1 INTRODUCTIONThis chapter gives a general overview of the topic at hand, its constituents, and importance of

the topic, problem statement, barriers, purpose, significance, the research questions and

research objectives.

Howard Dresner, who is presumed to be the father of Business Intelligence in his interview

with Hannah Smalltree, a News Writer from SearchBusinessAnalytics in 2006 laid down

Business Intelligence as simply the methodology of aligning people and process with

purpose. He further went on that the next high ground is business performance management

(BPM) [also called corporate performance management], which is what Business Intelligence

becomes when it grows up or as he calls it, Business Intelligence with a purpose.

Business Intelligence is getting the right information to the right people at the right time to

support better decision making and gain competitive advantages (Waite, 2006).

Wikitionary defines Business intelligence as any information that pertains to the history,

current status or future projections of a business organization and any information that can be

of strategic use to an organization.

Business Intelligence enables the comprehension, understanding and profit from experience.

Business data and information is the soil that grows Business Intelligence, which provides the

capability to reason, plan, solve problems, think abstractly, comprehend ideas and language,

and learn from business data and information. Business intelligence is fueled from the

utilization of information aligned with business performance. Business intelligence is

constructed on the identification and modeling of focused business information. Asking the

right questions is the precursor to making intelligent decisions (Annie, 2007).

1.1 Elements of Business IntelligenceWordweb dictionary defines “Elements” as “An abstract part of something”, "a component or

constituent element of a system". So basically, elements of Business Intelligence are the

component parts that make the whole Business Intelligence system. Business Intelligence can

be classified into two components as below.

Hard Business Intelligence: is that aspect of Business intelligence that is seeable and not just

an abstract matter. It is that part that includes hardware, software, tools e.t.c and mostly used

by IT side of business.

1 | P a g e

Soft Business Intelligence: Evolves from the 4-step process of how we get knowledge

(Knowledge Management), how we analyze (Business Analysis &Analytics), how we store

(Data warehouse), how we retrieve and use information (Data Mining). Business intelligence

(BI) uses Knowledge Management, data warehouse, data mining and business analysis to

identify, track and improve key business processes and data, as well as identify and monitor

trends in corporate, competitor and market performance(Jayanthi, 2008;Dataflux,2009).

1.1.1 Knowledge Management

Knowledge is the most paramount asset any organization will be willing to have at their

disposal and the way it is gotten and used actually help maintain a higher competitive edge.

This can be seen from the way they improvise creation of knowledge, sharing of knowledge,

usage of knowledge and management of knowledge into their business processes.

(ExecutiveBrief, 2009).Knowledge is a fluid mix of framed experiences, values, contextual

information, and expert insight that provides a framework for evaluating and incorporating

new experiences and information. It originates and is applied in the minds of professionals in

the field. In organizations, it often becomes embedded not only in documents or repositories

but also in organizational routines, processes and norms (Davenport and Prusak, 1998). In

addition, Gartner Group defines Knowledge Management as a discipline that promotes an

integrated approach to identifying, managing and sharing all of an enterprise's information

assets. It is the discipline applied to manage intellectual capital."

1.1.2 Business Analysis and Analytics

Analysis as defined by Webster’s dictionary is the separation of a whole into its components

parts”. It is also defined as a process of inspecting, cleaning, transforming, and modeling data

with the goal of highlighting useful information, suggesting conclusions, and supporting

decision making.”(Wikipedia) On the other hand, defines business analysis as : “The

discipline of identifying business needs and determining solutions to business problems.

Solutions often include a systems development component, but may also consist of process

improvement or organizational change or strategic planning and policy development.” These

two definitions may explain why the IT and business views of analysis sometimes differ. IT

often defines data analysis as covering the complete information life cycle from cleaning and

transforming source data making it ready for analysis, to analyzing the transformed data and

creating analytics. Business users, on the other hand, view business analysis as a set of

techniques for defining analyses and creating analytics on the transformed data.

2 | P a g e

Analytics can also be known as metrics, measurements, and indicators which could also be

defined in different forms. It is Note-worthy that analytics is more often defined as “the

science of analysis” rather than the “results of analytical processing.” Since business

intelligence is about providing business users with intelligence about the business, Business

analysis can be said to be the process of analyzing trusted data with the goal of highlighting

useful information, supporting decision making, suggesting solutions to business problems,

and improving business processes. (Hass et.al, 2008)

1.1.3 Data Warehousing

Different people have different definitions for a data warehouse. The most popular definition

came from Bill Inmon, who provided the following:

A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of

data in support of management's decision making process.

Subject-Oriented: A data warehouse can be used to analyze a particular subject area. For

example, "sales" can be a particular subject.

Integrated: A data warehouse integrates data from multiple data sources. For example, source

A and source B may have different ways of identifying a product, but in a data warehouse,

there will be only a single way of identifying a product.

Time-Variant: Historical data is kept in a data warehouse. For example, one can retrieve data

from 3 months, 6 months, 12 months, or even older data from a data warehouse. This

contrasts with a transactions system, where often only the most recent data is kept. For

example, a transaction system may hold the most recent address of a customer, where a data

warehouse can hold all addresses associated with a customer.

Non-volatile: Once data is in the data warehouse, it will not change. So, historical data in a

data warehouse should never be altered.

Data warehousing is a foundational practice that supports enterprise reporting, business

intelligence and decision support. Data warehouses and data marts are created across levels of

sophistication and different philosophical approaches, but typically involve extracting and

transforming data from operational/transactional databases and loading it to a repository for

shared use and analysis. (1keydata.com, 2001)

3 | P a g e

1.1.4 Data Mining

Data mining is the process of extracting hidden knowledge from large volumes of raw data. It

can also be defined as the process of extracting hidden predictive information from large

databases. Data mining is not an “intelligence” tool or framework. Business intelligence,

typically drawn from an enterprise data warehouse, is used to analyze and uncover

information about past performance on an aggregate level. Data warehousing and business

intelligence provide a method for users to anticipate future trends from analyzing past

patterns in organizational data. Data mining is more intuitive, allowing for increased insight

beyond data warehousing. An implementation of data mining in an organization will serve as

a guide to uncovering inherent trends and tendencies in historical information. It will also

allow for statistical predictions, groupings and classifications of data. (Mladenic et. al, 2003)

Most companies collect, refine and deduce massive quantities of data. Data mining

techniques can be implemented rapidly on existing software and hardware platforms to

enhance the value of existing information resources, and can be integrated with new products

and systems as they become part of the system. When implemented on high performance

client/server or parallel processing computers, data mining tools can analyze massive

databases to deliver answers to many different types of predictive questions.

1.2 The Importance of Business IntelligenceThe instability of Business conditions is in a constant state of flux. Sales patterns change

from place to place and from time to time. Currency valuations shift and alter profit margins

based on economic conditions. Suppliers change their delivery schedules and their prices.

Advent and progression of the internet have made customers more aware of market trends

and therefore more demanding. Balancing on this shifting terrain, business managers are

expected to deliver steady earnings growth. Somehow, they must smooth out the bumps and

anticipate the changes (Chee et.al, 2009).

The importance of Business Intelligence is to enable organizations understand change, to

identify causal factors through analysis of data by region, currency, customer or other

relevant dimensions. As the CIO of a large chemical company said recently, “If you rely on

averages, it’s easy to be misled, but if you slice through the data, it’s possible to see exactly

what’s affecting costs.” For example WalMart captures point-of-sale transactions from over

2,500 stores in six countries in its tera-scale data warehouse. The information gotten is used

to ask questions like “which stores and for which months was a particular product in high

4 | P a g e

demand but short supply?” The answer gotten from that can be used to optimize inventory

and capitalize on pricing opportunities that exist transiently at any one local store. The

importance of BI is not limited to retailers. Telecommunications carriers also use it to

identify and mitigate fraud. A delivery service might predict which vehicles are most likely to

break down and its location using GPS and Business intelligence (Olszak, 2002).

A bank might use Business intelligence to identify customers who, based on their recent

activity, are likely to transfer their account to another financial institution. The possibilities

and opportunities are enormous and limitless.

Business Intelligence makes enterprise data actionable. It is the spice for any successful

business. It uncovers trends and patterns that might otherwise go undetected. Managing a

business on intuition, educated guesses or averages isn’t good enough anymore. To be

successful, a company needs a foundation of accurate, current and complete information and

only Business intelligence can get this to work (Netezza, 2004).

1.3 Problem StatementDaniel (2007) analyzes the three weaknesses of Business Intelligence in this era. They are

that “Business Intelligence tools provide out of date information, Business intelligence tools

fail to identify process problems, Business intelligence tools can’t be used as predictors”.

From the above, we can see that it all had to do with tools. This is a glaring instance that not

just tools are enough for successful implementation of business intelligence in an

organization. For many years, people have debated about the need for Business Intelligence.

But the assumption has been that Business Intelligence is rather a tool than a set of

components or methodologies. This initial perception fails to take into account those tools

without methodologies or practices will have an adverse effect on business. People have

failed to realize the essence of these components and only focus on what they call the big

picture (Turban et al., 2007). If this continues, business will not reap bountifully. By

rethinking our approach to what Business Intelligence actually is, we can make essence of

and maximize business processes. My urge to do this topic is to challenge the status quo of

the ground meaning of Business intelligence and help understand the subject matter.

1.4 Barriers to Business Intelligence implementationBusiness Intelligence Guide (2009), an aggregation of all accumulated research papers on

Business intelligence, points out that even though Business Intelligence it is the most highly

5 | P a g e

desired technology spanning a $10 billion a year market and growing at 10% a year, it still

suffers from a ‘relative inability to prove its value’( Agha et.al,2009)

Economist Intelligence Unit Study (2007) lists the following barriers or problems quoted

below:

Departmental silos remain the biggest barrier to data sharing with 63% of executives

agreeing.

New obstacles such as data access and clean data are also causing problems with 41%

respondents agreeing.

Employee resistance to adoption of new technology, fear of misinterpretation of data

with 78% still using old spreadsheet technology.

Lack of CIO participation in decision making process with only 22% companies who

allow CIO involvement.

1.5 Failure of Business IntelligenceBusiness intelligence failure can be attributed to a lot of factors. But a few of them as below

are based on the final output of what business intelligence is supposed to be like;

Inadequate management support.

Inadequate monitoring.

Reliant on non Real-time data.

Reactive rather than proactive.

Summarizing past rather than looking forward.

Esoteric nature of organizations.

Employees with necessary business and technical skills are rare.

1.6 Sectors that use Business IntelligenceBusiness Intelligence can be used in all walks of life. Basically it can be used by both the

Public sector, private sector and non-governmental or charity organizations.

1.7 Purpose of ResearchThe purpose of this research is to challenge the status quo and bring out a new reasoning on

what Business Intelligence really is.

6 | P a g e

1.8 Significance of ResearchThe significance of this research is to show that Business Intelligence is more than just a tool

but also involves methodologies. Everyone included in the business chain will benefit from

this. The organization can make better decisions based on accurate data, the suppliers also

can know when to supply, while customers will gain customized offerings and will be better

served.

1.9 Research QuestionsTo what extent will the implementation of BI impact the success of an organization.

1.10 Research Objectives To identify the Critical Success Factors for implementation of Business Intelligence.

To evaluate the benefit and impact of BI on organizations.

To develop a theoretical framework to test the underlying factors.

To empirically test and validate the framework created.

To propose recommendation.

7 | P a g e

2 LITERATURE REVIEWThis chapter critically evaluates past literature in the attempt to amassing wealth of

knowledge critically analyze past literatures. It then moves into the critical factors responsible

to make it successful, and it also sees the relationship or fusion of each of the success factors

to the topic and finally a framework is created based on the factors.

Pattron (2009) describes literature review as a Systematic review of available resources

which involves;

Theoretical and conceptual concepts

Identification of independent and dependent variables

Measurement and operational definitions

Selection of appropriate research technique

Sampling strategy

Statistical technique

Findings and conclusions of similar studies studied

The rapid pace of today’s business environment has made Business Intelligence

indispensable to any organization’s success. Even though the term is not used explicitly, the

way and manner organizations analyze and make decisions all wrap around business

intelligence. Business Intelligence systems turn a company's raw data into useable

information that can help management identify important trends, analyze customer behavior,

and make intelligent business decisions quickly. Over the past few years, business

intelligence has been used to understand and address back office needs such as efficiency and

productivity. Now organizations are increasingly using Business Intelligence to analyze

customer behavior, understand market trends, and search for new opportunities. So they see it

as a tool rather than a set of components coming together to achieve the main purpose of

business (Sun, 2005).

The current trend in which most people and even practitioners see Business Intelligence is as

a tool which helps them get insights into data and business processes. Past literature shows

that this thought about business intelligence started out from its founder. According to Gibson

et.al (2004), the term Business Intelligence and its key concepts originated with Gartner

8 | P a g e

Research in 1989. Howard Dresner of Gartner Research, who is also widely recognized as the

father of Business Intelligence, first coined the term as “a broad category of software and

solutions for gathering, consolidating, analyzing and providing access to data in a way that

lets enterprise users make better business decisions”. Since then, leading vendors,

practitioners and prominent authors have used various other definitions to capture the essence

of Business Intelligence.

These definitions are summarized in Table 1. A comparison of the definitions reveals that

they generally fall into three main categories, namely the management (a.k.a. process) aspect,

the technological aspect, and the product aspect. The management and the technological

aspects recognize the traditional separation between technical and managerial approaches and

are in line with Petrini and Pozzebon’s observation(Petrini and Pozzebon,2002). Following

Chang’s suggestion, the third aspect (i.e., product) is added to capture the view of those who

see BI from a solution’s perspective(Chang,2006). Table 2 categorizes existing definitions of

Business Intelligence using the three categories found here.

Table 2.0: Summary of varied Business Intelligence definitions (Source: Developed for this

thesis, adapted from Chee et.al, 2009)

Author Own definition of Business Intelligence

Gartner Research(Hostmann

2007)

An umbrella term that includes the analytic applications, the infrastructure

and platforms, as well as the best practices.

Dresner (1989) broad category of software and solutions for gathering, consolidating,

analyzing and providing access to data in a way that lets enterprise users

make better business decisions

Business Objects

(Business Objects 2007)

The use of an organization’s disparate data to provide meaningful

information and analysis to employees, customers, suppliers, and partners

for more effective decision making.

SAS Institute (Ing 2007) Delivering the right information to the right people at the right time to

support better decision making and to gain competitive advantage.

Oracle (Oracle 2007) A portfolio of technology and applications that provides an integrated, end-

to end Enterprise Performance Management System, including financial

9 | P a g e

performance management applications, operational BI applications, BI

foundation and tools, and data warehousing.

Turban et al. (2007) An umbrella term that encompasses tools, architectures, databases, data

warehouses, performance management, methodologies, and so forth, all of

which are integrated into a unified software suite.

Pirttimäki et.al,(2007) Business intelligence (BI) is a managerial concept and tool that is used to

help organizations to manage business information and to make effective

decisions

Adelman and Moss

(2000)

A term encompasses a broad range of analytical software and solutions for

gathering, consolidating, analyzing and providing access to information in

a way that is supposed to let an enterprise’s users make better business

decision.

Wikitionary (accessed 7th

May 2010)

Any information that pertains to the history, current status or future

projections of a business organization and any information that can be of

strategic use to an organization.

Annie(2007) Provides the capability to reason, plan, solve problems, think abstractly,

comprehend ideas and language, and learn from business data and

information. Business intelligence is fueled from the utilization of

information aligned with business performance

Olszak (2002) Set of concepts, methods and processes that aim at not only improving

business decisions but also at supporting realization of an enterprise’s

strategy.

Chase(2001) Acquisition and utilization of fact based knowledge to improve a

business’s strategic and tactical advantage in the marketplace.

Moss(2003) It is an architecture and a collection of integrated operational as well as

decision-support applications and databases that provide the business

community easy access to business data.

Azvine(2006) how to capture, access, understand, analyze and turn one of the most

valuable assets of an enterprise – raw data – into actionable information in

order to improve business performance

10 | P a g e

Table 2.1: Three approaches to the definition of Business Intelligence (Source: Adapted from

(Petrini and Pozzebon, 2004), (Chang, 2006)).

Approach Managerial/Process Technological Product

Definition Focus on the process of gathering

data from internal and external

sources and of analyzing them in

order to generate relevant

information for improved

decision making.

Focus on the tools and

technologies that allow

the recording, recovery,

manipulation and analysis

of Information.

Describe BI as the

emerging result/product

of in-depth Analysis of

detailed business data as

well as analysis

practices using BI tools.

Author Whitehorn & Whitehorn (1999);

Business Objects (2007); Cognos

(2004); SAS Institute (2007);

Moss & Hoberman (2005);

Hostmann (2007); Oracle (2007);

Turban et al. (2007); Markarian,

Brobst & Bedell

(2007),wikitionary(en.wiktionary

.org/wiki/business_intelligence,

accessed 7th May 2010)

Moss & Atre (2003);

Moss & Hoberman

(2004); Adelman &

Moss (2000);Turban et al.

(2007); Oracle

2007);Hostmann (2007)

* Note: The definition of

Hostmann (2007) and

Moss &

Hoberman (2005) spans

across

both process and

technological

approaches.

Chang (2006);

Gangadharan &Swami

(2004); Kulkarni &

King, (1997); Turban et

al. (2007)

* Note: The definition of

Turban et al. (2007)

spans across all three

approaches.

11 | P a g e

Business Intelligence

Critical Success Factors

Definition(s) Variable(s) Definition(s) References

Knowledge Management fluid mix of framed

experiences, values, contextual

information, and expert insight

that provides a framework for

evaluating and incorporating

new experiences and

information (Davenport and

Prusak, 1998)

Leadership Process of social influence in

which one person can enlist the

aid and support of others in the

accomplishment of a common

task.

Chemers (2002)

Culture Combination of shared history,

expectations, unwritten rules,

and social customs that compel

behaviors. It is the set of

underlying beliefs that, while

rarely exactly articulated, are

always there to influence the

perception of actions and

communications of all

employees

Hasanali(2002)

Structure, Roles and

responsibilities

different ways and manners

organizations structure the

governance of their Knowledge

Management initiatives

Hasanali(2002)

IT infrastructure The use of IT in any

organization is to facilitate

Hasanali(2002)

12 | P a g e

better work environment and

conditions

Measurements any process by which a value is

assigned to the level or state of

some quality of an object of

study

Lord Kelvin(1929)

Business Analysis and

Analytics

process of inspecting, cleaning,

transforming, and modeling

data with the goal of

highlighting useful information,

suggesting conclusions, and

supporting decision making

Hass et. Al(2008)

Data Warehousing foundational practice that

supports enterprise reporting,

business intelligence and

decision support

(1keydata.com,

accessed 20th

May 2010)

Data Mining Process of extracting hidden

predictive information from

large databases. Data mining is

not an “intelligence” tool or

framework

Mladenic et. al,

(2003)

Table 2.2

13 | P a g e

Business Intelligence Outcome Performance Outcomes

Business Performance

Business Performance is the

ability of an organization to

achieve the maximum level of

profitability possible given the

human, financial, capital, and

other resources it possesses.

(Luftig, 1998)

ROI A measure of a corporation's profitability, equal to a fiscal year's income divided bycommon stock and preferred stock equity plus long-term debt. ROI measures how effectively the firm uses its capitalto generate profit; the higher the ROI, the better(carmel,2001).

Turnover the ratio of annual sales to inventory; or equivalently, the fraction of a year that an average item remains in inventory. Low turnover is a sign of inefficiency, since inventory usually has a rate of return of zero. here also called inventory turnover. For a mutual fund, the number oftimes per year that an average dollar of assets is reinvested(Bodieet.al, 2004).

Market Share portion or percentage of sales of a particular product or service in a given region that are controlled by a company(carnammie,2007)

Sales Volume Quantity or number o f goods sold or services rendered in the normal operations of a firm in a specified period(danny et.al,2007)

Competitive advantage

A competitive advantage is an advantage over competitors gained by offering consumers greater value, either by means of lower prices or by providing greater benefits and service that justifies higher prices (Porter, 1998).

 Reputation of a company is its important and valuable asset. A

14 | P a g e

Company reputation

positive one may bring many benefits to a company, when a negative one may significantly harm it. A company reputation is closely tied up with its stakeholders' emotional beliefs about it. In this article you will find out about some key factors of the reputation, its relation to the company's stakeholders and its consequences of an organization (Chang, 2006).

2.2 Business Intelligence and its Critical Success FactorsMind tools (accessed 12th June 2010), simply explains critical success factors as Identifying the

things that really matter for success. It further went on to explain CSFs as the essential areas of

activity that must be performed well if a business is to achieve the mission, objectives or goals

set by it. As stated out in the table above, The critical success factors for successful

implementation of Business Intelligence cuts through aspects of the 4-step process in chapter one

of this thesis on how we get knowledge (Knowledge Management), how we analyze (Business

Analysis &Analytics), how we store (Data warehouse) and finally, how we retrieve and use

information (Data Mining) (Jayanthi, 2008;Dataflux,2009).

2.2.1 Knowledge Management

Knowledge is the most paramount aspect of any organization aspiring success or already

successful and the way it is gotten and used actually help them maintain a higher competitive

edge. This can be seen from the way they improvise creation of knowledge, sharing of

knowledge, usage of knowledge and management of knowledge into their business processes.

(Executive Brief, [Accessed 25th May 2010]).Knowledge is a fluid mix of framed experiences,

values, contextual information, and expert insight that provides a framework for evaluating and

incorporating new experiences and information. It originates and is applied in the minds of

professionals in the field. In organizations, it often becomes embedded not only in documents or

repositories but also in organizational routines, processes, and norms (Davenport and Prusak,

1998). In addition, Gartner Group defines Knowledge Management as a discipline that promotes

15 | P a g e

an integrated approach to identifying, managing and sharing all of an enterprise's information

assets. It is the discipline applied to manage intellectual capital."

Bailey and Clarke (2008) describes Knowledge Management as ``how managers can generate,

communicate and exploit knowledge (usable ideas) for personal and organizational benefit'' and

highlights not only the organizational importance of Knowledge Management, but also its

relevance for individual managerial action. ``Organization benefit'' means improving the

effectiveness of organization strategy, operational processes, and change management, thus

ensuring that the Knowledge Management focus is currentmnjh. ``Personal benefit'' means that

the individual manager is able to identify ``what's in it for me to adopt a Knowledge

Management perspective?'', thus capturing the personal motivation for adopting a KM frame of

reference, the importance of which has been largely omitted from discussions on the subject

(Hackett, 1999; McAdam and McCreedy, 1999). Finally, the words ``how managers can'' are

important here too. For managers to do anything with ideas that are personally relevant and

organizationally important, they need to be able to see Knowledge Management as within the

range of actions available to them within their role, level, power etc.

Some inherent critical success factors are built into this definition because without them,

knowledge will not be able to be gotten, refined or kept for further use. Knowledge Management

is a set of strategies and approaches, which denotes a definite structure or a way to do things.

Another critical piece of this definition is that this approach enables the flow of information to

the right person at the right time; otherwise, an organization would be managing its knowledge

just for the sake of managing it and not to create value. That brings us to the most critical aspect

of Knowledge Management: creating more value for the organization. The most elaborate

knowledge-sharing procedures will not help if the knowledge shared within an organization does

not enable its recipient(s) to create value, be it through increased revenue or time or cost savings

(Hasanali, 2002)

The successful implementation of Knowledge Management initiative depends on many factors,

some within the organization’s control, others not. Typically, some factors have effects on

Knowledge Management and can be grouped into five primary categories as below:

Leadership:

16 | P a g e

Organizational viability depends in part on effective leadership. Effective leaders engage in both

professional leadership behaviors (e.g. setting a mission, creating a process for achieving goals,

aligning processes and procedures) and personal leadership behaviors (e.g. building trust, caring

for people, acting morally). Interestingly, most of what we know about leadership comes from

the examination of how employees relate to their immediate supervisors. However, examining

individual perceptions of “leadership” at the organizational level is an interesting proposition

(Bailey and Clarke, 2008). At first glance, it may seem that professional leadership behaviors

such as aligning processes and procedures may be more easily conceptualized at the

organizational level than personal leadership behaviors such as acting morally. However, recent

events such as Enron and WorldCom suggest the important impact of personal leadership. In

these cases, negative personal leadership behaviors were present throughout the organization and

the consequences were dramatic (Hill and Knowlton, 2007).

Ideal leaders do not exist in practice. Thus, we can relate to leadership as a progressive

development only. Since we humans cannot be fully conscious of our emotions, a posteriori, we

cannot fully mobilize them in order to understand and attain our life goals and purpose. Because

our purpose remains opaque at best, it follows that leaders will act unethically even when they do

so unwillingly or unconsciously. The only way for leaders to improve their ethical position is to

interact with others in society to help them reveal their hidden agenda over time (Kotter, 1990).

The particular worldview, in turn, shapes these agendas, either Theta or Lambda that a person

embodies in his search for greater self-awareness and contextualization with his external

environment. Both the behavioral perspective as well as the economic model examines

leadership as a role whose purpose is to assist an organization to adapt. That is how an individual

practicing leadership can help an organization to affect adaptive change (Heifetz, 1998; Nanus,

1995)

– The theory of leadership

Adding to Kurt Lewin’s (1945) observation that “there is nothing as practical as a good theory”,

Whetten (2002) suggests that only a good theory is practical. Hence, we have two successive

goals:

we should understand the components that comprise theory; and

17 | P a g e

we should incorporate this knowledge into the theory of leadership.

Like any theory, leadership theory has to answer to three key questions – what, why and how

(Whetten, 2002). “What” refers to the constructs analyzed, or the target of theorizing; “how”

explains the methods we use to create interrelationships between constructs of the theory; and

“why” represents the conceptual assumptions behind these relationships. Thus, in leadership

theory the “what” represents the goal that the leader looks to attain, the “how” explains the way

the leader reaches the goal, and the “why” explains the reasons behind selecting this particular

method for attaining the goal. However, we contend that while the literature into leadership deals

with what leaders do or how they do it, it is silent about the reasons for why leaders are

motivated to pursue such activities.

Chemers (2002) describes leadership as the process of social influence in which one person can

enlist the aid and support of others in the accomplishment of a common task. Leadership plays a

key role in ensuring success in almost any initiative within an organization. Its impact on

Knowledge Management is even more pronounced because the more experienced a leader is, the

more he or she can make meaning out of Knowledge available. Nothing makes greater impact on

an organization than when leaders model the behavior they are trying to promote among

employees. The CEO at Buckman Laboratories, a chemicals company, orchestrates the

Knowledge Management initiative within the organization and personally reviews submissions

to its knowledge bank. When he notices that a particular employee has not had been active

within the system, he sends a message that reads: "Dear associate, you haven't been sharing

knowledge. How can we help you? All the best, Bob." (Hasanali, 2002)

Several other best-practice organizations have demonstrated this commitment to Knowledge

Management. At the World Bank, the president's support led to the creation of an infrastructure

that promoted and supported the growth of communities of practice (CoPs) not only throughout

the organization, but also around the globe. Today, the World Bank has sustained its Knowledge

Management initiative through its CoPs. Its knowledge managers constantly search for new

approaches to knowledge sharing.

18 | P a g e

Although leadership plays a critical role in the success of the Knowledge Management initiative,

the "culture" factor can be even more important to the success of Knowledge Management. This

moves us into the next section.

Culture:

From the Corporate perspective, culture helps explain why some companies are more successful

than others. Kotter and Heskett(1992) investigated the relationship of culture to corporate

performance, they summarized their research by means of four conclusions as below

Corporate culture can have a significant impact on a firm’s long-term economic

performance.

Corporate culture will be an even more important factor in determining the success or

failure of firms.

Cultures that inhibit strong long-term financial performance are common, and they

develop easily even when employees are reasonable and intelligent people

Although tough to change, corporate cultures can be made more performance enhancing

The effects of culture on the performance of an organization depend, not on the strength of the

overall culture, but on the mix and weightings of the components of that culture. An example is

the component of conflict, which may be a healthy incentive for action and competition when

present in some forms and degrees, but can be damaging when it becomes the culture’s dominant

feature and its existence is not acknowledged. Research theory in the management of non-profits

emphasizes the need for consonance and deplores the existence of conflict; however, research

shows that some community organizations do not fit the model presented in the literature and

that conflict does exist in these organizations and can cripple their ability to function in goal-

setting, staffing, the conduct of meetings, problem solving and decision making, the

identification and utilization of individual skills, and writing submissions for government

funding(Heskett,1999).

The concept of organizational culture has been well documented in the literature, though there is

still inconsistency in definitions and therefore uncertainty in the meaning of the term. While

some authors see culture as basic assumptions held by organization members (Sathe, 1983;

Schein, 1984; Lewis, 1992), most authors prefer to view it as a combination of assumptions,

19 | P a g e

feelings, beliefs, values and behaviour. Lewis (1996) believes that this preference could be either

a result of the culture model’s basing itself on the organizational development model, which

takes this broad view of culture; or a result of the important influence of the books of Peters and

Waterman (1982) and Deal and Kennedy (1982), who also propounded the “combination”

theory. Whichever view of culture is taken, culture itself is a contributing factor to what makes

one organization different from another. It is the essence of an organization – its character, its

personality. It is therefore long-term and very difficult to change. Some researchers (Uttal, 1983)

argue it is almost impossible to change. Also according to the literature, culture can have a

significant impact on the effectiveness and competitive advantage of an organization (Bettinger,

1989; Brown, 1992; Fiol, 1991; Kilmann, 1989; Petrock, 1990; Sherwood, 1988; Whipp,

Rosenfeld and Pettigrew, 1989).

Culture is the combination of shared history, expectations, unwritten rules, and social customs

that compel behaviors. It is the set of underlying beliefs that, while rarely exactly articulated, are

always there to influence the perception of actions and communications of all employees.

(Hasanali, 2002)

Cultural issues concerning Knowledge Management initiatives usually arise due to the following

factors:

Lack of time :

Life is time the meaning of life is to enrich the lives of others. The way you manage your time is

the way in which you manage your life. Infact, time cannot be managed. However, you can

manage the activities in your life. (Dobbins and Pettman1998).Dobbins and Pettman(1998), also

identifies most common time waster which are as below.

They say that managers attribute their time wasting to the following causes:

Telephone calls

Unexpected visitors

Poor delegation

Ineffective, prolonged, unnecessary meeting

No clarity of objectives, planning

20 | P a g e

Fire fighting, being reactive rather than proactive

Trying to juggle too many balls at the same time

Inability to make decisions, delayed decisions

Failing to say “NO”

Poor communications, unclear instructions

Unavailable, inaccurate information

Poor self discipline

Incompetent staff

Social “business”

According to Drucker (1967), executives do not manage their time well. We assume that this

issue applies to all professions (e.g. Dahl, 1990). Currently there is a major shift of the workforce

from manual work to knowledge and service work. According to Drucker, we have since

Scientific Management been concerned with the most effective use of time where it matters least

– manual work. Here the difference between time-use and time-waste is primarily efficiency and

cost. “But we have not applied it to the work that matters increasingly, and that particularly has

to cope with time: the work of the knowledge worker and especially of the executive. Here the

difference between time-use and time-waste is effectiveness and results” (Drucker, 1967, p. 35).

Hence, with an increasing number of knowledge workers, it becomes more and more vital to

make time effective.

We forget that our preoccupation with time as measured by the hands of a clock is a relatively

modern phenomenon. In Western Europe most cities and market towns had public clocks only by

the seventeenth century, and even then ones that had to be frequently reset using a sundial, such

was their inaccuracy. Timekeeping was imprecise, but this changed with the advent of the

industrial revolution which “required worker discipline if machine and man were to be

integrated” (Thrift, 1990). The industrial revolution enabled the mass manufacture of watches –

portable, personal timekeeping devices, which became at once both a status symbol to own and a

source of enslavement that served to entrap all future generations within a chronological

paradigm of time: time is duration that can be, indeed must be, timed.

Over the centuries precision in timekeeping has increased not only because watches and clocks

have become more reliable but also because the integration of modern society has demanded it.

21 | P a g e

Topik (1992) suggested, for example, that precision in timekeeping in modern China began only

when transport and travel became more rapid and the need to integrate such systems increased.

The study of the content of the advertising in one American magazine led Gross and Sheth(1989)

to infer that consumers became more concerned with clocktime as society became more

industrialized and urbanized.

The goal is not to encourage the employees to work more, but to work more effectively. The

processes, technologies, and roles designed during a Knowledge Management initiative must

save employees' time, not burden them with more work. This can only be accomplished if the

employees' work patterns are accounted for during the initial design and planning phase of the

initiative.

Unconnected reward systems:

There is a substantial body of theoretical literature that links organizational strategy, human

resource (HR) practices, and performance (Balkin and Gomez- Mejia, 1987; Hambrick and

Snow, 1989; Lawler, 1986a; Lawler, 1986b; Ulrich and Lake, 1990; Waldman, 1994). This

literature typically suggests that human resource practices should be selected which complement

and support an organizational strategy. More specifically, the reward system should be aligned to

motivate employee performance that is consistent with the firm's strategy, attract and retain

people with the knowledge, skills and abilities required to realize the firm's strategic goals, and

create a supportive culture and structure (Galbraith, 1973; Kilmann, 1989; Nadler and Tushman,

1988). Furthermore, the literature argues that alignment of the reward system with organizational

strategy helps to determine organizational effectiveness.

A review of the literature which links organizational strategy and human resource practices by

Becker and Gerhart (1996) suggests that the human resource system can be a unique source of

competitive advantage, especially when its components have a high degree of internal and

external fit. Another review by Gomez-Mejia and Balkin (1992) contends that the old model of

compensation (with pay structures based on job analyses, descriptions, specifications, and

classifications) is no longer effective in today's business environment. They conclude that

modern organizations must align their reward system practices with their organizational strategy

in order to achieve higher levels of performance at both the individual and organizational level.

22 | P a g e

At this point, the literature has remained mostly at the conceptual level in discussing the link

between organizational strategy, the reward system and firm performance. These propositions

have remained largely untested and there is a recognized need for empirical work in this area

(Lawler and Jenkins, 1994; Ledford, 1995; Waldman, 1994).

Reward and recognition for individual employees remains one of the controversial areas of

quality management. Notable authors such as Deming (1986) believed that fair ratings in such

systems were impossible due to supervisor biases, worker competition and organizational

politics. More recently, Scholtes (1995) has listed five reasons to explain why reward,

recognition and incentive systems do not work:

no data to show long term benefits;

they set up internal competition;

reward systems undermine teamwork and co-operation;

they often reward those who are lucky and pass by those who are unlucky; and

they create cynics and losers.

Organizations have to maintain a balance between intrinsic and explicit rewards in order to

encourage employee behavior. The most effective use of explicit rewards has been to encourage

sharing at the onset of a Knowledge Management initiative. If the attendees don't find value in

either the meetings or the information on the system, providing incentives will not sustain their

participation. People share because they want to, they like to see their expertise being used, and

they like being respected by their peers.

Lack of common perspectives - Sharing must be inspired by a common vision. The

people affected by the new process or technology must all buy in to this vision and

believe it will work. This usually happens when those in charge are from different work

background, so they have different beliefs and different attitudes to work.

No formal communication - Internal communication has for many years been the

stepchild of public relations and communication management. Clutterbuck and James

(2001) quote a survey by Business Intelligence that two thirds of businesses in the UK

formed internal communication departments only within the past few years. The internal

communication discipline has, however, begun to compensate for its Cinderella image. In

23 | P a g e

a Hill and Knowlton survey among more than 250 senior corporate communications

officers 90 per cent of the participants list building support among employees and other

key stakeholders as critical to their programme(Hill and Knowlton ,1997) . Employee

communication is rated second, after addressing company changes, as an issue facing

corporations in five years.

When designing and implementing Knowledge Management initiatives, the Knowledge

Management charter should ensure that employees and customers know about the changes

occurring in the organization. It has been hypothesized that a person needs to hear the same

message at least three times before it registers in the brain. Hence, communication should be

pervasive and reiterative from time to time. When an organization designs Knowledge

Management initiatives around its culture, it will be able to initiate a cultural change.

Most material dealing with effective organizational communication assumes that one individual

is the sole receiver of that communication. In actual practice, much organizational

communication involves communication aimed at groups. This communication often takes place

in meetings. Therefore, organizational communication directed toward groups and transmitted

within meetings needs study and attention (Spinks and Wells, 2006). Implication for

organizational communication is that communication must not be directed towards individuals

alone, but must be directed towards groups, formal and informal, which exist in the organization.

Herein lies a serious problem. The power to influence the success or failure of organizational

activities that is wielded by informal groups is so great that those groups cannot be ignored, but

must be recognized and dealt with as real entities. However, in so doing, leaders must be careful

not to usurp the legitimate and rightful role of the formal organization and its formal groups.

Striking a balance – a happy medium between these two factors – is a hallmark of a good

organizational leader (Spinks, 2007).

Structure, Roles and Responsibilities:

Staniforth (1994) explains that some 72 per cent of manufacturing firms in a survey, report

significant changes in their organization structures in the last two years. Structure is seen by

many as a powerful tool in mobilizing resources in both an efficient and effective manner. The

desire for change is clearly present, but whether positive outcomes will result is another matter.

24 | P a g e

What is the role of structure? What factors affect structure? Which structural types work best?

These are some of the key questions many senior managers have to grapple with. Organization

structure is the formal presentation of systems of positions and relationships within the firm. It

should be an operational statement of the firm’s goals.

It specifies formal communication channels, who does what and who is responsible for

whom/what. Structure may be seen as a statement from senior management as to how they wish

the firm to work (Leavitt, 1978). In essence the structure of the firm should reflect the activities

of the firm. As trends towards team working, empowerment, total quality management, etc.

gather pace, structure needs to facilitate these initiatives. While many firms are now much better

at displaying mission statements, quality definitions and other corporate data, many people inside

the firm remain unaware of the organization chart and its true significance.

In any group there is differentiation between the group members in terms of the functions they

perform (Hare, 1994).These different functions constitute the roles of the group members

whether they be formal (such as chairperson or secretary) or informal (such as facilitator or

joker). Although a great deal has been written about social roles (Biddle, 1979; Mills, 1984),

little research of an empirical nature has been carried out into the different types of roles in small

groups. Most research on roles has come either from sociologically-oriented psychologists

(Heiss, 1981; Stryker and Statham, 1985) who focus on theoretical accounts of roles or from

management psychologists who tend to rely on descriptive case studies (Adair, 1986; Handy,

1985).Even social psychologists have tended to concentrate almost exclusively on just one role

in the group – the leader.

Although the leader is the single most influential member of a group, the collective influence of

the remaining members can easily exceed the leader’s influence (Hare and Kent, 1994). One

exception to this is work based on Belbin’s team-role model (Fisher et al., 1998, 2000, 2002).

Belbin (1981, 1993) argued that an individual member of a group usually adopts a specific way

of interacting with other members. Some behaviours are either favoured or resisted by

individuals, who choose particular roles according to their natural disposition. Individuals find it

easier to play a role that fits their personality characteristics and this result in effective

participation within a group. According to Belbin, the useful people to have in teams are those

who possess the strengths or characteristics which serve a particular need without duplicating

25 | P a g e

those already there. What is needed for effective teams is not well-balanced individuals but

individuals who balance well with one another. In this way, weaknesses can be compensated and

strengths used to full advantage.

Governance of Knowledge Management initiatives differ within different organizations. A

steering committee should be put in place to oversee the general success and progress of the

knowledge Pool. The steering committee usually consists of executives at the top level. They

promote the concept and provide guidance, direction, and support. The central Knowledge

Management group is typically made up of three to four people who provide the initial support

for projects or initiatives, which are usually handed over to the business owners once they are

implemented.

The central group usually consists of people with advanced project management, facilitation, and

communication skills. The stewards, or owners, are responsible for knowledge sharing and

acquisition within the business units. Like the core Knowledge Management group, the stewards

are change agents for the organization. They model and teach employees the principles of

knowledge sharing using a common vocabulary. All of these participants work as a team to

prevent a silo mentality and incorporate resistant employees in the process (Hill and Knowlton,

2007).

Although the structure is put in place to establish ownership and accountability, if there is no

overall ownership of knowledge and learning within the organization and the leadership does not

"walk the talk," it will be difficult to sustain any sharing behavior.

IT infrastructure

As the global business environment has become more dynamic and complex, competition among

companies has become increasingly intense amid ever tighter budget constraints. This tension

has forced organizations to make the management of all its resources a priority. The

improvement of productivity, cycle times, customer service and responsiveness has become ever

more critical. At the same time, business executives are expected to make quick but careful

decisions that will take advantage of emerging opportunities. Therefore, they are beginning to

realize the importance of information technology (IT) and understand its role in changing and

improving the way businesses operate.

26 | P a g e

Although IT is an important tool in attaining the desired growth and competitiveness of today’s

businesses, it may also constitute a major portion of an organization’s capital investment

(Alshawi et al., 2003; Kumar, 2004; Huang et al., 2006). As reported by Cuneo (2005), average

IT spending among the companies in InformationWeek 500 during 2001-2005 was

approximately US$ 300 million per year. Moreover, IT spending in the US economy has

increased by more than 200 per cent since 1970 (Mistry, 2006). IT investment and its payoffs

have always been important to executives but now there is another issue which is increasingly

concerned under ever-changing business environments. The question is, with a large investment,

how can IT infrastructure be managed to best achieve today’s business goals as well as future

demand? The simple answer is that IT infrastructure must be flexible enough to handle changes.

However, there are two questions that must be answered first: what is ‘‘IT infrastructure

flexibility’’ and what characteristics of IT infrastructure are considered ‘‘flexible’’?

Firstly, IT is constantly evolving and change happens very quickly. Improved IT products and

services are released every day. In most cases, it is difficult for organizations to implement new

IT systems without a large re-investment and without affecting regular business operations.

Secondly, IT infrastructure is a long-term asset, a long-term shareholder value and it represents

the long-term options of an organization (Weill and Broadbent, 1998). Since IT infrastructure

involves a large investment and affects the entire organization, it is difficult to change in a short

period of time. Therefore, it must be able to support change without having to start from scratch

every time a new development is introduced because that costs too much and takes too long to

implement (Robertson and Sribar, 2002; Schalken et al., 2005). Thirdly, although some research

has been conducted concerning IT infrastructure flexibility, the concept itself is still vaguely

understood and not fully developed.

The use of IT in any organization is to facilitate better work environment and conditions.

Without a solid IT infrastructure, an organization cannot enable its employees to share

information on a large scale. Yet the trap that most organizations fall into is not a lack of IT, but

rather too much focus on IT. A Knowledge Management initiative is not a software application;

having a platform to share information and to communicate is only part of a Knowledge

Management initiative. Following are some Knowledge Management success factors related to

IT.

27 | P a g e

Approach – Has to do with the ideas or actions intended to deal with a problem or

situation. The people who are charged with implementing Knowledge Management must

take the time to understand their users' needs. Matching the Knowledge Management

system with the Knowledge Management objectives is essential.

Content - With a similar focus on users' needs, establishing great content involves having

processes in place to acquire, manage, validate, and deliver relevant information, when

and where it is needed.

Common platforms - Standard companywide architecture ensures the sustainability and

scalability of Knowledge Management efforts. By understanding the organization's

infrastructure at a high level, the steering committee can guide the Knowledge

Management team in picking the appropriate technology. This is mostly done with IT

people. It is best to have as part of the top Management an IT person with knowledge of

business. This easily comes if the individual comes from an IT background and went

further to study business or management. Sometimes organizations realize that they need

a complete overhaul of their IT infrastructure before they can expect their employees to

share knowledge. Many organizations have eliminated or are in the process of phasing

out customized legacy systems and replacing them with market-standard operating

systems. This enables organizations to build on the existing architecture by using off-the-

shelf software that was written to support these platforms, thus avoiding costly

customized packages.

Simple technology – The effectiveness of GOMS comes with usability of IT systems,

GOMS an acronym for Goals, Operators, Methods, And Selection Rules is a kind of

specialized human information process or model for human computer interaction

observation. GOMS reduces a user's interaction with a computer to its elementary actions

(these actions can be physical, cognitive or perceptual) (Stuart et.al 1983). If it takes

more than three clicks to find knowledge on your system, users will get frustrated. That

also has to be tempered with the amount of information being delivered and the

complexity of information demanded by the user. Another common mistake made in

information delivery is the emphasis on explicit knowledge. Although technology is

primarily used to deliver explicit knowledge, placing too much emphasis on it causes the

28 | P a g e

user to lose the context in which the information was shared and leads to

misunderstanding on how to interpret the knowledge.

Adequate training - Knowledge Management is enabled by adequate technology and

people who know how to use it. Best-practice examples reveal that the central

Knowledge Management group should spend most of its time (after deployment)

teaching, guiding, and coaching users how to use the system to interact, communicate,

and share information and knowledge with one another.

Measurements

Lord Kelvin, way back as 1906 explains measurement to be any process by which a value is

assigned to the level or state of some quality of an object of study. Most people fear

measurement because they see it as synonymous with ROI, and they are not sure how to link

Knowledge Management efforts to ROI (Compton, 1992). Although the ultimate goal of

measuring the effectiveness of a Knowledge Management initiative is to determine some type of

ROI, there are many intervening variables that also affect the outcomes.

In order for companies to ensure achievement of their goals and objectives, performance

measures are used to evaluate, control and improve production processes. Performance measures

are also used to compare the performance of different organizations, plants, departments, teams

and individuals, and to assess employees. Heim and Compton(1992) quoted the following words

of Lord Kelvin (1824-1907): ”When you can measure what you are speaking about and express

it in numbers, you know something about it … (otherwise) your knowledge is a meager and

unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely in thought

advanced to the stage of science.” In fact, the importance of performance measures was clearly

emphasized by the Foundation of Manufacturing Committee of the National Academy of

Engineering where one of the ten foundations of world-class practices states: “World-class

manufacturers recognize the importance of metrics in helping to define the goals and

performance expectations for the organization. They adopt or develop appropriate metrics to

interpret and describe quantitatively the criteria used to measure the effectiveness of the

manufacturing system and its many interrelated components (Edosomwan, 1990).

29 | P a g e

Companies will lose market share to overseas competitors who are able to provide higher-quality

products with lower costs and more variety. To regain a competitive edge companies will not

only shift their strategic priorities from low-cost production to quality, flexibility, short lead time

and dependable delivery, but also implemented new technologies and philosophies of production

management (i.e. computer integrated manufacturing (CIM), flexible manufacturing systems

(FMS), just in time (JIT), optimized production technology (OPT) and total quality management

(TQM)). The implementation of these changes revealed that traditional performance measures

have many limitations and the development of new performance measurement systems is

required for success (Ghalayini and Noble, 1996).

Because many variables may affect an outcome, it is important to correlate Knowledge

Management activities with business outcomes, while not claiming a pure cause-and-effect

relationship. Increased sales may be a result not only of the sales representatives having more

information, but also of the market turning, a competitor closing down, or prices dropping 10

percent. Due to the inability to completely isolate knowledge-sharing results, tracking the

correlations over time is important (Hasanali, 2002).

2.2.2 Business Analysis and Analytics

Analysis as defined by Webster’s dictionary is the separation of a whole into its components

parts”. It is also defined as a process of inspecting, cleaning, transforming, and modeling data

with the goal of highlighting useful information, suggesting conclusions, and supporting decision

making”(Wikipedia [assessed 20th May 2010]). On the other hand, business analysis is defined

as: “The discipline of identifying business needs and determining solutions to business problems.

Solutions often include a systems development component, but may also consist of process

improvement or organizational change or strategic planning and policy development.” These two

definitions may explain why the IT and business views of analysis sometimes differ. IT often

defines data analysis as covering the complete information life cycle from cleaning and

transforming source data making it ready for analysis, to analyzing the transformed data and

creating analytics. Business users, on the other hand, view business analysis as a set of

techniques for defining analyses and creating analytics on the transformed data.

Analytics can also be known as metrics, measurements, and indicators which could also be

30 | P a g e

defined in different forms. It is Note-worthy that analytics is more often defined as “the science

of analysis” rather than the “results of analytical processing.” Since business intelligence is about

providing business users with intelligence about the business, Business analysis can be said to be

the process of analyzing trusted data with the goal of highlighting useful information, supporting

decision making, suggesting solutions to business problems, and improving business processes

(Hass et. al, 2008).

2.2.3 Data Warehousing

Data warehousing enables each user not only to share a common, widely distributed, diverse

database but also to analytically explore, discover, and better comprehend fundamental trends

and relationships using all of the available data quickly and correctly. The data warehouse, which

is applications transparent, allows users to take full advantage of cheaper storage, faster

computing speeds, heterogeneous interfaces, and ever-burgeoning networks. The data warehouse

architecture consists of a consolidated, consistent relational database and san information

management system server which is the focal point of all end user queries, as well as, the data

access mechanism for analytical and quantitative studies. Raw data are extracted, scrubbed, and

integrated into the warehouse from a variety of external sources. Metadata, information

concerning data describing the warehouse, are also an integral part of the system. The warehouse

architecture must manage standard information delivery systems and data queries, interfaces with

applications development platforms and executive information systems (EISs), and online

analytical processing (OLAP), in addition to advanced information technology data mining tools.

By employing an interactive prototyping methodology and ensuring both scalability and

flexibility, the data warehouse will continually evolve and grow rapidly from a relatively small

repository of data, information, and knowledge to a very large one (Berson and Smith, 1997).

Different people have different definitions for a data warehouse. The most popular definition

came from Bill Inmon, who provided the following:

A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of

data in support of management's decision making process.

Subject-Oriented: A data warehouse can be used to analyze a particular subject area. For

example, "sales" can be a particular subject.

31 | P a g e

Integrated: A data warehouse integrates data from multiple data sources. For example, source A

and source B may have different ways of identifying a product, but in a data warehouse, there

will be only a single way of identifying a product.

Time-Variant: Historical data is kept in a data warehouse. For example, one can retrieve data

from 3 months, 6 months, 12 months, or even older data from a data warehouse. This contrasts

with a transactions system, where often only the most recent data is kept. For example, a

transaction system may hold the most recent address of a customer, where a data warehouse can

hold all addresses associated with a customer.

Non-volatile: Once data is in the data warehouse, it will not change. So, historical data in a data

warehouse should never be altered.

Data warehousing is a foundational practice that supports enterprise reporting, business

intelligence and decision support. Data warehouses and data marts are created across levels of

sophistication and different philosophical approaches, but typically involve extracting and

transforming data from operational/transactional databases and loading it to a repository for

shared use and analysis. (1keydata.com [Accessed 21st May 2010]).

Data warehousing is a methodology that combines and coordinates many sets of diversified data

into a unified and consistent body of useful information. In larger organizations, many different

types of users with varied needs must utilize the same massive data warehouse to retrieve those

pieces of information which best suit their unique requirements (Gargano and Raggad ,1999)

2.2.4 Data Mining

Data mining is concerned with discovering new, meaningful information, so that decision makers

can learn as much as they can from their valuable data assets. Using advanced information

technologies, knowledge discovery in databases (KDD) can uncover veins of surprising and

golden insights in a mountain of factual data. Data mining searches for hidden relationships,

patterns, correlations, and interdependencies in large databases that traditional information

gathering methods (e.g. report creation, pie and bar graph generation, user querying, decision

support systems (DSSs), etc.) might overlook. Data mining uses a motley toolkit of novel

algorithmic models that help to automatically solve user defined questions. Each model in this

panoply of powerful tools is intuitive, easy to explain, understandable, and simple to use. These

32 | P a g e

tools include artificial intelligence methods (e.g. expert systems, fuzzy logic, etc.), decision trees,

rule induction methods, genetic algorithms and genetic programming, neural networks (e.g.

backpropagation, associative memories, etc.), and clustering techniques. Data visualization is

also used as an important ancillary aid in the development, creation, and interpretation of data

driven knowledge discovery (Gargano and Raggad ,1999).

Data mining is a term used to describe a range of activities in the extraction and transformation

of data sets, and presentation of the results in a useful form. It is a form of information retrieval

in which the user seeks a manageable yet pertinent number of returns from search terminology,

though unlike most information retrieval techniques, data mining is focused on data stored in

structured form with fixed format fields of numeric values, character codes, or short strings.

More recently data mining practitioners have commenced trying to extract data from materials as

images, with the objective of turning such data into a structured format. In particular, data

mining is targeted at large legacy databases that often hold huge quantities of data that are never

utilized (Calvert, 2005). Often, data mining attempts to discover underlying patterns, trends and

relationships in the original data sets. The techniques of data mining can be extended to examine

very large enterprise or scientific databases, whether they are located in a single location or

distributed globally. This is also the case with the World Wide Web, considered in this context as

a massive database (Scarecrow, 2005).

It is also the process of extracting hidden knowledge from large volumes of raw data. It can also

be defined as the process of extracting hidden predictive information from large databases. Data

mining is not an “intelligence” tool or framework. Business intelligence, typically drawn from an

enterprise data warehouse, is used to analyze and uncover information about past performance on

an aggregate level. Data warehousing and business intelligence provide a method for users to

anticipate future trends from analyzing past patterns in organizational data. Data mining is more

intuitive, allowing for increased insight beyond data warehousing. An implementation of data

mining in an organization will serve as a guide to uncovering inherent trends and tendencies in

historical information. It will also allow for statistical predictions, groupings and classifications

of data (Mladenic et. al, 2003).

Most companies collect, refine and deduce massive quantities of data. Data mining techniques

can be implemented rapidly on existing software and hardware platforms to enhance the value of

33 | P a g e

existing information resources, and can be integrated with new products and systems as they

become part of the system. When implemented on high performance client/server or parallel

processing computers, data mining tools can analyze massive databases to deliver answers to

many different types of predictive questions.

Data mining software allows users to analyze large databases to solve business decision-making

problems. Data mining tools predict future trends and behaviors, allowing businesses to make

proactive, knowledge-driven decisions. Data mining tools can answer business questions that

traditionally were too time-consuming to resolve. Data mining is, in some ways, an extension of

statistics, with a few artificial intelligence and machine learning twists thrown in. Like statistics,

data mining is not a business solution, it is just a technology. (Mladenic et. al, 2003)

Since our objective is to show that the above components are factors that influence Business

Intelligence, it is necessary to relate each of the components to business intelligence as below.

2.3 Business Intelligence and Knowledge ManagementBusiness Intelligence and Knowledge Management have the same significant objective which is

to focus on improving business performance. Knowledge Management aims at achieving a

higher degree of understanding of an organization’s working environment that can either help

them move forward or keep wallowing in retardation if it is not successfully implemented.

Business Intelligence has almost similar workability, Since it is comprised of customer,

competitor and market intelligence and since the main purpose of implementing Business

Intelligence in an organization is to support strategic-decision making, grow business and

monitor competitors, then we recognize that these are definite similarities with Knowledge

Management. (McCarthy, 2009).Even though Business Intelligence and Knowledge

Management share a common high level objective, there are some fundamental differences.

These differences are to be found in the manner in which they are applied and implemented

towards achieving that goal. The value of Business Intelligence and its product, opportunity

analysis, is found in its usefulness as a decision making tool; the value of Knowledge

Management lies in the ability of the organization to identify, capture and reuse knowledge and

in particular best practices in such a manner that it saves the organization time, effort and

resources -translated and measured in cost (Cody et al., 2002).

34 | P a g e

Marco (2002) contends that a ‘‘true’’ enterprise-wide Knowledge Management solution cannot

exist without a BI-based meta data repository. In fact, a metadata repository is the backbone of a

Knowledge Management solution. That is, the BI meta data repository implements a technical

solution that gathers, retains, analyses, and disseminates corporate ‘‘knowledge’’ to generate a

competitive advantage in the market. This intellectual capital (data, information and knowledge)

is both technical and business-related. Marco says that most magazines that discuss Knowledge

Management fail to mention a meta data repository. He believes this ‘‘glaring oversight’’ exists

because most Knowledge Management professionals focus on a limited portion of the

Knowledge Management equation. However, implementers, he asserts, realize that a meta data

repository is the technical solution for Knowledge Management.

Cook and Cook (2000) note that many people forget that the concepts of KM and BI are both

rooted in pre-software business management theories and practices. They claim that technology

has served to cloud the definitions. Defining the role of technology in KM and BI – rather than

defining technology as KM and BI – is seen by Cook and Cook as a way to clarify their

distinction.

2.4 Business Intelligence and Business Analysis and Analytics(couldn’t find materials)

A business intelligence environment helps organizations and business users move from manual

to automated business analysis. Important results from business analysis include historical,

current and predictive metrics, and indicators of business performance. These results are often

called analytics. (Hass et. al, 2008)

2.5 Business Intelligence and Data WarehouseOne vehicle to deliver business intelligence is data warehousing. In other words, data

warehousing is a subcomponent of and a vehicle for delivering business intelligence. Since

Business intelligence refers to the use of existing data/information/knowledge within an

enterprise and refers to systems and technologies that provide the business with the means for

decision-makers to extract personalized meaningful information about their business and

industry, not typically available from internal systems alone. This includes advanced decision

support tools and back-room systems and databases to support those tools. The data warehouse is

that back-room database. Combine that with the support tools required to build and maintain the

35 | P a g e

data warehouse, such as data cleansing and extract, transform and load tools and you have what

many call data warehousing. Data warehousing and business intelligence provide a method for

users to anticipate future trends from analyzing past patterns in organizational data

We can think of the data warehouse as the back office and business intelligence as the entire

business including the back office. The business needs the back office on which to function, but

the back office without a business to support, makes no sense (Tannenbaum, 2001).

2.6 Business Intelligence and Data Mining(same with this)Data mining allows users to sift through the enormous amount of information available in data

warehouses; it is from this sifting process that business intelligence gems may be found. Data

mining systems can discover various types of knowledge including but not limited to association

rules, characteristic rules, classification rules, clustering evolution, and deviation analysis. The

knowledge may be classified into general knowledge, primitive-level knowledge, and multiple

level knowledge.

2.7 Business Intelligence OutcomesBefore the critical evaluation of the success factors of business intelligence, we already know

what output we are testing these factors on. These are the benefits the organization will acquire

by the success of these above factors which is Business Performance. Here performance could be

positive or negative. A better insight into Business Performance is given below.

2.7.1 Business Performance

Business Performance is the ability of an organization to achieve the maximum level of

profitability possible given the human, financial, capital, and other resources it possesses.

(Luftig, 1998)

2.7.2 Performance Measurement

The selection of performance measures that reflect the true situation of businesses with some

degree of certainty and reliability is indeed a crucial process (Murphy, Trailer, and Hill, 1996).

The lack of universally accepted standard business performance measures left the door open to

business organizations to decide and choose its own performance measure that might not truly

reflect its performance. Such performance measures include but not limited to: market share,

sales volume, company reputation, return-on-investment (ROI), profitability, and established

36 | P a g e

corporate identity. These could be seen as Tangible and Intangible. In all cases, regardless of

what measure should be used, this thesis will be using multiple business performance indicators

to test out the variables as above (Corchran and Wood, 1984; Hall, 1982; and Ibrahim and Rue,

1998).The diverse range of measures that can be adopted to define success can lead to a false

judgment on the actual performance.

For example, a business with declining profits or market share could be seen as failing when in

fact its owners/managers are satisfied with the overall business performance. Turnover growth is

an objective measure that is relatively easy to get due to data availability and common use and is

also a good indicator of firm size and a proxy for overall business growth. In this respect,

Barkham (1996) concluded that an analysis of a company’s growth should, at least in part, be

based on changes in turnover. Business success can be defined in many different ways. A study

by Beaver and Jenning (1995) stated that the most commonly adopted definition of success is

financial growth with adequate profits. The study concluded that being able to define success,

whether generally or specifically, is not the same as explaining success. Other definitions of

success are equally applicable. For example, some entrepreneurs regard success as the job

satisfaction they derive from achieving desired goals. However, financial growth due to

increasing profits has been widely (Murphy, Trailer, and Hill, 1996).

37 | P a g e

2.8 Research Framework

38 | P a g e

3 Research MethodologyIntroduction

This chapter aims to provide a synopsis of the methodological approaches, strategy and research

design for the successful implementation of Business Intelligence. In its exploration of the

critical success factors, this thesis shall investigate how the impact of the factors that affect

Business Intelligence leads to an improved Business Performance. Lastly, following the analysis

of primary data collected through a set of web survey administered online with CIO’s and top

managers, the thesis shall critique on the outcome of the survey.

In order to plan and carry out research, it is necessary to know what we mean by research-in

general, as well as in the specialized fields of language teaching and language acquisition. The

word research is derived from the French word recherché. Its literal meaning being:

The systematic process of collecting and analyzing information (data) in order to discover of new

knowledge or expand and verify of the existing one (e.g. theory - law) (Al-Mishri, 2005).

Research is an organized and systematic way of finding answers to questions

Systematic because there is a definite set of procedures and steps required to be followed. There

are certain things in the research process which are always done in order to get the most accurate

results.

Organized in the sense that there is a structure or method for undertaking a research. It is a

planned procedure, not a spontaneous one. It is focused and limited to a specific scope.

Finding Answers is the ultimate goal of all research. Whether it is the answer to a hypothesis or

even a simple question, research is successful when answers can be deduced. Sometimes the

answer is no, but it is still an answer.

Questions are central to research. If there is no question, then there is no answer and then

anything inferred is of no use. Research is focused on relevant, useful, and important questions.

Without a question, research has no focus, drive, or purpose (Al-Mishri, 2005).

Research involves finding something new. ‘New’ may simply mean ‘new to everyone’, or it may

simply mean ‘new to the researcher’. The first of these meanings, ‘new to everyone’, is usually

39 | P a g e

known as primary research. The second, ‘new to researcher but not to everyone’, is usually

known as secondary research. So, for example, if you check the timetable to find out the time of

the last train home at the subway on a Sunday evening, that’s secondary research. If you count

the number of types of bird at your bird table on Sunday morning, that’s a modest piece of

primary research. They’re different things, and they both have their uses. Secondary research is

also very important when you’re doing the preparatory work before some primary research, since

it vastly reduces the risk that the researcher will simply reinvent the wheel through not knowing

what has been done before.

Again, the importance of the secondary research is pretty specific to the one doing the

preparatory work, and the secondary research doesn’t take on wider importance until the primary

research is realized. Although secondary research is very useful for numerous purposes, it

doesn’t usually lead to breakthroughs in human knowledge (for instance, discovering the cause

of diabetes, or finding a better way of teaching people with dyslexia). So, although secondary

research is useful, it’s usually primary research that answers the important questions, the ones

that other people also want answered. Breakthroughs usually come through primary research,

which is why primary research is so highly valued in academia and in fields tackling big

unsolved problems. (Rugg and Petre, 2007)

3.1 STYLES OF RESEARCHMuhammad (2006) stated out that there are basically three styles of research as below;

Applied Research

Pure/Basic Research

Business Research

3.1.1 Applied Research

Most research driven or technological organizations engages in applied research. Applied

research can be defined as any fact gathering project that is conducted with the aim of acquiring

and applying knowledge that will address a specific problem or meet a specific need within the

scope of the entity. Any organization can benefit from engaging in applied research. (Tatum,

2010)

40 | P a g e

Applied research is designed to solve practical problems of the modern world, rather than to

acquire knowledge for knowledge's sake. One might say that the goal of the applied scientist is to

improve the human condition.

For example, applied researchers may investigate ways to:

improve agricultural crop production

treat or cure a specific disease

improve the energy efficiency of homes, offices, or modes of transportation

(Smoot, 2007).

3.1.2 Basic Research

Basic research also known as fundamental or pure research is driven by a researcher’s curiosity

or interest in a research context. The main ideology is to expand man's knowledge, not to create

or invent anything new. There is no obvious commercial value to the discoveries that result from

basic research (Muhammad, 2006).

For example, basic science investigations probe for answers to questions such as:

How did the universe begin?

What are protons, neutrons, and electrons composed of?

How do slime molds reproduce?

What is the specific genetic code of the fruit fly?

Most scientists believe that a basic, fundamental understanding of all branches of science is

needed in order for progress to take place. In other words, basic research lays down the

foundation for the applied science that follows. If basic work is done first, then applied spin-offs

often eventually result from this research. Smoot (2007) says, "People cannot foresee the future

well enough to predict what's going to develop from basic research. If we only did applied

research, we would still be making better spears."

3.1.3 Business Research

In a general viewpoint, business research has to do with any type of researching done when a

business is about to be started or when there is need for it while running any kind of business. As

a general notion, starting any type of business requires research into the target customer,

41 | P a g e

competition, industry and market to create a business plan. Conducting business market research

in existing businesses is helpful in keeping in touch with consumer demand. Small business

research begins with researching an idea and a name and continues with research based on

customer demand and other businesses offering similar products or services. All business

research is done to learn information that could make the company more successful and become

more competitive (Cyprus, 2010).

The two main types of business research conducted are business market research and advertising

research, other than that, researching is done to provide information for investors. Business

people aren't likely to invest in a company or organization without adequate research and

statistics to show them that their investment is likely to pay off. Large or small business research

can also help a company analyze its strengths and weaknesses by learning what customers are

looking for in terms of products or services the business is offering. Then a company can use the

business research information to adjust itself to better serve customers, gain over the competition

and have a better chance of staying in business (Cyprus, 2010).

For the sake of this research project, I will be conducting a Basic research based on its definition

because I am trying to “expand man's knowledge” (Muhammad, 2006).

3.2 Types of Research StudiesResearch studies also known as Research design can be classified into four major groups. These

include observational research, correlation research, true experiments, and quasi-experiments.

(Woolf, 2010).Each of these will be discussed further below;

3.2.1 Observational research

A lot of studies could be defined or tagged as observational research. They include but not

limited to case studies, ethnographic studies, ethological studies, just to mention a few. The

major viewpoint or characteristic of each of these types of studies is that processes are being

observed and recorded. Often times, the studies are qualitative in nature. For example, a

psychological case study of how employees work under pressure would entail extensive notes

based on observations of and interviews with the workers. A detailed report with analysis would

42 | P a g e

be written and reported constituting the study of this individual case. These studies may also be

qualitative in nature or include qualitative components in the research. For example, an

ethological study of primate behavior in the wild may include measures of behavior durations ie.

the amount of time an animal engaged in a specified behavior. This measure of time would be

qualitative. Surveys are often classified as a type of observational research.

3.2.2 Correlation research

In general, a correlation research is conducted to examine the covariation of two or more

variables. A candid example will be, the early research on cigarette smoking examines the

covariation of cigarette smoking and a variety of lung diseases. These two variables, smoking

and lung disease were found to covary together. Correlational research can be accomplished by a

variety of techniques which include the collection of empirical data. Often times, correlational

research is considered type of observational research as nothing is manipulated by the

experimenter or individual conducting the research. For example, the early studies on cigarette

smoking did not manipulate how many cigarettes were smoked. The researcher only collected

the data on the two variables. Nothing was controlled by the researchers. It is important to note

that correlational research is not causal research. In other words, we cannot make statements

concerning cause and effect on the basis of this type of research. There are two major reasons

why we cannot make cause and effect statements. First, we dont know the direction of the cause.

Second, a third variable may be involved of which we are not aware. To reiterate, it is

inappropriate in correlationional research to make statements concerning cause and effect.

Correlation research is often conducted as exploratory or beginning research. Once variables

have been identified and defined, experiments are conductible.

3.2.3 True Experiments

Before now, true experiments have been mistaken for only laboratory studies. However, this is

not always the case. A true experiment is defined as an experiment conducted where an effort is

made to impose control over all other variables except the one under study. It is often easier to

impose this sort of control in a laboratory setting. Thus, true experiments have often been

erroneously identified as laboratory studies. To understand the nature of the experiment, a few

terms need to be defined as below:

43 | P a g e

Experimental or treatment group - this is the group that receives the experimental treatment,

manipulation, or is different from the control group on the variable under study.

1. Control group - this group is used to produce comparisons. The treatment of interest is

deliberately withheld or manipulated to provide a baseline performance with which to compare

the experimental or treatment group's performance.

2. Independent variable - this is the variable that the experimenter manipulates in a study. It can

be any aspect of the environment that is empirically investigated for the purpose of examining its

influence on the dependent variable.

3. Dependent variable - the variable that is measured in a study. The experimenter does not

control this variable.

4. Random assignment - in a study, each subject has an equal probability of being elected for

either the treatment or control group.

5. Double blind - neither the subject nor the experimenter knows whether the subject is in the

treatment of the control condition.

6. Now that we have these terms defined, we can examine further the structure of the true

experiment.

First, every experiment must have at least two groups: an experimental and a control group. Each

group will receive a level of the independent variable. The dependent variable will be measured

to determine if the independent variable has an effect. As stated previously, the control group

will provide us with a baseline for comparison. All subjects should be randomly assigned to

groups, be tested as simultaneously as possible, and the experiment should be conducted double

blind. Perhaps an example will help clarify these points.

3.2.4 Quasi-Experiments

Quasi-experiments are very similar to true experiments but use naturally formed or pre-existing

groups. For example, if we wanted to compare young and old subjects on lung capacity, it is

impossible to randomly assign subjects to either the young or old group (naturally formed

groups). Therefore, this can not be a true experiment. When one has naturally formed groups, the

44 | P a g e

variable under study is a subject variable (in this case - age) as opposed to an independent

variable. As such, it also limits the conclusions we can draw from such a research study. If we

were to conduct the quasi-experiment, we would find that the older group had less lung capacity

as compared to the younger group. We might conclude that old age thus results in less lung

capacity. But other variables might also account for this result. It might be that repeated exposure

to pollutants as opposed to age has caused the difference in lung capacity. It could also be a

generational factor. Perhaps more of the older group smoked in their early years as compared to

the younger group due to increased awareness of the hazards of cigarettes. The point is that there

are many differences between the groups that we can not control that could account for

differences in our dependent measures. Thus, we must be careful concerning making statement

of causality with quasi-experimental designs. Quasi-experiments may result from studying the

differences between naturally formed groups (ie. young & old; men & women).

However, there are also instances when a researcher designs a study as a traditional experiment

only to discover that random assignment to groups is restricted by outside factors. The researcher

is forced to divide groups according to some pre-existing criteria. For example, if a corporation

wanted to test the effectiveness of a new wellness program, they might decide to implement their

program at one site and use a comparable site (no wellness program) as a control. As the

employees are not shuffled and randomly assigned to work at each site, the study has pre-

existing groups. After a few months of study, the researchers could then see if the wellness site

had less absenteeism and lower health costs than the non-wellness site. The results are again

restricted due to the quasi-correlational nature of the study. As the study has pre-existing groups,

there may be other differences between those groups than just the presence or absence of a

wellness program. For example, the wellness program may be in a significantly newer, more

attractive building, or the manager from hell may work at the non-wellness program site. Either

way, it a difference is found between the two sites it may or may not be due to the

presence/absence of the wellness program.

To summarize, quasi-experiments may result from either studying naturally formed groups or

use of pre-existing groups. When the study includes naturally formed groups, the variable under

study is a subject variable. When a study uses pre-existing groups that are not naturally formed,

the variable that is manipulated between the two groups is an independent variable (With the

45 | P a g e

exception of no random assignment, the study looks similar in form to a true experiment). As no

random assignment exists in a quasi-experiment, no causal statements can be made based on the

results of the study.

3.3 RESEARCH BACKGROUND

3.3.1 Research Question

To what extent will the implementation of BI impact the success of an organization.

3.3.2 Research Objectives

The main objective of this research is to analyze the factors that help in making Business

Intelligence Successful in an organization. The following objectives ad below is stated.

To identify the Critical Success Factors for implementation of Business Intelligence.

To evaluate the benefit and impact of BI on organizations.

To develop a theoretical framework to test the underlying factors.

To empirically test and validate the framework created.

To propose recommendation.

3.3.3 Research Design and Methodology

To understand the use of statistics, one needs to know a little bit about experimental design or

how a researcher conducts investigations. A little knowledge about methodology will provide us

with a place to hang our statistics. In other words, statistics are not numbers that just appear out

of nowhere. Rather, the numbers (data) are generated out of research. Statistics are merely a tool

to help us answer research questions. As such, an understanding of methodology will facilitate

our understanding of basic statistics (Aicha 2007).Design is needed to because it facilitates

smoothing moving of research therefore making research more efficient yielding maximum

information with minimum money and time. (Kothari 2004)

Memo (2006) describes Research methodology as the analysis of principles of methods, rules

and techniques which involves the systematic study of methods which are applied to analyze a

specific project or study. He further explains that, In order to make research organized and to

increase its reliability, different methodologies are adopted.

46 | P a g e

It can also be said to be the system of collecting data for research projects. The data may be

collected for either theoretical or practical research for example management research may be

strategically conceptualized along with operational planning methods and change management.

Some important factors in research methodology include validity of research data, Ethics and the

reliability of measures most of the work is finished by the time the final data is

analyzed(Aicha,2007).

3.4 Research PurposeSaunders et al (2003), Patton (1990) identifies four main rationales behind research studies or

activities. These are the exploratory, the descriptive, explanatory and prescriptive purposes. It is

necessary to identify the purpose of a research study by correlating the research questions to the

research objectives. They are all outlined below.

3.4.1 Exploratory

Robson (2002) explains, exploratory research investigates a specified problem/phenomenon for

the purpose of shedding new light upon it and, consequently, uncovering new knowledge. The

research objectives directly tie in with, and complement one another. They additionally correlate

to research objectives 1-4 and are fundamentally explorative in nature.

3.4.2 Explanatory

This is the amplification of relationship between variables and the componential elements of the

research problem. Explanatory research, in other words, functions to highlight the complex

interrelationships existent within, and around, a particular phenomenon and contained within the

research problem (Miles and Huberman, 1994).

3.4.3 Prescriptive

is not a very familiar word to many people because prescription researches are not so common

among academic assessments. For this type of research, the researcher gives a suggestion for the

improvement or correction of a given topic. Since the research paper of this nature is based on

logical or creative thinking and circumstantial evidence, it is not easy to handle such an

assignment successfully (Patton, 1990)

47 | P a g e

3.4.4 Descriptive

Descriptive research entails the thorough examination of the research problem, for the specified

purpose of describing the phenomenon, as in defining, measuring and clarifying it (Dane, 1990).

Jackson (1994) contends that all research is partly descriptive in nature. The descriptive aspect of

a research is, simply stated based on the ‘who’, ‘what’, ‘when’, ‘where’, ‘why’, and ‘how’ of the

study.

The proposed research is descriptive in nature rather than experimental. Descriptive research,

according to Best (1981), can be distinguished from other forms of research on the basis of the

following characteristics:

Descriptive research is non-experimental in that it deals with relationships between non-

manipulated variables in a natural rather than artificial setting. Since the events or

conditions have already occurred or exist, relevant variables are merely selected for an

analysis of their relationships.

Descriptive research involves hypothesis formulation and testing.

Descriptive research uses logical methods of inductive and deductive reasoning in order

to arrive at generalizations.

All of the variables and procedures used in descriptive studies are described as

completely and accurately as possible so as to permit future replication.

Descriptive research often employs methods of randomization so that error can be

estimated when inferring population characteristics from observations of samples.

Because the study is not experimental, it is technically inappropriate to refer to the variables of

interest as dependent or independent factors (Kiess and Bloomquist, 1985).

3.5 Research ApproachResearch Approach refers to the approach or the methodology that has been adopted to conduct a

particular research work. It basically involves the selection of research questions, the conceptual

framework that has to be adopted, the selection of appropriate research method such as primary

research, secondary research etc (Aicha, 2007).

Selection of a suitable research approach is a critically important decision. It gives the researcher

the opportunity to critically consider how each of the various approaches may contribute to, or

48 | P a g e

limit, his study, allow him/her to satisfy the articulated objectives and design an approach which

best satisfies the research’s requirements (Creswell, 2003). According to Hair et al. (2003),

research approach has its ideology around the quantitative comparison to the qualitative and the

deductive comparison to the inductive. Each set of approaches is commonly perceived of as

referring to polar opposites (Hair et al., 2003). Jackson (1994) takes issue with this perception

and contends that a researcher should not limit himself to a particular approach but, instead

should use a variety of approaches, if and when required by his study. When one starts to think

about research methodology, it is needed to think about the differences between qualitative and

quantitative research.

3.5.1 Qualitative research 

It explores attitudes, behavior and experiences through such methods as interviews or focus

groups. It attempts to get an in-depth opinion from participants. As it is attitudes, behavior and

experiences which are important, fewer people take part in the research, but the contact with

these people tends to last a lot longer. Under the umbrella of qualitative research there are many

different methodologies (Denzin and Lincoln, 1994).

3.5.2 Quantitative research 

It generates statistics through the use of large-scale survey research, using methods such as

questionnaires or structured interviews. If a market researcher has stopped you on the streets, or

you have filled in a questionnaire which has arrived through the post, this falls under the

umbrella of quantitative research. This type of research reaches many more people, but the

contact with those people is much quicker than it is in qualitative research (Creswell, 2003).

3.5.3 Qualitative versus quantitative inquiry

Over the years there has been a large amount of complex discussion and argument surrounding

the topic of research methodology and the theory of how inquiry should proceed. Much of this

debate has centred on the issue of qualitative versus quantitative inquiry – which might be the

best and which is more ‘scientific’. Different methodologies become popular at different social,

political, historical and cultural times in our development, and as it generally is, all

methodologies have their specific strengths and weaknesses. These should be acknowledged and

addressed by the researcher. Certainly, this helps the researcher think about his or her research

methodology in considerable depth.

49 | P a g e

. Table 3 Differences between Qualitative and Quantitative Research Methods cdcynergy

(2008)

Qualitative Methods Quantitative Methods

Methods include focus groups, in-depth

interviews, and reviews

Surveys

Primarily inductive process used to

formulate theory

Primarily deductive process used to test pre-

specified concepts, constructs, and hypotheses

that make up a theory

More subjective: describes a problem or

condition from the point of view of those

experiencing it

More objective: provides observed effects

(interpreted by researchers) of a program on a

problem or condition

Text-based Number-based

More in-depth information on a few cases Less in-depth but more breadth of information

across a large number of cases

Unstructured or semi-structured response

options

Fixed response options

No statistical tests Statistical tests are used for analysis

Can be valid and reliable: largely depends

on skill and rigor of the researcher

Can be valid and reliable: largely depends on

the measurement device or instrument used

Time expenditure lighter on the planning

end and heavier during the analysis phase

Time expenditure heavier on the planning

phase and lighter on the analysis phase

Less generalizable More generalizable

50 | P a g e

3.6 Deciding which methodology is right

One major mistake many researchers do is thinking that quantitative research is ‘better’ than

qualitative research. Neither is one better than the other – they are just different and both have

their strengths and weaknesses. What matters most is justifying a chosen methodology (sanchez,

2006). From the differences between the two approaches above, it can be justified that

quantitative method is the right method for this thesis. Based on what Quantitative method is

defined as above (Creswell, 2003).

3.6.1 Deductive and Inductive

Alternatively, there are two types of reasoning or approaches a researcher can take while doing a

research, they are Inductive and Deductive approaches (Burney, 2008)

(By author)

Deductive Research Approach: This type of reasoning works from the more general to

more specific instances of research. Sometimes it is informally called the “Top-Down

Approach”. The conclusion of a deductive research approach follows logically from

premises which are available facts.

51 | P a g e

Inductive Research Approach: The Inductive reasoning is the inverse or directly

opposite of deductive reasoning. It works the other way,, moving from specific

observations to broader generalizations and theories. Informally, it is sometimes called

the “Bottom-up approach”. In contrast to deductive approach; its conclusion is likely

based on premise and involves a higher degree of uncertainty.

Deductive Vs Inductive

Induction is usually described as moving from the specific to the general,, while

deduction begins with the general and ends with the specific.

52 | P a g e

Arguments based on laws,, rules and accepted principles are generally used for Deductive

reasoning. Observations tend to be used for Inductive Arguments.

In this research author will use both approaches. The reason being that some theories will be

tested (deduction) and there are new factors (induction) which will be analyzed to discover

relation on variables.

3.6.2 Justification of Methods

The method that was chosen was based on the research objectives specified at the start of the

research process. Quantitative method and mixed method (deductive and inductive) were chosen

to meet following objective:

To identify the Critical Success Factors for implementation of BI

To evaluate the benefit and impact of BI on organizations

To develop a theoretical framework to test the underlying factors

To empirically test and validate the framework created

To propose recommendation

The researcher has chosen these methods to answer research question and to meet research

objectives. Based on secondary data there are several theories concerning research question, for

testing theory, researchers usually use deductive method and quantitative data. However the

researcher will propose new independent variables which can impact on dependent variables.

53 | P a g e

3.7 InstrumentationThis section has to do with the way data is collected. When describing instruments to be used,

some issues such as the number of questions, length of administration, readability and scoring

need to be reviewed. After describing the instrument, then there is the need to review the

reliability (e.g. alpha coefficients, inter-rater reliability, test retest reliability, split half reliability)

and validity of the instrument (content validity, external validity and discriminant validity).

The theoretical constructs that the survey is attempting to measure is the viability of business

intelligence.

3.7.1 Quantitative Sample

The questionnaire for this survey is designed to determine the viability and usability of Business

Intelligence and its impact on organizations. It is majorly design to do quantitative survey of both

sample size of Nigeria and Malaysia managers.

Part 1: Demographic Data of Organization

Part 2: measure the respondent’s view on various variables/factors considered for successful

implementation of Business Intelligence. The ‘Likert Scale’ will be used throughout the

questionnaire. The 'Likert scale' is a common interval-based multiple-choice style of question

used in questionnaires.The ‘Likert’ scale measurement of 1 to 5, with 1 as strongly disagree,

disagree, neutral, agree and 5 as strongly agree will be used.

Part 3: This will finalize the questionnaire by allowing the respondents suggestionsin order to

get their own viewpoint of the subject matter which was not covered within the questionnaire.

3.8 POPULATION AND SAMPLINGThe basic research paradigm is to:

Define the population

Draw a representative sample from the population

Do the research on the sample

Infer your results from the sample back to the population

When conducting a research, the researcher must often use a sample of the population as

opposed to using the entire population. Before we go further into the reasons why, let us first

54 | P a g e

discuss what differentiates between a population and a sample. A population can be defined as

any set of persons/subjects having a common observable characteristic. For example, all

individuals who reside in the United States make up a population. Also, all pregnant women

make up a population. The characteristics of a population are called a parameter. A statistic can

be defined as any subset of the population. The characteristics of a sample are called a statistic.

As it can be seen from above, it all begins with a precise definition of the population. The whole

idea of inferential research (using a sample to represent the entire population) depends upon an

accurate description of the population. After completion of the research, statements are made

based on the results (Walonick, 2005).

The population for this study is based on companies in Malaysia and Nigeria who use business

intelligence in one way or the other or potential business intelligence users. There are numerous

sampling methods from which to choose. There are several ways to get the population sample

and a few methods are outlined below.

3.8.1 Why Sample?

This brings us to the question of why sample. Why should we not use the population as the focus

of study? There are at least four major reasons to sample. First, it is usually too costly to test the

entire population. The United States government spends millions of dollars to conduct the U.S.

Census every ten years. While the U.S. government may have that kind of money, most

researchers do not. The second reason to sample is that it may be impossible to test the entire

population. For example, let us say that we wanted to test the 5-HIAA (a serotonergic

metabolite) levels in the cerebrospinal fluid (CSF) of depressed individuals. There are far too

many individuals who do not make it into the mental health system to even be identified as

depressed, let alone to test their CSF. The third reason to sample is that testing the entire

population often produces error. Thus, sampling may be more accurate. Perhaps an example will

help clarify this point. Say researchers wanted to examine the effectiveness of a new drug on

cancer. One dependent variable that could be used is an Activities of Daily Living Checklist. In

other words, it is a measure of functioning o a day to day basis. In this experiment, it would

make sense to have as few of people rating the patients as possible.

55 | P a g e

If one individual rates the entire sample, there will be some measure of consistency from one

patient to the next. If many raters are used, this introduces a source of error. These raters may all

use slightly different criteria for judging Activities of Daily Living. Thus, as in this example, it

would be problematic to study an entire population. The final reason to sample is that testing

may be destructive. It makes no sense to lesion the lateral hypothalamus of all rats to determine

if it has an effect on food intake. We can get that information from operating on a small sample

of rats. Also, you probably would not want to buy a car that had the door slammed five hundred

thousand time or had been crash tested. Rather, you probably would want to purchase the car that

did not make it into either of those samples.

3.8.2 Sampling Methods

As stated above, a sample consists of a subset of the population. Any member of the defined

population can be included in a sample. A theoretical list (an actual list may not exist) of

individuals or elements who make up a population is called a sampling frame. There are five

major sampling procedures. There are two main ways sampling can be represented. They are

non-probability sampling and probability sampling. Probability sampling involves setting up

processes that ensure that every unit in the population has an equal probability of being chosen.

This involves some random sampling. The major difference between probability sampling and

non probability sampling is that non probability does not use random sampling. Although most

researchers prefer probabilistic sampling, non probability sampling is of use when the

circumstances are not practical or theoretically enough to use random sampling. Non

probabilistic sampling can be divided into two: accidental and purposive sampling. Purposive

sampling is the most commonly used type of non probability sampling. It is sampling with a

purpose in mind.

It is based on the researcher’s discretion to clearly define the target population. There are no

strict rules to follow; all that is needed is for the researcher to rely on logic and judgment. The

population is defined in keeping with the objectives of the study. Sometimes, the entire

population will be sufficiently small, and the researcher can include the entire population in the

study. This type of research is called a census study because data is gathered on every member of

the population. Usually, the population is too large for the researcher to attempt to survey all of

56 | P a g e

its members. A small, but carefully chosen sample can be used to represent the population. The

sample reflects the characteristics of the population from which it is drawn (Westfall, 2008).

3.8.3 Justification of sampling method chosen

A purposive, non-probabilistic method is chosen to select respondents from Malaysia and

Nigeria. This is because the researcher is not sure of the population and will only draw from

samples chosen. This then moves us to the type of non probabilistic method chosen again. The

researcher will be using a combination of expert and quota sampling. There are two forms of

quota sampling as below;

Quota Sampling

With proportional quota sampling the aim is trying to get a sample that represents each of two or

more subgroups in the same proportions that they are represented in the population, but the

researcher doesn’t need to be bothered with stratified random sampling. Suppose your

population is undergraduate students at UCSI University. We assume that 40% are new intakes

while 60% are returning students. So if a researcher wants 50, so he or she needs need 20 new

intakes and 30 returning students. Once the researcher has the predetermined number of subjects

in any category, he or she no longer accept participation by any additional persons in that

category. With non-proportional quota sampling, the researcher specifies the minimum number

of subjects he or she wants in each subgroup and then just keep gathering data until he or she

has at least that many in each subgroup(Wuensch, 2003).

Expert sampling is further explained below

Expert Sampling

Here the researcher gathers a group of persons known to be expert with respect to the

information being sought after. For example, suppose a researcher is constructing a

questionnaire that is designed to measure how social networking affects productivity. The

researcher writes a large number of potential items and now he or she wants some help in

determining which items would be most appropriate to include on such a questionnaire. The

researcher then gathers a group of psychologists with expertise in constructing questionnaires

like this and asks their opinions about the items (Wuensch, 2003).

57 | P a g e

Both methods employed are a little less restrictive compared to other non probability techniques.

In both methods, the primary concern is to obtain a sufficient target sample size in the population

and not concerned with having numbers that match the whole population. It typically ensures

that smaller groups are adequately represented in the sample (Hair et al., 2003).

3.9 Target SampleThe target sample will include small, medium and large business corporations in Malaysia and

Nigeria. The research will focus on managers, CEO’s and decision makers. The purpose of

having this focus is that since some of the success factors are IT terms and just these people

might have the understanding on this and since they know how Business Intelligence is

implemented in their organizations. The sample size will be about 450 respondents with 200 of

these in Nigeria and the remainder 250 in Malaysia. This size will not be biased and will include

both male and female decision makers.

The sample size will be calculated with sample size formula as below to get the appropriate

number of questionnaires to be distributed. The determination of sample size is a common task

58 | P a g e

for many organizational researchers. The accuracy of research is influenced by the

appropriateness, adequateness, and inexcessive sample size (Cochran, 1977). The formula used

to determine the number of respondents is describe below.

3.10 Questionnaire distributionThe questionnaires will be distributed and administered mainly online. This is because of the

nature of the jobs of the target sample. So a web link will be emailed to the respondents in both

in Nigeria and Malaysia. The paper questionnaires will be collected within four days of

distribution while those on the internet will be collected within a week and a half of distribution.

The web based questionnaires are used because the respondents cannot be approached in person

by the researcher.

Part 1: Demographic Data of Organization

Part 2: measure the respondent’s view on various variables/factors considered for successful

implementation of Business Intelligence. The ‘Likert Scale’ will be used throughout the

questionnaire. The 'Likert scale' is a common interval-based multiple-choice style of question

used in questionnaires.The Likert scale measurement of 1 to 5, with 1 as strongly disagree,

disagree, neutral, agree and 5 as strongly agree will be used.

Part 3: This will finalize the questionnaire by allowing the respondents suggestions in order to

get their own viewpoint of the subject matter which was not covered within the questionnaire.

3.11 Limitations of the questionnaireLike any form and part of research, questionnaires also has some limitation due to the fact that

most of the web-survey, I will be using might not truly reflect the right opinion of the respondent

given the fact that the respondent might not understand some of the questions and I am not there

to guide them. And finally, the questionnaire only gives small sample size of the target

population and might not truly reflect the actual opinion of entire population.

59 | P a g e

3.12 Administering the questionnaire

3.12.1 Web based survey

This survey will be administered through a website that host questionnaires and a link will be

sent out to the respondents through email especially to all respondents.

3.12.2 Data Coding

Coding in computer terms may refer to the particular language software understands or can relate

to (Fischer, 2003). Here in research, it is the development of a language that will be used to

transfer data from the research instrument into the computer. In other words, it is representing

the data collected from the questionnaire in a way that the SPSS software can easily understand,

manipulate and output results successfully. Since computers only understand ones and zeros(1,0)

or bits, it is best to represent the responses received from the administered questionnaires in

numbers and the best way to do this is to create a codebook, otherwise known as a data

descriptor which shows how each question is represented. Below is a table showing the various

variables and how they will be coded in SPSS.

Table for Coding of Data in SPSS

Section1: Company Demographics

What is the name of your organization?

Which of the following best describes your organization's industry or function?

How long has your company existed?

How many people are employed in your entire organization including all branches, divisions, and subsidiaries?

What is your primary job function within your organization?

What is your primary job title? (SENIOR IT MANAGEMENT: CORPORATE MANAGEMENT:

Are you involved in setting the direction for your Company's IT Budget or Strategy?

Which technologies are currently implemented in your organization?

a) Internet [ ] b) Data warehousing [ ]

60 | P a g e

c) Intranet [ ] d) Knowledge Management software [ ]

e) Extranet [ ] f) Decision support system [ ]

g) Groupware [ ] h) Data management system [ ]

i) E Commerce [ ] j) Automated Manufacturing

Section 2: Measurement of CSF of BI

Knowledge Management Q1: My organization has a well written Knowledge Management policy or strategy.

Q2: My organization has a values system or culture intended to promote knowledge sharing.

Q3: My organization captures and uses knowledge obtained from public research institutions including universities and government laboratories and other industry sources such as industrial associations, competitors, clients and suppliers.

Q4: My organization provides formal and informal training related to Knowledge Management and uses formal mentoring practices, including apprenticeships and internships.

Q5: My organization encourages experienced workers to transfer their knowledge to new or less experienced workers.

Q6: encourages workers to continue their education by reimbursing tuition fees for successfully completed work-

61 | P a g e

related courses and offers off-site training to workers in order to keep skills current.

Business Analysis and Analytics Q1: Business goals are clearly

communicated and understood, and we

know how success is measured.

Q2: My organization is doing very good

now as compared with last year and three

years ago

Q3: My organization is moving in with

current business trends and developments

Q4: My organization utilizes cost-saving

measures effectively without

compromising the quality of its products

or services.

Q5: My organization selects technology

to help the business achieve its mission

and goals.

Q6: My organization has established

realistic business goals

Data Warehousing Q1: We have a formal data warehouse

development methodology.

Q2: the business role of the data

warehouse is understood

Q3: We understand the role of the data

62 | P a g e

warehouse in changing business

processes.

Q4: Data is integrated throughout the

organization

Q5: Our organization can ensure the

quality of data warehouse deliverables.

Data Mining

Data Mining is the process of

finding new and potentially useful

knowledge

Q1: there is a well written Policy for

data retrieval organization-wide

Q2: Critical business information needs

are easily met with access to operational

databases.

Q3: We understand the role of business

processes in producing information.

Q4: The business units openly share

information across organizational lines

Q5: Data is easily retrieved in a timely

manner and fashion

Section 3: Open Ended Questions Q1: DO you agree that with the correct

Knowledge Management practices and

business analysis and analytics

methodology and the efficacy of a data

warehouse and the correct data retrieval

methods makes up better business

intelligence.

63 | P a g e

Q2: Can you summarize business

intelligence as; how we get knowledge

(Knowledge Management), how we

analyze (Business Analysis &Analytics),

how we store (Data warehouse), how we

retrieve and use information (Data

Mining).

3.12.3 Procedure and time frame

The research will run for two semesters which is the May-August Semester and September-

December Semester. It is fully depicted in the gannt chart.

64 | P a g e

3.12.4 Data Analysis Procedures

The analysis plan has to do with each research question being analyzed thoroughly. Thus, the

research questions will be addressed one at a time followed by a description of the type of

statistical tests that will be performed to answer that research question. Also, variables that will

be included in the analyses and identify the dependent and independent variables if such a

relationship exists. Decision making criteria (e.g., the critical alpha level) will also be stated, as

well as the computer software that will be used. Ultimately, the data collected is used to inform

the research findings. If the data is not verifiable, the implication is that the findings are

potentially suspected. Accordingly, it is incumbent upon the researcher to validate his/her

findings (Sekaran, 2003). Beyond that, Miles and Huberman (1984) contend that it is equally

important for the researcher to evaluate the quality of his/her data prior to its exploitation.

65 | P a g e

Fig 3. Processing of data from analysis to synthesis (Hirsjarvi and Hurme, 2000)

3.13 Statistical Analysis and Hypothesis Testing

For a researcher to know if a hypothesis is correct or not, he or she needs to determine this using

statistics. Using statistics in research involves a lot more than making use of statistical formulas

or getting to know statistical software.

Making use of statistics in research basically involves

1. learning basic statistics

2. understanding the relationship between probability and statistics

3. Comprehension of the two major branches in statistics, descriptive statistics and

inferential statistics.

4. Knowledge of how statistics relates to the scientific method.

Statistics in research is not just about formulas and calculation. (Many wrong conclusions have

been conducted from not understanding basic statistical concepts)

66 | P a g e

Statistics inference helps us to draw conclusions from samples of a population.

Hypothesis testing is conducted by formulating an alternative hypothesis which is tested against

the null hypothesis, the common view. The hypotheses are tested statistically against each

other.The researcher can work out a confidence interval, which defines the limits when he or she

will regard a result as supporting the null hypothesis and when the alternative research

hypothesis is supported (Experiment Resources,2009).

The survey data will be analyzed using SPSS statistical package. SPSS is an acronym for

Statistical Package for the Social Sciences which is a computer program or software used for

statistical analysis.It gives a higher output on analytical data which will increase the quality and

reliability to the research depending on the data input in the system. Alternatively, STATSPAC

could be used instead of SPSS. They function same way, but STATSPAC is easier and simpler to

use and administer.

3.13.1 Descriptive statistics

Descriptive statistics are used to describe the basic features of the data in a study. They provide

simple summaries about the sample and the measures. Together with simple graphics analysis,

they form the basis of virtually every quantitative analysis of data. With descriptive statistics you

are simply describing what is or what the data shows (Trochim, 2006).The descriptive statistics

will be used to explore the data and to summarize and describe the observations. Prior to

performing in-depth analysis on data gathered, descriptive analysis will be performed to

understand profile of respondents and their organization.

3.14 STATISTICAL TESTS

3.14.1 Correlation (Linear Relationship)

According to Choudhury (2009), statistical correlation is a statistical technique which tells us if

two variables are related. Aldrich (1995),further stressed that correlation is one of the most

common and most useful statistics. A correlation is a single number that describes the degree of

relationship between two variables. Correlations are useful because they can indicate a predictive

relationship that can be exploited in practice. In general statistical usage, correlation or co-

relation can refer to any departure of two or more random variables from independence, but most

commonly refers to a more specialized type of relationship between mean values. There are

67 | P a g e

several correlation coefficients, often denoted ρ or r, measuring the degree of correlation. The

most common of these is the Pearson correlation coefficient, which is mainly sensitive to a linear

relationship between two variables. Therefore the sample correlation coefficient, can be used to

estimate the population Pearson correlation between X and Y. The sample correlation coefficient

is written as

Where, x and y are the sample means of X and Y, sx and sy are the sample standard

deviations of X and Y.

3.15 Validity and Reliability testAccording to Mertler & Charles (2005), validity and reliability are not two distinct concepts, but

in fact they are sharing an important relationship in that It is possible for research’s data to be

reliable (consistent) but not valid (measures something that is not intended to measure).

Reliability is a necessary factor, but not sufficient as quoted from Mertler & Charles (2005):

“A valid test is always reliable, but reliable test is not necessarily valid”.

Here the researcher will be describing the steps taken to validate and measure reliability of the

survey. Validity refers to the accuracy or truthfulness of a measurement. There are no statistical

tests to measure validity. All assessments of validity are subjective opinions based on the

judgment of the researcher. Nevertheless, there are at least three types of validity that should be

addressed and stated what steps taken to assess validity. Face validity refers to the likelihood that

a question will be misunderstood or misinterpreted. Pre-testing a survey is a good way to

increase the likelihood of face validity.

3.15.1 Reliability test

According to Mertler (2006), reliability refers to the consistency of collected data . There are a

number of different reliability coefficient but the most commonly used is Cronbach’s alpha

which is based on the average correlation of item within the test if the items standardised

(Coakes & Steed, 1999). Cronbach's (alpha) is a statistical formula which has an important use

68 | P a g e

as a measure of the reliability of a psychometric instrument. It was first named as alpha by

Cronbach (1951), as he had intended to continue with further instruments The Cronbach’s alpha

for this research will be calculated with SPSS 13.0. Variable can be said as good in reliability if

the cronbach’s alpha is higher than 0.7 (Kirkpatrick & Feeney, 2006).

Cronbach's is defined as

where is the number of components (items or testlets), is the variance of the observed total

test scores, and is the variance of component i.

Alternatively, the standardized Cronbach's can also be defined as

where N is the number of components (items or testlets), equals the average variance and is the

average of all covariance’s between the components.

3.16 FACTOR ANALYSISFactor analysis is a statistical approach that can be used to analyze large number of interrelated

variables and to categorize these variables using their common aspects The approach involves

finding a way of representing correlated variables together to form a new smaller set of derived

variables with minimum loss of information. It helps in reducing data and also helps to remove

redundancy or duplication from a set of correlated variables. Also, factors are formed that are

relatively independent of one another. But since it require the data to be correlated, so all

assumptions that apply to correlation are relevant here (Choudhury, 2009).

3.16.1 MAIN TYPES

The two main types of factor analysis are described below.

Principal component analysis: provides a unique solution so that the original data can be

reconstructed from the results. Thus, this method not only provides a solution but also

works the other way round, i.e., provides data from the solution. The solution generated

includes as many factors as there are variables.

69 | P a g e

Common factor analysis: uses an estimate of common difference or variance among the

original variables to generate the solution. Due to this, the number of factors will always

be less than the number of original factors. So, factor analysis actually refers to common

factor analysis.

3.16.2 MAIN USES

Though Factor Analysis can be used in plethora of ways, but its main uses can be summarized as

below.

Identification of underlying factors: the aspects common to many variables can be

identified and the variables can be clustered into homogeneous sets. Thus, new sets of

variables can be created. This allows us to gain insight to categories.

Screening of variables: helps to identify groupings so that we can select one variable to

represent many.

3.16.3 EXAMPLE

An example to understand the use of factor analysis is given below.

Suppose a researcher wants to know certain aspects such as “Knowledge

Management” ,”Business Analysis and Analytics”, “Data Warehousing” and “Data Mining”

attribute to the “Business Intelligence” which later increases “Business Performance and

Productivity”. The researcher could prepare a questionnaire with 20 items, 5 each pertaining to

“Knowledge Management” ,”Business Analysis and Analytics”, “Data Warehousing” and “Data

Mining”.

Before using the questionnaire on the sample, the researcher could use it on a small group of

people, who are like those in the survey. When the researcher analyzes the data, he or she tries to

see if there are really these above outlined factors and if those factors represent the aspects of

“Knowledge Management” ,”Business Analysis and Analytics”, “Data Warehousing” and “Data

Mining”. In this way, factors can be found to represent variables with similar aspects.

3.17 AssumptionsAll research studies make assumptions. The most obvious is that the sample represents the

population. Another common assumption is that an instrument has validity and is measuring the

70 | P a g e

desired constructs. Still another is that respondents will answer a survey truthfully. The

important point is for the researcher to state specifically what assumptions are being made.

3.18 Scope and limitationsAll research studies also have limitations and a finite scope. Limitations are often imposed by

time and budget constraints. They are outlined below

Time constraints

Financial consideration

Anticipating and avoiding problems

Equipment limitations

Human resource limitations

“Out of the box” thinking

“In the box” thinking

71 | P a g e

QUESTIONNAIRES

Dear All,

I am a student at the UCSI University, Kuala Lumpur, Malaysia and I am conducting a Masters in Business Administration (MBA) thesis on critical success factors for Business Intelligence. I am now gathering data to be able to analyze and adjudge if the factors would lead to the successful implementation of Business Intelligence which finally yields tangible and intangible benefits and it would be a great help if you could take the time to fill out my online questionnaire.

http://www.xxxxxxxxxx.com

All responses will be treated in confidence and the survey should take only a few minutes. The questions are simple and basic and not too probing!

Business users should answer from their personal experience while consultants should take one (or more if they can) situation(s) and answer from that perspective.

This will be of great benefit to my thesis and I thank you all in advance.

Many Thanks,

Fabiyi Olawale Adefisayo

Note: If anyone has any queries regarding the validity of this survey, please do not hesitate to contact :

Dr Keoy Kay Hooi BSc PhD CSSBB CSSGB CProjMgmtE

Head

Centre of Excellence for Research, Value Innovation and Entepreneurship (CERVIE)

UCSI University, Kuala Lumpur Campus

 

Tel: 03 91018880 Ext-3355

E-mail: keoykh@ucsi.edu.my

72 | P a g e

Section1: Company Demographics

Q1: What is the name of your organization?

Q2: Which of the following best describes your organization's industry or function?

Q3: How long has your company existed?

Q4: How many people are employed in your entire organization including all branches, divisions, and subsidiaries?

Q5: What is your primary job function within your organization?

Q6: What is your primary job title? (SENIOR IT MANAGEMENT: CORPORATE MANAGEMENT:

Q7: Are you involved in setting the direction for your Company's IT Budget or Strategy?

Q8: Which technologies are currently implemented in your organization?

a) Internet [ ] b) Data warehousing [ ]

c) Intranet [ ] d) Knowledge Management software [ ]

e) Extranet [ ] f) Decision support system [ ]

g) Groupware [ ] h) Data management system [ ]

i) E Commerce [ ] j) Automated Manufacturing

73 | P a g e

Section 2: Measurement of CSF of BI

stro

ngly

disa

gree

disa

gree

neut

ral

agre

e

stro

ngly

agre

e

1My organization has a well written Knowledge Management policy or strategy.

1 2 3 4 5

2My organization has a values system or culture intended to promote knowledge sharing. 1 2 3 4 5

3has policies or programs intended to improve worker retention

1 2 3 4 5

4

My organization Captures and uses knowledge obtained from public research institutions including universities and government laboratories and other industry sources such as industrial associations, competitors, clients and suppliers.

1 2 3 4 5

5

My organization Provides formal and informal training related to Knowledge Management and uses formal mentoring practices, including apprenticeships and internships.

1 2 3 4 5

6My organization encourages experienced workers to transfer their knowledge to new or less experienced workers

1 2 3 4 5

7

My organization encourages workers to continue their education by reimbursing tuition fees for successfully completed work-related courses and offers off-site training to workers in order to keep skills current

1 2 3 4 5

74 | P a g e

8Business goals are clearly communicated and understood, and we know how success is measured.

1 2 3 4 5

9My organization is doing very good now as compared with last year and three years ago

1 2 3 4 5

10My organization is moving in with current business trends and developments

1 2 3 4 5

11My organization utilizes cost-saving measures

effectively without compromising the quality of

its products or services.

1 2 3 4 5

12 My organization selects technology to help the

business achieve its mission and goals.

1 2 3 4 5

13My organization has established realistic business goals

1 2 3 4 5

14My organization has a formal data warehouse development methodology.

1 2 3 4 5

15 the business role of the data warehouse is

understood

1 2 3 4 5

16

We understand the role of the data warehouse in

changing business processes.1 2 3 4 5

17 Data is integrated throughout the organization 1 2 3 4 5

18My organization ensures the quality of data

warehouse deliverables.1 2 3 4 5

19there is a well written Policy for data retrieval organization-wide

1 2 3 4 5

75 | P a g e

20

Critical business information needs are easily met with access to operational databases. 1 2 3 4 5

21We understand the role of business processes in

producing information.1 2 3 4 5

22The business units openly share information

across organizational lines1 2 3 4 5

23Data is easily retrieved in a timely manner and

fashion1 2 3 4 5

Section 3: Open Ended Questions

Q1: DO you agree that with the correct Knowledge Management practices and business analysis

and analytics methodology and the efficay of a data warehouse and the correct data retrieval

methods makes up a better business intelligence and why?

Q2: Can you summarize business intelligence as; how we get knowledge (Knowledge

Management), how we analyze (Business Analysis &Analytics), how we store (Data warehouse),

how we retrieve and use information (Data Mining) and why?

Q3: Anyother matter not covered within the questionnaire

76 | P a g e

References

Shaun McCarthy (2009) 'Business Intelligence versus Knowledge Management-Inside

Knowledge'. InsideKnowledge. 23rd May 2010, pp. 2-6.

wikitionary.org (2009) Business Intelligence [Online]. Los Angeles, United States: Wild

West Domains, Inc.. Retrieved from: http://en.wiktionary.org/wiki/business_intelligence

[Accessed 23rd May 2010].

Annie Green (2007) 'KNOWLEDGE VALUATION', Business information – a natural path to

business intelligence: knowing what to capture, Vol. 1, No. 10, pp.

Dataflux (2009) Business Intelligence [Online]. NC 27513-2414, USA : SAS Institute Inc.

Retrieved from: http://www.bettermanagement.com/topic/default.aspx?f=10 [Accessed 21st May

2010].

Thomas H. Davenport ,Laurence Prusak (1998) ,Working Knowledge: How Organisations

Manage What They Know, (Harvard Business School Press,Boston, Massachussettes, 1998),

page 5.

ExecutiveBrief (2009) Success Factors in Knowledge Management [Online]. USA: Project

Team Blog. Retrieved from: http://www.projectteamblog.com/?p=169 [Accessed 25th May

2010].

Kathleen B Hass, Richard Vander Horst, Kimi Ziemski (2008). From Analyst to Leader:

Elevating the Role of the Business Analyst Management Concepts, 2008. ISBN 1567262139.

p94: "As the discipline of business analysis becomes professionalized"

1keydata.com (2001) Datawarehousing [Online]. USA: 1keydata.com. Retrieved from:

http://www.1keydata.com/datawarehousing/datawarehouse.html [Accessed 21th May 2010].

Farida Hasanali (2002) 'Critical Success Factors of Knowledge Management', Knowledge

77 | P a g e

Management, Lessons Learned, Vol. 1, No. 1, pp. 1-4.

Card, Stuart; Thomas P. Moran and Allen Newell (1983). The Psychology of Human Computer

Interaction. Lawrence Erlbaum Associates. ISBN 0-89859-859-1.

Adrienne Tannenbaum (2001) 'Metadata Solutions: Using Metamodels, Repositories, XML, and

Enterprise Portals to Generate Information on Demand '. USA: Addison-Wesley Professional.

Porter, Michael E. (1998) Competitive Advantage: Creating and Sustaining Superior

Performance. 4th ed. USA: Simon & Schuster.

Dunja Mladenic, Nada Lavrac, Marko Bohanec and Steve Moyle (2003) 'Data Mining and

Decision Support: Integration and Collaboration', Journal of Documentation, Vol. 61, No.

3, pp. 443-445.

Netezza (2004) 'Business Intelligence in a Real-Time World', White Paper, Vol. 1, No. 1, pp. 1-

2.

Agha Muhammad Ali Khan,Nadia Amin,Nick Lambrou (2009) 'Drivers And Barriers To

Business Intelligence Adoption: A Case Of Pakistan', European and Mediterranean Conference

on Information Systems 2010, Vol. 1, No. 1, pp. 3-5.

Virpi Pirttimäki, Antti Lönnqvist and Antti Karjaluoto (2007) 'in a Finnish Telecom-munications

Company', Measurement of Business Intelligence, Vol. 3, No. 1, pp. 3-6.

Gibson, M, Arnott, D, Jagielska, I & Melbourne, A 2004,'Evaluating the Intangible Benefits of

Business Intelligence: Review & Research Agenda', Proceedings of the 2004 IFIP International

Conference on Decision Support Systems (DSS2004): Decision Support in an Uncertain and

Complex World, pp. 295-305.

78 | P a g e

Timothy Chee, Lee-Kwun Chan, Min-Hooi Chuah, Chee-Sok Tan, Siew-Fan Wong,William

Yeoh (2009) 'Business Intelligence Systems', State-Of-The-Art Review And Contemporary

Applications, Vol. 1, No. 1, pp. 3-4.

Olszak, C. M. (2002). The Purposes and Assumptions of Business Intelligence. In: Sroka, H.

(Eds): Organizational Support Systems. Katowice: Univerity of Economics.

Dick Chase (2001) 'Business Intelligence', Business Intelligence: With or Without the E, Vol. 1,

No. 1, p. 2.

B.Azvine, Z. Cui, D.D. Nauck, B. Majeed.Real Time Business Intelligence for the Adaptive

Enterprise. In: E-Commerce Technology, 2006. The 8th IEEE International Conference on and

Enterprise Computing, E-Commerce, and EServices,The 3rd IEEE International Conference on,

29- 29

L. T. Moss and S. Atre. Business Intelligence Roadmap: The Complete Project Lifecycle for

Decision-Support Applications. Addison-Wesley Longman Publishing Co., Inc., Boston, MA,

USA, 2003.

Petrini, M & Pozzebon, M 2004, 'What Role Is “Business Intelligence” Playing in Developing

Countries? A Picture of Brazilian Companies', Cahier du GReSI, vol. 4, p. 16.

Chang, E 2006, 'Advanced BI Technologies, Trust,Reputation and Recommendation Systems',

presented atthe 7th Business Intelligence Conference (Organised byMarcus Evans), Sydney,

Australia.

Chemers, M. M. (2002). Meta-cognitive, social, and emotional intelligence of transformational

leadership: Efficacy and Effectiveness. In R. E. Riggio, S. E. Murphy, F.

J. Pirozzolo (Eds.), Multiple Intelligences and Leadership.

Lord Kelvin http://findarticles.com/p/articles/mi_m2267/is_2_68/ai_771877171/

79 | P a g e

Sun (2005) 'Business Intelligence and Data Warehousing', Transform raw data into business

results, Vol. 1, No. 1, p. 1.

Jayanthi Ranjan (2008) 'Business justification with business intelligence', Business Intelligence

and Corporate Strategy, Vol. 1, No. 1, pp. 464-465.

Diann Daniel (2007) Three Weaknesses in Business Intelligence Today [Online]. USA: CXO

Media Inc. Retrieved from:

http://www.cio.com/article/148303/Three_Weaknesses_in_Business_Intelligence_Today[Access

ed 15th June 2010].

Angelo Mastrangelo,Erik R. Eddy,Steven J. Lorenzet (2004) 'The importance of personal and

professional leadership', Leadership Analysis, Vol. 1, No. 1, p. 3.

Dianne S. Lewis,Erica French,Peter Steane (1997) 'A culture of conflict', Leadership &

Organization Development, Vol. 18, No. 6, pp. 275-282.

Richard Dobbins,Barrie Pettman (1998) 'Creating More time', Equal Opportunities Intl, Vol. 17,

No. 2, pp. 18-22.

Thrift, N., “The Making of Capitalist Time Consciousness”, in Hassard, J. (Ed.), The Sociology

of Time, Macmillan, Basingstoke, Hampshire, 1990, ch. 7, p. 114.

Topik, S., “How Time Got That Way”, World Trade, Vol. 5 No. 3, 1992, pp. 128-30.

Gross, B.L. and Sheth, J.N., “Time-orientated Advertising: A Content Analysis of United States

Magazine Advertising, 1890-1988”, Journal of Marketing, Vol. 53 No. 4, 1989, pp.76-83

Hill and Knowlton (2007) ‘The state of corporatecommunications’.

80 | P a g e

Catherine Bailey,Martin Clarke (2008) 'How do managers use knowledge about Knowledge

Management', Knowledge Management, Vol. 1, No. 1, p. 237.

David Staniforth (1994) 'To Restructure or Not? That Is the Managerial Question', THE TQM

MAGAZINE, Vol. 6, No. 5, p. 28.

Drucker, P.F., The Practice of Management,Heinemann, London, 1961.

Leavitt, H.J., Managerial Psychology,University of Chicago Press, IL, 1978.

Heim, J.A. and Compton, W.D. (Eds), Manufacturing Systems: Foundations of World-

ClassPractice, National Academy of Engineering, Washington, DC, 1992.

Edosomwan, J.A., Integrating Productivity and Quality Management, Marcel Dekker, New

York, NY, 1990.

Alaa M. Ghalayini and James S. Noble (1996) 'The changing basis of performance

measurement', International Journal of Operations & Production Management, Vol. 16, No. 8,

pp. 63-80.

Philip Calvert (2005) 'Encyclopedia of Data Warehousing and Mining'. 2nd ed. Hershey, PA:

Idea Group Publishing.

Michael L. Gargano and Bel G. Raggad (1999) 'Data mining', a powerful information creating

tool, Vol. 15, No. 2, pp. 81-90

Dr. Abdul majid Aljilani Al-Mishri (2005) 'Introduction to Research Methodology', Research

Methodology, Vol. 1, No. 5, pp. 2-3.

Aicha (2007) Research Approach [Online]. usa: BlurtIT. Retrieved from:

http://www.blurtit.com/q417261.html [Accessed 28th June 2010].

81 | P a g e

Memo (2006) Research Approach [Online]. usa: BlurtIT. Retrieved from:

http://www.blurtit.com/q417261.html [Accessed 28th June 2010].

David S. Walonick (2005) Elements of a research proposal and report [Online]. Bloomington:

StatPac Inc. Retrieved from: http://www.statpac.com/research-papers/research-proposal.htm

[Accessed 28th June 2010].

Gordon Rugg and Marian Petre (2007) A Gentle Guide to Research Methods.

Berkshire,England: Open University Press.

NIK MAHERAN NIK MUHAMMAD (2006) 'RESEARCH DESIGN', RESEARCH

METHODOLOGY WORKSHOP, Vol. 1, No. 1, pp. 1-35.

Malcolm Tatum (2010) What is Applied Research? [Online]. US: Wise Geek. Retrieved from:

http://www.wisegeek.com/what-is-applied-research.htm [Accessed 6th July 2010].

George Smoot (2007) Basic vs. Applied Research [Online]. USA: LBNL. Retrieved from:

http://www.lbl.gov/Education/ELSI/research-main.html [Accessed 6th July 2010].

Sheri Cyprus (2010) What Is Business Research? [Online]. US: Wise Geek. Retrieved from:

http://www.wisegeek.com/what-is-business-research.htm [Accessed 6th July 2010].

Linda Westfall (2008) The Certified Software Quality Engineer Handbook. 2nd ed. Australia:

ASQ Quality Press.

Creswell, J. W. (1994) Research design : qualitative and quantitative approaches. - Thousand

Oaks, Calif.; London : Sage Publications, ISBN 0803952546

Aqil Burney S. M. (2008) 'Inductive & Deductive Research Approach', Research Methodologies,

Vol. 1, No. 1, pp. 2-22.

82 | P a g e

Linda M. Woolf (2010) Research Methods [Online]. USA: Webster University. Retrieved

from:http://www.webster.edu/~woolflm/statmethods.html [Accessed 10th July 2010].

Deryck D. Pattron (2009) 'Research Methodology', Research, Vol. 1, No. 1, p. 10.

Pattron, D. 2000. Fundamentals of Scientific Research. New York: Scientific

Publishershttp://owl.english.purdue.edu Publication Manual of the American Psychological

Association, 5th ed. http://www.apastyle.org

andrew sanchez (2006) the difference between qualitative and quantitative research [Online].

usa: free article.Retrieved from: http://e-articles.info/e/a/title/THE-DIFFERENCE-BETWEEN-

QUALITATIVE-AND-QUANTITATIVE-RESEARCH/ [Accessed 10th July 2010].

cdcynergy (2008) Differences Between Qualitative and Quantitative Research Methods [Online].

usa: cdcynergy. Retrieved from:

http://www.orau.gov/cdcynergy/demo/Content/activeinformation/tools/toolscontent/

SOC_qual_quant_chart.htm [Accessed 11th July 2010].

Steven R. Fischer, A history of language, Reaktion Books, 2003, ISBN 186189080X, p. 205

William M.K. Trochim (2006) Descriptive Statistics [Online]. usa: Social Research Methods.

Retrieved from: http://www.socialresearchmethods.net/kb/statdesc.php [Accessed 12th July

2010].

Experiment Resources (2009). Statistics Tutorial [Online]. Retrievedfrom Experiment

Resources: http://www.experiment-resources.com/statistics-tutorial.html [Accessed 12th July

2010].Amit Choudhury (2009)

83 | P a g e

Choudhury, Amit (2009). Correlation(Linear Relationship)[Online]. Retrieved from Experiment

Resources: http://www.experiment-resources.com/statistics-tutorial.html [Accessed 12th July

2010].

Choudhury, Amit (2009). Factor Analysis. Retrieved from Experiment Resources:

http://www.experiment-resources.com/factor-analysis.html [Accessed 12th July 2010].

84 | P a g e