Description
Recently business intelligence (BI) applications have been the primary agenda for many CIOs.
BUSINESS INTELLIGENCE SYSTEMS: STATE-OF-THE-ART REVIEW
AND CONTEMPORARY APPLICATIONS
Timothy Chee, Lee-Kwun Chan, Min-Hooi Chuah, Chee-Sok Tan, Siew-Fan Wong,William Yeoh
Faculty of Information and Communication Technology
University Tunku Abdul Rahman, Malaysia
{cheech; chanlk; chuahmh; cstan; wongsf; yeohgs}@utar.edu.my
ABSTRACT
Recently business intelligence (BI) applications have
been the primary agenda for many CIOs. However, the
concept of BI is fairly new and to date there is no
commonly agreed definition of BI. This paper explores
the nebulous definitions and the various applications of
BI through a comprehensive review of academic as
well as practitioner’s literature. As a result, three main
perspectives of BI have been identified, namely the
management aspect, the technological aspect, and the
product aspect. This categorization gives researchers,
practitioners, and BI vendors a better idea of how
different parties have approached BI thus far and is
valuable in their, design, planning, and implementation
of a contemporary BI system in the future. The
categorization may even be a first effort towards a
commonly agreed definition of BI.
Keywords - Business Intelligence Systems, Review,
Evolution, BI Applications
1. INTRODUCTION
The business intelligence (BI) market has recently
experienced high growth as vendors continue to report
substantial profits [1, 2]. The BI applications have
emerged as the top spending priority for many CIOs
and remain the most important technologies to be
purchased [3, 4, 5]. CIOs realize that data is one of
their more valuable assets because data is used to
generate information. The increasing needs for prompt
decision making leads to the generation of information
at an increasing pace. Data analysis, reporting, and
query tools in BI systems can help business users wade
through a sea of data to generate valuable information
from it.
With its rapid growth, business intelligence as a
relatively new area in information system [6] warrants
academic attention [7]. However, most of the BI
literature has come from within the business world, the
IT industry, and vendors [8]. Academic research within
the information system field is still at an early stage [8,
9, 10] and so there is no commonly agreed definition of
BI. Vitt et al. acknowledged that the term is
multifaceted and is “used by different pundits and
software vendors to characterise a broad range of
technologies, software platforms, specific applications,
and processes” [6]. Thus it is a content-free expression
and means different things to different people. In view
of this, this paper first presents the various definitions
and categories of BI and suggests how the categories
can fit into different dimensions of BI.
2. BUSINESS INTELLIGENCE
According to Gibson et al, the term business
intelligence and its key concepts originated with the
Gartner Research in 1989 [7]. Howard Dresner of
Gartner Research, who is also widely recognised as the
father of BI, first coined the term as “a broad category
of software and solutions for gathering, consolidating,
analysing and providing access to data in a way that
lets enterprise users make better business decisions”
[7]. However, this research found that the term
business intelligence was used as early as 1958 by
Luhn in an IBM journal article entitled “A Business
Intelligence System”. Using a selective dissemination
of information (SDI) technique, Luhn’s paper presents
a concept which is similar to the modern notion of
business intelligence. The following is the original
definition from Luhn [11]:
“Business is a collection of activities carried
on for whatever purpose, be it science,
technology, commerce, industry, law,
government, defense, et cetera. The
communication facility serving the conduct of a
business (in the broad sense) may be referred
to as an intelligence system. The notion of
intelligence is also defined here, in a more
general sense, as "the ability to apprehend the
interrelationships of presented facts in such a
way as to guide action towards a desired
goal.”
Since then, leading vendors and prominent authors
have used various other definitions used to capture the
essence of BI. 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 recognizes
the traditional separation between technical and
managerial approaches and are in line with Petrini and
Pozzebon’s observation [12]. 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 [13]. Table 2 categorizes existing
definitions of BI using the three categories found here.
Symposium on Progress in Information & Communication Technology 2009
96
An interesting finding worth mentioning here is the fact
that while some definitions fall strictly into one
category, others span two or three categories. This
highlights the currently multi-faceted definition of BI,
and hence the lack of agreement on how different
parties perceive a BI system.
Table 1: Summary of varied BI definitions (Source: Developed for this research)
BI Vendor / Author Definition of BI
Turban et al. (2007) An umbrella term that encompasses tools, architectures, databases, data
warehouses, performance management, methodologies, and so forth, all of
which are integrated into a unified software suite.
Moss and Atre (2003) It is an architecture and a collection of integrated operational as well as
decision-support applications and databases that provide the business
community easy access to business data.
Chang (2006) The accurate, timely, critical data, information and knowledge that supports
strategic and operational decision making and risk assessment in uncertain and
dynamic business environments. The source of the data, information and
knowledge are both internal organisationally collected as well as externally
supplied by partners, customers or third parties as a result of their own choice.
Gangadharan and
Swami (2004)
The result of in-depth analysis of detailed business data, including database
and application technologies, as well as analysis practice.
Kulkarni and King
(1997)
A product of analysing business data using business intelligence tools. It
emerges as a result of this analysis.
Moss and Hoberman
(2004)
The processes, technologies, and tools needed to turn data into information,
information into knowledge and knowledge into plans that drive profitable
business action. BI encompasses data warehousing, business analytics tools
and content/knowledge management.
Adelman and Moss
(2000)
A term encompasses a broad range of analytical software and solutions for
gathering, consolidating, analysing and providing access to information in a
way that is supposed to let an enterprise’s users make better business decision.
Gartner Research
(Hostmann 2007)
An umbrella term that includes the analytic applications, the infrastructure and
platforms, as well as the best practices.
IBM (Whitehorn &
Whitehorn 1999)
An umbrella term that broadly covering the processes involved in extracting
valuable business information from the mass of data that exists within a typical
enterprise.
Business Objects
(Business Objects 2007)
The use of an organisation’s disparate data to provide meaningful information
and analysis to employees, customers, suppliers, and partners for more
effective decision making.
Cognos (Cognos 2007) Business intelligence brings people and data together, offering a variety of
ways to see the information that backs fact-based decision-making.
SAS Institute (Ing
2007)
Delivering the right information to the right people at the right time to support
better decision making and to gain competitive advantage.
Oracle (Oracle 2007) A portfolio of technology and applications that provides an integrated, end-to-
end Enterprise Performance Management System, including financial
performance management applications, operational BI applications, BI
foundation and tools, and data warehousing.
Informatica, Teradata,
MicroStrategy
(Markarian, Brobst &
Bedell 2007)
An interactive process for exploring and analysing structured, domain-specific
information (often stored in a data warehouse) to discern trends or patterns,
thereby deriving insights and drawing conclusions.
Symposium on Progress in Information & Communication Technology 2009
97
Table 2: Three approaches to the definition of BI (Source: Adapted from [12], [13])
3. ADDRESSING THE MULTI-FACETED
DEFINTION OF BUSINESS INTELLIGENCE
From the technological standpoint, BI is considered as
a broad category of tools, software, solutions, and
technologies that enables decision-makers to find,
accumulate, organize, and access a wider range of
information from disparate data sources [15, 18, 19,
25]. In this context, the emphasis of BI is not on the
process itself, but on the technologies that support the
gathering, storage, consolidation, analysis, and mining
of corporate data. The desired output is the unveiling of
‘insights’ that might be deeply embedded in the data, if
there is a right mix of data warehousing and data
mining [27].
The managerial approach presents BI as a process in
which data from both internal and external sources are
integrated so as to generate actionable information for
improved decision support, and to realise the benefits
of the deployment of integrated transaction processing
systems and enterprise applications [18, 20, 22, 23, 24].
So, the primary focus is on the coordination and
management of the process by which different
information sources from diverse operational and
transactional systems (both inside and outside the
company) can be integrated and analysed coherently to
support the decision making process [12].
From the perspective of the product approach, BI is
deemed to be a product (i.e., a result) emerging from
advanced processing of high-quality data, information
and knowledge, and analytical practices that support
decision-making and performance assessment [13]. In
this regard, various analytical and mining tools from BI
vendors such as Cognos, Business Objects, and the
SAS Institute are applied. The sources of data include
those supplied from operational, transactional and
legacy systems within the organisation and from
suppliers, business partners, customers or third parties
such as government agencies, industry benchmarks,
and information service providers [13].
In order to clearly define the meaning of the term
business intelligence in the light of these three
interpretations, this study suggests that the
technological aspect of BI to be considered as a BI
System, whereas the process perspective is regarded as
the implementation of BI systems. The product
perspective is the result (i.e. actionable information) of
analysis of business data which originated from various
sources. The characteristics of actionable information
are described in Table 3.
Table 3: Characteristics of BI from the product perspective (Source: Adapted from [28])
Characteristics Descriptions
Integrated Must have a single, enterprise-wide view
Data integrity Information must be accurate and must conform to business rules
Accessible Easily accessible with intuitive access paths, and responsive for analysis
Credible Every business factor must have one and only one value
Timely Information must be available within the stipulated time frame
Approach Managerial/Process Technological Product
Definition Focus on the process of
gathering data from internal
and external sources and of
analysing 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)
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.
Symposium on Progress in Information & Communication Technology 2009
98
4. BUSINESS INTELLIGENCE SYSTEMS
According to Reinschmidt and Francoise, a BI system
is “an integrated set of tools, technologies and
programmed products that are used to collect,
integrate, analyse and make data available” [29].
Negash states that a BI system “combines data
gathering, data storage, and knowledge management
with analytical tools to present complex and
competitive information to planners and decision
makers” [10]. The objectives are to enable business
managers and analysts of all levels to readily access
any data in the organisation and to conduct appropriate
manipulation and analysis [17]. Implicit in this
definition is the notion that a BI system can improve
the timeliness and quality of the input to the decision
making process [10], and thus corporate data is
transformed from quantity to quality [16]. However,
the idea is not new and fifty years ago Luhn of IBM
wrote that [11]:
“Information is now being generated and
utilised at an ever-increasing rate because
of the accelerated pace and scope of
human activities. At the same time the
growth of organizations and increased
specialization and divisionalization have
created new barriers to the flow of
information”. Intrinsically, he notes that “a
comprehensive system may be assembled
to accommodate all information problems
of an organisation. We call this a Business
Intelligence System.”
In other words, a BI system can be viewed as enterprise
architecture for an integrated collection of operational
and decision support applications and databases [15],
which provides various business stakeholders with easy
access to the required information. Moreover, it
facilitates the analysis and sharing of information and
helps with the making of informed business decisions
[16]. Negash and Gray argue that a BI system is not
revolutionary technology, but rather a “natural
outgrowth of a series of previous systems designed to
support decision making” [9].
In terms of its key components, Fisher et al. assert that
a BI system is composed of a set of three
complementary data management technologies, namely
data warehousing, online analytical processing
(OLAP), and knowledge discovery which is
predominantly aided by data mining techniques [30].
More specifically, Olszak and Ziemba posit that a BI
system is composed of the following essential
components [31]:
? ETL (Extraction-Transformation-Load) tools
that are responsible for data transfer from
operational or transaction systems to data
warehouses;
? data warehouses to provide some room for
thematic storing of aggregated and analysed
data;
? OLAP tools which allow users access and
which analyse and model business problems
and share information that is stored in data
warehouses;
? data mining tools for determining patterns,
generalisations, regularities and rules in data
resources;
? reporting and ad hoc inquiry tools for creating
and utilising different synthetic reports; and
? presentation layers that include customised
graphical and multimedia interfaces to provide
users with information in a comfortable and
accessible form.
5. COMTEMPORARY APPLICATIONS OF BI
SYSTEMS
To date, BI systems have been applied in various
industries such as transportation, banking, health care,
retail, manufacturing, and pharmaceuticals. Table 4
depicts the contemporary applications of BI systems in
various areas.
Table 4: Contemporary applications of BI systems
(Source: Adapted from [32], [33], [34], [35], [36])
BI Applications Benefits
Transportation Industry
? Generally, transportation service providers utilize
several tools and platforms provided by Business
Intelligence (BI) vendors which enabling the delivery of
information to decision makers such as Query Tools
Standard, Reporting Tools, Online Analytical
Processing (OLAP) tools, Data Visualization Tools, and
Data Mining Tools.
? Airline industry uses text mining to automatically
extract useful information from different written
resources such as incident reports.
? The direct benefits of the usage of a BI solution in
transportation industry are reduction in the turnaround time
for preparation of reports, direct and faster access to the
data needed to support decision-making, analyze the flow
of businesses across services, regions, clients, pricing,
currencies, and market factors in time etc.
? The huge databases maintained by airlines have limited
human interpretation and the terminology appears different
to a computer. Thus, discovery of new, previously
unknown knowledge can be found in a timely manner by
using text mining.
Symposium on Progress in Information & Communication Technology 2009
99
? An incident report is prepared whenever an event occurs
that might lead to a problem. Text mining of airline
incident reports can identify potential dilemma. Text
mining can be used with this large set of incident data
reported to validate predetermined theories and to cull new
patterns of knowledge.
Banking Industry
? Banking industry relies on the BI platform to make
more effective decisions in a few areas such as
Customer Analysis, Operations & Financial Analysis,
Sales & Marketing Analysis, Promotion Analysis, and
Risk & Fraud Analysis.
? BI applications help management to improve operational
and strategic decisions based on better and timely
information.
? Potential customers are identified through the analysis of
purchasing data. Cross-selling opportunities will be
recognized via analysis of customer behavior.
Health Care Industry
? The implementation of BI in health care industry has
enabled data to be delivered beyond administrative
offices and directly to clinical staffs who can make the
most use of it.
? In order to foster a broader adoption, interactive and
user-friendly interfaces have been designed to provide
users with simple and relevant data like the number of
patients, treatments needed, and their hospitalization
period.
? Business decision making process has become more
effective where users can access any type of information
with a fast and consistent response time, independent of the
data volumes analyzed or questions asked.
? The inter-operability application in BI reduces the
operation cost in health care industry by eliminating
expensive custom-integration in the computing system.
Retail Industry
? BI is implemented for demand forecasting in the retail
industry by generating reliable estimates for both short
term and long term demand based on the available
customer data.
? Apart from demand forecasting, BI is also used to
monitor customer loyalty by evaluating which
customers are loyal and which are likely to leave.
? When reliable estimates of customer demand are generated,
service and product distribution plans of a company would
always be in place to meet its customer expectations.
? By monitoring customer loyalty, factors that influence their
decisions to stay or go could be determined in order to
devise better strategies to retain them.
Manufacturing Industry
? BI systems allow manufacturers to track their inventory
usage across location and time by using alerts for
instant notification of low inventory levels.
? Besides this, BI systems allow manufacturers to analyze
data from multiple sources in order to set performance
goals and create sophisticated profitability and financial
models.
? With the functionality of inventory monitoring,
manufacturers can reduce over-capacity and ensure
sufficient supplies for their production.
? Apart from this, BI systems also help manufacturers in
financial management by identifying areas where they can
increase profits and improve efficiency.
Pharmaceuticals Industry
? BI systems help pharmaceuticals companies to identify
which products are most profitable and monitor
customer behavior in purchasing products.
? By closely tracking sales performance and consumer
behavior, pharmaceuticals companies are able to set better
marketing strategies and ensure proper allocation of
marketing funds.
6. CONCLUSION
This paper has reviewed the nebulous definitions and
applications of business intelligence systems. It first
addressed the multi-faceted definitions of BI by
categorizing them into three main perspectives,
namely technological, managerial, and product
aspects. Then it outlined the key components of a
typical BI system. It is noted that a BI system is not
regarded as a completely new system, but an
evolutionary, integrated product of a variety of tools
and computing techniques. These include but are not
limited to data warehousing, online analytical
processing, visualization techniques, data mining,
data quality, and web technologies. Also, the
literature review has demonstrated that applications
of BI systems are really broad and can be tailored to
various industrial needs. Due to the unique business
nature of every industry, organizations have different
requirements in their business intelligence systems.
As illustrated in Table 4 above, a banking company
has significantly different applications of BI systems
as compared to a transportation enterprise.
Consequently, to ensure a successful BI system
implementation, BI stakeholders have to take into
account not only the core business needs, but also the
substantial benefits a BI system could bring into their
organisations rather than the diverse features as
trumpeted by BI vendors.
Symposium on Progress in Information & Communication Technology 2009
100
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Recently business intelligence (BI) applications have been the primary agenda for many CIOs.
BUSINESS INTELLIGENCE SYSTEMS: STATE-OF-THE-ART REVIEW
AND CONTEMPORARY APPLICATIONS
Timothy Chee, Lee-Kwun Chan, Min-Hooi Chuah, Chee-Sok Tan, Siew-Fan Wong,William Yeoh
Faculty of Information and Communication Technology
University Tunku Abdul Rahman, Malaysia
{cheech; chanlk; chuahmh; cstan; wongsf; yeohgs}@utar.edu.my
ABSTRACT
Recently business intelligence (BI) applications have
been the primary agenda for many CIOs. However, the
concept of BI is fairly new and to date there is no
commonly agreed definition of BI. This paper explores
the nebulous definitions and the various applications of
BI through a comprehensive review of academic as
well as practitioner’s literature. As a result, three main
perspectives of BI have been identified, namely the
management aspect, the technological aspect, and the
product aspect. This categorization gives researchers,
practitioners, and BI vendors a better idea of how
different parties have approached BI thus far and is
valuable in their, design, planning, and implementation
of a contemporary BI system in the future. The
categorization may even be a first effort towards a
commonly agreed definition of BI.
Keywords - Business Intelligence Systems, Review,
Evolution, BI Applications
1. INTRODUCTION
The business intelligence (BI) market has recently
experienced high growth as vendors continue to report
substantial profits [1, 2]. The BI applications have
emerged as the top spending priority for many CIOs
and remain the most important technologies to be
purchased [3, 4, 5]. CIOs realize that data is one of
their more valuable assets because data is used to
generate information. The increasing needs for prompt
decision making leads to the generation of information
at an increasing pace. Data analysis, reporting, and
query tools in BI systems can help business users wade
through a sea of data to generate valuable information
from it.
With its rapid growth, business intelligence as a
relatively new area in information system [6] warrants
academic attention [7]. However, most of the BI
literature has come from within the business world, the
IT industry, and vendors [8]. Academic research within
the information system field is still at an early stage [8,
9, 10] and so there is no commonly agreed definition of
BI. Vitt et al. acknowledged that the term is
multifaceted and is “used by different pundits and
software vendors to characterise a broad range of
technologies, software platforms, specific applications,
and processes” [6]. Thus it is a content-free expression
and means different things to different people. In view
of this, this paper first presents the various definitions
and categories of BI and suggests how the categories
can fit into different dimensions of BI.
2. BUSINESS INTELLIGENCE
According to Gibson et al, the term business
intelligence and its key concepts originated with the
Gartner Research in 1989 [7]. Howard Dresner of
Gartner Research, who is also widely recognised as the
father of BI, first coined the term as “a broad category
of software and solutions for gathering, consolidating,
analysing and providing access to data in a way that
lets enterprise users make better business decisions”
[7]. However, this research found that the term
business intelligence was used as early as 1958 by
Luhn in an IBM journal article entitled “A Business
Intelligence System”. Using a selective dissemination
of information (SDI) technique, Luhn’s paper presents
a concept which is similar to the modern notion of
business intelligence. The following is the original
definition from Luhn [11]:
“Business is a collection of activities carried
on for whatever purpose, be it science,
technology, commerce, industry, law,
government, defense, et cetera. The
communication facility serving the conduct of a
business (in the broad sense) may be referred
to as an intelligence system. The notion of
intelligence is also defined here, in a more
general sense, as "the ability to apprehend the
interrelationships of presented facts in such a
way as to guide action towards a desired
goal.”
Since then, leading vendors and prominent authors
have used various other definitions used to capture the
essence of BI. 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 recognizes
the traditional separation between technical and
managerial approaches and are in line with Petrini and
Pozzebon’s observation [12]. 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 [13]. Table 2 categorizes existing
definitions of BI using the three categories found here.
Symposium on Progress in Information & Communication Technology 2009
96
An interesting finding worth mentioning here is the fact
that while some definitions fall strictly into one
category, others span two or three categories. This
highlights the currently multi-faceted definition of BI,
and hence the lack of agreement on how different
parties perceive a BI system.
Table 1: Summary of varied BI definitions (Source: Developed for this research)
BI Vendor / Author Definition of BI
Turban et al. (2007) An umbrella term that encompasses tools, architectures, databases, data
warehouses, performance management, methodologies, and so forth, all of
which are integrated into a unified software suite.
Moss and Atre (2003) It is an architecture and a collection of integrated operational as well as
decision-support applications and databases that provide the business
community easy access to business data.
Chang (2006) The accurate, timely, critical data, information and knowledge that supports
strategic and operational decision making and risk assessment in uncertain and
dynamic business environments. The source of the data, information and
knowledge are both internal organisationally collected as well as externally
supplied by partners, customers or third parties as a result of their own choice.
Gangadharan and
Swami (2004)
The result of in-depth analysis of detailed business data, including database
and application technologies, as well as analysis practice.
Kulkarni and King
(1997)
A product of analysing business data using business intelligence tools. It
emerges as a result of this analysis.
Moss and Hoberman
(2004)
The processes, technologies, and tools needed to turn data into information,
information into knowledge and knowledge into plans that drive profitable
business action. BI encompasses data warehousing, business analytics tools
and content/knowledge management.
Adelman and Moss
(2000)
A term encompasses a broad range of analytical software and solutions for
gathering, consolidating, analysing and providing access to information in a
way that is supposed to let an enterprise’s users make better business decision.
Gartner Research
(Hostmann 2007)
An umbrella term that includes the analytic applications, the infrastructure and
platforms, as well as the best practices.
IBM (Whitehorn &
Whitehorn 1999)
An umbrella term that broadly covering the processes involved in extracting
valuable business information from the mass of data that exists within a typical
enterprise.
Business Objects
(Business Objects 2007)
The use of an organisation’s disparate data to provide meaningful information
and analysis to employees, customers, suppliers, and partners for more
effective decision making.
Cognos (Cognos 2007) Business intelligence brings people and data together, offering a variety of
ways to see the information that backs fact-based decision-making.
SAS Institute (Ing
2007)
Delivering the right information to the right people at the right time to support
better decision making and to gain competitive advantage.
Oracle (Oracle 2007) A portfolio of technology and applications that provides an integrated, end-to-
end Enterprise Performance Management System, including financial
performance management applications, operational BI applications, BI
foundation and tools, and data warehousing.
Informatica, Teradata,
MicroStrategy
(Markarian, Brobst &
Bedell 2007)
An interactive process for exploring and analysing structured, domain-specific
information (often stored in a data warehouse) to discern trends or patterns,
thereby deriving insights and drawing conclusions.
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97
Table 2: Three approaches to the definition of BI (Source: Adapted from [12], [13])
3. ADDRESSING THE MULTI-FACETED
DEFINTION OF BUSINESS INTELLIGENCE
From the technological standpoint, BI is considered as
a broad category of tools, software, solutions, and
technologies that enables decision-makers to find,
accumulate, organize, and access a wider range of
information from disparate data sources [15, 18, 19,
25]. In this context, the emphasis of BI is not on the
process itself, but on the technologies that support the
gathering, storage, consolidation, analysis, and mining
of corporate data. The desired output is the unveiling of
‘insights’ that might be deeply embedded in the data, if
there is a right mix of data warehousing and data
mining [27].
The managerial approach presents BI as a process in
which data from both internal and external sources are
integrated so as to generate actionable information for
improved decision support, and to realise the benefits
of the deployment of integrated transaction processing
systems and enterprise applications [18, 20, 22, 23, 24].
So, the primary focus is on the coordination and
management of the process by which different
information sources from diverse operational and
transactional systems (both inside and outside the
company) can be integrated and analysed coherently to
support the decision making process [12].
From the perspective of the product approach, BI is
deemed to be a product (i.e., a result) emerging from
advanced processing of high-quality data, information
and knowledge, and analytical practices that support
decision-making and performance assessment [13]. In
this regard, various analytical and mining tools from BI
vendors such as Cognos, Business Objects, and the
SAS Institute are applied. The sources of data include
those supplied from operational, transactional and
legacy systems within the organisation and from
suppliers, business partners, customers or third parties
such as government agencies, industry benchmarks,
and information service providers [13].
In order to clearly define the meaning of the term
business intelligence in the light of these three
interpretations, this study suggests that the
technological aspect of BI to be considered as a BI
System, whereas the process perspective is regarded as
the implementation of BI systems. The product
perspective is the result (i.e. actionable information) of
analysis of business data which originated from various
sources. The characteristics of actionable information
are described in Table 3.
Table 3: Characteristics of BI from the product perspective (Source: Adapted from [28])
Characteristics Descriptions
Integrated Must have a single, enterprise-wide view
Data integrity Information must be accurate and must conform to business rules
Accessible Easily accessible with intuitive access paths, and responsive for analysis
Credible Every business factor must have one and only one value
Timely Information must be available within the stipulated time frame
Approach Managerial/Process Technological Product
Definition Focus on the process of
gathering data from internal
and external sources and of
analysing 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)
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.
Symposium on Progress in Information & Communication Technology 2009
98
4. BUSINESS INTELLIGENCE SYSTEMS
According to Reinschmidt and Francoise, a BI system
is “an integrated set of tools, technologies and
programmed products that are used to collect,
integrate, analyse and make data available” [29].
Negash states that a BI system “combines data
gathering, data storage, and knowledge management
with analytical tools to present complex and
competitive information to planners and decision
makers” [10]. The objectives are to enable business
managers and analysts of all levels to readily access
any data in the organisation and to conduct appropriate
manipulation and analysis [17]. Implicit in this
definition is the notion that a BI system can improve
the timeliness and quality of the input to the decision
making process [10], and thus corporate data is
transformed from quantity to quality [16]. However,
the idea is not new and fifty years ago Luhn of IBM
wrote that [11]:
“Information is now being generated and
utilised at an ever-increasing rate because
of the accelerated pace and scope of
human activities. At the same time the
growth of organizations and increased
specialization and divisionalization have
created new barriers to the flow of
information”. Intrinsically, he notes that “a
comprehensive system may be assembled
to accommodate all information problems
of an organisation. We call this a Business
Intelligence System.”
In other words, a BI system can be viewed as enterprise
architecture for an integrated collection of operational
and decision support applications and databases [15],
which provides various business stakeholders with easy
access to the required information. Moreover, it
facilitates the analysis and sharing of information and
helps with the making of informed business decisions
[16]. Negash and Gray argue that a BI system is not
revolutionary technology, but rather a “natural
outgrowth of a series of previous systems designed to
support decision making” [9].
In terms of its key components, Fisher et al. assert that
a BI system is composed of a set of three
complementary data management technologies, namely
data warehousing, online analytical processing
(OLAP), and knowledge discovery which is
predominantly aided by data mining techniques [30].
More specifically, Olszak and Ziemba posit that a BI
system is composed of the following essential
components [31]:
? ETL (Extraction-Transformation-Load) tools
that are responsible for data transfer from
operational or transaction systems to data
warehouses;
? data warehouses to provide some room for
thematic storing of aggregated and analysed
data;
? OLAP tools which allow users access and
which analyse and model business problems
and share information that is stored in data
warehouses;
? data mining tools for determining patterns,
generalisations, regularities and rules in data
resources;
? reporting and ad hoc inquiry tools for creating
and utilising different synthetic reports; and
? presentation layers that include customised
graphical and multimedia interfaces to provide
users with information in a comfortable and
accessible form.
5. COMTEMPORARY APPLICATIONS OF BI
SYSTEMS
To date, BI systems have been applied in various
industries such as transportation, banking, health care,
retail, manufacturing, and pharmaceuticals. Table 4
depicts the contemporary applications of BI systems in
various areas.
Table 4: Contemporary applications of BI systems
(Source: Adapted from [32], [33], [34], [35], [36])
BI Applications Benefits
Transportation Industry
? Generally, transportation service providers utilize
several tools and platforms provided by Business
Intelligence (BI) vendors which enabling the delivery of
information to decision makers such as Query Tools
Standard, Reporting Tools, Online Analytical
Processing (OLAP) tools, Data Visualization Tools, and
Data Mining Tools.
? Airline industry uses text mining to automatically
extract useful information from different written
resources such as incident reports.
? The direct benefits of the usage of a BI solution in
transportation industry are reduction in the turnaround time
for preparation of reports, direct and faster access to the
data needed to support decision-making, analyze the flow
of businesses across services, regions, clients, pricing,
currencies, and market factors in time etc.
? The huge databases maintained by airlines have limited
human interpretation and the terminology appears different
to a computer. Thus, discovery of new, previously
unknown knowledge can be found in a timely manner by
using text mining.
Symposium on Progress in Information & Communication Technology 2009
99
? An incident report is prepared whenever an event occurs
that might lead to a problem. Text mining of airline
incident reports can identify potential dilemma. Text
mining can be used with this large set of incident data
reported to validate predetermined theories and to cull new
patterns of knowledge.
Banking Industry
? Banking industry relies on the BI platform to make
more effective decisions in a few areas such as
Customer Analysis, Operations & Financial Analysis,
Sales & Marketing Analysis, Promotion Analysis, and
Risk & Fraud Analysis.
? BI applications help management to improve operational
and strategic decisions based on better and timely
information.
? Potential customers are identified through the analysis of
purchasing data. Cross-selling opportunities will be
recognized via analysis of customer behavior.
Health Care Industry
? The implementation of BI in health care industry has
enabled data to be delivered beyond administrative
offices and directly to clinical staffs who can make the
most use of it.
? In order to foster a broader adoption, interactive and
user-friendly interfaces have been designed to provide
users with simple and relevant data like the number of
patients, treatments needed, and their hospitalization
period.
? Business decision making process has become more
effective where users can access any type of information
with a fast and consistent response time, independent of the
data volumes analyzed or questions asked.
? The inter-operability application in BI reduces the
operation cost in health care industry by eliminating
expensive custom-integration in the computing system.
Retail Industry
? BI is implemented for demand forecasting in the retail
industry by generating reliable estimates for both short
term and long term demand based on the available
customer data.
? Apart from demand forecasting, BI is also used to
monitor customer loyalty by evaluating which
customers are loyal and which are likely to leave.
? When reliable estimates of customer demand are generated,
service and product distribution plans of a company would
always be in place to meet its customer expectations.
? By monitoring customer loyalty, factors that influence their
decisions to stay or go could be determined in order to
devise better strategies to retain them.
Manufacturing Industry
? BI systems allow manufacturers to track their inventory
usage across location and time by using alerts for
instant notification of low inventory levels.
? Besides this, BI systems allow manufacturers to analyze
data from multiple sources in order to set performance
goals and create sophisticated profitability and financial
models.
? With the functionality of inventory monitoring,
manufacturers can reduce over-capacity and ensure
sufficient supplies for their production.
? Apart from this, BI systems also help manufacturers in
financial management by identifying areas where they can
increase profits and improve efficiency.
Pharmaceuticals Industry
? BI systems help pharmaceuticals companies to identify
which products are most profitable and monitor
customer behavior in purchasing products.
? By closely tracking sales performance and consumer
behavior, pharmaceuticals companies are able to set better
marketing strategies and ensure proper allocation of
marketing funds.
6. CONCLUSION
This paper has reviewed the nebulous definitions and
applications of business intelligence systems. It first
addressed the multi-faceted definitions of BI by
categorizing them into three main perspectives,
namely technological, managerial, and product
aspects. Then it outlined the key components of a
typical BI system. It is noted that a BI system is not
regarded as a completely new system, but an
evolutionary, integrated product of a variety of tools
and computing techniques. These include but are not
limited to data warehousing, online analytical
processing, visualization techniques, data mining,
data quality, and web technologies. Also, the
literature review has demonstrated that applications
of BI systems are really broad and can be tailored to
various industrial needs. Due to the unique business
nature of every industry, organizations have different
requirements in their business intelligence systems.
As illustrated in Table 4 above, a banking company
has significantly different applications of BI systems
as compared to a transportation enterprise.
Consequently, to ensure a successful BI system
implementation, BI stakeholders have to take into
account not only the core business needs, but also the
substantial benefits a BI system could bring into their
organisations rather than the diverse features as
trumpeted by BI vendors.
Symposium on Progress in Information & Communication Technology 2009
100
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