Critical Success Factors of BI Implementation

Description
Today more and more business organizations are turning towards BI for making better business decisions.


Critical Success Factors of
BI Implementation
Master’s Thesis Report

Naveen K. Vodapalli
([email protected])
CPR Nr. 290576-XXXX
02/11/2009






Supervisor: Frantisek Sudzina
Asst. Prof. Board of Studies for EBUSS
[Today more and more organizations are turning towards BI for making better business
decisions. This is true based on the fact from the latest Gartner’s research report (Gartner
Research, 2009), which states, “For the fourth year in a row, BI applications have been
ranked the top technology priority in the 2009 Gartner Executive programs survey of more
than 1,500 Chief Information Officers (CIOs) around the world.” BI implementations
usually take very long time, consume a lot of resources and are also very expensive. They
often do not bring expected results or do not fulfill vaguely defined expectations. And since
turnover, is lower than in not so far past, it is more crucial to complete BI implementation
with time and budget, well in order to improve business performance and to satisfy the
sponsor of BI project. Thus making, the study of Critical Success Factors in BI
implementation vital to study. In this project I gathered first-hand data of what industry
professionals feel are critical for the implementation of Business Intelligence and then
compared the results with the factors I have analyzed in my literature review to gain better
understanding of the factors and also see if there is any difference in the results.]
2
Contents
Table of Contents
1 INTRODUCTION ............................................................................................. 11
1.1 Chapter introduction ................................................................................. 11
1.2 Project background ................................................................................... 11
1.3 Motivation for the project ......................................................................... 13
1.4 Project objective ....................................................................................... 13
1.5 Significance of the project ......................................................................... 13
1.6 Project scope and Limitations ................................................................... 14
1.7 Chapters alignment ................................................................................... 14
2 BUSINESS INTELLIGENCE: AN OVERVIEW ............................................... 17
2.1 Chapter Introduction ................................................................................. 17
2.2 Evolution of BI ......................................................................................... 17
2.3 Definition of BI......................................................................................... 18
2.4 BI System ................................................................................................. 20
2.5 BI Architecture ......................................................................................... 21
2.5.1 Components of BI Architecture ..................................................... 23
2.6 Key Characteristics of a BI System ........................................................... 26
2.7 The BI Stack ............................................................................................. 26
2.8 Drivers of BI Implementation ................................................................... 27
2.9 BI Implementation decision factors ........................................................... 31
2.10 Relationship between BI and Data warehouse ........................................... 32
2.11 Benefits of BI Implementation .................................................................. 34
3 LITERATURE REVIEW ................................................................................... 35
3.1 Chapter Introduction ................................................................................. 35
3.2 Critical Success Factors ............................................................................ 35
3.3 Distinguishing CSFs from KPIs ................................................................ 36
3.4 List of CSFs .............................................................................................. 36
3.5 Categorization of CSFs: ............................................................................ 41
3.6 Analysis of CSFs from the literature review .............................................. 42
4 RESEARCH METHODOLOGY ....................................................................... 46
4.1 Chapter Introduction ................................................................................. 46
4.2 Research Purpose ...................................................................................... 46
3
4.3 Research Approach ................................................................................... 47
4.4 Research Strategy ..................................................................................... 47
4.5 Sample Selection ...................................................................................... 47
4.6 Data Collection Method ............................................................................ 48
4.7 Data Analysis ............................................................................................ 48
4.8 Validity and Reliability ............................................................................. 59
5 FINDINGS & CONCLUSION ........................................................................... 60
5.1 Chapter Introduction ................................................................................. 60
5.2 Findings .................................................................................................... 60
5.3 Discussion of findings ............................................................................... 69
5.4 Implications and Recommendations .......................................................... 70
5.5 Implications for Future Research ............................................................... 71
6 BIBLIOGRAPHY .............................................................................................. 72
7 APPENDIX ....................................................................................................... 79
7.1 Questionnaire ............................................................................................ 79
7.1 Interview of Respondent1 ......................................................................... 84
7.2 Interview of Respondent2 ......................................................................... 89
7.3 Interview of Respondent3 ......................................................................... 94
7.4 Interview of Respondent4 ......................................................................... 99
7.5 Interview of Respondent5 ....................................................................... 104
7.6 Interview of Respondent6 ....................................................................... 109
7.7 Interview of Respondent7 ....................................................................... 114




4

List of Tables
Table 1.1: Chapters Alignment ................................................................................16
Table 2.1: Summary of varied BI definitions ...........................................................19
Table 3.1: List of CSFs from literature review .........................................................36
Table 3.2: Categorization of CSFs ...........................................................................42
Table 3.3: Analysis of CSFs from the literature review ............................................43
Table 3.4: Priority order of CSFs from the Literature Review ..................................44
Table 4.1: List of CSFs from the questionnaire ........................................................50
Table 4.2: Critical Scale Analysis of CSFs in the Organization Perspective .............51
Table 4.3: Critical Scale Analysis of CSFs in the Process Perspective .....................52
Table 4.4: Critical Scale Analysis of CSFs in the Technology Perspective...............53
Table 4.5: Priority Analysis of CSFs in the Organization Perspective ......................54
Table 4.6: Priority Analysis of CSFs in the Process Perspective ..............................56
Table 4.7: Priority Analysis of CSFs in the Technology Perspective ........................57
Table 4.8: Priority Analysis of all the CSFs with respect to BI Implementation .......58
Table 4.9: Priority Order of CSFs with respect to overall BI implementation ...........59
Table 5.1: Critical Scale Analysis of CSFs from the Perspectives ............................61
Tabel 5.2: Critical order of the CSFs .......................................................................63
Table 5.3: Priority Analysis of CSF from the perspectives ......................................64
Table 5.4: Priority order of the CSFs .......................................................................66
Table 5.5: Analysis of priority order of CSFs in tables 4.9 & 5.4 .............................66
Tabel 5.6: Priority order of CSFs .............................................................................67
5
Table 5.7: Analysis of Research Tables ...................................................................68
Table 5.8: Priority Order of CSFs developed from my Research ..............................69

List of Figures
Figure 2.1: Traditional BI Architecture....................................................................22
Figure 2.2: Technical Architecture of BI .................................................................23
Figure 2.3: ETL Process ..........................................................................................24
Figure 2.4: The Business Intelligence Stack ............................................................27
Figure 2.5: Drivers of Business Intelligence ............................................................29
Figure 2.6: BI Value Chain against Cost and Time ..................................................30
Figure 2.7: The BI Value Chain...............................................................................31
Figure 3.1: Grouping critical perspectives of CSFs ..................................................41
Figure 3.2: Analysis of CSFs sited in literature review ............................................44
Figure 4.1: Critical Scale Analysis of Organizational Perspective ............................52
Figure 4.2: Critical Scale Analysis of Process Perspective .......................................53
Figure 4.3: Critical Scale Analysis of Technology Perspective ................................54
Figure 4.4: Priority Analysis of Organization Perspective........................................55
Figure 4.5: Priority Analysis of Process Perspective ................................................56
Figure 4.6: Priority Analysis of Technology Perspective .........................................57
Figure 4.7: Priority Analysis of all CSFs .................................................................58
Figure 5.1: Critical Scale Analysis of CSFs from the Perspectives ...........................62
Figure 5.2: Priority Analysis of CSF from the perspectives ...................................65
Figure 5.3: Analysis of priority order of CSFs in table 5.5 .......................................67
6
Figure 5.4: Analysis of Research Tables ..................................................................68


7

List of acronyms used in this project
BI – Business Intelligence
CEO – Chief Executive Officer
CIO – Chief Information Officer
CRM – Customer Relationship Management
CSFs – Critical Success Factors
DSS – Decision Support System
DW – Data Warehouse
ESS – Executive Support System
ERP – Enterprise Resource Planning
ETL – Extract, Transform and Load
IS – Information System
IT – Information Technology
KPIs – Key Performance Indicators
MIS – Management Information System
OLAP – Online Analytical Processing
OLTP – Online Transactional Processing


8


Declaration


I confirm that this project work has been carried out by me and this work has not been
submitted for a degree or diploma in any university. This work do not contain any materials
previously published by other persons except where due references are made in text. All the
contributions by others, including joint publications are clearly acknowledged with due
references.



Naveen K Vodapalli
November, 2009




9

Acknowledgements

All good things have to come to an end sooner than later. And the accomplishments during
this jouney could not have been possible without support and assistance of so many people.

My foremost gratitude goes to the most instrumental person for this thesis, that is my
supervisor, Asst. Prof. Frantisek Sudzina, who has been helpful to me all along right from the
project proposal phase to providing contacts for the interviews, further helping out with
formulation of the questionnaire followed by suggestions and corrections to this report. He
has been there for me at every phase of this project.

I am greatful to all the respondents who have come forward despite their busy schedules and
without whom my project would not have gone ahead. Thanks especially to Jørgen Steines
and Kasper Damsø for providing references so that I could gather more responses. Also many
thanks to Henrik Terkelsen, who despite busy schedule, has accepted to participate in the
interview in last hours before my deadline. Many thanks to michael Rosairus and Manish for
their valuable inputs.

Many thanks to my friends Chandra, Ravi and Kiran, who have given me a helpful hand
reviewing, editing and sometimes engaging in data entry for my graphical presentations.

I would like to express my gratitude to all my family members; although far from them for
past few years, it is through their continued love and encouragement I have reached this stage
of my life.


10




Dedication






To Ananya
My lovely daughter
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1 INTRODUCTION
1.1 Chapter introduction
In this chapter, I would like to discuss about the project background, motivation behind
taking up this project, its objective, scope and limitations of the project, overview of the
methodology I have adopted during the project and the chapters alignment in this report.
1.2 Project background
Whenever turbulent times block the pathway, it is natural for any human to turn inquisitive
and try to overcome the situation with deep analysis and take preventive measures in due
course of time. Similarly, it is but natural that we turn inquisitive towards ongoing crisis in
the business world and try to understand what constitutes towards this problem and at the
same time what needs to be done to overcome this currently and may not face similar
situation in future. Obviously, there have been some bad decisions made by the management
of various organizations running the business. Bad decision making could be attributed to
lack of information or improper utilization of the information available. In the view of above
circumstances, BI could be a handy tool in the hands of management which could help the
management in making better decisions by providing on the fly analysis, generation of
reports and by providing predictive analysis of information available etc.

Today more and more business organizations are turning towards BI for making better
business decisions. This is true based on the fact from the latest Gartner’s research report
(Gartner Research, 2009), which states, “For the fourth year in a row, BI applications have
been ranked the top technology priority in the 2009 Gartner Executive programs survey of
more than 1,500 Chief Information Officers (CIOs) around the world.”

According to Gartner Research (June, 2009), “Worldwide Business Intelligence, Analytics
and Performance Management software revenue reached $8.8 billion in 2008, a 21.7 percent
increase from 2007 revenue of $7.2 billion.” Gartner maintains the forecast growth rates it
previously published, BI market will show a five-year Compound Annual Growth Rate
(CAGR), in revenue terms, of 8.6% from 2006 through 2011 (Gartner, 2008). As per Mr. Dan
Sommer, Senior Research Analyst at Gartner, “In tough times, the first step is to increase
transparency which helps identify cost-centers, and then to more tightly align strategy with
12
execution. This is why demand for BI, analytics and performance management is relatively
strong even in a bearish economy.”

A recent Gartner report (January, 2009), also predicts the following aspects for 2009 and
beyond:
• Citing lack of information, processes and tools, through 2012, more than 35 per
cent of the top 5,000 global companies will regularly fail to make insightful
decisions about significant changes in their business and markets.
• By 2012, business units will control at least 40 per cent of the total budget for BI.
• By 2010, 20 per cent of organizations will have an industry-specific analytic
application delivered via Software as a Service (SaaS) as a standard component of
their BI portfolio.
• In 2009, collaborative decision making will emerge as a new product category that
combines social software with BI platform capabilities.
• By 2012, one-third of analytic applications applied to business processes will be
delivered through coarse-grained application mash-ups.

Also in the same report (Gartner Research, January 2009), Nigel Rayner, research vice
president of Gartner says, “Organizations will expect IT leaders in charge of BI and
performance management initiatives to help transform and significantly improve their
business”.

According to Online DM (2006), DataMicron Inc.,( 2008) and Business Performance
Management Software (2009), “Business Intelligence means using your data assets to make
better business decisions. It is about access, analysis and uncovering new opportunities”

Today, as the world is facing tougher times in the name of recession and with the world
financial crisis making it even tougher for the managers to utilize their resources in a better
way, the significance of BI increases heavily. This could result in temptation for business
organizations and managers to implement BI within their business units or organizations.

BI implementations usually take very long time, consume a lot of resources and are also very
expensive. They often do not bring expected results or do not fulfill vaguely defined
expectations. And since turnover, thus available resources, is lower than in not so far past, it
is more crucial to complete BI implementation with time and budget, well in order to improve
business performance and to satisfy the sponsor of BI project. Thus making, the study of
critical success factors in BI implementation vital to study.
13
1.3 Motivation for the project
I have always been inclined to know more about business side of the technological aspects
especially in IT. This project has provided me a wonderful opportunity to explore both sides
of the coin namely business and IT. The idea of knowing more about these two aspects,
especially how they can interlink, exchange and wonderfully overlap each other’s
functionalities by mutual co-operation, overall, this has been a wonderful journey.

An important motivation factor for this project has been my supervisor Asst. Professor
Frantisek Sudzina who had proposed me idea of this project in our first meeting itself. He had
a clear vision and planning of how to proceed with this project and along with it, full
confidence in me to complete it in time. He had been a real motivation factor and inspiration
behind taking up this project.
1.4 Project objective
The objective of this project is to find out the factors which are very crucial in turning any
business intelligence implementation into success thus forming success criteria. These criteria
thus form base of any BI implementation and when handled properly can make it successful.
1.5 Significance of the project
This project explores various aspects of BI implementation and the CSFs impacting the
implementation process. This project is expected to contribute both in theory and in practice.

In theoretical terms, this project is expected to:
• Add and contribute to the already existing literature in the area of BI, in particular
towards CSFs in the implementation process.
• Identify the issues and criteria which determine the success of a BI
implementation.
• Examine previous literature, if any, available on CSFs of BI systems
implementation and compare them with emerging CSFs obtained by first hand data
available by means of interviews and thus extend significantly the literature on BI
implementation.
• Validate and extend knowledge of current CSFs in BI systems.

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In practical terms, this project is expected to:
• Identify the CSFs and associated contextual elements that impact on BI systems
implementation, so enabling stakeholders to better use their scarce resources by
focusing on those key areas that are most likely to have a greater impact (Yeoh,
2008).
1.6 Project scope and Limitations
The scope of this project is limited to finding out of CSFs first by literature review and later
by interviews conducted within Denmark. This research does not include implementation of
any kind.

During the research in my literary review section, I have consciously tried to gather the CSFs
which are most recent and mostly from the corporate world. I have limited my research list
with 20 authors although there could be many more available.

This project does not aim at finding of CSFs in any particular industry segment but across all
industries on a whole.

Interviews for this project were conducted by meeting them personally; on limited number of
people who are confined to Denmark. The participants of the interview have been contacted
by searching for professionals in area of BI across the professional online network
(www.LinkedIn.com) and few by references from other participants and friends.
1.7 Chapters alignment
It is of prime importance for anyone to present his/her ideas in a meaningful way / order.
Hence here I would like to describe how I chose to place them along this report.

In chapter 1, I have discussed the project background, Scope & limitations of the project
along with the project significance which give details of why I have taken up this project, the
motivation behind it and the limitations to this project and what makes this project
significant.

In chapter 2, I would like to give an overview of business intelligence, its evolution,
definitions, architecture, components of the system, presentation of BI stack which gives full
15
picture of BI applications, business drivers of BI implementation, benefits of BI
implementation, followed by relation between BI and Data warehousing, and then I conclude
the chapter with BI implementation decision factors.

In chapter 3, here I would like to definie CFS’s, what it means to organizations and then
distinguish CSFs from the KPIs. Further I would present the list of CSFs I sited from my
literary review followed by analysis of the listed factors.

In chapter 4, I would like to discuss in detail about the research methodology I have adopted.
The issues discussed are the purpose of research, approach towards it, research strategy,
selection of sample, data collection method I have adopted, analysis of data obtained
followed by validity and reliability of the data gathered.

In chapter 5, I have discussed in detail about the findings of my research, its implications for
organizations taking up BI project and managers followed by the conclusion. I have then
discussed what could be implications of my project towards future research prospects.

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Table 1.1: Chapters Alignment
Source: Developed for this research





































Chapter 1: Introduction
Project background
Motivation for the project
Objective of the project
Significance of the project
Project scope & limitations

Chapter 2: Business Intelligence: an overview
Evolution and definition of BI
BI System and architecture
Key Characteristics of BI System
The BI Stack
Drivers of BI Implementation
BI Implementation decision factors
Relationship between BI and Data
Chapter 3: Literature Review
Definition of CSFs
Difference between CSFs & KPIs
List of CSFs
Categories of CSFs
Analysis of CSFs
Chapter 4: Research Methodology
Research purpose
Research approach
Research strategy
Data collection method
Validity & reliability
Chapter 5:Findings & Conclusion
Discussion of findings
Implications & recommendations
Implications for future research

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2 BUSINESS INTELLIGENCE: AN OVERVIEW
2.1 Chapter Introduction
In this chapter, I would like to give an overview of Business Intelligence, its definitions
followed by discussing what constitutes a BI system, key characteristics of a BI system,
importance of it with respect to business, its relation with Data Warehousing and Reporting,
typical architecture of an Business Intelligence system followed by project life cycle of
Business Intelligence system.
2.2 Evolution of BI
Many in the industry believe the term “Business Intelligence” was first coined by “Howard
Dresner” of Gartner Research in 1989, where he termed BI as “a broad category of software
and solutions for gathering, consolidating, analyzing and providing access to data in a way
that lets enterprise users make better business decisions” (Gibson et al, 2004; Yeoh, 2008).
However according to Yeoh (2008), Imhoff (2009) and Answers.com (2009), it was first
coined by “H.P.Luhn” in an IBM journal titled “A Business Intelligence System” (Luhn,
October, 1958). Following is the original definition of Luhn as defined in 1958, as per Yeoh
(2008):

“Business is a collection of activities carried on for whatever purpose, be it science,
technology, commerce, industry, law, government, defense, et cetera. The communication
facility serving the conduct of a business (in the broad sense) may be referred to as an
intelligence system. The notion of intelligence is also defined here, in a more general sense,
as “the ability to apprehend the interrelationships of presented facts in such a way as to
guide action towards a desired goal.”

However, it is widely accepted that Howard Dresner was the one to familiarize the world
with the term “Business Intelligence” when he proposed BI as an umbrella term to describe
“concepts and methods to improve business decision-making by using fact-based support
systems.” (Answers.com, 2009)
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2.3 Definition of BI
Business Intelligence or BI in short, as the name suggests corresponds to business and similar
to many terms in this area, do not have a standard definition like that of many scientific
terms. However, most of the BI literature has come from within the business world, the IT
industry, and vendors (Jagielska et al 2003; Yeoh, 2008).

Here are few definitions from my research:
Davenport (2007): “BI is a set of technologies and processes that use data to understand and
analyze organization performance”.

Platon A/S (2009): the term BI covers “the use of information to drive business insight.
Basically it’s about providing a better foundation for decision makers by providing
information in the right form, in the right quality, at the right time”

Elizabeth Vitt et al (2002): the term BI is “used by different pundits and software vendors to
characterize a broad range of technologies, software platform, specific applications, and
processes”. Thus it is clear that many people have viewed or expressed BI differently and
from various contexts.

Also during my research, respondents have defined the term BI different from each other.
The following are the definitions given by them.

According to Respondent2, “BI is a tool for monitoring of business activities and it usually
produces better results when integrated with the business model”.

Respondent1 views it as “data created for the end users for the purpose of their needs” and
for him the data generated is of primary concern rather than how we are generating it, aka
data is of primary importance rather than the applications or tools used for generation of it.

As per Respondent3, BI is “provisioning of decision worthy information in proper context,
time and quality”.

Respondent4 views BI as “a means to provide business information for end-users and
decision makers of the organization” and opines that “it is business driven rather than
technology driven”.

19
Following are few definitions rendered by various business vendors and authors with respect
to BI:
Table 2.1: Summary of varied BI definitions
Source: Adopted from Yeoh (2008)

BI Vendor / Author Definition of BI
Turban et al (2007) An umbrella term that encompasses tools, architectures,
databases, data warehouses, performance management,
methodologies, and so forth, all of which are integrated
into a unified software suite.
Moss & Atre (2003) It is an architecture and a collection of integrated
operational as well as decision-support applications and
databases that provide the business community easy
access to business data.
Chang (2006) The accurate, timely, critical data, information and
knowledge that supports strategic and operational
decision making and risk assessment in uncertain and
dynamic business environments. The source of the data,
information and knowledge are both internal
organizationally collected as well as externally supplied
by partners, customers or third parties as a result of their
own choice.
Gangadharan & Swami (2004) The result of in-depth analysis of detailed business data,
including database and application technologies, as well
as analysis practice.
Kulkarni & King (1997) A product of analyzing business data using business
intelligence tools. It emerges as a result of this analysis.
Moss, L & Hoberman S (2004) The processes, technologies, and tools needed to turn
data into information, information into knowledge and
knowledge into plans that drive profitable business
action. BI encompasses data warehousing, business
analytics tools and content/knowledge management.
Adelman & Moss (2000) A term encompasses a broad range of analytical software
and solutions for gathering, consolidating, analyzing and
providing access to information in a way that is supposed
to let an enterprise’s users make better business decision.
Gartner Research (Hostmann, An umbrella term that includes the analytic applications,
20
2007) the infrastructure and platforms, as well as the best
practices.
IBM (Whitehorn & Whitehorn,
1999)
An umbrella term that broadly covering the processes
involved in extracting valuable business information
from the mass of data that exists within a typical
enterprise.
Business Objects (2007) The use of an organization’s disparate data to provide
meaningful information and analysis to employees,
customers, suppliers, and partners for more effective
decision making.
Cognos (2007) Business intelligence brings people and data together,
offering a variety of ways to see the information that
backs fact-based decision-making.
SAS Institute (Ing, 2007) Delivering the right information to the right people at the
right time to support better decision making and to gain
competitive advantage.
Oracle (2007) A portfolio of technology and applications that provides
an integrated, end-to-end Enterprise Performance
Management System, including financial performance
management applications, operational BI applications, BI
foundation and tools, and data warehousing.
Informatica, Teradata,
MicroStrategy (Markarian et al
2007)
An interactive process for exploring and analyzing
structured, domain-specific information (often stored in a
data warehouse) to discern trends or patterns, thereby
deriving insights and drawing conclusions.
2.4 BI System
According to Olszak & Ziemba (2007), Business Intelligence Systems provide a proposal that
faces needs of contemporary organizations. Main tasks that are to be faced by the BI systems
include intelligent exploration, integration, aggregation and a multidimensional analysis of
data orginating from various information resources. Systems of a BI standard combine data
from internal information systems of an organization and they integrate data coming from the
particular environment e.g. statistics, financial and investment portals and miscellaneous
databases. Such systems are meant to provide adequate and reliable up-to-date information on
different aspects of enterprise activities.

21
As per NAV (2007), BI system when implemented gives the ability to access, use and share
data and information in an efficient and relevant way helps improve business performance.
Business intelligence capabilities empower employees to:
• Align day-to-day operations with overall company strategy and objectives
• Identify and understand the relationship between business processes and their
impact on performance
• Access information relevant to specific user roles and responsibilities
• Analyze data from documents and spreadsheets in an easy way
• Gain contextual insight into business drivers
• Monitor the vital business indicators that are needed to move an organization
forward such as:
o Current status and trend of essential financial ratios
o Effectiveness and profitability of sales channels
o Crucial operational metrics.
In short, business intelligence helps companies gain a comprehensive and integrated view of
their business and facilitate better and more effective decision-making (NAV, 2007).
2.5 BI Architecture
As per David Loshin (2008), the objective of the business information analysis was, and still
is, the ability to evaluate how efficiently the organization operates and subsequently seek out
opportunities for exploiting actionable knowledge. This notion of performance management
and improvement through reporting and analytics has evolved into what is commonly
referred to as “business intelligence.” While many front-end presentation, reporting, and
visualization products help in communicating the results of this analysis, business
intelligence remains inextricably linked to the technical infrastructure of the data warehouse.

The tactical challenges for providing a framework for business intelligence involve collecting
data from disparate distributed systems, consolidating that data into a centralized model, and
organizing the data to feed the front-end applications for driving business analysis and
reporting. As is seen in Figure 2.1, this requires data connectors to extract data from the
sources, integration tools to assimilate and consolidate the data, servers and storage used to
house the data warehouse and its database management system, all feeding the front-end
application architecture for reporting, dashboards, data mining, and dimensional analysis
(David Loshin, 2008).
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Figure 2.1: Traditional BI Architecture
Source: Adopted from David Loshin (2008)


However the traditional architeture could also be illustrated with its designated technological
components presented in the following Figure 2.2.









23



Figure 2.2: Technical Architecture of BI
Source: Adopted from kJube (2002)

2.5.1 Components of BI Architecture
The components of the above technical architecture are described as follows (kJube, 2002):

1. Source systems: It consists of all the data that an organization might require for the
analysis. It may consists of data from a source database, external data feeds from the
partners in XML format, data from the click-stream analysis captured from the
organizational website based on customer behavior, a connection from application
server level ERP connection, data from Excel files and many more sources. (kJube,
2002).
2. ETL tools: ETL is a process to extract data, mostly from different types of system,
transform it into a structure that’s more appropriate for reporting and analysis and
finally load it into the database. The following Figure displays the steps involved in
the process:





24






Figure 2.3: ETL Process
Source: Adopted from Passionned Nederland B.V (2009)

3. Data Quality: According to Passionned Nederland B.V (2009), today ETL does
much more than what it is known for. It also covers data profiling, data quality
control, monitoring and cleansing, real-time and on-demand data integration in
Service Oriented Architecture (SOA), and Metadata Management.
4. Data mining: As per Kurt Thearling (2009), Data mining, the extraction of hidden
predictive information from large databases, is a powerful new technology with great
potential to help companies focus on the most important information in their data
warehouses. Data mining tools predict future trends and behaviors, allowing
businesses to make proactive, knowledge-driven decisions. The automated,
prospective analyses offered by data mining move beyond the analyses of past events
provided by retrospective tools typical of decision support systems. Data mining tools
can answer business questions that traditionally consuming a lot of time to resolve.
5. Data staging: In the data warehousing process, the data staging area is composed of
the data staging server application and the data store archive (repository) of the results
of extraction, transformation and loading activity. The data staging application server
temporarily stores and transforms data extracted from OLTP
1
(Online Transaction
Processing) data sources and the archival repository stores cleaned, transformed

1
Online Transaction Processing is a class of program that facilitates and manages transaction-oriented
applications, typically for data entry and retrieval transactions in a number of industries (SearchDataCenter.com,
2009).
25
records and attributes for later loading into data marts and data warehouses (Firestone,
2002).
6. Operational Data Store (ODS): According to Inmon (1998), an ODS is an
integrated, subject-oriented, volatile (including update), current-valued structure
designed to serve operational users as they do high performance integrated
processing. The essence of an ODS is the enablement of integrated, collective on-line
processing. An ODS delivers consistent high transaction performance – two to three
seconds. An ODS supports on-line update. An ODS is integrated across many
applications. An ODS provides foundation for collective, up-to-the-second views of
the enterprise. And, at the same time, the ODS supports decision support processing.
7. Multi-dimensional data warehouse: As per kJube (2002), the multi-dimensional
data warehouse is the core of the business intelligence environment. Basically it is a
large database containing all the data needed for performance management. The
modeling techniques used to build up this database are crucial for the functioning of
the BI solution. Typical characteristics of multi-dimensional data warehouse are that it
contains invariant, integrated, and atomic data. A good understanding of multi-
dimensional modeling techniques is necessary for better understanding of such data
warehouse.
8. Data mart: A data mart is a smaller version of a data warehouse, typically containing
data related to one functional area of the firm or with limited scope in some other
way. It can be a useful step to a full-scale data warehouse (Rai & Storey, 2001).
9. OLAP: According to Rai & Storey (2001), Online Analytical Processing or OLAP as
it is popularly known, is a technology for real-time ad-hoc data access and analysis. It
is a vendor-driven solution to meet business needs. The OLAP software may require
scheduling, queuing, monitoring capabilities to handle thousands of users.
10. Semantic layer: According to Vezzosi (2009), Semantic layer reduces time to
Business Intelligence. It provides a secured access to data with simple business terms.
It can open access to any client and user, data type. It can combine with Data
Federation for more agility. Semantic layers acts as a intermediary between
presentation layer, which normally is the user interface for all end-users, business
analysts, and administrators and the data storage area.
11. Single point of information access: As a BI solution can generate various types of
outputs such as reports, dashboards, scorecards etc. These outputs may sometimes
contain tags, comments, interpretations etc which is in multiple forms and needs to be
stored in a storage location which can be attached to these reports and hence may
consume huge amounts of data storage space. So it is often advisable to have a single
point of access through a portal solution, where it is also possible to attach a business
user, scope of the report along with accessibility options (kJube, 2002).
26
2.6 Key Characteristics of a BI System
Characteristics in general refer to features of a particular entity. Key characteristics of a BI
system here refer to the advantages that can be reaped when a BI system is implemented. As
per Dr. Saadia Asif (2009), the following are the key characteristics of a BI System:
• Integrated data from multiple sources
• Consistent data across organizational systems
• Cleansed current and historical data
• Data analyzed across dimensions and hierarchies
• Fast delivery of analysis
2.7 The BI Stack
More often than not during my research, I have met BI consultants and managers frowning
and fretting over about their colleagues when it is interchangeably used with Reporting and
Data warehousing. “Reporting and analytics – or the so-called ‘presentation’ layer of BI – is
just a small part of the BI architectural stack” (Evelson, 2008). The following figure2.4
presents the Business Intelligence Stack:
27

Figure 2.4: The Business Intelligence Stack
Source: Adopted from Evelson (2008)


2.8 Drivers of BI Implementation
Gone are the days when key decision makers in the business organizations used to rely
mainly on their instincts or gut-feelings to take important decisions. Business Intelligence has
28
now started to emerge and “is playing a key role in business decisions by providing proven
solutions to help leverage best business practices to deliver the timely granular intelligence
needed to create increasingly effective business strategies, continuously strengthen
competitive positions, and steadily improve revenue streams” (Peter Callaghan, 2005).

“BI has a reputation for being a resource sink that delivers reports almost no one reads. It
doesn’t have to be that way. And you can no longer afford to let it be” (Gruman, 2007).
Gruman also says, by treating BI as a set of technologies, most organizations veer off track,
building ever-more-complex systems that fail to meet user needs – while what’s really
needed is a better understanding of the underlying data and business requirements.

As per Gruman (2007), Business drives BI:
• At 74% of companies, management is driving BI as a key priority. Of these, 48%
are driven by the executive level and 30% by line-of-business management.
• 74% of companies see BI as having high or critical priority in three to five years –
and 66% in the coming year.
• 61% of respondents say business analysts are playing a more strategic role in their
organizations. 39% of these organizations plan to hire more business analysts in
the next year.

Gruman (2007) also mentions of How BI is used:
• Predictive analytics is a big use of BI, with 62% of respondents using it this way or
planning to within a year. Sales (56%), finance (54%), marketing (54%),
logistics/materials management (35%), and customer service/call center (35%)
departments use predictive analysis the most.
• The most important BI capabilities are drill-down and drill-through (67%), sorting
and filtering (60%), and decision evaluation and optimizaation (45%).
• 36% of respondents are extremely or very confident in the relevance and accuracy
of reports and dashboards. 10% have no confidence in them.
• 54% of respondents would consider replacing BI-generated reports with search
technology that lets users get their own results. But 43% of those respondents say
their companies don’t have the internal expertise to do so.

Why the business world is so obsessed with BI and what are the driving forces of it, for
whom is it useful and for what purposes is well illustrated with the following Figure2.5:

29


Figure 2.5: Drivers of Business Intelligence
Source: Adopted from Platon A/S (2009), Connelly, McNeil, & Mosimann (1997)

As per NAV (2007), BI helps drive businesses to better performance by enabling all decision
makers – essentially empowering all employees throughout the organization – to make better
decisions. There are 3 kinds of decisions that an organization makes:
• Strategic decisions – these are the “big” decisions that companies make (e.g.
should we buy a partner, should we enter a new market). The value of these
decisions is large – but the quantity is few.
• Tactical decisions – this has been where business intelligence traditionally has
been implemented. This is the product manager deciding what discount schedule to
put in place or a pricing decision for a new product.
• Operational decisions – these are the business decisions (often made by people that
have never heard of “business intelligence”) that happen a lot on a daily basis –
yet, they have smaller business impact when measured by themselves. However, in
aggregate, multiple operational decisions add up to a lot of value – and can drive a
better business.

30
As per Platon A/S (2009), the real drivers for an organization to take up implementation of a
BI system should be business initiatives or legal requirements rather than the technology or
IT initiatives. BI implementation must be carried out in close co-operation with the business
areas. The following figure 2.6 illustrates the business value of BI against cost and time:



Figure 2.6: BI Value Chain against Cost and Time
Source: Adopted from Platon A/S (2009)

Figure 2.7 illustrates the value chain of the BI system across various levels of decision
making across the organization, the type of managers or decision makers involved, along
with the characteristics of data at that level.





31



Figure 2.7: The BI Value Chain
Source: Adopted from Manglik (2009)

According to Platon A/S (2009), the value chain of business intelligence lies where data,
which represents facts about events, transforms into information, which makes you able to
react on the events and then into knowledge, which makes it possible to proactively carry out
actions on events.
2.9 BI Implementation decision factors
There are number of factors which might affect decision making process while taking up of a
BI project. Given below are the categorized lists of factors that affect the decision making
process followed by the aspects that need to be pondered upon:
Source: Adopted from Dr. Saadia Asif (2009),
1. Reporting and Analysis Tools:
a. Features and functionality
b. Scalability and deployability
c. Usability and manageability
d. Ability to customize


32
2. Databases:
a. Scalability and performance
b. Manageability and availability
c. Security and customization
d. Ability to Write back
3. ETL Tools
a. Ability to read any source
b. Efficiency and productivity
c. Cross platform support
4. Costs involved
a. Hardware costs (actual or opportunity)
b. Costs of software (ETL, databases, applications and front-end)
c. Internal development costs
d. External developments costs
e. Internal training
f. Ongoing maintenance
5. Benefits
a. Time savings and operational efficiencies
b. Lower cost of operations
c. Improved customer service and satisfaction
d. Improved operational and strategic decision making
e. Improved employee communications and satisfaction
f. Improved knowledge sharing
2.10 Relationship between BI and Data warehouse
More often than not, it has been observed that people interchangeably use the word BI with
that of an Data warehouse. Let us now examine the relation between these two terms.

According to Platon A/S (2009), the term Data warehouse covers the management of data as
opposed to the term Business Intelligence that focus on the usage of data. The purpose of data
warehousing is to provide integrated, consistent, structured, correct and timely data for any
system or user that requires information. Data is extracted from operational systems and
integrated in the data warehouse environment in order to provide an enterprise wide
perspective, one version of the truth. Whereas, the term business intelligence covers the use
of information to drive business insight. Basically it’s about providing a better foundation for
33
decision makers by providing information in the right form, in the right quality and at the
right time.

As per Dr. Saadia Asif (2009), “BI is used for exploitation of data where as Data warehouse
is used for integration of the data” and hence it is understandable that they cannot be used
interchangeably.

The following are the views expressed by Moss et al (2003):

According to Larissa Moss, BI refers to the capability of providing a 360
0
view of the
business and is a framework of cross-organizational disciplines and an enterprise architecture
for the construction and management of an integrated pool of operational as well as decision
support applications and databases that provides the business community easy access to their
business data and allows them to make accurate business decisions. One vehicle to delivery
business intelligence is data warehousing; another vehicle is CRM and so on. In other words,
data warehousing is a subcomponent of and a vehicle of delivering business intelligence.

As per Adrienne Tannenbaum, BI refers to the use of existing data / information / knowledge
within an enterprise. In general, most data is currently diverse and sporadically integrated
(such as with a single data warehouse). In a BI framework, all of this is looked at as part of a
bigger whole and becomes available via one search. Documentation, business rules, search
criteria and existing reports are examples of things that are accessible from one place,
typically via organized and related search terms.

As per Scott Howard, BI refers to systems and technologies that provide the business with the
means for decision-makers to extract personalized meaningful information about their
business and industry, not typically available from internal systems alone. This includes
advanced decision support tools and back-room systems and databases to support those tools.
The data warehouse is that back-room database. Combine that with the support tools required
to build and maintain the data warehouse, such as data cleansing and extract, transform and
load tools and you have what many call data warehousing.

Think of the data warehouse as the back office and business intelligence as the entire
business including the back office. The business needs the back office on which to function,
but the back office without a business to support, makes no sense.

Chuck Kelley feels, you build a data warehouse to put a tool on top of it to do business
intelligence. So data warehousing is the foundation that business intelligence is built upon.
34

Clay Rehm feels, the data warehousing stemmed from the terms ‘decision support’ and
‘mangement reporting’ many years ago. BI sought to encapsulate more processes that include
data warehousing. If you notice, many vendors use the term BI to describe their services; to
show that they provide more services than just data warehousing.
2.11 Benefits of BI Implementation
As per Dr. Saadia Asif (2009), implementation of a BI system can offer:
1. Enhanced Revenue
o Through new customer acquisitions
o Cross selling and up selling to existing customers
2. Reduced operating costs
o Reallocate resources to low cost channels
o Lower operating costs
o Enhance product offerings and improve customer service
3. Competitive advantage
o Greater share of customers mind and wallet by identification of key
customer needs and service opportunities.

According to Schiff (2009), implementing a BI system in an organization also helps:
• Improve the overall efficiency and effectiveness of your organization
• Help organization make better decisions – by provision of customized applications
or tools aimed at users of various cadres.
• Allow business users to analyze and better understand their organization’s plans
and results.
• Alert organizations to potential issues when exceptional conditions occur – such as
sales dropping 20% below forecast or inventory falling below a threshold value.





35
3 LITERATURE REVIEW
3.1 Chapter Introduction
In this chapter, I would like to discuss what constitutes the CSFs, how to distinguish them
from the KPIs. Further I would like to list all the CSFs I have cited in various articles,
reports, executive white papers etc then categorize them into different perspectives and
analyze them accordingly and present graphical representation of the analysis.
3.2 Critical Success Factors
Certain areas need to perform at satisfactory levels for a system or organization to run
successfully in competetive conditions. These areas thus become important ingredients of a
successful system and hence called Critical Success Factors. Most importantly these factors
are not measurable.

According to Rockart (1979), CSFs are the limited number of areas in which satisfactory
results will ensure successful competitive performance for the individual, department or
organization. CSFs are the few areas where “things must go right” for the business to flourish
and for a manger’s goals to be attained.

CSFs are the characteristics, conditions or variables that can significantly impact on the
success of a firm competing in particular industry given that the variables, conditions and
characteristics are well sustained, maintained or managed (Leidecker & Bruno, 1987) and
identifying them can help to clarify the nature and scope of resources that must be gathered to
permit the project team to concentrate its efforts on priority issues rather than wasting time
considering what the available technologies will allow (Greene & Loughridge, 1996). A set
of CSFs identified for the development of any major information system, such as a BI
system, is fundamentally different from the set of interlinked detailed tasks which must be
accompanied to ensure a project’s completion (Dobbins, 2000; Yeoh, 2008). Hence, a mere
ensuring of successful execution of CSFs may not guarantee success of a project
implementation but surely it can give a prolonged run to the project.
36
3.3 Distinguishing CSFs from KPIs
KPIs or Key Performance Indicators usually are a measurement of progress towards
achieving goals of a project-in-hand, system or organization. KPIs allow management to take
quick actions when there are well defined performance indicators in place all along the way
in achieving organizational or project goals.

According to Cooper (2006), KPIs represent a particular value or characteristic that is
measured to assess whether an orgnization’s goals are being achieved. They reflect the
critical success factors, stakeholder needs, and the expectations of the organization. For KPIs
and their measures to be effective, the organizations’s goals need to be specific, measurable,
agreed, realistic and time-based. KPI’s can use both financial and non-financial metrics.
According to him, KPIs need to be specific, measurable, realistic and time based.

Cooper (2006) also believes KPI’s are used in conjunction with CSF’s and must have a target
that is to be achieved. The target for a KPI can be expressed as a percentage, a simple ratio,
an index, a composite average or in a statistical context. Whatever is chosen as a KPI and a
target must be actually measurable. At the outset, keeping the number of KPI’s for a single
CSF in the range of 3-5 is recommended.

Hence in a broader picture, KPI’s can be part of CSF’s but not in vice versa.
3.4 List of CSFs
As a part of my research, I have extensively searched for the CSFs with respect to
implementation of BI as mentioned by various authors in various articles, journals,
periodicals, reports, and executive white papers etc. Here, I have hence decided to list them in
Table 3.1 along with the authors. Some of the CSFs might be overlapping in the list hence I
would like to further utilize the space in analysing the CSFs.


Table 3.1: List of CSFs from literature review
Source: Developed for this research
1.Claudia Imhoff,
President, Intelligent
Solutions (Claudia
Imhoff, 2004)
• A dependable architecture
• Strong partnership between the business community and IT
• A different kind of methodology
• Well-defined business problems
• A willingness to accept change
37
2.Ferenc Mantfeld,
CTO, Siebel
(Mantfeld, 2006)
As per Ferenc, BI Implementations fail because of the following
reasons:
• Lack of upfront Planning
• Data Quality Issues
• Not Anticipating Change
• Differences in Perceived need
• One-Stop Shopping
• Dashboards as a generic cure
• Outsourcing
• Performance Considerations
3.Wayne W.
Eckerson, Director of
Research, The Data
Warehousing
Institute (Eckerson,
2005)
• Support all users via integrated BI suites
• Conforms to the way users work
• Integrates with Desktop and operational applications
• Delivers actionable information
• Foster rapid development
• Provide a robust, extensible platform
4.Cindi Howson,
Founder of
BIScorecard
(Howson, 2006)
• Measure Success
• Develop a data strategy
• Manage the program well
• Ensure executive support
• Choose appropriate tools
• Standardize on a platform
• Align BI strategy with business
5.Keith Gile, Senior
Industry Analyst,
Forrester Research
(Gile, 2003)
• Create a formal plan
• Classify analytic end users / Know the users (Producers and
Consumers)
• Map the solutions to the users (Determine what you already
have and fill in the gaps)
6. Dan Murray, VP
& CIO of Blastrac
Manufacturing (Dan
Murray, 2009)
• Define the problem clearly – avoid scope creep
• Identify user’s specific issues and needs – solve their
problems
• Understand the underlying data issues – BI is not just
software
• Select the appropriate toolset – look for fast, easy, lightweight
and low cost application
• Build for flexibility and responsiveness – know that users will
require changes early on
• Leverage existing staff – eliminate solutions that require new
staff and/or consultants
• Lead from the top – leverage your authority and executive
buy-in
7.Ilya Dmitriev,
Head of BI and
Geospatial
department, Affecto
• Common language (“Rosetta stone”)
• Less is more
• Balance approach
• Manage organizational resistance
• Resource planning
38
Latvia, Ltd
(Dmitriev, 2008)
8.InetSoft, Executive
White paper
(InetSoft
Technology, 2007)
• Economic model: Operational BI as a regular application
• Application model: Active involvement for all stakeholders
• Data model: Operational data store and unstructured data
9.William Yeoh,
Andy Koronios, Jing
Gao (Yeoh et al,
2008)
• Committed management support & sponsorship
• Business user-oriented change management
• Clear business vision & well established case
• Business driven methodology & project management
• Business centric championship & balanced project team
composition
• Strategic & extensible technical framework
• Sustainable data quality & governance framework
10.U.S. Army
Enterprise Solutions
Competency Center
(ESCC, 2009)
• High level executive, financial, and IT involvement
• Engaged end users
• Business and IT alignment
• Pervasive appreciation for value of data throughout the
enterprise
• Implement five key BI best practices
1) Business Information governance programs
2) Enterprise information strategy
3) Information quality program
4) Enterprise data warehouse
5) Organizational competency center
• Ensure Clean and High quality data relevant to the business
• Define clear role to the Competency center for BI success
which includes ‘champion the BI technologies and define
standards. It focuses on business-alignment, project
prioritization, and resolving management and skills issues
associated with significant BI projects’
• Have a continuous improvement mentality in order to realize
and achieve the full business value of having a BI solution.
11. Faun deHenry,
FMT Systems Inc.,
(deHenry, 2009)
• Executive Sponsorship
• Realistic Expectations
• Methodology*
• Team*
• Proper Technical Architecture and tools*
• Quality data*
• Limited Scope Changes
• Fast payback projects
• *Note: key areas where DW/ETL tools and BI consultants
can add value.
12. Steve Williams &
Nancy Williams,
Elsevier, Inc.
According to them common mistakes that are made while
establishing and managing BI program are
• Using ad hoc practices to select and fund BI projects
39
(Williams &
Williams, 2007)
• Providing inadequate governance for the BI program
management
• Establishing de facto program governance based on the initial
BI project
• Failing to strategically position BI in the business
organization
• Not providing adequate resources and funding for supporting
efforts needed for a successful BI initiative
13James Markarian,
CTO, Informatica;
Stephen Brobst,
CTO, Teradata; and
Jeff Bedell, CTO,
MicroStrategy in a
joint whitepaper
(Markarian et al,
2007)
• Data freshness, cleansing, accuracy and completeness
• Scalability in terms of concurrent users by delivery
mechanism
• Mixed workload management to ensure service level
performance goals
• Tactical query response time measures by type of user and
analytic
• High availability metrics by user community
14. Neil McMurchy
of Gartner Research
(Neil McMurchy,
2008)

• A strategic view of BI with a phased implementation, focused
on benefit realization
• Strong business ownership of the BI strategy and its outcomes
• Fixing data quality and data governance issues early in the
process
• Constantly seeking to expand the scope of the BI strategy
• Substantial and sustained investment in BI skills and
competencies
15. Lyndsay Wise
(Wise, 2007)
• Identifying the Business Problem
• Determining the Expectations of Use
• Understanding Delivery of Data
• Rolling Out of Training Initiatives
• Choosing a Vertical – or Horizontal based Solution
16. Knightsbridge in
partnership with
BusinessWeek
Research Services
(Knightsbridge,
2006)
• Business Information governance programs
• Enterprise information strategy
• Information quality programs
• Enterprise data warehouse
• BI Competency centers
BI Pitfalls to watch for:
• Lack of user adoption
• Incomplete or inaccurate business requirements
• Disconnect between BI and performance management in the
organization
• Lack of cooperation or collaboration between business and IT
• Lack of required business skills within the organization
• Insufficient funding to do it right
• Lack of required IT skills within the organization
17. Dan Vesset, in
• Defining system requirements
• Transforming disparate data into a single model
40
Keys to Successful
Business Intelligence
Deployment for IDC,
commissioned by
Oracle and titled
“The State of
Business Analytics-
Best Practices,
Benefits, Challenges
and Shortcomings”
(Vesset, 2005)
• Managing user expectation/experience
• Guaranteeing system availability
18. Anthony Mano,
CEO, Lisys
Technology
Solutions (Mano,
2009)
• Business requirements : should meet Consistency,
Completeness, Clarity and Compliance
• Business data: Accessibility, Completeness and Quality are
foundations of successful implementation.
• Technology: should complement existing systems; available
at an affordable cost; provide rapid development; adaptable
and flexible for custom development; control for change and
meta-data management
• Expertise: know-how, aptitude and attitude makeup an expert
technical resource.
19. Christopher
Meister, Director,
Management
Consulting, Auxis
Inc., (Meister, 2009)
Challenges in successful BI Implementation
• Identifying key requirements and validating the proof of
concept
• Evaluating the quality and sufficiency of source data
• Insuring flexibility in the final implementation
Steps to insure BI implementation success
• Think big, start small
• Use iterative prototyping techniques in defining requirements
and scope
20. John Schwechel,
Senior Project
Manager with Retail
Process Engineering,
LLC. (Schwechel,
2005)
• Set expectations: Define the project
• Get organized: Create the project plan
• Start at the top: Gain executive support
• The best teams deliver the best results: Project team
management
• Prepare for the inevitable: Risk management
• Stick to the plan: Scope management
• Deliver on the plan: Quality assurance
• Spread the news: Communicate early and often
• Gain user acceptance: Change management
• Quantify results: Measure project success
41
3.5 Categorization of CSFs:
According to Yeoh, 2008, CSFs can be broadly calssified into 4 categories namely
Organizational, Process, Technical and Environmental dimensions or perspectives. But
during my study of these CSFs, Environmental perspective is deemed unfit for this research
as none of the factors listed above in Table 3.1 refer to this perspective. The factors that could
be considered in each perspective could be be better illustrated with the following Figure 3.1.







Figure 3.1: Grouping critical perspectives of CSFs
Source: Adopted from Yeoh, 2008

Technological Perspective
• Technical
• Infrastructure
• System
• BI development
• BI architecture
• Data
Organizational Perspective
• Goals & Objective
• Organization
• Management
Process Perspective
• Project
• Project
Management
• Skills
• User
Environmental
Perspective
? = CSFs Perspectives
42
Based on the Figure3.1, I have categorized all the CSFs listed in Table 3.1 according to
respective perspectives and arrived at Table 3.2 as a result. Thus, Table 3.2 would now
represent all the CSFs listed in Table 3.1.

Table 3.2: Categorization of CSFs
Source: Developed for this research
Organization Perspective Process Perspective Technology Perspective
1. Partnership between
Business Community &
IT
5. Business driven
methodology & Project
management
11. Robust & extensible
framework
2. Committed
management support
and sponsorship
6. Usage of iterative
prototyping to define
requirements & scope
12. Data quality &
management issues
3. Information
governance via BI
competency center
7. Map the solutions to
the users
13. Appropriate
technology / tools
4. Clear business vision &
planning
8. Balanced team
composition
14. Integrated BI
applications
9. User training & support 15. Performance
considerations
10. Change management
3.6 Analysis of CSFs from the literature review
For analysis of the listed CSFs I have tabulated the CSF variables according to respective
perspectives and further, the factors listed by various authors are marked accordingly in Table
3.3.









43

Table 3.3: Analysis of CSFs from the literature review
Source: Developed for this research
Authors Organization
Perspective
Process Perspective Technical
Perspective
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Claudia Imhoff
(2004)
* * * *
Mantfeld (2006) * * * * * * *
Eckerson (2005) * * * *
Howson (2006) * * * * * * *
Gile (2003) * *
Dan Murray,
(2009)
* * * * * * * *
Dmitriev (2008) * * * *
InetSoft
Technology
(2007)
* * *
Yeoh et al
(2008)
* * * * * * * *
ESCC (2009) * * * * * * *
deHenry (2009) * * * * * * *
Williams &
Williams (2007)
* * * * *
Markarian et al,
(2007)
* * * *
Neil McMurchy
(2008)
* * * * * *
Wise (2007) * * * *
Knightsbridge
(2006)
* * * * * * *
Vesset (2005) * * * *
Mano (2009) * * * * *
Meister (2009) * * * *
Schwechel
(2005)
* * * * * * *

Total 5 10 5 15 5 5 11 6 5 5 6 14 5 2 8

44



Analysis of CSFs:



Figure 3.2: Analysis of CSFs sited in literature review
Source: Developed for this research

From the figure 3.2 it can be read that higher the bar length of the CSF, the higher priority to
be given to the CSF while implementation.

Based on the analysis, given below is the list of CSFs in the order of priority:

Table 3.4: Priority order of CSFs from the Literature Review
Source: Developed for this research

Priority Order CSF
1 Clear vision & planning
2 Data quality & management issues
3 Map the solutions to the users
4 Committed management support and sponsorship
5 Performance considerations
5
10
5
15
5 5
11
6
5 5
6
14
5
2
8
0
2
4
6
8
10
12
14
16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Analysis of CSFs from the literature review
45
6 Balanced team composition
Robust & extensible framework
7 Partnership between Business Community & IT
Information governance via BI competency center
Business driven methodology & Project management
Usage of iterative prototyping to define requirements & scope
User training & support
Change management
Appropriate technology / tools
8 Integrated BI applications


46
4 RESEARCH METHODOLOGY
4.1 Chapter Introduction
In this chapter, I would like to give an idea about the research process on whole, reasons
behind the research, approach process adopted and the reasons behind it, sample selection
and my approach towards them, problems faced during the process followed by data analysis.
Overall, this chapter would give an impression of reliability and validity of both approach
and the data.
4.2 Research Purpose
According to Yin (2003, as cited by Kalbasi, 2007), the purpose of an academic study can be
exploratory, descriptive or explanatory.
• Exploratory studies are practical if you wish to clarify your understanding of a
problem (Saunders et al, 2000, as cited by Kalbasi, 2007). Exploratory studies are a
method of finding out “what is happening; to seek new insights; to ask questions and
to assess phenomena in a new light” (Robson, 1993 as cited by Saunders et al, 2000
and Kalbasi, 2007).
• Descriptive studies are appropriate when you wish to portray phenomenon such as
events, situations or process. Furthermore, a descriptive study is also appropriate
when a problem is clearly structured, but the intention is not to conduct research about
the connections between causes and symptoms (Kalbasi, 2007).
• Explanatory studies are useful when you wish to establish casual relationship between
variables. The emphasis in this sort of study is to examine a situation or a problem in
order to explain the relationship between variables (Saunders et al, 2000 as cited by
Kalbasi, 2007)

My research work here is exploratory as I wish to verify and validate the results obtained on
analysis of CSFs in my literature review. In the process of achieving the overall objective of
the project, I have already gone through various literatures and have done analysis of the
CSFs of the BI implementation process in chapter 3. The purpose of this research is to give
me better understanding of the quality of data I have gained and analyzed, context of it and
relevance to the current project.
47
4.3 Research Approach
My approach towards this research has been mostly qualitative rather than quantitative. I
have contacted as many professionals as possible and conducted personal interviews based on
the questionnaire presented in Appendix 1. I have chosen interview method as the research
methodology for this project because that would assure me that the data obtained is of highest
quality and is first-hand. That would also give me valuable insights into how the BI industry
is functioning in Denmark and at the same time get an impression of prospective employers.
But due to many constraints (time, finance, availability of respondents etc) conducting
interviews for all people was not feasible; also if we approach survey method there can be
serious ambiguities about the quality of data obtained. Hence, I have decided to take
interviews of a limited number of people for this research purpose.
4.4 Research Strategy
The data generated by the interview process would then be analyzed and the output would
then be compared with the results obtained from the literature review to find if there are any
differences in outputs generated by this research and the literature review.
4.5 Sample Selection
Sample selection for this research has been randomly identified by searching on professional
networking website www.LinkedIn.com and requests for interviews has been sent to people
after going through their profiles and on confirming that their current work location is in
Denmark. Out of six to seven requests sent randomly through the LinkedIn network only two
responded positively and hence I had to look for other contacts. I had then communicated
with Human Resource departments of few consulting companies and other organizations
referred by my friends. During the interview process I had two respondents who were helpful,
by providing one reference each for the interviews. For a long time I was held up at six
interviews in total, then fortunately during the last 2 weeks, I got a positive response from
one person who was referred by a respondent and the interview took place on last working
day before submission of this project. That made the total number of interviews taken for this
research seven.
48
4.6 Data Collection Method
Although Time-consuming, overall interview process for this research has been very
satisfactory. All the interviews were taken by meeting the respondents personally at their
office locations. The questionnaire for the first two interviews had only 19 questions,
questions 20 and 21 were added later to the list considering they would be more helpful in
analysis of the results. The first two respondents were contacted again and informed about
addition of two more questions, the additional questions were then sent to both respondents
one and two by email and they have replied back with answers at a later stage according to
their convenience. Respondent2 has wished to give same priority level to some of the factors
and hence have answered them accordingly. The emailed answers were then added to rest of
their answers in the questionnaire.

4.7 Data Analysis
Analysis of the data recorded during the interviews could be used to understand the CSFs
better and to analyze if there are any differences generated from this output to that of results
obtained from the literature review.

Here are some facts about the interviews taken for this research:
• Although, I tried to have as many interviews as possible. I could only manage total
7 interviews for this research.
• Only 2 respondents have responded positively when asked whether I could
mention their name and the organization they are working. 3 other respondents had
no issues mentioning their name in this report but for some reasons did not wish to
relate their views with the organization they are working for, hence wanted me to
keep their company name anonymous. 2 respondents wanted to keep both the
details as anonymous.
• Average experience of the respondents in IT industry was 14 years. The minimum
experience level recorded was of 7 years, while the maximum experience level was
of 25 years.
• Average experience of the respondents in BI field was 11 years. The minimum
experience level recorded was of 5 years, while the maximum experience level was
of 20 years.
49
• Average number of implementations done by the respondents was 12. While the
minimum implementations recorded by a respondent were 4, maximum
implementations were 35.
• Out of the 7 respondents, 3 belonged to the health-care / pharmaceutical / medico
technique (as respondent would like to call it) sector of the industry, 2 belonged to
the financial services / banking, and 1 each from the IT services / consulting and
Kids / Toys segment.
• Section1 of the questionnaire is aimed at getting to know the person, his
experience, business sector of the organization and the quality of experience on a
whole.
• Section2 of the questionnaire is aimed at gaining more insight into the
respondent’s work related aspects, his views, definitions, type of applications,
vendors, tools and architecture they deal in day-to-day work life.
• Section3 of the questionnaire deals more with the CSFs trying to extract their
views, opinions and priorities as viewed by them with respect to BI
implementation. And thus more useful for our analysis.
• Two CSFs namely, ‘Involvement of top management’ and ‘Involvement of end-
users’ have been added to the list of organization perspective as compared to that
of list of CSFs from the literature review.
• As mentioned before, Section1 and Section2 is meant only for knowing and
understanding the existing system in organizations of various respondents, I would
like to analyze only Section3 for this research.

Table 4.1 consists of all the CSFs I have used during my interview process from questions 17
through 21 and have been arranged beneath each perspective I believe they correspond to.
The alphabets placed in front of the CSFs separating them by a hyphen correspond to
individual CSF and have been used to refer them during further analysis and graphical
representation.










50
Table 4.1: List of CSFs from the questionnaire
Source: Developed for this research

Organization
Perspective
Process
Perspective
Technical
Perspective
A - Clear vision &
planning
G - Business driven
methodology &
project Management
L - Robust &
extensible framework
B - Committed
management support
& sponsorship
H - Balanced team
composition
M - Data
management &
quality issues
C - Involvement of
top management
I - Usage of iterative
prototyping to define
requirements &
scope
N - Appropriate
technology / tools
D - Partnership
between business
community & IT
J - Map the solutions
to the users
O - Integrated BI
applications
E -Information
governance via BICC
K - Change
Management
P - Performance
considerations
F - Involvement of
end-users

Q - User training &
support

We are analyzing the responses of section 3 only for this research as they are most relevant
for this research. When the responses from all the respondents are observed for the questions
13 to 16, some factors mentioned by them like ‘Support from key personnel’, ‘Quality of the
process’, ‘Project management’, ‘High maintainability’ etc. have already been considered and
are placed under respective perspectives they belong to hence those data are ignored. And
further questions beyond 16 have been chosen to analyze.


While answering the questions from 17 to 19 respondents were asked to rate the CSFs in a
range of 1 to 5, where, 1 corresponds to not critical and 5 corresponds to most critical. Hence,
higher the total value of a particular CSF, more essential the CSF would be. Similarly, in
graphical representation, higher the bars are placed more critical or essential that particular
CSF could be considered.


51
Analysis of question 17:


Table 4.2: Critical Scale Analysis of CSFs in the Organization Perspective
Source: Developed for this research

Respondents A. Clear
Vision &
Objectives
B.
Committed
management
support &
sponsorship
C.
Involvement
of top
management
D.
Partnership
between
business
community
& IT
E.
Information
governance
via BI
competency
center
F.
Involvement
of end-users
Respondent
1
3 3 2 3 3 3
Respondent
2
5 3 5 2 2 3
Respondent
3
3 4,5 3,5 3 3,5 3,5
Respondent
4
5 5 4 4 4 5
Respondent
5
5 4 4 5 5 2
Respondent
6
5 5 5 4 4 4
Respondent
7
5 5 3 5 3 3

Total: 31 29,5 26,5 26 24,5 23,5


52


Figure 4.1: Critical Scale Analysis of Organizational Perspective
Source: Developed for this research

Analysis of question 18:
Table 4.3: Critical Scale Analysis of CSFs in the Process Perspective
Source: Developed for this research

Respondents
G. Business
driven
methodology
& project
management
H. Balanced
team
composition
I. Usage of
iterative
prototyping to
define
requirements
& scope
J. Map the
solutions to
the users
K. Change
management
Respondent 1 4 3 3 3 1
Respondent 2 3 1 4 3 4
Respondent 3 4 3 4 4 4
Respondent 4 4 4 3 5 4
Respondent 5 5 3 5 3 3
Respondent 6 4 4 5 4 4
Respondent 7 5 3 2 3 5

Total: 29 21 26 25 25


31
29.5
26.5
26
24.5
23.5
0
5
10
15
20
25
30
35
A B C D E F
Critical Scale Analysis of Organizational
Perspective
53

Figure 4.2: Critical Scale Analysis of Process Perspective
Source: Developed for this research

Analysis of question 19:

Table 4.4: Critical Scale Analysis of CSFs in the Technology Perspective
Source: Developed for this research

Respondents
L. Robust &
extensible
framework
M. Data
management
& quality
issues
N.
Apprppriate
technology
/ tools
O.
Integrated
BI
applications
P.
Performance
considerations
Q. User
training &
support
Respondent
1
3 5 5 1 3 3
Respondent
2
3 5 2 1 5 3
Respondent
3
3 4 3,5 2 3 3
Respondent
4
4 5 4 4 5 5
Respondent
5
4 4 3 3 2 4
Respondent
6
5 4 4 5 5 4
Respondent
7
5 5 5 5 5 5

Total: 27 32 26,5 21 28 27
29
21
26
25 25
0
5
10
15
20
25
30
35
G H I J K
Critical Scale Analysis of Process
Perspective
54



Figure 4.3: Critical Scale Analysis of Technology Perspective
Source: Developed for this research

While answering the questions 20 and 21, respondents are asked to prioritize the CSFs in the
order of its priority in implementation of a BI system. Hence, the lower value (i.e., 1) given to
a particular CSF could mean a higher priority among the other relevant factors. Hence, lower
the total value of a particular CSF, that particular CSF could be considered more essential.
Similarly, in graphical representation, lower the bars are placed more critical or essential that
particular CSF could be considered.


Analysis of question 20:

Organization Perspective

Table 4.5: Priority Analysis of CSFs in the Organization Perspective
Source: Developed for this research

Respondents A. Clear
Vision &
Objectives
B.
Committed
management
support &
sponsorship
C.
Involvement
of top
management
D.
Partnership
between
business
community
& IT
E.
Information
governance
via BI
competency
center
F.
Involvement
of end-users
27
32
26.5
21
28
27
0
5
10
15
20
25
30
35
L M N O P Q
Critical Scale Analysis of Technology
Perspective
55
Respondent
1
3 2 5 3 4 1
Respondent
2
2 1 1 1 2 1
Respondent
3
4 1 3 5 6 2
Respondent
4
1 2 6 5 4 3
Respondent
5
1 4 5 3 2 6
Respondent
6
2 3 1 4 5 6
Respondent
7
3 1 6 2 4 5

Total: 16 14 27 23 27 24



Figure 4.4: Priority Analysis of Organization Perspective
Source: Developed for the research
16
14
27
23
27
24
0
5
10
15
20
25
30
A B C D E F
Priority Analysis of Organization
Perspective
56
Process Perspective

Table 4.6: Priority Analysis of CSFs in the Process Perspective
Source: Developed for this research

Respondents G. Business
driven
methodology
& project
management
H. Balanced
team
composition
I. Usage of
iterative
prototyping to
define
requirements
& scope
J. Map the
solutions to
the users
K. Change
management
Respondent 1 1 2 4 4 5
Respondent 2 1 2 2 2 1
Respondent 3 4 5 1 3 2
Respondent 4 3 2 5 1 4
Respondent 5 2 3 1 5 4
Respondent 6 1 3 2 5 4
Respondent 7 1 4 5 3 2

Total:
13 21 20 23 22




Figure 4.5: Priority Analysis of Process Perspective
Source: Developed for this research

13
21
20
23
22
0
5
10
15
20
25
G H I J K
Priority Analysis of Process Perspective
57

Technology Perspective
Table 4.7: Priority Analysis of CSFs in the Technology Perspective
Source: Developed for this research
Respondents
L. Robust &
extensible
framework
M. Data
management
& quality
issues
N.
Apprppriate
technology
/ tools
O.
Integrated
BI
applications
P.
Performance
considerations
Q. User
training &
support
Respondent
1
5 4 1 6 2 3
Respondent
2
2 1 2 2 1 1
Respondent
3
4 1 3 6 5 2
Respondent
4
4 1 5 6 2 3
Respondent
5
1 2 5 4 6 3
Respondent
6
1 2 3 5 4 6
Respondent
7
3 2 6 4 1 5

Total:
20 13 25 33 21 23


Figure 4.6: Priority Analysis of Technology Perspective
Source: Developed for this research

20
13
25
33
21
23
0
5
10
15
20
25
30
35
L M N O P Q
Priority Analysis of Technology Perspective
58

Analysis of question 21:

Table 4.8: Priority Analysis of all the CSFs with respect to BI Implementation
Source: Developed for this research

Respondents
Organization perspective Process perspective Technical perspective
A B C D E F G H I J K L M N O P Q
Respondent 1 2 1 11 3 4 6 8 12 13 5 17 14 9 15 16 10 7
Respondent 2 3 1 1 1 2 1 2 2 2 2 1 2 2 3 4 1 1
Respondent 3 1 4 5 10 15 6 7 14 3 17 8 12 2 13 16 11 9
Respondent 4 1 3 8 11 9 2 14 10 17 4 15 12 6 13 16 7 5
Respondent 5 1 9 10 3 2 12 5 6 4 17 14 7 8 11 13 16 15
Respondent 6 3 2 1 9 16 7 4 12 10 15 11 5 8 6 13 14 17
Respondent 7 6 1 13 4 10 11 7 17 16 15 14 5 3 12 8 2 9


Total 17 21 49 41 58 45 47 73 65
75 80 57 38 73 86 61 63




Figure 4.7: Priority Analysis of all CSFs
Source: Developed for this research

The responses to the above question 21 are for the priority order of the CSFs hence the lowest
to highest total value in Table 4.8 and the shortest bar to the longest in the Figure 4.7
17
21
49
41
58
45
47
73
65
75
80
57
38
73
86
61
63
0
10
20
30
40
50
60
70
80
90
100
A B C D E F G H I J K L M N O P Q
Priority Analysis of all the CSF's
59
represent CSFs of decreasing priority order. The priority order for this data can be illustrated
with the following Table:
Table 4.9: Priority Order of CSFs with respect to overall BI implementation
Source: Developed for this research

Priority Order CSFs
1 A – Clear vision & planning
2 B – Committed management support & sponsorship
3 M – Data management & quality issues
4 D – Partnership between business community & IT
5 F – Involvement of end-users
6 G – Business driven methodology & project management
7 C - Involvement of top management
8 L – Robust & extensible framework
9 E – Information governance via BI competency center
10 P – Performance considerations
11 Q – User training & support
12 I – Usage of iterative prototyping to define requirements & scope
13 H – Balanced team composition
N – Appropriate technology / tools
14 J – Map the solutions to the users
15 K – Change management
16 O – Integrated BI applications

4.8 Validity and Reliability
As all the data gathered during this research is of first-hand, I consider all the data as
valid and reliable to the best of my knowledge. Responses to the questionnaire, by
each respondent have been provided in Appendix 7.2 to 7.8 at the end of this report
for further verifications, clarifications and analysis.


60
5 FINDINGS & CONCLUSION
5.1 Chapter Introduction
In this chapter I would like to present and discuss the findings of my analysis. Further I
would like to discuss what these results could imply followed by the future research prospects
in this area to conclude with.
5.2 Findings
After going through the analysis of data done in Data Analysis section 4.7, it is hard to
understand the results of it by looking at that section. Hence, I have decided to make it easy
for common people to understand it by joining the data in Tables 4.2, 4.3 and 4.4 for
understanding the critical nature of all the CSFs. I would then prepare a table with list of
CSFs in order of critical scale.

At the same time I would join the Tables 4.5, 4.6 and 4.7 to make it easy to understanding the
priority order of CSFs when considered with each perspective. This would then give result to
a table with priority order of CSFs with respect to the perspectives.

After generating both these tables I would analyze these tables with that of the priority table I
have generated for the question 21, which is the priority table of CSFs with respect to overall
BI implementation.


61

Table 5.1: Critical Scale Analysis of CSFs from the Perspectives
Source: Developed for this research


Organization Perspective Process Perspective Technology Perspective
Respondents A B C D E F G H I J K L M N O P Q
Respondent
1
3 3 2 3 3 3 4 3 3 3 1 3 5 5 1 3 3
Respondent
2
5 3 5 2 2 3 3 1 4 3 4 3 5 2 1 5 3
Respondent
3
3 4,5 3,5 3 3,5 3,5 4 3 4 4 4 3 4 3,5 2 3 3
Respondent
4
5 5 4 4 4 5 4 4 3 5 4 4 5 4 4 5 5
Respondent
5
5 4 4 5 5 2 5 3 5 3 3 4 4 3 3 2 4
Respondent
6
5 5 5 4 4 4 4 4 5 4 4 5 4 4 5 5 4
Respondent
7
5 5 3 5 3 3 5 3 2 3 5 5 5 5 5 5 5


Total: 31 29,5 26,5 26 24,5 23,5 29 21 26 25 25 27 32 26,5 21 28 27



62

Figure 5.1: Critical Scale Analysis of CSFs from the Perspectives
Source: Developed for this research

As the Table 5.1 and Figure 5.1 represent Critical Scale analysis of the CSFs, it can be
understood that higher the total value in the table or bar line in the graphical presentation;
higher is the critical nature of that particular CSF. Hence the order of CSFs with decreasing
critical natures is represented in the following Table 5.2:

31
29.5
26.5
26
24.5
23.5
29
21
26
25 25
27
32
26.5
21
28
27
0
5
10
15
20
25
30
35
A B C D E F G H I J K L M N O P Q
Critical Scale Analysis of all Perspectives
63

Tabel 5.2: Critical order of the CSFs
Souce: Developed for this research
Critical Order CSF
1 M – Data management & quality issues
2 A – Clear vision & planning
3 B – Committed management support & sponsorship
4 G – Business driven methodology & project management
5 P – Performance considerations
6 L - Robust & extensible framework
Q – User training & support
7 N – Appropriate technology / tools
C – Involvement of top management
8 D – Partnership between business community & IT
I – Usage of iterative prototyping to define requirements & scope
9 J – Map the solutions to the users
K – Change management
10 E - Information governance via BI competency center
11 F – Involvement of end users
12 H – Balanced team composition
O – Integrated BI applications


Now, I would join the Tables 4.5, 4.6 and 4.7 to get the priority order of the CSFs when
considered with each perspective.

64

Table 5.3: Priority Analysis of CSF from the perspectives
Source: Developed for this research

Respondents
Organization Perspective Process Perspective Technology Perspective
A B C D E F G H I J K L M N O P Q
Respondent
1
3 2 5 3 4 1 1 2 4 4 5 5 4 1 6 2 3
Respondent
2
2 1 1 1 2 1 1 2 2 2 1 2 1 2 2 1 1
Respondent
3
4 1 3 5 6 2 4 5 1 3 2 4 1 3 6 5 2
Respondent
4
1 2 6 5 4 3 3 2 5 1 4 4 1 5 6 2 3
Respondent
5
1 4 5 3 2 6 2 3 1 5 4 1 2 5 4 6 3
Respondent
6
2 3 1 4 5 6 1 3 2 5 4 1 2 3 5 4 6
Respondent
7
3 1 6 2 4 5 1 4 5 3 2 3 2 6 4 1 5



Total: 16 14 27 23 27 24 13 21 20 23 22
20 13 25 33 21 23



65

Figure 5.2: Priority Analysis of CSF from the perspectives
Source: Developed for this research

As this analysis is being done for the priority order of the CSFs, the lowest the total value in
Table 5.2 has the highest priority order; similarly lower the bar level in the graphical
representation gives highest priority to the CSF. The priority order in this case could be given
by the following Table 5.4.

16
14
27
23
27
24
13
21
20
23
22
20
13
25
33
21
23
0
5
10
15
20
25
30
35
A B C D E F G H I J K L M N O P Q
Priority Analysis of all Perspectives
66

Table 5.4: Priority order of the CSFs
Source: Developed for this research
Priority Order CSF
1 G - Business driven methodology & project management
M – Data management & quality issues
2 B - Committed management support & sponsorship
3 A - Clear vision & planning
4 I - Usage of iterative prototyping to define requirements & scope
L – Robust & extensible framework
5 H – Balanced team composition
P – Performance considerations
6 K - Change management
7 D - Partnership between business community & IT
J – Map the solutions to the users
Q – User training & support
8 F - Involvement of end-users
9 N – Appropriate technology / tools
10 C – Involvement of top management
E – Information governance via BI competency center
11 O – Integrated BI applications

At this stage we have 3 Tables, Table5.2 representing Critical Scale order of the CSFs,
whereas, 4.9, and 5.4 both representing Priority Order of the CSFs. Hence to avoid the
confusion, I would like to join the tables 4.9 and 5.4 to arrive at a single table for Priority
Order of the CSFs.

Table 5.5: Analysis of priority order of CSFs in tables 4.9 & 5.4
Source: Developed for this research

Table Number A B C D E F G H I J K L M N O P Q
Table 4.9 1 2 7 4 9 5 6 13 12 14 15 8 3 13 16 10 11
Table 5.4 3 2 10 7 10 8 1 5 4 7 6 4 1 9 11 5 7

Total 4 4 17 11 19 13 7 18 16 21 21 12 4 22 27 15 18

The graphical representation of the above table can be given with the following figure:
67

Figure 5.3: Analysis of priority order of CSFs in table 5.5
Source: Developed for this research

From the data analysis of above table 5.5, given below is the priority order of the CSFs :
Tabel 5.6: Priority order of CSFs
Source: Developed for this research

Priority Order CSFs
1 A – Clear vision & planning
B – Committed management support & sponsorship
M – Data management & quality issues
2 G – Business driven methodology & project management
3 D – Partnership between business community & IT
4 L – Robust & extensible framework
5 F – Involvement of end-users
6 P – Performance considerations
7 I – Usage of iterative prototyping to define requirements & scope
8 C – Involvement of top management
9 H – Balance team composition
Q – User training & support
10 E – Information governance via BI competency center
11 J – Map the solutions to the users
K – Change management
4 4
17
11
19
13
7
18
16
21 21
12
4
22
27
15
18
0
5
10
15
20
25
30
A B C D E F G H I J K L M N O P Q
Priority Order of the CSFs
68
12 N – Appropriate technology / tools
13 O – Integrated BI applications



Table 5.7: Analysis of Research Tables
Source: Developed for this research

Table Number A B C D E F G H I J K L M N O P Q
Table 4.9 1 2 7 4 9 5 6 13 12 14 15 8 3 13 16 10 11
Table 5.2 2 3 7 8 10 11 4 12 8 9 9 6 1 7 12 5 6
Table 5.4 3 2 10 7 10 8 1 5 4 7 6 4 1 9 11 5 7

Total 6 7 24 19 29 24 11 30 24 30 30 18 5 29 39 20 24

The graphical representation of the following table is illustrated in figure 5.3:




Figure 5.4: Analysis of Research Tables
Source: Developed for this research

From the above data which is in order of decreasing priority, lowest total value in Table 5.5
represents CSF of highest priority and vice versa; at the same time in the Figure 5.3 bar lines
with shortest length are CSFs of highest priority and vice versa.

6
7
24
19
29
24
11
30
24
30 30
18
5
29
39
20
24
0
5
10
15
20
25
30
35
40
45
A B C D E F G H I J K L M N O P Q
Analysis of Research Tables
69
Thus, we can now generate an overall priority order of CSFs with the data we have gained
through my interview responses, it is tabulated in the following table:

Table 5.8: Priority Order of CSFs developed from my Research
Source: Developed for this research

Priority Order CSF(s)
1 Data management & quality issues
2 Clear vision & planning
3 Committed management support & sponsorship
4 Business drivenmethodology & project management
5 Robust & extensible framework
6 Partnership between business community & IT
7 Performance considerations
8 Involvement of top management
Involvement of end-users
Usage of iterative prototyping to define requirements & scope
User training & support
9 Information governance via BI competency center
Appropriate technology / tools
10 Balanced team composition
Map the solutions to the users
Change management
11 Integrated BI applications

From the above research, addition of two new CSFs ”Involvement of top management” and
”Involvement of end-users” has been justified as they are placed well above the CSFs like
”Map the solutions to the users” and ”Balanced team composition” which were placed much
higher in the CSFs list obtained from the data analysis part of literature review.

5.3 Discussion of findings
If we compare the Tables 3.4 and 5.8 we can observe that there is not much difference in the
priority order of the CSFs listed in both the tables except that the CSFs ”Map the solutions to
the users” and ”Balanced team composition” have been given more importance in the
literature review, where as the respondents have placed them in the bottom of the list. Rather
amusingly, the newly included CSFs in my interviews ”Involvement of top management” and
70
”involvement of the end-users” were placed higher above them. Both the literature study and
the interview process confirm placing the CSF ”Integrated BI applications” at the bottom of
the list.

CSFs are those areas of the project that are absolutely essential to its success. Whilst there is
no hard and fast rule, it’s useful to limit the number of CSFs to absolute essentials. This helps
you maintain the impact of your CSFs, and so give good direction and prioritization to other
elements of your project strategy (AAHA, 2009). If we observe the Tables 3.4 and 5.6 each
consists of at least 15 CSFs listed and when the list of CSFs become huge it would be
impossible to concentrate on the various aspects of the project. Hence, I have decided to take
top 5 CSFs from both the lists and merge them so that there would be a shorter list which
could be more meaningful and hence allowing managers to focus more on the project. The
list of CSFs thus evolved in alphabetical order is:

• Business driven methodology & project management
• Clear vision & planning
• Committed management support & sponsorship
• Data management & quality issues
• Map the solutions to the users
• Performance considerations and
• Robust & extensible framework

I would recommend the managers handling BI implementation aspects to focus on these
above mentioned factors to have a prolonged run in keeping the project and process alive.
5.4 Implications and Recommendations
There are numerous factors that could affect the implementation process of BI system. The
choice of CSFs may actually vary depending on which perspective the manager is looking
from and hence the list of tables 4.2, 4.3 and 4..4 each show credibility of CSFs with an
critical scale order and tables4.5, 4.6 and 4.7 each show credibility of CSFs in their priority
order from each perspective point of view.

Table 4.8 shows priority order of CSFs when viewed from overall perspective of the
business. Table 5.1 shows critical scale order of CSFs when viewed from overall business
perspective. Table 5.3 shows combined priority order of CSFs when viewed from individual
71
perspective and Table 5.5 shows priority order of CSFs when tables 4.8 and 5.3 are combined
together.

To make it easy to understand, I would recommend managers to follow Table 5.1 when they
want to view CSFs in critical scale order and Table 5.5when they want to view CSFs in
priority order of CSFs. And from overall BI implementation point of view I would
recommend them to visit the list of CSFs given in section 5.3 of this chapter.
5.5 Implications for Future Research
During this project I have only recorded the CSFs as observed from various literatures and
have then analyzed them. This seems to be a common approach but it does not give practical
insight into the implementation aspects which could be more meaningful for the topic.
Participation in the implementation process while reading all the variables (both internal and
external) would have been a perfect approach for this kind of studies. Hence further research
in these lines is recommended.


72
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79
7 APPENDIX
7.1 Questionnaire
Critical Success Factors of Business
Intelligence Implementation

Interviewee’s name: ________________________
Organization: ________________________
Location: ________________________
Date: ________________________
Start time: ________________________
Finish Time: ________________________

Section 1: General Information

Please tell me about yourself:
1. Your background
a. In what industry do you work? (E.g. IT services, Healthcare, Public Sector,
Banking, etc)

b. Size of your organization (no. of employees / active users, approximately)

c. What is your current position in the organization?

d. Whom do you report to?

e. Total number of years in the industry?

f. Number of years in BI area?

g. Number of implementations done?

Section 2: Business Intelligence in general

2. How do you define Business Intelligence?


80



3. Most often than not Business Intelligence is interchangeably used with Data
Warehousing and Reporting (Generation of reports), How would you relate BI with
Data warehousing and Reporting?






4. What are the primary motives for implementing BI in an organization?



a. The problems before implementation


b. The benefits perceived after implementation


5. Please describe your organization’s BI system
a. Implementation duration and stage (E.g., in-use, partially in-use or planning
stage?)


b. Primary BI applications or tools used (E.g., Dashboards, Scorecards, Mash-
ups etc)


c. Types of business users


d. Applied BI vendors


e. BI architecture





81
6. How would you define success or failure of BI implementation?



7. Choose all you wish to include in defining success factors of an BI implementation
a. Support to key stakeholders
b. Cost savings
c. Improved business performance
d. Better access to data
e. Return on Investment
f. User perception
g. Number of active users
h. Further comments:


8. What is the success rate of your implementations? (optional)
a. (Mostly) Failure
b. Slightly Failure
c. Moderately Successful
d. Successful


9. To what extent does the BI implementation contribute to your company’s
performance?
a. Not at all
b. Slightly
c. To some extent
d. Significantly
10. For how many years has your BI deployment been available?



11. How would you categorize your BI implementation?
a. Departmental implementation (E.g. Finance, Marketing, HR, Production etc)
b. Unit wide (All Departments of the branch)
c. Enterprise-wide implementation (all branches / all countries)
12. If data were available in tables, would you like to work on normalized form of data or
data in dimensional model and why?


Section 3: Critical Success Factors
13. In terms of Organization or Business entity perspectives what do you think may
influence implementation of BI system?



82

14. In terms of Process perspective what do you think may influence implementation of a
BI system?



15. In terms of Technology perspective what do you think may influence implementation
of a BI system?



16. Are there any other factors you feel important and why?



Please rate the following BI perspectives using the scale:
1. Not important 2. Little importance 3. Important 4. Very important
5. Essential / critically important

17. Organization Perspective
a. Clear vision and planning 1 2 3 4 5
b. Committed management support and sponsorship 1 2 3 4 5
c. Involvement of top management 1 2 3 4 5
d. Partnership between business community & IT 1 2 3 4 5
e. Information governance via BI competency center 1 2 3 4 5
f. Involvement of end-users 1 2 3 4 5

18. Process Perspective
a. Business driven methodology & project management 1 2 3 4 5
b. Balanced team composition 1 2 3 4 5
c. Usage of iterative prototyping to define requirements & scope 1 2 3 4 5
d. Map the solutions to the users 1 2 3 4 5
e. Change management 1 2 3 4 5
19. Technology perspective
a. Robust & extensible framework 1 2 3 4 5
b. Data management & quality issues 1 2 3 4 5
c. Appropriate technology/tools 1 2 3 4 5
d. Integrated BI applications 1 2 3 4 5
e. Performance considerations 1 2 3 4 5
f. User training & support 1 2 3 4 5

20. Please prioritize the following factors with respect to each perspective
Organization Perspective
[ ] a. Clear Vision and planning
[ ] b. Committed management support and sponsorship
[ ] c. Involvement of top management
83
[ ] d. Partnership between business community & IT
[ ] e. Information governance via BI competency center
[ ] f. Involvement of end-users
Process Perspective
[ ] a. Business driven methodology & project management
[ ] b. Balanced team composition
[ ] c. Usage of iterative prototyping to define requirements & scope
[ ] d. Map the solutions to the users
[ ] e. Change Management
Technology Perspective
[ ] a. Robust & extensible framework
[ ] b. Data management & quality issues
[ ] c. Appropriate technology / tools
[ ] d. Integrated BI applications
[ ] e. Performance considerations
[ ] f. User training & support

21. Please organize the following Critical Success Factors in a priority order you would
like to place them with respect to an BI implementation
[ ] a. Clear Vision and planning
[ ] b. Committed management support and sponsorship
[ ] c. Involvement of top management
[ ] d. Partnership between business community & IT
[ ] e. Information governance via BI competency center
[ ] f. Involvement of end-users
[ ] g. Business driven methodology & project management
[ ] h. Balanced team composition
[ ] i. Usage of iterative prototyping to define requirements & scope
[ ] j. Map the solutions to the users
[ ] k. Change Management
[ ] l. Robust & extensible framework
[ ] m. Data management & quality issues
[ ] n. Appropriate technology / tools
[ ] o. Integrated BI applications
[ ] p. Performance considerations
[ ] q. User training & support


Thank you very much
84
7.1 Interview of Respondent1
Critical Success Factors of Business
Intelligence Implementation

Interviewee’s name: Respondent1
Organization: ________________________
Location: ________________________
Date: 29.09.2009
Start time: 9.15AM
Finish Time: 10.30AM

Section 1: General Information

Please tell me about yourself:
1. Your background
a. In what industry do you work? (E.g. IT services, Healthcare, Public
Sector, Banking, etc) – Finance

b. Size of your organization (no. of employees / active users,
approximately) – 800 in Denmark,

c. What is your current position in the organization? – Manager, BI

d. Whom do you report to? – Internal Service Director (part of IT)

e. Total number of years in the industry? – 25 years

f. Number of years in BI area? – 5 years

g. Number of implementations done? – 7 to 8 in 2009 (mostly minor)

Section 2: Business Intelligence in general

2. How do you define Business Intelligence? – BI is data created for end users
for the purpose of their needs.



85


3. Most often than not Business Intelligence is interchangeably used with Data
Warehousing and Reporting (Generation of reports), How would you relate BI
with Data warehousing and Reporting? – BI only uses the information
gathered by the Data warehouse


4. What are the primary motives for implementing BI in an organization?

a. The problems before implementation – Build Data warehouse with
SAS tools, Smart reporting, Automatic data gathering

b. The benefits perceived after implementation

5. Please describe your organization’s BI system
a. Implementation duration and stage (E.g., in-use, partially in-use or
planning stage?) – 2 week

b. Primary BI applications or tools used (E.g., Dashboards, Scorecards,
Mash-ups etc) – Risk and fraud dept., Financial transactions.

c. Types of business users – Employees, specialists in data
transformations, day to day users, CEO, controlling persons,
communication department.

d. Applied BI vendors – SAS

e. BI architecture – Windows, Mainframes, SAS tables (flat), SPD (of
SAS)

6. How would you define success or failure of BI implementation? – Gathering
data and using it will define the success or failure

7. Choose all you wish to include in defining success factors of an BI
implementation
a. Support to key stakeholders - Yes
b. Cost savings
c. Improved business performance - Yes
d. Better access to data - Yes
e. Return on Investment
f. User perception - Yes
g. Number of active users
h. Further comments:
86


8. What is the success rate of your implementations? (optional)
a. (Mostly) Failure
b. Slightly Failure
c. Moderately Successful
d. Successful – Mostly


9. To what extent does the BI implementation contribute to your company’s
performance?
a. Not at all
b. Slightly
c. To some extent
d. Significantly
10. For how many years has your BI deployment been available? – 2.5 years

11. How would you categorize your BI implementation?
a. Departmental implementation (E.g. Finance, Marketing, HR, Production
etc)
b. Unit wide (All Departments of the branch)
c. Enterprise-wide implementation (all branches / all countries)
12. If data were available in tables, would you like to work on normalized form of
data or data in dimensional model and why?


Section 3: Critical Success Factors
13. In terms of Organization or Business entity perspectives what do you think may
influence implementation of BI system? – Support from key personnel, being
able to show results based on Business Objectives.

14. In terms of Process perspective what do you think may influence
implementation of a BI system? – Automization, Quality of the process,
project management

15. In terms of Technology perspective what do you think may influence
implementation of a BI system? – High availability of the system, high
maintainability

16. Are there any other factors you feel important and why? – Persistency in
Organization thought process


Please rate the following BI perspectives using the scale:
87
1. Not important 2. Little importance 3. Important 4. Very important
5. Essential / critically important

17. Organization Perspective
a. Clear vision and planning 1 2 3 4 5
b. Committed management support and sponsorship 1 2 3 4
5
c. Involvement of top management 1 2 3 4 5
d. Partnership between business community & IT 1 2 3 4 5
e. Information governance via BI competency center 1 2 3 4 5
f. Involvement of end-users 1 2 3 4 5

18. Process Perspective
a. Business driven methodology & project management 1 2 3 4 5
b. Balanced team composition 1 2 3
4 5
c. Usage of iterative prototyping to define requirements & scope 1 2 3 4
5
d. Map the solutions to the users 1 2 3
4 5
e. Change management 1 2 3
4 5
19. Technology perspective
a. Robust & extensible framework 1 2 3
4 5
b. Data management & quality issues 1 2 3
4 5
c. Appropriate technology/tools 1 2 3
4 5
d. Integrated BI applications 1 2 3 4 5
e. Performance considerations 1 2 3
4 5
f. User training & support 1 2 3 4 5

20. Please prioritize the following factors with respect to each perspective
Organization Perspective
[3] a. Clear Vision and planning
[2] b. Committed management support and sponsorship
[5] c. Involvement of top management
[3] d. Partnership between business community & IT
[4] e. Information governance via BI competency center
[1] f. Involvement of end-users

Process Perspective
88
[1] a. Business driven methodology & project management
[2] b. Balanced team composition
[4] c. Usage of iterative prototyping to define requirements & scope
[3] d. Map the solutions to the users
[5] e. Change Management

Technology Perspective
[5] a. Robust & extensible framework
[4] b. Data management & quality issues
[1] c. Appropriate technology / tools
[6] d. Integrated BI applications
[2] e. Performance considerations
[3] f. User training & support



21. Please organize the following Critical Success Factors in a priority order you would
like to place them with respect to an BI implementation
[2] a. Clear Vision and planning
[1] b. Committed management support and sponsorship
[10] c. Involvement of top management
[3] d. Partnership between business community & IT
[4] e. Information governance via BI competency center
[6] f. Involvement of end-users
[7] g. Business driven methodology & project management
[11] h. Balanced team composition
[12] i. Usage of iterative prototyping to define requirements & scope
[5] j. Map the solutions to the users
[16] k. Change Management
[13] l. Robust & extensible framework
[8] m. Data management & quality issues
[14] n. Appropriate technology / tools
[15] o. Integrated BI applications
[9] p. Performance considerations
[6.5] q. User training & support



Thank you very much

89
7.2 Interview of Respondent2
Critical Success Factors of Business
Intelligence Implementation

Interviewee’s name: Respondent 2
Organization: ________________________
Location: ________________________
Date: 30.09.2009
Start time: 2.05PM
Finish Time: 3.15PM

Section 1: General Information

Please tell me about yourself:
1. Your background
a. In what industry do you work? (E.g. IT services, Healthcare, Public Sector,
Banking, etc) - IT consultancy services with Information Management /
BI

b. Size of your organization (no. of employees / active users, approximately) -
220

c. What is your current position in the organization? – Practice Director, BI
advisory

d. Whom do you report to? – CEO

e. Total number of years in the industry? – 13

f. Number of years in BI area? – 13

g. Number of implementations done? - 10

Section 2: Business Intelligence in general

2. How do you define Business Intelligence? – Use of information for decision-
making
90
3. Most often than not Business Intelligence is interchangeably used with Data
Warehousing and Reporting (Generation of reports), How would you relate BI with
Data warehousing and Reporting? – Data warehouse is more like a backend part.
BI is actively used part of the backend which uses information. BI is palette of
things and reporting is part of it.


4. What are the primary motives for implementing BI in an organization?

a. The problems before implementation - Monitoring the Business


b. The benefits perceived after implementation – Better monitoring of the
Business activities, integrate with Business model


5. Please describe your organization’s BI system
a. Implementation duration and stage (E.g., in-use, partially in-use or planning
stage?) – 3-6 months


b. Primary BI applications or tools used (E.g., Dashboards, Scorecards, Mash-
ups etc) – Dash boards, score cards, reporting & analysis, solutions,
integration into various applications, CRM, data mining


c. Types of business users – Knowledge workers (day to day users), managers


d. Applied BI vendors – Oracle, Informatica, Business Objects, SAP,
Microsoft, IBM


e. BI architecture – Hop-n-spoke

6. How would you define success or failure of BI implementation?
Does it chantge the way people do their job ? and how they make their decision ?


7. Choose all you wish to include in defining success factors of an BI implementation
a. Support to key stakeholders - Yes
b. Cost savings - value generation
c. Improved business performance - Yes
d. Better access to data - Yes
e. Return on Investment - Yes
f. User perception - Yes
91
g. Number of active users
h. Further comments: - does it change the way people do their job and how
they make their decisions?


8. What is the success rate of your implementations? (optional)
a. (Mostly) Failure
b. Slightly Failure
c. Moderately Successful
d. Successful


9. To what extent does the BI implementation contribute to your company’s
performance?
a. Not at all
b. Slightly
c. To some extent
d. Significantly
10. For how many years has your BI deployment been available?

11. How would you categorize your BI implementation?
a. Departmental implementation (E.g. Finance, Marketing, HR, Production
etc)
b. Unit wide (All Departments of the branch)
c. Enterprise-wide implementation (all branches / all countries)
12. If data were available in tables, would you like to work on normalized form of data or
data in dimensional model and why?


Section 3: Critical Success Factors
13. In terms of Organization or Business entity perspectives what do you think may
influence implementation of BI system? – ROI, Increasing effectiveness

14. In terms of Process perspective what do you think may influence implementation of a
BI system? – Speed with which it can be developed, how it can be integrated,
iterative

15. In terms of Technology perspective what do you think may influence implementation
of a BI system? – user common toolset, training & support, concrete guidelines on
development, maintainability and flexibility.


16. Are there any other factors you feel important and why?



92
Please rate the following BI perspectives using the scale:
1. Not important 2. Little importance 3. Important 4. Very important
5. Essential / critically important

17. Organization Perspective
a. Clear vision and planning 1 2 3 4 5
b. Committed management support and sponsorship 1 2 3 4 5
c. Involvement of top management 1 2 3 4 5
d. Partnership between business community & IT 1 2 3 4 5
e. Information governance via BI competency center 1 2 3 4 5
f. Involvement of end-users 1 2 3 4 5

18. Process Perspective
a. Business driven methodology & project management 1 2 3 4 5
b. Balanced team composition 1 2 3 4 5
c. Usage of iterative prototyping to define requirements & scope 1 2 3 4 5
d. Map the solutions to the users 1 2 3 4 5
e. Change management 1 2 3 4 5
19. Technology perspective
a. Robust & extensible framework 1 2 3 4 5
b. Data management & quality issues 1 2 3 4 5
c. Appropriate technology/tools 1 2 3 4 5
d. Integrated BI applications 1 2 3 4 5
e. Performance considerations 1 2 3 4 5
f. User training & support 1 2 3 4 5

20. Please prioritize the following factors with respect to each perspective
Organization Perspective
[2] a. Clear Vision and planning
[1] b. Committed management support and sponsorship
[1] c. Involvement of top management
[1] d. Partnership between business community & IT
[2] e. Information governance via BI competency center
[1] f. Involvement of end-users

Process Perspective
[1] a. Business driven methodology & project management
[2] b. Balanced team composition
[2] c. Usage of iterative prototyping to define requirements & scope
[2] d. Map the solutions to the users
[1] e. Change Management

Technology Perspective
[2] a. Robust & extensible framework
[1] b. Data management & quality issues
93
[2] c. Appropriate technology / tools
[2] d. Integrated BI applications
[1] e. Performance considerations
[1] f. User training & support


21. Please organize the following Critical Success Factors in a priority order you would
like to place them with respect to an BI implementation
[3] a. Clear Vision and planning
[1] b. Committed management support and sponsorship
[1] c. Involvement of top management
[1] d. Partnership between business community & IT
[2] e. Information governance via BI competency center
[1] f. Involvement of end-users
[2] g. Business driven methodology & project management
[2] h. Balanced team composition
[2] i. Usage of iterative prototyping to define requirements & scope
[2] j. Map the solutions to the users
[1] k. Change Management
[2] l. Robust & extensible framework
[2] m. Data management & quality issues
[3] n. Appropriate technology / tools
[4] o. Integrated BI applications
[1] p. Performance considerations
[1] q. User training & support




Thank you very much
94
7.3 Interview of Respondent3

Critical Success Factors of Business
Intelligence Implementation

Interviewee’s name: Respondent3
Organization: ________________________
Location: ________________________
Date: 05.10.2009
Start time: 10.30 AM
Finish Time: 12.00 Noon

Section 1: General Information

Please tell me about yourself:
1. Your background
a. In what industry do you work? (E.g. IT services, Healthcare, Public Sector,
Banking, etc) - Healthcare

b. Size of your organization (no. of employees / active users, approximately) –
12000 globally

c. What is your current position in the organization? – BI Manager

d. Whom do you report to? – Marketing Manager – Director of Services -
CMO

e. Total number of years in the industry? -

f. Number of years in BI area? – 20 years

g. Number of implementations done? – 35 (100 – 200 analytic models, OLAP)

Section 2: Business Intelligence in general

2. How do you define Business Intelligence? - Decision worthy information in proper
context at proper time and quality.
95
3. Most often than not Business Intelligence is interchangeably used with Data
Warehousing and Reporting (Generation of reports), How would you relate BI with
Data warehousing and Reporting? - It is Process and tools to solve business
problems. Reporting is one aspect of BI. It is abstraction of reporting. Decision
support system is BI in broad scale

4. What are the primary motives for implementing BI in an organization?

a. The problems before implementation – no availability of information or
data and processes are with a gap

b. The benefits perceived after implementation

5. Please describe your organization’s BI system
a. Implementation duration and stage (E.g., in-use, partially in-use or planning
stage?) – 3 to 9 months in Denmark


b. Primary BI applications or tools used (E.g., Dashboards, Scorecards, Mash-
ups etc) – Reporting, Dash Boards, Predictive Analytics/Tree diagram

c. Types of business users – Cashier to CEO

d. Applied BI vendors – Product or Services

e. BI architecture – Good User Interface, embedded in process, meeting
expectations, structured, step-by-step, scalable or federated solution, user
process and architecture choices

6. How would you define success or failure of BI implementation? – User satisfaction,
sponsor satisfied, budget to maintain or improve, process improvement, meeting
SLA’s, develop/transfer knowledge.

7. Choose all you wish to include in defining success factors of an BI implementation
a. Support to key stakeholders
b. Cost savings
c. Improved business performance
d. Better access to data
e. Return on Investment
f. User perception
g. Number of active users
h. Further comments: All can be factors based on context.


8. What is the success rate of your implementations? (optional)
a. (Mostly) Failure
b. Slightly Failure
96
c. Moderately Successful
d. Successful
Predictive Analytics – 80% and reporting 50%

9. To what extent does the BI implementation contribute to your company’s
performance?
a. Not at all
b. Slightly
c. To some extent
d. Significantly
10. For how many years has your BI deployment been available?


11. How would you categorize your BI implementation?
a. Departmental implementation (E.g. Finance, Marketing, HR, Production
etc)
b. Unit wide (All Departments of the branch)
c. Enterprise-wide implementation (all branches / all countries)
12. If data were available in tables, would you like to work on normalized form of data or
data in dimensional model and why?


Section 3: Critical Success Factors
13. In terms of Organization or Business entity perspectives what do you think may
influence implementation of BI system? – CSF’s and KPI’s should be clubbed in
real life scenario. Following development model, meeting investment predictions,
implementation on time will influence the implementation


14. In terms of Process perspective what do you think may influence implementation of a
BI system? – Scalable, fast responsiveness, being satisfied with development
process, project meeting the business needs with respect to technical or budget ,
vision of IT responsible group, quality, scalable, changeable and respectable.


15. In terms of Technology perspective what do you think may influence implementation
of a BI system?





16. Are there any other factors you feel important and why?
97
Please rate the following BI perspectives using the scale:
2. Not important 2. Little importance 3. Important 4. Very important
5. Essential / critically important

17. Organization Perspective
a. Clear vision and planning 1 2 3 4 5
b. Committed management support and sponsorship 1 2 3 4.5 5
c. Involvement of top management 1 2 3.5 4 5
d. Partnership between business community & IT 1 2 3 4 5
e. Information governance via BI competency center 1 2 3.5 4 5
f. Involvement of end-users 1 2 3.5 4 5

18. Process Perspective
a. Business driven methodology & project management 1 2 3 4 5
b. Balanced team composition 1 2 3 4 5
c. Usage of iterative prototyping to define requirements & scope 1 2 3 4 5
d. Map the solutions to the users 1 2 3 4 5
e. Change management 1 2 3 4 5
19. Technology perspective
a. Robust & extensible framework 1 2 3 4 5
b. Data management & quality issues 1 2 3 4 5
c. Appropriate technology/tools 1 2 3.5 4 5
d. Integrated BI applications 1 2 3 4 5
e. Performance considerations 1 2 3 4 5
f. User training & support 1 2 3 4 5

20. Please prioritize the following factors with respect to each perspective
Organization Perspective
[4] a. Clear Vision and planning
[1] b. Committed management support and sponsorship
[3] c. Involvement of top management
[5] d. Partnership between business community & IT
[6] e. Information governance via BI competency center
[2] f. Involvement of end-users

Process Perspective
[4] a. Business driven methodology & project management
[5] b. Balanced team composition
[1] c. Usage of iterative prototyping to define requirements & scope
[3] d. Map the solutions to the users
[2] e. Change Management

Technology Perspective
[4] a. Robust & extensible framework
[1] b. Data management & quality issues
98
[3] c. Appropriate technology / tools
[6] d. Integrated BI applications
[5] e. Performance considerations
[2] f. User training & support



21. Please organize the following Critical Success Factors in a priority order you would
like to place them with respect to an BI implementation
[1] a. Clear Vision and planning
[4] b. Committed management support and sponsorship
[5] c. Involvement of top management
[10] d. Partnership between business community & IT
[15] e. Information governance via BI competency center
[6] f. Involvement of end-users
[7] g. Business driven methodology & project management
[14] h. Balanced team composition
[3] i. Usage of iterative prototyping to define requirements & scope
[17] j. Map the solutions to the users
[8] k. Change Management
[12] l. Robust & extensible framework
[2] m. Data management & quality issues
[13] n. Appropriate technology / tools
[16] o. Integrated BI applications
[11] p. Performance considerations
[9] q. User training & support





Thank you very much

99
7.4 Interview of Respondent4
Critical Success Factors of Business
Intelligence Implementation

Interviewee’s name: Respondent 4
Organization: ________________________
Location: ________________________
Date: 07.10.2009
Start time: 02.05 PM
Finish Time: 03.20 PM

Section 1: General Information

Please tell me about yourself:
1. Your background
a. In what industry do you work? (E.g. IT services, Healthcare, Public Sector,
Banking, etc) – FMCG(toys)-retail organization

b. Size of your organization (no. of employees / active users, approximately) –
2000 to 3000

c. What is your current position in the organization? – BI consultant

d. Whom do you report to? - MD

e. Total number of years in the industry? – 10 years

f. Number of years in BI area? – 10 years

g. Number of implementations done? – 10 major projects

Section 2: Business Intelligence in general

2. How do you define Business Intelligence? – Providing Business information for
end users, decision makers of the organization. It focuses more on what can be
delivered rather than technology

100

3. Most often than not Business Intelligence is interchangeably used with Data
Warehousing and Reporting (Generation of reports), How would you relate BI with
Data warehousing and Reporting? – Data warehouse is the provider of data for BI.


Reporting part of BI


Provider of data for BI


4. What are the primary motives for implementing BI in an organization? – Business
driven requirements



a. The problems before implementation – Reports of absence, requirement
specification analysis, etc were not found


b. The benefits perceived after implementation – faster information,
qualitative, valuable business insight, better decision making.


5. Please describe your organization’s BI system
a. Implementation duration and stage (E.g., in-use, partially in-use or planning
stage?) - Partially


b. Primary BI applications or tools used (E.g., Dashboards, Scorecards, Mash-
ups etc) – Cognos – Adhoc analysis on cubes, reports


c. Types of business users – Sales management, merchandising, marketing,
day to day users and middle level management


d. Applied BI vendors - Cognos, BI suit, Oracle Datawarehouse, Oracle
warehouse builder.


e. BI architecture – Hub-n-spoke

Data
warehouse
BI
101




6. How would you define success or failure of BI implementation? – User acceptance,
usage is the criteria



7. Choose all you wish to include in defining success factors of an BI implementation
a. Support to key stakeholders (for few solutions)
b. Cost savings (sometimes)
c. Improved business performance - Yes
d. Better access to data - Yes
e. Return on Investment (sometimes)
f. User perception - Yes
g. Number of active users
h. Further comments:


8. What is the success rate of your implementations? (optional)
a. (Mostly) Failure
b. Slightly Failure
c. Moderately Successful
d. Successful
e. Mostly Successful


9. To what extent does the BI implementation contribute to your company’s
performance?
a. Not at all
b. Slightly
c. To some extent
d. Significantly
10. For how many years has your BI deployment been available? – 1.5 years



11. How would you categorize your BI implementation?
a. Departmental implementation (E.g. Finance, Marketing, HR, Production
etc)
b. Unit wide (All Departments of the branch)
c. Enterprise-wide implementation (all branches / all countries)
12. If data were available in tables, would you like to work on normalized form of data or
data in dimensional model and why?

102

Section 3: Critical Success Factors
13. In terms of Organization or Business entity perspectives what do you think may
influence implementation of BI system? – User adoption, user needs, requirements
vision. When it does not support user needs, it fails

14. In terms of Process perspective what do you think may influence implementation of a
BI system? – Analysis of requirements

15. In terms of Technology perspective what do you think may influence implementation
of a BI system? – Software policies of the company, technical perspective is not a
major hindrance.

16. Are there any other factors you feel important and why?

Please rate the following BI perspectives using the scale:
3. Not important 2. Little importance 3. Important 4. Very important
5. Essential / critically important

17. Organization Perspective
a. Clear vision and planning 1 2 3 4 5
b. Committed management support and sponsorship 1 2 3 4 5
c. Involvement of top management 1 2 3 4 5
d. Partnership between business community & IT 1 2 3 4 5
e. Information governance via BI competency center 1 2 3 4 5
f. Involvement of end-users 1 2 3 4 5

18. Process Perspective
a. Business driven methodology & project management 1 2 3 4 5
b. Balanced team composition 1 2 3 4 5
c. Usage of iterative prototyping to define requirements & scope 1 2 3 4 5
d. Map the solutions to the users 1 2 3 4 5
e. Change management 1 2 3 4 5
19. Technology perspective
a. Robust & extensible framework 1 2 3 4 5
b. Data management & quality issues 1 2 3 4 5
c. Appropriate technology/tools 1 2 3 4 5
d. Integrated BI applications 1 2 3 4 5
e. Performance considerations 1 2 3 4 5
f. User training & support 1 2 3 4 5

20. Please prioritize the following factors with respect to each perspective
Organization Perspective
[1] a. Clear Vision and planning
[2] b. Committed management support and sponsorship
[6] c. Involvement of top management
103
[5] d. Partnership between business community & IT
[4] e. Information governance via BI competency center
[3] f. Involvement of end-users

Process Perspective
[3] a. Business driven methodology & project management
[2] b. Balanced team composition
[5] c. Usage of iterative prototyping to define requirements & scope
[1] d. Map the solutions to the users
[4] e. Change Management

Technology Perspective
[4] a. Robust & extensible framework
[1] b. Data management & quality issues
[5] c. Appropriate technology / tools
[6] d. Integrated BI applications
[2] e. Performance considerations
[3] f. User training & support


21. Please organize the following Critical Success Factors in a priority order you would
like to place them with respect to an BI implementation
[1] a. Clear Vision and planning
[3] b. Committed management support and sponsorship
[8] c. Involvement of top management
[11] d. Partnership between business community & IT
[9] e. Information governance via BI competency center
[2] f. Involvement of end-users
[14] g. Business driven methodology & project management
[10] h. Balanced team composition
[17] i. Usage of iterative prototyping to define requirements & scope
[4] j. Map the solutions to the users
[15] k. Change Management
[12] l. Robust & extensible framework
[6] m. Data management & quality issues
[13] n. Appropriate technology / tools
[16] o. Integrated BI applications
[7] p. Performance considerations
[5] q. User training & support



Thank you very much

104
7.5 Interview of Respondent5
Critical Success Factors of Business
Intelligence Implementation

Interviewee’s name: Respondent5
Organization: ________________________
Location: ________________________
Date: 08.10.2009
Start time: 2.10 PM
Finish Time: 3.15 PM

Section 1: General Information

Please tell me about yourself:
1. Your background
a. In what industry do you work? (E.g. IT services, Healthcare, Public Sector,
Banking, etc) – Healthcare

b. Size of your organization (no. of employees / active users, approximately) –
12000 globally

c. What is your current position in the organization?

d. Whom do you report to? – BI Manager

e. Total number of years in the industry? – 16 years

f. Number of years in BI area? – 12 years

g. Number of implementations done? – 4

Section 2: Business Intelligence in general

2. How do you define Business Intelligence? – It is a medium of presenting existing
information


105
3. Most often than not Business Intelligence is interchangeably used with Data
Warehousing and Reporting (Generation of reports), How would you relate BI with
Data warehousing and Reporting? – Data warehouse has Data modeling, star
schemas, labels, cubes, extracts, etc. Reports have excel sheets, ppt’s etc. BI
brings together data warehouse and Reporting.


4. What are the primary motives for implementing BI in an organization? – Need and
wish to have new information or knowledge to build new KPI’s, Technology
driven, initiatives driven by bad data / information quality.

a. The problems before implementation

b. The benefits perceived after implementation – not always achieved ,
specially of data quality

5. Please describe your organization’s BI system
a. Implementation duration and stage (E.g., in-use, partially in-use or planning
stage?) – 2 years duration, 2 not in use, 2 in full usage


b. Primary BI applications or tools used (E.g., Dashboards, Scorecards, Mash-
ups etc) – reports with analytical possibilities


c. Types of business users – Top management – VP and above


d. Applied BI vendors – SAP, Microsoft, Cognos and Business Objects


e. BI architecture



6. How would you define success or failure of BI implementation? – User initiating
some change based on information fetched from the system generated report.



7. Choose all you wish to include in defining success factors of an BI implementation
a. Support to key stakeholders
b. Cost savings (could be)
c. Improved business performance
d. Better access to data (could be)
e. Return on Investment
106
f. User perception (could be)
g. Number of active users (could be)
h. Further comments:

8. What is the success rate of your implementations? (optional)
a. (Mostly) Failure as compared to what we started to achieve
b. Slightly Failure
c. Moderately Successful
d. Successful

9. To what extent does the BI implementation contribute to your company’s
performance?
a. Not at all
b. Slightly
c. To some extent
d. Significantly
10. For how many years has your BI deployment been available?
One is 8 years and other 6 years

11. How would you categorize your BI implementation?
a. Departmental implementation (E.g. Finance, Marketing, HR, Production etc)
b. Unit wide (All Departments of the branch)
c. Enterprise-wide implementation (all branches / all countries)
12. If data were available in tables, would you like to work on normalized form of data or
data in dimensional model and why? – Dimensional for better performance

Section 3: Critical Success Factors
13. In terms of Organization or Business entity perspectives what do you think may
influence implementation of BI system? – Organisation is ready to do performance
management, structured management of KPI’s rather than gut feeling – KPI
driven




14. In terms of Process perspective what do you think may influence implementation of a
BI system? – short lead times, change to KPI’s faster, going live whenever
something is built successfully. Iterative approach both in development,
implementation and maintenance.

15. In terms of Technology perspective what do you think may influence implementation
of a BI system? – Flexible framework

16. Are there any other factors you feel important and why? – BI system is presenting
KPI’s but more often than not, it is the gut feeling rather than the KPI’s.
BI is all about what customer needs and how they want to use it.

107
Please rate the following BI perspectives using the scale:
4. Not important 2. Little importance 3. Important 4. Very important
5. Essential / critically important

17. Organization Perspective
a. Clear vision and planning 1 2 3 4 5
b. Committed management support and sponsorship 1 2 3 4 5
c. Involvement of top management 1 2 3 4 5
d. Partnership between business community & IT 1 2 3 4 5
e. Information governance via BI competency center 1 2 3 4 5
f. Involvement of end-users 1 2 3 4 5

18. Process Perspective
a. Business driven methodology & project management 1 2 3 4 5
b. Balanced team composition 1 2 3 4 5
c. Usage of iterative prototyping to define requirements & scope 1 2 3 4 5
d. Map the solutions to the users 1 2 3 4 5
e. Change management 1 2 3 4 5
19. Technology perspective
a. Robust & extensible framework 1 2 3 4 5
b. Data management & quality issues 1 2 3 4 5
c. Appropriate technology/tools 1 2 3 4 5
d. Integrated BI applications 1 2 3 4 5
e. Performance considerations 1 2 3 4 5
f. User training & support 1 2 3 4 5

20. Please prioritize the following factors with respect to each perspective
Organization Perspective
[1] a. Clear Vision and planning
[4] b. Committed management support and sponsorship
[5] c. Involvement of top management
[3] d. Partnership between business community & IT
[2] e. Information governance via BI competency center
[6] f. Involvement of end-users

Process Perspective
[2] a. Business driven methodology & project management
[3] b. Balanced team composition
[1] c. Usage of iterative prototyping to define requirements & scope
[5] d. Map the solutions to the users
[4] e. Change Management

Technology Perspective
[1] a. Robust & extensible framework
[2] b. Data management & quality issues
108
[5] c. Appropriate technology / tools
[4] d. Integrated BI applications
[6] e. Performance considerations
[3] f. User training & support

21. Please organize the following Critical Success Factors in a priority order you would
like to place them with respect to an BI implementation
[1] a. Clear Vision and planning
[9] b. Committed management support and sponsorship
[10] c. Involvement of top management
[3] d. Partnership between business community & IT
[2] e. Information governance via BI competency center
[12] f. Involvement of end-users
[5] g. Business driven methodology & project management
[6] h. Balanced team composition
[4] i. Usage of iterative prototyping to define requirements & scope
[17] j. Map the solutions to the users
[14] k. Change Management
[7] l. Robust & extensible framework
[8] m. Data management & quality issues
[11] n. Appropriate technology / tools
[13] o. Integrated BI applications
[16] p. Performance considerations
[15] q. User training & support





Thank you very much

109
7.6 Interview of Respondent6
Critical Success Factors of Business
Intelligence Implementation

Interviewee’s name: Respondent6
Organization:
Location: ________________________
Date: 10.10.2009
Start time: ________________________
Finish Time: ________________________

Section 1: General Information

Please tell me about yourself:
1. Your background
a. In what industry do you work? (E.g. IT services, Healthcare, Public Sector,
Banking, etc) IT

b. Size of your organization (no. of employees / active users,
approximately)4000

c. What is your current position in the organization? IT Consultant.

d. Whom do you report to? Reporting Manager.

e. Total number of years in the industry? 7

f. Number of years in BI area? 5

g. Number of implementations done? 7

Section 2: Business Intelligence in general

2. How do you define Business Intelligence? Business intelligence (BI) refers to skills,
technologies, applications and practices used to help a business acquire a better
understanding of its commercial context. Business intelligence may also refer to
the collected information itself.
110

3. Most often than not Business Intelligence is interchangeably used with Data
Warehousing and Reporting (Generation of reports), How would you relate BI with
Data warehousing and Reporting? BI applications use data gathered from a data
warehouse or a data mart. However, not all data warehouses are used for
business intelligence nor do all business intelligence applications require a data
warehouse. Reports are generated by using BI applications data.





4. What are the primary motives for implementing BI in an organization?
a. The problems before implementation – Data source, Data Collection and
missing Business knowledge expert. Data inconsistency, Lack of
knowledge of using BI tools by end users.

b. The benefits perceived after implementation – Tools and techniques that
support better business decisions. Turns data into meaningful
interpretation and gives data a better insight. It is widely used in
Performance Management. It help management to set process and
objectives, goals , plans, metrics and Key Performance Indicators (KPI)


5. Please describe your organization’s BI system
a. Implementation duration and stage (E.g., in-use, partially in-use or planning
stage?) in-use

b. Primary BI applications or tools used (E.g., Dashboards, Scorecards, Mash-
ups etc) Scorecards and KPI’s (in built OLAP tool)

c. Types of business users – All from top to bottom. (approx 10000 users)

d. Applied BI vendors – MS, IBM

e. BI architecture – Re-use and enhancement of existing framework
developed in organization.

6. How would you define success or failure of BI implementation?

The following are the key challenges for BI implementation:

a) Understand the conflicting requirement between BI and operational BI.
b) Becoming a default excel , one BI platform
c) DW will deliver or required data and will solve all access information
d) Assuming average user know how to use BI tools and have insight to data.
111

If organization will able to overcome all above challenges fully (or more than
70 %) then it is a success in my eyes otherwise failure.

7. Choose all you wish to include in defining success factors of an BI implementation
a. Support to key stakeholders
b. Cost savings
c. Improved business performance
d. Better access to data
e. Return on Investment
f. User perception
g. Number of active users
h. Further comments: Employee goals are cleared towards target.

8. What is the success rate of your implementations? (optional)
a. (Mostly) Failure
b. Slightly Failure
c. Moderately Successful
d. Successful

9. To what extent does the BI implementation contribute to your company’s
performance?
a. Not at all
b. Slightly
c. To some extent
d. Significantly
10. For how many years has your BI deployment been available? 4 years

11. How would you categorize your BI implementation?
a. Departmental implementation (E.g. Finance, Marketing, HR, Production etc)
b. Unit wide (All Departments of the branch)
c. Enterprise-wide implementation (all branches / all countries) 10000 users
approx.

Section 3: Critical Success Factors
12. In terms of Organization or Business entity perspectives what do you think may
influence implementation of BI system?

It must benefit the organization in terms of setting goals and objectives. BI data
should be used to evaluate the performance that is actual vs planned.

a. Support All Users Via Integrated BI Suites.
b. Role and need based user segmentation.
c. Common Framework.
d. It should be Conforms to the Way Users Work.


112
13. In terms of Process perspective what do you think may influence implementation of a
BI system?

a. Delivers Actionable Information
b. It should suit to Business driven methodology and project Management.
c. Quick rapid development environment to build prototype.


14. In terms of Technology perspective what do you think may influence implementation
of a BI system?

a. Provide a Robust and Extensible Platform
b. BI software – tools, platforms, applications and alike.
c. Integrates with Desktop and Operational Applications
d. User manual and training.

15. Are there any other factors you feel important and why?

BI platforms should provide tight linkages to data integration tools and quicker
metadata exchange to deliver actionable information at right-time.

Please rate the following BI perspectives using the scale:
5. Not important 2. Little importance 3. Important 4. Very important
5. Essential / critically important

16. Organization Perspective (make a bracket around your choice)
a. Clear vision and planning 1 2 3 4 5
b. Committed management support and sponsorship 1 2 3 4 5
c. Involvement of top management 1 2 3 4 5
d. Partnership between business community & IT 1 2 3 4 5
e. Information governance strategy (BI competency center) 1 2 3 4 5
f. Involvement of casual users (ad-hoc / shop floor level users) 1 2 3 4 5

17. Process Perspective
a. Business driven methodology & project management 1 2 3 4 5
b. Balanced team composition 1 2 3 4 5
c. Usage of iterative prototyping to define requirements & scope 1 2 3 4 5
d. Map the solutions to the users 1 2 3 4 5
e. Change management 1 2 3 4 5
18. Technology perspective
a. Robust & extensible framework 1 2 3 4 5
b. Data management & quality issues 1 2 3 4 5
c. Appropriate technology/tools 1 2 3 4 5
d. Integrated BI applications 1 2 3 4 5
e. Performance considerations 1 2 3 4 5
f. User training & support 1 2 3 4 5

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19. Please prioritize the following factors with respect to each perspective
Organization Perspective
[ 2 ] a. Clear Vision and planning
[ 3 ] b. Committed management support and sponsorship
[ 1 ] c. Involvement of top management
[ 4 ] d. Partnership between business community & IT
[ 5 ] e. Information governance via BI competency center
[ 6 ] f. Involvement of end-users

Process Perspective
[ 1 ] a. Business driven methodology & project management
[ 3] b. Balanced team composition
[ 2] c. Usage of iterative prototyping to define requirements & scope
[ 5] d. Map the solutions to the users
[ 4] e. Change Management

Technology Perspective
[ 1 ] a. Robust & extensible framework
[ 2 ] b. Data management & quality issues
[ 3 ] c. Appropriate technology / tools
[ 5 ] d. Integrated BI applications
[ 4 ] e. Performance considerations
[ 6 ] f. User training & support

20. Please organize the following Critical Success Factors in a priority order you would
like to place them with respect to an BI implementation
[ 3 ] a. Clear Vision and planning
[ 2 ] b. Committed management support and sponsorship
[ 1 ] c. Involvement of top management
[ 9] d. Partnership between business community & IT
[ 16] e. Information governance via BI competency center
[ 7 ] f. Involvement of end-users
[ 4] g. Business driven methodology & project management
[ 12 ] h. Balanced team composition
[ 10 ] i. Usage of iterative prototyping to define requirements & scope
[ 15] j. Map the solutions to the users
[ 11] k. Change Management
[ 5] l. Robust & extensible framework
[ 8] m. Data management & quality issues
[ 6] n. Appropriate technology / tools
[ 13 ] o. Integrated BI applications
[ 14 ] p. Performance considerations
[ 17 ] q. User training & support
Thank you very much
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7.7 Interview of Respondent7
Critical Success Factors of Business
Intelligence Implementation

Interviewee’s name: Respondent 7
Organization: ________________________
Location: ________________________
Date: 30/10/2009
Start time: 02:50 PM
Finish Time: 04:25 PM

Section 1: General Information

Please tell me about yourself:
1. Your background
a. In what industry do you work? (E.g. IT services, Healthcare, Public Sector,
Banking, etc) – Medico Technique

b. Size of your organization (no. of employees / active users, approximately) –
7000 to 8000

c. What is your current position in the organization? – Senior Manager

d. Whom do you report to? – Head of BICC

e. Total number of years in the industry? – 9 years

f. Number of years in BI area? – 9 years

g. Number of implementations done? – 10 to 15 major ones

Section 2: Business Intelligence in general

2. How do you define Business Intelligence? - The process, concept or methodology
we use to improve decision making in the business. It also requires applications,
ETL tools, Database, etc.



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3. Most often than not Business Intelligence is interchangeably used with Data
Warehousing and Reporting (Generation of reports), How would you relate BI with
Data warehousing and Reporting? – Data warehouse is the process of storing the
data, staging and dimension modeling. BI is more of a business goal. It is not
necessary to have a data warehouse. We need fact based system.








4. What are the primary motives for implementing BI in an organization? – Tool to
make business decision, how to optimize a process to get hight quality data


a. The problems before implementation – lack of ownership in the business,
quality of data


b. The benefits perceived after implementation – better data quality; optimized
process of reporting; transparency of data; focus on core business


5. Please describe your organization’s BI system
a. Implementation duration and stage (E.g., in-use, partially in-use or planning
stage?) - 3 to 6 months


b. Primary BI applications or tools used (E.g., Dashboards, Scorecards, Mash-
ups etc) – Cognos8, Datastage (ETL), DB2


c. Types of business users – Reports for middle management / Executive
management, packages for operational mnagement


d. Applied BI vendors – IBM, Erwin for data modeling


e. BI architecture – Hub-n-spoke with dimensional modeling
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6. How would you define success or failure of BI implementation? – If it is used for
business decision making, it is a success or else a failure



7. Choose all you wish to include in defining success factors of an BI implementation
a. Support to key stakeholders
b. Cost savings
c. Improved business performance
d. Better access to data
e. Return on Investment
f. User perception
g. Number of active users
h. Further comments: b,c – cost benefits and a, c, d are success factors


8. What is the success rate of your implementations? (optional)
a. (Mostly) Failure
b. Slightly Failure
c. Moderately Successful
d. Successful


9. To what extent does the BI implementation contribute to your company’s
performance?
a. Not at all
b. Slightly
c. To some extent
d. Significantly
10. For how many years has your BI deployment been available?
9 years


11. How would you categorize your BI implementation?
a. Departmental implementation (E.g. Finance, Marketing, HR, Production etc)
b. Unit wide (All Departments of the branch)
c. Enterprise-wide implementation (all branches / all countries)
12. If data were available in tables, would you like to work on normalized form of data or
data in dimensional model and why?
Depends on persons accessing the data

Section 3: Critical Success Factors
13. In terms of Organization or Business entity perspectives what do you think may
influence implementation of BI system? - support business analysts to setup
business requirements, strategy or vision with respect to BI, cost of BI
implementation

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14. In terms of Process perspective what do you think may influence implementation of a
BI system? - Project management, process to maintain or operate the system,
process to monitor the system before and after the implementation, right
resources, prioritization of resorce, availability of quality resources

15. In terms of Technology perspective what do you think may influence implementation
of a BI system? – bad data quality, toolset not suitable for the business
requirements.

16. Are there any other factors you feel important and why?

Please rate the following BI perspectives using the scale:
6. Not important 2. Little importance 3. Important 4. Very important
5. Essential / critically important

17. Organization Perspective
a. Clear vision and planning 1 2 3 4 5
b. Committed management support and sponsorship 1 2 3 4 5
c. Involvement of top management 1 2 3 4 5
d. Partnership between business community & IT 1 2 3 4 5
e. Information governance via BI competency center 1 2 3 4 5
f. Involvement of end-users 1 2 3 4 5

18. Process Perspective
a. Business driven methodology & project management 1 2 3 4 5
b. Balanced team composition 1 2 3 4 5
c. Usage of iterative prototyping to define requirements & scope 1 2 3 4 5
d. Map the solutions to the users 1 2 3 4 5
e. Change management 1 2 3 4 5
19. Technology perspective
a. Robust & extensible framework 1 2 3 4 5
b. Data management & quality issues 1 2 3 4 5
c. Appropriate technology/tools 1 2 3 4 5
d. Integrated BI applications 1 2 3 4 5
e. Performance considerations 1 2 3 4 5
f. User training & support 1 2 3 4 5

20. Please prioritize the following factors with respect to each perspective
Organization Perspective
[3] a. Clear Vision and planning
[1] b. Committed management support and sponsorship
[6] c. Involvement of top management
[2] d. Partnership between business community & IT
[4] e. Information governance via BI competency center
[5] f. Involvement of end-users

118
Process Perspective
[1] a. Business driven methodology & project management
[4] b. Balanced team composition
[5] c. Usage of iterative prototyping to define requirements & scope
[3] d. Map the solutions to the users
[2] e. Change Management

Technology Perspective
[3] a. Robust & extensible framework
[2] b. Data management & quality issues
[6] c. Appropriate technology / tools
[4] d. Integrated BI applications
[1] e. Performance considerations
[5] f. User training & support

21. Please organize the following Critical Success Factors in a priority order you would
like to place them with respect to an BI implementation
[6] a. Clear Vision and planning
[1] b. Committed management support and sponsorship
[13] c. Involvement of top management
[4] d. Partnership between business community & IT
[10] e. Information governance via BI competency center
[11] f. Involvement of end-users
[7] g. Business driven methodology & project management
[17] h. Balanced team composition
[16] i. Usage of iterative prototyping to define requirements & scope
[15] j. Map the solutions to the users
[14] k. Change Management
[5] l. Robust & extensible framework
[3] m. Data management & quality issues
[12] n. Appropriate technology / tools
[8] o. Integrated BI applications
[2] p. Performance considerations
[9] q. User training & support



Thank you very much


doc_894079011.pdf