This document discusses operational business intelligence and how it helps agile enterprises. It discusses how vendors are developing products that provide real-time intelligence and decision-making capabilities. It also discusses how tools like machine learning, semantic search, and data visualization are helping organizations gain insights from large amounts of structured and unstructured data to make better operational decisions.

1. Operational Business Intelligence for Agile Enterprises
By Kishore Jethanandani

2. VENDOR STRATEGIES AND PRODUCT OFFERINGS
Agile enterprises need business intelligence to improve their sensory perceptions; the ability to
gauge reality, to view their resource flows and alacrity to remain on top of events. Business
intelligence vendors are increasingly conscious that customers hate to be hobbled by their sunk
costs in Information Technology. Instead, customers want to be able to rejig their existing
technologies to adapt in fluid situations. Information needs to flow unimpeded by clunky
technologies. Technology inadvertently has often, in the past, become a millstone instead of a
lubricant of change. Customers are increasingly looking for technologies that read the pulse of
their business activity and funnel information to all their employees who can communicate and
collaborate and act in time to respond to events.
The concerted effort that customers are making to lower latencies in data collection and decision
making is best illustrated by the effort investment banks are making to speed up the processes to
refresh their data that influences their decisions on portfolio management. They are looking to
receive information directly from the exchanges, option trading exchanges and ECNs so that they
can weigh the impact of events on any of the securities that they hold in their portfolios. Automatic
trading tools enable traders to complete the calculus of risk and return when they buy or sell
securities and need to evaluate the impact of major changes such as prices and interest rates.
Their information architecture has to be so constructed that it can tap market data from a variety
of servers, with their own data formats, and convert them into a single format and move the data,
aided by middleware, to the enterprise data infrastructure.
In the past, information systems were tenuously linked to the levers that companies could use to
act as situations changed. Increasingly, enterprises are looking to integrate decision making
processes and business processes so that the lag time between the receipt of information and
the response is minimized. They want to build in the rules for predictable responses to known
problems so that human resources can be reallocated to attend to more knotty problems. Where
human intervention is required, companies want to be able to quickly visualize a situation and
size up a problem before they act.
Above all, the best decisions happen when all the related information is brought to bear on a
course of action. In addition, decision-makers want to simulate alternative scenarios, visualize
them before they take their decisions.
The business intelligence industry is still evolving and the jury is still out on who is eventually
going to win. Clearly, established players with experience in implementing large size deals in the
enterprise software industry have the best chance to integrate several technologies required to
gather information, analyze data and communicate decisions. Business intelligence projects also
require consulting services that are so necessary for successful implementations of especially the
operational applications.

3. INTELLIGENCE ON TAP
Business managers, in operational situations, get the “missed the bus” feeling when they are
unable to take decisions at the right time; their calculations can go haywire as the ground shifts
underneath them. Well timed moves help them to grab opportunities and get ahead of the
competitors. Companies need to also take decisions before a problem snowballs into a crisis.
Delays in decision making can cause grievous losses to businesses. In the pharmaceutical
industry, for example, counterfeiting, medical errors and poor quality of products can undermine
confidence in companies and medical groups. The actual manufacturers could be blamed for the
harm done by counterfeit or an odd batch of poorly manufactured products causes harm to
patients. The healthcare industry is now well equipped with laser vision, RFID and other
technologies to eliminate errors in data entry and data gathering technologies which aggregate
data so that causes of any damage done can be traced back to specific deliveries and preemptive
action is taken.
Edge Dynamics Inc is one of the companies with applications for real time decision making for the
pharmaceutical industry. Typically, a complex set of contracts, deals and regulatory policy bind
the stakeholders in the supply chain consisting of the manufacturers, wholesalers and retailers.
Edge Dynamic has the software which is able to capture transaction order stream data originating
from an EDI or other such sources such as a Web-service B2B network. The data is analyzed for
discrepancies from forecasted numbers or any deviations from agreements initiated at the outset.
As data is received and analyzed, the partners in the supply chain will discover flaws in its design
and work towards managing inventories better. They will find better ways to optimize and
reevaluate their partners and their logistical planning.
The technology that goes into gaining visibility into the operations of businesses is illustrated by
the implementation of Siebel Analytics at Jostens which sells class rings, graduation
announcements, and yearbooks to schools across the country. Siebel Analytics is able to
aggregate data from the Oracle data warehouse, a Microsoft SQL Web/e-commerce application,
and Microsoft Access for use by Jostens sales staff which can view the results on role-based,
interactive dashboards. The Siebel Analytics software enables Jostens to keep track of the sales
performance data by each segment of the business. With real time data feeds, Jostens’ sales
staff can spot opportunities for cross-selling, up-selling, etc.
Retail stores present a familiar scenario where markdowns happen almost everyday when
inventories pile up unexpectedly. All too often, retail store managements are taken by surprise as
preferences change, the media influences attitudes, new promotions are announced by
competitors, seasons change or events affect purchases by consumers. At the local level,
consumer behavior can be quirky and the inventory in stock may not excite them. Retail stores
have to learn to stock an assortment of products that are in tune with the tastes of customers for

4. each of their stores spread around the country, ensure that they will be profitable and manage the
supply chain so that the products will available in time for the season.
When supply closely matches demand, companies can not only pass the benefits of lower losses
from stock outs to consumers in the form of lower prices but also offer products that closely
match their needs. Zara, a Spanish clothing company, takes less time than its competitors to
respond to market need. The managers at its stores send information about customer
preferences by handheld devices and it is all aggregated rapidly so that the most relevant
products are displayed in their stores. The dyeing and printing is done only after the customer
information is available.
The management of demand and supply has gotten more difficult as the product life cycles get
shorter and the supply chains get longer as goods are sourced from more distant places.
Increasingly, companies are looking at software that can aggregate point-of-sale data from
multiple sources, analyze it to predict demand for individual categories of products, optimize the
supply chain and help in pricing.
Several different pieces of software are used in the management of demand and supply. One of
them is revenue optimization software which takes into account information on demand, the costs
and determines the best price to offer based on elasticity of demand. Conversely, it can take the
prices offered by competitors as given and throw up the numbers for the desired demand and
supply. Such software can also pinpoint customer segments most likely to respond to a particular
offer. Manugistics Inc. is one company that leads in this segment of the market. However,
revenue optimization software does takes into account existing demand before it cranks out
figures on prices and potential segments to target.
The suppliers would rather that they could forecast demand accurately and produce as much so
that they can receive better price deals. This is best achieved by using demand forecasting
software. The successful implementation of demand forecasting tools presupposes the collection
of point-of-sales data and the willingness of retailers to share such information with their vendors.
Besides supply chain management software providers such as i2 and Manugistics, Business
Intelligence vendors such as NCR Teradata, Business Objects, Cognos, and Prescient are the
players in the segment.
An additional piece of software for collaborating in real time is the software for supply chain
management to collaborate with vendors, manage logistics and to share information. Oracle's 11i
E-Business Suite, for example, includes iSupplier and Collaborative Planning portal to
communicate with offshore contract manufacturers and suppliers with web based tools.
MACHINE LEARNING FOR INSIGHTS
The growing size of data sets has changed the analytical paradigm. Well known techniques, such
as statistical techniques, are overwhelmed by the colossal volumes of data. Typically, statistical
techniques begin with a hypothesis and a model, based on domain knowledge such as

5. psychology, which they seek to validate. The involvement of human beings and uncertain
processes preclude the use of insights in real time.
When data sets are large and chaotic, it is much harder to decide on the methodology for
verification. The dimensions overwhelm human cognition’s ability to see the connections and be
able to this quickly enough to make decisions. Increasingly, machine learning methods are
required to reduce raw data into patterns before humans can look for the story that is relevant for
decision making purposes. These automated methods of finding patterns look for correlations of
data over periods of time (time series), find clusters in activities such as crime or classify data
such as in decision trees. Market basket analysis, for example, looks for combinations of products
customers tend to buy.
The kind of situations in which machine learning has a compelling value is searches on the web.
Intelligence agencies have to look for terrorist activity, competitive intelligence analysts look for
information on rivals or content creators have to look for intellectual property rights violations.
Companies, such as FAST, have created tools that are able to extract insights from such a
labyrinth. Reuters, for example, uses FAST’s search tool to zero down on content that looks
suspiciously like its own.
Machine learning plays an important role in functions such as fraud detection, stock trading, and
customer segmentation to extract intelligence that cannot wait for an analyst to extract
intelligence. Neural Network software systems, for example, have reduced fraud in UK banks by
as much as 30%.
The vendors in the space include SAS, STATISTICA Data Miner, S-Plus, Fair Isaac, SPSS
Clementine, IBM Intelligent Miner Affinium Model, Insightful Miner and KXEN, IBM Intelligent
Miner and Genelytics.
Tools that understand fuzzy concepts
Companies are best able to extract insights when they can search across all their data and
classify and correlate it. For decision support knowledge, companies have to be able to conduct
searches on both structured and unstructured information.
The urgency to search for unstructured information is more urgent now as it has wide range of
applications such as especially law enforcement, customer service, drug discovery, knowledge
management, etc. Companies are beginning to discover the enormous benefits of mining text and
other unstructured information. The pharmaceutical industry, for example, is discovering that it
can reduce the time required to commercialize new drugs if only it could search and analyze
information pouring in from clinical trials for all drugs. When data on safety is available for all
clinical trials, the regulatory bodies can look for patterns that will help them to come to decisions
about accepting drugs for human use faster than is the case now. XML is the bedrock for linking
related databases and to search them with text mining tools.

6. Customers need a common language and search tools to parse all the relevant data and to
analyze it. Natural language is best able to express the nuances in human thought processes.
Inevitably, natural language can have a variety of meanings, synonyms, connotations and usage.
New tools are required to see words in their context before any meaning can be drawn from
them. This is best achieved when search engines have semantics capability.
The traditional and the most widely used method of searching databases, the Structured Query
Language, is inadequate for heterogeneous environments where data descriptions vary between
databases. This form of querying is relevant only for structured data and it presumes knowledge
of the specific information a person is searching. In most cases, people have a knowledge of the
theme they are interested in exploring. In heterogeneous environments, searching by using SQL
would be impossible since the number of series, as well its heterogeneity, overwhelms its ability
to extract meaningful information and knowledge.
With the advent of XML technologies it is now possible to classify unstructured information as
well. Individual elements of unstructured information can be described by tags or the metadata
that describes the information content in there. The detailed description of the content helps to
search repositories with large volumes of content much like SQL queries can extract information
from relational databases. XQuery can search both content repositories and databases and
extracted related quantitative and qualitative information. Microsoft’s SQL Server 2000 is one
product which supports XQuery and is able to use both structured and unstructured data for
analytical purposes.
Another approach to searching unstructured data is to use search engines. However, a search
conducted on unstructured data all too often yields a jumble of results which is an all too familiar
experience of users of the World Wide Web. Similar searches on corporate intranets are worse
since the information in not even linked as is the case with the World Wide Web.
Search technologies for corporate databases seek to look for the significance in a mass of words.
For example, someone maybe looking for information on a crime committed by a suspect named
John Lear of San Francisco will be able to find meaningful information when inter-related
information about the background of the person, the time, location, previous associations with the
victim is presented. Databases of unstructured information can have variables such as time,
location, biographical data, etc., as the dimensions of a data warehouse and they can store
related facts associated with each of them. A search conducted on such databases is more likely
to find related results instead of a jumble.
At the center of semantic search technologies is ontology or the knowledge base that helps to
define the “being” or the personae that are pivotal to understanding the universe under
consideration. For example, students and professors form the axis of the universe of an
educational institution. Semantic search tools create taxonomy to describe the entities in a

7. universe and their relationships with the world around them. The information about the university
is classified by entities which helps to create the links between the available records.
Search engines for unstructured data are now able to find information in an organized way by
using tokenization, linking and taxonomies. In essence, these methods look for patterns in the
unstructured data. The tools are so designed that they look for associated text; a word like crime
is related to gang membership, academic performance of the person, incidents of drug or alcohol
abuse, etc., and the information is presented in its relevant context.
The impact of correlating structured and unstructured data can be easily visualized if we look at
the decision analysis that is required for store location analysis. Typically, retail companies will
need structured data such as the demographics of the neighborhood. They will need also map
information in the form of satellite imagery. Also, they would like to have unstructured information,
such as crime, to gauge the attractiveness of the location besides lifestyle trends in the region.
One example of the use of intelligent search engines is the case of ISYS search engine from
Odyssey Development. The Ventura County in California searches through its numerous
repositories to find related information. It could, for example, use data of blood examination, from
a structured database, and find related information on several burglaries committed by the same
individual from several other repositories.
One of the several semantic search tools in the market has been created by Semagix for
searching media sources. The ontology is a hierarchy of categories beginning with general
classifications like News, Business and Entertainment and then more specific terms like cricket,
soccer, etc. The searches could be done by themes such as cricket tournaments which exclude
the possibility of tangential information, such as tournaments of all sports, appearing.
IBM is one company bringing a great deal of intellectual property to the table for searching
unstructured data. IBM WebSphere Information Integrator OmniFind Edition has pushed the
envelope by launching its Unstructured Information Management Architecture (UIMA), a platform
for integrating structured data and unstructured information. The platform supports a variety of
functions such as linking analytics software and enterprise applications, tools for developers to
conveniently create new or reusable text analytics components. With this architecture,
unstructured data in a host of formats or languages, whether it is located in databases, e-mail
files, audio recordings, pictures or video images can be searched.
The searches are unlike the familiar keyword searches; they use concepts to look for related
pieces of information. Text analytic components, supported by UIMA, can use WebSphere
Information Integrator OmniFind Edition to define the ontology, look for relationships in data, mine
text to find hidden knowledge and extract useful business information. An example of how these
kinds of search engines can look for inter-related information would be the case of customer
satisfaction; it would be possible to search for data in maintenance records, market research

8. studies, call center records and warranty claims to find the products that customers find most
satisfactory or vice versa.
Altogether a total of fifteen companies plan to use this architecture and they include Attensity,
SPSS, Endeca, Factiva, Kana ClearForest, Cognos, and SAS. Factiva and QL2 will provide data
for analysis.
They get it with visuals
Decision-makers are constantly intimidated by information clutter and are looking for tools to help
them digest information rapidly. There is a great deal of noise in large volumes of information
while the noteworthy nugget could well elude the decision makers. In industries such as the
securities industries, the value of information decays quickly unless the substance is absorbed
quickly.
Visualization is an indispensable tool for real time assimilation of relationships in large volumes of
data and their implications for decision making. One instance of this is American Water which has
to monitor the threat of a hostile intrusion on its IT network. It receives thousands of alerts and the
large majority of them are false alarms. Visualization tools help it to map the source address of a
packet and its destination to help isolate any suspicious activity.
Decision makers prefer interactive visualization tools to help them test their hypothesis visually.
Excel type of static graphics have been the staple for visualization in enterprises. Decision
makers need to be able to examine alternative scenarios and they like to have visuals that are
three-dimensional, pliable enough for impromptu reconfiguration to respond quickly to queries
and they like to flip them to view a problem from a variety of angles. The visuals are made lifelike
by the use of artifacts, colors and animations to convey the meaning of the information displayed.
All of these attributes are meant to contribute to effective communication of a message. None of
the widely available visuals available with spreadsheets have the capability to achieve this.
Visual queries are one of the means to isolate relevant data from a clutter and portray it visually.
Much like the structured query language, a visual query extracts specific pieces of information
from a mass of relational database and displays it on a graph. An alternative way to zero down on
selected information is by the choice of dimensions; an analyst might want to compare the bad
debt losses by regions, such as mid-west and the west coast, which is possible when a cube is
created. Cognos Visualizer, which works in combination with Cognos Powerplay for aggregation
of data from multiple sources, is one product that enables users to choose their dimensions and
the corresponding numbers they want to display graphically.
The vendors in this field include the Business Intelligence vendors and another group is
specialists with a focus on visualization. Among the leading BI vendors are Cognos, Business
Object and SAS. On the other hand, the specialists are companies like Vizible Corporation, Visual
Mining and visualization platform providers such as Antarctica System’s Visual Net and Spotfire’s
DecisionSite. The platform providers are the most versatile as they are designed to use data from

9. any source and they can customize analytical tools to conduct the desired kind of visualization.
Typically, the platform providers focus on industries that generate enormous quantities such as
the pharmaceutical industry or the natural resources industry.
Spreadsheets are forever
An aspect of real time access to data for decision making is also the ability of users to have the
option to continue to use familiar tools. Spreadsheets have been the most widely used for
analytics required for decision making purposes. Integration of spreadsheets with business
intelligence software is critical to their widespread adoption in the enterprise.
Excel spreadsheets are ubiquitous in enterprises despite the fact that they inexorably fragment
the data sources. The flexibility of Excel allows users to create their own data marts and they can
add formulas of their own choice. On the other hand, they contribute to fragmentation of data
sources and perceptions which conflicts with the objectives of gaining a consistent view of the
enterprise. Excel spreadsheets are also not scalable and are not the tool of choice when it large
teams have to work together. In addition, Excel spreadsheets do not have the ability to manage
business processes based on the analysis conducted. Above all, Excel spreadsheets do not have
the ability to pull together information from a diverse set of corporate databases.
Business Intelligence vendors have, in the past, provided partial integration with Excel
spreadsheets by adding features for exporting data to Excel sheets which can be loaded on to a
server. However, Excel users routinely create their own formulas to create additional data series
which were not included in the process of integration. One of the exceptions was SRC Software,
recently acquired by Business Objects, which had closely integrated a variety of databases with
an Excel interface.
Lately, business intelligence vendors have changed course and have offered Excel as the
interface to their business performance or business intelligence software. The software provides
a consistent view of the data and uploads the formulas together with information on changes
made by any of the workers in the enterprise so that they all have access to the same
information.
Beyond a patchwork of integration
The issue of integration has gained urgency as companies seek to manage their sprawling supply
chains, collaborate in product development, offer self-service options to customers and outsource
business processes. A common denominator in these applications is that they span several
systems and applications; enterprises need conduits for information to flow across all of them.
In the past, middleware was the accepted way to integrate applications; it was a step forward
from arduous custom coding that was the norm. While custom coding is the recommended
method for joining two applications, middleware simplifies matters when an application has to be
connected to several others. The middleware is the intermediate junction which allows
information to flow to several different directions. On its way, the middleware prepares the data,

10. the reformatting, merging, etc., which allows it to be accepted in another database or application.
Data transfers, in a message-oriented architecture, are akin to e-mail which remains in a server
queue till it can move through networks and eventually downloaded onto a desktop when desired.
Middleware created a patchwork of joins which grew in numbers and complexity over a period of
time and was increasingly difficult to manage. At this point of time, a need was felt for a platform
that would manage the gamut of middleware and associated software such as business process
management, be managed from a single platform. Enterprise Application Integration (EAI) suites
meet these needs. An EAI network has subscribing applications which replicate data received by
any one of them into another application.
Integration of applications is not limited to specialized EAI vendors such as Tibco, WebMethods
and SeeBeyond. These vendors have strengths in integrating several applications into a
continuous process but are less likely to be able to combine a broad range of complex business
processes. Another group of vendors are the large enterprise software companies offering
platforms such as IBM, Microsoft, BEA. Finally, the application vendors are also offering a series
of integrated applications such as SAP’s Netweaver, Siebel’s Universal Application Network
(UAN).
In the early stages of integration, message-oriented EAI software was most widely used for
transportation of data from one end to another. In a typical message oriented integration
technology, data can be transferred in an asynchronous manner so that current applications don’t
have to interrupt their functions to receive new data feeds. An intermediate middleware received
the data feeds and transmits to another application without the need to alter the two applications
in any way. The EAI network facilitates the navigation of data over a variety of networks,
programming languages and applications. When two applications are integrated, a point-to-point
integration will suffice. On the other hand, a publish/subscribe methodology will work better when
data transfers have to be undertaken to multiple applications.
One case study of the implementation of the EAI is Cincinnati-based HealthBridge which acts as
the junction for flows of information from hospitals and providers in the metropolitan area of
Cincinnati. The company navigates the data streams from 28 hospital clinical applications and
delivers more than 940,000 clinical results per month to 2,900 physicians. HealthBridge network
uses clinical messaging software to transmit data from one application in a hospital to another in
a physician’s office.
EAI makes a departure from piecemeal integration of some of the applications of an enterprise by
tying them with middleware. While integration by means of middleware is much less expensive
than an EAI implementation, the benefits too are far more limited. By integrating the entire
enterprise, an EAI also enables companies to view their business processes and applications in
their entirety. The process parameters are monitored so that companies can monitor their
performance. They can optimize their business processes by modeling them, simulating the

11. impact of alternative designs which could help to lower costs and redesigning their business
processes.
The IBM MQ series is the market leader in the space for message oriented integration
technologies with an estimated 65% of the market share. The leading position of IBM is
accounted for by open architecture which means that applications in diverse range of platforms
can be integrated without any special programming. On the other hand, Microsoft, the other major
player in the market, does not support any other platform other than its own without custom code
to make it happen. IBM’s WebSphere MQ connects applications and passes messages between
them. It has a library of connectors to Oracle, SAP, and Siebel Systems applications, as well as
mainframe systems such as CICS and IMS. BEA Systems offers WebLogic Integration and uses
XML based application adaptors to inter-link applications.
The other method of integration is to execute it at the level of data. This method relies on
database technologies like gateways, metadata, queries, data set transfers, and bulk data loading
tools such as ETL or data grids. The most commonly used methods are ETL tools, traditionally
used to feed data into Data Warehouses, are products like Data Junction's Integration Studio and
Engine, Informatica's PowerCenter. These tools extract data from operational data stores,
transform the data and load them into data warehouses.
Increasingly, the former specialists in ETL have morphed into data integration specialists.
Informatica, for example, has incorporated PowerAnalyzer into its data integration product
PowerCenter Advanced Edition and is working closely with Composite Software for EII type of
integration. IBM extended its data integration capability by its acquisition of Ascential and has
incorporated its ETL engine, renamed DataStage TX, which also includes EAI capabilities that
came with the acquisition of Mercator Software Inc. The IBM WebSphere Information Integrator
plays the role of data integration.
Integration of data sources is crucial to not only consolidate data but also to check for its quality.
This is illustrated by the case of The Scotts Company, Marysville, Ohio, the lawn and garden
products company, which needed to find a way to forecast consumer demand with greater
accuracy. In the past, it had to depend on its own shipment data instead of the point of sales data
which was scattered and could only be accessed from EDI systems that were routinely used by
retail chains. The data integration major, Ascential, designed a solution that allows the company
to access consolidated data from its POS of sales services in a way that could be read by its SAP
applications. A comparison of the actual shipment, production and inventory data with demand
helps to determine the trends in consumer demand and to make relevant adjustments.
Enterprise Information Integration (EII) is a set of tools which integrate a federation of data
sources besides applications. EII provides a single point access to all the data in the enterprise,
whatever its format, with metadata to describe all the data. The better known products in this
space include BEA's Liquid Data and IBM's DB2 Integration Integrator. Among the new

12. companies are Attunity, Avaki, Composite, and MetaMatrix. Composite has an alliance with
Cognos to integrate business intelligence software and its product Composite Information Server
3.0 starts at a price of $100,000. Avaki’s first product Avaki 6.0 was launched at $50,000 while
the total costs of deployment averages $175,000 to $250,000.
These systems have two important components; they need a data model to aid the process of
conversion of data from one source to another. These tools also provide graphical tools that show
the configuration of the network of applications and data sources and a directory of terms to
access methods and fields in the data sources.
Among the key players are start-ups such as MetaMatrix which has a partnership with Business
Objects and Hyperion while Composite Software has alliances with Informatica and Cognos. Both
these vendors come from a background in relational databases and their products afford SQL.
Another category of vendors, such as Ipedo, provide XML based query techniques. Composite
Software enables the aggregation of data into a portal view while any kind of manipulation of this
data has to be done manually by the user.
A much desired integration method is to orchestrate business processes in order to enhance the
ability to control the levers that will help to respond quickly to changes in the business
environment. The ability to manipulate business processes puts business executives in control of
IT and they can direct enterprise resources independent of the IT department. The need to
automate business processes had been alluded to by vendors in the content management and
workflow management as well as in the EAI space. However, the management of business
processes has been piecemeal so far and has not progressed to a level where all them can be
managed from a single platform of its own independent of other divisions.
A single platform, for business process management, provides the means to adapt business
processes to changing requirements rather than be set in an application stone. One of the key
barriers to configuring a series of business processes is that they have always been embedded in
applications. Once they are decoupled from applications, business processes can be broken up
into their components, reused for a variety of tasks and connected to complete a series of tasks
to complete a job at hand. The Business Process Modeling Language (BPML) provides the
means to create a path for the flow of business processes. For execution purposes, the Business
Process Execution Language (BPEL) plays a complementary role in that it manages the flow of
business processes. The conceptual bedrock of an independent management of business
processes is Pi Calculus which provides a method for unifying them and reuniting them for
another purpose. Just like properties define an object, Pi Calculus is like metadata which spells
out the tasks an individual unit of a business process can complete and the roles it expects
related processes to complete. In such a world, business processes are akin to packets in a
network which can be made to follow different routes depending on the addresses where they are
directed to move towards.

13. Intalio is one of the pioneers in the design of platforms for the management of business
processes in their own right rather than as a component of an application. Other notable products
in the same space are Microsoft BizTalk server and Holosofx which was acquired by IBM was
renamed as Business Integration Modeler and incorporated into its Websphere platform.
Siebel’s Universal Application Network (UAN) creates a process centric environment for the
integration of applications and data in an enterprise. At the heart of this strategy is a library of
business processes such as quote to cash, campaign to lead, order to pay, etc. which can be
used to compose solutions. An overarching SOA architecture enables other vendors to tie their
applications in the overall solution. UAN incorporates vendor-neutral interfaces based on SOAP
(simple object access protocol), WSDL (Web service definition language) and XML (extensible
markup language) to create an environment for a diverse range of vendors to plug in their
applications. The UAN has used the syntax of the BPEL4WS (Business Process Execution
Language for Web Services) standard to define all business processes which implies that
individual vendors can hook in their own servers.
Lately, another variety of data integration software for interlinking data sources across a grid has
appeared. This kind of software, such as the Avid Data Grid, spans a wide area network and
allows access to the resources available across enterprises without going through the
cumbersome process of using multiple passwords to access them. The data access is made
possible by a universal directory which provides the access to the data source. With just this one
access point, users are able to extract data from numerous sources and are delivered for specific
users.
Some of the key players in the grid computing industry are HP’s Adaptive Enterprise, IBM’s On
Demand and Oracle’s 10G and Sun’s NI. IBM’s On Demand program uses its WebSphere and
Tivoli products for policy-based management. HP has a similar product called OpenView Platform
for the management of the grid and integrates Talking Blocks Web services into it.
The key advantage of Grids is the possibility of lowering latencies afforded by a cluster of servers
and storage devices which are not clogged when traffic spikes unexpectedly which would tend to
happen when numerous applications have to be operated simultaneously. An array of storage
and servers helps to spread the load over several devices which also are better utilized because
they are not dedicated to specific applications. Charles Schwab was able to lower to lower query
response from four minutes to as low as fifteen seconds.
One of the earliest applications of Grid computing is the case of Hewitt Associates, a global HR
company, which has deployed an IBM WebSphere-based grid to operate software to calculate
pensions.
Corporate radars
The payoff of integration of information assets in an enterprise is the ability to monitor any sign of
a threat or an opportunity so that enterprises can take timely actions. Business Activity Monitoring

14. servers play the role of radar which can spot any exceptional events, such as missed schedules
in transportation, followed by the analysis of the impact of untoward events on related activities
such as communicating to a truck which could use its spare capacity to take on a load that the
assigned truck missed. Business Activity Monitoring involves a series of related activities of
observing business processes, comparing them with metrics, analyzing and visualizing its
implications and communicating for corrective action.
One of the applications of real time activity monitoring is the case of Brocade communications
which needed to keep track of the performance of its contract manufacturers to make decisions
about its outsourcing decisions. It acquired a business activity monitoring tool to be able to do this
in real time.
Some of the leading players in the industry are Informatica which has incorporated its Business
Activity Platform, developed jointly with WebMethods, in its PowerCenter RT, its integration
platform. WebMethods Application Integration capabilities and Informatica’s information
integration have been combined while they have, at best, lightweight business process
management capabilities. Ascential Software’s DataStage, now integrated with IBM WebSphere
Datastage has a Real Time Integration (RTI) Services component.
There are also companies from the business process domain, such as Microsoft BizTalk Server
2004, which has added a Business Activity Monitoring engine. BizTalk Server 2004 allows users
of Microsoft Office 2003 to monitor business processes from a desktop. Middleware specialist
TIBCO offers BusinessFactor, the technology it acquired from Praja. Celequest is among the
more prominent pure-play companies in the domain with its ActivityServer suite which has the
ability to stream data from operational systems and compares business rules with metrics to
determine whether an alert needs to be sent. Among the database companies, Teradata with its
Active Data warehousing product is focused on business activity monitoring.
A nose for bad data
In its early stages of growth, the data quality industry was largely populated by independent
players. In more recent years, the independent players have been bought over by the large
business intelligence companies. A prominent example of this is the acquisition of DataFlux by
SAS, Ascential bought Vality and was in turn bought by IBM. FirstLogic, an innovator in the
space, was bought by Pitney Bowes. Group 1, a data quality vendor, took a different course and
acquired an ETL vendor, Sagent, before Pitney Bowes purchased it. The process of extracting,
transforming and loading is expensive and companies hope to lower their costs by merging the
associated routines of validation, transformation, filtering or standardization. These processes are
optimal way to improving data quality when companies want to do data mining in a data
warehouse environment while much more needs to be achieved in a real time environment.
An example of the functionality available with such products that offer both data consolidation and
data quality services is IBM’s WebSphere Data Integration Suite. Its ProfileStage component

15. automates the process of matching the data structures and data formats, from different
databases, so that the data from the source and the target are consistent. Similarly, the
QualityStage component standardizes data for individual entities, such as a customer, and
ensures that disparate conventions in storing data don’t contribute to inconsistencies. The data
quality components are provided with the DataStage, an ETL tool.
When data is updated in a real time environment, using technologies such as messaging queues,
there is a need to execute data quality functions early on in the transactional databases or by
other means such as metadata. The automation of data quality functions presupposes a
comprehensive solution to provide universal definitions of data and a means to convert from one
definition applicable to a specific application to another, removing duplications and correcting
errors in addresses, names, etc. Master data management is a means by which a database of
metadata is created and rules for converting from one format to another.
A key problem with current methodologies is that the data cleansing is done after the data has
already been extracted so that the source of the error is not detected. When data reconciliation
takes place with the help of master data management systems, the source of the error is also
identified.
It is now possible to buy rudimentary master data management products from a variety of
vendors. Among the platform vendors, the leaders are IBM and HP, PeopleSoft and SAP are the
players in the applications vendor category and Ascential, Infomatica among integration vendors
besides system integrators and Hyperion from the BI category.
An example of the implementation of a master data management system is the case of Unilever
which needed a centralized way of managing its data to pursue a global policy for brand
management and supply chain management. Historically, Unilever followed a decentralized policy
for the management of its subsidiaries which meant that its IT system was fragmented. It has now
implemented a master data repository which helps to align its transactional systems for a
consistent view of its data.
Sensors everywhere
Automation of data collection is one of the means to collect data free from errors. Sensors also
help companies gain visibility into their environment and to expand the universe of problems they
are able to address. The volumes grow with the use of sensors and present new challenges in
data processing. Increasingly, RFIDs and other type of sensors are available for commercial
application. For mass adoption, on the other hand, the costs of RFIDs would have to be
substantially lower before they will be accepted in applications such as supply chain
management.
General Electric has been one of the early pioneers in the use of sensors for analytical purposes.
These sensors gather data on the state of health of its jet engines. The data is gathered at one
place where analytical software looks for signs of trouble and provide early warning to their

16. customers. The military has often been a leader in adoption in new technologies and its
willingness to accept the RFID technology is a pointer to wider diffusion in industry at large. Cost
factors are less binding in situations where high value activities, such as the manufacture of
aircraft engines, is involved or in the military where security is an overriding consideration.
The use of RFIDs in mass applications such as supply chain management is going to bring it
closer to ubiquity. In the early days, the industry had to improve the technology, the ability to read
information, so that it would work in commercial environments. Wal-mart is working with a
hundred of its partners to expand the use of RFIDs for supply chain management. NEC of Japan
has reported early successes in the use of RFID in its PC assembling plants. Unlike bar codes,
RFIDs do not require manual scanning of bar codes; the productivity at its Yonezawa Plant, as a
result, increased by 10% besides the benefits of just-in-time replenishment of inventories.
It will take middleware or some other form of integration technologies for companies to be able to
receive information from a variety of sources and funnel it to a central database. One of the
significant initiatives, to popularize the use of RFIDs, is the partnership between Oracle and Intel
and Xpaseo to supply tools for the management of information received from sensors. These
tools will mediate the flow of information from sensors and integrate with existing products from
Oracle and Intel, including Oracle Application Server 10g, Oracle Database 10g and Oracle E-
Business Suite. In addition, the partnership will extend the scope of pervasive computing by
linking data from other devices such as hand held devices, PC, mobile devices using Intel
communication and server platforms.
Other companies who have RFID offerings include SAP, IBM and SUN. There are also smaller
specialist companies who offer middleware to integrate RFID technology with the rest of the
enterprise software.
THE BIG PICTURE
Real time enterprise has to put in place several moving parts before the entire vehicle for rapid
response takes shape. Vendors have been able to offer most of the components of the solutions
of an adaptive enterprise and are acquiring companies to consolidate their products to provide a
complete package. Some remarkable breakthroughs have already been achieved especially in
the analysis of data. Real time interpretation of data, aided by machine learning techniques, and
predictive analytics capabilities has equipped enterprises to grasp the dimensions of the
problems they encounter and to anticipate the outcomes and consequences. The imminent
prospect of automated data gathering and business process design will trigger as much
excitement as dashboards have done in the recent past. Larger vendors such as Oracle, IBM,
Microsoft, SAS, SAP, Business Objects and Hyperion are emerging stronger than before and are
best equipped to emerge as leaders. In all probability, infrastructure providers from among the
former ERP companies will have the strongest foundations to supply the products and the
consulting skills to win over customers in the future.