Description
In recent years Business Intelligence (BI) systems have consistently been rated as one of the highest priorities of Information Systems (IS) and business leaders.
International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 2012
DOI : 10.5121/ijcses.2012.3201 1
The necessities for building a model to evaluate
Business Intelligence projects- Literature Review
Vahid Farrokhi
1
and Lászl? Pokorádi
2
1, 2
Doctoral School of Informatics, University of Debrecen, Debrecen, Hungary
1
[email protected] and
2
[email protected]
Abstract
In recent years Business Intelligence (BI) systems have consistently been rated as one of the highest
priorities of Information Systems (IS) and business leaders. BI allows firms to apply information for
supporting their processes and decisions by combining its capabilities in both of organizational and
technical issues. Many of companies are being spent a significant portion of its IT budgets on business
intelligence and related technology. Evaluation of BI readiness is vital because it serves two important
goals. First, it shows gaps areas where company is not ready to proceed with its BI efforts. By identifying
BI readiness gaps, we can avoid wasting time and resources. Second, the evaluation guides us what we
need to close the gaps and implement BI with a high probability of success. This paper proposes to
present an overview of BI and necessities for evaluation of readiness.
Key words: Business intelligence, Evaluation, Success, Readiness
1. Introduction
Many firms have realized that the only way to continually compete and profit in the global
marketplace of today is to utilize the power of information [1-3]. In today’s highly competitive
world, the quality and timeliness of business information for an organization is not just a choice
between profit and loss; it may be a question of survival or bankruptcy [4] . The business needs
to know what is happening right now, faster, in order to determine and influence what should
happen next time [5]. Companies spend billions of dollars annually on implementation and
maintenance of IS [6]. Estimates are that IS expenses constitute the largest portion of
organizational expenditures [7, 8]. Given the size of these expenditures, companies expect to
gain benefits commensurate with the money being spent. Unfortunately recent figures estimated
that nearly half of IS project did not result in the anticipated benefits [8]. So it is important to
know how companies can get a benefit and suitable return on their investments.
Previous information systems like maintaining accounting ledgers or processing financial
transactions were applied to automate manual processes. The benefits from these types of
systems resulted from increases in efficiency or effectiveness of the underlying processes
resulting in measurable cost saving or revenue increases [9]. Traditional enterprises may
normally face issues such as the overflow of data, the lack of information, the lack of knowledge
and insufficiency of reports [10]. Top managers used to make and take decisions based on their
experiences which these lead to more risk of decision failure and reducing the value of the
decision. As worldwide competition is maturating, past decision-making modes can no longer
satisfy the requirements of enterprises for decision efficiency and benefits; enterprises must
make good use of electronic tools to quickly extract useful information from huge volume of
data by providing the skills of fast decision-making [11]. Socio-economic reality of
contemporary organizations has made organizations face some necessity to look for instruments
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that would facilitate effective acquiring, processing and analyzing vast amounts of data that
come from different and dispersed sources and that would serve as some basis for discovering
new knowledge [12]. Recent years, there are many software packages which can provide a set of
complete solutions for the operation and management processes of organizations. Nowadays, the
individual-system approach applied to decision-support such as Decision Support Systems
(DSS) has been substituted by a new environmental approach [13]. With the potential to gain
competitive advantage when making important decisions, it is vital to integrate decision support
into the environment of their enterprise and work systems. Business Intelligence can be
embedded in these enterprise systems to obtain this competitive advantage [14, 15]. BI systems
provide benefits by supporting analytical processes that provide recommendations for changing
products or processes in ways that improve their competitiveness or operational efficiency [16].
And practitioners design and implement Business Intelligence as umbrella concept create a
decision-support environment for management in enterprise systems [17].
However, the effects of the implementation of electronization tools vary that the probability of
failure is higher than that of the success [18]. Therefore, the ability to implement BI project and
support it, depends on readiness of companies.
Farrokhi and Suhaimi in their article addressed about importance of information and its flow
in companies and developed a model based on Business Process Modeling (BPM) [19]. Business
intelligence technology gives this ability to the managers and experts of these companies. But
nowadays BI systems include one of the largest and fastest growing areas of IT expenditure in
companies and if the BI project fail, they will lose a lot of money. For reducing costs through BI
implementation and preventing from fail of BI project, we need to evaluate readiness of these
companies from two aspects: Organizational and Technical.
This paper presents an overview of BI and necessities for building a model which enables us
to assay and evaluate readiness of companies from technical and organizational aspects when
they want to implement BI project. The outline of the paper is as follows: Section 2 shows an
overview of BI and its definitions. Section 3 presents the necessities for building an evaluation
model of readiness. Finally, section 4 presents the conclusions and prospective.
2. Business Intelligence
Business Intelligence or BI is a grand, umbrella term, introduced by Howard Drenser of the
Gartner Group, in 1989, to describe a set of concepts and methods to improve business decision
making by using fact-based, computerized support systems [20]. The first scientific definition by
Ghoshal and kim [21] referred to BI as a management philosophy and tool that helps
organizations to manage and refine business information for the purpose of making effective
decisions. The goal of BI systems [5] is to capture (data, information, knowledge) and respond
to business events and needs better, more informed, and faster, as decisions. BI was considered
to be an instrument of analysis, providing automated decision making about business conditions,
sales, customer demand, product preference and so on [22]. The Data Warehousing Institute, a
provider of education and training in data warehouse and BI industry defines business
intelligence as: The processes, technologies, and tools needed to turn data into information,
information into knowledge, and knowledge into plans that drive profitable business action.
Business intelligence encompasses data warehousing, business analytic tools, and
content/knowledge management.
1
Business intelligence has been defined as “business
information and business analyses within the context of key business processes that lead to
decisions and actions and that result in improved business performance” [23]. Another
definitions is “a set of processes and technologies that transform raw, meaningless data into
useful and actionable information” [24]. It utilizes a substantial amount of collected data during
the daily operational processes, and transforms the data into information and knowledge to avoid
1
The Data Warehouse Institute Faculty Newsletter, Fall 2002.
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the supposition and ignorance of the enterprises [25]. Golfarelli at al. argue that BI is the process
that transforms data into information and then into knowledge [26]. It is the process of gathering
high-quality and meaningful information about the subject matter being researched that will help
the individual(s) to analyze the information, draw conclusions or make assumptions [27].
Stackowiak et al. opine that BI is the process of taking large amounts of data, analyzing that
data, and presenting a high-level set of reports that condense the essence of that data into the
basis of business actions, enabling management to make fundamental daily business decisions
[28]. Zeng et al. have put forth that BI is “The process of collection, treatment and diffusion of
information that has an objective, the reduction of uncertainty in the making of all strategic
decisions [29]. Ranjan [30] considers BI as the conscious methodical transformation of data
from any and all data sources into new forms to provide information that is business-driven and
results-oriented. Eckerson [31] understood that BI must be able to provide the following tools:
production reporting, end-user query and reporting, OnLine Analytical Processing (OLAP),
dashboard/screen tools, data mining tools, and planning and modeling tools. It uses huge-
database (data-warehouse) analysis, and mathematical, statistical and artificial intelligence, as
well as data mining and OLAP [32]. BI includes a set of concepts, methods and processes to
improve business decisions, using information from multiple sources and applying past
experience to develop an exact understanding of business dynamics [33]. It has emerged as a
concept for analyzing collected data with the purpose to help decision making units get a better
comprehensive knowledge of an organization’s operations, and thereby make better business
decisions [4]. A BI system is a data-driven DSS that primarily supports the querying of a
historical database and the production of periodic summary reports [34]. It can be presented as
an architecture, tool, technology or system that gathers and stores data, analyzes it using
analytical tools, facilities reporting, querying and delivers information and/or knowledge that
ultimately allows organizations to improve decision making [35-42]
Lönnqvist and Pirttimäki [43] stated that term, BI, can be used when referring to the following
concepts:
1. Related information and knowledge of an organization, which describe the business
environment, the organization itself, the conditions of the market, customers and
competitors and economic issues;
2. Systemic and systematic processes by which organizations obtain, analyse and distribute
the information for making decisions about business operations.
BI allows firms to apply information for supporting their processes and decisions by
combining its capabilities in both of organizational and technical issues. Put another way,
“business intelligence allows people at all levels of an organization to access, interact with, and
analyze data to manage the business, improve performance, discover opportunities, and operate
efficiently” [44]. Problems and a huge amount of data of enterprises are input into data mining
systems for data analysis so that decision makers can obtain useful information promptly for
making correct judgment; that is, in regard to enterprise operating contents, abilities of fast
understanding and deducing are provided, and thus enhancing the quality of decision-making
and improving performance and expediting processing speed [45]. From a technical perspective,
BI systems offer an integrated set of tools, technologies and software products that are used to
collect heterogenic data from dispersed sources in order to integrate and analyse data to make it
commonly available [12].
In some research, BI is concerned with the integration and consolidation of raw data into key
performance indicators (KPIs). KPIs represent an essential basis for business decisions in the
context of process execution. Therefore, operational processes provide the context for data
analysis, information interpretation, and the appropriate action to be taken [46]. Figure 1 depicts
this concept.
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Figure 1: KPIs and BI Components (Source: [47])
Therefore, BI covers a wide range of tools and broad scope, and among the commonly
mentioned important applications are data warehouse, data mining, OLAP, DSS, Balance
Scorecard (BSC), etc [10]. However, in the overall view, there are two important issues. First,
the core of BI is the gathering, analysis and distribution of information. Second, the objective of
BI is to support the strategic decision-making process [22]. By strategic decisions, we mean
decisions related to implementation and evaluation of organizational vision, mission, goals and
objectives with medium to long-term impact on the organization, as opposed to operational
decisions, which are day-to-day in nature and more related to execution [48].
3. Necessities for evaluation of readiness
In recent years Business Intelligence systems have consistently been rated as one of the
highest priorities of IS and business leaders [24, 49, 50]. Winning companies, such as
Continental Airlines, have seen investments in BI generate increases in revenue and produce
cost savings equivalent to a 1,000% return on investment (ROI) [51]. Many of companies are
being spent a significant portion of its IT budgets on business intelligence and related
technology. Estimates of the amount spent on BI in 2006 range from $14 to $20 Billion, with
growth estimates of from 10% to 11% per year for the foreseeable future [44, 52]. A Gartner
Executive Program survey, as shown in figure 2, conducted in 2008 across 1,500 organizations
in Western Europe found that BI is the top technology priority for CIOs.
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Figure 2: BI Spend Prediction (Source: [53])
In spite of these investments only 24% of BI implementations were identified as being very
successful in a recent survey of companies using BI systems [44]. Losing companies have spent
more resources than their competitors with a smaller ROI, all while watching their market share
and customer base continuously shrink [54]. The complexity of business intelligence – data
warehouse systems is very high so it is better to consider from the beginning various foreseen
aspects that could impact the overall cost and increase the initial investment of the project but
even with a good analysis there are still remaining a large numbers of variables to be considered
[55]. The companies which are investing heavily in BI must expect to achieve benefits from
their investments. How can some organizations achieve to these benefits while others don’t?
What are differences between those companies which gain benefits from BI implementation
with the companies lost their money? Unfortunately, while much has been written about how to
effectively implement and use business intelligence technology [23, 44, 56, 57], research on BI
and specifically detailing how an organization can achieve benefits from BI is sparse [58].
3.1. BI Success
The stakes are high for organizations to develop successful BI implementations [59]. When
we want to research how BI can be considered successful we have to be able to define what we
mean by success. As we know, BI is a class of information system and it is better that we begin
to clarify how success is measured for IS in general. Many IS researchers have tried to evaluate
success [60-65]. Early work focused on multiple criteria including “profitability, application to
major problems of the organization, quality of decisions or performance, user satisfaction and
wide-spread use” [61]. The appropriate success measure depended upon the perspective of those
evaluating success or the nature of the problem being addressed [66].
While multiple criteria measures are useful in IS success but many of those criteria are
difficult to measure. As a result, much of the work on IS success has focused on system use as a
proxy for success [67-69]. In other words, the authors advised that capability of system usage is
an important clue for its success. Usage of an information system means that the system can be
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accepted by users, and users’ work-related needs can be met and the objective at the initial
implementation can be achieved. Still it was recognized that “a better measure of IS success
would probably be some weighted average of the criteria” [61]. So the advantages of an
information system differ and it depends on the type of system being implemented and the
stakeholder of it. This guides us that success measures for the research is necessary to be based
on BI specific characteristics. BI systems are implemented to provide analytical capability to
offer recommendations to improve operational or strategic processes or product characteristics
[23, 44]. Value of BI for business is predominantly expressed in the fact that such systems cast
some light on information that may serve as the basis for carrying out fundamental changes in a
particular enterprise, i.e. establishing new co-operation, acquiring new customers, creating new
markets, offering products to customers [70-72]. This means that using a BI system is not
enough to say it is successful but using recommendations and advices is the key factor. Thus we
should consider the achievement of organizational benefits to be appropriate measure of BI
success.
3.2. BI Readiness
BI readiness means that the essential prerequisites for BI success are in place. BI readiness
assessments are used at the front end of BI projects to determine the degree to which a given
company is prepared to make the changes that are necessary to capture the full business value of
BI [23]. The BI Readiness Assessment is a series of tasks that analyzes several key areas across
an organization to evaluate how prepared an organization is to begin short term tactical
deployment of Business Intelligence solutions and mature it practice over the long term [73].
Evaluation of BI readiness is vital because it serves two important goals. First, it shows gaps
areas where company is not ready to proceed with its BI efforts. By identifying BI readiness
gaps, we can avoid wasting time and resources. Second, the evaluation guides us what we need
to close the gaps and implement BI with a high probability of success.
3.3. Necessities for building a model
The bottom line in any evaluation program is the finding of problems and the demonstration
that the system under evaluation satisfies its requirements. It is unfortunate that, in many cases,
the evaluating program is actually aimed at showing that the BI system, as implemented, runs as
it is requested by the users. That is, the evaluations are aimed at showing that the BI project does
not fail, rather than that it fulfills its requirements.
There are a few books that discuss exactly on BI readiness. Williams and Williams (2007)
identified seven factors defining “business intelligence readiness” as being:
i. Strategic Alignment;
ii. Continuous Process Improvement Culture;
iii. Culture Around Use of Information and Analytics;
iv. BI Portfolio Management;
v. Decision Process Engineering Culture;
vi. BI & DW Technical Readiness;
vii. Business/IT Partnership [23].
The authors (S. Williams, and Williams, N.) suggested that only when an organization can
gain the benefits of BI, if it has this readiness. Davenport and Harris in their book “Competing
on Analytics,” [56] focused on the impact of BI systems on organizations. They identified
something that called an analytical capability, which was their conception of the ability of an
organization to use BI and as consisting of organizational acumen and technology factors
[56]. They suggest that an organization need to have capability in both organizational and
technology factors. But they provide a high level view of these factors without discuss in detail.
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Jourdan et al. have collect, synthesizes, and analyzes 167 articles on a variety of topics closely
related to business intelligence published from 1997 to 2006 in ten leading Information Systems
journals [59]. Based on their research, there are only 35 articles in BI implementation category
which covers issues in a variety of BI contexts including data warehousing, data mining,
Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), Knowledge
Management Systems (KMS), and eBusiness projects.
Research in information systems is generally focused on either developing theories that
explain related phenomena or on verifying existing theories [74]. Analysis of the research
strategies (in BI Research) over the ten year period from 1997 to 2006 illustrates that Formal
Theory/Literature Review, Field Study-Primary Data, Field Study-Secondary Data, and Sample
Survey are represented in almost every year of the time frame [59]. These four strategies are
exploratory in nature and indicate the beginnings of a body of research [75]. BI research covers
diverse subjects ranging from practical applications of neural networks [76], to end-user
satisfaction [77], to the use of clustering as a business strategy to gain a competitive advantage
[78].
Based on the journals and the books mentioned above and previous sections, there is not any
research of evaluation of BI readiness in companies. So we need to:
i. investigate and determine BI readiness factors and their associated contextual elements
that influence implementation of BI systems in companies
ii. developing a model for evaluation of BI readiness in companies
4. Conclusions and Prospective
In this paper an attempt has been made to depict an overview of BI and the necessities for
building a model to evaluate readiness of companies in implementing BI project. It was shown
that in today’s highly competitive world, using BI is vital and no business organization can deny
the benefit of BI. BI technologies are applied by profit and non-profit firms and business users
became increasingly proactive. Successful BI project is an important issue for both researchers
and practitioners; however, not many studies have done on BI readiness. Although some
guidelines for implementation exist, few have been subjected to model building. During
prospective scientific research related to this study, the authors will work out models to evaluate
readiness of companies in implementing BI projects.
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doc_818491770.pdf
In recent years Business Intelligence (BI) systems have consistently been rated as one of the highest priorities of Information Systems (IS) and business leaders.
International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 2012
DOI : 10.5121/ijcses.2012.3201 1
The necessities for building a model to evaluate
Business Intelligence projects- Literature Review
Vahid Farrokhi
1
and Lászl? Pokorádi
2
1, 2
Doctoral School of Informatics, University of Debrecen, Debrecen, Hungary
1
[email protected] and
2
[email protected]
Abstract
In recent years Business Intelligence (BI) systems have consistently been rated as one of the highest
priorities of Information Systems (IS) and business leaders. BI allows firms to apply information for
supporting their processes and decisions by combining its capabilities in both of organizational and
technical issues. Many of companies are being spent a significant portion of its IT budgets on business
intelligence and related technology. Evaluation of BI readiness is vital because it serves two important
goals. First, it shows gaps areas where company is not ready to proceed with its BI efforts. By identifying
BI readiness gaps, we can avoid wasting time and resources. Second, the evaluation guides us what we
need to close the gaps and implement BI with a high probability of success. This paper proposes to
present an overview of BI and necessities for evaluation of readiness.
Key words: Business intelligence, Evaluation, Success, Readiness
1. Introduction
Many firms have realized that the only way to continually compete and profit in the global
marketplace of today is to utilize the power of information [1-3]. In today’s highly competitive
world, the quality and timeliness of business information for an organization is not just a choice
between profit and loss; it may be a question of survival or bankruptcy [4] . The business needs
to know what is happening right now, faster, in order to determine and influence what should
happen next time [5]. Companies spend billions of dollars annually on implementation and
maintenance of IS [6]. Estimates are that IS expenses constitute the largest portion of
organizational expenditures [7, 8]. Given the size of these expenditures, companies expect to
gain benefits commensurate with the money being spent. Unfortunately recent figures estimated
that nearly half of IS project did not result in the anticipated benefits [8]. So it is important to
know how companies can get a benefit and suitable return on their investments.
Previous information systems like maintaining accounting ledgers or processing financial
transactions were applied to automate manual processes. The benefits from these types of
systems resulted from increases in efficiency or effectiveness of the underlying processes
resulting in measurable cost saving or revenue increases [9]. Traditional enterprises may
normally face issues such as the overflow of data, the lack of information, the lack of knowledge
and insufficiency of reports [10]. Top managers used to make and take decisions based on their
experiences which these lead to more risk of decision failure and reducing the value of the
decision. As worldwide competition is maturating, past decision-making modes can no longer
satisfy the requirements of enterprises for decision efficiency and benefits; enterprises must
make good use of electronic tools to quickly extract useful information from huge volume of
data by providing the skills of fast decision-making [11]. Socio-economic reality of
contemporary organizations has made organizations face some necessity to look for instruments
International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 2012
2
that would facilitate effective acquiring, processing and analyzing vast amounts of data that
come from different and dispersed sources and that would serve as some basis for discovering
new knowledge [12]. Recent years, there are many software packages which can provide a set of
complete solutions for the operation and management processes of organizations. Nowadays, the
individual-system approach applied to decision-support such as Decision Support Systems
(DSS) has been substituted by a new environmental approach [13]. With the potential to gain
competitive advantage when making important decisions, it is vital to integrate decision support
into the environment of their enterprise and work systems. Business Intelligence can be
embedded in these enterprise systems to obtain this competitive advantage [14, 15]. BI systems
provide benefits by supporting analytical processes that provide recommendations for changing
products or processes in ways that improve their competitiveness or operational efficiency [16].
And practitioners design and implement Business Intelligence as umbrella concept create a
decision-support environment for management in enterprise systems [17].
However, the effects of the implementation of electronization tools vary that the probability of
failure is higher than that of the success [18]. Therefore, the ability to implement BI project and
support it, depends on readiness of companies.
Farrokhi and Suhaimi in their article addressed about importance of information and its flow
in companies and developed a model based on Business Process Modeling (BPM) [19]. Business
intelligence technology gives this ability to the managers and experts of these companies. But
nowadays BI systems include one of the largest and fastest growing areas of IT expenditure in
companies and if the BI project fail, they will lose a lot of money. For reducing costs through BI
implementation and preventing from fail of BI project, we need to evaluate readiness of these
companies from two aspects: Organizational and Technical.
This paper presents an overview of BI and necessities for building a model which enables us
to assay and evaluate readiness of companies from technical and organizational aspects when
they want to implement BI project. The outline of the paper is as follows: Section 2 shows an
overview of BI and its definitions. Section 3 presents the necessities for building an evaluation
model of readiness. Finally, section 4 presents the conclusions and prospective.
2. Business Intelligence
Business Intelligence or BI is a grand, umbrella term, introduced by Howard Drenser of the
Gartner Group, in 1989, to describe a set of concepts and methods to improve business decision
making by using fact-based, computerized support systems [20]. The first scientific definition by
Ghoshal and kim [21] referred to BI as a management philosophy and tool that helps
organizations to manage and refine business information for the purpose of making effective
decisions. The goal of BI systems [5] is to capture (data, information, knowledge) and respond
to business events and needs better, more informed, and faster, as decisions. BI was considered
to be an instrument of analysis, providing automated decision making about business conditions,
sales, customer demand, product preference and so on [22]. The Data Warehousing Institute, a
provider of education and training in data warehouse and BI industry defines business
intelligence as: The processes, technologies, and tools needed to turn data into information,
information into knowledge, and knowledge into plans that drive profitable business action.
Business intelligence encompasses data warehousing, business analytic tools, and
content/knowledge management.
1
Business intelligence has been defined as “business
information and business analyses within the context of key business processes that lead to
decisions and actions and that result in improved business performance” [23]. Another
definitions is “a set of processes and technologies that transform raw, meaningless data into
useful and actionable information” [24]. It utilizes a substantial amount of collected data during
the daily operational processes, and transforms the data into information and knowledge to avoid
1
The Data Warehouse Institute Faculty Newsletter, Fall 2002.
International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 2012
3
the supposition and ignorance of the enterprises [25]. Golfarelli at al. argue that BI is the process
that transforms data into information and then into knowledge [26]. It is the process of gathering
high-quality and meaningful information about the subject matter being researched that will help
the individual(s) to analyze the information, draw conclusions or make assumptions [27].
Stackowiak et al. opine that BI is the process of taking large amounts of data, analyzing that
data, and presenting a high-level set of reports that condense the essence of that data into the
basis of business actions, enabling management to make fundamental daily business decisions
[28]. Zeng et al. have put forth that BI is “The process of collection, treatment and diffusion of
information that has an objective, the reduction of uncertainty in the making of all strategic
decisions [29]. Ranjan [30] considers BI as the conscious methodical transformation of data
from any and all data sources into new forms to provide information that is business-driven and
results-oriented. Eckerson [31] understood that BI must be able to provide the following tools:
production reporting, end-user query and reporting, OnLine Analytical Processing (OLAP),
dashboard/screen tools, data mining tools, and planning and modeling tools. It uses huge-
database (data-warehouse) analysis, and mathematical, statistical and artificial intelligence, as
well as data mining and OLAP [32]. BI includes a set of concepts, methods and processes to
improve business decisions, using information from multiple sources and applying past
experience to develop an exact understanding of business dynamics [33]. It has emerged as a
concept for analyzing collected data with the purpose to help decision making units get a better
comprehensive knowledge of an organization’s operations, and thereby make better business
decisions [4]. A BI system is a data-driven DSS that primarily supports the querying of a
historical database and the production of periodic summary reports [34]. It can be presented as
an architecture, tool, technology or system that gathers and stores data, analyzes it using
analytical tools, facilities reporting, querying and delivers information and/or knowledge that
ultimately allows organizations to improve decision making [35-42]
Lönnqvist and Pirttimäki [43] stated that term, BI, can be used when referring to the following
concepts:
1. Related information and knowledge of an organization, which describe the business
environment, the organization itself, the conditions of the market, customers and
competitors and economic issues;
2. Systemic and systematic processes by which organizations obtain, analyse and distribute
the information for making decisions about business operations.
BI allows firms to apply information for supporting their processes and decisions by
combining its capabilities in both of organizational and technical issues. Put another way,
“business intelligence allows people at all levels of an organization to access, interact with, and
analyze data to manage the business, improve performance, discover opportunities, and operate
efficiently” [44]. Problems and a huge amount of data of enterprises are input into data mining
systems for data analysis so that decision makers can obtain useful information promptly for
making correct judgment; that is, in regard to enterprise operating contents, abilities of fast
understanding and deducing are provided, and thus enhancing the quality of decision-making
and improving performance and expediting processing speed [45]. From a technical perspective,
BI systems offer an integrated set of tools, technologies and software products that are used to
collect heterogenic data from dispersed sources in order to integrate and analyse data to make it
commonly available [12].
In some research, BI is concerned with the integration and consolidation of raw data into key
performance indicators (KPIs). KPIs represent an essential basis for business decisions in the
context of process execution. Therefore, operational processes provide the context for data
analysis, information interpretation, and the appropriate action to be taken [46]. Figure 1 depicts
this concept.
International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 2012
4
Figure 1: KPIs and BI Components (Source: [47])
Therefore, BI covers a wide range of tools and broad scope, and among the commonly
mentioned important applications are data warehouse, data mining, OLAP, DSS, Balance
Scorecard (BSC), etc [10]. However, in the overall view, there are two important issues. First,
the core of BI is the gathering, analysis and distribution of information. Second, the objective of
BI is to support the strategic decision-making process [22]. By strategic decisions, we mean
decisions related to implementation and evaluation of organizational vision, mission, goals and
objectives with medium to long-term impact on the organization, as opposed to operational
decisions, which are day-to-day in nature and more related to execution [48].
3. Necessities for evaluation of readiness
In recent years Business Intelligence systems have consistently been rated as one of the
highest priorities of IS and business leaders [24, 49, 50]. Winning companies, such as
Continental Airlines, have seen investments in BI generate increases in revenue and produce
cost savings equivalent to a 1,000% return on investment (ROI) [51]. Many of companies are
being spent a significant portion of its IT budgets on business intelligence and related
technology. Estimates of the amount spent on BI in 2006 range from $14 to $20 Billion, with
growth estimates of from 10% to 11% per year for the foreseeable future [44, 52]. A Gartner
Executive Program survey, as shown in figure 2, conducted in 2008 across 1,500 organizations
in Western Europe found that BI is the top technology priority for CIOs.
International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 2012
5
Figure 2: BI Spend Prediction (Source: [53])
In spite of these investments only 24% of BI implementations were identified as being very
successful in a recent survey of companies using BI systems [44]. Losing companies have spent
more resources than their competitors with a smaller ROI, all while watching their market share
and customer base continuously shrink [54]. The complexity of business intelligence – data
warehouse systems is very high so it is better to consider from the beginning various foreseen
aspects that could impact the overall cost and increase the initial investment of the project but
even with a good analysis there are still remaining a large numbers of variables to be considered
[55]. The companies which are investing heavily in BI must expect to achieve benefits from
their investments. How can some organizations achieve to these benefits while others don’t?
What are differences between those companies which gain benefits from BI implementation
with the companies lost their money? Unfortunately, while much has been written about how to
effectively implement and use business intelligence technology [23, 44, 56, 57], research on BI
and specifically detailing how an organization can achieve benefits from BI is sparse [58].
3.1. BI Success
The stakes are high for organizations to develop successful BI implementations [59]. When
we want to research how BI can be considered successful we have to be able to define what we
mean by success. As we know, BI is a class of information system and it is better that we begin
to clarify how success is measured for IS in general. Many IS researchers have tried to evaluate
success [60-65]. Early work focused on multiple criteria including “profitability, application to
major problems of the organization, quality of decisions or performance, user satisfaction and
wide-spread use” [61]. The appropriate success measure depended upon the perspective of those
evaluating success or the nature of the problem being addressed [66].
While multiple criteria measures are useful in IS success but many of those criteria are
difficult to measure. As a result, much of the work on IS success has focused on system use as a
proxy for success [67-69]. In other words, the authors advised that capability of system usage is
an important clue for its success. Usage of an information system means that the system can be
International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 2012
6
accepted by users, and users’ work-related needs can be met and the objective at the initial
implementation can be achieved. Still it was recognized that “a better measure of IS success
would probably be some weighted average of the criteria” [61]. So the advantages of an
information system differ and it depends on the type of system being implemented and the
stakeholder of it. This guides us that success measures for the research is necessary to be based
on BI specific characteristics. BI systems are implemented to provide analytical capability to
offer recommendations to improve operational or strategic processes or product characteristics
[23, 44]. Value of BI for business is predominantly expressed in the fact that such systems cast
some light on information that may serve as the basis for carrying out fundamental changes in a
particular enterprise, i.e. establishing new co-operation, acquiring new customers, creating new
markets, offering products to customers [70-72]. This means that using a BI system is not
enough to say it is successful but using recommendations and advices is the key factor. Thus we
should consider the achievement of organizational benefits to be appropriate measure of BI
success.
3.2. BI Readiness
BI readiness means that the essential prerequisites for BI success are in place. BI readiness
assessments are used at the front end of BI projects to determine the degree to which a given
company is prepared to make the changes that are necessary to capture the full business value of
BI [23]. The BI Readiness Assessment is a series of tasks that analyzes several key areas across
an organization to evaluate how prepared an organization is to begin short term tactical
deployment of Business Intelligence solutions and mature it practice over the long term [73].
Evaluation of BI readiness is vital because it serves two important goals. First, it shows gaps
areas where company is not ready to proceed with its BI efforts. By identifying BI readiness
gaps, we can avoid wasting time and resources. Second, the evaluation guides us what we need
to close the gaps and implement BI with a high probability of success.
3.3. Necessities for building a model
The bottom line in any evaluation program is the finding of problems and the demonstration
that the system under evaluation satisfies its requirements. It is unfortunate that, in many cases,
the evaluating program is actually aimed at showing that the BI system, as implemented, runs as
it is requested by the users. That is, the evaluations are aimed at showing that the BI project does
not fail, rather than that it fulfills its requirements.
There are a few books that discuss exactly on BI readiness. Williams and Williams (2007)
identified seven factors defining “business intelligence readiness” as being:
i. Strategic Alignment;
ii. Continuous Process Improvement Culture;
iii. Culture Around Use of Information and Analytics;
iv. BI Portfolio Management;
v. Decision Process Engineering Culture;
vi. BI & DW Technical Readiness;
vii. Business/IT Partnership [23].
The authors (S. Williams, and Williams, N.) suggested that only when an organization can
gain the benefits of BI, if it has this readiness. Davenport and Harris in their book “Competing
on Analytics,” [56] focused on the impact of BI systems on organizations. They identified
something that called an analytical capability, which was their conception of the ability of an
organization to use BI and as consisting of organizational acumen and technology factors
[56]. They suggest that an organization need to have capability in both organizational and
technology factors. But they provide a high level view of these factors without discuss in detail.
International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 2012
7
Jourdan et al. have collect, synthesizes, and analyzes 167 articles on a variety of topics closely
related to business intelligence published from 1997 to 2006 in ten leading Information Systems
journals [59]. Based on their research, there are only 35 articles in BI implementation category
which covers issues in a variety of BI contexts including data warehousing, data mining,
Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), Knowledge
Management Systems (KMS), and eBusiness projects.
Research in information systems is generally focused on either developing theories that
explain related phenomena or on verifying existing theories [74]. Analysis of the research
strategies (in BI Research) over the ten year period from 1997 to 2006 illustrates that Formal
Theory/Literature Review, Field Study-Primary Data, Field Study-Secondary Data, and Sample
Survey are represented in almost every year of the time frame [59]. These four strategies are
exploratory in nature and indicate the beginnings of a body of research [75]. BI research covers
diverse subjects ranging from practical applications of neural networks [76], to end-user
satisfaction [77], to the use of clustering as a business strategy to gain a competitive advantage
[78].
Based on the journals and the books mentioned above and previous sections, there is not any
research of evaluation of BI readiness in companies. So we need to:
i. investigate and determine BI readiness factors and their associated contextual elements
that influence implementation of BI systems in companies
ii. developing a model for evaluation of BI readiness in companies
4. Conclusions and Prospective
In this paper an attempt has been made to depict an overview of BI and the necessities for
building a model to evaluate readiness of companies in implementing BI project. It was shown
that in today’s highly competitive world, using BI is vital and no business organization can deny
the benefit of BI. BI technologies are applied by profit and non-profit firms and business users
became increasingly proactive. Successful BI project is an important issue for both researchers
and practitioners; however, not many studies have done on BI readiness. Although some
guidelines for implementation exist, few have been subjected to model building. During
prospective scientific research related to this study, the authors will work out models to evaluate
readiness of companies in implementing BI projects.
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