What to focus on when choosing a Business Intelligence tool?

A BUYER’S GUIDE

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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

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

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

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 providersWhat should be considered when selecting a BI solution?

„Pure-play” providers or „traditional” BI?

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

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

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

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

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

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?

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.

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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?

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.

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