictions based on query
governance definitions.
In-memory caching is not supported.
Each report is time-stamped with information about when the data was last
updated. This information is not automatically displayed.
Management of users
Typically, DecisionSuite is designed to permit simultaneous read-only access.
The DecisionSuite Server uses a flexible security model to connect to the
RDBMS, with anything from a one-to-one user to connection relationship, to
all users sharing the same connection. User profiles grant access to parts of
the DecisionSuite application environment and metadata. Profiles can be
assigned on an individual or workgroup basis. The profiles are also closely
linked to categories, which define user access to parts of the data warehouse
and available calculations and filters.
Query governance
Administrators can define the maximum number of concurrent processes
used by DecisionSuite Server at any given time. They can also control the
maximum number of rows returned and processed on a user profile basis,
and specify the maximum time a query is allowed to run in the database.
There is no support for restricting queries to specific times of the day.
The category definition controls access to the metadata a user can access.
This definition determines the model metadata, calculations and filters that
can be included in a report for a particular user or groups of users.

Adaptability
Summary
1 2 3 4 5 6 7 8 9 10
DecisionSuite’s metadata layer allows for an adaptable business model. New
dimensions and measures can easily be defined and re-used across models.
All additions are automatically time-stamped. Model metadata can be
referenced to ensure that reports are kept synchronised at all times, but there
are no facilities for keeping data sources and models in line. There is no
possibility to change the architecture from ROLAP to MOLAP mode.
New dimensions can be easily added to the metadata tables and subse-
quently used in reports. Each addition is time-stamped, but there is no
direct support for change management.
New dimension definitions are stored in the metadata tables and can be re-
used across multiple models depending on the access rights assigned to
them.
New measures can be added to models at any time by developers and Analy-
sis users provided they have the necessary access rights. Folders exist within
the model in which to save the calculation definitions and Unix-style secu-
rity is applied to them.
When an end user creates a new calculated measure, the specification is
stored in metadata tables and is available for use by other users with the
appropriate access rights.
The Information Advantage tools are all ROLAP-based; there is no possibil-
ity of changing the architecture to MOLAP.
Users are not automatically informed of changes in the data warehouse
when a report is opened.
As the data warehouse is the only source of information for DecisionSuite,
new members are automatically available. However, there is no support to
lock a level to prevent new members being automatically updated.

Metadata
A validation function exists to ensure that categories are synchronised with
the metadata tables each time a report is created.
Impact analysis
DecisionSuite does not provide any metadata audit trail facilities.
DecisionSuite integrates with Informatica’s Metadata Exchange architec-
ture. This enables developers to view extraction and transformation
metadata about the columns in the data warehouse that provide the data for
the model.
Summary
1 2 3 4 5 6 7 8 9 10
DecisionSuite utilises the strengths of relational database technology, while
ensuring that processing is optimised between the DecisionSuite Server and
the database. It also provides a number of performance-tuning services aimed
primarily at minimising access times, such as multipass SQL, native SQL
access and SMP parallelism.
ROLAP
Multipass SQL
DecisionSuite automatically generates multipass SQL statements.
An important feature of DecisionSuite is its ability to intelligently balance
SQL processing between the DecisionSuite Server and the database.
The server cache is volatile, and cannot be stored and revised.
Aggregate navigator
DecisionSuite can automatically access the highest level aggregate tables in
the database. It calculates the Cartesian cross-product of dimensional data
models which then produces aggregate-level priority information.
MOLAP
DecisionSuite is a ROLAP tool.

Information Advantage claims that the DecisionSuite Server-based architec-
ture can support many users without degrading performance. It has many
customer sites with more than 1,000 concurrent users running
DecisionSuite reports against large data warehouses.
Processing
DecisionSuite uses native SQL interfaces to connect to all the major
RDBMSs. It also uses ODBC for Unix to connect to Red Brick, Teradata and
HP-Intelligent Warehouse data warehouses.
A client request is automatically routed to the least utilised DecisionSuite
Server for processing. There is no automatic load balancing between these
servers, because each functions independently.
It is, however, possible to balance processing between the database server
and the DecisionSuite Server.
SMP support
DecisionSuite Server takes full advantage of SMP technology.
Customisation
Summary
1 2 3 4 5 6 7 8 9 10
DecisionSuite provides limited support for application development. Add-ins
and a procedural scripting language are available to customise applications
and reports. Application development relies on the tool’s API, and using
external development tools that can use the same DLL that links the
DecisionSuite client modules to the server.
Customisation
Various aspects of the DecisionSuite tools’ interface can be modified to
provide restricted or extended views and functionality.
Application Workbench provides an ‘add-in’ facility to extend or link pre- and
post-process operations for reports. Typically, these are calls to an external
procedure, such as a Windows application or a Unix shell script, and are
used to customise the execution or results of a report, or add new capabili-
ties.

Applications
A web gateway API is provided for the development of simple EIS interfaces
in HTML or JavaScript.
DecisionSuite does not have a visual development environment. It does
provide a scripting language for defining server-based procedures for inter-
action with external systems or data. The scripting language is a cross
between Visual Basic and Unix shell scripts, and uses the standard ‘vi’
editor.
DecisionSuite client DLLs can be called by development tools such as Visual
Basic, PowerBuilder and Visual C++.
DecisionSuite has language support for English, French and German.

Deployment
Platforms
Client
DecisionSuite clients run on Windows 3.1, Windows 95 and Windows NT.
WebOLAP runs on standard web browsers including Netscape, Microsoft
and Mosaic.
Server
DecisionSuite Server runs exclusively on Unix: HP-UX, IBM AIX, NCR, SGI
IRIX, Sequent, Sun Solaris, Data General DG/UX, Digital Alpha, Siemens
Reliant and Unisys SVR4.
Data access
DecisionSuite provides native access to the following relational databases:
Oracle, DB2, Sybase, Informix, Tandem and MDI. ODBC for Unix drivers are
supported to provide access to Teradata, HP-Intelligent Warehouse and Red
Brick.
Standards
DecisionSuite has its own proprietary server and client APIs. The
DecisionSuite OLE DB connection provides support for Microsoft’s OLE DB
for OLAP API. WebOLAP supports HTML, Java and JavaScript.
Information Advantage has not published any OLAP benchmarks.
Price structure
Pricing depends on the number of servers and registered users, and the size
of the underlying database. Typical entry level pricing is $150,000 for the
DecisionSuite Server and 50 users. Clients are priced separately:
• NewsLine costs $200
• Analysis costs $1,200
• Data Workbench costs $16,500
• Application Workbench costs $6,600.

Summary
At a glance .............................................................................................. 2
Ovum’s verdict......................................................................................... 4
Distribution ............................................................................................ 14
Product evaluation
Web support .......................................................................................... 19
Management ......................................................................................... 20
Adaptability ............................................................................................ 22
Customisation ....................................................................................... 25
Deployment
Platforms ............................................................................................... 26
Data access .......................................................................................... 26
Standards .............................................................................................. 26
Price structure ....................................................................................... 26

At a glance
Developer
Microsoft, Redmond WA, USA
Versions evaluated
Microsoft SQL Server 7.0 OLAP Services (Beta 3 and Final Feature
Editions)
Key facts
• A multidimensional engine that can support MOLAP, ROLAP and
HOLAP
• OLAP Services runs on Windows NT; end-user tools runon Windows 9x
and NT
• Comes free with SQL Server 7.0 Enterprise and Standard Edition
Strengths
• Extensive wizard support makes it very easy to use
• Easy to move between MOLAP, ROLAP and HOLAP storage options
• Wide range of end-user tools available from third parties
Points to watch
• Not a total OLAP solution, requires an end-user tool
• Has no ‘ready-to-run’ web features
• Not yet integrated with the Microsoft Repository
Ratings
Web support
Management
Adaptability
Customisation
1 2 3 4 5 6 7 8 9 10

Cube
Microsoft’s term for the multidimensional business model.
HOLAP
The details are stored in a relational database, and the aggregates in the
MDDB.
Library
The library supports the re-use of dimensions, mappings to data sources and
roles. When defining a model, dimensions can be freshly defined (and option-
ally stored in the library) or library definitions used.
MOLAP
The details and aggregates are stored in the MDDB.
Partition
A model can have multiple partitions, all of which have the same dimen-
sions. However, the partitions can be stored in different locations, have
different data storage options (that is MOLAP, ROLAP or HOLAP) and
different degrees of optimisation.
ROLAP
The details and aggregates are stored in the RDBMS.

Ovum’s verdict
What we think
OLAP Services is a product that sets new standards of ease of use and
confirms OLE DB for OLAP as the de facto standard for accessing multidi-
mensional databases. Outside of its use in a small workgroup, it is more
accurately described as a component rather than a complete solution.
The outstanding features of Microsoft’s OLAP Services are its ease of use
and its architectural flexibility. It has more than 30 wizards, which enable
straightforward multidimensional models to be built entirely using point-
and-click. One of the most impressive wizards is the data storage and aggre-
gation one, which enables the data storage architecture (MOLAP, ROLAP or
HOLAP) to be selected with one click. The visual display of the trade-off
between size and performance makes optimisation, even for the naïve user,
an easy operation. This ease of use, combined with the fact that OLAP
Services is are bundled with most versions of SQL Server 7, makes it an
appealing introduction to OLAP.
While the tool has high initial appeal, it is not a complete corporate solution.
The most obvious need is for an end-user tool, but this poses little difficulty
because entry-level tools can be freely downloaded from the Web, and many
third-party tools use the OLE DB for OLAP interface. However, a basic end-
user tool may still only provide a minimal system. Within a corporate envi-
ronment, OLAP requires report production and distribution facilities, web
access, customised applications, advanced analytics and metadata for end
users. OLAP Services provides very little of this. In this context it is a useful
component, but requires considerable supplementing.
When to use
The OLAP Services multidimensional engine should be considered if you
want:
• ease of use as a priority
• flexibility of storage options
• to be able to use a wide range of end-user tools
• a low-cost introduction to OLAP.
It is not suitable if you want:
• built-in financial or statistical functions for complex analytics
• to define complex models with user-defined levels
• more security than that provided by Windows NT
• a complete (client and server) solution from one vendor.

Product overview
Components
The main components of SQL Server 7 Enterprise and Standard Edition are:
• Data Transformation Services (DTS)
• the SQL Server Engine
• the Microsoft Repository
• Microsoft SQL Server 7.0 OLAP Services.
SQL Server 7 does not include a front-end tool for OLAP Services. The
Microsoft product for this will be Microsoft Excel version 9 (not released at
the time of writing).
The main focus of this evaluation is OLAP Services.
Figure 1 shows whether the component usually runs on the client or the
server, and its primary purpose.
OLAP Services
OLAP Services is a multidimensional engine that can access data from any
OLE DB source, and in turn can be accessed by any tool with an OLE DB for
OLAP as a consumer interface. It offers a variety of storage options, includ-
ing detail and aggregates stored in the MDDB (MOLAP), details and aggre-
gates stored in an RDBMS (ROLAP) and a hybrid combination in which
details are stored in an RDBMS and aggregates in the MDDB (HOLAP).
Several multidimensional business models can be combined to form a virtual
model. The most likely reason for this is that security is defined at model
level, so this necessitates the provision of separate models for user groups.
Groups requiring a more overall view will generally work with virtual
models to avoid duplication of data and effort.
Models can be partitioned. All partitions share the same dimensions, but
each partition can have a different storage option and degree of
optimisation. As in relational databases, the rationale for partitioning is
performance gain.
OLAP Services provides a wizard-driven environment with the facility to
drop down into an editor to make alterations.
Data Relational Storage of OLAP engine End-user tool
extraction data storage DTS metadata and data store for OLAP
Client Microsoft Excel
Mid-tier Data SQL Server The Microsoft OLAP Services
server Transformation Engine Repository
Services
Main purpose

Data Transformation Services
In theory, the Data Transformation Services can extract data from any OLE
DB-accessible source and move them to any other OLE DB target, but in
practice the target is likely to be SQL Server 7 because this is part of the
package.
As with OLAP Services, the interface is primarily through wizards, and
enhancements can be added through a GUI editor and/or programmatically.
Using the main import wizard, the DTS effectively maps one source only
onto a database with no transformations. Using the editor, multiple sources
can be joined, with SQL queries and tables used as a data source. Transfor-
mations can be written in VB script, J script and Perl script. For greater
flexibility, COM components can be called by a transformation.
As well as data manipulation functions, the DTS also provides management
facilities, including scheduling.
The Microsoft Repository
The Microsoft Repository is designed as a unifying thread to tie together
data warehousing tools. This will be achieved through the definition of Open
Information Models (OIM) to describe data transformations and
multidimensional models. Thus, transformations specified by the DTS (or
third-party tools) and multidimensional models specified by OLAP Services
(or third-party tools) will be stored and exchanged through the use of the
repository.
At the time of the release of SQL Server 7.0, the OIM for data transforma-
tions was usable – but the one for OLAP was not. Thus, the version 7.0
release of OLAP Services uses the Microsoft Repository to store DTS and
schema metadata, but does not use it to store multidimensional models. A
private repository is used. The company intends to enable models stored in
the private repository to be exported to the Microsoft Repository when the
OIM models for OLAP are defined.
A major feature of the SQL Server 7.0 architecture is that although the DTS,
Repository, SQL Server engine and OLAP Services come as a complete
datamart package, each of these has an open API. This means that DTS can
feed into any OLE DB target, and OLAP Services can take data from any
OLE DB source and feed it to any OLE DB for OLAP consumer. Similarly,
within each of the components (that is, DTS and OLAP Services) the wizard-
driven interface is useful for defining 80% of the required functionality, and
what cannot be added with scripting can generally be added with a COM
component.
There are two full mid-tier options:
• detailed and aggregate data are stored in the mid-tier in the OLAP
Services (MOLAP)
• aggregate data is stored in the OLAP Services, but detailed data remains
in the source database (HOLAP).

Changing between these architectures is simply a matter of making a
selection in the Data Storage and Aggregation Wizard (a fuller description is
given below).
There is no in-built support for distributed servers. No end-user tool is
provided with the SQL Server 7.0 package; the options are either a third-
party tool or Microsoft’s Excel version 9. Thus the choice of the tool
determines whether the client is thin or fat. The architectures supporting
web access are described below.
The light mid-tier option, in which the data is stored separately on an
RDBMS and the mid-tier provides the engine to manipulate this, is an
additional option. As with the full mid-tier options, the ROLAP architecture
is selected using the Data Storage and Aggregation Wizard. Similarly, the
client can be fat or thin depending on the end-user tool chosen.
OLAP Services is the server part of a client-server solution, so the desktop
architecture using a two-tier model is not supported.
Mobile architecture
The mobile architecture is supported by PivotTable Services, a COM compo-
nent on the client; this enables drill-down and similar pivot table features.
In many ways, PivotTable Services is a ‘lite’ OLAP Services.
Client tools incorporating this component can load and cache data, and can
then disconnect from the data source. It is not possible to store this data
persistently on the client.
Web architectures
Most OLAP tools use a four-tier architecture for web access, based on CGI
with an OLAP web server between the generic web server and the OLAP
database. Microsoft does not include a web server in the SQL Server 7.0
package, but has two alternative means of giving the user web access using
the PivotTable Services COM component and Active Server Pages.
PivotTable Services
The use of this COM component results in what appears to be a thin client,
inasmuch as a browser interface is used, but is in fact a fat client with the
processing being carried out on the local machine.
When the user accesses the web page, the first thing that happens is that
Microsoft’s PivotTable Service COM component is automatically
downloaded. The data for the model is then downloaded and the browser
interface used to locally process this. There is no generation of HTML pages,
so the web connection can be severed and the user can still continue to
manipulate the data.
Active Server Pages
The second architecture makes use of Active Server Pages, Microsoft’s
proprietary server-side scripting technology on the IIS Web Server. (The
URL of the Active Server Pages ends in ‘.asp’ rather than ‘.html’.)

The Active Server Pages are made up of embedded HTML, and script that is
interpreted by the web server at runtime. Generally, the script will establish
the connection, passing the user ID and password to OLAP Services and
then issuing some MDX commands. The result of these is passed back to the
Internet Information Server (IIS), which generates the HTML for the
browser.
Using OLAP Services
In this section, we examine the use of OLAP Services and describe some of
the features that differentiate it from other OLAP tools.
Extensive use of wizards
The whole OLAP Services environment is wizard-driven. It is possible to
build a model using nothing but point-and-click and minimal decision mak-
ing. Figure 2 shows the wizard used in defining the dimensions, and Figure
3 shows the wizard for optimising performance.
Figure 2 Dimension definition wizard

Figure 3 Performance optimisation wizard
Independence of OLAP Services from the SQL Server 7.0 relational data store
OLAP Services is provided with SQL Server 7.0, so it is likely that many
users will use the two together. However, it is important to stress that OLAP
Services can access data in any database supporting OLE DB (and using the
OLE DB to ODBC mapper this includes ODBC-compliant databases).
Ease of changing storage options
Storage options for a model are defined using the Data Storage and Aggre-
gation Wizard.
This wizard first offers a choice of three storage options: MOLAP (detailed
data and aggregates are stored in the MDDB); ROLAP (detailed data and
aggregates are held in a RDBMS) and HOLAP (detailed data is held in the
RDBMS and aggregates in the MDDB).
It then provides the designer with visual information about how the size of
the model is related to the degree of aggregation selected, and thus the
difference in performance that will result from different levels of stored

aggregates. Using ‘under the bonnet’ algorithms, the system dynamically
calculates this and draws the relationship between the two. This is shown in
Figure 4.
From the information generated, the designer selects a percentage of
aggregations to be pre-calculated and stored. Selecting ‘save and process
aggregates’ results in the model actually being built.
To see the results of this, the designer can use the cube browser. This is, in
effect, a cut-down browser. It provides drill-down and enables dimensions to
be changed, but there are no graphics facilities.
Partitions are supported
A model can be made up of multiple partitions. Partitions within a model
have the same dimensions and securities, but can have different measures
associated with them, a different storage mechanism and a different level of
aggregation. The main use of partitions is for incremental update and
scalability. There is no specified limit to the number of partitions a model
may have.
Partitions are added via a wizard going through the same data storage and
aggregation stages described above.
Figure 4 Calculation of performance levels

The range of end-user tools
Once the multidimensional model has been built, it can be accessed by any
client tool with an OLE DB for OLAP consumer interface. This is the only
interface that can be used to access data from OLAP Services. Microsoft
provides a list of compatible client tools at http://www.microsoft.com/sql/dss.
As OLE DB for OLAP becomes established, there is an increasing number of
compatible ‘off-the-shelf’ end-user tools. These range from freely
downloadable tools (in monetary terms and the degree of effort required)
from the Web or tools from more established vendors such as Cognos and
Business Objects.
Building your own end-user tool
Developers can build their own client tools using Microsoft’s ADO MD and
the underlying OLAP provider, Microsoft PivotTable Service. The PivotTable
Service provides a client-side cache and calculation engine, thus reducing
network traffic and minimising response time. PivotTable Service ships with
Microsoft’s Data Access SDK and also comes with Decision Support Service.
If the end-user tool is written in Visual Basic, the developer uses ADO MD to
access schema details of the multidimensional model. Information required
to answer end-users’ queries is retrieved (and written back if this is sup-
ported by the database) using multidimensional expressions (MDX). MDX is
a syntax for specifying a dataset that is defined in OLE DB for OLAP. A
typical MDX statement has the same format as SQL, but contains multidi-
mensional model details rather than table details. An example is shown
below.
SELECT axis specification
FROM cube specification
WHERE slicer specification
Supports member properties
Member properties are attributes (for example, colour and size) that are
attached to members as a means of handling information that is particular
to individual members. For instance, in the product-level of a dimension
there may be members as diverse as tennis racquets and blouses. The prop-
erties that you might want to store about tennis racquets could be weight
and material, whereas for blouses you would want information on colour and
sleeve length. If these are defined as member properties, they can be used in
filtering and sorting queries, and as ‘virtual’ dimensions. This also helps to
deal with sparsity issues.
Balances the workload between server and client
When a client, using the PivotTable Services COM component, processes a
query, the work is done on the client by default. However, if the number of
computations is large (as determined automatically by OLAP Services), the
computation is moved to the server.

Future enhancements
Microsoft intends to release the next significant version of SQL Server 7 at
the end of 1999 or the beginning of 2000. Plans for this release are still at a
very early stage. The company’s intention is to include data mining function-
ality in this release. One of the uses of this will be to assist the designer in
choosing what should be in the dimensions.
A further enhancement, which may be released as a point release earlier
than 1999 or 2000, is integration with the Microsoft Repository to allow
OLAP Services to natively use it. Currently, the metadata is stored in a
private repository and does not make use of the Microsoft Repository.

Company background
Microsoft was founded in 1975 by Bill Gates and Paul Allen. Incorporated in
1981, it has become the largest independent software vendor in the world.
Fiscal 1998 revenues rose 27.5% to $14.49 billion, and net income increased
30% to $4.48 billion.
The style of Microsoft’s growth has been to combine internal product devel-
opment with the acquisition of companies or important personnel. If the
company perceives that a small company has developed a solution, it will
attempt to buy that company in its entirety. In the case of larger companies,
the same result is achieved by tempting away influential members of staff.
Microsoft entered the industrial-strength relational database market by first
licensing SQL Server from Sybase in 1987. In 1994, a new licensing agree-
ment gave Microsoft full ownership of the source code of its version, and a
clean break with Sybase.
In the OLAP area, Microsoft bought OLAP technology and the RD team
from Panorama Software Systems in Tel Aviv, Israel in October 1996. In the
wider data warehousing market, Microsoft’s activities were geared towards
building up partnerships for its ‘Alliance for Data Warehousing’. During
1997, the most tangible activity was the announcement, in September, of the
beta specification of OLE DB for OLAP, a set of COM interfaces extending
OLE DB for access to multidimensional data.
In 1998, Microsoft delivered products that will ensure it is regarded as a
serious competitor in this area. The company has developed a ‘Data Ware-
housing Framework’ that combines interface specifications and products.
The interface specifications are OLE DB for OLAP (version 1 was made
available via the Web in February 1998) and the extensions of the Database
Information Model to cover the storage of metadata about data transforma-
tions and multidimensional business models in the Microsoft Repository.
The main business of the company has traditionally been the provision of
shrink-wrapped desktop products that have been characterised by ease of
use and low cost. In recent years, Microsoft has been seeking to move into
the enterprise markets, as evidenced by its ‘scalability’ days in 1997 and the
development of a website (www.teraserver.com) to demonstrate that SQL
Server 7.0 is capable of dealing with terabytes of data.
In the database area, as well as the trend towards greater scalability, there
is an acknowledgement that there is more growth in decision support than
OLTP. Microsoft has an agnostic approach to OLAP, with OLAP Services
supporting MOLAP, ROLAP and HOLAP. OLAP Services is part of
Microsoft’s data warehousing framework, covering all aspects of the process
from data extraction through to storage and multidimensional analysis.
OLAP Services is designed as a commodity data warehousing product, so the
prevailing themes are ease-of-use and a concentration on providing 80% of
the functionality that most users require, backed up with an open architec-
ture enabling third-party tools to fill in the gaps.

The scope of the SQL Server 7.0 solution and the competitive pricing (al-
though details have yet to be announced, this is a low-risk prediction) will
place Microsoft in a strong position to exploit the data warehousing and
decision support markets. At the lower end of the market, the question is not
‘will the company dominate the space?’ but ‘to what extent?’
At the higher end of the market, the effect is less predictable. Microsoft is
developing its indirect channels and intends packaged applications devel-
oped by ISVs to be a major outlet for SQL Server 7.0.
Customer support
Support
SQL Server 7.0 comes with the standard Microsoft helpline and support
services.
Training
The company has stated that it has plans to support an extensive number of
short courses, but details are not yet available.
Consultancy is not provided by Microsoft, but is available through its
partners.
Distribution
US
Microsoft Corporate Headquarters
One Microsoft Way
Redmond, WA 98052-6399
USA
Tel: +1 425 882 8080
Fax: +1 425 936 7329
Europe
Microsoft Europe
Microsoft Properties France
Tour Pacific
Cedex 77
92977 Paris-La Defense
France
Tel: +1 33 1 46 35 1010
Fax: +1 33 1 46 35 1030
Asia-Pacific
Microsoft Asia-Pacific headquarters
65 Epping Road
North Ryde, NSW 2113
Australia
Tel: +61 2 870 2200
Fax: +61 2 870 2769
http://www.microsoft.com/

Product evaluation
Summary
1 2 3 4 5 6 7 8 9 10
The end-user functionality is almost entirely dependent on the end-user client
tool used. The features described here are found in entry level tools based on
Microsoft’s PivotTable Service, a COM component. More extensive features,
such as subscription and distribution services, are outside of the scope of this
component, but are provided by more powerful tools that can access OLAP
Services models using the OLE DB for OLAP interface (such as Cognos’s
PowerPlay and Business Object’s BusinessObjects, both of which are evalu-
ated elsewhere in this report).
Entry-level tools based on Microsoft’s PivotTable Service do not provide
support for searching using metadata, and rely on models being grouped
appropriately in folders.
No metadata is provided for end users.
There is no support for additional annotations by end users.
Using the model
The PivotTable Service offers all the standard OLAP functionality for drill-
down, pivot and changing dimensions.
There is no direct support for changing the position of members within a
dimension level.
It is possible using the PivotTable Service to provide a hierarchical view of
the dimensions.
This depends on how the storage for the model has been specified. If MOLAP
mode is used, detailed data is available if stored within the cube. If ROLAP
or HOLAP mode is used, the detailed data (in the relational database) can be
accessed.

Any front-end tool offering an OLE DB for OLAP interface can access mod-
els in OLAP Services. These are listed on Microsoft’s web page,
www.microsoft.com/sql/dss. As this becomes the de facto standard, most
major front-end tools will become compatible.
All the entry-level products offer a range of graphs, but do not usually
include maps. Graphs and tables can be viewed on the same page.
Saving and sharing results is outside the scope of the PivotTable Service and
is generally not available in entry-level end-user tools. Enhancements, such
as designing and publishing reports, distribution via e-mail and subscription
services are features provided by more powerful (and generally more expen-
sive) client tools.
Other end-user features
Web applications built using the PivotTable Service (PTS) can take advan-
tage of its data caching facility. These applications download the PTS COM
component and the data for the model, then when the network connection is
severed the user can still continue to manipulate the data.
Summary
1 2 3 4 5 6 7 8 9 10
The distinguishing feature of building a multidimensional business model in
OLAP Services is the extent to which the designer is assisted by wizards.
While the product cannot be faulted on ease of use, it is prevented from
getting a higher score in this criteria by its limited support for customising
the dimensions and measures. Additionally, and possibly because the model
so closely maps onto the columns in the data warehouse, there is no support
to collect metadata about the model or its components. A further weakness, if
the tool is to be used in a corporate setting, is the lack of support for multiple
designers.
Basic design
Design interface
The interface is easy to use and wizard assistance is available at all stages.
There are three levels to the design interface: the high-level wizard-driven
approach; editors for adding dimensions and calculated members; and the
programmatical enhancement through the addition of COM components.
When using the cube wizard to specify the data source, a sample of data is
shown.

There is no support for a universally available mapping layer.
When the model is being built, there are no prompts for metadata about the
model or its components.
There is no direct mechanism for storing the metadata about the multidi-
mensional model in the Microsoft Repository.
Columns are easily selected using point-and-click.
There is no direct support for selecting members in dimension levels when
the model is being built.
The dimension hierarchy can be defined by the order of selecting the col-
umns, so the system assumes that the second column selected is the child of
the first.
Additionally, columns can be promoted or demoted by clicking on arrow keys.
The system warns the designer if the current child has fewer members than
the parent.
It is not possible to define unbalanced dimensions. User-defined levels can
be specified by typing in SQL statements, using the Dimension Editor.
Time dimension
A dimension can be either ‘standard’ or ‘time’. If it is the latter, then a vari-
ety of time hierarchies is automatically available to the designer.
Dimensions have only one name, so there is no support for a shorter name to
appear on tables and charts as well as a more descriptive, longer name.
The default level of a hierarchy is always the top level. It is not possible to
change this.
Calculated measures
Calculated measures are specified using the ‘calculated member box’. It
provides limited built-in functionality with arithmetic operators and a small
range of functions.
The tool supports multiple measures with a set of dimensions.

Multiple designers
Multiple designers
There is no support to prevent ‘lost updates’ if multiple designers are work-
ing on a model.
There are two very useful wizards to aid optimisation: the Data Storage and
Aggregation Wizard and the Usage-Based Optimisation Wizard. The former
visually graphs the increase in size against the percentage of pre-calculated
aggregates and the latter provides statistics on use.
Summary
1 2 3 4 5 6 7 8 9 10
OLAP Services’ limited support for advanced analytical power emphasises
that this is a tool for ease and speed of implementation and should not, by
itself, be used for powerful analytical work. It is possible to extend the ana-
lytical functionality by incorporating COM components; this requires an
additional sophisticated skill set.
When writing applications that access data stored in OLAP Services, the user
can use some multidimensional expression (MDX) functions. However, the
functions available within the tool are those that reference a member rather
than those that process sets. It thus provides very limited assistance in
defining complex analytical functions using OLAP Services’ Calculated
Member Builder. End-user tools accessing OLAP Services could make use of
the MDXs mentioned below to provide users with some specialised analytical
support.
It is possible to access models developed in OLAP Services using Excel 97,
although this requires some technical skills. When Excel 9 is released in
1999, Microsoft has stated that integration will be well supported, which
should improve the score in this area. At the time of writing, none of the
major third-party analytical tools (for example, SPSS, SAS ETS or Gauss)
provide interfaces.
Ranking and sorting
Supported by the MDX functions of rank, top-count, bottom-count and tail.
There is no support for complex mathematical models.

Financial functions
There are no complex financial functions
Statistical models
There is some support using the MDX functions of standard deviation and
variance.
Trend analysis
There is no support for complex trend analysis.
Simple regression
Some support is provided by the MDX linear regression function.
There are no functions to support forecasting.
The limited built-in functionality can be enhanced by writing the function as
a ‘.dll’ file and then registering it as a function.
Write back is supported. When the updates are written back, they are stored
in a relational partition in SQL Server 7.0 or a relational database accessible
using OLE DB.
Incorporating non-numerical analysis
There is no support for non-numerical analysis.
Data mining
There is no support for data mining functionality.
Web support
Summary
1 2 3 4 5 6 7 8 9 10
OLAP Services, by itself, does not provide web support. As with several of the
features considered in this evaluation, if they are required, they have to be
added on by the use of an appropriate third-party and/or end-user tool.
Some of the entry level tools offer web access using a COM component or
Active Server Pages on IIS. This enables users to explore models using
browser access, but there is no support for the creation of models, nor for
using the Web and the Internet as distribution mechanisms.
OLAP Services does not provide a component to support end-user function-
ality via the Web. It is, however, a feature of several of the end-user tools
that can access OLAP Services via the OLE DB for OLAP interface. These

tools do this either by downloading the PivotTable Services COM component
or through the use of Active Server Pages, as described in the earlier section
on Web architectures.
Supports registered and unregistered web access
It is possible for known users and ‘guests’ to use the web access described
above.
There is no support for producing EIS-style reports for viewing with a web
browser.
There is no support for this.
This is outside the scope of the tool.
This is outside the scope of the tool.
Management
Summary
1 2 3 4 5 6 7 8 9 10
OLAP Services has good support for managing size and partitioning the
data, but poor support for scheduling uploads and providing user security
and controls.
OLAP Services provides a published API, Decision Support Objects (DSO), to
control the management aspects of the tool. This is used, for instance, by the
cube-building wizards. Microsoft has not yet produced easy-to-use functional-
ity to support the scheduling of loads and updates, so users have to either do
this manually or write their own applications using the DSO.
The most obvious enhancement needed in this area is the provision of wizard
support for the management of data.
The management of data, models and users is carried out through a variety
of interfaces.
Security of models
OLAP Services relies on NT authenticating the user.

Query monitoring
Information on queries is via the Usage-Based Optimization Wizard, which
enables the administrator to select a model and a partition within it, and
can then see how many queries were made after a date, how many took
longer than a specified time and which queries are popular. While it suggests
which aggregates should be added or replaced, it does not suggest which
aggregates should be deleted because they are not used.
Management of data
OLAP Services offers three options for storing data:
• MOLAP (both aggregates and detail data are stored in the MDDB)
• ROLAP (both aggregates and detail data are stored in the RDBMS)
• HOLAP (aggregates are stored in the MDDB and detail data in the
RDBMS).
There is no direct support for this. Scheduling has to be done manually or
developers could write an application using the Decision Support Objects
API to control this. This requires programming skills.
There is no direct support for this. It could be done using DSO.
There is no support for scheduling.
Partitions of a model can be distributed on different servers.
Sparsity is handled ‘under the bonnet’ and the designer does not have to
make any decisions about it.
The Data Storage and Aggregation Wizard gives an excellent visual repre-
sentation of the effects of trading off size (a function of the amount of pre-
calculated aggregates) for performance gain. From this representation, the
user makes a choice.
There is no support for in-memory caching.
There is no support to enable end users to be informed of the currency of the
data.

Management of users
Not applicable because write back is not supported.
There is no real support for defining user profiles, because user access to
models is limited to read or not. Security is controlled by creating different
models for different users. To minimise duplicated data, senior managers see
a virtual model made up of several physical ones.
Query governance
There is no support for query governance. While this is not a problem when
OLAP Services is in MOLAP mode, it may be required when used in ROLAP
mode.
There is no metadata other than the model itself. While there is a mecha-
nism to prevent users accessing the model, there is no improvement of the
security offered by NT to restrict the visibility of the model.
Adaptability
Summary
1 2 3 4 5 6 7 8 9 10
The most notable strength of the tool with regard to adaptability is the ease
of changing the storage architecture. It is also easy to add dimensions and
measures. There is effectively no metadata to synchronise, which – although it
is a limitation in other areas – does at least make implementing the changes
straightforward. The tool is prevented from getting a higher score by the lack
of support to track and predict the impact of changes.
New dimensions are added using the Cube Editor, but there are no change
management facilities to track these changes.
Dimension definitions can be stored in the library and re-used from there.
New measures are added using the Cube Editor.

Measure definitions cannot be saved and re-used.
A particular strength of OLAP Services is the ease of changing the architec-
ture to reflect different business needs. When a new partition is defined, the
designer can switch between MOLAP, ROLAP and HOLAP modes.
If all the data for the model is stored in the MDDB (via MOLAP), it will
always be synchronised with the model. However, if either the detail data
and/or aggregate data is stored in an RDBMS (HOLAP and ROLAP) then
changes to the data sources in the RDBMS would lead to a lack of synchroni-
sation between the data and the model. There is no automatic mechanism to
forewarn the user if this is the case.
New dimension members are automatically imported. There is no direct
support for limiting the import of new members.
Metadata
Not applicable, because the only metadata held is schema details.
Impact analysis
There is no support to show the end user the history of the metadata. How-
ever, there is very little metadata so this is not significant.
There is no integration with extraction tools to access metadata generated
upstream.
Summary
1 2 3 4 5 6 7 8 9 10
The administrator has most scope for performance tuning when the tool is
used in MOLAP mode. The most useful feature is the visualisation of the
relationship between database size and performance. In ROLAP and HOLAP
mode there is limited scope for performance tuning.

ROLAP
Multipass SQL
OLAP Services uses multipass SQL when in ROLAP mode.
There are no options for specifying where the processing takes place. It is
always carried out on the database server.
When in ROLAP mode, retrieved data is cached for the duration of the
session. When the cache is full, some data will be lost.
Aggregate navigator
There is no support enabling SQL queries to transparently make use of
summary tables.
MOLAP
One of the most impressive features of OLAP Services is its wizard support
for trading off the percentage of pre-calculated aggregates (size) against the
performance gain. This plots a graph in real-time showing the relationship
between the two, and the user can then choose what percentage of aggre-
gates to have.
As well as reducing the number of pre-calculated aggregates, reducing load
time can be done by limiting the recalculation of aggregates when new data
is entered, so that only aggregates affected by the new data are recalculated.
In OLAP Services, this is achieved by loading the new data into partitions
leaving the original data as it was. The drawback of this is that end-user
queries may then require access to multiple partitions. This can be coun-
tered by merging partitions, and will generally be done at weekends or when
the system is quiet.
Multiple users
OLAP Services has not been available long enough to establish the degree of
support for multiple users.
Processing
All data extraction is carried out using OLE DB for OLAP.
There is no support for distributing the processing between multiple OLAP
Services servers.
SMP support
The architecture is multi-threaded and can take advantage of SMP.

Customisation
Summary
1 2 3 4 5 6 7 8 9 10
The low score in this criteria reflects the absence of support provided by the
tool to develop customised interactive applications. However, if viewed as a
component within an application, then the ubiquity of the API and the low
cost makes it attractive to developers. It cannot be used to customise, but is
itself customisable.
OLAP Services is a component, with an open API, that can be used within a
customised application. The development of the application can be carried
out in any COM-compliant environment such as Visual Basic or C++, but
OLAP Services itself does not provide an environment for developing these.
Customisation
This is a feature of the end-user tool. It is generally not an option in the
entry-level tools considered in this evaluation.
There is no direct support within OLAP Services to provide a customised,
easy-to-use environment. It could either be achieved via an end-user tool or
by incorporating OLAP Services as a component within an application.
Applications
There is no direct support to build simple EIS applications to run in a
browser.
No OLAP-specific development environment is provided.
Applications that access data in OLAP Services using the OLE DB for OLAP
interface can be developed in any COM-compliant development
environment.

Deployment
Platforms
OLAP Services runs on Windows NT, end-user tools on Windows 95/ 98, and
NT.
Using Windows 95 or 98 is appropriate for personal use, but not as a server
for multiple users.
Data access
OLAP Services can access any data available via OLE DB or ODBC.
Standards
OLAP Services uses Microsoft’s OLE DB for OLAP API.
There are no published benchmarks for OLAP Services.
Price structure
SQL ServerOLAP Services is not available separately, but is included in the
Enterprise and Standard Editions of SQL Server 7.0.
At the time of writing (pre-launch), the price structure was not available, but
Microsoft states that it will be similar to SQL Server 6.5. (SQL Server NT
6.5 with ten-user licences costs approximately £1,500 in the UK.)

Microstrategy – DSS
Product Suite
Summary
At a glance .............................................................................................. 3
Ovum’s verdict......................................................................................... 5
Distribution ............................................................................................ 17
Product evaluation
Web support .......................................................................................... 24
Management ......................................................................................... 25
Adaptability ............................................................................................ 28
Customisation ....................................................................................... 31
Deployment
Platforms ............................................................................................... 33
Data access .......................................................................................... 33
Standards .............................................................................................. 33
Price structure ....................................................................................... 33

2
Evaluation: Microstrategy – DSS Product Suite

3
At a glance
Developer
Microstrategy, Vienna, Virginia, USA
Version evaluated
DSS Product Suite version 5.5, consisting of: DSS Architect, DSS Agent, DSS
Server, DSS Administrator, DSS Web, DSS Broadcaster, DSS Executive and
DSS Objects
Key facts
• A ROLAP product with a client-based engine generating SQL
• End-user tools run on Windows 3.x, 95, 98, NT and OS/2; server runs on
Windows NT. Web access is supported
• Microstrategy is repositioning itself to provide commercial business
intelligence applications for the e-commerce market
Strengths
• Easy to build and access models if data is stored in a data warehouse
using a snowflake or star schema
• Strong data analysis and broadcasting capabilities via the Web
• Includes development tools for EIS applications, and an API for building
applications in an OLE-aware language
Points to watch
• Highly dependent on the design and processing capabilities of the data
warehouse for performance
• Limited support for specialised analysis – relies heavily on Excel for
analytical capabilities
• Large implementations involve a wider data warehousing consideration –
a significant amount of consulting is usually required
Ratings
Web support
Management
Adaptability
Customisation
1 2 3 4 5 6 7 8 9 10

4
Agent
A collection of reports, usually grouped under business themes.
Attribute
A level in a dimension hierarchy that either maps directly on to a column in
the database, or can be a derived column.
Broadcasting
The timely delivery of business intelligence information to end users via a
variety of devices.
Cache
A Microstrategy cache is persistent. It can be stored either on the server or
the client. The place and duration (from hours to months) of storage is
defined when the model is created in DSS Architect. Microstrategy refers to
a ‘datamart’ as a cached subset of the data warehouse.
Element
The term used to refer to a member.
Filter
Used to constrain the data that appears in a report. In SQL terms it is the
‘where’ part of the query.
Metric
Corresponds to Ovum’s definition of a calculated measure.
Project
This includes both the multidimensional business model and the location of
the data referenced by it. It is used as a basis for defining reports.
Template
Defines the layout, base content and presentation of a report. In SQL terms
it is the ‘select’ part of the query.

5
Ovum’s verdict
What we think
The DSS tools are well integrated and are becoming increasingly web-
focused. They are also highly dependent on a data warehouse for design and
performance. Any purchase decision will therefore involve a wider data
warehousing consideration. Large implementations usually come with a lot
of consulting, with rollouts taking from 6 to 18 months to complete.
The ROLAP architecture makes the DSS tools well suited to the routine
analysis of large volumes of data. The end-user tools are easy to use and
provide most of the functionality required by casual and regular OLAP
users. DSS Architect’s well designed interface simplifies the mapping of
relational to multidimensional structures.
DSS Broadcaster is a major component of the toolkit. It provides excellent
support for distributing information in a timely manner via the Web and
other wireless channels. The sophisticated reporting and broadcasting
capabilities are in line with Microstrategy’s belief in the commercial benefits
of distributing (and selling) analytical information to a wide range of users.
However, the significant investment required to structure the data in order
to take advantage of the features of the toolkit leads to a degree of lock-in.
While the snowflake schema does not preclude using other reporting and
query tools, it is unlikely to be the preferable design schema if other tools
are to be used. However, the advantage of the Microstrategy approach is that
the company can provide full support for building, maintaining and using a
data warehouse for OLAP.
Although DSS Web exploits the Excel function library, the client-server tools
offer limited support for complex and specialised analytics. This is primarily
a consequence of the ROLAP architecture, which uses specialised analytics
created and stored in the data warehouse, rather than creating them within
the OLAP tool. Microstrategy has yet to introduce DSS Server as an OLE DB
for OLAP data provider; this limits end users to the vendor’s own front ends.
When to use
The Microstrategy tools are suitable if you:
• have already built a data warehouse using a snowflake schema
• have customer management or other applications where the data
volumes are large and there are many members in each dimension
• need good broadcasting support using a variety of devices.
It is less suitable:
• as a departmental OLAP solution, because of its dependency on the data
warehousing architecture
• if you require many different or rapidly changing business models
• if you want to develop highly customised OLAP applications
• if you require specialised and highly complex metrics beyond those
provided by Excel.

6
Product overview
Components
The main components of DSS Suite (all version 5.5) are:
• DSS Architect
• DSS Agent
• DSS Server
• DSS Administrator
• DSS Web
• DSS Broadcaster
• DSS Executive
• DSS Objects.
Figure 1 shows whether the component runs on the client or the server, the
stage at which it is typically used, and its primary function.
DSS Architect
This is a developer tool used to build multidimensional models and define
the mappings to the physical database schema. It is also used to specify how
the model appears to the user.
The Microstrategy solution is closely tied to the dimensional design of the
data warehouse, so the manual gives extensive advice about the appropriate
warehouse schema to use. This generally involves using the snowflake
schema, although a star schema can be used for simplicity (but will not give
such good performance).
DSS Agent
An end-user tool used for report development (for example, applying filters
and templates to the model defined in DSS Architect to specify a report) and
ad hoc analysis of the business model.
Modelling OLAP Web access Management Distribution Development
analysis
Client DSS DSS Agent DSS Objects
Architect
Server DSS Server DSS Web DSS DSS DSS Executive
Administrator Broadcaster

7
DSS Agent generates the SQL to retrieve data from the data source and can
be configured in two-tier mode, with direct access to the data warehouse, or
three-tier mode, with DSS Server as an intermediary. It also integrates with
ETL tools to allow users to view metadata from data warehouses.
DSS Server
This is the core server component for the DSS Suite. SQL queries, whether
generated by DSS Agent or DSS Web, are passed through this server. The
server redirects queries to either the source database (where all processing
occurs) or to cached datasets.
DSS Server provides three tools:
• Scheduler – uses scheduled agents to refresh caches and reports based on
time- or event-driven criteria. The scheduling component can be run as a
Windows NT Service
• Cache Manager – a graphical console for monitoring report caches
• a runtime management environment – allows information to be displayed
about jobs that are running. A transaction log is provided to store
statistical information about system usage and SQL queries.
DSS Administrator
This is the main administration component for the DSS Suite. It consists of
two main interfaces:
• Warehouse Monitor – provides information about performance trends,
usage trends and statistics by user, report and time. It can be used to
assist performance-tuning of the data warehouse (for example, the need
for aggregation tables and indexing), and to determine the best time to
schedule jobs and cache results
• Object Manager – controls the management of users and DSS Suite
objects (such as reports, templates, filters and measures). It can be used
to generate user and group profiles, and to define access rights to
application objects and system functions.
DSS Web
This is a web server that enables a web browser to be used as a thin client
(an alternative interface to DSS Agent), as well as providing an environment
for developing an EIS or a customised front end. Development is achieved
with a combination of HTML and JavaScript, with some Java applets.
DSS Web requires a four-tier architecture. Messages are passed from the
browser to the standard Internet server (for example, Microsoft’s IIS), then
to DSS Web Server where the query engine generates the necessary SQL,
which is passed to DSS Server via RPC and then on to the data warehouse.
DSS Web is licensed either for full functionality (DSS Web Professional) or
as a viewing tool for pre-built reports, but without the facilities to drill
anywhere or create reports (DSS Web Standard Edition).

8
DSS Broadcaster
This is an information distribution and broadcasting server. DSS
Broadcaster uses reports created in DSS Agent or DSS Web. It manages the
distribution of these reports to a variety of end-user devices, including
mobile phones, pagers, PDAs and fax terminals, and via e-mail clients. DSS
Broadcaster includes a sophisticated HTML generation engine that exploits
XML (extensible mark-up language) and XSL (extensible style-sheet
language), to deliver formatted and highly functional e-mails.
DSS Broadcaster’s administration console offers a number of graphical tools
to manage content and set up the distribution environment. Reports can be
scheduled or event-driven, and dynamic distribution lists can also be defined.
DSS Executive
This is an easy to use development environment that is used to create EIS-
like front-end interfaces for casual users of DSS Agent. It provides a number
of ready-to-use EIS objects (buttons and icons) that can be mapped on to
reports to create simple briefing-book applications.
DSS Objects
This acts as an interface to DSS Server that allows for the development of
custom applications using OLE-enabled application development languages
(such as Visual Basic, Visual C++, Visual Basic for Applications and Delphi).
The API provided in DSS Objects enables the application to make high-level
function calls to the query engine and server, making use of predefined
metrics, templates and filters. The developer can create user-defined filters
and templates using the API. There are, however, no ready-to-use components
that provide a GUI interface for creating these objects when building an
application.
Microstrategy only supports a ROLAP approach.
The Microstrategy toolset offers two variants of the light mid-tier architecture:
• fat client
• thin client.
The fat-client configuration uses DSS Agent as the client. SQL is generated
by DSS Agent, but is then passed to the data source via DSS Server. (The
primary role of DSS Server is not to generate the SQL but to monitor and
manage the flow of data in realtime.)
With a three-tiered architecture, the client can use centrally cached data. All
processing of SQL queries is carried out on the database server.
The thin client configuration is used for web access and requires the inclu-
sion of both DSS Server and DSS Web. With this configuration, DSS Web
Server, is the engine that generates the SQL to retrieve the data from the
database. The role of DSS Server is to manage and monitor the data.

9
Running DSS Agent directly against the data source is the simplest two-
tiered configuration. DSS Agent generates the multipass SQL, which is
processed on the database, and the resulting datasets are manipulated in
DSS Agent. This configuration is usually only used for small projects and
testing.
Mobile architecture
There is no direct support to download a subset of data from the relational
database and run queries against this. By definition, ROLAP tools are not
geared to support this architectural configuration.
Using the DSS Product Suite
The need for a data warehouse
Microstrategy provides a set of components to develop, use and manage
multidimensional models. The tools do not provide data cleansing and
transformation functions because the toolkit is intended for use against
pre-cleansed data stored in a data warehouse (using a snowflake schema).
An initial requirement therefore, which is not supported by the toolkit, is to
design an appropriate data model and build a data warehouse. There is
advice about this in the DSS Architect manual.
As shown in Figure 2, building the dimension hierarchy assumes that the
structure of the data source is a look-up table with an ID and description
column.
The use of cached data
In the ROLAP model, data for a multidimensional model is retrieved as
needed from a relational database. However, retrieved data can be cached in
two of the following ways:
• locally – in a binary format including crosstab data
• centrally on the server – in a relational format without pre-calculated
crosstabs.
The cache is non-volatile and available over several sessions. DSS Server
dynamically directs repeat queries to caches when information is currently
available, rather than executing a fresh query on the data warehouse.
Feeding a datamart
The toolset provides the option of feeding retrieved data into a relational
database on the network. Microstrategy calls this ‘dynamic datamarting’. It
is used as a means of passing data to other tools for further analysis.
Division of responsibility
The tool lends itself to a clear division of responsibilities between designer
and user. The mapping layer is defined by the designer using DSS Architect,
and reports are defined by the user in DSS Agent.

10
Figure 2 Building the dimension hierarchy
Within DSS Architect, the designer defines the information that can be used
to build multidimensional business models, maps this on to the source data
and specifies how it is presented to the user.
A report is based on a project defined in DSS Architect. The report adds
templates (which define the slice of the model to be used) and filters (the
rows to be included). In DSS Agent, users can build new reports, run previ-
ously created reports or run ad hoc queries.
Support for casual users
When the user first opens DSS Agent they have to elect to work in either
EIS or DSS mode. Users work in EIS mode if they wish to use an EIS appli-
cation developed with DSS Executive. Working in DSS mode gives the user
full access to all the DSS Agent functionality. Three choices of interface are
available:
• power user – allows full functionality to develop filters, templates, reports
and agents
• DSS Analyst – provides reduced functionality
• high-level user – runs previously-defined reports.
Users can easily build a simple report using basic components
Assuming that there are some previously defined templates and filters, the
simplest way to create a report is to use the Report Wizard. This first guides
the user to select a template from a list of those available, as shown in
Figure 3.

11
Figure 3 Template list
A selected filter can then be modified if required. The results of a report can
be viewed in grid, graph, map or alert mode. For alert mode, data meeting
previously specified criteria is displayed with headlines, optional headers
and optional footers.
Building more sophistication into a report
Define new measures
The power user may wish to define new measures to add to a report. This is
considered to be an advanced feature within the toolkit.
New measures are defined using a calculator-like interface that provides
arithmetic operators and the SQL aggregate functions. If the database being
accessed is Red Brick, the additional RISQL functions (such as rank, tertile
and cumulative) are also available. Compound measures can be created by
combining standard mathematical operators. Values can also be incorporated,
and can be defined as non-aggregable (for example, profit).
All new measure definitions are SQL expressions. They can be re-used in a
number of reports and edited if required.
Define a filter
A filter is a set of conditions that the data must meet in order to be included
in the report. It can be used to define a subset of rows (for example,
‘product=spam’ or ‘sales1,000’) and to handle data attributes. Compound
filters can be built.
A filter is prepared using the Filter Editor. As shown in Figure 4, there is a
tab for each dimension in the Filter Editor, and one for each measure. Within
each dimension, attributes dragged into the right-hand box open an at-
tribute qualification box. This provides operands such as ‘’, ‘’, ‘like’, ‘not
like’ and ‘between’.

12
Similarly, measures can be qualified based on value, rank or percentile. A
further option is to use auto-prompts, which effectively parameterise the
filter and prompt the user to insert a value when the filter is applied.
Define a template
A template specifies how the data retrieved from the data warehouse will be
displayed in each of the reporting modes (that is, crosstab or tabular). If a
report is created using the Report Wizard in DSS Agent, then the template
definition is part of that process. Alternatively, it can be designed separately
using the Template Editor. Essentially, it enables the developer to drag-and-
drop attributes to define the row and column headers and then add measures
to this layout.
Information broadcasting
A key differentiator of DSS Suite is the importance it places on ‘push’ tech-
nology (Microstrategy calls this ‘broadcasting’) in distributing information to
large communities of users, both inside and outside an organisation.
DSS Broadcaster is an information broadcast server component that enables
large-scale report distribution. It includes a web-based self-subscription
interface through which users can register themselves, subscribe to services
and choose device types for delivery of information. Wizards are provided to
guide administrators through the configuration of personalised broadcasting
criteria, such as schedules, styles (using content filters) and threshold
conditions.
Figure 4 Filter Editor

13
Internet e-mail is the main gateway out of DSS Broadcaster, although a
range of devices are supported, including pagers, mobile phones, PDA
handheld devices and fax terminals. DSS Broadcaster includes a sophisticated
XML generation engine that exploits XML (extensible mark-up language)
and XSL (extensible stylesheet language) technology to deliver customised
and functionally rich HTML output to remote users.
E-mails can act as small applications in their own right. They can be used as
a starting point for accessing additional information or to drill deeper into
the data for further analysis. Figure 6 shows that e-mails can include alert
messages, as well as dynamically generated Excel workbooks with detailed
reports and DSS Web URL links for ad hoc analysis capabilities.
DSS Broadcaster also provides an administration console for controlling the
broadcast environment. Monitoring and browsing tools allow systems
administrators to view the status of services, schedules and style objects to
which a user or group is subscribed.
Figure 5 A functionally rich broadcast e-mail

14
Future enhancements
The next major release of DSS Suite is scheduled for late 1999. The new
version will support a new server-centric architecture, with thinned down
clients, allowing easier deployment and centralised management. The web
tools will exploit XML and XSL technology for report generation and format-
ting. Microstrategy has given no further details of the new release; formal
announcements are expected in mid-1999.
Microstrategy is still considering support for Microsoft’s OLE DB for OLAP
as a data provider support, but it has not announced a definite date for
delivery.

15
Company background
Microstrategy was founded in 1989 by the existing president, Michael Saylor.
The company originally offered consultancy rather than products. It built
custom decision-support applications for large companies such as DuPont,
Merck and Xerox.
DSS Agent was released in 1994, and was the first in a line of decision-
support software based on ROLAP. In 1995, DSS Server and DSS
Administrator were released. The first web program was DSS Web, which
was released in 1996; DSS Broadcaster was released in August 1998.
In 1999 the company restructured itself into three principal business units:
• Business Intelligence: focuses on traditional business intelligence
solutions using its ROLAP tools and a central data warehouse
• Commercial Intelligence: develops decision support applications to
support business-to-business/customer/partner information provision,
focusing specifically on the e-commerce market
• Consumer Intelligence: provides business-to-customer information
delivery applications and services to industry sector consumers such as
telcos and ISPs. These applications are targeted directly at information
consumers and revenue is derived mainly from advertising and
subscription fees. One example is DSS Stockmarket, which delivers
personalised stock market reports to subscribers
Microstrategy is one of the fastest growing software companies in the OLAP
industry. The company was entirely self-financing until it went public in
June 1998, which raised $48 million. Microstrategy now employs over 900
people. It has its headquarters in Vienna, Virginia with 27 offices and a
worldwide network of VARs and distributors. Microstrategy has had an
impressive growth rate since going public. Revenues for 1998 increased by
99% to $106.4 million, and net income was $6.2 million. While originally a
consultancy-based company, Microstrategy now generates around 70% of its
revenue through software.
Technically, the philosophy behind the Microstrategy product strategy is a
total commitment to the ROLAP approach. Its core focus is on large ROLAP
implementations, which account for 80–90% of revenue. The ROLAP solution
is dependent on the design of the data warehouse for optimisation, so the
company is also in a strong position to design this.
Microstrategy intends to make information universally accessible to all
types of user. The company’s motto is ‘information is water’. This vision is
characterised by Michael Saylor’s ‘query-tone’ concept: just as the dial-tone
makes telecoms universally available, the query-tone makes access to infor-
mation in a data warehouse a ubiquitous utility.

16
What differentiates Microstrategy from other OLAP vendors is its strong
belief in the commercial opportunities of selling information resulting from
OLAP queries, and in the importance of personalised information broadcast-
ing technology in distributing information. Microstrategy has restructured
its core business to deliver business intelligence applications that allow
companies to strengthen their relationships with customers and suppliers.
The DSS Broadcaster component of the toolset is a major part of this strategy,
and pushes Microstrategy’s established ROLAP technology into the informa-
tion broadcasting business.
Microstrategy has around 700 customers worldwide and is active in the
retail, finance and telecoms sectors. Large customers include Air Canada,
Bank of America, General Motors, Kmart, Hallmark, MCI Xerox and
Vodafone. The need for a data warehouse means that many of
Microstrategy’s accounts are extremely large. In the company’s experience,
the cost of a total data warehouse solution may typically be in the region of
$2.5 million.
Microstrategy markets its software products and services mainly through a
direct sales force, but is increasingly starting to use indirect sales channels.
The company has more than 150 systems integrators, application develop-
ment partners and platform partners using its tools. These include: Acxiom,
Andersen Consulting, IBM, HNC Software, Intrepid Systems, NCR and
Retek.
Customer support
Support
Microstrategy offers worldwide support at various levels according to the
maintenance agreement in place. This includes 24×7 and full support account
management.
Support is provided through two major centres located in Washington DC,
US and Slough, UK. Local support numbers are available for all supported
countries.
Training
Microstrategy runs a variety of courses for its customers and partners,
including a foundation and advanced course (one day each), a two-day data
warehouse design course and a three-day installation and management
course.
The company also provides a partner programme aimed at systems integra-
tors, VARs, OEMs, partners and distributors, comprising all the above
training plus certification.
Consultancy is available directly from Microstrategy, and from partners such
as Andersen Consulting and Renaissance Worldwide.

17
Distribution
US
Microstrategy
8000 Towers Crescent Drive
Vienna
Virginia 22182
USA
Tel: +1 703 848 8600
Fax: +1 703 848 8610
Europe
Microstrategy
St Martin’s Place
51 Slough Rd
Slough
Berkshire SL1 3UF
UK
Tel: +1 44 1753 826100
Fax: +1 44 1753 826101
Asia-Pacific
Microstrategy
41 Dillon Street
Paddington
Sydney, NSW
Australia
Tel: +61 2 9360 0240
Fax: +61 2 9331 3542
http://www.strategy.com
E-mail: info@strategy.com

18
Product evaluation
Summary
1 2 3 4 5 6 7 8 9 10
End users can easily access the multidimensional model via a desktop or web
interface. Flexible access via the desktop is facilitated by the ability to select a
‘high level’ or ‘analyst’ interface. In general, the end-user interface provides
most of the features that OLAP users expect in a well designed GUI.
DSS reports provide a range of sophisticated presentation features, including
mapping capabilities. DSS Broadcaster also adds some very useful publishing
capabilities for easy and flexible distribution, including the ability to dynami-
cally define address lists.
Reports are collected together in report folders and can also be stored in an
agent. An agent is used to group logically similar reports. If an agent is
double-clicked, all of its reports are executed and each one is displayed in a
separate window. There are, however, no search facilities for the end user.
All objects, except fields (for example, reports, templates, filters and
projects), can have a description attached to them. This is defined by designers
in Object Manager, and is visible to end users in reports.
DSS Agent can integrate with ETL products. This enables the end user (or
developer) to view extraction and transformation metadata about the fields
in the data warehouse that provided data for dimensions and measures.
However, caching has to be disabled for this to work. End users simply right-
click on a data element in a model to view additional metadata (such as the
source of the data and the date or time that the data was last updated).
It is not possible for the end user to add annotations.
Using the model
DSS Agent provides all the expected facilities to drill-up, down and across
and to pivot the model. Nested crosstabs are supported. Users can also
specify stoplightling and alert functions.
Ease-of-use is increased by the option of three types of interface in DSS
Agent: power user (with full functionality), DSS Analyst (customised) and
high-level user (for running reports).

19
It is not possible to reposition members within a dimension level.
There is a visual representation of the dimension hierarchy in the
component window, but there is no link to the report to show which level you
are currently in.
The default allows users to drill-down to the detailed data.
Locking prevents users from viewing all the members via the component
window. This is implemented when the mapping layer is defined in DSS
Architect.
Within a report, users can be prevented from retrieving all the data using
‘drill governing’, which restricts the data retrieved from a report (for exam-
ple, to the top five or over a certain value).
Reports can be run in DSS Agent, a web browser, a customised EIS environ-
ment (developed with EIS Executive) or a customised application (developed
with DSS Objects).
Apart from exporting data to Microsoft Excel or Lotus 1-2-3, there is no
direct integration with third-party OLAP clients. There is no support for
Microsoft’s OLE DB for OLAP interface.
Reports can be displayed in crosstabs, graphs or with the alerts highlighted.
DSS Agent (and DSS Web) also support the visualisation of data in maps
and organisational charts. It is possible to combine crosstabs and graphs in
reports and create multiple report workbooks.
Designing a report
A range of sophisticated formatting and design options are provided:
• wizards simplify the report building process
• a number of grid formatting templates are also provided
• a library of statistical arithmetical and date string functions is
provided to define local calculations in reports.
Publishing a report
DSS Broadcaster gives an enormous range of publishing options for reports.
They can be published to dynamically defined address lists, to a wide variety
of delivery devices including PCs, mobile phones (that support alpha
messaging), pagers, handheld devices (for example, Palm Pilots and Windows
CE machines) and fax terminals. Publishing schedules can be based on time-
or event-driven criteria.

20
E-mail is the main gateway out of DSS Broadcaster. An active report can be
distributed via Lotus Notes and Microsoft Exchange Server through a
toolbar button. Gateways are also provided to Microsoft Mail and cc:Mail.
E-mails can support plain text, rich text and HTML content. Dynamic e-mail
address lists are also supported.
Users can subscribe to specific services and reports using DSS Broadcaster’s
subscription templates. Custom subscription interfaces can also be built by
writing to DSS Broadcaster’s COM API.
Summary
1 2 3 4 5 6 7 8 9 10
DSS Architect provides a clear and easy-to-use set of graphical tools to
design and use multidimensional models. Much of the work that results in
the multidimensional model is done upstream when designing the tables in
the data warehouse and defining the mapping layer in DSS Architect. This
makes the building of models easy, but limits the flexibility due to the high
dependency on the data warehouse for the model-building process. For
example, it is not possible to introduce user-defined levels in the dimensions.
Nor is there any tool support for defining the time dimension – it is limited to
what is available in the data warehouse.
Basic design
Design interface
The mapping layer for the reports is defined in DSS Architect using an
intuitive graphical interface. All aspects of the mapping process – establishing
connectivity, selection of warehouse tables, identification of fact columns and
the definition of dimensions and metrics – is simplified with point-and-click
functionality.
A report (which is a set of dimensions, measures and filters) can be generated
from this using a Report Wizard. The design interface, in all cases, is simple
and easy to use.
The tables in the source database are listed and the structure of each can be
optionally displayed. A sample of data from the tables can be viewed using
the component window.
There is partial support for this, because the project serves as a mapping
layer. This stores the metadata that describes the relationship between the
logical model and the database. However, this layer is not universally
available.

21
The developer is not prompted to add metadata when designing reports or
creating the semantic layer.
In DSS Architect the columns for the dimensions are easily selected using
point-and-click. The tool provides development hints by evaluating defini-
tions in the model and suggesting appropriate tables and columns from the
data warehouse.
Row selection is achieved by defining a filter in DSS Agent. Although the
Filter Editor is defined in the manual as an ‘advanced topic’, it is a well
designed and easy-to-use point-and-click facility.
Parent/child relationships between attributes are defined in DSS Architect.
It is possible to have alternative drill-down paths, but there is no support for
unbalanced hierarchies or for inserting user-defined levels.
Time dimension
The tool does not provide a time dimension. The assumption is that the data
will be organised into appropriate summary tables in the data warehouse.
The tool can only use these tables as they are and cannot generate new time
categories. Thus, a time dimension can be built, but all levels must be
mapped on to tables in the database.
In DSS Architect, the developer provides a single term to describe the
dimension. There is no support for a more detailed description.
The definition of a report includes a default level within the hierarchy.
Calculated measures
New measures are defined using a calculator-like interface, which provides
arithmetic operators and the SQL aggregate functions of ‘sum’, ‘average’,
‘maximum’, ‘minimum’ and ‘count’. The designer can also use functions
provided by the database being accessed. However, they need to know what
these functions are because no drop-down list is available.
There is support for multiple measures with a set of dimensions.
Multiple designers
Multiple designers
Only users with the appropriate security can edit objects, but within this
group there is no mechanism to support multiple designers simultaneously
working on objects.

22
There are search facilities that use Object Manager (one of the interfaces in
DSS Objects) to aid developers in finding components.
DSS Architect provides error checking that alerts developers to specification
errors, such as logical violations in the model and assignment errors.
Summary
1 2 3 4 5 6 7 8 9 10
Standard ranking and sorting functions are particularly well supported by
the DSS Server, but there is limited support for specialised analytics above
that provided by the data warehouse. This is primarily a consequence of its
ROLAP architecture, which exploits the analytical functions created and
stored in the data warehouse.
Within DSS Agent there is some scope for extending the range of analytics by
combining basic facts from the data warehouse or embedding conditions
inside the available metrics. DSS Web provides enhanced analytical capabili-
ties through its integration with Excel. Datamining is not available.
DSS Web (but not DSS Agent) can integrate with Microsoft’s Excel function
libraries: web reports can include any Excel function and can easily be
applied to data.
There is no integration with other third-party analytical tools.
Ranking and sorting
Measures can have qualifiers attached to them to limit the items returned. A
measure can be qualified by value (‘’, ‘=’, ‘=’), rank or per cent. Ranking
can be the top or bottom n, or n%. Per cent is used to define the percentage
of the values being ranked (for example, 80:20 reporting). Quartile analysis
is also supported.
Data in reports can be sorted on one or multiple keys, and can be specified
as ascending or descending.
Support is provided for standard arithmetic (for example, minimum, maxi-
mum, average and absolute totals) and algebraic calculation functions. If
advanced mathematical functions are accessible in the database, they can be
used. For example, if the database being accessed is Red Brick, the additional
RISQL functions, such as rank, tertile and cumulative, are also available.

23
Financial functions
There are no specialised financial functions provided by DSS Agent. Some
support is available in DSS Web (via Excel).
Statistical functions
This is not provided by DSS Agent. It is only supported in DSS Web through
its integration with Excel.
Trend analysis
This is not provided by DSS Agent. However, DSS Web users can use Excel
for simple trend analysis.
Simple regression
This is not provided by DSS Agent. However, DSS Web users can use Excel’s
linear regression techniques.
There are no specialised time-series forecasting functions.
There is no scripting language available to extend the range of functions.
However, there is considerable flexibility to build and re-use new measures
in three ways:
• using the function builder interface allows users to create their own
functions and apply them to a data series for display in report writer
mode
• combining basic facts from the data warehouse in reports to build more
advanced metrics (for example, combine the ‘inventory’ and ‘sales figures’
to calculate new measures such as ‘turnover’ and ‘sell-through’)
• embedding conditions inside calculations, and then adding qualifications
at runtime or building ‘self-adjusting’ measures that can be re-used
across the enterprise and multiple reports.
There is no support for writeback analysis.
There is no support for the analysis of non-numerical data.
Datamining
There is no support for datamining.

24
Web support
Summary
1 2 3 4 5 6 7 8 9 10
Microstrategy offers good web support via two products: DSS Web and DSS
Broadcaster. DSS Web has several modes: it provides a development environ-
ment, acts as a server enabling thin client creation and access to sophisticated
reports, and provides administrative control of web usage. It also integrates
with the Excel function library to provide greater analytical capabilities than
the DSS Agent client.
DSS Broadcaster is designed to distribute reports to users. A unique feature
is the ability to distribute these to a range of different devices simply by using
a pull-down menu.
DSS Web uses both ActiveX and Java within the browser to provide a similar
interface to that of DSS Agent. Web access is similar to desktop access,
except users cannot move or pivot dimensions. A range of report-design
features are supported, and asynchronous query handling and status moni-
toring facilities for reports are provided via the web client.
DSS Web provides tight integration with Excel to offer a range of statistical
functions.
Supports registered and unregistered web access
DSS Web employs a flexible multi-tiered approach to security. Security is
flexible enough to be configured to support unregistered users.
The development options offered by DSS Web enable suitable web interfaces
to be created for power users and occasional users.
Editing the mapping layer cannot be done via a browser. It has to be done
using DSS Architect on the desktop.
The underlying model definitions cannot be changed. However, the web API
allows applications to be built using a Report Wizard Java applet, which
enables new reports to be created from existing templates and filters, or by
creating a new template.

25
DSS Agent does not allow reports in HTML format to be saved for loading
on to web pages. However, DSS Web does integrate an HTML editor to
provide support for writing HTML-based reports. There is no support to
generate Java code.
DSS Broadcaster is specifically geared to dynamically distribute reports
over the Internet via e-mail. DSS Broadcaster supports plain text, RTF and
HTML e-mail types. Customisable stylesheet templates are also provided for
the enhanced presentation and layout of data in e-mails.
Reports can also be posted to a web server to create personalised web pages
for access. DSS Broadcaster supports Microsoft’s web casting and CDF
standards.
The applications generated using DSS Web are HTML-based, so they can
include URL references. E-mail sent using DSS Broadcaster can also contain
URLs.
There is no integration with middleware to support distributed processing.
Management
Summary
1 2 3 4 5 6 7 8 9 10
Management of models and users is supported in several of the components
of the toolkit and is easy to use. As expected from a ROLAP tool, there is
comprehensive support for query management and monitoring the usage of
the system. However, some management features, notably user security, are
largely delegated to the data warehouse and are not provided by the toolset.
In ROLAP, persistent models are a pragmatic convenience rather than a
fundamental aspect of the system. However, they still require management,
and the product would be enhanced by more sophisticated cache refresh
functions and more information on refresh schedules.
There are two management interfaces:
• DSS Server – for realtime management
• DSS Administrator – for monitoring and tuning the system.
Both provide graphical interfaces and most administration operations are
supported by point-and-click and drag-and-drop.

26
Security of models
The Microstrategy tools are based on the assumption that most of the
security functions are provided by the database used for the data
warehouse. Within the toolset there is limited support provided by the log-
on ID of the creator of an object determining whether it can be seen by a
group or an individual. The rationale of this facility is to share, rather than
create, a secure environment.
Query monitoring
DSS Administrator’s Warehouse Monitor provides an easy-to-use interface
for a range of useful monitoring information, including a list of most
frequently used reports, a breakdown of web and non-web usage, user
statistics (such as resource consumption and data volume per user) and
information for load balancing.
The information obtained includes:
• table-hit frequency – to identify aggregate table utilisation and
partitioned table utilisation
• individual query statistics – such as total query execution time, SQL
generation time, queue time and SQL execution times.
Management of data
The Microstrategy toolkit does not automatically create persistent versions
of all models, but there is an option to cache data. In this case, a cache is not
a volatile store but a file that can be saved either locally or on the central
server. The two advantages of this are that it:
• reduces the processing load on the data warehouse
• speeds up retrieval time.
Scheduling of loads and updates
The default schedule for refreshing the cache can be specified in DSS Agent,
DSS Web and DSS Broadcaster. The schedule includes details about where
the cache is to be stored and its duration. The units used to define the expiry
date are days, weeks, months and years.
DSS reports can have individual refresh schedules.
Administrators can create a schedule that refreshes the cache after certain
events; for example, a data warehouse load, a general ledger update or a
critical threshold within the data warehouse.
Failed loads and updates
There is no support to inform the administrator that a refresh schedule has
failed.
Caches can be optionally stored on the client or server. A mixture of caching
options is supported.

27
This is not applicable.
This is a minor issue, as the administrator only has to manage the size of
the cached data. This data is subject to the size restrictions on reports
defined in DSS Server. This enables the administrator to limit the number
of rows in the report and/or the scheduled job time. Cached data is also
subject to compression.
This is not applicable, because there is no tool support for in-memory cach-
ing.
When a cached report is used, the status bar displays the date and time
that the data was last updated.
Management of users
This is not applicable: write facilities are not provided.
There is limited support to define security profiles for individuals or groups
from within the tool. Users can, optionally, be allocated to groups by adding
lines to the configuration file, and this will define the objects (that is, reports,
metrics, templates and filters) that they can see. This is, however, more of a
convenience than a security feature.
Most of the security functions are provided by the database.
Query governance
DSS Agent generates a report cost estimate, which can be viewed by the
user, and is also passed to DSS Server for governing and prioritising queries.
A time warning can be attached to a report that generates a message box if a
threshold is exceeded.
Within DSS Server, the emphasis is on governing the overall set of query
processes rather than particular users. These include size limits on number
of rows in a results set, time-outs for long running queries and the maximum
concurrent jobs per user.
There is no facility to limit queries to particular times of the day or week.
There is some control over the visibility of the metadata because, in the first
instance, objects can only be seen by their creator, and then Object Manager
is used to share the visibility. If the designer logs in as an individual user
rather than a system user, this will limit the original visibility.

28
Adaptability
Summary
1 2 3 4 5 6 7 8 9 10
Adaptability in DSS Suite is generally a case of being able to add new
dimensions and measures to a model and re-use these definitions. DSS Agent
also provides access to metadata repositories from third-party data
warehousing tools.
There are a lack of facilities for keeping the data sources, multidimensional
business models and the metadata all synchronised. Changes to the structure
of the data warehouse therefore remain transparent to DSS Suite, but analys-
ing the impact of changes made to dimensions and measures in a model on
associated reports is supported. There is no possibility of adapting the
ROLAP architecture to support MOLAP and hybrid architectures.
A new dimension can be added to the metadata definitions using DSS
Architect and can then be used in reports. There are no management facilities
to track the changes.
As the dimension definitions are stored as metadata they can be re-used.
New measures are added via DSS Agent. A wizard is provided to help users
assemble calculations on-the-fly and add them to a model.
Measures are named and described and can be re-used. All measure defini-
tions are stored centrally.
The Microstrategy tools are strictly ROLAP-based. There is no possibility of
changing the architecture to MOLAP or hybrid modes to support changing
business needs.
There is no support to inform the user when a report is opened if some of the
data sources referenced by the model are no longer available.
ROLAP systems work directly against the source data, so any new values in
an attribute (for example, new product ID) are automatically included in a
report.

29
There is no direct support to prevent new members being automatically
incorporated, but an equivalent effect could be produced by defining a
timestamp filter that does not admit members after a certain date.
Metadata
The metadata is generated by DSS Architect (and stored as a project) and
the multidimensional business models (known as reports) are created from
this using DSS Agent. If some of the fields used in a report are removed
from a project, the report will run – but without the relevant fields. The user
is not informed of what has been lost.
Impact analysis
There is no support to inform the administrator of the effect that changing
the structure of the data warehouse will have on reports. However, Object
Manager lets administrators assess the impact of changes to a metric defini-
tion on reports.
There is no support for a metadata audit trail.
Integration with ETL products enables the developer to view extraction and
transformation metadata about the columns in the data warehouse that
provided data for the measures. However, caching has to be disabled for this
to work.
Metadata can be accessed from a range of ETL tools, including Informatica,
Acta, Ardent, Constellar, D2K, ETI, Prism, Relational Matters and
Systemfabrik.
Summary
1 2 3 4 5 6 7 8 9 10
In a ROLAP model such as Microstrategy’s, the main performance issues are
how to minimise access time, particularly with complex queries. This is
achieved by providing options to cache data and use multipass SQL, ena-
bling queries of greater complexity to be handled. DBAs can also change the
aggregate table or structure, or create table partitions to improve query
response times.
The Microstrategy approach is for all the SQL processing is done on the
database server so that minimum data is retrieved, which reduces the network
load, but increases the load on the database server. A typical Microstrategy
implementation also includes numerous aggregate tables to boost query
performance (but which slow deployment and increase the management
burden). Load balancing across multiple DSS Servers is not supported.

30
ROLAP
Multipass SQL
DSS Server uses multipass SQL.
The processing of SQL queries is always carried out on the source database
(usually a data warehouse). DSS Server can take full advantage of database-
specific optimisation techniques such as hash and start joins, SQL extensions
and predicate clause optimisation.
Non-volatile caches are used. The refresh schedule for these is defined in
DSS Agent.
The information provided by Warehouse Monitor can be used to define an
aggregate strategy in the data warehouse that would speed up user access.
The tool depends on database features (such as table partitioning) to help
speed-up data access.
Aggregate navigator
DSS Server is aggregate-aware. It automatically directs queries in order to
reference smaller, precalulated aggregate tables, thus improving performance.
For queries that require an aggregate table that does not exist, DSS Server
performs a ‘tree-walk’ to reference the most appropriate table from which to
calculate the aggregation.
MOLAP
DSS Suite does not support a MOLAP architecture.
The main limit on the number of users that can be simultaneously supported
is the ability of the data warehouse to cope with the number of temporary
tables generated when the SQL is processed.
The number of users that can be supported is therefore related to the com-
plexity of the queries and capacity of the data warehouse.
Processing
DSS Server uses 16- and 32-bit ODBC connectivity to connect to leading
relational databases. The queries use the pass-through facility of ODBC to
use native SQL to retrieve data.
The processing of the SQL queries is always carried out on the source data-
base (typically the data warehouse). The administrator can use DSS Server
in realtime to tune the number of processes simultaneously running on the
data warehouse. Dynamic database thread allocation and management is
supported to prevent overloading of the data warehouse.

31
However, there is no automatic load balancing between the DSS Servers
because each functions independently. However, using the Warehouse Moni-
tor, the administrator can see which servers are overloaded and move
projects accordingly.
SMP support
SMP support is provided by the database.
Customisation
Summary
1 2 3 4 5 6 7 8 9 10
DSS Suite provides three development environments:
• an API to support application development in OLE-enabled application
development language
• development for the Web using DSS Web
• the production of custom EIS interfaces using DSS Executive.
None of these environments provide support for OLAP-aware components
(such as crosstab and graphical objects), which limits the type of application
that can be easily produced.
Customisation
This is well supported by a selection of interfaces in DSS Agent. When DSS
Agent is opened, users can elect whether to work in EIS or DSS mode. The
range of interfaces supports simple, intermediate, advanced and custom
functionality.
The production of EIS reports is supported by DSS Executive. An EIS is a
guided tour through a project with limited support for analytical exploration.
Using point-and-click, the important objects (buttons, images, labels, grids,
graphs and textboxes) are positioned and their properties specified. This
feature is well supported in an easy-to-use, drag-and-drop environment.
Applications
Web applications are developed via DSS Web, using HTML authoring tools,
JavaScript or Visual Basic Script.
There is no provision of a development environment specialised for OLAP
applications and providing ‘OLAP-aware’ components.
DSS Executive only supports standard EIS objects and visual layout func-
tions that can be used to build briefing books and simple EIS front ends.

32
DSS Objects provides an API to support application development in OLE-
enabled application development languages such as Visual Basic, Visual
C++, Visual Basic Applications and Delphi.
Other support for customisation
The DSS tools support English, Spanish, German, French, Italian and
double-byte Korean language versions.

33
Deployment
Platforms
Client
Clients run on Windows 3.x, 95, 98, NT and OS/2. Web access requires
Microsoft Internet Explorer or Netscape Navigator browsers.
Server
The server-based tools (DSS Server, DSS Web and DSS Administrator) run
only on Windows NT.
Data access
DSS Servers uses ODBC to connect to the following relational databases:
Oracle, Informix, Teradata, Tandem, DB2, Sybase, Microsoft SQL Server,
Red Brick, ADABAS D and Microsoft Access. The queries use the pass-
through facility of ODBC to use native SQL to retrieve data.
There is no direct support for accessing specialised data sources such as SAP
BW and ERP operational data. This is regarded as a responsibility of the
database rather than the OLAP tool and is achieved mainly through the use
of ETL partners such as Prism, Informatica, ETI, Acta and Systemfabrik.
Standards
DSS Objects offers an OLE-based API for software development using a
COM-compliant language.
Microstrategy has not participated in any external benchmarking tests.
However, it has conducted its own internal ‘stress test’ benchmarks.
Price structure
Server pricing
Pricing for server components, including support for up to 50 users, is as
follows:
• DSS Server – $21,125
• DSS Web – $11,375
• DSS Broadcaster – $11,375.
Pricing for all servers increases incrementally with user-number categories
(for example, the next category is 51–200 users).

34
Development tools
The development tools are priced on a per-user model:
• DSS Architect (one user) – $9,750
• DSS Executive (one user) – $9,750
• DSS Administrator (one user) – $19,500
• Development Bundle (two users) – $45,500.
Interfaces
The interface components are priced on a per-user basis, but discounted
rates can apply according to total deal volume:
• DSS Agent (one user) – $1,670
• DSS Objects (one user) – $1,335
• DSS Web PE (one user) – $1,335
• DSS Web SE (one user) – $830
• DSS Broadcaster (one user) – $495.

Oracle Express
Development Tools
Summary
At a glance .............................................................................................. 2
Ovum’s verdict......................................................................................... 4
Distribution ............................................................................................ 13
Product evaluation
Web support .......................................................................................... 18
Management ......................................................................................... 19
Adaptability ............................................................................................ 21
Customisation ....................................................................................... 24
Deployment
Platforms ............................................................................................... 26
Data access .......................................................................................... 26
Standards .............................................................................................. 26
Price structure ....................................................................................... 26

At a glance
Developer
Oracle, Redwood Shores, CA, USA
Versions evaluated
Oracle Express Server, version 6.2; Oracle Objects and Express Analyzer,
version 2.2; Oracle Web Publisher, version 2.0
Key facts
• A MDDB with an integrated development environment. Can also be
configured for ROLAP
• Web- and Windows-based clients and Unix- and NT-based server
• Oracle also produces sales and financial OLAP applications
Strengths
• A mature MDDB engine with a range of financial and analytical
functions
• An easy-to-use – but extensible – development environment, which can
be used to create EIS-style and industrial strength applications
• A complete package, including an end-user tool that can support power
users and has a web publishing component
Points to watch
• Requires 4GL coding skills to supplement the GUI environment; for
instance, to specify security levels for database objects
• Limited support for the publication and distribution of reports
• Not yet seamlessly integrated with other Oracle business intelligence
products
Ratings
Web support
Management
Adaptability
Customisation
1 2 3 4 5 6 7 8 9 10

Formulae
Formulae are calculated measures, but they are created dynamically and not
stored.
SPL
The stored procedure language, also known as Express language, used in
Express Server. It is a 4GL-like language used to define analytical functions
and some Express operations.
SNAPI
Structured n-dimensional API. It is a C language interface for accessing
Oracle Express Server and Personal Express, and used when developing
applications in C/C++ or languages that support calls to C functions in DLLs
such as Visual Basic. It is used by front-end tool vendors such as Business
Objects and Cognos to access Express data.
Variable
The Express term for a measure. A variable is cross-referenced only against
selected dimensions.

Ovum’s verdict
What we think
Oracle Express Server and its associated components have notable strengths
on several fronts. Its multidimensional database is a mature product that
combines GUI ease-of-use with the fully featured Express language giving
procedural control. The application tools, Oracle Express Objects and Oracle
Express Analyzer, enable applications and reports to be quickly prototyped
and more fully developed if required. Underpinning all aspects is a wide
range of analytical functions, particularly in the financial and forecasting
area. A further strength is the possibility of changing the data storage
architecture from MOLAP to ROLAP if the requirements change.
There is good, but not yet seamless, integration with other Oracle business
intelligence tools (such as Oracle Reports and Discoverer) and partnerships
with data mining vendors if this type of analysis is required.
Although generally Express is a GUI-based environment, it requires coding
in some important aspects, such as the specification of user access controls.
Express could be strengthened by the addition of more comprehensive
publication and distribution facilities.
Overall, however, the tool offers an excellent scalable and extensible data-
base and development environment, with the support facilities of a major
database player.
When to use
Oracle Express should be on your shortlist if you:
• are developing medium- to large-scale projects requiring customised
applications of varying complexity
• have complex analytical requirements where the ability to write new,
complex functions is important
• wish to enhance and further develop one of Oracle’s packaged
applications (such as Financial Analyzer or Sales Analyzer).
• want an ‘out-of-the-box’ solution for end users
• do not have access to the coding skills necessary to fully exploit the tools
• want sophisticated publication and distribution support.

Product overview
Components
The main components of the Oracle Express Development toolkit are:
• Oracle Express Server version 6.2
• Oracle Express Objects version 2.2
• Oracle Express Analyzer version 2.2
• Oracle Web Publisher version 2.0.
Figure 1 shows whether the component runs on a client or server and its
primary purpose.
Oracle Express Server
Express Server is at the heart of Oracle’s support for multidimensional
analysis. It can function in three modes:
• as a multidimensional store and engine
• as a ROLAP engine accessing data in a star or snowflake schema in a
data warehouse
• in a hybrid (HOLAP) mode in which the summary details are held in the
multidimensional store, and the detailed data retrieved as necessary
from a relational database.
Data is physically stored in Oracle Express in multi-cubes. Three kinds of
dimensions can be defined: time, text and integer (which is used when
unique names, such as for employees, cannot be guaranteed).
Calculated measures (known as formulae) are defined in a Stored Procedure
Language (SPL) and are usually calculated as required. Using SPL with the
large collection of financial and statistical functions means that complex
business models (including ‘what-if’ scenarios) can be created. When defin-
ing a model, the user has to make a decision as to whether the dimensions
are sparse or not.
When Oracle Express Server is configured as a ROLAP engine, it uses
dynamic reach through SQL to access data in a relational database; the data
is cached in the Express Server and then accessed in the usual way by end
users.
Develop applications OLAP Develop web pages
Client Express Objects Express Analyzer
Server Express Server Web Publisher
Main purpose

Oracle Express includes query monitoring, security and scheduling facilities.
Express Server includes (at no extra cost) Oracle Web Server, Oracle Web
Agent, Oracle Express Administrator and Oracle Batch Manager.
Oracle Express Objects
Oracle Express Objects is a visual development environment that can be
used to develop applications or to enhance Oracle’s packaged financial and
sales applications. It uses Express Basic to give programmatic control. It is
similar in style to Visual Basic, with the addition of multidimensional objects
such as table objects and graph objects and dimensionally-aware list boxes.
The multidimensional aspects are defined through a database browser.
It is an object-oriented environment offering inheritance and polymorphism.
The development environment can be enhanced by the creation of new
objects, which can then be added to the toolbox.
An application is developed as a series of pages.
Oracle Express Analyzer
Analyzer can be used to carry out ad hoc exploration of the database models,
to access applications written in Express Objects or as a visual development
environment to create a briefing report consisting of customised pages
containing tables and graphs. The reports can contain OLE objects, and
include URLs giving web access, as well as all the usual drill-up/down, pivot
and ranking features. If required, any of the properties (such as the dimen-
sion bar or drill-down) can be turned off to provide a less complex report
with reduced functionality.
When published as a briefing, the user navigates the report using VCR-like
buttons to move from page to page. Briefings can also be exported in web or
Excel format.
Oracle Web Publisher
Oracle Web Publisher is for developing web pages that can then be loaded
onto a web server. It is an easy-to-use wizard-driven tool for placing tables
and graphs onto web pages and adding text. The tool enables the developer
to select whether Java code or HTML will be generated. (The HTML is
offered because Java requires a 32-bit operating system and therefore
cannot run on PCs with Windows 3.x.)
The look-and-feel of the resulting pages is more elegant if the Java option is
selected.
Other Oracle business intelligence products
The main Oracle tools supporting business intelligence are Oracle Reports,
Oracle Discoverer and Oracle Express. In addition, the company provides
two packaged applications, Oracle Financial Analyzer and Oracle Sales
Analyzer. These packaged applications are based on Oracle Express and the
components described in this evaluation can be used to enhance and develop
the packaged applications.

Oracle provides OLAP functionality in Oracle Discoverer and Oracle Ex-
press. In this evaluation we have focused on Oracle Express because it is a
more powerful environment, offering a richer set of analytical functions and
development tools. Oracle Discoverer has ‘out-of-the-box’ ease of use,
whereas Oracle Express is more of a toolkit.
Oracle Express Server can be configured to support multiple architectures.
The ‘best fit’ configuration is with Oracle Express as an MDDB server and
engine, the full mid-tier option. The main client options are listed below.
Using Oracle tools
The three most common end-user tools for Oracle Express Server are:
• Express Analyzer
• Microsoft Excel, using an add-in
• a web browser.
Using Oracle’s end-user tool, Oracle Analyzer, users can carry out ad hoc
exploration of the database models, write and run a briefing report, or run
an application written using Oracle Express Objects.
With an Excel add-in, users can view and explore models. A further means of
access is through a web browser if pages have been prepared in Web Pub-
lisher or exported from one of the other Express tools that have been put on
the web server.
Via the API
The Structured n-dimensional Application Programming Interface (SNAPI)
enables client applications running in a Windows environment to retrieve
and use data stored in Oracle Express Server and Personal Express
databases. Approximately 12 user vendors, the most notable being Business
Objects, Cognos and SPSS, have used this interface to enable their tools to
work with Express data.
Light mid-tier option
Using Relational Access Manager, Express can directly access data in an
RDBMS and store it in a cache in Express, where it is available for analysis.
In this configuration, it is comparable to a ROLAP tool, except that the
administrator has some additional work to do in defining the handling of
calculated measures.
The choice of front end-user tools is the same as with the full mid-tier
option.
The desktop and mobile user configurations require the use of Personal
Express, a small footprint version of Oracle Express Server.

Using Oracle Express
Supports MOLAP, ROLAP and HOLAP
Oracle Express can operate in MOLAP mode (with the data for the model
stored in the MDDB), in ROLAP mode (with the data stored in a relational
database system) or in HOLAP mode (with some data in the MDDB and
some in a relational database). However, there is a considerable difference
between the ease of use in the three different modes. The use with best fit is
MOLAP, and when working in this mode the designer implements most of
the features using a GUI.
In ROLAP mode, a more technical skill set is required, because the adminis-
trator has to take special steps to enable the end-user tools to use measures
that include qualified date references, time series functions or ampersands
(alternatively, the administrator should tell the users to avoid using the
functions and syntax that the Express client products cannot parse).
In HOLAP mode, the most usual design is to store the summary information
in the MDDB and then define the dimensions so that when more detailed
information is required, the server generates dynamic reach through SQL to
retrieve the data. Thus, from the users’ point of view, the source of data is
irrelevant. As with ROLAP, some technical skill is required to design the
model to run in this way.
The administrator’s tasks to enable the retrieval of data from a RDBMS to
supplement the data stored in the MDDB include:
• preparing the code that connects to the SQL database
• using the programs provided to make the connection
• implementing the calculated measures in the Express database so they
can be used for SQL reach-through.
As this list suggests, this is not a set of point-and-click exercises.
Provides an extensible environment
When building the business model, most operations can be done at a basic
level using a GUI environment and then extended using the stored proce-
dure language.
Wizard support to build a business model based on data sources
There are several ways of using Oracle Express Server to build the business
model. It can be used in a ‘top down’ manner, in which the business model is
first constructed and then the data sources are mapped onto it. Alterna-
tively, a simple business model based on data sources can be built using the
database wizard.
A sample of data can be seen. The developer can specify various options on
the data to be loaded, including splitting fields, adding or deleting prefixes
and suffixes, specifying values where none are available in the source data
and replacing values.
When dimensions are defined they are designated as sparse (or not) and
hierarchies are created.

Three ways of writing web applications
The Oracle Express development tools enable developers to build web
applications in three ways:
• using Web Publisher. These web applications enable users to interact
with, but not create, models. Web Publisher is an application developed
using Express Objects and accesses Express Server via the CGI provided
by Web Agent
• using any third-party authoring tool and using Express HTML tags to
gain access to the Express Server via the CGI provided by Web Agent
• using the Express language to build a data driven web application. This
requires substantial coding skills.
A wide range of functions can be used to define the calculated measures
A rich set of functions is available for inclusion in the calculated expression,
primarily supporting financial and time series operations. They are defined
by typing in an expression in Express Basic rather than using a calculator-
type interface. This is shown in Figure 2. Further functions can be defined in
the stored procedure language.
Figure 2 Function definition

In Oracle Express, measures have a data type of Boolean, date, decimal, ID,
integer, shortDecimal, shortInteger or text. In Oracle Express, the calculated
measures are not usually stored, but produced as required to reduce the size
of the database. If they are particularly complex and/or used frequently, then
they can be pre-calculated and stored.
Support for incremental roll-ups
The roll-up wizard is used to define the scheduling of the calculation of
aggregates. It supports incremental roll-ups. The Selector tool is used to
select members for roll-ups in the Administrator tool. It is also used in the
end-user tool to specify which members should be included in tables and
graphs. This is shown in Figure 3.
Support for write back
‘What-if’ models can be built using the write-back facility. Measures are
defined that depend on other measures. For example, profits depend on sales
figures. Sales figures, like all components of Express, are objects with prop-
erties. To develop a ‘what-if’ model, the ‘cell entry’ property of the sales
figures is changed to allow editing. The user can then enter a different value
in that cell, and click a button to write this value back to the model, which
then re-calculates the profit figure.
This adjustment to the properties of a cell could also be done in the end-user
tool.
Figure 3 Roll-up wizard

Future enhancements
Oracle believe that the two main issues that will dominate the decision
support tools marketplace over the next few years are the need for a single
integrated framework for delivering business intelligence and the need to
extend DSS tools to a much wider community of users across the enterprise.
Oracle’s main development efforts are therefore focused on two important
areas:
• the further integration of its family of decision support products
• the further development of these tools under Oracle’s Network
Computing Architecture. NCA represents Oracle’s implementation of an
open Internet standards model.
DSS family integration
The current focus is on three principal areas:
• common Java objects
• extended integration with Oracle Server and data warehousing
initiatives
• implementation of common warehouse metadata standards.
Java objects
Oracle’s integration strategy is focused on Internet computing, and the
company sees open and portable components as critical to this approach.
Oracle has begun by re-engineering the decision support clients into a
collection of objects in the form of JavaBeans.
Data warehousing
A major Oracle goal is to provide an environment to cover all phases of
warehouse definition and operation, including integrated access tools.
Initiatives include further data warehousing support in Oracle 8.x and closer
integration with Oracle Express Server.
Metadata standard
Oracle has been participating in an industry initiative to provide a common
warehouse metadata standard. The company intends to use this in all future
developments, thus integrating Oracle’s decision support tools at the
metadata level.
DSS Over the Web
Oracle intends to develop itsDSS Family in line with its Network Computing
Architecture. Discoverer, Express client tools and Express applications will
be implemented as Application Servers. These will support open Internet
standards (that is, CORBA/IIOP), allowing the different components and
tiers to interact, and ensuring that the solution is platform- and operating
system-independent.

Company background
Oracle was founded in 1977 and in 1979 brought the first SQL-based com-
mercial relational database system to market. The company has grown year
on year since its inception. In the early 1990s, it appeared to be faltering, but
made its comeback under the direction of Ray Lane, who was promoted as
Oracle’s president of worldwide operations in October 1993 and is now the
chief operating officer.
Revenues grew by a factor of four between 1991 and 1996, while income
before tax expanded almost tenfold between 1992 and 1996. In the fiscal
year 1997, revenue grew 35% to $5.7 billion, and in the year ending May
1998 the revenues were $7.1 billion, up 17% on 1997 and profits for the year
were $955 million compared to $845 million in 1997.
A breakdown of 1998 results points towards the main growth being in the
services arm, Oracle Consulting, which comprises half Oracle’s business. On
the software side, database software did better than applications.
Express was originally owned and developed by Information Resources (IRI).
In 1985, IRI acquired Management Decision Systems, a consulting company
that had developed the first commercially available multidimensional
database, Express, which was first released in 1972.
In 1995, Oracle purchased the Express technology from IRI Development
still continues in IRI’s original base at Waltham, MA. At that time, approxi-
mately 600 of IRI’s 900 staff joined Oracle.
Ovum estimates that Oracle’s annual revenue from Oracle Express product
suite licences (including packaged applications) is in the region of $180
million, with services and support bringing the total revenue in this area to
$250 million.
Oracle aims to produce a spectrum of business intelligence tools covering the
range of user requirements. The company is agnostic about whether OLAP is
used in an independent datamart infrastructure, within a dependent
datamart data warehousing framework or embedded in operational applica-
tions. The set of tools on offer covers all the architectural configurations and
MOLAP, ROLAP and HOLAP options. The only visible gap is a data mining
tool, and the company has no stated plans to acquire data mining technology.
The Oracle Express Server, considered in this evaluation, is the MDDB
component of the flexible architectures the company aims to support. The
philosophy behind this particular product is that users need a range of
analytical features that can be extended, and a development environment
tailored for multidimensional analysis. If the purchaser is looking for a
simpler environment, the company claims to meet these needs with Oracle
Discoverer.
The only part of the company philosophy that lacks certainty is how to
handle the rapidly growing acceptance of Microsoft’s OLE DB for OLAP
standard. While not ruling adoption of this out, Oracle is still putting its
weight behind the OLAP Council’s MDAPI standard.

Approximately 60% of Oracle Express sales are through its direct channels
and the rest through indirect resellers, ISVs and SIs. Direct sales are gener-
ally to major accounts with an annual turnover in excess of $200 million. It
is anticipated that indirect sales will grow to around 60% in the next few
years.
Customer support
Support
Support is not included in the package, but offered as an additional service.
Support programs include telephone support, around-the-clock coverage, on-
site assistance, dedicated support account managers and priority call
handling.
Support is payable annually and is charged in line with software licensing
(that is, per named or concurrent user). Prices are available upon request.
Training
Oracle Education has 217 education centres in 63 countries, offering a
variety of courses.
Consultancy is not packaged with the product. Service support is available
from Oracle Partners as well as Oracle Consulting.
Distribution
US
Oracle
500 Oracle Parkway
Redwood Shores
CA 94065
USA
Tel: +1 415 506 7000
Fax: +1 415 506 7200
Rijnzathe 6
NL 3453 PV De Meern
The Netherlands
Tel: +31 30 669 4211
Fax: +31 30 666 5603
Asia-Pacific
5 Tamasek Boulevard
#15-03 Suntec City Tower
Singapore 038985
Tel: +65 337 3797
Fax: +65 337 6109
http://www.oracle.com

Product evaluation
Summary
1 2 3 4 5 6 7 8 9 10
The end-user functionality depends on the tool used to access the model or
application. Here, we mainly assess the functionality offered by Analyzer
when accessing a multidimensional business model. Although the model is
easy to use and offers the expected range of drill-down and pivot features, the
product is prevented from getting a higher score for this criteria by the lack of
support for cataloguing, publishing and distributing models and reports. The
tool could benefit from absorbing some of the features of Oracle Reports.
(There is no integration between the two tools.)
There is no tool support for cataloguing models.
Most objects, such as dimension fields and functions, have a description
property that can be viewed in Express Analyzer. Using the Express lan-
guage, additional properties can be added to hold additional metadata.
The end user cannot annotate the model.
Using the model
The expected functionality, such as drilling, pivoting and defining alerts, is
available using point-and-click.
The desktop end-user tools use drag-and-drop, and in the web browser this
can be done using the Selector tool.
The cursor shape tells the user whether they can drill-down. Hierarchies can
be viewed using the Selector’s List tool.
If the model has been defined so that the summary details are in the multi-
dimensional database and the detailed data is retrieved as necessary from
relational tables (‘hybrid mode’), then the user can drill-down to detailed
data.

The ready-to-use front-end tools are Oracle Analyzer, Microsoft Excel with
an add-in, and a web browser. Vendors such as Cognos, Business Objects and
SPSS have used the open API to develop interfaces to access Express
databases.
Tables and graphs can be seen in the same window. Map information can be
incorporated using OCXs available from Map Info.
Designing a report
Reports can include text and OLE objects.
Publishing a report
There is no direct support in Analyzer for the publishing and distribution of
reports.
There is no direct support within Analyzer to e-mail a report. This function-
ality could, however, be implemented using Oracle Express Objects.
There is no mechanism for seeing available reports and subscribing to them.
Summary
1 2 3 4 5 6 7 8 9 10
The tool provides a flexible and powerful interface to define the business
model, and prototypes can be quickly built using the database wizard. It
offers a full range of features for building the multidimensional business
model. The main ways in which the tool could be enhanced are through more
structured support for the collection of metadata (to collect richer informa-
tion) and by the provision of version control.
Basic design
Design interface
The business model is defined using dialogue boxes and point-and-click.
Source data can be seen.
There is no direct support for a universally available mapping layer.

The developer is prompted for short names (for graphs) and a longer, more
descriptive, term for objects such as dimension levels and measures.
Columns are selected using point-and-click.
Members are selected using point-and-click in the Selector tool.
User-defined levels can be inserted. Unbalanced hierarchies and alternative
drill-down paths can be defined.
Time dimension
If the type property of a dimension is set to time, the developer can use the
range tool in the Selector to create dynamic time periods such as ‘year to
date’ and ‘period ago’ measures.
Long and short annotations are supported.
The developer can define different default levels for different users.
Calculated measures
Logical and arithmetic operators and a wide range of functions can be used
to define the calculated measures, but the definition has to be typed in.
Multiple measures can be attached to a set of dimensions.
Multiple designers
Multiple designers
The need for control applies both to the database and the development
environment. In the database, only one user can have write access at any
one time. Oracle supports multi-write access to its Financial Analyzer
application.
Within the development environment, designers work on project libraries.
Two or more users work on project libraries, which are then merged and
compiled into a single project.
There is no support for versioning in Oracle Express.
A prototype model can be quickly built using the Database Wizard.

Summary
1 2 3 4 5 6 7 8 9 10
Support for advanced analytics is one of Oracle Express’s strengths. It has a
rich collection of ready-to-use functions supporting financial, time series and
forecast analysis. Users, with appropriate permissions, can change the prop-
erty of cells and write values back so that values in the model can be re-
calculated. If required, additional functions can be written in the stored
procedure language. The product would be enhanced by the addition of data
mining functionality.
There is an Excel plug-in and an SPSS interface to enable users to access
data in Express Server and use the analytical power of these tools.
Ranking and sorting
This is supported in both Oracle Express and Oracle Analyzer.
Mathematical models
There is no direct support for mathematical functions such as matrix alge-
bra, linear and quadratic equations, cubes and polynomials.
Financial functions
There is a wide range of financial functions. For example, for calculating the
depreciation of assets, a fixed interest schedule and an internal rate of
return.
Statistical models
There are a few statistical functions, such as multiple linear regression.
Trend analysis
There are functions for exponential smoothing, moving annual totals and
moving monthly averages.
Simple regression
There is no support for regression algorithms.
There are lots of functions; for example, functions for comparing values in
one time series with those in another, calculating the series of averages over
time, and showing the percentage difference between the current value of a
time series and the value of a previous one.
Forecasts are supported with functions based on three methods: the
straight-line trend, exponential growth and Holt-Winters extrapolation.

User-defined functions can be written using the Express language. There is
excellent documentation and online help for this.
If the ‘edit’ property of a cell is set appropriately, users can write back values
to the database and new values are calculated from this.
Functions are available to handle date and text as well as numerical data,
but there are no functions to directly support the analysis of non-numerical
data.
Data mining
Data mining is not supported.
Web support
Summary
1 2 3 4 5 6 7 8 9 10
The Oracle Express development tools enable users to access models using
Web Agent (which is bundled with Express Server) and to publish pages
using Web Publisher. As with most OLAP tools, there is support for web access
but not for the creation of new models.
The main focus of web support is the publication of a web page. There is no
support for using the Internet for personalised distribution.
Web access to models created using Web Publisher is very similar to access
via desktop tools. The most obvious differences are that pull-down menus
replace drag-and-drop to change dimensions and some of the charting is less
elegant if the web pages have been generated as HTML rather than Java.
A user has the same rights whether viewing via the desktop or a web
browser. New users can log-on as a ‘guest’ with limited access rights.
Web Publisher can be used to produce simple. non-interactive reports with-
out drill-down or a dimension bar (by turning off properties), as well as
OLAP reports.
Not applicable because no mapping layer is used.

There is no support to build models via the Web.
Users with appropriate permissions can generate HTML pages from
Analyzer.
There is no support to define a dynamic address list for web distribution
from the desktop tool, Oracle Analyzer.
URLs can be included in reports generated using Web Publisher.
Oracle Express uses the conventional architecture of messages passing from
the browser to the web application server and then, using CGI, to the Ex-
press Web Agent and then to Express Server. Web Agent generates HTML
pages and data for Java graphs in response to queries.
There is thus no distribution of web server processing.
Management
Summary
1 2 3 4 5 6 7 8 9 10
The Oracle Express development tools offer developers a large number of
options and configurations, and managing these is unlikely to be a simple
task. While support for most of the important management functions is
available, it assumes a DBA mind and skill set. Some tasks (for example,
defining securities) require the administrator to write stored procedures in
the Express language, rather than doing most of the work in a GUI environ-
ment with the occasional need to script. Thus most of the expected features
are available, but the task of the administrator could be eased with wizard
support and more intuitive interfaces.
Many of the management tasks are done using Oracle Express Administra-
tor. Scheduling, however, is organised using the Express Batch Manager.
Security of models
Security is defined using both the operating system and Oracle Express’s
functions. It is defined using the Express language.

Query monitoring
Through the ‘query statistics’ option, the administrator can get information
about performance statistics and about how the levels in the dimensions are
being used and infer the need for summary tables.
Management of data
When Express is used in MOLAP mode it is stored in a multidimensional
database. When it is used in ROLAP mode it is stored in a cache in the
MDDB.
Scheduling is supported through Express Batch Manager (a graphical utility
to create, monitor and control batch processes) which comes with Express
Server.
Event-driven scheduling, contingent on flags, the existence of a file or time-
stamps, is supported by the administrator writing scripts in the Express
language.
If an upload fails there is error reporting, but there is no facility to specify
that the schedule is automatically re-run.
The database can be stored wherever the developer wishes.
Oracle Express has a method to handle the indexing of sparse measures, but
does not offer wizard support for this.
The simplest method of reducing indexing, if the measure is sparse in only
one direction, is to specify this as the last dimension. This will reduce the
size of the database because pages containing ‘n/a’ values are not saved.
If sparsity is more randomly distributed the measure is defined as a ‘sparse
variable’ along one or more of its dimensions. When a measure is defined as
sparse in this way the system automatically creates a ‘composite’, which is
the list of dimension value combinations that provides an index into one or
more sparse variables. For efficiency, measures can share composites so that
one combination of dimension values is used to access more than one meas-
ure. If the sparsity patterns are different, individual composites can be
defined.
By default, composites are created using BTREE algorithms but, optionally,
a HASH method can be chosen. There is no system support to help the user
see the benefits of the alternatives.
There are two main ways of managing the size of the database:
• the multi-cube architecture reduces the size, as cubes are generally
comprised of dimensions that are densely populated with regard to each
other

• by processing calculated measures as required.
There is no wizard support for these administrative decisions.
No options are available.
There is no direct support to inform the user about when the data being
accessed was last uploaded.
In ROLAP mode the data may be freshly retrieved, cached during the user
session or cached more permanently. Using Analyzer for ad hoc queries, the
user is not aware of the type of cache in use.
Applications built using Express Objects can include information to inform
the end user of the currency of the data.
Management of users
As described in Building the business model, only one user can write to the
model at any one time.
User access is defined using the Express language.
Query governance
Query governance is primarily needed when the tool is used in ROLAP
mode. There is no direct support.
The visibility of models and their metadata can be controlled using ‘permit’
commands in the Express language.
Adaptability
Summary
1 2 3 4 5 6 7 8 9 10
The most useful feature in Oracle Express supporting adaptability is the
ability to change the data storage architecture. The system can be configured
so that all data is held in the multidimensional database or, using Express
Relational Access Administrator, data stored in a relational database can be
used in the model. Finally, the system can be configured using Express
Administrator, so that some (usually the summary) data is held in the MDDB
and the rest is retrieved using SQL commands (HOLAP). Although the toolset
offers a great deal of flexibility, it does not offer wizard support and requires
a competent DBA to manage it.

Adding new dimensions and measures is straightforward, but the tool lacks
facilities to re-use and track these changes. The simplicity of the metadata,
while in other contexts a negative feature, does simplify the process of adapt-
ing the model.
Adding a new dimension to a model is straightforward, but there are no
change management facilities to track these.
It is not possible to name, save and thus re-use a dimension definition.
Adding a new measure to a model is straightforward, but there are no
change management facilities to track these.
Re-use of measure definition
A measure can be saved as ‘custom measure’, with a description and either
local or global availability.
Oracle Express can operate in MOLAP mode (with the data for the model
stored in the MDDB), in ROLAP mode (with the data stored in a relational
database system) or in HOLAP mode (with some data in the MDDB and the
rest in a relational database).
There is no direct support for this when Oracle Express in being used in
ROLAP mode, but a stored procedure could be written to check the data
sources before the query was executed.
The way to lock a dimension so that new members are not automatically
added is to specify in the data load definition that new data should be
matched rather than appended. This is done on a per-dimension basis.
Metadata
This is not an issue because there is little metadata to synchronise.
Impact analysis
There is no direct support for impact analysis.
There is no integration with third-party tools to give access to metadata
generated at an earlier stage.

Summary
1 2 3 4 5 6 7 8 9 10
Express Server can operate in both MOLAP and ROLAP mode, so it could
potentially be finely tuned for both approaches. As expected, its tunability
strengths are as a MOLAP engine.
The appropriate design of multi-cubes can enhance performance, but there is
no automatic support for this. The tool supports SMP.
One weakness of the tool, when used in ROLAP mode, is that the users have
no direct way of knowing how long the data they are viewing has been
cached.
ROLAP
Multipass SQL
Multipass SQL is supported.
The SQL statements are always processed on the database host machine.
Retrieved data is stored in a cache in Express. Caching options are:
• transient cache, in which the data is held in an Express cache for the
duration of the user session
• do not cache, but always query the RDBMS
• permanent cache – all or some levels of the data are permanently stored
in the Express cache.
It is expected that the user will know what form of cache is being used
because it will be a result of their requirements, thus there is no direct
means of indicating the currency of the data to the end user.
Aggregate navigator
Express can transparently make use of summary tables. Information about
summary tables is entered using Express Relational Access Administrator.
MOLAP
The multi-cube architecture assists in size reduction.
When specifying the cube definition in Express Administrator, any measure
defined as a formulae (a calculated measure) can be pre-calculated or not.
There is no software limit to the number of users that can simultaneously
access Express Server. Typically, such limitations are imposed by the hard-
ware and communications set-up.

Processing
In general ODBC is used, although as expected there is native access to
Oracle’s relational database.
There are three main processing options determined by the architecture,
rather than under administrative control:
• thin client/web, in which all processing is done on the Express Server
• client-server, in which the balance is application-dependent. Functions
and stored procedures can be processed on the server or data can be
extracted and processed locally on the client
• processing on the RDBMS, using embedded SQL in the Express language,
stored procedures on the RDBMS and aggregate SQL commands.
SMP support
Express Server uses a multi-threaded architecture which can exploit SMP.
Customisation
1 2 3 4 5 6 7 8 9 10
Summary
Oracle Express and its associated products provide excellent support for
application development. Reports with multidimensional features can be
developed using Oracle Express Analyzer’s visual development environment.
Using Oracle Express Objects, fully featured applications can be developed
combining ease of use with the power of a procedural language. Finally, Web
Publisher enables ‘browser aware’ applications to be created.
Customisation
There is no direct support for users to use a restricted interface.
Using the visual development environment in Oracle Express Analyzer,
users can create a customised report with EIS-type functionality.
Applications
These can be developed using Web Publisher.
Oracle Express Objects is an environment and a set of components. It uses
Express Basic to give programmatic control. In style it is similar to Visual
Basic, with the addition of multidimensional objects such as table objects

and graph objects and dimensionally aware list boxes. The multidimensional
aspects are defined through a database browser.
It is an object-oriented environment offering inheritance and polymorphism.
The development environment can be enhanced by the creation of new
objects, which can then be added to the toolbox.
An application is developed as a series of pages.
SNAPI provides a C language interface to Express Server, and can be used
to write programs in C/C++ or any other Windows programming environ-
ment (such as Visual Basic or Delphi) that supports calls to C functions in
DLLs.

Deployment
Platforms
Oracle Express Server is available for Microsoft NT and various Unix plat-
forms, including IBM AIX, Sun Solaris, HP-UX and Digital Unix.
Personal Express is available for Windows 95 and NT.
Data access
ODBC is used to access the data sources, thus any data source for which
there is an ODBC driver can be accessed.
Standards
The published API is Structure n-Dimensional Application Programming
Interface (SNAPI). This is compliant with the OLAP Council’s specification.
In May 1998, Oracle published figures for the OLAP Council’s APB-1 OLAP
benchmarks.
Price structure
Oracle Express Service (including Oracle Express Web Agent, Oracle Ex-
press Administrator, Oracle Express Spreadsheet-In and Relational Access
Manager) is licensed on a concurrent basis. Prices start at $4,995 for three
concurrent users. The single user version of the Server, Personal Express, is
$870 per named user.
Oracle Express Objects (including Oracle Express Web Publisher) is priced
at $4,995 per named user, with Oracle Express Analyzer at $745 per named
user.
All prices are Oracle’s standard global prices. Contact your local Oracle office
for local country pricing.

Pilot Software – Pilot Decision
Support Suite
Summary
At a glance .............................................................................................. 3
Ovum’s verdict......................................................................................... 5
Distribution ............................................................................................ 17
Product evaluation
Web support .......................................................................................... 24
Management ......................................................................................... 26
Adaptability ............................................................................................ 28
Customisation ....................................................................................... 31
Deployment
Platforms ............................................................................................... 33
Data access .......................................................................................... 33
Standards .............................................................................................. 33
Price structure ....................................................................................... 33

2
Evaluation: Pilot Software – Pilot Decision Support Suite

3
At glance
Developer
Pilot Software, Cambridge, Massachusetts, USA
Versions evaluated
Pilot Decision Support Suite version 6.1
Key points
• A hybrid OLAP server with client-server developer and end-user tools
• Server runs on Windows NT and Unix; clients support Windows 95/98/NT.
Web access is provided
• Pilot has developed analytical applications for retail, CRM and business
performance measurement
Strengths
• A mature MDDB engine that supports a range of analytical functions
• Dynamic dimensions and hierarchies increase the scalability and
flexibility of multidimensional models
• Supports an extensible library of analytical modules for immediate
analysis of data
Points to watch
• Pricing is geared for high-end sites – hybrid OLAP and web access are
expensive options, instead of being bundled with the core product
• Server set-up and management can be complex
• Questions still remain about the company’s stability and growth
Ratings
Web support
Management
Adaptability
Customisation
1 2 3 4 5 6 7 8 9 10

4
Analytical applets
Predefined analytical applications that are provided with Pilot Desktop. The
applications are driven by the business model and can be used interchange-
ably across different models.
Dynamic dimensions
Dimensions that are calculated on-the-fly at runtime. They typically relate
to attributes of a customer (such as salary or age) or product (such as colour
or size) that normally generate very sparse datasets when cross-dimensioned
with other categories.
Dynamic hierarchies
Groups of members that are dynamically defined at runtime in order to
create new aggregation levels. A salesperson may, for example, want to
create their own custom hierarchies based on best and worst customers.
Structural dimensions
Dimensions defined in a business model and preconsolidated in the Pilot
MDDB.

5
Ovum’s verdict
What we think
Pilot Decision Support Suite (PDSS) is a good choice for companies that
want to perform complex analysis against large multidimensional datasets.
The analytical power is expensive, however, making the product a high-end
solution.
PDSS offers high performance and scalability. The tools are built on a ma-
ture multidimensional database (MDDB) engine and a well-conceived
architecture that gives database administrators (DBAs) considerable flexibil-
ity over where data is stored – in a MDDB or relational database, or combi-
nations of both. Pilot’s dynamic dimensions and hierarchies are unique
features that significantly increase adaptability, enabling end users to define
new member groups in the model on-the-fly without having to rely on DBAs
to redesign the core model. They provide an efficient means of tackling the
requirements of large customer analysis or product management applica-
tions that analyse data at an attribute level.
The prebuilt analytical applets included in the Analysis Library are an
important aid to productivity, allowing for fast and easy analysis of data
from different perspectives. Developers are spared the effort of building
these applications from scratch and have access to a set of customisable
objects as a basis for further development.
PDSS is priced as a high-end tool – the hybrid functionality is considered a
luxury rather than a core part of the product set, and is priced at 50% above
the standard Analysis Server. The set-up and management of the MDDB can
be complex, and the product lacks graphical tools and aids that help to build
and maintain the hybrid system – many functions still rely on Pilot’s com-
mand-line interface. Apart from this shortcoming, there are few reasons to
criticise PDSS in technical terms. The greatest challenge for Pilot is to
improve its commercial standing after some turbulent years. To its credit,
the company has implemented wholesale changes to its sales and marketing
organisation and is targeting new markets via partnerships and the provi-
sion of vertically-focused analytical applications.
When to use
PDSS is most suitable if you:
• are developing medium- to large-scale customer analysis or product
management applications that need attribute-level analysis
• wish to analyse data over a range of time periods, using a variety of
forecasting methods
• want the flexibility to store data in a MDDB, a relational database or
combinations of both
• wish to provide users with out-of-the-box analysis with a minimal
development effort.

6
• do not have a well structured and highly-cleansed data source – there are
no data extraction or transformation capabilities, so third-party tools will
have to be licensed
• are looking for an inexpensive and easy-to-use OLAP solution
• have users that want to build their own custom models by sharing and
re-using dimension and measure definitions – the Pilot tools are geared
towards building fewer (but larger) business models that are centrally
maintained by IS
• have the necessary DBA skills in-house to set up and maintain the
servers.

7
Product overview
Components
Pilot Decision Support Suite 6.1 consists of the following components:
• Pilot Analysis Server
• Pilot Designer
• Pilot Desktop
• Pilot Internet Publisher
• Pilot Desktop Reporting
• Pilot Data Mining (Discovery Server).
Pilot Analysis Server
Analysis Server is Pilot’s MDDB. Three features are particularly notable:
• dynamic dimensions and dynamic hierarchies
• flexible time handling
• integrated forecasting and modelling functions.
It also includes a command-line ‘administrator’ interface for managing data,
and a ‘supervisor’ graphical interface for administering end users and
security.
Hybrid option
Analysis Server has a ‘hybrid option’ that provides a relational storage
facility for model data. This option supports any amount of consolidation:
• a fully-consolidated model stored in relational and multidimensional
structures
• an input-only ROLAP structure where all consolidation is done on-the-fly
• anything in between.
OLAP analysis and Web access Data mining Development
reporting
Client Pilot Desktop Pilot Designer
Pilot Desktop Reporting Pilot Internet Pilot Data Mining
Server Pilot Analysis Server Publisher (Discovery Server)
Analysis Server Hybrid Option

8
Pilot Designer
This is a client-server development environment for building analytical
applications. Designer also provides a Visual Basic-like scripting language
for development.
The development environment is closely integrated with the Analysis Server.
Components of the development environment – the table object for building
screens, for example – are ‘dimensionally aware’ of the models that they are
working with.
Pilot Desktop
This is a client-server end-user tool that creates and analyses multidimen-
sional models. Desktop provides a client runtime environment for Designer
applications and includes a standalone version of Analysis Server that data
can be extracted to from a network server.
One aim of Desktop is to provide support for mobile computing.
Desktop integrates three components:
• Model Builder
• Pilot Analysis Library
• Pilot Excel Add-In.
Model Builder
A graphical development tool used to create multidimensional models. Model
Builder can be used to build models in Analysis Server or the PC version of
the OLAP engine.
Pilot Analysis Library
A library of prebuilt ‘analytical applets’ for ad hoc OLAP analysis and more
specialised analysis, such as complex ranking, Pareto analysis, trend-line,
budgeting, exception reporting and forecasting. The applets can be used
across any Pilot model or customised using the Designer component. There
are more than 14 prebuilt analytical applets provided in the Analysis Library.
Pilot Excel Add-In
A DLL add-in that enables Microsoft Excel users to access and analyse
models in Analysis Server. It provides a dimensional selection object, and
drill-down and rotate/pivot facilities. Users can also format and display data
using standard Excel tools.
Pilot Internet Publisher
An application server that provides a web interface to Analysis Server. It
accesses multidimensional data using a combination of HTML, ActiveX and
Java.
Significantly, Pilot has rejected the standard CGI approach for connectivity
in favour of writing directly to Microsoft’s Internet Application Program-
ming Interface (IAPI) on Microsoft’s Internet Information Server (IIS).

9
Pilot Desktop Reporting
An integrated module for advanced reporting capabilities. Desktop Report-
ing is an OEM version of Seagate Software’s Crystal Reports and is tightly
integrated with the PDSS tools and applications.
Pilot Data Mining
In September 1998, Pilot announced that it was phasing out its Discovery
Server and has now aligned itself with Thinking Machines, a data mining
tool vendor. Pilot Data Mining is the integrated result of Darwin and the
Pilot Decision Support Suite. It is available as an add-on component and
provides data mining against relational data; the results of the mining
operation are stored in a table in the relational database management
system (RDBMS) and loaded into the Analysis Server like any other source
data.
Pilot Data Mining is targeted at the business user rather than the data
mining specialist, and focuses on the specific needs of the marketing depart-
ment. Pilot provides two applications (Segment Viewer and Profit Chart)
that are aimed at the marketing manager and make impressive use of the
data mining results within an OLAP environment.
One of the most important features of PDSS is its flexibility in supporting a
range of OLAP architectures for both client-server and web implementations.
This is the ‘natural’ architecture of PDSS. In a full mid-tier architecture,
aggregated data is loaded and stored persistently on a mid-tier server
(where all OLAP calculations are done). When detail data is requested, the
system drills through the multidimensional database to the underlying
relational database.
PDSS supports ‘full’ and ‘thin’ client implementations:
• full client – uses the Desktop module to directly access Analysis Server’s
MDDB
• thin client – needs the inclusion of a web server, and an Internet
Publisher server and clients. Internet Publisher uses Microsoft’s ISAPI
(rather than CGI) for connectivity.
The full client architecture uses a separate queue on the server to service
each Windows client. By contrast, Internet Publisher uses a shared queue to
allow multiple web users to access analysis applications.
Analysis Server supports a ROLAP mode (where model data is stored per-
sistently in a relational database) in a star schema. It also supports a
metadata layer to access the relational dimensional structures for analysis
on the server.
A hybrid (MDDB and ROLAP) architecture is supported, allowing users to
store, access and analyse data in a multidimensional or relational database.

10
The Desktop component includes a standalone version of Analysis Server for
desktop and mobile architectures. This allows data to be downloaded from
the server on to a desktop PC for local processing, or a laptop for offline
analysis.
Using PDSS
Hybrid OLAP
PDSS is a hybrid OLAP tool that provides a server-based multidimensional
database as well as a relational storage option. What differentiates PDSS
from other hybrid OLAP tools is how it implements the hybrid system. In
most hybrid systems, the upper level aggregate data is stored in the MDDB;
lower level (or detail) aggregate data is stored in the relational format. When
detail data is requested, the system drills through the MDDB to the rela-
tional database in order to retrieve the data.
PDSS can store data in a multidimensional or relational database by analys-
ing usage. For example, if the upper tier of aggregate data is rarely accessed,
it can then be stored in the relational database with the detail data; mid-
level aggregate data that is frequently accessed can be stored in the MDDB
for quick access. Although Pilot gives administrators considerable flexibility
over where the data is stored, the set-up and management of the servers can
be complex.
Figure 2 is a typical PDSS architecture that shows how different levels of
data can be stored in an MDDB server or a relational database.
The benefits of dynamic dimensions and hierarchies
Dynamic dimensions
A unique feature of PDSS is its support for dynamic dimensions. Dynamic
dimensions are calculated on-the-fly at runtime (as opposed to ‘structural’
dimensions, which are preconsolidated), but they act and appear as normal,
precalculated dimensions. For example, two dynamic dimensions can also be
cross-tabulated.
For users, dynamic dimensions offer two main benefits:
• scalability – very large models can be defined with tens (or hundreds) of
dimensions without the resource problems typical of MDDBs
• efficient analysis of data at an attribute level – dynamic dimensions
relate to attributes of a customer (age or salary) or product (colour or
size). These dimensions normally generate very sparse datasets when
cross-dimensioned with other categories. Most other MDDBs allow users
to select dimension members on the basis of attributes only (all red
garments, for example). But Analysis Server allows these attributes to be
used for multidimensional analysis.
Dynamic hierarchies
Dynamic hierarchies enable groups of members to be defined on-the-fly,
creating new aggregation levels; a salesperson may, for example, want to
create their own groups based on best and worst customers.

11
Figure 2 Hybrid architecture
Pilot Desktop Web browserPilot Excel Add-In
Pilot Data
Mining
Pilot Analysis Server
(OLAP Engine and MDDB)
Highly aggregated and frequently-accessed data
Pilot Internet Publisher
Web server
OLAP metadata
RDBMS
Detail and/or rarely-accessed
aggregate data
The main advantage for end users is flexibility when working with a busi-
ness model. Users can define their own custom aggregation levels on-the-fly,
without the need to wait for a DBA to redefine the core business model.
Built-in time dimensionality
PDSS has always had strong time-handling functions. The built-in time
dimensionality within models automatically comprehends how data changes
over time. Data collected from predefined timeframes is automatically
consolidated, without additional programming or maintenance. Time intelli-
gence also ensures that measures that do not add over time (such as ratios)
are correctly calculated and displayed within the requested time periods.
In a Pilot model, time is held at the lowest level of granularity within Analy-
sis Server. Aggregation levels for time are dynamically created within the
server, which understands standard and non-standard calendars (for exam-
ple, months based on the pattern of four or five weeks). Many predefined
views are also available, including ‘prior period’, ‘moving average’ and ‘roll-
ing views’. A custom view capability accommodates special timeframes such
as promotional and seasonal periods.

12
Pilot Analysis Library
From an end-user perspective, PDSS’s analytical capability lies in its Analy-
sis Library. The library consists of a series of point-and-click ‘analytical
applets’ that can be used out-of-the-box for immediate analysis.
The term ‘applet’ is slightly misleading, as they are in fact fully-functional
applications built with Designer; they can also be customised using the
Designer tools. In practice, however, they function as ‘templates’ because
they are data-driven, picking up all relevant values dynamically from a
multidimensional model (including all dimensions, aggregation hierarchies
and measures). An analytical applet therefore works automatically against
data from any Pilot business model. The applets provide a range of function-
ality from a standard tabular display of multidimensional data to an advanced
application for trend forecasting. Some applets come in multiple versions,
each of which offers specialised variations of a similar analytical theme.
The main analytical applets available are:
• Navigator, which supports ad hoc analysis. It provides a table view on a
business model, as well as the basic OLAP functions such as drill-down,
dimension selection from your model, rotation, and row and column
calculations
• Exception Reporting and Exception Stars (for exception and variance
reporting over two time periods)
• Ranking Analysis, as shown in Figure 3, for ranking dimensions
according to a single measure. Ranking Stars ranks a measure for two
time periods; Ranking Plus displays two measures side by side (for
example, sales and margins)
• 80/20 Analysis, which is used, for example, to determine what percentage
of total business is represented by any group of products, regions,
accounts, customers or channels. 80/20 Stars analyses two time periods;
80/20 Plus displays analysis for two measures side by side
• Trend Forecast, which allows users to select from the various forecasting
algorithms that are supplied to produce a trend analysis on the basis of
any selected data
• Quadrant Analysis, which creates a four-square grid based on two
selected business measures and shows the correlation between the two.
If, for example, the measures are sales and margins, the top quadrant
shows products with high sales and high margins.
By default, the bottom half of an analytical screen shows what dimensions
and measures are available – users can choose their preferred type of analy-
sis. These controls can be hidden to provide an ‘executive’ view, showing only
results.
Moving from one kind of analysis to another keeps the same perspective on
the data. Whatever view users have of data, when they switch applications –
from 80/20 to Quadrant Analysis, for example – the new application looks at
the data from the same view.

13
Figure 3 Ranking Stars application
Developing applications
PDSS provides Designer, its own integrated development environment,
which offers a number of graphical tools for creating analytical applications
easily. Designer applications are based on the concept of ‘sheets’, which are
composed of ‘objects’. Each object has associated properties defined in a set
of tabbed dialogues.
Designer has a Visual Basic-like scripting language that is used to define
dialogues or fine-tune functionality. Most development is done by tailoring
presupplied objects such as tables, charts, OLE containers or listboxes.
The Object Manager interface gives a hierarchical view of components of a
sheet, as shown in Figure 4. Its aim is to make the task of managing complex
screens easier.
The table object is particularly important, because it provides cross-tabular
functionality for multidimensional analysis and no additional programming
is needed. The table object can be linked to a dimensional model, SQL proce-
dure, text file or DDE source.
When using the table object against a business model, you select a view on
the model and the application runs immediately, providing default cross-
dimensional analysis capabilities. Default features include:
• charting
• the selector object (for choosing measures and dimensions)
• an inherent understanding of drill-down
• access to Pilot’s calendar functions.

14
Figure 4 Object Manager interface
The features that are made visible to the end user can be limited by the
developer.
As with prebuilt analytical applets, applications that are created in Designer
are inherently data-driven rather than procedurally defined. This means
that an application should run against any model without change.

15
Future enhancements
Version 6.2 of PDSS is due in mid-1999. It will include the following en-
hancements:
• model partitioning – allowing data to be stored across separate models
with shared structures. The models can then be treated independently,
and can be loaded and consolidated incrementally by different processes
• the ability to re-use and share dimensional structures and measure
definitions between models
• writeback capabilities via the Excel Add-In facility
• agent-based distribution services – through an ongoing partnership with
Blue Isle Software, Pilot will provide agent-driven analysis and
distribution capabilities. For example, users will be able to create an
agent process to watch for specific criteria (for example, exceptions or
other events in the database), triggering data loads or updates and then
notifying users via e-mail (or other devices such as pagers and mobile
phones)
• extended data access from Internet Publisher – web users will be able to
access MDDB and relational data from the same screen without
additional third-party data access tools.
Tight integration with Thinking Machines’ data mining tool, Darwin, is also
scheduled for 1999. The aim is to have the data mining engine talking to the
Analysis Server, so that results from data mining will feed directly into the
MDDB server model. The integration will provide the focus for developing
new fraud detection applications that extensively integrate data mining
functionality.
Pilot intends to develop additional vertical applications that build OLAP
analysis solutions on top of CRM vendor packages. It is seeking to form
partnerships with campaign management, salesforce automation and call
centre software vendors.

16
Company background
Pilot Software was incorporated in the US in 1983. It is one of the longest-
established OLAP vendors. Pilot launched one of the first EIS tools (Com-
mand Center) in 1984 and became a leading vendor in the mainframe EIS
market. The company has claimed a number of ‘technology firsts’, including
time-series analysis and the use of a multidimensional database. Pilot
Lightship, first launched in 1992, marked a departure from a mainframe-
centric approach towards a client-server architecture. Lightship has now
been replaced by PDSS.
In 1994, Pilot was bought by Dun Bradstreet and was incorporated as part
of its Cognizant business division. The takeover provided Pilot with re-
sources to complete its transition to a provider of client-server tools. How-
ever, it did not deliver the growth in revenues that was expected; conflicting
visions between the two management teams resulted in significant problems
with Pilot’s sales and marketing activities. In late 1997, Pilot was sold to
Platinum Equity Holdings, a US company that specialises in buying high-
tech companies. A new business model that focused on channels and partners
was then defined, and unprofitable operations were shut down. Pilot also
restructured its salesforce and management team, and appointed a new
CEO.
Pilot employs around 175 people. Ovum estimates that the company had
revenues of approximately $35 million for the 1998 fiscal year. The compa-
ny’s corporate headquarters is in Cambridge, Massachusetts, USA, with
regional offices and distributors worldwide.
Pilot Software has been in the business intelligence market for several
years, and was a founding member of the OLAP Council. The company’s
reputation for technical innovation and extensive experience make it an
important player in this market.
Despite Pilot’s established presence in the market and the steady evolution
of its OLAP products, it has been unable to build up any serious commercial
momentum. The takeover by Platinum Equity Holdings, however, has intro-
duced wholesale changes; Pilot’s newly-appointed CEO now refers to the
company as a ‘mature start-up’.
Pilot’s traditional focus was on sales and marketing applications. This
horizontal focus has been further refined though important partnerships to
develop and market packaged analytical applications for vertical industry
sectors, including:
• retail – Retail Performance Monitor and Retail StoreCard
• financial services – Commercial Credit
• consumer banking – Customer Attrition
• telcos – Churn Prophet and Channel Wizard.

17
Pilot has more than 500 customers and has sold more than 100,000 user
licences worldwide. Most of the customers are large Global 2,000 companies.
Pilot has a particularly strong presence in the retail, telco and consumer
packaged goods sectors. Large customers include ATT, Office Max, Burger
King, Kmart, Baskin Robbins, Whirlpool and Lucent Technologies.
The company sells its products mainly through direct channels, but is in-
creasingly using channel partners to penetrate new markets such as cus-
tomer relationship management, manufacturing and healthcare, and bal-
anced scorecard. Major application partners include Foresight Software,
Lightbridge, American Software, Synertech, IMS Health and Touch.
Customer support
Support
Telephone, e-mail and fax support is available worldwide; it is primarily
aimed at developers rather than end users. Support centres are located in
the US, Europe and Australia. Support is priced at around 20% of the licence
fee.
Training
Pilot offers training for all components of PDSS, which is available either on-
site or from its worldwide offices.
Pilot provides a range of consultancy services for PDSS implementation and
application development. Most consultancy work focuses on defining business
requirements, building models and implementing data access strategies.
Distribution
US
Pilot Software
1 Canal Park
Cambridge, MA 02141
USA
Tel: +1 617 374 9400
Fax: +1 617 374 1110
Europe
Pilot Software
Maxfli Court
Riverside Way
Camberley
Surrey GU15 3YL
UK
Tel: +44 1276 687000
Fax: +44 1276 687077

18
Asia-Pacific
Pilot Software
Level 1, Building A
Forest Corporate Park
18 Rodborough Road
Frenchs Forest
NSW 2086
Australia
Tel: +61 2 9975 2380
Fax: +61 2 9975 2386
http:// www.pilotsw.com
E-mail: info@pilotsw.com

19
Product evaluation
Summary
1 2 3 4 5 6 7 8 9 10
Users that are familiar with analysis tools will find Desktop easy to navigate,
but OLAP novices may initially feel overwhelmed by the interface. Desktop
comes with a number of point-and-click analytical applets that can be run
immediately against any model. The look-and-feel of the navigation is simi-
lar, regardless of the analysis at hand; once users learn how to navigate one
application, the others will be easy to use. Advanced reporting relies on an
OEM-enabled version of Crystal Reports, but customers must pay extra for this.
The client tools would benefit from more metadata (to help end users under-
stand the model better) and the ability to drill through to source data. Publish-
and-subscribe capabilities for widespread distribution are not supported.
A Portfolio applet allows specific data views to be collected and used as a
starting point for analysis. Apart from this, there is no support for logically
grouping business models or searching for them using keywords.
There is no scope for viewing metadata about a model.
Annotation by end users
The direct annotation of models by end users is not supported.
Using the model
The Navigator applet can provide ad hoc OLAP analysis functions, such as
drill-down, drill-up, and pivot and rotation.
A dialogue box is provided for re-ordering members in a dimension level.
The Selector dialogue box provides a graphical representation of the drill-
down hierarchies. There is no support for showing the users’ position within
it, however.
It is not possible to drill-down through models to source transactional data.

20
Desktop supports more than 14 analytical applets that can be used as front-
end interfaces. A Microsoft Excel add-in is also provided. Custom front-ends
can also be developed by third-party tools such as Visual Basic and
PowerBuilder, and integrated using the Designer tool. Third-party OLE DB
for OLAP clients cannot access Analysis Server, however.
PDSS supports more than 50 business charts. OLAP (drill-down and rota-
tion) from within charts and graphs is not automatic, but can be built-in by
developers. PDSS integrates mapping technology from MapInfo for
geospatial presentation and analysis of data.
Designing a report
PDSS does not have a dedicated report design tool; reports are based on
tables that users can add headers and footers to. For advanced report design,
PDSS relies mainly on Pilot Reporting, an OEM version of Seagate Soft-
ware’s Crystal Reports. The Desktop Reporting component uses ActiveX to
feed data from Pilot applications to a report template – the report template
can have any structure supported by Crystal Reports. Nested crosstabs are
supported, as is the ability to embed OLE objects in report templates. Local
calculations can also be defined.
Publishing a report
There is no direct support for publishing a report. The only way to do so is to
set up a shared server environment and allow users to publish reports in a
public portfolio for access by others.
PDSS is MAPI-enabled and integrates with Windows-based e-mail systems.
It also works with Lotus Notes – the Notes link provides an e-mail and a
document interface. Notes documents can be integrated into a ‘sheet’ in a
Pilot application.
Report subscription services are not provided.
Summary
1 2 3 4 5 6 7 8 9 10
Model Builder provides an easy-to-use graphical environment in which to
create a basic model structure. The modelling capabilities are quite sophisti-
cated. The tool’s efficient handling of time series calculations and the ability
to define dynamic dimensions and hierarchies at runtime is unique among
OLAP tools, and simplifies the task of building and maintaining large
models. However, developers will often have to use Pilot’s proprietary command-
line language IDQL (Interactive Dimensional Query Language) to add
greater complexity to the multidimensional structure.

21
Basic design
Design interface
Model Builder is a menu-driven GUI tool for building business models and is
part of the Desktop and Designer clients. Model designers can select the
dimensions, hierarchies and measures required in a model through a set of
menus, pick lists and dialogue boxes.
Model Builder is best used to build a standard base model, but lacks ad-
vanced design functions. This can be remedied using the IDQL (Interactive
Dimensional Query Language) command-line interface.
It is not directly possible to visualise the data source, but a check-box is
associated with every field listing and allows developers to display values
from the data source during the design process.
A universally available mapping layer is not supported.
The level of metadata captured during the design process is minimal.
To create a new model, developers select a source and define a SQL query to
retrieve information on the target data structure. Model Builder provides a
set of dialogue boxes and menus to select columns for dimensions from the
results set.
Members can be selected using point-and-click or SQL.
Developers can define their own ad hoc aggregation hierarchies using point-
and-click. Multiple hierarchies in a dimension are also supported. It is not
directly possible, however, to specify ragged (that is, unbalanced) dimensions.
Time dimension
PDSS makes time an implicit part of the data model. Model designers do not
have to define the time dimension; only a fiscal calendar has to be specified,
so that period calculations can be done.
Time is held at the lowest level of granularity (determined by the source
data); any required aggregation levels are generated dynamically. Analysis
can be based on a range of time factors, including daily or hourly, and vari-
ous non-standard time periods such as retail calendars based on four- or
five-week months and 13-month lunar calendars. Users can also define their
own calendars and periods, including dynamic periods. The time options also
ensure that measures that are not additive over time – for example, balance
sheet items or ratios – are correctly calculated and displayed.

22
Model designers can define long and short names for dimensions. End users
can choose which type of name to use for display in reports and charts.
Default dimension levels are typically specified at the application design
level.
Calculated measures
Calculated measures can be defined graphically using a formula. But the
range of predefined functions provided is limited. Advanced calculations can
be created through logic sets such as ‘if, when and else’ statements; these
can be applied to stored measures or virtual measures (calculated at
runtime).
This is supported.
Multiple designers
Multiple designers
Other than locking a model, there is no shared repository to support
multidesigner environments.
Versioning control is not supported.
Summary
1 2 3 4 5 6 7 8 9 10
Pilot’s Analysis Library is the most significant resource for analysis. The
analytical applets fulfil standard and more specific analysis tasks, and
support a wide range of statistical analyses, correlation methods and fore-
casting techniques. The Analysis Library is powerful and easy-to-use.
Most applets can be used out-of-the-box and can be applied to data from any
Pilot model; however, users would benefit from additional help or tutorials to
interpret the results. Developers can also customise the applets (using the
Designer tools) and create new functions using IDQL, the non-procedural
language. Data mining capabilities are also provided.

23
Integration with Microsoft Excel and data mining tools is provided. There is
no integration with other analysis front-end tools, such as SPSS.
Ranking and sorting
The Analysis Library includes four ranking modules that support basic
ranking on a single measurement and ranking a dimension based on different
measures. Support is also provided for Pareto analysis. The Rising Stars
application enables users to analyse the fastest gains and losses at regular
time intervals.
Support is provided for a range of advanced mathematical functions. These
functions are extensively used by the analytical methods discussed in this
section.
Financial functions
Support is provided for standard financial functions such as net present
value (NPV), internal rate of return (IRR) and depreciation.
Statistical models
A built-in statistical library for trend and forecasting techniques is provided.
A number of correlation statistics are supported, including moving averages,
standard deviation, skew, kurtosis and the DurbinWatson method.
Trend analysis
Support is provided for a variety of curve fitting and smoothing techniques.
The Trend and Forecast analytical object makes many of these functions
readily accessible to the end user. The user selects from the various forecast-
ing algorithms supplied to produce a trend analysis for any selected dataset.
The user picks the forecasting and smoothing method to be used from a
menu.
Simple regression
Support is provided for linear, polynomial and stepwise regression techniques.
A range of complex forecasting functions are available as menu options.
These include extrapolation, exponential, hyperbola, quadratic and rational.
Users can also extend functions and build their own functions using IDQL,
Pilot’s proprietary language. IDQL is a simple non-procedural language
similar in syntax to Pascal. The functions that are created can be stored in a
common repository for re-use.

24
The Analysis Server only supports single-user writeback. For ‘what if?’
models or budgeting applications that require changes to the model data, a
user can save a ‘snapshot’ of the data for local access. The data can be loaded
back into the server later.
Pilot also provides a special What If? application that can be used to create
business scenarios by increasing and changing the values of various meas-
ures in a model. The What If? application, developed in partnership with
Extend (a Brazilian partner), is fully integrated with the PDSS tools but is
sold as an optional component.
Analysis Server can store and manipulate textual data within the multidi-
mensional database. It does not provide any text analysis functions.
Data mining
Predictive data mining capabilities are available via Discovery Server;
however, Pilot is in the process of phasing out this application, and now has
an agreement with Thinking Machines to integrate with (and resell) its
Darwin data mining tool. Further information on Darwin can be found at
http://www.think.com.
A number of analytical objects, as discussed in Using PDSS, are included in
the Analysis Library for specialised analysis.
Web support
Summary
1 2 3 4 5 6 7 8 9 10
Given the analytical nature of Pilot applications, Internet Publisher is most
useful for internal or intranet use rather than deploying applications over the
Internet. The web interface provides a similar level of analytical capabilities
as the Desktop clients through a combination of HTML and Java applets.
Users that need more complex functionality will be better served using the
Desktop client. By artificially limiting the maximum number of server
resources, administrators can control and balance server loads on a shared
system. Desktop also provides server caching and resource pooling for
increased performance and scalability.
Internet Publisher works exclusively with Microsoft’s Internet Information
Server – connectivity (via CGI) to a wider range of web servers is not supported.

25
Internet Publisher’s web interface uses HTML to present the data and most
of the interface functions to the browser; Java is used for complex analytical
features. The web interface supports picklist-driven views – users can select
and combine picklist choices to perform drill-down and rotation functions.
Web users can also create their own dimension member groups or summa-
rised results for custom analysis needs.
The same analytical functions included in the Analysis Library are available
as Java or ActiveX applets, but there is no support for writeback or locally-
defined calculations via the Web.
All web users must be registered and named users.
Range of users supported by the Web interface
Internet Publisher generates HTML tables that can be colour-coded for
casual users requiring simple reports or EIS interfaces. Power users are
supported by Java-based analytical applets.
There is no support for creating and editing models via the Web.
Internet Publisher generates HTML tables that can be displayed in web
pages, but does not provide its own web page editor. It can also provide a
datastream for Java or ActiveX objects.
In partnership with Blue Isle Software, Pilot enables users to distribute
models as personal models via the Internet as a compressed e-mail attach-
ment. Models can also be distributed via URL links in an e-mail or personal
web page.
It is possible to include URLs in reports.
Internet Publisher can distribute processing over a heterogeneous cluster of
servers. Administrators can define maximum server processes for each
server. When the maximum load is exceeded, additional processing requests
are automatically queued and dispatched in a balanced fashion.
Alternatively, administrators can select groups of users that have their own
pool of dedicated server resources. This guarantees optimal performance for
users that require preferred response rates.

26
Management
Summary
1 2 3 4 5 6 7 8 9 10
PDSS gives administrators considerable flexibility over where the data is
stored. Detailed monitoring tools are provided to refine the system’s configu-
ration based on the most popular queries and usage patterns. The servers are
complex to set up, however, and would benefit from graphical tools and aids
to make the process less time-consuming. Most management functions are
accessible via a proprietary command-line interface; the only exception is the
Supervisor utility, which provides a graphical (but inflexible) environment
for defining user groups and access rights.
Developers can build a set of procedures that can be run to refresh the model.
Analysis Server does not have its own scheduling services, however; they must
be performed using operating system functions.
The Administrator interface is used to manage models and data. The inter-
face has a command line; administrative functions are accessed by program-
ming directly in IDQL.
Security of models
Three access modes are available for models:
• exclusive – enables only one user to access the model at a time in the
read or write modes
• read – enables all users to access the model in read mode
• shared – enables all users to access the model in the read or write modes.
While one user is using the model in shared mode, no other user can
access it in the exclusive or read modes. Because Analysis Server must
continually check whether the data has changed, the shared mode results
in a much slower response time and is not recommended.
User and/or group access to models can also be limited by dimensions,
aggregation levels or measures. Users are simply provided with filtered
views of the business model and are not aware that they are excluded from
elements of the model.
Query monitoring
The TrackerTable monitors queries that are made to the MDDB and rela-
tional database. The number of hits and the time taken to process a query
based on an intersection of specific model elements is recorded and stored in
a relational table for further analysis and performance tuning.

27
Management of data
Data can be stored in a MDDB, a relational database, or combinations of
both.
Scheduling of loads and updates
Once a model is defined, Model Builder creates a set of procedures that can
be run to update a model. The Analysis Server does not, however, have its
own scheduling services; this has to be done using operating system functions.
Event-driven scheduling is not supported.
Failed loads and updates
All loads and updates to a model are logged and a trace is provided of any
rejected data.
Data can be stored in Analysis Server’s MDDB or in the relational database
as base level data or in summary tables. This flexibility allows DBAs to
configure where the different aggregate layers in a model are stored. Data
can also be stored locally on the client.
Sparsity handling is automatically defined – the MDDB only stores and
indexes data that exists. Analysis Server uses hashing techniques to handle
sparse data.
Storage requirements for large models can be controlled through dynamic
dimensions and the ability to selectively cross-dimension measures (for
example, price) may relate dimensionally to product but not to geography.
Dynamic measures, calculated at runtime, can also be defined to save space.
In-memory caching options are not supported.
There is no provision in the default applications for informing end users of
the currency of the data that they are working with. A ‘text’ measure called
‘last_update’ (or similar), with no dimensions associated with it, can be used.
With each load, a string is stored in this measure with the time and date of
the last update (which can subsequently be queried in reports).
Management of users
Only single-user writeback is supported. When the writeback mode is used,
the entire database is locked and write access is exclusive to a single user.

28
The Supervisor interface provides a graphical environment for managing
end users and setting up groups. But there is no support for creating and
re-using individual user profiles.
Query governance
Query governance is not supported.
There are no facilities to restrict resource-intensive processing to specific
times of the day.
In the case of PDSS, the visibility of data is principally governed by the
access rights granted to specific users. Additionally, any controlled view of
data can be defined in IDQL.
Adaptability
Summary
1 2 3 4 5 6 7 8 9 10
A significant feature of PDSS is its inherently model-driven approach. All
analytical applications can run against any model; changes to a model are
automatically reflected in analytical applications. Within a Pilot model,
adaptability is usually a case of being able to add new dimensions and
measures. This is a simple process, although there are no change manage-
ment facilities provided to track the changes being made. The use of dynamic
dimensions and hierarchies increase adaptability by allowing changes to be
implemented with minimal effect.
Each Pilot model is distinct, however, and there is no scope for re-using
definitions across different models. Because PDSS has no native control over
loading schedules, data sources and models are not automatically synchro-
nised. Impact analysis and access to upstream metadata is not supported.
New dimensions can be added to a model using Model Builder or IDQL’s
command-line interface. There are no change management facilities to track
the changes made to a model.
It is not possible to re-use dimension definitions across multiple models.

29
New measures can be added to a model using Model Builder or IDQL’s
command-line interface; however, there are no change management facilities
to track the changes made to a model.
Measure definitions cannot be re-used across different models.
PDSS is a hybrid OLAP product that can be configured for ROLAP and
MOLAP modes of operation. The availability of a standalone desktop version
of Analysis Server extends the flexibility of the product to support mobile
architectures.
There are no automatic facilities to keep the relational data source and
model synchronised; this is a manual process. Administrators must rebuild
the structure of each ‘load and consolidate’ process by querying the relational
source to keep the two synchronised.
New members are picked up by the data loading procedures and added to
the business model under the control of the DBA. There are no automatic
facilities to identify and remove non-existent items in the model; these items
must be deleted manually.
If a new member has no associated hierarchy in the incoming data, then it is
added to the model as a data point but it is not included in any calculations;
Pilot refers to this as an ‘orphan member’ because it has no aggregate
parents.
Metadata
Apart from structural metadata, which remains synchronised in the MDDB
at all times, there is little metadata to synchronise.
Impact analysis
A metadata audit trail is not supported.
There is no access to upstream metadata from data warehousing tools.

30
Summary
1 2 3 4 5 6 7 8 9 10
The ultimate limit on scalability is the capacity of Analysis Server; however,
performance depends largely on the design of the system and the model – the
excessive use of dynamic dimensions will inevitably affect performance.
Analysis Server provides dynamic monitoring tools that help administrators
to determine the optimal server configurations, including the specification of
consolidations and where they should be stored, and to tune the system for
quicker data access.
Internet Publisher connects to web servers directly using Internet Application
Programming Interface (IAPI). While this effectively restricts the choice of
web servers that can be used, Pilot also claims that it provides a faster and
more secure gateway, and avoids the bottlenecks associated with CGI.
ROLAP
Multipass SQL
Multipass SQL is not supported.
Processing is carried out on the database, whereas advanced and time-based
calculations are carried out on Analysis Server.
Server-based caching of frequently-requested data is supported. If the
underlying data is changed, however, then the cache is automatically deleted.
Aggregate navigator
PDSS is aggregate-aware. By using TableTracker and metadata, the SQL
generator is aware of the nearest neighbour for consolidations and uses that
data.
MOLAP
Dynamic dimensions remove the need to reconsolidate the model every time
new data is loaded into the MDDB. There is a trade-off in terms of
performance, however, for models that analyse a large number of dimension
attributes.
Analysis Server provides a graphical interface specifying preconsolidation or
consolidation on-the-fly based on usage statistics returned from the
TableTracker monitoring tool.
Pilot claims that some of its larger customer sites have several thousand
users. The company has shown usage logs of up to 500 concurrent users on a
single Unix Analysis Server.

31
Processing
Native access is only provided for Oracle and Sybase relational databases.
Access to other RDBMSs is via ODBC.
PDSS does not support distributed server architectures (for example,
through peer-to-peer processing between Analysis Servers).
Internet Publisher does not support load balancing across multiple servers.
SMP support
There is no support for SMP parallelism.
Customisation
Summary
1 2 3 4 5 6 7 8 9 10
Designer provides graphical tools and a Visual Basic-like scripting language
to customise analysis modules or create new applications. Components of the
development environment are ‘dimensionally aware’ of the models that they
are working with; however, the development approach lacks the features that
come with fully object-oriented environments. The client interface can be
extended with Windows development tools through the OLE interface or by
extending the Designer environment using the Pilot Software Development
Kit (SDK).
Customisation
There is no provision for giving users the option of a restricted interface.
A portfolio can be defined for presenting a series of EIS-style reports in a
briefing book paradigm. Similarly, a personal page can be created to define
EIS-style web homepages.
Applications
Web developers can build custom Internet Publisher applications using
HTML, JavaScript, Java, VBScript or ActiveX programs.
Designer provides a graphical object-based development environment.
Applications are built from a set of a standard visual objects (such as push
buttons or dialogues) and special dimensionally-aware objects such as tables

32
and charts. OLAP functionality can be built-in to applications easily using
the predefined objects without programming. A Visual Basic-like scripting
language is available to tailor applications further and add more custom
functionality.
The development approach, however, is object-based; it lacks features such
as inheritance and polymorphism that come with fully object-oriented
environments. Although this limits the re-usability of application components,
it does make it easier for novice developers to understand and maintain
Pilot applications.
Third-party development tools (such as Visual Basic, C++ and PowerBuilder)
can access Analysis Server functions and objects through the OLE2 interface.
A SDK is also available to support third-party development using the
Analysis Server API.
Localisation
Analysis Server handles multibyte character sets that allow users to run
Pilot applications against non-English data warehouses. The language
display and format is fully client-controlled.

33
Deployment
Platforms
Client
The Desktop and Designer clients run on the Window 95/98/NT formats.
Internet Publisher supports Microsoft Internet Explorer and Netscape web
browsers.
Server
Analysis Server runs on Windows NT and Unix (Solaris, HP-UX, AIX, Digital,
ATT, Pyramid, NCR and Sequent). AS/400 is supported through an exclu-
sive partnership with SystemSource.
Internet Publisher server runs on Windows NT and Solaris. It uses
Microsoft’s Internet Information Server (IIS) as its web server.
Data access
PDSS provides native access to Oracle and Sybase relational databases.
Other relational sources are accessed via ODBC. It can also access ASCII
files. SDKs are provided to access ERP data sources, such as SAP.
Standards
PDSS does not support Microsoft’s OLE DB for OLAP API; support as a
consumer is planned.
Pilot has not participated in, or published any, OLAP benchmarks.
Price structure
Pricing for Analysis Server starts at $25,000 for up to ten users for Windows
NT and Unix platforms. The hybrid Analysis Server OLAP option is an
additional 50% of the server price.
The Internet Publisher prices start at $10,000 for up to ten users. Internet
users are charged $50 each; Desktop users are charged $895 each (which
includes the Analysis Library). A Designer developer licence costs $4,000.
The Reporting module costs $9,995.
The price of the packaged applications starts at $12,500 for ten users; please
note that these applications also need Analysis Server and Desktop licences.

34

SAP Business Information
Warehouse
Summary
At a glance .............................................................................................. 2
Ovum’s verdict......................................................................................... 4
Distribution ............................................................................................ 16
Product evaluation
Web support .......................................................................................... 21
Management ......................................................................................... 22
Adaptability ............................................................................................ 24
Customisation ....................................................................................... 26
Deployment
Platforms ............................................................................................... 28
Data access .......................................................................................... 28
Standards .............................................................................................. 28
Price structure ....................................................................................... 28

2
Evaluation: SAP AG – SAP Business Information Warehouse
At a glance
Developer
SAP AG, Walldorf, Germany
Version evaluated
SAP Business Information Warehouse (BW), version 1.2A
Key facts
• A data warehouse that is preconfigured to work with SAP R/3 data
• Server runs on Windows NT and Unix; clients run on Windows 95 and
Windows NT and use Microsoft Excel as a presentation layer
• BW is an independent product and has a separate release cycle from SAP
R/3
Strengths
• SAP delivers preconfigured business content and plans to add more in
future versions
• Preconfigured multidimensional models, extraction routines and reports
make initial implementation quick and easy if solely SAP data is being
used
• Central and easy to use tools for administering the whole data warehouse
Points to watch
• Limited range of end-user tools – no web access to models
• Building models from non-SAP data relies on R/3 expertise and the use of
third-party tools
• Requires SAP skills to set up, enhance and manage
Ratings
Web support
Management
Adaptability
Customisation
1 2 3 4 5 6 7 8 9 10

3
BAPIs
Business APIs. A published programming interface that potentially gives
access to software applications from a range of vendors. BAPIs are propri-
etary, though SAP has a certified partner programme to encourage use by
third parties.
Business content
A key feature of the BW philosophy is that the information is organised into
meaningful ‘business content’. The preconfigured data extraction, storage
and presentation content is designed with business needs in mind.
Characteristic
SAP term for a dimension used in an InfoCube.
InfoCube
Conceptually, an InfoCube is a multidimensional database that is used for
analysis and reports. It is implemented using a set of relational tables
arranged in a star schema. An InfoCube contains InfoObjects and corre-
sponds closely to Ovum’s definition of a business model. BW comes with
more than 20 preconfigured InfoCubes.
InfoObjects
A generic term for business objects, such as ‘customer’ and ‘revenue’. They
are similar in function to a data structure, but with additional meta
information.
InfoSource
InfoSources are the main method of feeding data into BW. For data to be
used in BW, it must be part of an InfoSource. BW provides predefined
InfoSources for accessing R/3 data. New InfoSources can be created using
the Administrator Workbench tool.
Key figures
These are quantifiable values, such as ‘revenue’, which are either extracted
from a source system or derived from a calculation. This corresponds with
Ovum’s definition of a calculated measure.

4
Ovum’s verdict
What we think
SAP’s Business Information Warehouse (BW) comes fully equipped for SAP
infrastructures and will therefore slot smoothly into any R/3 environment. If
the preconfigured data extraction routines, multidimensional models and
reports that come with BW map closely to your organisation’s business
intelligence needs, then implementation can be relatively quick and easy. If
there is also a preference to ‘buy from a single vendor’, then BW will be a
compelling product for users wanting OLAP access to large amounts of SAP
data.
The tight integration with the R/3 OLTP modules and preconfigured busi-
ness content are undoubtedly the greatest strengths of the product, and
should allow SAP to carve itself a niche for quick turnkey implementations.
However, the downside is that it can best be used and extended by develop-
ers with strong SAP skills. Business users are therefore highly dependent on
IS to set up and supply them with specialised models. BW is primarily
geared to use R/3 data and SAP has yet to prove that it is easy to integrate
with non-SAP data sources and end-user tools; this relies on proprietary
BAPIs, expensive R/3 skills and third-party applications and tools.
SAP does not have great OLAP experience and BW, in the first release at
least, falls short of providing the advanced modelling, analytical and report-
ing capabilities found in more mature OLAP tools. Nor is it clear whether
SAP can rollout functionality fast enough to satisfy complex user needs.
Although the Excel client is sufficient for standard OLAP, BW is hindered by
its lack of front-end tools. The market is flooded with OLAP clients that
provide more flexible and powerful front ends.
BW comes with a degree of openness not usually associated with SAP.
However, SAP has not yet published the specifications for the BAPI that
OLAP tool vendors would use to get access to BW data. Full support for the
OLE DB for OLAP interface is also lacking.
When to use
SAP BW is most suitable if you:
• have most of your corporate data in R/3, and need direct analysis of data
in the transaction databases for decision support
• can closely map BW’s preconfigured models and extraction routines
against your organisation’s business intelligence needs
• want to buy a turnkey data warehouse package from a single, well-known
vendor, rather than build your own
• have SAP development skills in your organisation.

5
It is less suitable if:
• the amount of SAP data required for your decision support needs is
minimal
• you have already made a large investment in a data warehouse
• you want to support a small proof of concept project, with a small
investment
• you have users with minimal SAP skills that want to build their own
models from a variety of data sources
• you have significantly customised your base SAP system, and thus need
to develop a significant number of additional extraction routines and
models.

6
Product overview
Components
The key OLAP components of BW version 1.2A are:
• BW Server
• Administrator Workbench
• Business Explorer
• Data Extractors (for R/3)
• BAPIs (Business APIs).
BW Server
A mid-tier server that includes an OLAP engine, a metadata repository and
a database – all of which are preconfigured for R/3. The server processes all
OLAP requests and returns results data to clients.
InfoCubes
The BW database is structured into self-contained multidimensional data
‘containers’, called InfoCubes. An InfoCube is stored in a number of rela-
tional tables in a star schema. The database can reside within BW Server or
on a remote database server. SAP provides more than 20 preconfigured
InfoCubes. Users can also extend existing InfoCubes and create additional
ones.
InfoCubes contain InfoObjects (dimensions and measures). They are fed
from InfoSources that extract data from R/3 systems or external systems
such as relational data warehouses, flat files or other source systems.
OLAP Processor
An OLAP engine that is used for processing data in InfoCubes. It provides
the methods needed to query data and perform OLAP analysis.
OLAP analysis Warehouse management Integration
administration
Client Business Explorer Administration Workbench
Server BW Server Data Extractors BAPIs

7
Staging Engine
The Staging Engine requests an extract from an InfoSource and performs
the necessary mappings and transformations needed to create InfoCubes. It
uses SAP’s ALE (Application Link Embedding) middleware for data
transport.
Metadata repository
The metadata repository stores business-related and technical metadata in
catalogues. ‘Business metadata’ includes content definitions, descriptions
and rules. ‘Technical metadata’ describes structures and transformation and
mapping rules for the data extraction and staging process.
Operational Data Store
An optional component that temporarily stores transactional data in BW.
The data format remains unchanged; no aggregations or transformations
take place. The Operational Data Store (ODS) is organised as a set of flat
tables, each assigned to an InfoSource. The ODS is primarily used as an
intermediate store for the staging process, allowing custom data scrubbing
and transformation tasks to be performed (using either SAP or third-party
tools) on a complete extract before it is mapped to InfoCubes. It also provides
a method for end users to drill-down to transaction-level data without
entering the OLTP system.
Administrator Workbench
An administration tool for managing and extending the data warehouse
environment. It provides a graphical interface for scheduling data loads/
updates and monitoring processing tasks. Graphical tools are also provided
for defining and maintaining InfoCubes, InfoSources, metadata, setting
security and maintaining a report catalogue.
Business Explorer
The client component for BW. It consists of two parts:
• Report Browser – a web interface that enables the end user to display
metadata information about reports, and choose what models to explore
• Analyzer – an ad hoc query and analysis interface that uses Microsoft
Excel to display data. A BW add-on provides OLAP capabilities directly
from the spreadsheet.
The report catalogue can be consulted via the Internet using a web browser.
If the user wishes to interact with the data, Business Explorer fires up
Analyzer, which is really Excel with BW extensions. BW’s OLAP engine is
only activated when the data needs to be refreshed or a new view of the data
needs to be computed.
Data Extractors
A set of programs for the extraction of transaction data from R/3 OLTP
applications into BW. BW provides extract programs for all the major R/3
applications, including Logistics, Controlling, Finance and HR (human
resources). Tools are provided to extend the extractor routines. Initially, the
extractor programs pull the entire dataset across; on subsequent extractions,
they pull only incremental changes.

8
Non-SAP data sources must be extracted using third-party or custom extrac-
tion tools.
BAPIs
BAPIs (Business APIs) are proprietary programming interfaces for business
applications, which SAP promises will remain stable. There are more than
400 provided by SAP. BAPIs are published at www.sap.com/bapi.
BW supports BAPIs for loading data from non-SAP data sources into BW
and integrating with third-party applications. SAP has four certified BAPI
partners for data extraction – ETI, Informatica, Prism and TSI – and is
working closely with a number of front-end OLAP tool vendors for integra-
tion.
Programmers can include BAPIs in programming languages such as Visual
Basic, Java and C, as well as SAP’s own development language – ABAP/4.
SAP BW does not support a full mid-tier MDDB architecture.
BW is a ROLAP-oriented tool and is based on a light mid-tier architecture.
Figure 2 depicts the architecture of BW.
Data is stored in a star schema in a relational database, either in the BW
Server or on a separate database server. BAPIs provide back-end links for R/
3 transactional systems and other external data sources, and front-end links
to Business Explorer clients. When used with R/3, BW Server resides inde-
pendently of the OLTP applications, although there is some sharing of the
kernel technology. Users can also access native R/3 reporting tools.
The standard BW configuration uses a central data warehouse. However, it
is also possible to create multiple, independent warehouses for specialist
needs. The architecture allows consolidation on several different levels and
Business Explorer provides a single front-end for one or more BW Servers.
SAP plans to develop a BAPI for the extraction of data from BW (but with no
official release date). This may result in more architectures where BW acts
as the hub feeding several non-SAP datamarts.
Desktop and mobile architecture
SAP BW does not support desktop or mobile architectures.
Using SAP BW
Business content
A key feature of the BW philosophy is that the information is organised into
meaningful ‘business content’ – a term that SAP uses to describe
preconfigured storage, presentation and data extraction objects that are
designed with business needs in mind. The objects provided by BW are based
on business processes that are executed in the R/3 system, of which there
are more than 900.

9
Figure 2 BW’s architecture
Favourites
Business Explorer
Reporting and
analysis for Excel
Report builder
Catalogue browser
Administration
Scheduling
Monitor
Administration
workbench
BAPI
OLAP processor
Business information
warehouse server
Metadata
repository
InfoCubes
Metadata
Manager
Data
Manager
Staging
engine
Operational
datastore
Non-R/3 OLTP applications
OTLP
reporting
R/3-OLTP applications
BAPI
Non-R/3 production
Data Extractor
Production
Data Extractor
SAP is uniquely positioned to understand the semantics behind R/3 data.
The company therefore views the business content context of BW as its
unique selling point. SAP provides a wealth of business content, and plans to
add extra content in future releases. There are four main classes of business
content:
• InfoCubes – SAP supplies more than 20 preconfigured InfoCubes for
analysing key sets of R/3 data. The InfoCubes provided are primarily
function-based, but some also reflect a task-based view of data. Examples
include profitability analysis, market segment analysis, financial
overviews, stock inventory analysis and corporate indicator systems
• InfoSources – BW comes with 50 preconfigured InfoSources to build
InfoCubes from. Each InfoSource is assigned to a R/3 application
component

10
• Data Extractors – data extractor programs are supplied for all the major
R/3 modules, including Finance, Controlling, Logistics and Human
Resources. SAP also provides customised routines to access data in older
R/2 systems or proprietary file structures
• report templates – BW includes a range of predefined reporting
templates for particular user types, such as production planners,
financial controllers, product managers and human resources managers.
Task-related reports (or queries) combine information from related
InfoCubes and organise them into report clusters which, in turn, form the
basis for a channel, in which a so-called ‘business role’ is defined.
Templates are also available for commonly required business parameters,
such as contribution margin.
Metadata repository
BW has a central metadata repository that contains information about both
the meaning of BW data, and its origins and transformations. The metadata
repository is preconfigured for R/3 and is dynamically linked to the enter-
prise data model. All developer activities are automatically captured in the
repository.
The repository organises the information into four catalogues:
• InfoObject catalogue – all the attributes and measures are described.
These InfoObjects can be re-used within multiple InfoSources and
InfoCubes
• InfoCube catalogue – stores the definitions of InfoCubes (the attributes
and measures contained within each InfoCube)
• Report catalogue – contains report descriptions and definitions. Using
Business Explorer the end user can view these and select reports to open
• InfoSource catalogue – as well as the InfoSource definitions, the
catalogue stores information about the mappings on to InfoCubes.
The Administrator Workbench provides a metadata management tool
(Metadata Manager) for maintaining the different catalogues.
Building new models in BW
Creating new models (InfoCubes) based on previously defined dimensions
and measures (generically called InfoObjects in BW) can be easily achieved
in the Administrator Workbench. A R/3 system connected to BW provides a
wealth of predefined InfoObjects, as well as InfoCubes and InfoSources. All
these objects can be easily loaded into BW and referenced in the appropriate
catalogue in the metadata repository.
However, competitive advantage is likely to require that new InfoCubes be
defined which integrate a wider range of data, including non-SAP data.
In order to understand how this is achieved, it is first necessary to under-
stand how data is transferred into BW and mapped to multidimensional
OLAP structures using a metadata layer.
Within BW, there are three layers of representation (source data,
InfoSources and InfoCubes), with two levels of mapping.

11
Mapping source data to InfoSources
The first level of mapping is the source data mapped to the InfoSources
layer.
Extract programs deliver source data in an extract source structure. A
source structure can be transaction data (dimensions and measures, which
are called InfoObjects) or master data attributes, texts and hierarchies.
Multiple extract source structures are mapped to a single transfer and
communications structure called an InfoSource. InfoSources are data provid-
ers for BW. They contain data, information about the communication proto-
col to be used when transferring the data and any rules about the data
transfer and updates.
InfoSources are the main method of feeding data into BW. Preconfigured
InfoSources and extraction routines supplied by SAP allow for quick and
easy development of InfoCubes based on R/3 data. However, the InfoSources
provided are intended to be used as they are, rather than modified. It is
possible to create new InfoSources in BW, using the Administrator Work-
bench, although this requires familiarity with R/3.
Data from other non-SAP sources is loaded using BAPIs. This can be
achieved either by users writing applications, the flat file extraction facilities
or through the use of third-party ETL vendors that have been certified by
SAP. However, the complexity and difficulty of extracting data from systems
outside R/3 to load into BW should not be underestimated. The extract,
transform and load (ETL) process is widely acknowledged to be the most
technically challenging task in data warehousing.
SAP’s data extraction capabilities are beyond the scope of this report, and
are examined in greater detail in Ovum Evaluates: Data Warehousing Tools.
Creating InfoCubes from InfoSources
The second level involves mapping InfoSources to InfoCubes. This is
achieved using the Administrator Workbench tool. InfoCube designers are
provided with a metadata management tool for accessing predefined
InfoSources, InfoCubes and InfoObjects in the metadata repository.
A hierarchical overview of all the metadata catalogues is provided by Ad-
ministrator Workbench. Using this interface, designers can assign
InfoObjects to InfoSources to define new InfoCubes. Menu-driven dialogues
are provided for defining attributes, properties, transfer and communication
structures and accessing further functions. Business logic can also be ap-
plied, ranging from simple aggregations to arbitrary calculations on key
figures and additional attributes and user-defined functions.
Using OLE DB for OLAP to access the data
Version 1.2A of BW provides limited support for the use of OLE DB for
OLAP to access data. Client applications that use OLE DB for OLAP as a
consumer have to use an OLE DB for OLAP ‘wrapper’ object provided by
SAP. This wrapper maps the methods of the OLE DB for OLAP objects on to
the SAP OLAP API.
Only a subset of the MDX grammar specified in OLE DB for OLAP is sup-
ported by the wrapper.

12
Using the Analyzer component of Business Explorer, queries – with dimen-
sions and measures – can be defined from InfoCubes. The data for these is
retrieved using the OLE DB for OLAP calls, which are then interpreted by
the SAP wrapper. Effectively, Excel is used for the presentation of the infor-
mation and the processing is carried out by the OLAP server.
Using the Business Explorer
Business Explorer is the primary front end for BW and is used to:
• define queries and save them in reports
• analyse data by navigating through the queries
• select and manage reports.
Defining queries
In Business Explorer, a query represents a subset of data that is ‘cut’ from
an InfoCube. Queries are defined graphically by selecting characteristics and
key figures (InfoObjects) and distributing them via drag-and-drop to filters,
rows, columns and user-defined characteristics to create a view of the data.
A preview of the results area of the query is also provided. SAP also provides
a number of predefined query templates for InfoCubes.
Figure 3 shows the Business Explorer interface for defining queries.
Figure 3 Queries interface

13
Queries are saved as part of an Excel workbook as a report. A report can
have multiple queries attached to it, each of which represents a different
slice of the data.
Analysing data
If the user wishes to interact with the data, Business Explorer fires up the
Analyzer, which is effectively a local copy of Excel with a BW add-on.
Users analyse data by navigating through queries in order to generate
different views of the data (known as navigational states). The navigation of
the query can include functions such as drill-down, slice-and-dice, sorting
and filtering.
Selecting and managing reports
Reports are accessed by end users via channels, which deliver collections of
reports grouped according to topic areas or user roles and business proc-
esses. Within a channel, reports can be logically grouped into clusters.
BW administrators assign channels and reports to end users from a central
InfoCatalog in the Administrator Workbench. The InfoCatalog provides a
grouped and structured display for report management. Access rights can
also be defined for channels, reports or elements of data within a report.
Reports are accessed and managed by end users via a web browser, using
the Report browser facility shown in Figure 4. Users can also assemble
personal folders of reports. Frequently accessed reports can be assigned as
‘favourites’ for quick and direct access.
Figure 4 Report browser

14
Future enhancements
SAP is developing BW and its partnerships. The next planned release is
version 1.2B, due in the first quarter of 1999. The release will mainly include
additional business content rather than major technological developments.
OLAP-specific enhancements will include drill-through to transaction-level
data in OLTP and databases directly from the Business Explorer interface,
archiving and data replication, server load balancing and capacity planning
functions, and enhanced data visualisation capabilities.
SAP plans to extend the range of servers supported to include MVS in early
1999, and AS400 at a future (as yet unspecified) date.
The next significant release (version 2.0) is planned for the third quarter of
1999 and will include technology and content improvements.
SAP plans to provide BAPIs for accessing data in BW, but these are not due
for release until the end of 1999.

15
Company background
SAP (Systems, Applications and Products in Data Processing) is the fourth-
largest independent software company in the world. The company was
founded in 1972 in Mannheim, Germany, by five ex-IBM software engineers.
SAP has not changed direction radically over the years and has been suc-
cessful in extending its market through renewing and expanding its core
business application software offering. SAP’s first major product was its
mainframe R/2 manufacturing solution. In 1991, SAP released R/3, the first
fully configurable client-server ERP system available on Unix. R/3’s business
process orientation, which permitted views of management accounts based
on multiple business views, was very much in tune with the business process
re-engineering movement at the time. This has allowed SAP to successfully
pilot R/3 to a leading position in the US and Europe. Ovum’s figures indicate
around a 40% share of the software licence revenues for the worldwide ERP
market, with more than 17,000 R/3 installations worldwide. SAP BW was
first released in September 1998, although the pilot programme started in
May 1997. By the end of 1998, SAP claimed around 300 shipments of the
product. As expected, almost all of these were to current SAP R/3 users.
SAP is publicly held and has its headquarters in Walldorf, Germany, with
offices worldwide. The company is structured around 17 industry-specific
business units and employs more than 17,000 people. SAP is listed on sev-
eral exchanges, including the Frankfurt stock exchange, the Swiss stock
exchange and the New York stock exchange. Figures for fiscal 1998 show
that revenues grew 40% to DM8.4 billion ($4.8 billion). However, pretax
profit growth of 15% was well below expectations. This shortfall was attrib-
uted to the weak Asian market and the decline in SAP’s Japanese activities.
SAP has made no secret of its desire for its users to use SAP technology to
support as much as possible (if not all) of its business. SAP BW fits neatly
into this all-conquering vision, by keeping the data warehouse inside the
‘SAP space’ while allowing data to be imported from external systems; SAP
even suggests that non-R/3 users may use BW.
Most BW users are R/3 customers. Early adopters include Colgate-Palmolive,
Intel, DEC and Bay Networks. Historically, SAP has targeted large compa-
nies. The company has long been interested in increasing its products’
attractiveness to the SME market, but has yet to succeed in this aim outside
its home market. In order to do this, it is starting to use VARs in the US
market.
SAP is developing a partner programme for the product at both the back and
front end. ETI, Informatica, Prism (recently acquired by Ardent) and TSI
have been BAPI-certified to load non-SAP data into BW; being ‘certified’ is
primarily a stamp of quality and commitment. At the front end, SAP is
working with Business Objects, Arcplan and Cognos, with plans to add IBI to
the list. The planned result of the partnership is that tools from these ven-
dors will integrate so that they can seamlessly make use of the InfoCubes
built in BW.

16
Customer support
Support
SAP offers the same level of support for BW and R/3. This includes around-
the-clock telephone hotline and on and offsite support worldwide.
Training
BW training is available from most of SAP’s subsidiaries worldwide. SAP
has only fully translated training materials from German into English.
SAP provides consulting services for BW for implementation, application
creation and use. Consulting accounts for around 20% of SAP’s revenues and
is growing faster than software licence revenues. SAP also has partnerships
with global management consultants and system integrators.
Distribution
Europe
SAP
Neurottstrasse 16
69190 Walldorf
Germany
Tel: +49 6227 747 474
Fax: +49 6227 757 575
Americas
SAP America
701 Lee Road
Wayne, PA 19087
USA
Tel: +1 610 725 4500
Fax: +1 610 725 4555
Asia-Pacific
SAP Asia
750A Chai Chee Road
7th Floor Chai Chee
Industrial Park
Singapore 469001
Tel: +65 446 1800
Fax: +65 249 1818
E-mail: info@sap.com
http://www.sap.com

17
Product evaluation
Summary
1 2 3 4 5 6 7 8 9 10
Business Explorer uses Microsoft Excel (with special BW extensions) for the
analysis and presentation of data. The interface is easy to use and flexible
enough to support a significant degree of ad hoc query and analysis, but
lacks the advanced functionality found in more mature OLAP client tools.
Preconfigured report templates are provided to quickly build up a catalogue
of reports that can be consulted via a web browser. There is no integration
with third-party OLAP front ends, and support for the targeted distribution
of reports is limited.
Typically, end users do not access models directly; they access reports that
contain specific views (queries) of the model data. Graphical access to
reports is provided by the Report Browser. Reports can be grouped logically
into clusters and stored in public and private directories. ‘Favourite’ reports
can be selected from the file system. Search facilities are not provided, but it
is possible to display a preview of queries and reports to ease selection.
When a report is opened, it shows metadata such as author details, the
query name, when it was last changed, the source InfoCube and a textual
description.
Models cannot be directly annotated by end users. Excel features can be
used to edit comments to spreadsheet data.
Using the model
Standard OLAP functions, such as slice-and-dice and drill-down and drill-
across, can be defined using the BW add-on facility that is embedded within
the Microsoft Excel worksheet. Exceptions can be set for traffic-lighting
applications.
Members within a dimension level can be repositioned, and the sequence of
the display can also be sorted in ascending or descending order.
End users can bring up a full visual representation of the hierarchies.

18
Drill-down to records in the OLTP or other source systems is not supported.
However, it is possible to drill-down to transaction data that has been ex-
tracted to BW’s Operational Data Store.
The primary front-end tool is Microsoft Excel. BW supports a subset of
Microsoft’s OLE DB for OLAP protocol, but makes no claims about this
enabling access by any end-user tools other than Excel. BW can also be
accessed by other SAP applications, such as Advanced Planner and
Optimizer (APO), SAP Sales and SAP Marketing.
BW uses Excel to display results data in tabular and graphical format. There
is no integration with GIS mapping tools.
Designing a report
Report design relies entirely on Excel’s formatting and layout features.
Multiple queries can be included in a single report. SAP provides a number
of predefined templates for specific reporting needs.
Publishing a report
Reports can be published using channels.
Targeted distribution via email
Reports can be saved as static Excel files and attached to an e-mail in the
usual way.
BW does not support conditional subscription services.
Summary
1 2 3 4 5 6 7 8 9 10
SAP provides preconfigured models that are designed for analysing R/3
data. New models based on previously defined dimensions and measures can
be easily built in the Administrator Workbench. If there is a need to integrate
non-SAP data in models, then new InfoSources will have to be defined. The
InfoSources provided by BW are intended to be used as they are; creating new
ones requires designers to have strong SAP development skills and use third-
party tools.

19
Basic design
Design interface
InfoCubes can be easily designed using the menu-driven tools provided by
Administrator Workbench. If predefined dimensions and measures are
available then this is a straightforward process. Otherwise, developers will
need to use additional SAP and third-party tools to define upstream integra-
tion with data sources.
The data source is only visualised upstream, when defining the InfoSources.
It is not possible to bring up a sample of data when creating InfoCubes.
Dimensions and measures defined in BW’s metadata layer can be re-used
across models. However, a universal mapping layer is not directly supported.
Designers are prompted to include descriptive metadata, such as owner
details, contact details, description and rationale when building InfoCubes.
The preconfigured InfoCubes automatically map dimensions to source R/3
data. Mapping dimensions to non-SAP sources is performed when creating
new InfoSources. This requires a familiarity with R/3, ABAP and third-party
tools.
Once a dimension has been created, members in a dimension level can be
selectively included or excluded using point-and-click.
Dimension hierarchies can be defined graphically. Custom, alternative,
unbalanced and time-dependent hierarchies are supported.
Time dimension
Standard fiscal year variants and quarters are supported. Custom and
dynamic time periods (for example, the last six months) can also be defined.
Short name and long text descriptions can be assigned to dimensions. End
users can decide whether short or long text is displayed on screen.
A default view of dimensional hierarchy is established based on the first
navigational state of a query definition.

20
Calculated measures
A formula editor is provided to create calculations. Standard arithmetic
operators and logical operands can be included in a formula workspace using
drag-and-drop. More than 100 metrics are included in the preconfigured
InfoCubes.
Multiple measures can be included with a set of dimensions.
Multiple designers
Multiple designers
BW does not provide any special support for multi-designer environments.
However, developers can use the R/3 Basis system to provide change man-
agement, check-out/in and multi-user locking facilities for the repository.
Version management is provided by CTO.
Summary
1 2 3 4 5 6 7 8 9 10
BW supports a useful collection of ready-to-use functions that can be used for
general business analysis. More than 100 metrics are included in the
preconfigured InfoCubes. End users rely on Excel functions for forecasting,
and write-back is not supported for ‘what-if’ applications. There is limited
support from SAP for the extraction of data from BW for use in third-party
analytical tools.
BW integrates with Microsoft Excel. However, there is no integration with
other third-party analysis tools.
Ranking and sorting
Ranking and sorting functions are available including top/last n and top/last
n%. Sequence functions are also provided for 80/20 analysis, including
cumulative sum, tertiles, quartiles, classification and dual classification.
There is support for standard arithmetic operator functions and more
advanced methods, such as trigonometric functions.
Financial functions
Financial functions are provided, including complex currency translations
(the euro, exchange rate types for bank buying/selling rates and EMU legal
directives), ABC analysis and internal business volume elimination.

21
Statistical models
Support is provided for a number of statistical functions, including
percentage share, ratio, correlation, percentage difference and variance.
Trend analysis
Average over period sum/count functions can be used for establishing trends.
Otherwise, BW relies on Excel for trend analysis functions.
Simple regression
BW relies on the linear regression functions provided by Excel.
Simple time-related comparisons and trends are supported via Excel.
However, there is no support for advanced time-series forecasting methods.
Simple functions can be defined locally in a model using the formula editor
or the Excel macro functions. However, a procedural language is not
supported.
Write-back to models is not supported.
SAP BW is designed for the analysis of numerical data only.
Data mining
There is no support for data mining.
Web support
Summary
1 2 3 4 5 6 7 8 9 10
Business Explorer offers a web interface, but only for browsing the metadata
catalogue to see what reports are available. Ad hoc query and analysis is via
Analyzer and requires a copy of Excel (with a BW add-on) to be installed
locally on the client machine. Effectively, there is no OLAP support via the
Web. However, if required, it can be provided by tools from some of SAP’s
partners.
The web interface only allows users to browse through the metadata
catalogue to select reports. OLAP functions are not available via this web
interface.
All SAP clients must be registered named-users.

22
The web interface is limited to browsing through a catalogue of predefined
reports.
Modelling capabilities via the Web are not supported.
Apart from e-mailing a static report via the Internet, there are no special
publishing and distribution facilities provided by BW.
Management
Summary
1 2 3 4 5 6 7 8 9 10
BW provides a central and easy to use administration tool to supervise and
control the operation of the warehouse. Strong facilities are provided for
managing data loads and updates and monitoring processing in the
warehouse environment. To get the best from BW, administrators should have
a good knowledge of R/3 and third-party tools, particularly if additional
SAP and non-SAP data sources are used. Security is based on standard R/3
authorisations.
The Administrator Workbench provides a graphical overview of InfoObjects
and corresponding source systems, InfoSources and InfoCubes. These objects
can be easily managed using drag-and-drop functions.
Security of models
Access rights (authorisations) can be defined for queries, models or indi-
vidual InfoObjects. Security can be modelled freely for all elements in a
model, right down to field values.
Query monitoring
The ‘monitor’ facility provides detailed statistics on the frequency of query
execution and usage of summary levels. BW supports its own statistics
InfoCube for analysing and reporting on the collected data.
Management of data
Data is stored persistently in relational database (in a star schema). The
database can reside in the BW Server or a separate database server.

23
SAP BW’s Scheduler facility is based on R/3’s scheduling system. It provides
a graphical interface that is used to define extract and load schedules ac-
cording to time and date criteria (hourly, daily, weekly, monthly or other
periods). Full or delta loads/updates are supported.
It is possible to schedule a data load or an update to the data warehouse
based on external events (for example, a specific transaction in R/3).
The ‘monitor’ facility supervises the load and staging processes. It provides
detailed statistics on current and completed load jobs and notifies the ad-
ministrator of exceptions such as failures. An ‘assistant’ feature helps with
the analysis of failed and incorrect data loads/updates.
Data can be stored in separate BW Servers or remote database servers. No
data is stored on the client.
Sparse data handling is the responsibility of the relational database, and is
not an issue in BW.
Typically, the size of InfoCubes runs into tens of gigabytes, rather than
hundreds. However, the maximum size is limited only by the RDBMS. BW
administrators can decide on the number of aggregate tables created to
control the size of the database.
R/3’s extensive memory management and caching functions are available for
fine-tuning performance.
All reports are time-stamped for each new data load.
Management of users
Write-back to InfoCubes is not supported. However, certain SAP applications
that work with BW do allow write-back to the source operational systems.
User security is based on individual and group authorisation profiles. BW
uses the same authorisation schema used in R/3.
Query governance
SAP BW does not support query governance.

24
It is not possible to restrict usage patterns and queries to specific times of
the day.
SAP authorisation checks can be used to restrict access to any functions or
objects in the SAP BW environment. User authorisations are summarised in
the form of profiles. Different authorisations are required for working with
the Administrator Workbench and the Business Explorer.
Adaptability
Summary
1 2 3 4 5 6 7 8 9 10
Dimensions and measures can easily be added to models. All these defini-
tions are stored and maintained in a single metadata repository, allowing for
easy re-use within multiple models. If SAP data is being used, structural
changes to the data source are automatically synchronised within BW. How-
ever, if customised ABAP programs are used for accessing non-SAP data, the
maintenance overheads can rise considerably.
New dimensions (characteristics) can be added to a model by selecting
InfoObjects from the InfoObject catalogue.
InfoObject definitions can be re-used across multiple InfoCubes.
New measures (key figures) can be added to models, either as a predefined
measure or a local calculation. However, there is no support for tracking the
changes made to the base model.
Measure definitions are stored in the InfoObject catalogue and can be re-
used across multiple InfoCubes. Additionally, key figure templates can also
be applied to InfoCubes.
There is no support for changing the architecture to a full MOLAP mode.
Desktop and mobile architectures are not supported.

25
Metadata from R/3 systems is automatically transferred to BW. The
metadata repository is updated every time there is a change in the underly-
ing data source.
If InfoObjects have changed in the source system, the changed properties
need to be transferred in BW using the appropriate maintenance dialogue.
Dimension members are automatically updated during the update process.
Metadata
InfoCubes are automatically synchronised with the metadata repository.
However, descriptive metadata assigned to InfoCubes must be maintained
manually.
Impact analysis
There is no direct support for anticipating the consequences of changes in
data sources to models and reports.
The metadata repository provides a history of the metadata.
Metadata from a R/3 system can be imported into BW. Access to metadata
from data warehousing tools is via BAPIs. SAP has four certified partners in
this space: ETI, Informatica, Prism and TSI.
Summary
1 2 3 4 5 6 7 8 9 10
BW resides on its own dedicated server and is separate from the OLTP
system and other source systems. Scalability is achieved using multipass
SQL, distributed processing and SMP technology. The caching mechanisms
within the tool have also been carefully designed to maintain performance.
Overall, getting the best performance from BW can be expensive, although
justifiable.
ROLAP
Multipass SQL
BW automatically generates multipass SQL.
Data processing is optimised between the OLAP Processor and the BW
database.

26
Once an InfoCube has been queried, it caches the data in memory. The
cached data can be stored persistently for subsequent access rather than
(re)querying the InfoCube.
Aggregate navigator
The Data Manager maintains aggregations to speed up queries. An aggre-
gate optimiser determines the best fit aggregate tables that satisfy the
query. Administrators can also add or remove summary levels according to
usage patterns.
MOLAP
BW is not a MOLAP tool.
A single BW Server can support around 200 concurrent users. Multiple
application servers can be added as the number of users grows.
Processing
BW supports native SQL generation for accessing its relational database.
Multiple BW Servers can be connected. However, there is no support for
intelligently balancing processing across the servers.
SMP support
SAP BW supports SMP parallelism.
Customisation
Summary
1 2 3 4 5 6 7 8 9 10
SAP BW is positioned as a ready-to-go data warehouse. Customers can use
SAP’s proprietary ABAP fourth generation programming language for
application development. However, ABAP coders are a much valued species
and are available only at high prices. Accessing R/3 data for decision sup-
port using customised ABAP programs also has the danger of high mainte-
nance overheads. The SAP BAPIs can be accessed by third-party development
tools.
Customisation
There are no facilities for providing restricted interfaces.
There is no direct support for producing EIS-style reports. This can only be
achieved using third-party development tools.

27
Applications
There is no direct support for developing web applications.
R/3 customers can use SAP’s proprietary ABAP/4 fourth generation pro-
gramming language. SAP provides ABAP Workbench with the SAP R/3
system, which is a development platform for client-server applications. It
includes a repository, editor, dictionary, function builder, screen/menu paint-
ers and tools for testing and debugging R/3 applications. ABAP can be used
to communicate with both the application server layer and the client. All BW
objects are accessible to the ABAP Workbench.
BAPIs can be accessed from development environments such as Visual
Basic, Visual J++ and Visual Age.

28
Deployment
Platforms
Client
SAP BW Business Explorer runs on Windows 95 and Windows NT. A local
copy of Microsoft Excel is required on the client to run Analyzer.
Server
SAP BW Servers and database servers run on Windows NT and all the
major versions of Unix.
Data access
SAP BW is primarily designed to work with SAP R/3 data or it can easily
access data in other BW systems. SAP can provide customised routines to
enable customers to access data in R/2 systems and from content providers
such as Dun and Bradstreet. There is a flat file load interface for feeding in
flat files. Data from other non-SAP sources is loaded using BAPIs. This can
be achieved either by users writing applications or through the use of SAP’s
partners that have been certified, such as ETI, Informatica, Prism and TSI.
The first three of these are evaluated in Ovum Evaluates: Data Warehousing
Tools.
Standards
SAP BW supports SAP’s proprietary BAPIs to feed data into BW.
For accessing data in BW, SAP provides a subset of Microsoft’s OLE DB for
OLAP protocol for accessing data. This is used by Microsoft Excel when
running as Business Analyzer.
SAP BW does not have any published OLAP benchmarks.
Price structure
Pricing for SAP BW is based on named users. The entry level cost for BW
Server is DM250,000 (approximately $144,200) for 250 users. Existing SAP
users can upgrade to SAP BW for DM1,000 ($575).

Seagate Holos
Summary
At a glance .............................................................................................. 2
Ovum’s verdict......................................................................................... 4
Distribution ............................................................................................ 15
Product evaluation
Web support .......................................................................................... 21
Management ......................................................................................... 22
Adaptability ............................................................................................ 24
Customisation ....................................................................................... 27
Deployment
Platforms ............................................................................................... 28
Data access .......................................................................................... 28
Standards .............................................................................................. 28
Price structure ....................................................................................... 28

At a glance
Developer
Seagate Software Information Management Group, Scotts Valley, CA, USA
Versions evaluated
Seagate Holos version 7.0
Key facts
• A client-server application development environment for building OLAP
applications
• Runs on Windows NT, Unix and VMS servers; clients run on Windows 3.1,
Windows 95, Windows NT and Macintosh
• Part of a comprehensive suite of business intelligence applications being
assembled by Seagate Software
Strengths
• A functionally rich OLAP tool with strong support for custom application
development
• Supports a range of OLAP architectures, and provides flexible data
storage and access options
• Supports an extensive set of advanced analytical functions
Points to watch
• Little ‘out-of-the-box’ functionality
• Can be complex to set-up and manage – considerable thought has to go
into the model and application design process
• End users rely entirely on IS to build models and OLAP applications
Ratings
Web support
Management
Adaptability
Customisation
1 2 3 4 5 6 7 8 9 10

Agent
Used by Holos to perform time-consuming, complex or repetitive tasks in the
background, as batch programs.
Holos desktop
A custom end-user interface built using the Holos development tools. Usu-
ally called a Holos application.
Model
A collective term for a combination of structures that store data and rules
which define calculations to be performed on the data.
Open OLAP
An integration strategy that allows Holos to incorporate data stored in
third-party multidimensional databases into Holos structures.
Report
A common way of presenting information to the user of a Holos application.
A report is a view from a particular perspective into a multidimensional
model. It can also contain other types of information such as graphs, images
and OLE objects and controls.
Rules
Holos rules define relationships between dimension members and calculated
measures. An example of a rule could be a calculation such as variance.
Rules are generally held in a ruletable.
Structures
Define dimensions and their relationships and include information on how
the underlying data is stored and retrieved. Holos supports four types of
structure: memory-based, disk-based, relational and ‘Open OLAP’. A struc-
ture corresponds to Ovum’s definition of a business model.

Ovum’s verdict
What we think
Seagate Holos is aimed primarily at application developers. Holos’s powerful
4GL is purpose-designed for building OLAP applications and provides a
broad range of development options. The graphical development tools offer
some strong features for building sophisticated OLAP applications for
specialist analytical requirements.
One of Holos’s greatest strengths is its breadth of functionality. The tool
supports a wide range of OLAP architectures, and provides one of the
strongest sets of advanced statistical analysis and forecasting functions in
the OLAP market. Scalable performance is also achieved through the use of
optimised multi-cubes and distributed and parallel processing. However,
under the covers, Holos is a complex OLAP product, and its learning curve is
steeper than most other OLAP products. The compound multi-cube architec-
ture can be complex to set up and there are limited graphical management
facilities to maintain the system. The Holos Web Gateway is Seagate Soft-
ware’s first attempt at web access. Although it provides a functional web
interface, its HTML interface is far from elegant.
Holos is targeted at the high-end of the OLAP market, which consists of
large corporates seeking to deploy OLAP throughout the enterprise. Any
purchase decision therefore requires a strong commitment to the Holos
development philosophy, not to mention a significant entry-level investment.
The main challenge for Seagate Software is to straddle the requirements of
proprietary language-based development, and the provision of ‘ready-to-use’
tools that provide greater freedom for end users and remove the reliance on
developers and IS to deliver OLAP applications.
When to use
Holos is suitable if you:
• are willing to make a strategic commitment to Holos for decision support
• require highly customised OLAP applications
• have in-house development skills to exploit
• want applications with advanced statistical analysis and forecasting
• require flexible models built from disparate data sources.
• are looking for an ‘out-of-the-box’ OLAP solution that is quick and easy to
implement
• have users that require flexible, ad hoc business modelling
• do not wish to commit yourself to a significant development effort
• do not want IS involvement in supporting OLAP applications.

Product overview
Components
The main components of Holos 7.0 are:
• Holos development environment
• Holos Agents
• Seagate Worksheet
• Holos Web Gateway.
Figure 1 shows the primary functions of Holos components and whether
they run on the client or the server.
Holos is a client-server application development tool for building OLAP
applications. The tool is based on a powerful 4GL (called Holos Language),
which is purpose-built for developing business intelligence applications;
application components are created as Holos Language scripts.
Holos is a flexible hybrid OLAP product that supports relational and multi-
dimensional data storage options, with an interesting overlaying option.
OLAP processing and code development is usually completed on the server.
Holos extensively uses agents to automate a number of processing tasks. At
the client end, Holos relies mainly on the development of custom applica-
tions to access data and perform OLAP analysis. An ‘out-of-the-box’
spreadsheet-style interface is provided. Seagate Crystal Reports and
Seagate Info support read-only access to Holos. Holos can also be accessed by
ODBC-compliant applications. The Holos Web Gateway provides access to
the Holos server via a web browser; it supports HTML and Java tools.
Holos development environment
The Holos development environment provides a number of graphical devel-
opment tools to define models, interfaces and other elements of a Holos
application. The main interface for development is the Application Manager.
This is a graphical tool for creating and maintaining Holos applications
aimed at developers. It brings together the various elements (objects) of a
Holos application. It acts as a ‘shell’ for other Holos application development
components and provides support for multi-developer environments.
Holos application objects are defined using one or more of the graphical
Holos design tools, the most important of which are listed below.
Data storage Application OLAP analysis Web access
development
Client Holos development Holos development Holos clients Java Worksheet
environment environment Seagate Worksheet
Server Holos development Holos development Holos Agents Holos Web Gateway
environment environment

Data Manager
This provides a GUI point-and-click interface for creating Holos data struc-
tures. Structures define dimensions and their relationships. They also
include information on how the underlying data is stored and retrieved. A
Holos ‘model’ is a combination of a structure and a collection of business
rules that define further relationships between dimensions and additional
calculated measures.
The Data Manager is also used to define the loading of data into the struc-
tures from relational databases, flat files and other supported data sources.
Hierarchy Manager
Used to define simple parent/child relationships between dimension mem-
bers. It is also used to define additional drill hierarchies using a graphical
editor.
Data Filter
Used to define selection criteria for use with Holos reports, in the Worksheet
front-end tool or as standalone processes.
Report Designer
A tool for designing multidimensional reports, and enabling OLAP functions
to be defined within them.
Desktop Designer
Used to design custom application interfaces and desktops. It integrates
reports and application components in a single user interface. Developers
can also use the tool to tailor their own development workspaces.
Dialogue Designer
Used to create Visual Basic-style client dialogues for use in applications.
Seagate Worksheet
An end-user tool that presents a spreadsheet-style interface for ad hoc
analysis of models. The Worksheet is designed for power users that require
flexible ad hoc OLAP capabilities. The Worksheet interfaces directly with
models stored on the server. It can access all the analytical and data ma-
nipulation functions provided by Holos. A Java version is also available.
Holos Web Gateway
The Holos Web Gateway uses CGI to connect with a web server to provide
web access to Holos applications and data. The Gateway provides HTML and
Java interfaces:
• the HTML interface provides access to predefined Holos reports; a
transformer facility converts Holos Language code into HTML and
presents the report to the web browser as a series of ‘dynamic’ HTML
pages. Users can perform standard OLAP functions such as drill-down
using HTML tags
• the Java implementation provides a web interface for the Seagate
Worksheet. It uses Java applets and a Corba architecture to download a
structure and provide more interactive OLAP functions, including slice-
and-dice. Charting and graphing functions are available via a Java
applet.

Holos Agents
An important part of Holos is the use of Holos Agents to automate batch
processing tasks that are hidden from end users. An Agent toolkit provides
developers with functions to create, manage and use Agents. The Agent
Dialogue System provides users with a set of dialogues for setting up and
modifying Agents. Holos supports a number of predefined Agents, for OLAP
analysis, data mining and data loading.
The Holos development environment supports a wide range of application
development styles and implementation architectures. This flexibility is
possible due to the number of data storage and processing options that can
be configured by developers when designing OLAP applications.
Compound multi-cube OLAP
Before explaining the different architectural options supported by Holos, it
is important to first understand the compound multi-cube architecture that
underpins the tool and OLAP applications developed using it.
Hybrid storage options
Holos is a hybrid OLAP tool, supporting multidimensional, in-memory or
relational storage options. A Holos business model (structure) can consist of
different types of structures. This means that ‘virtual’ structures can be
created by referencing one or more physical structures. The structures can
be any of four physical kinds with an interesting over-laying option:
• disk-based structures; these are multidimensional data files stored
persistently on the Holos server. There are two forms of disk-based
structure – shared and hashed structures – that provide optimised
storage for dense and sparse data sets respectively
• relational structures; these map to data stored in RDBMS tables. In this
case, the structure of the cube is simply a mapping layer that holds
information about how requested data should be retrieved
• memory-based structures are in the form of ‘micro-cubes’ created on-the-
fly whenever data is needed. They are typically used for relatively small
amounts of data. Memory-based structures are created and held on the
Holos server and viewed from the client
• open OLAP structures, which are sourced from third-party
multidimensional databases such as Essbase and Microsoft SQL Server
OLAP Services.
A strong feature of the architecture is the ability to create compound
structures under the control of the Holos language at runtime in the
application, perhaps in response to end-user actions.
Racking and stacking
Different types of structures can be joined using racking and stacking, as
shown in Figure 2.

Figure 2 Structure joining
Jan
Feb
Mar
Jan MarFeb
Eastern
Southern
Northern
ProfitsCostsSales
Cars
Trucks
Motorcycles
Racked
Stacked
Cars
Trucks
Motorcycles
Apr
ProfitsCostsSales
Eastern
Southern
Northern
Racking occurs when multiple cubes are joined in parallel along a ‘backbone’
dimension. For example, three cubes containing sales data dimensioned by
region, product and line item can each hold data for different months. These
three cubes can be ‘racked’ together by introducing a time dimension to act
as a backbone – each of the monthly structures would contribute the data for
one field in the time dimension. The resulting compound cube behaves like a
standard cube dimensioned by region, product, line item and time, but would
contain no data of its own; instead it would provide pointers to data in the
base cubes.
Stacking is a less obvious form of joining. It connects two identically dimen-
sioned structures in a series – in this case, data is always read preferentially
from the first structure. When data is read from the resulting compound
cube, Holos first interrogates the top cube of the stack. If the data is found, it
is returned: if not, the bottom cube is interrogated. In contrast, when data is
written to the compound cube, it is only ever written to the top cube.

This is the ‘natural’ architectural configuration for Holos where data re-
trieval, manipulation and formatting are done on the server, with the client
interface handling user requests and presentation issues.
The full mid-tier architecture stores models and data in a mid-tier server.
OLAP calculations and processing are done on the server. Holos disk struc-
tures represent the MDDB, although an open OLAP structure could refer-
ence an Essbase or Microsoft SQL Server OLAP Services multidimensional
database. Typically, the MDDB is built using multiple structures (multi-
cubes) that can be linked together to create a ‘virtual’ structure.
The client interface can be an application created using the Holos develop-
ment tools, the Seagate Worksheet, an Excel add-in or a web browser (if the
Holos Web Gateway is also running on the server).
Holos’s ROLAP functionality supports a lighter mid-tier architecture
through its support for relational structures. These structures map to data
stored in relational tables on a database server, and provide information
about how requested data should be retrieved. SQL queries are executed at
runtime and data is cached on the server as a (hashed) multidimensional
structure for further analysis. There is the same range of client interfaces as
with the full mid-tier architecture.
A cut-down, standalone version of Holos is provided to support a two-tier
desktop architecture. This can be configured to work directly against an
RDBMS and carry out processing on the client machine.
Mobile architecture
The standalone version allows Holos applications to be run on a laptop
computer. A structure can be downloaded locally for offline analysis.
Using Holos
Holos is primarily an application development environment. As such, it
clearly defines roles and tools for developers and end users:
• the development environment and design tools provided are geared
towards experienced application developers with strong programming
skills. Typically, developers create the business model and the application
interface, including OLAP functions and access rights
• end users access models assigned to them via a custom OLAP interface,
or by using the Seagate Worksheet interface.

Principal development concepts
It is easier to understand the Holos approach to OLAP application develop-
ment if three important concepts – structures, rules and models – are clari-
fied.
• A structure is both a storage type and a definition of a set of dimension
and hierarchies. The structure is, in essence, a metadata layer that maps
dimensions, dimensions members and aggregation hierarchies on to a
physical storage format. Structures can be implemented in a number of
different ways. Different structures can be linked within a compound
structure to appear as a single structure.
• Holos rules define relationships between fields (dimension members and
measures). A rule may, for example, define a calculated measure such as
variance. A rule may also define the derivation rules for a hierarchical
relationship between members. For easier management, a set of rules can
be held together in a ruletable.
• A model is a collective term for a combination of structures (which store
data) and rules (which define calculations to be performed on that data).
Models are defined by declaring their constituent objects (structures and
rules). Holos provides automatic validation of models to ensure rules are
correctly applied. It also ensures that rules are always implemented in
the correct order (so that any dependencies between calculations are
handled correctly). A model can have a number of structures and
ruletables, and a structure can be used with different models.
Developing a custom OLAP application
A Holos application is defined as a series of interconnected ‘objects’, which
are linked together using a hierarchical browser facility in the Application
Manager. This is shown in Figure 3.
The hierarchical browser uses a dataflow metaphor to represent the links
between application items. Each block in the browser represents some
functionality within the application. The first ‘object’ within the application
will typically be a ‘data load’ module that has been defined in Data Manager.
Within Data Manager, this ‘data load’ object will itself be made up of a
number of steps that define a data source and a Holos data structure.
Developers use the other Holos graphical tools, such as the Desktop De-
signer, Report Designer and Dialogue Designer, to define the various compo-
nents of an application, but the Holos language remains an important part
of the development philosophy. All the graphical tools generate Holos lan-
guage scripts, which can be further customised or run independently.

Figure 3 Hierarchical browser
Designing a Holos report
Structures and models are used to build a report in the Report Designer.
They can also be used directly within Holos language programs.
Structures are also used in the Data Filter module, which is used to define
selection criteria for use in reports. A filter is built up as a hierarchical set of
procedures defined using dialogues and pick lists.
Analysing a model using Seagate Worksheet
As shown in Figure 4, Seagate Worksheet provides a spreadsheet-style,
multidimensional analysis environment. The Worksheet can be used by end
users and developers to build prototype models and reports. New rules can
be easily added from within the worksheet to enhance a model. All the
analytical functions provided by Holos are available directly from the
worksheet interface via drop-down lists.

Figure 4 Analysis environment
A worksheet view can also be saved to provide the basis of a report defini-
tion, which is tailored in the Report Designer tool.

Future enhancements
Seagate Software plans a number of enhancements to Holos with two major
releases.
• The ‘Tasman’ release, due in early 1999, will provide a number of
significant enhancements to Holos’s web offerings and its development
environment.
• The ‘Magellan’ release is scheduled for the second half of 1999 and will
focus on making Holos structures more scalable. It will also provide
greater management capabilities, specifically a toolkit for implementing
application and user security and a GUI tool for administering multiple
OLAP servers.
Seagate Software is also endeavouring to integrate its OLAP products more
closely to provide a business intelligence suite. The next version of Seagate
Info, code-named Polaris and scheduled for release in December 1998, will
allow Holos applications to be run directly from its interface.

Company background
The original developer of Holos was Holistic Systems, a British company
formed in 1987. In June 1996, Seagate Technology purchased Holistic Sys-
tems for $84 million as part of its strategy to build up a $1 billion software
arm.
Seagate Technology is perhaps best known as a manufacturer of disk drives,
but has been building up its software business. It acquired Crystal Services
in 1994, which developed the now ubiquitous Crystal Reports. This was
followed by Holistic Systems and several network storage management
software products. Together, these companies now form Seagate Software, a
wholly-owned subsidiary of Seagate Technology. The business intelligence
technologies have now been brought under the wing of Seagate Software’s
IMG.
Seagate Technology is a publicly held company with revenues of $6.8 billion.
Seagate Software’s revenues for fiscal 1998 grew 35% to $293 million.
Seagate Software is based in Scotts Valley, California, US, and employs 1,800
people. It also has representation in 40 countries worldwide.
Seagate Software is combining the technologies of its business intelligence
and its network and storage management units under a framework called
‘Enterprise Information Management’ (EIM). The EIM initiative aims to
provide enterprise customers with end-to-end information delivery, analysis
and availability. Hence, Seagate wants its customers to think of its back-up
and other management software products in the same light as its OLAP,
query, data mining and similar products. The plan involves some integration
of its product range, specifically the reporting and OLAP analysis capabili-
ties of Seagate Reports, Seagate Info, with the development capabilities of
Holos.
Seagate Software is attacking both the low and the high ends of the business
intelligence market. One of the challenges facing the company is to accom-
modate the different channel approaches for its OLAP products:
• high volume and VAR-oriented, for its low-end reporting and OLAP
analysis tools
• direct sales and strong customer focus, for large corporates deploying
OLAP on an enterprise scale.
Customer support
Support
Help-desk support is provided through all local offices via the telephone or
the Web.

Training
A range of Holos courses are provided for developers and end users. Develop-
ers may need some consulting support in addition to training to establish
first links between Holos structures and the underlying database. Computer-
based training is also available.
The worldwide professional services division is growing rapidly. Services
provided can be basic (installation or start-up) or can scale up to address
enterprise issues such as requirements analysis and application design.
Seagate also has referral partnerships with management consultants.
Distribution
North America
Seagate Software
Information Management Group
840 Cambie Street
Vancouver
British Columbia V6B 4J2
Canada
Tel: +1 604 681 34 35
Fax: +1 604 681 29 34
Seagate Software
The Broadwalk
54 The Broadway
Ealing
London W5 5JN
UK
Tel: +44 181 566 2330
Fax: +44 181 231 0600
Asia-Pacific
Seagate Software IMG Australia
Level 9, 42 Alfred Street
Milsons Point
New South Wales
Australia
Tel: +61 2 9955 4088
Fax: +61 2 9955 7682
Seagate Software also has Asia-Pacific offices in Singapore and Japan
covering local Asia-Pacific territories.
http://www.seagatesoftware.com
E-mail: info@seagatesoftware.com

Product evaluation
Summary
1 2 3 4 5 6 7 8 9 10
Core OLAP analysis is well supported via the Seagate Worksheet or custom
application interfaces. The Worksheet is well suited to power users wanting to
take full advantage of Holos’s advanced forecasting and modelling capabili-
ties from a ‘no-nonsense’ spreadsheet-like interface. However, Holos is a tool
for IS developers building applications for diverse user requirements. Holos
supports report distribution via the Web or by integrating with reporting
tools such as Seagate Info. Holos data can also be exported to Lotus Notes
databases as a shared resource, for distribution to group working environ-
ments. Report subscription services are not provided.
Access to specific models is generally defined by the developer of the applica-
tion and is entirely dependent on the user’s workspace definition.
Most of the metadata about models in Holos is aimed at developers only.
A ‘notes’ function allows users to attach descriptions to dimensions and cells
in a model. A flag can be displayed if a note is attached.
Using the model
A range of OLAP functionality (drill-down, slice-and-dice and pivot) can
easily be built into a custom application using the Holos development tools.
Seagate Worksheet provides a spreadsheet-like interface, from which core
OLAP functions are also accessible.
A range of filtering options is available for ordering the position of values in
a dimension level.
Visualising the drill down hierarchies
A dimension ‘Field Picker’ selector provides a visualisation of the drill-down
hierarchies, and the current position in it.

Developers can give end users the option of drilling through to transaction-
level data. This must be explicitly defined when the original structure is
created – Holos saves the SQL statement and column/dimension mapping
information in a script.
The Holos Open Client Interface makes Holos data available to any ODBC-
compliant desktop application. Custom applications developed using Visual
Basic can also access data from the Holos server. An Excel add-in is also
provided.
Seagate Crystal Reports and Seagate Info can access reporting data from
Holos, though the interface is read-only.
Holos supports a comprehensive gallery of standard business charts and
graphs. Multiple charts and data can be displayed on a report page. Double-
clicking on a chart item will allow drill-down. Holos provides its own
graphing capabilities. It also integrates a third-party graphing engine
(licensed from 3-D) to offer a wider range of data visualisation options.
Designing a report
Holos provides a Report Designer tool which allows point-and-click creation
of reports in a graphical, production-style environment. Developers and end
users can include OLE, sound and video clip objects in reports.
Publishing a report
Holos reports can be distributed using external tools such as Seagate Info.
Reports can also be published and distributed in HTML format using the
Web Gateway. Integration with Lotus Notes allows Holos reports to be sent
to a Notes database (as a shared object) for distribution into group working
environments.
Holos reports can be e-mailed to multiple users from within the Holos client
interface. Reports can be sent as static report views or ‘viewpoints’ that
direct recipients to specific parts of an application.
Holos integrates with Microsoft Mail (MAPI), Lotus cc:Mail (VIM), Unix Mail
and VMS Mail e-mail systems. There is no direct support for the creation of
dynamic e-mail lists; this requires Holos language scripts to be written and
interfaced through the supported mail APIs.
Typically, Holos reports are dynamic and are executed at runtime. There is
no support provided for subscribing to reports.

Summary
1 2 3 4 5 6 7 8 9 10
In Holos, business models are built exclusively by IS or developers. These
users are well supported by the Holos language and a number of graphical
tools for defining structures, hierarchies and adding calculations to models.
The combination of structures, models and rules provides tremendous flex-
ibility for designing a business model, and quite complex business models
can be built by overlaying different types of structures. But a single view of
the various types of structures and dimensions available would ease the
process considerably.
Basic design
Design interface
The Holos tools provide an easy-to-use point-and-click interface for creating
structures, dimensional hierarchies and calculations. However, there is no
single view of the various structures and dimensions that are available for
use in applications.
When building the structure, the database, flat file or source schema can be
viewed on-screen, and a sample of data displayed.
There is no direct support for providing end users with a universal mapping
layer.
Developers and end users are not automatically prompted to provide addi-
tional metadata during the model design, application development or report
design process. Optional metadata can be included. The metadata can be as
detailed as required, and can be stored with the object concerned or linked to
a database table.
Columns can be chosen selectively via point-and-click by using the SQL
Select Builder tool.
Dimension members are chosen using the graphical SQL Select Builder tool.
The Hierarchy Manager provides a visual interface for defining dimension
hierarchies and drill-down paths. Multiple hierarchies may be specified
within dimensions based on summary consolidations or average consolida-
tions. Holos also supports unbalanced hierarchies.

Time dimension
Holos provides a utility to simplify the process of creating time dimensions.
Standard time periods such as years, quarters, months and days can be
defined. These can be customised to reflect user-defined time periods. Stand-
ard operators for time analysis, such as lead and lag operations, are also
provided.
Dimension names can have two forms: a symbolic name for use within the
Holos language; and a long name that provides a fuller and more meaningful
description to end users in worksheets and custom applications.
Models can be designed to default to a specified dimension level when it is
opened or saved in an application.
Calculated measures
The Holos language provides arithmetic, conditional and looping constructs,
and many intrinsic functions for the manipulation and calculation of multi-
dimensional data. The Worksheet provides its own functions and operators,
including sum, avg, min, max, random, variance, exponential and square
root.
Multiple measures can be assigned to a set of dimensions. Measures can also
be arranged in a hierarchy.
Multiple designers
Multiple designers
Holos supports a built-in file control system that provides standard check-
out/check-in facilities.
Holos supports its own version control system, and can also link to external
versioning and change management systems.
Summary
1 2 3 4 5 6 7 8 9 10
Holos provides an extensible set of advanced analytical functions for statisti-
cal analysis, trend analysis and time series forecasting. Write-back is also
supported for ‘what-if’ analysis. End users have the flexibility to apply a
number of specialised pre-built functions to model data directly from a
custom application or the worksheet interface. The Holos language also
supports advanced data mining capabilities, though the integration of this
technology requires considerable programming effort. Holos does not help
analysts to interpret results of analyses.

Third party tool integration
An Excel add-in is provided. However, there is no integration with specialist
third-party analysis tools.
Ranking and sorting
Holos can rank values in either ascending or descending order (top/bottom-n
selections). It also supports Pareto analysis for more sophisticated classifica-
tion.
Holos supports advanced mathematical techniques, such as simultaneous
equations, linear and quadratic functions, algebra and nominal optimisation.
Financial functions
Holos provides a number of advanced financial consolidation routines,
including discounted cash flow, internal rate of return, net present value and
depreciation.
Statistical models
The Holos language supports an extensive set of statistical modelling func-
tions including moving averages, smoothing, range, standard deviation and
variance. The more powerful statistical functions assume an understanding
of statistics if the most is to be made from the results.
Trend analysis
Holos provides a number of sophisticated curve fitting techniques for analys-
ing trends. These include exponential, geometric, linear, quadratic, modified
exponential, modified hyperbolic, polynomial quadratic, rational and
semilog.
Simple regression
Holos provides a range of regression functions for forecasting, including:
multiple linear regression, univariate and multivariate regression.
Holos provides a comprehensive range of time-series forecasting functions,
including Holt-Winters method, Box Jenkins and Fourier analysis, additive
and multiplicative methods, single and double exponential smoothing.
The Holos external function interface can be used to access procedural
analytical functions created using external tools; for example, SAS. The
Holos language can also be used to extend the analytical capability of
applications.
Disk-based structures provide single-user write-back access for ‘what-if’
analysis.
Holos does not provide support for the analysis of non-numerical data.

Data mining
The Holos language provides advanced data mining capabilities through the
use of pattern matching, neural networks, cluster analysis and Chaid analy-
sis. The integration of this technology requires considerable programming,
although a wizard is provided to help generate the initial code. Pattern
matching is also available as an Agent process.
Holos supports a range of specialised analytical functions, including risk
analysis (Monte Carlo), impact analysis and target seeking.
Web support
Summary
1 2 3 4 5 6 7 8 9 10
Holos supports both HTML and Java-based web interfaces. The HTML
implementation is far from elegant, but does provide web users with a simple
and effective means of accessing and navigating through reports, albeit in a
restricted manner. The Java-implementation provides a more flexible inter-
face for OLAP analysis. The web tools are aimed at end users; there is no
support for designing models or developing applications.
The Web Gateway interface delivers standard OLAP capability via a series
of dynamic HTML pages. This allows a degree of interaction with reports,
but users are restricted by the limitations of HTML. Graphs are imple-
mented as Java applets.
The Java Worksheet provides a much slicker interface for OLAP analysis.
The Worksheet supports drag-and-drop operations for slice-and-dice and
filters for changing views.
The Web Gateway can support both registered and unregistered (guest)
users.
The Web Gateway is suitable for those users that require access to
predefined reports in a restricted way. The Java Worksheet is suitable for
power users that require direct access to models for ad hoc OLAP analysis.
It is not possible to edit the model-data source mapping layer using any of
the web tools.
It is not possible to create new models via the Web.

Holos can convert applications within Holos language code to HTML format
using a transformer facility. However, there is no support for generating
Java code.
There is no support for dynamic or targeted distribution of reports and
models via the Internet. Static HTML reports can be e-mailed via the
Internet.
It is possible to include multiple URLs in reports. URLs can reference other
Holos reports or applications.
There is no support to balance load-processing across separate application
servers. The Holos Web Broker can spread processing across many proces-
sors inside a single multi-processor application server.
Management
Summary
1 2 3 4 5 6 7 8 9 10
Holos lacks a separate graphical management console for administering
Holos models, data and end users. A command-line interface is provided to
define scheduling and user security. The security of models relies heavily on
the underlying operating system or database, though stricter access can be
programmed. It will be up to developers to define and maintain these
controls.
Application Manager is a general interface that is used to manage all as-
pects of the application development environment, including models.
Security of models
In general, Holos relies on the server operating system (Windows NT, Unix
or Open VMS) and the security in the target database to ensure access
controls. All Holos models can be defined as read-only or hidden from users.
Tighter controls can be built using the Holos language. For example, alias
definitions of structures can be used to restrict user access to parts of the
model.
Query monitoring
Holos provides query and application monitoring facilities. Statistics can be
collected and analysed on usage of models, reports and other system
components.

Management of data
Holos is a hybrid OLAP tool that supports both relational database and
multidimensional database storage options.
Data loading can be scheduled by attaching a loading script to an Agent that
defines the process for updating the models and applications with fresh data.
There is no point-and-click support for defining these schedules.
Agents can be defined that automatically watch for events such as an update
to the database or the creation of a new file, and subsequently execute a
schedule.
Holos automatically generates a comprehensive log of all load processes.
Holos language scripts determine how failed uploads and updates are
handled on a per-application basis. Agents can be linked to scripts to notify
administrators of failed loads and updates via e-mail.
Stored data can be distributed across multiple servers for storage. For
example, it is possible to store a multi-year time series as a set of ‘annual’
structures, each of a different type, and each stored on separate servers.
Holos provides facilities to calculate structures held in different servers.
Developers can specify sparse data processing algorithms according to the
type of structure and its degree of sparsity. Sparse disk structures are
indexed using a hash algorithm to provide optimised handling of sparse data
sets.
Holos’s smart consolidation facility can be used to calculate some values on
demand only. Developers can decide which values to precalculate, and which
to store for each model.
Various aspects of the server cache, such as bucket size, can be configured to
optimise performance.
A log is created each time data is loaded into a structure. The information in
the log can easily be displayed in client applications. However, there is no
support for accessing upstream load process metadata from the data ware-
house.

Management of users
Multi-user locking is automatic in relational structures. A toolkit is provided
to help manage multiple update users on a disk-based structure. However,
simultaneous user locks must be programmed in the Holos language.
User security profiles are defined using the Holos language. Profiles can be
assigned to individual users or groups of users.
Query governance
In the relational environment, the size of data blocks returned from the
database can be controlled.
There is no support provided for restricting queries according to time.
Developers can define the complete user environment, or set up application
development groups that restrict access to specific Holos components.
Adaptability
Summary
1 2 3 4 5 6 7 8 9 10
Within Holos, it is easy to adapt a model to support changing business
requirements; all model design operations produce Holos language scripts
that can be stored and re-used. Holos can also incorporate different types of
structures in a model and easily adapt from ROLAP to MOLAP modes and
vice versa. However, there is no direct support to ensure that data sources are
automatically synchronised with Holos structures and applications. Nor does
Holos provide any support for impact analysis.
Adding dimensions to models is straightforward, but adding them to struc-
tures requires updating the data in the structure and the objects dependent
on that structure. A tool to track these dependencies is available.
All dimension definitions can be saved as Holos language scripts and re-used
by application developers.
New measures can easily be added into any Holos model or application.

Measure definitions are re-usable.
Holos’s architecture is flexible in allowing for both ROLAP and MOLAP
types of data storage. When query times lag against the broader less sum-
marised ROLAP engine, a subset of the data can be loaded into a pre-aggre-
gated multidimensional database to obtain speedy response against a more
focused set of information.
The racking and stacking architecture lets users swap cube formats, or
replace a physical cube with a ‘virtual’ cube without having to modify the
application.
There is no direct support to ensure that the database schema is synchro-
nised with the application. However, developers could feasibly build Holos
language scripts to check on this before an application is run.
Updates to dimensions can be triggered through the use of agents.
Metadata
Apart from dimension and measure information, there is little model
metadata to synchronise in Holos.
Impact analysis
Holos does not provide any direct support for an audit trail. However, infor-
mation stored in scripts while creating and modifying the structures can
easily be logged to an external file for analysis.
There is no integration with externally generated metadata.
Summary
1 2 3 4 5 6 7 8 9 10
Holos provides strong tunabaility features for both MOLAP and ROLAP
operation. For ROLAP mode, Holos supports the generation of multipass SQL
and native SQL access to all the major relational databases. For MOLAP
configurations, multidimensional structures can be loaded incrementally. The
loading and precalucation of data can also be distributed across multiple
processors simultaneously using SMP technology.

ROLAP
Multipass SQL
Holos automatically generates multipass SQL.
The processing of SQL can be carried out either on the server or the data-
base.
Data access can be speeded up by caching queries on the server in an
optimised form. Users can access the cache for matching queries. The cache
can be defined to constantly refresh itself, though end users are not auto-
matically informed of its currency.
Aggregate navigator
The relational structure provides a caching mechanism that stores aggre-
gates on the server. This cache can be stored and reloaded as required.
MOLAP
Trading off time/size and performance
Large logical structures can be incrementally loaded. The underlying physi-
cal structures can be independently loaded or refreshed. Once the load is
complete, the multi-cubes can be ‘snapped’ back into the main compound
cube. Scripts can be developed to re-calculate only those values that have
changed during a refresh.
Disk-based (MDDB) structures provide single-user write-back access only.
Applications that require multiple write access to an RDBMS are supported
through the Holos language programs. Stacked cubes can also be used to
provide a ‘pseudo’ multi-user write-back environment.
For ROLAP architectures, the number of users that can be supported simul-
taneously depends on the complexity of the queries and capacity of the data
warehouse to cope with the number of temporary tables generated when the
SQL is processed.
Processing
Holos uses native SQL access to all the major RDBMSs. ODBC drivers are
supported on certain Unix platforms.
The loading and pre-calculation of data can be spread across many proces-
sors; either inside a single multi-processor system or across a loosely clus-
tered network of machines.
SMP support
Holos makes full use of SMP parallelism.

All the structure types have associated tuning mechanisms and can be
modified at runtime. Holos can alter the sparsity algorithms used to calcu-
late and consolidate data dynamically. This means that a structure can be
sparse at calculation time, and defined dense at runtime so that different
access methods can be employed.
Customisation
Summary
1 2 3 4 5 6 7 8 9 10
Holos is a powerful 4GL application development tool. The Holos language is
purpose-designed for building business intelligence applications, and is
supported by an integrated and easy-to-use set of graphical development
tools. The range of development options should satisfy most development
needs, from simple EIS reporting systems to advanced analytical applica-
tions.
Customisation
The Worksheet cannot be configured to provide a restricted interface. How-
ever, the Desktop Designer tool provides a point-and-click method for cus-
tomising the functionality of Holos applications, desktops and workspaces.
EIS-style reporting applications can easily be built using the graphical
development tools.
Applications
A toolkit is provided to develop applications that run through a web browser.
A transformer utility is provided to convert Holos language scripts into
HTML, and present them to web users as a series of dynamic HTML pages.
Web applications can access all the functions of the Holos server, but do not
support as many features as Holos client applications.
Holos provides a flexible and productive development environment. Easy-to-
use GUI tools allow most development work to be done via point-and-click. A
full programming environment for the Holos language is provided for writ-
ing custom procedures and linking them into applications.
Holos does not integrate with external development tools.

Holos has strong support for localisation; a single OLAP application can be
written to support different language interfaces including German, French,
Italian, Spanish, Portuguese and Japanese character sets.
Holos can also act as an OLE2 client.

Deployment
Platforms
Client
Holos clients run on Windows 3.1, Windows 95, Windows NT and Macintosh.
The Seagate Worksheet runs on Windows 95 and Windows NT and Java-
based web browsers.
Server
The Holos server runs on Windows NT, DEC VMS and the following Unix
flavours: HP-UX, AIX, Sequent, IRIX (Silicon Graphics and SGI), SunOS,
Digital, ATT, Pyramid, ICL DRS/NX and SGI Siemens Nixdorf.
Data access
Holos provides native access to the following RDBMSs: Oracle, Informix,
Sybase, Red Brick, Teradata, Rdb, Ingres, IBM DB2/6000 and HiRDB. Holos
also supports ODBC (Windows NT and Unix) and can access data from
third-party multidimensional databases (Essbase and Microsoft SQL Server
OLAP Services).
Holos can also access transactional data from SAP and Oracle ERP applica-
tions and can load data directly from Lotus Notes databases and flat file
systems. Links to external information providers, such as online news
services, are also supported.
Standards
Holos supports Microsoft’s OLE DB for OLAP and Hyperion Solutions’
Essbase API. A third interface – the OLAP Council’s MDAPI 2.0 specifica-
tion – is also under consideration.
The Holos Open Client Interface provides integration with ODBC-compliant
applications.
Holos does not provide any published OLAP benchmarks.
Price structure
Because of the fixed-price element of the host-based server, costs per user
start a little higher than other OLAP tools, but can be substantially lower for
large numbers of users.
The entry point for Holos is around $80,000. This enables five concurrent
server connections, ten licensed desktop users and one application developer.
The price includes training for the developer and end users.

Sterling Software –
Eureka:Suite
Summary
At a glance ............................................................................................. 2
Ovum’s verdict ........................................................................................ 4
Distribution ............................................................................................ 21
Product evaluation
Web support ......................................................................................... 28
Management ........................................................................................ 29
Adaptability ........................................................................................... 32
Customisation ....................................................................................... 35
Deployment
Platforms .............................................................................................. 37
Data access .......................................................................................... 37
Standards ............................................................................................. 37
Price structure ...................................................................................... 37

2
Evaluation: Sterling Software – Eureka:Suite
At a glance
Developer
Sterling Software (Business Intelligence Division), Eden Prairie, Minnesota,
USA
Versions evaluated
Eureka:Suite comprising of Eureka:Strategy 5.7.8, Eureka:Analyst 4.5,
Eureka:Intelligence 1.1, Eureka:Reporter 6.1.3 and Eureka:Portal 2.0
Key points
• A suite of tools that integrates ROLAP, multidimensional and query and
reporting systems through an Internet-based portal
• Servers run on Unix and Windows NT; clients run on Windows 3.1, 95, 98
and NT workstation. Web access is also provided
• Eureka:Suite integrates Information Advantage’s DecisionSuite ROLAP
tools and IQ Software’s SmartServer and Vision OLAP tools – both of
which were acquired by Sterling Software in August 1999
Strengths
• A comprehensive suite of business intelligence tools and services that are
easily accessible through a web-based portal interface
• Integrates a highly scalable ROLAP system – query processing is
automatically optimised between the server and the RDBMS
• Provides flexible scheduling, report sharing and messaging facilities that
are matched by few OLAP tools
Points to watch
• The ROLAP server runs exclusively on Unix
• The ROLAP server can only be accessed by Eureka clients, which are not
the strongest tools for highly specialised analysis
• Still considerable scope for integration between the back-end systems –
management of a Eureka system can be complex
Ratings
Web support
Management
Adaptability
Customisation
1 2 3 4 5 6 7 8 9 10

Ovum Evaluates: OLAP Evaluation: Sterling Software – Eureka:Suite
Calculations
A general term for any numeric fact that is included in a report. There are
three types of calculations:
• volumetric – stored values from the database fact tables
• calculated facts – are not stored but derived by the ROLAP engine
• custom calculations – combinations of existing facts, operators, constants
and dimension constraints that the user defines.
Categories
Database views that define the tables to be used for reports. Categories are
initially defined in the metadata tables; each metadata category identifies
the dimensions and attributes that are available. End users create reports,
calculations and filters based on metadata category definitions.
Facts
A generic term for data included in reports. Facts can be data items stored in
database tables or calculations derived from stored data items and formulae.
Facts are defined in the metadata tables and viewed in reports.
Filters
Allow users to limit the detail and amount of data in a report. Filters are re-
usable components stored in the metadata tables.
Hot Objects
Sterling’s term for hypertext support, which makes Eureka reports act like
web pages, with hot spots and drill-down capabilities.
Metadata tables
Metadata tables are relational database tables that map a data warehouse
structure to a business model and contains information about the
dimensions, attributes, drill paths and calculations that are used in reports.
Report
Reports consist of report documents that act as web pages. Report objects are
the primary source of information contained within report documents.

4
Ovum’s verdict
What we think
Before being bought by Sterling Software in 1999, Information Advantage
was one of the first OLAP vendors to introduce Internet portal concepts into
the business intelligence world. Eureka:Suite is its first attempt to integrate
two radically different product lines – DecisionSuite and IQ SmartServer and
IQ Vision, to offer a comprehensive business intelligence suite and, more
significantly, step away from its strict ROLAP stance.
Eureka’s portal approach has the potential to bring OLAP to a broader
audience. Users will either be delighted by the ease with which they can
access business intelligence data, or overwhelmed by the choice of tools and
breadth of information available – although users can easily personalise the
interface for specific information. However, the core strength of the product
remains its ROLAP capabilities (the focus of this evaluation), in particular
its ability to analyse large volumes of information with and high numbers of
attributes. Scalability is underpinned by a well designed server-based
architecture, including an object request broker and a proven ROLAP engine
that maximises the use of RDBMS technology while addressing the
limitations of SQL. The product’s flexible report scheduling, sharing and
distribution options are matched by few other ROLAP tools.
However, the ROLAP server is ‘closed’ in that it is not accessible as a data
provider to complementary third-party business intelligence tools. Users are
limited to Sterling’s own client offerings, which, although well integrated and
easy to use, are not necessarily ‘best-of-breed’. Sterling has committed to
supporting Microsoft’s OLE DB for OLAP standard, but has not yet
announced a date.
Under the portal interface, Eureka:Suite represents a collection of separate
business intelligence systems. There is still considerable scope for further
consolidation and integration and users should not underestimate the
management burden. The implementation of the ROLAP system can be
complex and any purchase decision usually involves a wider data
warehousing consideration. Customers without a data warehousing strategy
will almost always need to buy-in some consulting and migration assistance.
Large-scale rollouts can take between six and 18 months to complete.
When to use
Eureka:Suite is suitable if you:
• require access to corporate data stored in large, finely-tuned data
warehouses
• are already committed to a large-scale data warehouse strategy, or are
preparing for one
• want to analyse large amounts of customer- or product-centric data with
large numbers of attributes
• want to provide a broad range of users with personalised access to
business intelligence and unstructured corporate data
• have a requirement to easily distribute and share information and reports
across the enterprise

• have a strong commitment to Unix – the ROLAP server runs exclusively
in this environment.
• It is less suitable if you:
• want to develop highly specialised OLAP applications that require very
complex analysis
• want an out-of-the-box OLAP solution
• are willing to accept the effort and cost needed to implement Sterling’s
ROLAP tools
• want to use ‘best-of-breed’ tools to access and analyse OLAP data
• need a flexible business model – the ROLAP business model is tied closely
to the structure of the data warehouse.

6
Product overview
Components
‘Eureka’ is the colourful brandname for a suite of tools that integrates
ROLAP, multidimensional OLAP, query and reporting systems – technologies
that were originally developed by Information Advantage and IQ Software.
The five main components of the Eureka:Suite are:
Eureka:Strategy version 5.7.8 – for server-based ROLAP analysis
Eureka:Analyst version 4.5 – for multidimensional analysis at the desktop
Eureka:Intelligence version 1.1 – for web-based, integrated query reporting
and analysis
Eureka:Reporter version 6.1.3 – for server-based production reporting.
All of these components publish their results to, and can be accessed through,
the Eureka:Portal (version 2.0), a web-based portal that provides a
personalised, single entry point to a broad range of business intelligence and
corporate data.
Eureka:Suite is one of the largest business intelligence suites on the market.
But for the specific purposes of this evaluation, we focus on the ROLAP and
multidimensional OLAP capabilities provided by Eureka:Strategy and
Eureka:Analyst. However, we do acknowledge (where appropriate) the
functionality provided by the other components.
Eureka:Portal
Eureka:Portal is a web browser interface that allows users to access OLAP
reports and other corporate information. It applies the same principles used
by consumer Internet portals (such as MyYahoo!) to provide a personalised
single point of access to business intelligence and other corporate data via
URL links.
Query and reporting OLAP design and analysis Web access
Client Eureka:Analyst
Eureka:Portal
Server Eureka:Reporter Eureka:Strategy Eureka:Intelligence

Eureka:Portal is built on two components:
• Content Server, which maintains a repository of ‘objects’ created with
Eureka components and third-party systems. Users can publish objects
into the server, navigate through content using a search taxonomy, and
subscribe to ‘channels’ for automatic report distribution
• Content Administrator, which is a web-based environment for
administering the Content Server. It includes graphical interfaces for
setting up and managing user profiles and setting security, and tools for
monitoring the usage of repository objects and tuning performance.
The Eureka:Portal can be purchased out-of-the-box as a standalone portal
platform for the integration of unstructured tools through a personalised
interface. However, its benefits are fully realised when teamed with other
Eureka components. The portal integrates all the client Viewer interfaces for
the Eureka tools. Similarly, all the Eureka tools can also publish information
directly to the portal.
Eureka:Strategy
Eureka:Strategy is a Unix-based ROLAP server that processes client
requests against large data warehouses – typically to evaluate dimension
attributes for customer segmentation and inventory management
applications. The server carries out a significant amount of data processing
(joins, aggregations and calculations).
Eureka:Strategy uses an intermediary metadata layer to dynamically
generate SQL for a query, and delivers formatted content back to the
presentation tier. The metadata layer provides a business-oriented map of
the underlying database table structures, which automatically synchronises
applications with changes in the RDBMS. This information is stored in a
series of metadata tables, usually in the data warehouse. The metadata can
also map data stored in more than one RDBMS.
Eureka:Strategy includes a number of client components:
Designer
An end-user interface for defining ROLAP queries and new reports. Reports
can be enhanced by creating custom calculations directly from the Analysis
interface. A range of visualisation techniques are also provided.
Viewers
Provide a visual interface for analysing ROLAP models generated by the
ROLAP engine. Two types of ROLAP Viewers are available – for client-server
and a combination of HTML and Java:
• a client-server interface for casual users – it allows users to tailor reports
built with Analysis, or simply view predefined reports delivered by the
ROLAP server as part of a schedule or agent process
• a web interface – enables reports to be accessed and analysed from a web
browser. It is closely integrated with the ROLAP server (via CGI), with
reports dynamically generated in HTML.

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Administrator
A client-server tool aimed at model designers, DBAs and systems
administrators in the ROLAP environment. Interfaces are provide for:
• creating, validating and maintaining the metadata tables
• administering and managing the ROLAP environment.
Eureka:Analyst
Eureka:Analyst is a multidimensional analysis tool targeted at analysts with
calculation-intensive analytical needs – typically financial forecasting. The
tool is based on IQ Software’s Vision software – a proprietary OLAP client
that loads multidimensional cubes from MDDB servers and holds them in
memory on the desktop for offline analysis. It links directly to Applix’s TM1
MDDB engine and OLE DB for OLAP-compliant data sources.
Eureka:Analyst can be c

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