Description
Strategic Value Of Business Intelligence Systems, A Case Study Of Equity Bank Limited
STRATEGIC VALUE OF BUSINESS INTELLIGENCE SYSTEMS,
A CASE STUDY OF EQUITY BANK LIMITED
KAMARA DANIEL M
A RESEARCH PROJECT SUBMITTED IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS OF MASTER OF
BUSINESS ADMINISTRATION DEGREE, SCHOOL OF
BUSINESS, UNIVERSITY OF NAIROBI
NOVEMBER, 2014
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DECLARATION
This project is my original work and has not been presented for the award of a degree
in this or any other university.
Signed: _________________________ Date: _______________________
Kamara Daniel M.
REG. NO: D61/67494/2011
This project has been submitted for examination with my approval as University
supervisor.
Signed: _________________________ Date: ________________________
Mr. James T. Kariuki
Supervisor, School of Business
Department of Management Science
University of Nairobi
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ACKNOWLEDGEMENTS
I am most grateful to Mr. James T. Kariuki, my project supervisor, for his guidance
and assistance. The suggestions and criticisms at various stages of this work made it
possible for me to see it through. I will not forget my other lecturers who always
encouraged me to work hard. I would like to express my sincere appreciation to my
colleagues for their words of encouragement as we struggled through the semester.
Lastly but most important, God bless you all.
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DEDICATION
I dedicate this work to entire banking fraternity, my family members for great support
and encouragement.
May God Bless you all.
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ABSTRACT
The intent of any business intelligence System is simply to provide a system for
developing or improving processes through a structured approach, effective
deployment and better control. The objectives of this study were to identify the
strategic value of Business Intelligence (BI) system, the extent of use of BI system
and the challenges in the use of BI at Equity Bank. The study adopted a descriptive
design. The target population for the survey was 500 members of staff from various
departments in Equity Bank Head Office. Using a sample of 50 employees, the study
had 88.5% response rate. The primary data for the study was collected using a
structured questionnaire. Questionnaires collected were edited, coded and data entered
into Microsoft excel and Google Docs analytics which were used to analyse the data.
The study established that Equity bank gains strategic value from BI systems through
provision of information that facilitate handling of customer issues, predict their likes
and preferences, improve decision making and come up with innovative products and
services. On the usage of BI, the study found variation in the use and tools used. The
study found that management should provide full support of the business intelligence
system by ensuring that all the required resources are availed for the sustainability of
the business intelligence system. New employees should be inducted into the business
intelligence system in order to understand how to use the system and its benefits. All
employees should also be trained on BI and be incorporated into the team that is in
direct touch with the business intelligence systems. The researcher recommended
further research on cost benefit analysis of Business Intelligence (BI) systems since
the study focused on the strategy value of BI approach only.
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TABLE OF CONTENTS
DECLARATION ........................................................................................................ ii
ACKNOWLEDGEMENTS ...................................................................................... iii
DEDICATION ............................................................................................................ iv
ABSTRACT.................................................................................................................. v
TABLE OF CONTENTS ........................................................................................... vi
LIST OF TABLES ...................................................................................................... ix
ABBREVIATIONS ...................................................................................................... x
CHAPTER ONE: INTRODUCTION ........................................................................ 1
1.1 Background of the Study ...................................................................................... 1
1.1.1 Business Intelligence ..................................................................................... 2
1.1.2 Strategic Value of Business Intelligence ....................................................... 2
1.1.3 Equity Bank Limited ..................................................................................... 4
1.2 Statement of the Problem ..................................................................................... 7
1.3 Research Objectives ............................................................................................. 8
1.4 Value of study ...................................................................................................... 8
CHAPTER TWO: LITERATURE REVIEW......................................................... 10
2.1 Introduction ........................................................................................................ 10
2.2 Theoretical Orientation ....................................................................................... 10
2.3 The Origins of Business Intelligence Systems ................................................... 11
2.4 Components of Business Intelligence ................................................................ 12
2.5 Business Intelligence Process ............................................................................. 13
2.6 Strategic Value and Benefits of Business Intelligence ....................................... 14
2.7 Challenges of Business Intelligence ................................................................... 20
2.8 Summary ............................................................................................................ 23
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CHAPTER THREE: RESEARCH METHODOLOGY ........................................ 25
3.1 Introduction ........................................................................................................ 25
3.2 Research Design ................................................................................................. 25
3.3 Target Population ............................................................................................... 25
3.4 Sampling Design ................................................................................................ 25
3.5 Data Collection ................................................................................................... 26
3.6 Data Analysis ..................................................................................................... 26
CHAPTER FOUR: DATA ANALYSIS, RESULTS AND DISCUSSION ........... 27
4.1 Introduction ........................................................................................................ 27
4.2 Demographics ..................................................................................................... 27
4.2.1 Distribution of respondents by position ....................................................... 27
4.2.2 Distribution of Respondents by Department .............................................. 28
4.2.3 Years of Experience of the Respondents ..................................................... 29
4.2.4 Level of Education of the Respondents ....................................................... 30
4.2.5 Level of Education - ICT ............................................................................. 30
4.3 Extent to which Business Intelligence is used In Equity Bank. ......................... 31
4.3.1 Use of Business Intelligence systems (BI) in decision making ................... 32
4.3.2 Main areas of applications of Business Intelligence (BI) ............................ 34
4.3.3Knowledge and Training of Business Intelligence (BI) System .................. 35
4.4 Strategic Value of Business Intelligence (BI) Systems ...................................... 37
4.4.1 Sources of Information in Predicting Customers Issues, Improve Quality of
Goods/Services and Innovation ............................................................................ 37
4.4.2 Business Intelligence (BI) System Rating in Improving Quality of Decision
Made ..................................................................................................................... 39
4.5 Challenges of Business Intelligence (BI) systems ............................................. 41
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CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS ... 43
5.1 Introduction ........................................................................................................ 43
5.2 Summary of Findings ......................................................................................... 43
5.3 Conclusion .......................................................................................................... 45
5.4 Recommendations for Policy and Practice ......................................................... 46
5.5 Limitations of the Study ..................................................................................... 47
5.6 Suggestions for Further Study ............................................................................ 47
REFERENCES .......................................................................................................... 48
APPENDICES ............................................................................................................ 52
APPENDIX I: LETTER OF INTRODUCTION ..................................................... 52
APPENDIX II: QUESTIONNAIRE ........................................................................ 53
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LIST OF TABLES
Table 4.1: Distribution of Respondents by Position .................................................... 28
Table 4.2: Distribution of Respondents by Department .............................................. 28
Table 4.3: Years of Experience of the Respondents .................................................... 29
Table 4.4: Level of Education of the Respondents ...................................................... 30
Table 4.5: Education Level – ICT Related .................................................................. 31
Table 4.6: BI System use by Department .................................................................... 32
Table 4.7: Use of Business Intelligence Systems (BI) in Decision Making ............... 33
Table 4.8: Main Areas of Applications of Business Intelligence ................................ 34
Table 4.9: Knowledge and Training of Business Intelligence (BI) System ................ 36
Table 4.10: Source of Information in Predicting Customers Issues, Improve Quality of
Goods/Services and Innovation ................................................................................... 38
Table 4.11: Business Intelligence system rating in improving quality of decision
making.......................................................................................................................... 40
Table 4.12: Challenges of Business Intelligence (BI) systems .................................... 42
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ABBREVIATIONS
BI - Business Intelligence
CBK - Central Bank of Kenya
CDR - Call Detail Record
CIO - Chief Information Officer
CRM - Consumer Relationship Management
DW - Data Warehouse
EDI - Electronic Data Interchange
ERP - Enterprise Resource Planning
ETL - Extract, Transform and Load
HRM - Human Resource Management
ICT - Information and Communication Technology
IT - Information Technology
MDM - Master Data Management
OLAP - Online Analytical Processing
SCM - Supply Chain Management
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CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
Central Bank of Kenya has regulations that require banks to use computerized
information system, for internal or external application database validations to check
for any inconsistencies in the information provided particularly those containing
known fictitious application/ fraud information (CBK, 2013). An information system
(IS) is a formal network using computers to provide management information for
decision making with the main goal of providing the correct information to the
appropriate manager at the right time, in a useful form (Laudon & Laudon, 2009). An
information system (IS) can also generally be described as a collection of computer
hardware, software, people, procedures and communication devices used to capture
business data, process it and disseminate information for the purposes of decision
making within a business enterprise (University of Cape Town, 2014).
Developments in technology are changing the banking industry from paper, brick and
mortar banking, to digitized and networked banking services. Gachara (2012) found
that 83% commercial banks have increased new products over the recent past while a
majority 53% agrees that the electronic business processes have also increased. The
increased number of products and innovation is as a result of information systems
facilitation in doing business. Commercial banks needs a business Intelligence system
that can serve as an early-warning system for bank disruptive changes in the
competitive landscape from rival’s new products or pricing strategy or the entrance of
an unexpected player into the financial market.
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1.1.1 Business Intelligence
Nemati (2005) defines Business Intelligence (BI) as a suite of tools and technologies
that enhance the decision making process by transforming data into valuable and
actionable knowledge to gain a competitive advantage. According to Hannula &
Pirttimaki (2003), BI can broadly be defined as an organized and systematic process
which is used to acquire, analyse and disseminate information which is significant to
their business activities. BI is also defined as a set of technologies that gather and
analyse data to improve decision-making Herschel et al, (2005). Several
characteristics of BI emerge from these definitions, that is, it refers to both internal
and external information gathering, analysis and dissemination of valuable
information for decision making.
According to Olszak and Ziemba (2006) beneficiaries of Business Intelligence (BI)
systems include a wide group of user such as insurance companies, oil and mining
industry, security systems, banks and supermarkets. Banks are amongst the most
common sectors that use BI systems, BI systems also assist in determining the
profitability of individual customers who are current and long term. This provide the
basis for high profit sales and relationship banking, thus maximizing sales to high
value customers, reducing costs to low value customers. This provides a means to
maximise profitability of new innovative products and services therefore promoting
value creation in banks.
1.1.2 Strategic Value of Business Intelligence
Business intelligence (BI) empowers organizations with business insights that lead to
better, faster, more relevant decisions. This ensures the right information is gotten at
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the right time, and in the right format. According to Ubiparipovi? and ?urkovi?
(2011) BI systems enable banks to anticipate future behaviour of the customers and
most of their business indicators. They also enable modelling client behaviour not
only in terms of using new services but also from the perspective of potential risks.
Some of the notable areas where BI is applied in banks are analytical customer
relationship management, bank performance management, enterprise risk
management, asset and liability management and compliance.
Commercial banks in developing countries offer financial services through relying on
information gathered to provide superior value for the banks customers and improve
their satisfaction. According to (Porter, 1998) Technology intelligence exerts a
significant influence on the ability to innovate and is viewed both as a major source of
competitive advantage and of new product innovation. This strategy enables the banks
to provide considerable insulation from competition. It also forms a basis of
measuring the strategic value.
Organisations put in place a set of activities, methods, best practices, policies, and
automated tools that stakeholders use to develop and continuously improve
information systems and software. Business Intelligence (BI) maturity model
describes the stages that most organizations follow when evolving their BI
infrastructure from a low value, cost-centre operation, to a high value, strategic
function that drives market share. Examples of maturity models are Gartner’s
Maturity Model for Business Intelligence (BI) and Performance Management (PM)
which recognizes five levels of maturity: unaware, tactical, focused, strategic, and
pervasive. It is used for the assessment of the input effort, BI and PM maturity .The
other maturity model is AMR Research's Business Intelligence/Performance
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Management Maturity Model, the key characteristics of this model are reacting,
anticipating, collaborating and orchestrating (Rajteric, 2010).
Some models focus on the technical aspect and others on the business point of view.
Business Intelligence (BI) models help in identifying the existing problems of BI
implementation and provide symmetric guidelines (Chuah et al, 2013). Most
Information Technology (IT) driven BI and data warehousing initiatives tend to focus
on the technical aspects, therefore the technical challenges and trade-offs are at least
well understood ,attention now shifts towards the ways in which BI can be used to
deliver business value McIntyre (2009).
Whilst Business Intelligence (BI) remains one of the top technology issues for Chief
Information Officers (CIOs), little research has been done regarding the actual
business value realized as a result of BI investment (Negash & Gray, 2003). Apart
from operational and efficiency benefits, IT can offer payback on a strategic level,
making the prospect of clearly identifying the benefits an even more difficult
challenge (Gibson et al 2004). The strategic value of BI solutions is depicted by the
ability to manage and exploit the information potential of multitude of internal and
external data from sales, demographics, economic trends, competitive data, consumer
behavior, efficiency measures, financial calculations, and more (Ubiparipovi? &
?urkovi?, 2011).
1.1.3 Equity Bank Limited
The banking industry in Kenya is governed by the Companies Act, banking Act and
various prudential guidelines issued by the Central Bank of Kenya (CBK, 2014). The
industry has grown in double digits percentages over the last 5 years (CBK, 2013).
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The Kenyan Banking Sector recorded improved performance with the size of net
assets standing at Ksh. 2.97 trillion, loans & advances worth Ksh. 1.78 trillion, while
the deposit base was Ksh. 2.15 trillion and profit before tax of Ksh. 71.03 billion as at
30
th
June 2014. However in spite of this rapid growth in the banking sector,
competition has been a major concern making it more and more challenging for banks
to keep up with the changes and with the competition . This calls for change of tactics
by the industry.
Central Bank of Kenya (CBK) revised risk management guidelines for institutions
licensed under the banking Act. CBK has been regulating bank under Base I Capital
adequacy accord and though the sector has not fully adopted Base II, it is encouraging
to note that the new guidelines are features of Base III measures in capital adequacy
requirements (Think Business, 2013). The changes in regulatory frame and increased
competition led to banking industry in Kenya being shaky considering the major
failures that occurred between 1998 and 2005. Banks need to use all the tools at their
disposal to manage the many industry challenges and ensure their own financial
stability through intelligent business solutions (Microstrategy, 2008). One of the tools
at their disposal is Business Intelligence system.
Equity Bank started as a building society on registration in 1984 and converted to a
commercial bank 2004. With over 8 million accounts, accounting for over 50% of all
bank accounts in Kenya, Equity Bank is the largest bank in Africa in terms of
customer base and operates in Kenya, Uganda, South Sudan, Rwanda and Tanzania.
Its model of growth which includes rural banking orientation and promotion of
agribusiness is a significant and strategic intervention and contribution by Equity
Bank in Kenyans economic growth.
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Equity bank is one of the commercial banks in Kenya, as of 2013 it had over 6000
employees in Kenya. It enjoys the largest customer base mainly because of offering
products suited to low income areas, promotion of agribusiness and focusing on rural
banking. The company has attracted a lot of global accolades and recognition and
developing countries learn from the banks low margin high volumes model.
Equity Bank’s main strategy aims at maximizing the value of information technology
by aligning Information Technology (IT) investments with business objectives. To
achieve these strategies, there is need to review customer needs to identify unmet
demand, development of new cost effective online initiative and achieve the vision of
the business and review opportunities and challenges where Information Technology
(IT) can be leveraged. Amongst the major Information Technology (IT) investment
that Equity bank has made is the acquisition of a data center. This has helped
identified ways to incorporate an intelligent service platform to manage and map the
storage of data. The Business Intelligence (BI) system feeds on the data warehouse,
enabling it to fast-track decision making.
Equity Bank implemented Oracle Business Intelligence Enterprise (OBIE) in 2009,
which has worked well in functional decision making. However it seems not to
effectively support top level decision making, hence reducing the strategic value of
Business Intelligence. Once OBIE is properly deployed at the strategic level,
capability of solving past customer complaints and predicting competitiveness of the
industry will be improved. Other systems facing similar challenges include
solarwinds, Thomson Reuters/Bloomberg, Finonne and Siebel/ avaya call system.
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1.2 Statement of the Problem
Different departments in an organization use Business Intelligence (BI) systems
differently to serve their unique needs. Any employee in charge of making a decision
has to deal with a large amount of data, dashboards BI make it easier to comprehend
large amounts of data. In the scenario of business activity that lacks a dashboard, if an
executive wants to compare data and make any decision based on it, he or she needs
to go through a lengthy process to get the relevant data for comparison. Several times
the data is presented in different formats, which creates issues of compatibility. BI
helps in capitalizing the revenue and optimizing the business processes.
A decision-making process is an important process for any organization; decisions
made by managers or executives are very crucial for the success of any organization.
According to Venter and Tustin, (2009), in South Africa, whereas most people
understood how BI systems work in organizations, it is not readily available, when
they need it and in the format they require. Any large or small organization today
must optimize its strategic decision making process. With a sharp increase in data
collection due to the growing global market and customization, the decision making
process needs to be fast and more accurate.
Decision-making must be well supported by information about events within the
organization and in its environment. Organizations need reliable information systems
that enable analysts and managers access to the information required for quality and
effective decision-making (Puklavec, 2001).
Equity bank relies almost entirely on applications and databases, causing data and
storage needs to increase at astounding rates. It is therefore imperative for Equity to
optimize and simplify the complexity of managing its data resources. The challenge
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remains to proactively manage this data storage to the benefit of various departments,
divisions, geographical locations and business processes to achieve improved
efficiency and profitability.
The study sought answers on what is the extent of use of Business Intelligence (BI)
system in Equity Bank is, established the ways in which Equity Bank uses BI to gain
Strategic Value and the challenges in the application of BI system in Equity Bank?
1.3 Research Objectives
The main objective of the study was to identify the strategic value of Business
Intelligence (BI) system in Equity Bank. Specific objectives of the research were
1. To determine the extent to which BI system has been used by Equity Bank.
2. To establish the ways in which Equity Bank uses BI system to gain Strategic
Value.
3. To determine the challenges of using BI systems in Equity Bank.
1.4 Value of study
This study sought to determine the strategic value of Business Intelligence (BI)
system in Equity Bank. This research adds knowledge to the existing information
about the strategic value of Business Intelligence (BI) system. Academics (Students
and instructors) would benefit from the results of the research objectives so that they
can use the findings for further research on BI. It is a source of information and a
point of reference for future research. The research therefore is applied by researchers
all over the world. The study contributes to the body of knowledge on business
intelligence systems by outlining the effects of integrating it as a core business
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concept and that the findings will be used for further research on improving quality
systems as a strategic tool and recommendations that will be drawn will be used by
other organizations when developing and designing their frameworks related to
business intelligence systems.
In practice, the study is useful to different groups of people in different ways.
Regulators like Central Bank of Kenya will find this report useful in formulation of
policies and coming up with new prudential guidelines on BI. Business Intelligence
(BI) vendors and service providers could use this report to evaluate customers
concerns and satisfaction so that they can come up with ways to address the customer
concerns. Organizations will use the report to determine on how to leverage on BI for
improved financial performance, innovation and decision making.
Policy makers in the various organizations will gain useful information on the values
of BI in Kenya. They will also benefit from the findings of this study by adopting
findings of the study which will help them enhance responsible policy making and
governance which lead to sustained productivity and better organizational
performance.
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CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This chapter discusses the theoretical framework pertaining to strategic value of
business intelligence systems. This chapter also defines BI, BI tools and technologies
and describes the process of unlocking the power of data to impact on building
customer and business knowledge, identify new opportunities and manage and
mitigate risks facing the organization.
2.2 Theoretical Orientation
Emerging information technology cannot deliver improved organizational
effectiveness if it is not accepted and used by potential users. Technology Acceptance
Model (TAM) is one of the most successful measurements for information systems
usage among practitioners and academics. TAM is consistent with the theory on
diffusion of innovation where technology adoption is a function of a variety of factors
including relative advantage and ease of use. According to Kim et al (2009) TAM
explores the level of motivation and user attitude that determines whether the user
will actually use or reject the system.
TAM is widely used by researchers to provide explanations of usage behavior in
relation to adoption of information technology. TAM is implemented and tested in
online banking, online shopping, e-government, immigration, e-commerce. In TAM,
user’s beliefs determine the attitudes toward using the system. Behavioral intention, in
turn, is determined by these attitudes toward using the system. The concepts of
perceived usefulness and perceived ease of use are individual subjective judgments
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about the usefulness and ease toward specific system. Perceived usefulness and
perceived ease of use are distinct but related constructs. In TAM, perceived usefulness
is a major belief factor, and perceived ease of use is a secondary belief factor in
determining behavioral intentions toward using information technology.
TAM is determined by external variables which are effective technology and ease of
use for daily work and daily life, attitude toward using includes human attitudes
towards the use of either technology effectively in their daily lives and actual system
use which is the perceived usefulness and usage intentions in terms of social influence
and cognitive instrumental processes. In order to reduce cost benefit ratio, we must
examine the gap between system design and system acceptance. So the model of the
technology acceptance becomes very important and critical in relation to business
intelligence system.
2.3 The Origins of Business Intelligence Systems
Computer-based business intelligence systems go back a long way, in one case or
another, for close to forty years (Thomsen 2003). According to Thomsen (2003) BI as
a term replaced decision support, executive information systems, and management
information systems. With each new iteration, capabilities increased as enterprises
grew ever-more sophisticated in their computational and analytical needs and as
computer hardware and software matured. According to Hannula & Pirttimaki (2003),
in the 1980’s the term was identified with its emphasis on the need for continuous
monitoring of customers, competitors, suppliers, and other fields. Business
Intelligence systems therefore comprises a variety of intelligence information such as
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customer intelligence, competitor intelligence, market intelligence, technological
intelligence, product intelligence and environmental intelligence.
In the 1990s, much investment in Information Technology (IT) was focused on
enterprise applications such as Enterprise Resource Planning (ERP), Supply Chain
Management (SCM), and Consumer Relationship Management (CRM) and on
connectivity between trading partners via the Internet and more traditional means
such as Electronic Data Interchange (EDI). The business benefits of these investments
included transactional efficiency, internal process integration, back-office process
automation, transactional status visibility, and reduced information sharing costs
(Williams & Williams, 2003).
By the late nineties and early 2000, Data Warehousing (DW) was accepted in the
business arena. Although early justifications for data warehousing were primarily
driven by the needs to provide integrated reporting functionality, the value of data
warehousing became clear for carrying out large analysis tasks to assist data-driven
decision making both for tactical and strategic management decisions. As the role of
analysis expanded rapidly within an enterprise, teams of business analysts within an
enterprise were involved in extracting interesting patterns from enterprise wide data.
This notion of extracting and unlocking useful information from raw data is termed as
business intelligence.
2.4 Components of Business Intelligence
A business intelligence (BI) system does not exist as a final product, its producers
offer technological platforms and knowledge for their implementation (Ubiparipovi?
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& ?urkovi?, 2011). BI environment therefore often consists of many different
components, such as data integration, operational data stores, data warehouses, data
marts, cubes, reports, dashboards, alerts. This makes BI heavily dependent on and has
to be tightly integrated with other key platforms and applications such as data quality,
master data management (MDM), portals, security, mobile delivery and others.
According to Dayal, Castellanos, Simitsis, & Wilkinson (2009), BI architecture
typically consists of a data warehouse (or one or more data marts), which consolidates
data from several operational databases, and serves a variety of front-end querying,
reporting, and analytic tools. A data warehouse (DW) is a special type of database
where data is organized in a manner convenient for conducting analytical processes
on large data sets. It contains a copy of data isolated from operational databases and
structured specifically for reports and analyses. DW and On-line analytical processing
(OLAP) form the information basis for applying BI (Ubiparipovi? & ?urkovi?, 2011).
On-line analytical processing (OLAP) refers to the way in which business users can
slice and dice their way through data using sophisticated tools that allow for the
navigation of dimensions such as time or hierarchies. These systems process queries
required to discover trends and analyze critical factors. Advanced analytics is referred
to as data mining, forecasting or predictive analytics, this takes advantage of statistical
analysis techniques to predict or provide certainty measures on facts (Ranjan, 2009).
2.5 Business Intelligence Process
Business Intelligence (BI) enables the business to make intelligent, fact-based
decisions. The most cogent argument for establishing a new roadmap to business
Intelligence (BI) excellence is to rid the organization of the technology scramble and
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cobbled together solutions that Information Technology (IT) has had to deal with as it
struggled to meet business requirements. According to Ranjan (2009) a BI
organization fully exploits data at every phase of the BI architecture as it progresses
through various levels of informational metamorphosis.
Data is first collected including metadata, such as the creator or creating system, the
time of creation, the channel on which it was delivered, sentiment contained in plain
text, and so on. According to Olszak & Ziemba (2006) metadata facilitate the process
of extracting, transforming and loading data through presenting sources of data in the
layout of data warehouses. Metadata are also used to automate summary data creation
and queries management
For data to be used, it is important to ensure it is clean. Venter & Tustin (2009)
depicts that the purpose of a data warehouse is to provide rich, timely, clean and well-
structured information to BI analysis tools. Once that is done, the organization can
take advantage of the vast amounts of information, give it to users in a way they can
understand. Deliver predictive scores to the customer service representatives, so they
know which offers are most likely to result in a positive outcome. Provide
sophisticated visualization tools to analysts who can see patterns in millions of data
points. Deliver a dashboard to the Vice President (VP) of marketing with social media
sentiment scores about that new product.
2.6 Strategic Value and Benefits of Business Intelligence
English (2005) ascertains that the essential element of BI is the understanding of what
is happening within an organization and its business environment, as well as
appropriate action-taking for achieving organizational goals. From this, derives the
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importance of the human factor within BI. There is no such thing as business
intelligence without the people to interpret the meaning and significance of
information and to act on their knowledge gained (English, 2005). This is also
consistent with the findings from Finnish research (Hannula & Pirttimäki, 2003)
where around 75% of interviewees felt content and humane approaches are the key
aspects in success application of BI. BI provides employees with information to make
better business decisions, and can be used in environments ranging from workgroups
of 20 users to enterprise deployments exceeding 20,000 users.
In an extranet environment, BI is deployed in applications that allow organizations to
deliver new services and build stronger relationships with customers, partners, and
suppliers via the internet. Hence, English (2005) defines BI as “the ability of an
enterprise to act effectively through the exploitation of its human and information
resources.” Technology is the component that adds to quality information with which
business users can analyze business operations: what has happened, what is
happening, and what will happen in the future.
In enterprise performance management (EPM), organizations must understand and
have constant visibility into their key performance indicators and metrics that span
across their organizations. By doing this, organizations ensure their strategy is aligned
from top to bottom and across the organization from marketing to sales to
manufacturing to human resources. Providing this enterprise insight is a key strength
of BI. With business intelligence, users are able to turn this information into
knowledge, and knowledge into profit. BI enables the organization to track,
understand, and manage your business in order to maximize enterprise performance.
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With BI, organizations are able to improve operational efficiency, build profitable
customer relationships, and develop differentiated product offerings.
As Jakli? & Popovi? (2009) state, various recent international studies show a high
level of awareness by professionals about the potential benefits of business
intelligence in their business operations. For the fourth consecutive year, business
intelligence remains a top IT priority of major international companies, while
improved efficiency and operational performance are a key business priority for the
fifth year in a row (Jakli? & Popovi? 2009). Many companies have positioned
business intelligence and business performance management (‘BPM’) as their top
strategic priority for 2009 and 2010.
Strategic value can be measured by various aspects including increased turnover, an
improvement of customer satisfaction as a consequence of the faster response times to
their requests and expectations, a cost reduction due to time saving and reduced work
tasks, an expansion of market share due to the possibility of the transparent
monitoring of sales volumes, structures and trends, as well as the easier detection of
areas with poor sales, deviations from past trends, an increase in profit due to better
support for decision-making and due to time-saving, and faster decision-making
which may be critical to the survival of the company in a strong competitive
environment.
According to Porter (1990) Strategic value is about competitive pricing, cost, product
or market differentiation. Thus this research will focus on ways in which Equity Bank
uses Business Intelligence (BI) system to facilitate decision making, respond to
customer issues, innovate and improve quality of products and services.
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Cui et al (2007) view BI as way and method of improving business performance by
providing powerful assists for executive decision maker to enable them to have
actionable information at hand. BI tools are seen as technology that enables the
efficiency of business operation by providing an increased value to the enterprise
information and hence the way this information is utilized.
Tvrdíková (2007) describes the basic characteristic of BI tool as the ability to collect
data from heterogeneous source, to possess advance analytical methods, and the
ability to support multi users’ demands. Zeng et al. (2006) categorized BI technology
based on the method of information delivery; reporting, statistical analysis, ad-hoc
analysis and predicative analysis.
The concept of Business Intelligence (BI) was brought up by Gartner Group since
1996. It is defined as the application of a set of methodologies and technologies, such
as J2EE, DOTNET, Web Services, XML, data warehouse, OLAP, Data Mining,
representation technologies, etc, to improve enterprise operation effectiveness,
support management/decision to achieve competitive advantages. Business
Intelligence by today is never a new technology instead of an integrated solution for
companies, within which the business requirement is definitely the key factor that
drives technology innovation. How to identify and creatively address key business
issues is therefore always the major challenge of a BI application to achieve real
business impact. (Golfarelli et.al, 2004) defined BI that includes effective data
warehouse and also a reactive component capable of monitoring the time-critical
operational processes to allow tactical and operational decision-makers to tune their
actions according to the company strategy.
18
Gangadharan and Swamy (2004) widen the definition of BI as technically much
broader tools that include potentially encompassing knowledge management,
enterprise resource planning, decision support systems and data mining. BI includes
several software for Extraction, Transformation and Loading (ETL), data
warehousing, database query and reporting, (Berson et.al, 2002; Curt Hall, 1999)
multidimensional/on-line analytical processing (OLAP) data analysis, data mining
and visualization.
Banks must manage large volumes of data in the repositories; this data comes from
many sources, including a diverse customer base, extensive branch networks, and
shareholders. Banks needs to carry out an analysis to chart way for future action. To
derive real business value from this data, the right tools are needed to capture and
organize a wide variety of data types from different sources, and to be able to easily
analyse it within the context of all enterprise data (Dijicks, 2012). The tool required
for this job is Business intelligence. Stackowiaket al (2007) defines Business
intelligence (BI) is the process of taking large amounts of data, analysing that data,
and presenting in a high level set of reports that condense the essence of that data into
the basis of business actions, enabling management to make fundamental daily
business decisions.
The benefits of business intelligence, along with information systems, in general, can
be divided into various categories (Carver & Ritacco, 2006). Measurable or
quantifiable benefits are those that can be clearly measured, for example, reducing the
time needed to carry out certain tasks, savings achieved by purchasing one software
solution instead of another, an increase in revenue and profit.
19
Indirectly quantifiable benefits are usually related to customer satisfaction.
Introducing new technology can improve customer service, which has a positive
impact on their satisfaction, resulting in larger sales volumes, the increased loyalty of
customers returning to purchase again, the winning of new customers. According to
(Olszak & Ziemba, 2006) Business Intelligence (BI) systems enable both descriptive
and predictive segmentation of customer based on grouping customers in homogenous
segments. Banks are therefore able to assess the needs of each profile easily.
Customer satisfaction is typically assessed by surveys, by monitoring the volume of
business, the re-order ratio as well as other, less formal ways for example by visits
and dialogue with customers.
Non-measurable benefits include a higher quality of work, the better motivation of
employees, the effects of IT on an improvement of communication in the
organization, higher quality knowledge sharing between employees. These intangibles
benefits are difficult, sometimes impossible to quantify (Gibson et al 2004). The main
problem in assessing these benefits is that they may only be assessed in a subjective
way, which does not provide reliable information about their real value.
Unpredictable benefits can, for example, be new solutions and the ideas of creative
individuals.
Most Business Intelligence (BI) benefits are intangible. An empirical study for 50
Finnish companies found most companies do not consider cost or time savings as
primary benefit when investing in BI systems (Hannula & Pirttimaki, 2003). The hope
is that a good BI system will lead to a big return at some time in the future, this
research seeks to relate BI with improved organization performance, smarter decision
making and success of innovative products.
20
Organizations that are interested to improve quality of decision-making, image or
quality of partner services should incline towards the development of information
technology infrastructure that will represent a holistic approach to business
operations, customers, suppliers (Wells & Hess, 2004). Theory and practice show that
the above-mentioned requirements are largely met by Business Intelligence (BI)
systems (Gray, 2003; Liautaud, & Hammond, (2002); Olszak, & Ziemba, (2004);
Turban, & Aronson, (1998). Decision making therefore is one of the biggest
advantages of having BI in an organization.
2.7 Challenges of Business Intelligence
According to Chuah & Wong, (2013) Business Intelligence (BI) applications have
appeared the top spending priority for many Chief Information Officers (CIO) and it
remain the most important technologies to be purchased for past five years (Gartner
Research 2007; 2008; 2009). Although there has been a growing interest in BI area,
success for implementing BI is still questionable (Ang & Teo 2000; Lupu et.al (1997);
Computerworld (2003)). Lupu et.al (1997) reported that about 60% - 70% of business
intelligence applications fail due to the technology, organizational, cultural and
infrastructure issues. Furthermore, EMC Corporation argued that many BI initiatives
have failed because tools were not accessible through to end users and the result of
not meeting the end users’ need effectively.
The first challenge facing BI system is the cost. BI has evolved and everybody has
some form of BI in place now, as it is becoming a fairly substantial cost item. The
overall cost of BI – the cost of technology, upkeep and implementation – is certainly
one of the challenges that implementers are facing.
21
The second challenge is the number of users. The number of business users now
tapping into BI is increasing dramatically, especially as we begin to move into
operational intelligence. We’re seeing more naïve users – not the traditional analysts
or data scientists – so it is not only the number of users but an increase in support for
these users from an implementation standpoint.
The third challenge is in the area of operational BI and the new sources of data
available. We are seeing a tremendous increase in the volumes of data (big data)
being analyzed and stored in data warehouses and experimental areas. This data is
used for complex advanced, embedded and streaming analytics. There are now very
interesting sets of data in BI, which is certainly different from the traditional, more
strategic or tactical forms of BI. This doesn’t diminish the need for traditional BI; it
just means we must expand our BI architectures to embrace these new areas.
These big challenges lead to the fourth, which is the performance and scalability of
the environment. Obviously, if we are starting to bring in operational people,
operational BI, streaming analytics, big data applications, etc., it means that the
performance has to be a major focus of the BI implementers – sub-second response
time for many operational intelligence queries while simultaneously supporting the
more strategic or long running queries as well. It’s a mixed workload environment,
and that can cause a performance issue. So our technology also has to scale up to
handle it. A terabyte used to seem like a lot of data, but not anymore.
Computerworld (2003) stated that BI projects collapse because of failure to recognize
BI projects as cross organizational business initiatives, unengaged business sponsors,
unavailable or unwilling business representatives, lack of skilled and available staff,
22
no business analysis activities, no appreciation of the impact of dirty data on business
profitability and no understanding of the necessity for and the use of meta-data.
In the banking industry data sources can be from operational databases, historical
data, and external data for example, from market research companies or from the
Internet, or information from the already existing data warehouse environment. The
data sources can be relational databases or any other data structure that supports the
line of business applications. Data can also reside on many different platforms and
can contain structured information, such as tables or spreadsheets, or unstructured
information, such as plaintext files or pictures and other multimedia information. Big
data refers to large datasets that are challenging to store, search, share, visualize, and
analyze (Dijicks, 2012)
Banks are challenged by big data and require them to be proactive in managing and
utilizing corporate it if they want to keep up with or stay ahead of the competition.
Business intelligence (BI) gives enterprises the capability to analyze the vast amounts
of information they already have to make the best business decisions. Banks are able
to tap into their huge databases and deliver easy-to-comprehend insight to improve
business performance and maintain regulatory compliance (Nemati, 2005). The
applications of business intelligence in the banking are therefore far-reaching.
While the Business Intelligence (BI) solution typically contains the necessary data
that are required for identifying opportunities for improvement, significant effort is
often required to get to these insights. Often, the level of effort required to find
valuable data points exceed the cost of finding it. Moldovan (2011) studied the
23
financial industry and found that mining financial data presents some challenges,
difficulties and sources of confusion, especially when determining short term trends
and validating them.
Business Intelligence (BI) solutions require data from many different, and often
disparate, data sources. The unique aspects of each organization require significant
time and effort to get them up and running. At the end of the day, there is
considerable effort required to stand up and run these solutions. The most common
challenge companies are facing in the current competitive business environment is
management of its own data (Ponomarjovs, 2013). Once insight has been gained from
the Business Intelligence (BI) solution, there is no clear path to action, and often no
link to the underlying detailed data. Acting on the findings is limited, and is especially
challenging from the BI solution itself.
2.8 Summary
The empirical review above indicates that strategic value of business intelligence
determine the performance of commercial banks both in in improving their
competitiveness and handling customers issues and innovation. Both Dijicks (2012)
and (Ponomarjovs, 2013) indicated that its challenging for banks to manage the data.
This data according to (Moldovan, 2011) may cause confusion and difficulties.
However (Olszak and Ziemba, 2006) Business Intelligence (BI) enables organizations
to analyze and get insights from this data. Most studies on this subject were done in
different regions, on different Business intelligence systems with scanty studies done
in developing countries and particularly in Kenya. Kangogo (2013) indicates that
dynamism of the banking environment is posing a lot of challenges to all banks .BI
24
helps in anticipating future behavior and predicting most business indicators
(Ubiparipovi? & ?urkovi?, 2011). There is therefore a gap in literature in regard to
strategic value of Business Intelligence in commercial banks in Kenya. The current
study sought to bridge this gap by focusing on Equity Bank Kenya.
25
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Introduction
This chapter describes the research design and sampling design used in the survey and
also highlights on the population of study, data collection methods as well as data
analysis and presentation methods to be employed.
3.2 Research Design
This research employed descriptive survey method that helped in gathering
information about the strategic value of business intelligence system in Equity Bank.
This design was preferred because the study was concerned with answering questions
such as who, how, what, which, when and how much (Cooper & Schindler, 2003).
3.3 Target Population
The target population for the survey was 500 members of staff from various
departments in Equity Bank Head Office. These were the main users of Business
Intelligence (BI) systems at Equity Bank.
3.4 Sampling Design
Stratified random sampling was employed to identify respondents from the various
departments. Each stratum was composed of a department and levels within each of
these departments, employees were selected at random from the strategic, tactical and
operational levels. The sample size was drawn from the target population based on the
principle of 10% rule according to Mugenda and Mugenda (2003).
26
3.5 Data Collection
The data required for the study was obtained from primary sources. The researcher
collected primary data using a questionnaire. The questions were both open ended and
closed ended to give respondents enough space to express their views on Business
Intelligence use. Questionnaire was issued as follows: 15 from the IT Department, 7
from the Finance, 5 from Customer Service, 6 from Credit, 6 from Treasury and 7
from Marketing departments.
The researcher mailed the questionnaire with a personalized message. Additionally,
an introductory letter explaining the purpose of the study was also attached. A follow
up call was made to some of the respondents.
3.6 Data Analysis
The data was analyzed through descriptive statistics, this enabled the researcher to
organize data in an effective and meaningful way. The data generated by the study
after fieldwork was edited, coded then entered into a computer for processing using
Microsoft excel and Google doc analytics. By use of percentages, frequency
distributions, tables, charts, the researcher categorized the variables.
27
CHAPTER FOUR
DATA ANALYSIS, RESULTS AND DISCUSSION
4.1 Introduction
This chapter describes the analysis of data followed by a discussion of the research
findings. The findings relate to the research questions that guided the study. Data
were analyzed to identify, describe and explore the Strategic Value of Business
Intelligence (BI) systems at Equity Bank, extent of use, its benefits and challenges.
4.2 Demographics
The demographic data consisted of department of respondent, position held in the
department, years of service in current role and level of education. A sample of 52
respondents from different departments was targeted as respondents for the study, out
of which 46 participated. The study therefore had 88.5% response rate.
4.2.1 Distribution of respondents by position
The researcher wanted to find out the positions the respondents were holding in the
company. The study findings in table 4.1 indicated that majority of the respondents
(28%) held the position of officers in the operational level. Only 22% of the
respondents were supervisors in the in the tactical level of management, 20% of the
respondents were managers in the tactical level, 17% of the respondents were senior
managers in the strategic level and lastly 13% of the respondents were general
managers in the strategic level. The results are as shown on Table 4.1.
28
Table 4.1: Distribution of Respondents by Position
position
Number of
Respondents
% of
Respondents
Level of
Management
General
Manager
6 13% Strategic level
Senior
Manager
8 17% Strategic level
Supervisor
10 22% Tactical level
Manager
9 20% Tactical level
Officer
13 28% Operational level
Total
46 100.00%
Source: Researcher (2014)
4.2.2 Distribution of Respondents by Department
The study sought to find out the departments in which the respondents were working.
The findings are as shown on Table 4.2.
Table 4.2: Distribution of Respondents by Department
Department Number of Respondents % of Respondents
Customer
service 5 11%
Credit 6 13%
Treasury 6 13%
Finance 7 15%
Marketing 7 15%
ICT 15 33%
Total 46 100%
Source: Researcher (2014)
29
The study findings in table 4.2 indicated ICT department who were the majority
respondents were that at 33% response rate , followed by 15% of the respondents who
were working in the Finance and marketing departments respectively. In addition 13%
of the respondents were in the credit department and treasury department respectively.
Lastly 11% of the respondents were working in the customer service department.
4.2.3 Years of Experience of the Respondents
The researcher wanted to find out the years of experience of the respondents. The
results are as shown on Table 4.3.
Table 4.3: Years of Experience of the Respondents
Years of
Experience
Number of
Respondents % of Respondents
<2 Years
17 37%
2-5 Years
18 39%
6-10 Years
8 17%
>10 Years
3 7%
Total
46 100%
Source: Researcher (2014)
The respondents were required to indicate their years of experience. The study
findings in table 4.3 indicated that majority of the respondents were between 2-5 years
of experience at 39%. Those with <2 years and 6-10 years followed with 37% and
17% respectively. Those with more than 10 years of experience are at 7%.
30
4.2.4 Level of Education of the Respondents
The researcher wanted to find out the level of education of the respondents. The
respondents were required to indicate their level of education. Data collected was
analyzed and is shown in Table 4.4.
Table 4.4: Level of Education of the Respondents
Level of formal of education
attained - OTHERS Number of Respondents
% of
Respondents
Certificate 4 9%
Degree 26 57%
Diploma 4 9%
Masters 12 26%
Total 46 100%
Source: Researcher (2014)
The study findings in table 4.4 Majority of the respondents have degrees and
postgraduate level education both at 57% and 26% respectively, whereas 9% have
diploma level of education and certificate level of education.
4.2.5 Level of Education - ICT
The researcher sought to find out the level of ICT education attained by the
respondents. Respondents were required to indicate their level of education in ICT.
Data collected was analyzed and is shown in Table 4.5.
31
Table 4.5: Education Level – ICT Related
Level of formal education attained
- ICT RELATED
Number of
Respondents
% of
Respondents
Basic IT Training 8 17%
BSc. Degree in IT related area 9 20%
Diploma in IT related area 9 20%
Hands on Training 5 11%
IT Professional Certification 13 28%
Master’s Degree in IT related area 2 4%
Total 46 100%
Source: Researcher (2014)
The study findings in table 4.5 indicates that majority of the respondents (28% ) had
attained IT professional certificate, followed by 20% of the respondents who attained
BSc. Degree in IT related area and Diploma in IT related area respectively. In
addition, 17% of the respondents had attained Basic IT Training, 11% had attained
Hands on Training and lastly 4% of the respondents had attained Master’s Degree in
IT related area.
4.3 Extent to which Business Intelligence is used In Equity Bank.
The study sought to establish the extent to which Business Intelligence (BI) system is
used as contribution to strategic value at Equity Bank. The study had sought to
establish the frequency of usage of the BI systems, tools used and whether the users
were trained and knowledgeable on the BI systems currently implemented at Equity
Bank.
32
Table 4.6: BI System use by Department
BI System
Number of
Respondents
% of
Respondents
Finnone System 6 13%
Siebel/Avaya System 12 26%
OBIEE System 7 15%
Thomson Reuters/Bloomberg
System 6 13%
Solarwinds System 15 33%
Total 46 100%
Source: Researcher (2014)
The study findings in table 4.6 indicates that majority of the respondents (33%) used
Solarwinds, 26% used Siebel/Avaya system, 15% used OBIEE and Thomson
Reuter/Bloomberg and Finnone systems each had 13%.
4.3.1 Use of Business Intelligence systems (BI) in decision making
The researcher wanted to find out if the respondents used BI systems in making
decisions. The results are as shown on Table 4.7.
33
Table 4.7: Use of Business Intelligence Systems (BI) in Decision Making
BI system Department
Do you normally use
BI System reports
when making
decisions?
Number of
Respondents
% of
Respondents
Finnone System Credit Yes 6 13%
OBIEE System Finance Yes 7 15%
Siebel/Avaya
System
Customer
service Yes 5 11%
Marketing No 1 2%
Yes 6 13%
Solarwinds
System ICT No 1 2%
Yes 14 30%
Thomson
Reuters/Bloomb
erg System Treasury Yes 6 13%
Total
46 100%
Source: Researcher (2014)
The results in table 4.7 indicated that OBIEE was mainly used by Finance department,
15% of the respondents (represent all Finance department users) used BI reports for
decision making, Finonne system was mainly used by credit department representing
13% of which all respondents used BI reports to aid in decision making.
Reuters/Bloomberg system was mainly used by treasury department which constitute
13% of respondents.
The highest number of respondents – 32% used solarwinds system, this was mainly
ICT department. The only system used by more than one department was
Siebel/Avaya call system which was mainly used by Marketing and customer service
at 15% and 11% respectively. Only 4% of the respondents, 2% from Marketing and
2% from ICT who dint use BI reports for decision making.
34
4.3.2 Main areas of applications of Business Intelligence (BI)
The researcher wanted to find out the main areas of applications of business
intelligence. Respondents were requested to indicate key areas BI system was applied.
The results are as shown on Table 4.8.
Table 4.8: Main Areas of Applications of Business Intelligence
Key areas BI System is applied % of
Respondents
Finnone System 13%
Acquisition 2%
Customer & portfolio management, Bad debt management 2%
Customer & portfolio management, Bad debt management, Targeting &
prospecting, Acquisition
7%
Targeting & prospecting, Acquisition 2%
OBIEE System 15%
Drive innovation, Deploy business best practices , Make insights ,
Provide extreme performance
7%
Drive innovation, Make insights 4%
Drive innovation, Make insights , Provide extreme performance 2%
Make insights 2%
Siebel/Avaya System 26%
agent desktop analytics, agent quality and capability scorecards 2%
agent desktop analytics, agent quality and capability scorecards,
benchmarking of contact centre strategy and operations against industry
vertical, local geography and global best practice
4%
agent desktop analytics, agent quality and capability scorecards, speech
analytics
2%
agent desktop analytics, agent quality and capability scorecards, speech
analytics, post-call surveys, benchmarking of contact centre strategy and
operations against industry vertical, local geography and global best
practice
11%
35
Key areas BI System is applied % of
Respondents
agent desktop analytics, speech analytics 2%
agent desktop analytics, speech analytics, benchmarking of contact
centre strategy and operations against industry vertical, local geography
and global best practice
2%
speech analytics, post-call surveys 2%
Solarwinds System 33%
Database performance monitor 2%
Network performance monitor 2%
Network performance monitor, Database performance monitor 2%
Network performance monitor, Database performance monitor,
Security and compliance
2%
Network performance monitor, Optimize applications performance 2%
Network performance monitor, Optimize applications performance,
Database performance monitor, Security and compliance
17%
Optimize applications performance, Security and compliance 2%
Security and compliance 2%
Thomson Reuters/Bloomberg System 13%
Fx trading, Interest rates trading 11%
Interest rates trading 2%
Total 100%
Source: Researcher (2014)
4.3.3 Knowledge and Training of Business Intelligence (BI) System
Most of the respondents had good knowledge of the OBIEE system. 15% of Finance
department respondents had very good knowledge of the BI system. The research
shows that the user of OBIEE all had above average knowledge of BI. Only 2% of the
15% of the Finance department users had not received training of the BI system.
36
Most of the respondents had above average knowledge of the FINONNE system. All
the credit department respondents who were users of FINONNE had received
training.
Table 4.9: Knowledge and Training of Business Intelligence (BI) System
Knowledge of BI
system
Have you
ever received
training on
BI system
What BI system do you use? % of
Respondents
Average No OBIEE System 2%
Solarwinds System 4%
Yes Finnone System 4%
Solarwinds System 2%
Thomson Reuters/Bloomberg
System
2%
Good No Siebel/Avaya System 4%
Yes Finnone System 4%
OBIEE System 4%
Siebel/Avaya System 7%
Solarwinds System 15%
Thomson Reuters/Bloomberg
System
7%
Poor No Siebel/Avaya System 2%
Solarwinds System 2%
Very Good Yes Finnone System 4%
OBIEE System 9%
Siebel/Avaya System 13%
Solarwinds System 9%
Thomson Reuters/Bloomberg
System
4%
Total 100%
Source: Researcher (2014)
37
The study findings in table 4.9 indicated that Most of the respondents had above
average knowledge of the Reuters and Bloomberg system. All the treasury
department respondents who were users of Reuters/bloomberg had received training.
Most of the respondents had above average knowledge of the solarwinds system.
Only 2% were below average. 26% of the 33% of ICT respondents had received
training, othe remainder 7% hadnt had any training.
Not all the respondents had above average knowledge of the Siebel/avaya system. 2%
were below average. 20% of the 26% of marketing/customer service respondents had
received training, other remainder of 7% hadnt had any training.
4.4 Strategic Value of Business Intelligence (BI) Systems
The study had sought to establish the Strategic Value of Business Intelligent (BI)
systems in the organization. The study also sought to find out whether Business
Intelligent (BI) systems facilitated decision making, improving quality of services and
encouraging innovations. These were the key measures of strategic value of Business
Intelligent (BI) systems. The results were as per below.
4.4.1 Sources of Information in Predicting Customers Issues,
Improve Quality of Goods/Services and Innovation
The reports from OBIEE were very useful in predicting customer preferences and
like. The credit department mainly used finnone to forecast probability of a loan
repayment and monitor usage behavior. The main areas solar winds was used for
decision making were speed foresic investigation and route cause analysis and also
dashboard alerting and reporting , this was 28% of the respondents each. The main
38
areas siebel/avaya was used was analysing call drivers,and understanding market
threats and opportunities by mining customer interractions , this wa 24% of the
respondents and 22% respectiely.
Spotting growth opportunities,monitoring market developments and reseach tied at
11% as areas reuters/bloomberg assisted in making decisions.
Table 4.10: Source of Information in Predicting Customers Issues, Improve
Quality of Goods/Services and Innovation
Source of information is used by Equity Bank to respond
to customer issues and improve the quality of
products/services
% of
Respondents
Finnone System 13%
Analysis from Business Intelligence (BI) system 2%
Analysis from Business Intelligence (BI) system, Internet
searches
2%
Analysis from Business Intelligence (BI) system, Internet
searches, Periodic reports from CBK
2%
Internet searches 2%
Media reports and articles, Analysis from Business
Intelligence (BI) system
4%
OBIEE System 15%
Analysis from Business Intelligence (BI) system 2%
Analysis from Business Intelligence (BI) system, Internet
searches
2%
Analysis from Business Intelligence (BI) system, Internet
searches, Periodic reports from CBK
2%
Analysis from Business Intelligence (BI) system, Periodic
reports from CBK
4%
Media reports and articles, Analysis from Business
Intelligence (BI) system
2%
Media reports and articles, Analysis from Business
Intelligence (BI) system, Periodic reports from CBK
2%
Siebel/Avaya System 26%
Analysis from Business Intelligence (BI) system 9%
Analysis from Business Intelligence (BI) system, Internet
searches
7%
39
Source of information is used by Equity Bank to respond
to customer issues and improve the quality of
products/services
% of
Respondents
Analysis from Business Intelligence (BI) system, Periodic
reports from CBK
4%
Media reports and articles, Analysis from Business
Intelligence (BI) system
4%
Periodic reports from CBK 2%
Solarwinds System 33%
Analysis from Business Intelligence (BI) system 11%
Analysis from Business Intelligence (BI) system, Internet
searches
13%
Analysis from Business Intelligence (BI) system, Periodic
reports from CBK
4%
Media reports and articles, Analysis from Business
Intelligence (BI) system, Internet searches, Periodic reports
from CBK
2%
Media reports and articles, Internet searches, Periodic
reports from CBK
2%
Thomson Reuters/Bloomberg System 13%
Analysis from Business Intelligence (BI) system 2%
Analysis from Business Intelligence (BI) system, Internet
searches
4%
Analysis from Business Intelligence (BI) system, Internet
searches, Periodic reports from CBK
2%
Media reports and articles, Analysis from Business
Intelligence (BI) system
4%
Total 100%
Source: Researcher (2014)
4.4.2 Business Intelligence (BI) System Rating in Improving Quality
of Decision Made
The study sought to explore whether business intelligence (BI) system was used to
improve quality of decision making. The results are as shown on Table 4.11.
40
Table 4.11: Business Intelligence system rating in improving quality of decision
making
BI system used Rating of BI system in
Improving Quality of
decisions made
% of
respondents
Finnone System Average 4%
Good 4%
Very Good 4%
OBIEE System Average 2%
Good 2%
Very Good 11%
Siebel/Avaya System Average 2%
Good 13%
Very Good 11%
Solarwinds System Average 4%
Good 11%
Poor 2%
Very Good 15%
Thomson
Reuters/Bloomberg
System
Good 7%
Very Good 7%
Total 100%
Source: Researcher (2014)
The study findings in table 4.11 indicated that all users of OBIEE who represent 15%
of total respondents used OBIEE. 13% of them of the respondents used OBIEE to
facilitate decision making. OBIEE was rated as very good in improving the quality of
decisions made.
In developing of new products/services and solving customer issues, all the users of
OBIEE used analysis from BI system highly compared to other sources. All users of
41
Finonne representing 13% of respondents used reports from Finnone when making
decisions. Finonne was rated as above average in improving the quality of decisions
made. 11% of them of the respondents used Finnone information to quickly to
respond to customer issues and improve quality of their services.
All users of reuters/bloomberg representing 13% of respondents used reports from
reuters/bloomberg when making decisions. Only 7% of them of the respondents rated
Reuters/Bloomberg both as good and very good in improving quality of decisions
made. A total of 13% of them of the respondents used Reuters/Bloomberg
information to quickly to respond to customer issues and improve quality of their
services.
Out of 33% of ICT department respondents, 30% used solarwinds when making
decisions. Only 15% and 11% of the respondents rated solarwinds both as very good
and good respectively in improving quality of decisions made. A total of 30% of the
respondents used solarwinds information to quickly to respond to customer issues and
improve quality of their services. Out of 26% of marketing and customer service
department respondents, 24% used Siebel/avaya when making decisions, 11% and
13% of the respondents rated Siebel/avaya call both as very good and good
respectively in improving quality of decisions made. Only 30% of the respondents
used Siebel/avaya information to quickly to respond to customer issues and improve
quality of their services. All the systems were equally used in each department.
4.5 Challenges of Business Intelligence (BI) systems
The main challenges were downtime at 43%, trainings needs at 26% and lack of
technical support at 30%. This is in line with Lupu et.al (1997) who reported that
42
about 60% - 70% of business intelligence applications fail due to the technology,
organizational, cultural and infrastructure issues.
Table 4.12: Challenges of Business Intelligence (BI) systems
Challenge Number of Respondent %
Downtime 20 43%
Training Needs 12 26%
Lack of technical support 14 30%
46 100%
Source: Researcher (2014)
43
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Introduction
This chapter presents the summary of the findings from chapter four, and also gives
conclusions and recommendations of the study based on the objectives of the study.
The researcher evaluates the findings and gives recommendations necessary on the
strategic value Business Intelligence (BI) systems at Equity bank.
5.2 Summary of Findings
The summary of findings for this study are divided into demographics information of
the respondents, strategic value of Business Intelligence (BI) systems ,extent of use of
BI systems and challenges of using them.
A total of 46 respondents out of a targeted 52 participated in the survey. This was an
88% response rate. Respondents were drawn from all levels of management. Most of
the respondents, 30% were from strategic level this included General Managers and
Senior Managers. A total of 41 % were from tactical level, this included Managers
and supervisors and 28% operational level. A total of 63% of the respondents had
worked in their current position for more than two years.
The respondents were drawn from different departments as follows 33% from the IT
Department, 15% from the Finance, 11% from Customer Service, 13% from Credit,
13% from Treasury and 15% from Marketing departments. Only 57% of the
respondents had a degree in other area other than ICT, 26% had masters. All the
respondents had at least hands on or basic IT training. The main BI systems used by
44
Equity Bank were as follows; 15% OBIEE, 13% Finonne, 13% Thomson
Reuters/Bloomberg, 33% were solarwinds and 26% was Siebel/avaya call. It is
important to note than different departments used the system specific to their area.
The study sought to determine to what extent Business Intelligence (BI) systems were
being used at Equity Bank. The study found that the BI systems were mainly used for
decision making, 96% of the respondents used BI system when arriving at a decision.
All the departments sampled used a BI system custom made to their operations.
OBIEE was used in Finance, Finnone in credit, Thomson Reuters/Bloomberg in
treasury, solarwinds in ICT and Siebel/avaya call in marketing and customer service
departments.
Thomson reuters /Bloomberg were mainly used to make decisions on forex and
interest rates trading, spot growth opportunities and monitor market developments,
OBIEE provided Finance department drive innovation and make business insights.
Finonne was mainly used by credit department for credit scoring, loan portfolio
management and acquisition targeting.
Both marketing and customer service used Siebel/avaya call was used to drive
intelligent customer interactions with unified view of their customer relationships
across the bank, effectively anticipate customer needs, improve customer retention
and identify opportunities to cross-sell and up-sell. Solarwinds was mainly used in
ICT for real-time predictive intelligence, real-time tactical support to drive enterprise
actions that react immediately to events as they occur Obtain broader compliance
support, stronger security intelligence, and a faster time-to-respond duration with
embedded file integrity monitoring and active response.
45
The finding of the study shows Equity Bank gained strategic value by using Business
Intelligence (BI) systems. Users mainly gained strategic value by using the BI
systems for decision making, predict customer’s preferences and likes, respond to
customer issues and drive innovation.
According to Ubiparipovi? and ?urkovi? (2011) BI systems enable banks to
anticipate future behaviour of the system and most of their business indicators, all
systems in the study could predict outcomes .OBIEE was able to manage and exploit
the information potential of multitude of internal and external data from sales,
demographics, economic trends, competitive data, consumer behavior, efficiency
measures, financial calculations. Siebel/avaya call system users were able to build
stronger relationships with customers, partners, and suppliers. According to (Olszak
& Ziemba, 2006) Business Intelligence (BI) systems enable both descriptive and
predictive segmentation of customer based on grouping customers in homogenous
segments; this was the case for Finonne which was used for credit scoring.
5.3 Conclusion
The findings show that Business Intelligence (BI) systems add strategic value at
Equity Bank. The finding of the study also shows that Equity Bank through BI
systems is able to improve service delivery by properly managing the organization
information and using it in improving decisions made by the organization. Therefore
BI systems have strategy value given that it has improved decision making, managing
customer’s issues and innovation.
The study indicates that Business Intelligence (BI) systems made contribution to
value networks and not merely financial benefits, but also knowledge, among other
46
benefits. The study confirmed that BI systems are important investment that
institutions need to consider to remain competitive. It is however important to ensure
that institutions that choose to invest in the BI systems consider the challenges
involved.
The study also revealed training and education as core to the operation of Business
Intelligence (BI) systems and for that reason the study concluded that adequate and
relevant training of staff is necessary in running of the BI systems. To address the
challenges arising from use of BI systems the study concludes that it is important to
consider system integration; it is important to ensure the adopted system is compatible
with the existing system and software. Finally the study concludes that it is important to
have correct budgetary allocations to overcome the challenges of cost overrun.
5.4 Recommendations for Policy and Practice
The study recommended that there should be more awareness on the use and Business
Intelligence (BI) systems in Equity bank. As much as extent of use is concerned, there
is need for the Equity Bank to explore what other ways it can leverage more on these
systems. There is also need for the organization to train its staff in the best use of
Business Intelligence systems in order to ensure that there is proper use for maximum
benefit of the concept and to benefit more on the value that the organization may get
from Business Intelligence.
Equity Bank needs to evaluate whether there has been proper investment in human
resource and systems as an integral part of the different resources that exist in the
organization and on whether the organization is maximizing on the benefits that are
47
associated with the use of BI systems as a strategic plan meant to add value to the
operations of the organization
5.5 Limitations of the Study
The study was a case study and was based on only one Bank; the findings of the study
may therefore not be fully applicable to other banks and or organizations as they may
have different experiences even under the same circumstances. Different banks or
organizations may view the strategic value of BI differently and therefore may not
approach it in the same way. The study was also online based, most users had to
inquire how to go about it, this might have limited the amount of information the
respondents gave.
5.6 Suggestions for Further Study
The researcher recommends further research on cost benefit analysis of Business
Intelligence (BI) systems since the study focused on the strategy value of BI approach
only. The researcher also recommends further research in the area of unutilized
modules and resource of BI. This also came up during the interviews hence the need
for an in-depth study of the same.
48
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52
APPENDICES
APPENDIX I: LETTER OF INTRODUCTION
TO WHOM IT MAY CONCERN
Dear Sir/Madam:
REQUEST FOR COLLECTION OF DATA
My name is Kamara Daniel M, a post-graduate student at the school of business,
University of Nairobi. I am conducting a research study titled “strategic value of
business intelligence systems”.
You have been selected to form part of this study. Kindly assist by filling in the
attached questionnaire. The information given will be treated in strict confidence and
will be purely used for academic purposes.
Your assistance and cooperation will be highly appreciated.
Yours Sincerely,
________________________
Kamara Daniel M (Student)
D61/67494/2011
53
APPENDIX II: QUESTIONNAIRE
THE STRATEGIC VALUE OF BUSINESS INTELLIGENCE SYSTEMS
This questionnaire is designed to collect information on the strategic value of business
intelligence systems in Kenya.
The information obtained will be used for academic purposes only. Confidentiality of
the information and the respondents will be highly observed.
This questionnaire will be completed by: 15 managers from the IT Department, 7
from the Finance, 7 from Customer Service, 8 from Credit, 6 from Treasury and 9
from Marketing departments.
Instructions:
1. Kindly respond to the following questions by placing a tick in front of the
most appropriate response.
2. Where explanations are required, use the space below the items.
3. Kindly answer all questions.
SECTION A: PERSONAL INFORMATION
1. What’s your role in the company and under which department are you in
ROLE DEPARTMENT
Director /CFO/CIO ICT
General Manager Finance
Senior Manager Treasury
Manager Credit
Supervisor Marketing
Officer Customer service
54
Others (please specify)
___________________ ________________
2. How long have you been working in the current position?
<2 Years 2-5Years 6-10 Years >10 Years
3. What’s your highest level of formal of education attained
ICT RELATED OTHERS
Master’s Degree in IT related area PHD
Diploma in IT related area Masters
IT Professional Certification Degree
Basic IT Training Diploma
Hands on Training Certificate
BSc. Degree in IT related area
Other_______________________ Other___________________
SECTION B: EXTENT TO WHICH BUSINESS INTELLIGENCE IS USED IN
EQUITY BANK.
4. What BI system do you use?
OBIEE
Finnone
Thomson Reuters
Bloomberg
Solarwinds
Siebel/Avaya
5. How often do you use Business Intelligence (BI) system?
Rarely
Always
Never
6. Please list areas you use Business Intelligence (BI) system for?
55
7. Please list features of Business Intelligence (BI) system that you have ever
interacted with in your current role?
8. What features do you feel if added to your Business Intelligence, (BI) system
will enable you to make better use of the system?
__________________________________________________________
9. Do you retrieve or receive any reports from the Business Intelligence (BI)
system?
Yes No
10. Have you ever received training on how to use the features provided by the
Business Intelligence (BI) system currently implemented by Equity Bank.
Yes No
11. How do you rate your knowledge of Business Intelligence (BI) system used by
Equity Bank
Very Good
Good
Average
Poor
Very Poor
SECTION C: WAYS IN WHICH EQUITY BANK USES BI SYSTEM TO
GAIN STRATEGIC VALUE.
12. Please list Business Intelligence (BI) system tools that you use to help you in
decision making?
13. Do you normally use reports provided by Business Intelligence (BI) system
when making decisions?
56
Yes No
14. Select at least one way that clearly describes how you arrive at decisions at
Equity Bank?
Personal Gut (individual feeling)
Peers group discussions
Using Highly Analyzed reports from BI system
Reports from Application system such ERP, CRM or HRM systems
Other:________
15. How would you rate the use of Business Intelligence (BI) system in helping
you improve the quality of the decision made on day to day basis?
Very Good
Good
Average
Poor
Very Poor
16. What source of information is used by Equity Bank to respond to customer
issues and improve the quality of the quality of their services: tick at least one
Media reports and articles
Analysis from Business Intelligence (BI) system
Internet searches
Periodic reports from CBK
Other: ________
17. How do you rate reports from Business Inte1ligence (BI) System in helping
predict customer preferences and like?
Very useful
Useful
Average
57
Not useful at all
I don’t use such analysis reports
18. From your opinion what else should done to help realize strategic value of
Business Intelligence System at Equity Bank (Be as detailed as possible)?
___________________________________________________________
SECTION D: CHALLENGES AND BENEFITS OF BUSINESS
INTELLIGENCE SYSTEM
19. What are the major benefits of using Business intelligence system?
___________________________________________________________
20. What challenges do you face when using Business Intelligence system?
___________________________________________________________
doc_478483902.pdf
Strategic Value Of Business Intelligence Systems, A Case Study Of Equity Bank Limited
STRATEGIC VALUE OF BUSINESS INTELLIGENCE SYSTEMS,
A CASE STUDY OF EQUITY BANK LIMITED
KAMARA DANIEL M
A RESEARCH PROJECT SUBMITTED IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS OF MASTER OF
BUSINESS ADMINISTRATION DEGREE, SCHOOL OF
BUSINESS, UNIVERSITY OF NAIROBI
NOVEMBER, 2014
ii
DECLARATION
This project is my original work and has not been presented for the award of a degree
in this or any other university.
Signed: _________________________ Date: _______________________
Kamara Daniel M.
REG. NO: D61/67494/2011
This project has been submitted for examination with my approval as University
supervisor.
Signed: _________________________ Date: ________________________
Mr. James T. Kariuki
Supervisor, School of Business
Department of Management Science
University of Nairobi
iii
ACKNOWLEDGEMENTS
I am most grateful to Mr. James T. Kariuki, my project supervisor, for his guidance
and assistance. The suggestions and criticisms at various stages of this work made it
possible for me to see it through. I will not forget my other lecturers who always
encouraged me to work hard. I would like to express my sincere appreciation to my
colleagues for their words of encouragement as we struggled through the semester.
Lastly but most important, God bless you all.
iv
DEDICATION
I dedicate this work to entire banking fraternity, my family members for great support
and encouragement.
May God Bless you all.
v
ABSTRACT
The intent of any business intelligence System is simply to provide a system for
developing or improving processes through a structured approach, effective
deployment and better control. The objectives of this study were to identify the
strategic value of Business Intelligence (BI) system, the extent of use of BI system
and the challenges in the use of BI at Equity Bank. The study adopted a descriptive
design. The target population for the survey was 500 members of staff from various
departments in Equity Bank Head Office. Using a sample of 50 employees, the study
had 88.5% response rate. The primary data for the study was collected using a
structured questionnaire. Questionnaires collected were edited, coded and data entered
into Microsoft excel and Google Docs analytics which were used to analyse the data.
The study established that Equity bank gains strategic value from BI systems through
provision of information that facilitate handling of customer issues, predict their likes
and preferences, improve decision making and come up with innovative products and
services. On the usage of BI, the study found variation in the use and tools used. The
study found that management should provide full support of the business intelligence
system by ensuring that all the required resources are availed for the sustainability of
the business intelligence system. New employees should be inducted into the business
intelligence system in order to understand how to use the system and its benefits. All
employees should also be trained on BI and be incorporated into the team that is in
direct touch with the business intelligence systems. The researcher recommended
further research on cost benefit analysis of Business Intelligence (BI) systems since
the study focused on the strategy value of BI approach only.
vi
TABLE OF CONTENTS
DECLARATION ........................................................................................................ ii
ACKNOWLEDGEMENTS ...................................................................................... iii
DEDICATION ............................................................................................................ iv
ABSTRACT.................................................................................................................. v
TABLE OF CONTENTS ........................................................................................... vi
LIST OF TABLES ...................................................................................................... ix
ABBREVIATIONS ...................................................................................................... x
CHAPTER ONE: INTRODUCTION ........................................................................ 1
1.1 Background of the Study ...................................................................................... 1
1.1.1 Business Intelligence ..................................................................................... 2
1.1.2 Strategic Value of Business Intelligence ....................................................... 2
1.1.3 Equity Bank Limited ..................................................................................... 4
1.2 Statement of the Problem ..................................................................................... 7
1.3 Research Objectives ............................................................................................. 8
1.4 Value of study ...................................................................................................... 8
CHAPTER TWO: LITERATURE REVIEW......................................................... 10
2.1 Introduction ........................................................................................................ 10
2.2 Theoretical Orientation ....................................................................................... 10
2.3 The Origins of Business Intelligence Systems ................................................... 11
2.4 Components of Business Intelligence ................................................................ 12
2.5 Business Intelligence Process ............................................................................. 13
2.6 Strategic Value and Benefits of Business Intelligence ....................................... 14
2.7 Challenges of Business Intelligence ................................................................... 20
2.8 Summary ............................................................................................................ 23
vii
CHAPTER THREE: RESEARCH METHODOLOGY ........................................ 25
3.1 Introduction ........................................................................................................ 25
3.2 Research Design ................................................................................................. 25
3.3 Target Population ............................................................................................... 25
3.4 Sampling Design ................................................................................................ 25
3.5 Data Collection ................................................................................................... 26
3.6 Data Analysis ..................................................................................................... 26
CHAPTER FOUR: DATA ANALYSIS, RESULTS AND DISCUSSION ........... 27
4.1 Introduction ........................................................................................................ 27
4.2 Demographics ..................................................................................................... 27
4.2.1 Distribution of respondents by position ....................................................... 27
4.2.2 Distribution of Respondents by Department .............................................. 28
4.2.3 Years of Experience of the Respondents ..................................................... 29
4.2.4 Level of Education of the Respondents ....................................................... 30
4.2.5 Level of Education - ICT ............................................................................. 30
4.3 Extent to which Business Intelligence is used In Equity Bank. ......................... 31
4.3.1 Use of Business Intelligence systems (BI) in decision making ................... 32
4.3.2 Main areas of applications of Business Intelligence (BI) ............................ 34
4.3.3Knowledge and Training of Business Intelligence (BI) System .................. 35
4.4 Strategic Value of Business Intelligence (BI) Systems ...................................... 37
4.4.1 Sources of Information in Predicting Customers Issues, Improve Quality of
Goods/Services and Innovation ............................................................................ 37
4.4.2 Business Intelligence (BI) System Rating in Improving Quality of Decision
Made ..................................................................................................................... 39
4.5 Challenges of Business Intelligence (BI) systems ............................................. 41
viii
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS ... 43
5.1 Introduction ........................................................................................................ 43
5.2 Summary of Findings ......................................................................................... 43
5.3 Conclusion .......................................................................................................... 45
5.4 Recommendations for Policy and Practice ......................................................... 46
5.5 Limitations of the Study ..................................................................................... 47
5.6 Suggestions for Further Study ............................................................................ 47
REFERENCES .......................................................................................................... 48
APPENDICES ............................................................................................................ 52
APPENDIX I: LETTER OF INTRODUCTION ..................................................... 52
APPENDIX II: QUESTIONNAIRE ........................................................................ 53
ix
LIST OF TABLES
Table 4.1: Distribution of Respondents by Position .................................................... 28
Table 4.2: Distribution of Respondents by Department .............................................. 28
Table 4.3: Years of Experience of the Respondents .................................................... 29
Table 4.4: Level of Education of the Respondents ...................................................... 30
Table 4.5: Education Level – ICT Related .................................................................. 31
Table 4.6: BI System use by Department .................................................................... 32
Table 4.7: Use of Business Intelligence Systems (BI) in Decision Making ............... 33
Table 4.8: Main Areas of Applications of Business Intelligence ................................ 34
Table 4.9: Knowledge and Training of Business Intelligence (BI) System ................ 36
Table 4.10: Source of Information in Predicting Customers Issues, Improve Quality of
Goods/Services and Innovation ................................................................................... 38
Table 4.11: Business Intelligence system rating in improving quality of decision
making.......................................................................................................................... 40
Table 4.12: Challenges of Business Intelligence (BI) systems .................................... 42
x
ABBREVIATIONS
BI - Business Intelligence
CBK - Central Bank of Kenya
CDR - Call Detail Record
CIO - Chief Information Officer
CRM - Consumer Relationship Management
DW - Data Warehouse
EDI - Electronic Data Interchange
ERP - Enterprise Resource Planning
ETL - Extract, Transform and Load
HRM - Human Resource Management
ICT - Information and Communication Technology
IT - Information Technology
MDM - Master Data Management
OLAP - Online Analytical Processing
SCM - Supply Chain Management
1
CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
Central Bank of Kenya has regulations that require banks to use computerized
information system, for internal or external application database validations to check
for any inconsistencies in the information provided particularly those containing
known fictitious application/ fraud information (CBK, 2013). An information system
(IS) is a formal network using computers to provide management information for
decision making with the main goal of providing the correct information to the
appropriate manager at the right time, in a useful form (Laudon & Laudon, 2009). An
information system (IS) can also generally be described as a collection of computer
hardware, software, people, procedures and communication devices used to capture
business data, process it and disseminate information for the purposes of decision
making within a business enterprise (University of Cape Town, 2014).
Developments in technology are changing the banking industry from paper, brick and
mortar banking, to digitized and networked banking services. Gachara (2012) found
that 83% commercial banks have increased new products over the recent past while a
majority 53% agrees that the electronic business processes have also increased. The
increased number of products and innovation is as a result of information systems
facilitation in doing business. Commercial banks needs a business Intelligence system
that can serve as an early-warning system for bank disruptive changes in the
competitive landscape from rival’s new products or pricing strategy or the entrance of
an unexpected player into the financial market.
2
1.1.1 Business Intelligence
Nemati (2005) defines Business Intelligence (BI) as a suite of tools and technologies
that enhance the decision making process by transforming data into valuable and
actionable knowledge to gain a competitive advantage. According to Hannula &
Pirttimaki (2003), BI can broadly be defined as an organized and systematic process
which is used to acquire, analyse and disseminate information which is significant to
their business activities. BI is also defined as a set of technologies that gather and
analyse data to improve decision-making Herschel et al, (2005). Several
characteristics of BI emerge from these definitions, that is, it refers to both internal
and external information gathering, analysis and dissemination of valuable
information for decision making.
According to Olszak and Ziemba (2006) beneficiaries of Business Intelligence (BI)
systems include a wide group of user such as insurance companies, oil and mining
industry, security systems, banks and supermarkets. Banks are amongst the most
common sectors that use BI systems, BI systems also assist in determining the
profitability of individual customers who are current and long term. This provide the
basis for high profit sales and relationship banking, thus maximizing sales to high
value customers, reducing costs to low value customers. This provides a means to
maximise profitability of new innovative products and services therefore promoting
value creation in banks.
1.1.2 Strategic Value of Business Intelligence
Business intelligence (BI) empowers organizations with business insights that lead to
better, faster, more relevant decisions. This ensures the right information is gotten at
3
the right time, and in the right format. According to Ubiparipovi? and ?urkovi?
(2011) BI systems enable banks to anticipate future behaviour of the customers and
most of their business indicators. They also enable modelling client behaviour not
only in terms of using new services but also from the perspective of potential risks.
Some of the notable areas where BI is applied in banks are analytical customer
relationship management, bank performance management, enterprise risk
management, asset and liability management and compliance.
Commercial banks in developing countries offer financial services through relying on
information gathered to provide superior value for the banks customers and improve
their satisfaction. According to (Porter, 1998) Technology intelligence exerts a
significant influence on the ability to innovate and is viewed both as a major source of
competitive advantage and of new product innovation. This strategy enables the banks
to provide considerable insulation from competition. It also forms a basis of
measuring the strategic value.
Organisations put in place a set of activities, methods, best practices, policies, and
automated tools that stakeholders use to develop and continuously improve
information systems and software. Business Intelligence (BI) maturity model
describes the stages that most organizations follow when evolving their BI
infrastructure from a low value, cost-centre operation, to a high value, strategic
function that drives market share. Examples of maturity models are Gartner’s
Maturity Model for Business Intelligence (BI) and Performance Management (PM)
which recognizes five levels of maturity: unaware, tactical, focused, strategic, and
pervasive. It is used for the assessment of the input effort, BI and PM maturity .The
other maturity model is AMR Research's Business Intelligence/Performance
4
Management Maturity Model, the key characteristics of this model are reacting,
anticipating, collaborating and orchestrating (Rajteric, 2010).
Some models focus on the technical aspect and others on the business point of view.
Business Intelligence (BI) models help in identifying the existing problems of BI
implementation and provide symmetric guidelines (Chuah et al, 2013). Most
Information Technology (IT) driven BI and data warehousing initiatives tend to focus
on the technical aspects, therefore the technical challenges and trade-offs are at least
well understood ,attention now shifts towards the ways in which BI can be used to
deliver business value McIntyre (2009).
Whilst Business Intelligence (BI) remains one of the top technology issues for Chief
Information Officers (CIOs), little research has been done regarding the actual
business value realized as a result of BI investment (Negash & Gray, 2003). Apart
from operational and efficiency benefits, IT can offer payback on a strategic level,
making the prospect of clearly identifying the benefits an even more difficult
challenge (Gibson et al 2004). The strategic value of BI solutions is depicted by the
ability to manage and exploit the information potential of multitude of internal and
external data from sales, demographics, economic trends, competitive data, consumer
behavior, efficiency measures, financial calculations, and more (Ubiparipovi? &
?urkovi?, 2011).
1.1.3 Equity Bank Limited
The banking industry in Kenya is governed by the Companies Act, banking Act and
various prudential guidelines issued by the Central Bank of Kenya (CBK, 2014). The
industry has grown in double digits percentages over the last 5 years (CBK, 2013).
5
The Kenyan Banking Sector recorded improved performance with the size of net
assets standing at Ksh. 2.97 trillion, loans & advances worth Ksh. 1.78 trillion, while
the deposit base was Ksh. 2.15 trillion and profit before tax of Ksh. 71.03 billion as at
30
th
June 2014. However in spite of this rapid growth in the banking sector,
competition has been a major concern making it more and more challenging for banks
to keep up with the changes and with the competition . This calls for change of tactics
by the industry.
Central Bank of Kenya (CBK) revised risk management guidelines for institutions
licensed under the banking Act. CBK has been regulating bank under Base I Capital
adequacy accord and though the sector has not fully adopted Base II, it is encouraging
to note that the new guidelines are features of Base III measures in capital adequacy
requirements (Think Business, 2013). The changes in regulatory frame and increased
competition led to banking industry in Kenya being shaky considering the major
failures that occurred between 1998 and 2005. Banks need to use all the tools at their
disposal to manage the many industry challenges and ensure their own financial
stability through intelligent business solutions (Microstrategy, 2008). One of the tools
at their disposal is Business Intelligence system.
Equity Bank started as a building society on registration in 1984 and converted to a
commercial bank 2004. With over 8 million accounts, accounting for over 50% of all
bank accounts in Kenya, Equity Bank is the largest bank in Africa in terms of
customer base and operates in Kenya, Uganda, South Sudan, Rwanda and Tanzania.
Its model of growth which includes rural banking orientation and promotion of
agribusiness is a significant and strategic intervention and contribution by Equity
Bank in Kenyans economic growth.
6
Equity bank is one of the commercial banks in Kenya, as of 2013 it had over 6000
employees in Kenya. It enjoys the largest customer base mainly because of offering
products suited to low income areas, promotion of agribusiness and focusing on rural
banking. The company has attracted a lot of global accolades and recognition and
developing countries learn from the banks low margin high volumes model.
Equity Bank’s main strategy aims at maximizing the value of information technology
by aligning Information Technology (IT) investments with business objectives. To
achieve these strategies, there is need to review customer needs to identify unmet
demand, development of new cost effective online initiative and achieve the vision of
the business and review opportunities and challenges where Information Technology
(IT) can be leveraged. Amongst the major Information Technology (IT) investment
that Equity bank has made is the acquisition of a data center. This has helped
identified ways to incorporate an intelligent service platform to manage and map the
storage of data. The Business Intelligence (BI) system feeds on the data warehouse,
enabling it to fast-track decision making.
Equity Bank implemented Oracle Business Intelligence Enterprise (OBIE) in 2009,
which has worked well in functional decision making. However it seems not to
effectively support top level decision making, hence reducing the strategic value of
Business Intelligence. Once OBIE is properly deployed at the strategic level,
capability of solving past customer complaints and predicting competitiveness of the
industry will be improved. Other systems facing similar challenges include
solarwinds, Thomson Reuters/Bloomberg, Finonne and Siebel/ avaya call system.
7
1.2 Statement of the Problem
Different departments in an organization use Business Intelligence (BI) systems
differently to serve their unique needs. Any employee in charge of making a decision
has to deal with a large amount of data, dashboards BI make it easier to comprehend
large amounts of data. In the scenario of business activity that lacks a dashboard, if an
executive wants to compare data and make any decision based on it, he or she needs
to go through a lengthy process to get the relevant data for comparison. Several times
the data is presented in different formats, which creates issues of compatibility. BI
helps in capitalizing the revenue and optimizing the business processes.
A decision-making process is an important process for any organization; decisions
made by managers or executives are very crucial for the success of any organization.
According to Venter and Tustin, (2009), in South Africa, whereas most people
understood how BI systems work in organizations, it is not readily available, when
they need it and in the format they require. Any large or small organization today
must optimize its strategic decision making process. With a sharp increase in data
collection due to the growing global market and customization, the decision making
process needs to be fast and more accurate.
Decision-making must be well supported by information about events within the
organization and in its environment. Organizations need reliable information systems
that enable analysts and managers access to the information required for quality and
effective decision-making (Puklavec, 2001).
Equity bank relies almost entirely on applications and databases, causing data and
storage needs to increase at astounding rates. It is therefore imperative for Equity to
optimize and simplify the complexity of managing its data resources. The challenge
8
remains to proactively manage this data storage to the benefit of various departments,
divisions, geographical locations and business processes to achieve improved
efficiency and profitability.
The study sought answers on what is the extent of use of Business Intelligence (BI)
system in Equity Bank is, established the ways in which Equity Bank uses BI to gain
Strategic Value and the challenges in the application of BI system in Equity Bank?
1.3 Research Objectives
The main objective of the study was to identify the strategic value of Business
Intelligence (BI) system in Equity Bank. Specific objectives of the research were
1. To determine the extent to which BI system has been used by Equity Bank.
2. To establish the ways in which Equity Bank uses BI system to gain Strategic
Value.
3. To determine the challenges of using BI systems in Equity Bank.
1.4 Value of study
This study sought to determine the strategic value of Business Intelligence (BI)
system in Equity Bank. This research adds knowledge to the existing information
about the strategic value of Business Intelligence (BI) system. Academics (Students
and instructors) would benefit from the results of the research objectives so that they
can use the findings for further research on BI. It is a source of information and a
point of reference for future research. The research therefore is applied by researchers
all over the world. The study contributes to the body of knowledge on business
intelligence systems by outlining the effects of integrating it as a core business
9
concept and that the findings will be used for further research on improving quality
systems as a strategic tool and recommendations that will be drawn will be used by
other organizations when developing and designing their frameworks related to
business intelligence systems.
In practice, the study is useful to different groups of people in different ways.
Regulators like Central Bank of Kenya will find this report useful in formulation of
policies and coming up with new prudential guidelines on BI. Business Intelligence
(BI) vendors and service providers could use this report to evaluate customers
concerns and satisfaction so that they can come up with ways to address the customer
concerns. Organizations will use the report to determine on how to leverage on BI for
improved financial performance, innovation and decision making.
Policy makers in the various organizations will gain useful information on the values
of BI in Kenya. They will also benefit from the findings of this study by adopting
findings of the study which will help them enhance responsible policy making and
governance which lead to sustained productivity and better organizational
performance.
10
CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This chapter discusses the theoretical framework pertaining to strategic value of
business intelligence systems. This chapter also defines BI, BI tools and technologies
and describes the process of unlocking the power of data to impact on building
customer and business knowledge, identify new opportunities and manage and
mitigate risks facing the organization.
2.2 Theoretical Orientation
Emerging information technology cannot deliver improved organizational
effectiveness if it is not accepted and used by potential users. Technology Acceptance
Model (TAM) is one of the most successful measurements for information systems
usage among practitioners and academics. TAM is consistent with the theory on
diffusion of innovation where technology adoption is a function of a variety of factors
including relative advantage and ease of use. According to Kim et al (2009) TAM
explores the level of motivation and user attitude that determines whether the user
will actually use or reject the system.
TAM is widely used by researchers to provide explanations of usage behavior in
relation to adoption of information technology. TAM is implemented and tested in
online banking, online shopping, e-government, immigration, e-commerce. In TAM,
user’s beliefs determine the attitudes toward using the system. Behavioral intention, in
turn, is determined by these attitudes toward using the system. The concepts of
perceived usefulness and perceived ease of use are individual subjective judgments
11
about the usefulness and ease toward specific system. Perceived usefulness and
perceived ease of use are distinct but related constructs. In TAM, perceived usefulness
is a major belief factor, and perceived ease of use is a secondary belief factor in
determining behavioral intentions toward using information technology.
TAM is determined by external variables which are effective technology and ease of
use for daily work and daily life, attitude toward using includes human attitudes
towards the use of either technology effectively in their daily lives and actual system
use which is the perceived usefulness and usage intentions in terms of social influence
and cognitive instrumental processes. In order to reduce cost benefit ratio, we must
examine the gap between system design and system acceptance. So the model of the
technology acceptance becomes very important and critical in relation to business
intelligence system.
2.3 The Origins of Business Intelligence Systems
Computer-based business intelligence systems go back a long way, in one case or
another, for close to forty years (Thomsen 2003). According to Thomsen (2003) BI as
a term replaced decision support, executive information systems, and management
information systems. With each new iteration, capabilities increased as enterprises
grew ever-more sophisticated in their computational and analytical needs and as
computer hardware and software matured. According to Hannula & Pirttimaki (2003),
in the 1980’s the term was identified with its emphasis on the need for continuous
monitoring of customers, competitors, suppliers, and other fields. Business
Intelligence systems therefore comprises a variety of intelligence information such as
12
customer intelligence, competitor intelligence, market intelligence, technological
intelligence, product intelligence and environmental intelligence.
In the 1990s, much investment in Information Technology (IT) was focused on
enterprise applications such as Enterprise Resource Planning (ERP), Supply Chain
Management (SCM), and Consumer Relationship Management (CRM) and on
connectivity between trading partners via the Internet and more traditional means
such as Electronic Data Interchange (EDI). The business benefits of these investments
included transactional efficiency, internal process integration, back-office process
automation, transactional status visibility, and reduced information sharing costs
(Williams & Williams, 2003).
By the late nineties and early 2000, Data Warehousing (DW) was accepted in the
business arena. Although early justifications for data warehousing were primarily
driven by the needs to provide integrated reporting functionality, the value of data
warehousing became clear for carrying out large analysis tasks to assist data-driven
decision making both for tactical and strategic management decisions. As the role of
analysis expanded rapidly within an enterprise, teams of business analysts within an
enterprise were involved in extracting interesting patterns from enterprise wide data.
This notion of extracting and unlocking useful information from raw data is termed as
business intelligence.
2.4 Components of Business Intelligence
A business intelligence (BI) system does not exist as a final product, its producers
offer technological platforms and knowledge for their implementation (Ubiparipovi?
13
& ?urkovi?, 2011). BI environment therefore often consists of many different
components, such as data integration, operational data stores, data warehouses, data
marts, cubes, reports, dashboards, alerts. This makes BI heavily dependent on and has
to be tightly integrated with other key platforms and applications such as data quality,
master data management (MDM), portals, security, mobile delivery and others.
According to Dayal, Castellanos, Simitsis, & Wilkinson (2009), BI architecture
typically consists of a data warehouse (or one or more data marts), which consolidates
data from several operational databases, and serves a variety of front-end querying,
reporting, and analytic tools. A data warehouse (DW) is a special type of database
where data is organized in a manner convenient for conducting analytical processes
on large data sets. It contains a copy of data isolated from operational databases and
structured specifically for reports and analyses. DW and On-line analytical processing
(OLAP) form the information basis for applying BI (Ubiparipovi? & ?urkovi?, 2011).
On-line analytical processing (OLAP) refers to the way in which business users can
slice and dice their way through data using sophisticated tools that allow for the
navigation of dimensions such as time or hierarchies. These systems process queries
required to discover trends and analyze critical factors. Advanced analytics is referred
to as data mining, forecasting or predictive analytics, this takes advantage of statistical
analysis techniques to predict or provide certainty measures on facts (Ranjan, 2009).
2.5 Business Intelligence Process
Business Intelligence (BI) enables the business to make intelligent, fact-based
decisions. The most cogent argument for establishing a new roadmap to business
Intelligence (BI) excellence is to rid the organization of the technology scramble and
14
cobbled together solutions that Information Technology (IT) has had to deal with as it
struggled to meet business requirements. According to Ranjan (2009) a BI
organization fully exploits data at every phase of the BI architecture as it progresses
through various levels of informational metamorphosis.
Data is first collected including metadata, such as the creator or creating system, the
time of creation, the channel on which it was delivered, sentiment contained in plain
text, and so on. According to Olszak & Ziemba (2006) metadata facilitate the process
of extracting, transforming and loading data through presenting sources of data in the
layout of data warehouses. Metadata are also used to automate summary data creation
and queries management
For data to be used, it is important to ensure it is clean. Venter & Tustin (2009)
depicts that the purpose of a data warehouse is to provide rich, timely, clean and well-
structured information to BI analysis tools. Once that is done, the organization can
take advantage of the vast amounts of information, give it to users in a way they can
understand. Deliver predictive scores to the customer service representatives, so they
know which offers are most likely to result in a positive outcome. Provide
sophisticated visualization tools to analysts who can see patterns in millions of data
points. Deliver a dashboard to the Vice President (VP) of marketing with social media
sentiment scores about that new product.
2.6 Strategic Value and Benefits of Business Intelligence
English (2005) ascertains that the essential element of BI is the understanding of what
is happening within an organization and its business environment, as well as
appropriate action-taking for achieving organizational goals. From this, derives the
15
importance of the human factor within BI. There is no such thing as business
intelligence without the people to interpret the meaning and significance of
information and to act on their knowledge gained (English, 2005). This is also
consistent with the findings from Finnish research (Hannula & Pirttimäki, 2003)
where around 75% of interviewees felt content and humane approaches are the key
aspects in success application of BI. BI provides employees with information to make
better business decisions, and can be used in environments ranging from workgroups
of 20 users to enterprise deployments exceeding 20,000 users.
In an extranet environment, BI is deployed in applications that allow organizations to
deliver new services and build stronger relationships with customers, partners, and
suppliers via the internet. Hence, English (2005) defines BI as “the ability of an
enterprise to act effectively through the exploitation of its human and information
resources.” Technology is the component that adds to quality information with which
business users can analyze business operations: what has happened, what is
happening, and what will happen in the future.
In enterprise performance management (EPM), organizations must understand and
have constant visibility into their key performance indicators and metrics that span
across their organizations. By doing this, organizations ensure their strategy is aligned
from top to bottom and across the organization from marketing to sales to
manufacturing to human resources. Providing this enterprise insight is a key strength
of BI. With business intelligence, users are able to turn this information into
knowledge, and knowledge into profit. BI enables the organization to track,
understand, and manage your business in order to maximize enterprise performance.
16
With BI, organizations are able to improve operational efficiency, build profitable
customer relationships, and develop differentiated product offerings.
As Jakli? & Popovi? (2009) state, various recent international studies show a high
level of awareness by professionals about the potential benefits of business
intelligence in their business operations. For the fourth consecutive year, business
intelligence remains a top IT priority of major international companies, while
improved efficiency and operational performance are a key business priority for the
fifth year in a row (Jakli? & Popovi? 2009). Many companies have positioned
business intelligence and business performance management (‘BPM’) as their top
strategic priority for 2009 and 2010.
Strategic value can be measured by various aspects including increased turnover, an
improvement of customer satisfaction as a consequence of the faster response times to
their requests and expectations, a cost reduction due to time saving and reduced work
tasks, an expansion of market share due to the possibility of the transparent
monitoring of sales volumes, structures and trends, as well as the easier detection of
areas with poor sales, deviations from past trends, an increase in profit due to better
support for decision-making and due to time-saving, and faster decision-making
which may be critical to the survival of the company in a strong competitive
environment.
According to Porter (1990) Strategic value is about competitive pricing, cost, product
or market differentiation. Thus this research will focus on ways in which Equity Bank
uses Business Intelligence (BI) system to facilitate decision making, respond to
customer issues, innovate and improve quality of products and services.
17
Cui et al (2007) view BI as way and method of improving business performance by
providing powerful assists for executive decision maker to enable them to have
actionable information at hand. BI tools are seen as technology that enables the
efficiency of business operation by providing an increased value to the enterprise
information and hence the way this information is utilized.
Tvrdíková (2007) describes the basic characteristic of BI tool as the ability to collect
data from heterogeneous source, to possess advance analytical methods, and the
ability to support multi users’ demands. Zeng et al. (2006) categorized BI technology
based on the method of information delivery; reporting, statistical analysis, ad-hoc
analysis and predicative analysis.
The concept of Business Intelligence (BI) was brought up by Gartner Group since
1996. It is defined as the application of a set of methodologies and technologies, such
as J2EE, DOTNET, Web Services, XML, data warehouse, OLAP, Data Mining,
representation technologies, etc, to improve enterprise operation effectiveness,
support management/decision to achieve competitive advantages. Business
Intelligence by today is never a new technology instead of an integrated solution for
companies, within which the business requirement is definitely the key factor that
drives technology innovation. How to identify and creatively address key business
issues is therefore always the major challenge of a BI application to achieve real
business impact. (Golfarelli et.al, 2004) defined BI that includes effective data
warehouse and also a reactive component capable of monitoring the time-critical
operational processes to allow tactical and operational decision-makers to tune their
actions according to the company strategy.
18
Gangadharan and Swamy (2004) widen the definition of BI as technically much
broader tools that include potentially encompassing knowledge management,
enterprise resource planning, decision support systems and data mining. BI includes
several software for Extraction, Transformation and Loading (ETL), data
warehousing, database query and reporting, (Berson et.al, 2002; Curt Hall, 1999)
multidimensional/on-line analytical processing (OLAP) data analysis, data mining
and visualization.
Banks must manage large volumes of data in the repositories; this data comes from
many sources, including a diverse customer base, extensive branch networks, and
shareholders. Banks needs to carry out an analysis to chart way for future action. To
derive real business value from this data, the right tools are needed to capture and
organize a wide variety of data types from different sources, and to be able to easily
analyse it within the context of all enterprise data (Dijicks, 2012). The tool required
for this job is Business intelligence. Stackowiaket al (2007) defines Business
intelligence (BI) is the process of taking large amounts of data, analysing that data,
and presenting in a high level set of reports that condense the essence of that data into
the basis of business actions, enabling management to make fundamental daily
business decisions.
The benefits of business intelligence, along with information systems, in general, can
be divided into various categories (Carver & Ritacco, 2006). Measurable or
quantifiable benefits are those that can be clearly measured, for example, reducing the
time needed to carry out certain tasks, savings achieved by purchasing one software
solution instead of another, an increase in revenue and profit.
19
Indirectly quantifiable benefits are usually related to customer satisfaction.
Introducing new technology can improve customer service, which has a positive
impact on their satisfaction, resulting in larger sales volumes, the increased loyalty of
customers returning to purchase again, the winning of new customers. According to
(Olszak & Ziemba, 2006) Business Intelligence (BI) systems enable both descriptive
and predictive segmentation of customer based on grouping customers in homogenous
segments. Banks are therefore able to assess the needs of each profile easily.
Customer satisfaction is typically assessed by surveys, by monitoring the volume of
business, the re-order ratio as well as other, less formal ways for example by visits
and dialogue with customers.
Non-measurable benefits include a higher quality of work, the better motivation of
employees, the effects of IT on an improvement of communication in the
organization, higher quality knowledge sharing between employees. These intangibles
benefits are difficult, sometimes impossible to quantify (Gibson et al 2004). The main
problem in assessing these benefits is that they may only be assessed in a subjective
way, which does not provide reliable information about their real value.
Unpredictable benefits can, for example, be new solutions and the ideas of creative
individuals.
Most Business Intelligence (BI) benefits are intangible. An empirical study for 50
Finnish companies found most companies do not consider cost or time savings as
primary benefit when investing in BI systems (Hannula & Pirttimaki, 2003). The hope
is that a good BI system will lead to a big return at some time in the future, this
research seeks to relate BI with improved organization performance, smarter decision
making and success of innovative products.
20
Organizations that are interested to improve quality of decision-making, image or
quality of partner services should incline towards the development of information
technology infrastructure that will represent a holistic approach to business
operations, customers, suppliers (Wells & Hess, 2004). Theory and practice show that
the above-mentioned requirements are largely met by Business Intelligence (BI)
systems (Gray, 2003; Liautaud, & Hammond, (2002); Olszak, & Ziemba, (2004);
Turban, & Aronson, (1998). Decision making therefore is one of the biggest
advantages of having BI in an organization.
2.7 Challenges of Business Intelligence
According to Chuah & Wong, (2013) Business Intelligence (BI) applications have
appeared the top spending priority for many Chief Information Officers (CIO) and it
remain the most important technologies to be purchased for past five years (Gartner
Research 2007; 2008; 2009). Although there has been a growing interest in BI area,
success for implementing BI is still questionable (Ang & Teo 2000; Lupu et.al (1997);
Computerworld (2003)). Lupu et.al (1997) reported that about 60% - 70% of business
intelligence applications fail due to the technology, organizational, cultural and
infrastructure issues. Furthermore, EMC Corporation argued that many BI initiatives
have failed because tools were not accessible through to end users and the result of
not meeting the end users’ need effectively.
The first challenge facing BI system is the cost. BI has evolved and everybody has
some form of BI in place now, as it is becoming a fairly substantial cost item. The
overall cost of BI – the cost of technology, upkeep and implementation – is certainly
one of the challenges that implementers are facing.
21
The second challenge is the number of users. The number of business users now
tapping into BI is increasing dramatically, especially as we begin to move into
operational intelligence. We’re seeing more naïve users – not the traditional analysts
or data scientists – so it is not only the number of users but an increase in support for
these users from an implementation standpoint.
The third challenge is in the area of operational BI and the new sources of data
available. We are seeing a tremendous increase in the volumes of data (big data)
being analyzed and stored in data warehouses and experimental areas. This data is
used for complex advanced, embedded and streaming analytics. There are now very
interesting sets of data in BI, which is certainly different from the traditional, more
strategic or tactical forms of BI. This doesn’t diminish the need for traditional BI; it
just means we must expand our BI architectures to embrace these new areas.
These big challenges lead to the fourth, which is the performance and scalability of
the environment. Obviously, if we are starting to bring in operational people,
operational BI, streaming analytics, big data applications, etc., it means that the
performance has to be a major focus of the BI implementers – sub-second response
time for many operational intelligence queries while simultaneously supporting the
more strategic or long running queries as well. It’s a mixed workload environment,
and that can cause a performance issue. So our technology also has to scale up to
handle it. A terabyte used to seem like a lot of data, but not anymore.
Computerworld (2003) stated that BI projects collapse because of failure to recognize
BI projects as cross organizational business initiatives, unengaged business sponsors,
unavailable or unwilling business representatives, lack of skilled and available staff,
22
no business analysis activities, no appreciation of the impact of dirty data on business
profitability and no understanding of the necessity for and the use of meta-data.
In the banking industry data sources can be from operational databases, historical
data, and external data for example, from market research companies or from the
Internet, or information from the already existing data warehouse environment. The
data sources can be relational databases or any other data structure that supports the
line of business applications. Data can also reside on many different platforms and
can contain structured information, such as tables or spreadsheets, or unstructured
information, such as plaintext files or pictures and other multimedia information. Big
data refers to large datasets that are challenging to store, search, share, visualize, and
analyze (Dijicks, 2012)
Banks are challenged by big data and require them to be proactive in managing and
utilizing corporate it if they want to keep up with or stay ahead of the competition.
Business intelligence (BI) gives enterprises the capability to analyze the vast amounts
of information they already have to make the best business decisions. Banks are able
to tap into their huge databases and deliver easy-to-comprehend insight to improve
business performance and maintain regulatory compliance (Nemati, 2005). The
applications of business intelligence in the banking are therefore far-reaching.
While the Business Intelligence (BI) solution typically contains the necessary data
that are required for identifying opportunities for improvement, significant effort is
often required to get to these insights. Often, the level of effort required to find
valuable data points exceed the cost of finding it. Moldovan (2011) studied the
23
financial industry and found that mining financial data presents some challenges,
difficulties and sources of confusion, especially when determining short term trends
and validating them.
Business Intelligence (BI) solutions require data from many different, and often
disparate, data sources. The unique aspects of each organization require significant
time and effort to get them up and running. At the end of the day, there is
considerable effort required to stand up and run these solutions. The most common
challenge companies are facing in the current competitive business environment is
management of its own data (Ponomarjovs, 2013). Once insight has been gained from
the Business Intelligence (BI) solution, there is no clear path to action, and often no
link to the underlying detailed data. Acting on the findings is limited, and is especially
challenging from the BI solution itself.
2.8 Summary
The empirical review above indicates that strategic value of business intelligence
determine the performance of commercial banks both in in improving their
competitiveness and handling customers issues and innovation. Both Dijicks (2012)
and (Ponomarjovs, 2013) indicated that its challenging for banks to manage the data.
This data according to (Moldovan, 2011) may cause confusion and difficulties.
However (Olszak and Ziemba, 2006) Business Intelligence (BI) enables organizations
to analyze and get insights from this data. Most studies on this subject were done in
different regions, on different Business intelligence systems with scanty studies done
in developing countries and particularly in Kenya. Kangogo (2013) indicates that
dynamism of the banking environment is posing a lot of challenges to all banks .BI
24
helps in anticipating future behavior and predicting most business indicators
(Ubiparipovi? & ?urkovi?, 2011). There is therefore a gap in literature in regard to
strategic value of Business Intelligence in commercial banks in Kenya. The current
study sought to bridge this gap by focusing on Equity Bank Kenya.
25
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Introduction
This chapter describes the research design and sampling design used in the survey and
also highlights on the population of study, data collection methods as well as data
analysis and presentation methods to be employed.
3.2 Research Design
This research employed descriptive survey method that helped in gathering
information about the strategic value of business intelligence system in Equity Bank.
This design was preferred because the study was concerned with answering questions
such as who, how, what, which, when and how much (Cooper & Schindler, 2003).
3.3 Target Population
The target population for the survey was 500 members of staff from various
departments in Equity Bank Head Office. These were the main users of Business
Intelligence (BI) systems at Equity Bank.
3.4 Sampling Design
Stratified random sampling was employed to identify respondents from the various
departments. Each stratum was composed of a department and levels within each of
these departments, employees were selected at random from the strategic, tactical and
operational levels. The sample size was drawn from the target population based on the
principle of 10% rule according to Mugenda and Mugenda (2003).
26
3.5 Data Collection
The data required for the study was obtained from primary sources. The researcher
collected primary data using a questionnaire. The questions were both open ended and
closed ended to give respondents enough space to express their views on Business
Intelligence use. Questionnaire was issued as follows: 15 from the IT Department, 7
from the Finance, 5 from Customer Service, 6 from Credit, 6 from Treasury and 7
from Marketing departments.
The researcher mailed the questionnaire with a personalized message. Additionally,
an introductory letter explaining the purpose of the study was also attached. A follow
up call was made to some of the respondents.
3.6 Data Analysis
The data was analyzed through descriptive statistics, this enabled the researcher to
organize data in an effective and meaningful way. The data generated by the study
after fieldwork was edited, coded then entered into a computer for processing using
Microsoft excel and Google doc analytics. By use of percentages, frequency
distributions, tables, charts, the researcher categorized the variables.
27
CHAPTER FOUR
DATA ANALYSIS, RESULTS AND DISCUSSION
4.1 Introduction
This chapter describes the analysis of data followed by a discussion of the research
findings. The findings relate to the research questions that guided the study. Data
were analyzed to identify, describe and explore the Strategic Value of Business
Intelligence (BI) systems at Equity Bank, extent of use, its benefits and challenges.
4.2 Demographics
The demographic data consisted of department of respondent, position held in the
department, years of service in current role and level of education. A sample of 52
respondents from different departments was targeted as respondents for the study, out
of which 46 participated. The study therefore had 88.5% response rate.
4.2.1 Distribution of respondents by position
The researcher wanted to find out the positions the respondents were holding in the
company. The study findings in table 4.1 indicated that majority of the respondents
(28%) held the position of officers in the operational level. Only 22% of the
respondents were supervisors in the in the tactical level of management, 20% of the
respondents were managers in the tactical level, 17% of the respondents were senior
managers in the strategic level and lastly 13% of the respondents were general
managers in the strategic level. The results are as shown on Table 4.1.
28
Table 4.1: Distribution of Respondents by Position
position
Number of
Respondents
% of
Respondents
Level of
Management
General
Manager
6 13% Strategic level
Senior
Manager
8 17% Strategic level
Supervisor
10 22% Tactical level
Manager
9 20% Tactical level
Officer
13 28% Operational level
Total
46 100.00%
Source: Researcher (2014)
4.2.2 Distribution of Respondents by Department
The study sought to find out the departments in which the respondents were working.
The findings are as shown on Table 4.2.
Table 4.2: Distribution of Respondents by Department
Department Number of Respondents % of Respondents
Customer
service 5 11%
Credit 6 13%
Treasury 6 13%
Finance 7 15%
Marketing 7 15%
ICT 15 33%
Total 46 100%
Source: Researcher (2014)
29
The study findings in table 4.2 indicated ICT department who were the majority
respondents were that at 33% response rate , followed by 15% of the respondents who
were working in the Finance and marketing departments respectively. In addition 13%
of the respondents were in the credit department and treasury department respectively.
Lastly 11% of the respondents were working in the customer service department.
4.2.3 Years of Experience of the Respondents
The researcher wanted to find out the years of experience of the respondents. The
results are as shown on Table 4.3.
Table 4.3: Years of Experience of the Respondents
Years of
Experience
Number of
Respondents % of Respondents
<2 Years
17 37%
2-5 Years
18 39%
6-10 Years
8 17%
>10 Years
3 7%
Total
46 100%
Source: Researcher (2014)
The respondents were required to indicate their years of experience. The study
findings in table 4.3 indicated that majority of the respondents were between 2-5 years
of experience at 39%. Those with <2 years and 6-10 years followed with 37% and
17% respectively. Those with more than 10 years of experience are at 7%.
30
4.2.4 Level of Education of the Respondents
The researcher wanted to find out the level of education of the respondents. The
respondents were required to indicate their level of education. Data collected was
analyzed and is shown in Table 4.4.
Table 4.4: Level of Education of the Respondents
Level of formal of education
attained - OTHERS Number of Respondents
% of
Respondents
Certificate 4 9%
Degree 26 57%
Diploma 4 9%
Masters 12 26%
Total 46 100%
Source: Researcher (2014)
The study findings in table 4.4 Majority of the respondents have degrees and
postgraduate level education both at 57% and 26% respectively, whereas 9% have
diploma level of education and certificate level of education.
4.2.5 Level of Education - ICT
The researcher sought to find out the level of ICT education attained by the
respondents. Respondents were required to indicate their level of education in ICT.
Data collected was analyzed and is shown in Table 4.5.
31
Table 4.5: Education Level – ICT Related
Level of formal education attained
- ICT RELATED
Number of
Respondents
% of
Respondents
Basic IT Training 8 17%
BSc. Degree in IT related area 9 20%
Diploma in IT related area 9 20%
Hands on Training 5 11%
IT Professional Certification 13 28%
Master’s Degree in IT related area 2 4%
Total 46 100%
Source: Researcher (2014)
The study findings in table 4.5 indicates that majority of the respondents (28% ) had
attained IT professional certificate, followed by 20% of the respondents who attained
BSc. Degree in IT related area and Diploma in IT related area respectively. In
addition, 17% of the respondents had attained Basic IT Training, 11% had attained
Hands on Training and lastly 4% of the respondents had attained Master’s Degree in
IT related area.
4.3 Extent to which Business Intelligence is used In Equity Bank.
The study sought to establish the extent to which Business Intelligence (BI) system is
used as contribution to strategic value at Equity Bank. The study had sought to
establish the frequency of usage of the BI systems, tools used and whether the users
were trained and knowledgeable on the BI systems currently implemented at Equity
Bank.
32
Table 4.6: BI System use by Department
BI System
Number of
Respondents
% of
Respondents
Finnone System 6 13%
Siebel/Avaya System 12 26%
OBIEE System 7 15%
Thomson Reuters/Bloomberg
System 6 13%
Solarwinds System 15 33%
Total 46 100%
Source: Researcher (2014)
The study findings in table 4.6 indicates that majority of the respondents (33%) used
Solarwinds, 26% used Siebel/Avaya system, 15% used OBIEE and Thomson
Reuter/Bloomberg and Finnone systems each had 13%.
4.3.1 Use of Business Intelligence systems (BI) in decision making
The researcher wanted to find out if the respondents used BI systems in making
decisions. The results are as shown on Table 4.7.
33
Table 4.7: Use of Business Intelligence Systems (BI) in Decision Making
BI system Department
Do you normally use
BI System reports
when making
decisions?
Number of
Respondents
% of
Respondents
Finnone System Credit Yes 6 13%
OBIEE System Finance Yes 7 15%
Siebel/Avaya
System
Customer
service Yes 5 11%
Marketing No 1 2%
Yes 6 13%
Solarwinds
System ICT No 1 2%
Yes 14 30%
Thomson
Reuters/Bloomb
erg System Treasury Yes 6 13%
Total
46 100%
Source: Researcher (2014)
The results in table 4.7 indicated that OBIEE was mainly used by Finance department,
15% of the respondents (represent all Finance department users) used BI reports for
decision making, Finonne system was mainly used by credit department representing
13% of which all respondents used BI reports to aid in decision making.
Reuters/Bloomberg system was mainly used by treasury department which constitute
13% of respondents.
The highest number of respondents – 32% used solarwinds system, this was mainly
ICT department. The only system used by more than one department was
Siebel/Avaya call system which was mainly used by Marketing and customer service
at 15% and 11% respectively. Only 4% of the respondents, 2% from Marketing and
2% from ICT who dint use BI reports for decision making.
34
4.3.2 Main areas of applications of Business Intelligence (BI)
The researcher wanted to find out the main areas of applications of business
intelligence. Respondents were requested to indicate key areas BI system was applied.
The results are as shown on Table 4.8.
Table 4.8: Main Areas of Applications of Business Intelligence
Key areas BI System is applied % of
Respondents
Finnone System 13%
Acquisition 2%
Customer & portfolio management, Bad debt management 2%
Customer & portfolio management, Bad debt management, Targeting &
prospecting, Acquisition
7%
Targeting & prospecting, Acquisition 2%
OBIEE System 15%
Drive innovation, Deploy business best practices , Make insights ,
Provide extreme performance
7%
Drive innovation, Make insights 4%
Drive innovation, Make insights , Provide extreme performance 2%
Make insights 2%
Siebel/Avaya System 26%
agent desktop analytics, agent quality and capability scorecards 2%
agent desktop analytics, agent quality and capability scorecards,
benchmarking of contact centre strategy and operations against industry
vertical, local geography and global best practice
4%
agent desktop analytics, agent quality and capability scorecards, speech
analytics
2%
agent desktop analytics, agent quality and capability scorecards, speech
analytics, post-call surveys, benchmarking of contact centre strategy and
operations against industry vertical, local geography and global best
practice
11%
35
Key areas BI System is applied % of
Respondents
agent desktop analytics, speech analytics 2%
agent desktop analytics, speech analytics, benchmarking of contact
centre strategy and operations against industry vertical, local geography
and global best practice
2%
speech analytics, post-call surveys 2%
Solarwinds System 33%
Database performance monitor 2%
Network performance monitor 2%
Network performance monitor, Database performance monitor 2%
Network performance monitor, Database performance monitor,
Security and compliance
2%
Network performance monitor, Optimize applications performance 2%
Network performance monitor, Optimize applications performance,
Database performance monitor, Security and compliance
17%
Optimize applications performance, Security and compliance 2%
Security and compliance 2%
Thomson Reuters/Bloomberg System 13%
Fx trading, Interest rates trading 11%
Interest rates trading 2%
Total 100%
Source: Researcher (2014)
4.3.3 Knowledge and Training of Business Intelligence (BI) System
Most of the respondents had good knowledge of the OBIEE system. 15% of Finance
department respondents had very good knowledge of the BI system. The research
shows that the user of OBIEE all had above average knowledge of BI. Only 2% of the
15% of the Finance department users had not received training of the BI system.
36
Most of the respondents had above average knowledge of the FINONNE system. All
the credit department respondents who were users of FINONNE had received
training.
Table 4.9: Knowledge and Training of Business Intelligence (BI) System
Knowledge of BI
system
Have you
ever received
training on
BI system
What BI system do you use? % of
Respondents
Average No OBIEE System 2%
Solarwinds System 4%
Yes Finnone System 4%
Solarwinds System 2%
Thomson Reuters/Bloomberg
System
2%
Good No Siebel/Avaya System 4%
Yes Finnone System 4%
OBIEE System 4%
Siebel/Avaya System 7%
Solarwinds System 15%
Thomson Reuters/Bloomberg
System
7%
Poor No Siebel/Avaya System 2%
Solarwinds System 2%
Very Good Yes Finnone System 4%
OBIEE System 9%
Siebel/Avaya System 13%
Solarwinds System 9%
Thomson Reuters/Bloomberg
System
4%
Total 100%
Source: Researcher (2014)
37
The study findings in table 4.9 indicated that Most of the respondents had above
average knowledge of the Reuters and Bloomberg system. All the treasury
department respondents who were users of Reuters/bloomberg had received training.
Most of the respondents had above average knowledge of the solarwinds system.
Only 2% were below average. 26% of the 33% of ICT respondents had received
training, othe remainder 7% hadnt had any training.
Not all the respondents had above average knowledge of the Siebel/avaya system. 2%
were below average. 20% of the 26% of marketing/customer service respondents had
received training, other remainder of 7% hadnt had any training.
4.4 Strategic Value of Business Intelligence (BI) Systems
The study had sought to establish the Strategic Value of Business Intelligent (BI)
systems in the organization. The study also sought to find out whether Business
Intelligent (BI) systems facilitated decision making, improving quality of services and
encouraging innovations. These were the key measures of strategic value of Business
Intelligent (BI) systems. The results were as per below.
4.4.1 Sources of Information in Predicting Customers Issues,
Improve Quality of Goods/Services and Innovation
The reports from OBIEE were very useful in predicting customer preferences and
like. The credit department mainly used finnone to forecast probability of a loan
repayment and monitor usage behavior. The main areas solar winds was used for
decision making were speed foresic investigation and route cause analysis and also
dashboard alerting and reporting , this was 28% of the respondents each. The main
38
areas siebel/avaya was used was analysing call drivers,and understanding market
threats and opportunities by mining customer interractions , this wa 24% of the
respondents and 22% respectiely.
Spotting growth opportunities,monitoring market developments and reseach tied at
11% as areas reuters/bloomberg assisted in making decisions.
Table 4.10: Source of Information in Predicting Customers Issues, Improve
Quality of Goods/Services and Innovation
Source of information is used by Equity Bank to respond
to customer issues and improve the quality of
products/services
% of
Respondents
Finnone System 13%
Analysis from Business Intelligence (BI) system 2%
Analysis from Business Intelligence (BI) system, Internet
searches
2%
Analysis from Business Intelligence (BI) system, Internet
searches, Periodic reports from CBK
2%
Internet searches 2%
Media reports and articles, Analysis from Business
Intelligence (BI) system
4%
OBIEE System 15%
Analysis from Business Intelligence (BI) system 2%
Analysis from Business Intelligence (BI) system, Internet
searches
2%
Analysis from Business Intelligence (BI) system, Internet
searches, Periodic reports from CBK
2%
Analysis from Business Intelligence (BI) system, Periodic
reports from CBK
4%
Media reports and articles, Analysis from Business
Intelligence (BI) system
2%
Media reports and articles, Analysis from Business
Intelligence (BI) system, Periodic reports from CBK
2%
Siebel/Avaya System 26%
Analysis from Business Intelligence (BI) system 9%
Analysis from Business Intelligence (BI) system, Internet
searches
7%
39
Source of information is used by Equity Bank to respond
to customer issues and improve the quality of
products/services
% of
Respondents
Analysis from Business Intelligence (BI) system, Periodic
reports from CBK
4%
Media reports and articles, Analysis from Business
Intelligence (BI) system
4%
Periodic reports from CBK 2%
Solarwinds System 33%
Analysis from Business Intelligence (BI) system 11%
Analysis from Business Intelligence (BI) system, Internet
searches
13%
Analysis from Business Intelligence (BI) system, Periodic
reports from CBK
4%
Media reports and articles, Analysis from Business
Intelligence (BI) system, Internet searches, Periodic reports
from CBK
2%
Media reports and articles, Internet searches, Periodic
reports from CBK
2%
Thomson Reuters/Bloomberg System 13%
Analysis from Business Intelligence (BI) system 2%
Analysis from Business Intelligence (BI) system, Internet
searches
4%
Analysis from Business Intelligence (BI) system, Internet
searches, Periodic reports from CBK
2%
Media reports and articles, Analysis from Business
Intelligence (BI) system
4%
Total 100%
Source: Researcher (2014)
4.4.2 Business Intelligence (BI) System Rating in Improving Quality
of Decision Made
The study sought to explore whether business intelligence (BI) system was used to
improve quality of decision making. The results are as shown on Table 4.11.
40
Table 4.11: Business Intelligence system rating in improving quality of decision
making
BI system used Rating of BI system in
Improving Quality of
decisions made
% of
respondents
Finnone System Average 4%
Good 4%
Very Good 4%
OBIEE System Average 2%
Good 2%
Very Good 11%
Siebel/Avaya System Average 2%
Good 13%
Very Good 11%
Solarwinds System Average 4%
Good 11%
Poor 2%
Very Good 15%
Thomson
Reuters/Bloomberg
System
Good 7%
Very Good 7%
Total 100%
Source: Researcher (2014)
The study findings in table 4.11 indicated that all users of OBIEE who represent 15%
of total respondents used OBIEE. 13% of them of the respondents used OBIEE to
facilitate decision making. OBIEE was rated as very good in improving the quality of
decisions made.
In developing of new products/services and solving customer issues, all the users of
OBIEE used analysis from BI system highly compared to other sources. All users of
41
Finonne representing 13% of respondents used reports from Finnone when making
decisions. Finonne was rated as above average in improving the quality of decisions
made. 11% of them of the respondents used Finnone information to quickly to
respond to customer issues and improve quality of their services.
All users of reuters/bloomberg representing 13% of respondents used reports from
reuters/bloomberg when making decisions. Only 7% of them of the respondents rated
Reuters/Bloomberg both as good and very good in improving quality of decisions
made. A total of 13% of them of the respondents used Reuters/Bloomberg
information to quickly to respond to customer issues and improve quality of their
services.
Out of 33% of ICT department respondents, 30% used solarwinds when making
decisions. Only 15% and 11% of the respondents rated solarwinds both as very good
and good respectively in improving quality of decisions made. A total of 30% of the
respondents used solarwinds information to quickly to respond to customer issues and
improve quality of their services. Out of 26% of marketing and customer service
department respondents, 24% used Siebel/avaya when making decisions, 11% and
13% of the respondents rated Siebel/avaya call both as very good and good
respectively in improving quality of decisions made. Only 30% of the respondents
used Siebel/avaya information to quickly to respond to customer issues and improve
quality of their services. All the systems were equally used in each department.
4.5 Challenges of Business Intelligence (BI) systems
The main challenges were downtime at 43%, trainings needs at 26% and lack of
technical support at 30%. This is in line with Lupu et.al (1997) who reported that
42
about 60% - 70% of business intelligence applications fail due to the technology,
organizational, cultural and infrastructure issues.
Table 4.12: Challenges of Business Intelligence (BI) systems
Challenge Number of Respondent %
Downtime 20 43%
Training Needs 12 26%
Lack of technical support 14 30%
46 100%
Source: Researcher (2014)
43
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Introduction
This chapter presents the summary of the findings from chapter four, and also gives
conclusions and recommendations of the study based on the objectives of the study.
The researcher evaluates the findings and gives recommendations necessary on the
strategic value Business Intelligence (BI) systems at Equity bank.
5.2 Summary of Findings
The summary of findings for this study are divided into demographics information of
the respondents, strategic value of Business Intelligence (BI) systems ,extent of use of
BI systems and challenges of using them.
A total of 46 respondents out of a targeted 52 participated in the survey. This was an
88% response rate. Respondents were drawn from all levels of management. Most of
the respondents, 30% were from strategic level this included General Managers and
Senior Managers. A total of 41 % were from tactical level, this included Managers
and supervisors and 28% operational level. A total of 63% of the respondents had
worked in their current position for more than two years.
The respondents were drawn from different departments as follows 33% from the IT
Department, 15% from the Finance, 11% from Customer Service, 13% from Credit,
13% from Treasury and 15% from Marketing departments. Only 57% of the
respondents had a degree in other area other than ICT, 26% had masters. All the
respondents had at least hands on or basic IT training. The main BI systems used by
44
Equity Bank were as follows; 15% OBIEE, 13% Finonne, 13% Thomson
Reuters/Bloomberg, 33% were solarwinds and 26% was Siebel/avaya call. It is
important to note than different departments used the system specific to their area.
The study sought to determine to what extent Business Intelligence (BI) systems were
being used at Equity Bank. The study found that the BI systems were mainly used for
decision making, 96% of the respondents used BI system when arriving at a decision.
All the departments sampled used a BI system custom made to their operations.
OBIEE was used in Finance, Finnone in credit, Thomson Reuters/Bloomberg in
treasury, solarwinds in ICT and Siebel/avaya call in marketing and customer service
departments.
Thomson reuters /Bloomberg were mainly used to make decisions on forex and
interest rates trading, spot growth opportunities and monitor market developments,
OBIEE provided Finance department drive innovation and make business insights.
Finonne was mainly used by credit department for credit scoring, loan portfolio
management and acquisition targeting.
Both marketing and customer service used Siebel/avaya call was used to drive
intelligent customer interactions with unified view of their customer relationships
across the bank, effectively anticipate customer needs, improve customer retention
and identify opportunities to cross-sell and up-sell. Solarwinds was mainly used in
ICT for real-time predictive intelligence, real-time tactical support to drive enterprise
actions that react immediately to events as they occur Obtain broader compliance
support, stronger security intelligence, and a faster time-to-respond duration with
embedded file integrity monitoring and active response.
45
The finding of the study shows Equity Bank gained strategic value by using Business
Intelligence (BI) systems. Users mainly gained strategic value by using the BI
systems for decision making, predict customer’s preferences and likes, respond to
customer issues and drive innovation.
According to Ubiparipovi? and ?urkovi? (2011) BI systems enable banks to
anticipate future behaviour of the system and most of their business indicators, all
systems in the study could predict outcomes .OBIEE was able to manage and exploit
the information potential of multitude of internal and external data from sales,
demographics, economic trends, competitive data, consumer behavior, efficiency
measures, financial calculations. Siebel/avaya call system users were able to build
stronger relationships with customers, partners, and suppliers. According to (Olszak
& Ziemba, 2006) Business Intelligence (BI) systems enable both descriptive and
predictive segmentation of customer based on grouping customers in homogenous
segments; this was the case for Finonne which was used for credit scoring.
5.3 Conclusion
The findings show that Business Intelligence (BI) systems add strategic value at
Equity Bank. The finding of the study also shows that Equity Bank through BI
systems is able to improve service delivery by properly managing the organization
information and using it in improving decisions made by the organization. Therefore
BI systems have strategy value given that it has improved decision making, managing
customer’s issues and innovation.
The study indicates that Business Intelligence (BI) systems made contribution to
value networks and not merely financial benefits, but also knowledge, among other
46
benefits. The study confirmed that BI systems are important investment that
institutions need to consider to remain competitive. It is however important to ensure
that institutions that choose to invest in the BI systems consider the challenges
involved.
The study also revealed training and education as core to the operation of Business
Intelligence (BI) systems and for that reason the study concluded that adequate and
relevant training of staff is necessary in running of the BI systems. To address the
challenges arising from use of BI systems the study concludes that it is important to
consider system integration; it is important to ensure the adopted system is compatible
with the existing system and software. Finally the study concludes that it is important to
have correct budgetary allocations to overcome the challenges of cost overrun.
5.4 Recommendations for Policy and Practice
The study recommended that there should be more awareness on the use and Business
Intelligence (BI) systems in Equity bank. As much as extent of use is concerned, there
is need for the Equity Bank to explore what other ways it can leverage more on these
systems. There is also need for the organization to train its staff in the best use of
Business Intelligence systems in order to ensure that there is proper use for maximum
benefit of the concept and to benefit more on the value that the organization may get
from Business Intelligence.
Equity Bank needs to evaluate whether there has been proper investment in human
resource and systems as an integral part of the different resources that exist in the
organization and on whether the organization is maximizing on the benefits that are
47
associated with the use of BI systems as a strategic plan meant to add value to the
operations of the organization
5.5 Limitations of the Study
The study was a case study and was based on only one Bank; the findings of the study
may therefore not be fully applicable to other banks and or organizations as they may
have different experiences even under the same circumstances. Different banks or
organizations may view the strategic value of BI differently and therefore may not
approach it in the same way. The study was also online based, most users had to
inquire how to go about it, this might have limited the amount of information the
respondents gave.
5.6 Suggestions for Further Study
The researcher recommends further research on cost benefit analysis of Business
Intelligence (BI) systems since the study focused on the strategy value of BI approach
only. The researcher also recommends further research in the area of unutilized
modules and resource of BI. This also came up during the interviews hence the need
for an in-depth study of the same.
48
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APPENDICES
APPENDIX I: LETTER OF INTRODUCTION
TO WHOM IT MAY CONCERN
Dear Sir/Madam:
REQUEST FOR COLLECTION OF DATA
My name is Kamara Daniel M, a post-graduate student at the school of business,
University of Nairobi. I am conducting a research study titled “strategic value of
business intelligence systems”.
You have been selected to form part of this study. Kindly assist by filling in the
attached questionnaire. The information given will be treated in strict confidence and
will be purely used for academic purposes.
Your assistance and cooperation will be highly appreciated.
Yours Sincerely,
________________________
Kamara Daniel M (Student)
D61/67494/2011
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APPENDIX II: QUESTIONNAIRE
THE STRATEGIC VALUE OF BUSINESS INTELLIGENCE SYSTEMS
This questionnaire is designed to collect information on the strategic value of business
intelligence systems in Kenya.
The information obtained will be used for academic purposes only. Confidentiality of
the information and the respondents will be highly observed.
This questionnaire will be completed by: 15 managers from the IT Department, 7
from the Finance, 7 from Customer Service, 8 from Credit, 6 from Treasury and 9
from Marketing departments.
Instructions:
1. Kindly respond to the following questions by placing a tick in front of the
most appropriate response.
2. Where explanations are required, use the space below the items.
3. Kindly answer all questions.
SECTION A: PERSONAL INFORMATION
1. What’s your role in the company and under which department are you in
ROLE DEPARTMENT
Director /CFO/CIO ICT
General Manager Finance
Senior Manager Treasury
Manager Credit
Supervisor Marketing
Officer Customer service
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Others (please specify)
___________________ ________________
2. How long have you been working in the current position?
<2 Years 2-5Years 6-10 Years >10 Years
3. What’s your highest level of formal of education attained
ICT RELATED OTHERS
Master’s Degree in IT related area PHD
Diploma in IT related area Masters
IT Professional Certification Degree
Basic IT Training Diploma
Hands on Training Certificate
BSc. Degree in IT related area
Other_______________________ Other___________________
SECTION B: EXTENT TO WHICH BUSINESS INTELLIGENCE IS USED IN
EQUITY BANK.
4. What BI system do you use?
OBIEE
Finnone
Thomson Reuters
Bloomberg
Solarwinds
Siebel/Avaya
5. How often do you use Business Intelligence (BI) system?
Rarely
Always
Never
6. Please list areas you use Business Intelligence (BI) system for?
55
7. Please list features of Business Intelligence (BI) system that you have ever
interacted with in your current role?
8. What features do you feel if added to your Business Intelligence, (BI) system
will enable you to make better use of the system?
__________________________________________________________
9. Do you retrieve or receive any reports from the Business Intelligence (BI)
system?
Yes No
10. Have you ever received training on how to use the features provided by the
Business Intelligence (BI) system currently implemented by Equity Bank.
Yes No
11. How do you rate your knowledge of Business Intelligence (BI) system used by
Equity Bank
Very Good
Good
Average
Poor
Very Poor
SECTION C: WAYS IN WHICH EQUITY BANK USES BI SYSTEM TO
GAIN STRATEGIC VALUE.
12. Please list Business Intelligence (BI) system tools that you use to help you in
decision making?
13. Do you normally use reports provided by Business Intelligence (BI) system
when making decisions?
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Yes No
14. Select at least one way that clearly describes how you arrive at decisions at
Equity Bank?
Personal Gut (individual feeling)
Peers group discussions
Using Highly Analyzed reports from BI system
Reports from Application system such ERP, CRM or HRM systems
Other:________
15. How would you rate the use of Business Intelligence (BI) system in helping
you improve the quality of the decision made on day to day basis?
Very Good
Good
Average
Poor
Very Poor
16. What source of information is used by Equity Bank to respond to customer
issues and improve the quality of the quality of their services: tick at least one
Media reports and articles
Analysis from Business Intelligence (BI) system
Internet searches
Periodic reports from CBK
Other: ________
17. How do you rate reports from Business Inte1ligence (BI) System in helping
predict customer preferences and like?
Very useful
Useful
Average
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Not useful at all
I don’t use such analysis reports
18. From your opinion what else should done to help realize strategic value of
Business Intelligence System at Equity Bank (Be as detailed as possible)?
___________________________________________________________
SECTION D: CHALLENGES AND BENEFITS OF BUSINESS
INTELLIGENCE SYSTEM
19. What are the major benefits of using Business intelligence system?
___________________________________________________________
20. What challenges do you face when using Business Intelligence system?
___________________________________________________________
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