We explain some essential issues about Business Intelligence, present leading BI solutions and top providers of data visualisation and discovery.

1. What to focus on when choosing
a Business Intelligence tool?
A BUYER’S GUIDE
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2. With data diversification and various data visualisation me-
thods, it is difficult to select the tool that will best meet our
expectations from the wide rage of available options.
The aim of this Guide is to:
 present selected crucial issues of the Business Intelligen-
ce area
 identify leading solutions related to Data Warehouses
 indicate leading providers in the area of data visualisa-
tion and discovery
Contents of the Guide:
3 Introduction
4 Business Intelligence market
6 BI providers
8 Pure-plays vs. mega-vendors
10 With or without a data warehouse?
12 Data visualisation and discovery
13 Tools for SMB, but not only
2

3. 3
The latest Gartner Business Intelligence report shows that
the main objective of BI software purchasers is to exploit Big
Data as far and as effectively as possible.
The main obstacle companies face is not technical and vi-
sualisation capabilities of Business Intelligence tools and
not data base support, but it is the fact that the majority
of companies fail to accurately evaluate their business ne-
eds in this area. While technical aspects are naturally impor-
tant for IT departments, business users often select tools
exclusively based on the opinion from the market, and fail
IT chooses technology; Business monitors the market.
Is that the best way?
to consider important details. Their analysis is limited to
Gartner’s quadrant for BI, with the most focus on the ease
of use. They choose user-friendly tools that allow for data
analysis and “submitting queries” without the involvement
of the IT department. In fact, the best BI implementations
come from the synergy between the IT department and the
business.
In this Guide, we present a selection of best practices advi-
sable to consider when selecting the most suitable Business
Intelligence solution.
Introduction

4. Noticeably, the market of Business Intelligence tools is divi-
ded into two groups of providers.
The first group are “pure-play” category providers who offer
tools focused on specific product and business areas and
clearly oriented towards business recipients, e.g. in the area
of data visualisation.
The second group - “integrated, traditional, so-called mega-
-vendors” - offer a significantly broader range of tools that
may additionally cover elements ranging from data wareho-
uses, visualisations or analyses to planning and budgeting.
Therefore, among leading Business Intelligence providers
we will find those who deliver traditional tools as well as “pu-
re-play” producers such as QlikView and Tableau.
Business
Intelligence
market
4

5. The leading providers to the BI market are:
 Qlik
 Tableau
 Microstrategy
 SAS Institute
 IBM
 Oracle
 Microsoft
 SAP
 FICO
 Information Builders
Given the diversity of Business Intelligence technology pro-
viders, it is essential for a business to identify its own needs
related to:
 Key recipients of analyses and data
 Analytical and reporting requirements
 Presentation layers and mobility requirements
 Data volumes and the question whether to build
a data warehouse or use association data models
or subject-area data marts instead
 The possibility to combine different sources of data
effortlessly
5
Leading BI providers
What should be considered
when selecting a BI solution?
„Pure-play” providers
or „traditional” BI?

6. If there is a data warehouse or repository already in place or
if a company uses BI class tools, then it would seem unjusti-
fied to implement a new technology of BI mega-vendor for
e.g. data visualisation and discovery. A provider would work
towards redevelopment of the data warehouse with applica-
tion of its own tools, and argument that it is for ensuring the
integrity of technology in its broad sense.
On the other hand, for organisations who decide to imple-
ment a BI system for the first time, it would be reasonable
to consider a provider that offers traditional integrated so-
lutions with a number of tools, from management and con-
struction of data warehouses, advanced data analyses and
exploitation to planning and budgeting.
For such buyers, it may be more reasonable to select this
kind of solution to carry out the undertaking, provided that it
is economically justified. In general, however, such solutions
are far more expensive and require more time for imple-
mentation.
When deciding between a “pure-play”
and a „mega-vendor”, it is advisable
to “stock take” the tools and technologies
a company has in use.
„Pure-plays”
vs. mega-vendors
6

7. 7
Enterprise class solutions „mega-vendors”
ADVANTAGES
 Improved capabilities of data warehousing
 Integration of many databases including Hadoop
 Ability to analyse vast amounts of data sets
 Supporting a large number of users
DISADVANTAGES
 Low satisfaction of business users
 More expensive user licenses
 It may take more time to have a query to a database
replied
 They require aggregated and “pre-arranged” data
waithing for the user
 Migration processes are more complex
 It requires highly-advanced devices and a number
of server installations
 It is necessary to have specialist knowledge on data
warehousing
Specialist „pure-play” solutions
ADVANTAGES
 Highly-developed and advanced data analyses and
visualisations
 Intuitive tools for data uploading from different data
sources
 Facilitated implementation
 Lower maintenance costs
 Satisfaction of business users
 Simplified migration of solutions
DISADVANTAGES
 Storing capabilities limited to hardware resources
 A cost per user may be higher than at the beginning
of the project
 Functionality may be limited in a stand-alone scenario
Advantages and disadvantages of tools

8. 8
With or without
a data warehouse?
The validity of creating EDW has been lately called into qu-
estion. In the past, BI vendors convinced companies that it
was necessary to build data warehouses for storing all data
from different operating systems in one central location.
Over the years and with the growing awareness of business,
the amount of so-called “pure data” has been decreasing and
replaced by a constantly growing amount of unspecified data.
In the context of EDW building, the integrity of such data be-
comes unworkable or difficult to perform due to the variety
of new sources of data, in particular, the unstructured data
from social media.
To address this variety of data and ever-changing business
needs, the Hadoop software has been developed. It allows
for exposing data from EDW or versatile sources of data for
databases oriented towards more detail analyses. Hadoop
enables a company to collect data for analysis from traditio-
nal relative bases or data warehouses without the need to
use a schema. In this way, “row” data may be aggregated from
almost any source, without the time-consuming and burden-
some activities of data cleaning.
Architects also raise their doubts about whether any form of
EDW could find application in business. The low flexibility of
transformation, cleaning or uploading data certainly does not
encourage companies to use it. This trend has also been no-
ted in the latest Gartner’s report. According to analysts, 85%
of currently operating data warehouses will loose their capa-
bility to adjust to the increasing amount of data and methods
of its processing and presentation within the next two years.
However, many companies still use the data warehouse ar-

9. 9
What other aspects should be considered?
 Scalability: The solution may be easily integrated with new sources of data to
capture significant increase of data volumes
 Access: Possibility to easily configure access to data to adjust it to company’s bu-
siness needs.
 Support: Technical and functional support services rendered by the provider
 Implementation: Many enterprises still choose local providers of data wareho-
uses due to profitability aspects.
chitecture due to its capability to provide accurate data for
data mining at the lowest level of information.
Considering the diversity of unstructured data, it has become
increasingly popular in the Business Intelligence environment
to integrate Hadoop into solutions offered by one of provi-
ders. Such integrations gain in importance, especially in the
case of companies who regularly process vast amounts of
data.
Proof of Concept – a good practice
Top EDW providers
Before selecting a data warehouse provider, the best practi-
ce would be to conduct a proof of concept. A company gives
access to real or untrue data, depending on its security poli-
cy, and creates a small data warehouse to run a pilot project.
In this way, both of the parties enjoy the opportunity to as-
sess potential issues or the level and quality of cooperation
prior to actual implementation.
 IBM
 TeraData
 SAP
 Oracle
 SAS

10. Data visualisation is one of the most popular Business Intel-
ligence tools. The growing demand for visualisation results
from the increase in the number of business users and their
desire to obtain access to reports without the need to enga-
ge the IT department.
Visualisation tools such as dashboards and scorecards may
cooperate with data warehouses or store them, but in a si-
gnificantly less volumes than EDW.
Visualisation tools are focused on improving a user’s expe-
rience by promoting Self-Service BI. Consequently, they in-
crease the freedom business users enjoy in their analytical
activities.
Data visualisation tools may also include complex analytical
functionalities such as data modelling and uploading. Such
tools often go beyond historical and current data visualisa-
tions, and are able to predict future behaviours of clients.
In many cases, visualisation tools allow for the analysis of
unstructured data through integration with Hadoop.
Data visualisation
and discovery
10

11. 11
It is also becoming increasingly popular to publish analytical
solutions in the Cloud. However, in the Polish market, such
activities develop at a significantly slower pace due to the
permanent “fear” of enterprises about the data security and
access.
Business users do not always represent the same employee
profiles. Corporate environments, marketing, controlling or
sale departments often employ so-called data experts who
would like to use BI for advanced normative analyses rela-
ted to econometrics, statistics. In medium organisations,
users are focused on creating a style template for reports
and simply activating their analyses in a planned manner.
In its best form, data visualisation may help an organisation
to conduct real-time analyses so that it is able to quickly re-
spond to its clients’ behaviours. Individual data visualisation
and discovery platforms should also offer the possibility to
integrate with generally accepted authentication and autho-
risation methods to ensure security.
Leading pure-play providers
 Qlik
 Tableau
 ClearStory
 LogiAnalytics
 LavaStorm Analytics
 SAP Lumira
To visualise
or not?

12. Solutions
for small
and medium
businesses,
but not only
Traditional Business Intelligence solutions offered by global
providers may be out of reach for small and medium enter-
prises due to the need to build data warehouses.
As the in-memory processing expands, many systems allow
for storing and processing of large volumes of data using the
RAM, without the need to have a centralised data storage.
Such systems exploit the tools of tabular association data
models that enable data compression to the maximum level
of 10-12/1 and, in practice, to about 6-7/1. The pioneer and
leader in the market of data compression and in-memory
processing is Qlik®
that has developed and continuously im-
proved this technology since 1993.
In its resent report, Dell stated that 41% of medium enter-
prises run at least one BI + Big Data project, while 55% is
planning to start one in the nearest future.
12

13. 13
BI in RAM.
Is it sufficient?
The answer is: Yes, but not in the „Big Data” sense. At this
point, it is important to emphasise that Big Data and Busi-
ness Intelligence are not synonyms. In fact, Big Data is the
source of BI, and not its fundamental element. BI existed
long before Big Data, and brought incredible results, whi-
le Big Data emerged relatively not long ago and is related
to such elements as data volumes, unstructured data, so-
cial media or weather report service to improve data-based
inference. For small companies who do not require all the
above-mentioned components, Big Data, BI is of enormous
importance. Each organisation that owns data that can be
used for creating and developing competitiveness should
have BI. Data analysis does not have to exceed data volu-
mes to be effective. It just needs to lead to solving a business
problem. Small companies are definitely able to draw much
meaningful nformation from their data, even if it is only data
from weather report services combined with transactional
data to build knowledge about clients’ behaviours. This kind
of analytical intelligence may still explore great business op-
portunities for an organisation.
Is a BI solution a reasonable option for
small and medium businesses?

14. Leading providers
to the SMB sector
Leading providers of analytical tools
for the SMB sector are Qlik, Tableau or Birst.
They offer similar technologies, but it is worthwhile to look into
advantages and disadvantages of individual solutions to meet
particular business needs. However, considering the number
of functionalities, the most advisable choice for data visualisa-
tion combined with heterogeneous data sources are definitely
the tools by Qlik®
.
The Qlik®
company offers data analysis and visualisation to en-
sure that insights and data transparency are available when
they needed the most: at the decision-taking time. In this way,
decisions for the entire organisation are made with confiden-
ce, while its business analysts and specialist employees beco-
me irreplaceable professionals. Qlik has always been focused
on innovative solutions that follow the ever-changing require-
ments of both individual users and entire organisations. Qlik is
a pioneer and leader in the Data Discovery segment, which has
been confirmed by independent analytical companies.
14

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Author: Damian Skipioł, Cooperation: Karolina Kruba, Katarzyna Kęcka