BUSINESS INTELLIGENCE
State of the Art, Trends, and Open Issues
Ana Azevedo and Manuel Filipe Santos
CEISE/STI, ISCAP/IPP, Rua Jaime Lopes de Amorim, s/n, 4465-004 S. Mamede de Infesta, Porto, Portugal
ALGORITMI Research Center, Minho University, Campus Azurém, 4800 Guimarães, Portugal
Keywords: Business Intelligence, Review, Associations.
Abstract: Business Intelligence (BI) is one emergent area of the Decision Support Systems (DSS) discipline. Over the
last years, the evolution in this area has been considerable. An overview of some aspects of the area is
presented in this article. The roots of BI and its usual associations with Knowledge Management Systems
(KMS), Competitive Intelligence (CI), and Artificial Intelligence (AI) are introduced. From the literature
review, it was observed that the definition of an underlying structure on the area is missing. Therefore, a
framework for BI is defined. The state of the art of BI research field was made, presenting recent trends and
open issues for research.
1 INTRODUCTION
BI can be presented as an architecture, tool,
technology or system that gathers and stores data,
analyzes it using analytical tools, and delivers
information and/or knowledge, facilitating reporting,
querying, and, ultimately, allows organizations to
improve decision making (Clark et al., 2007;
Kudyba & Hoptroff, 2001; Michalewicz et al.,
2007; Moss & Shaku, 2003; Negash, 2004;
Raisinghani, 2004; Thierauf, 2001; Turban et al.,
2008). To put it shortly, Business Intelligence (BI)
can be defined as the process that transforms data
into information and then into knowledge (Golfarelli
et al., 2004). More recently, Michalewicz
(Michalewicz et al., 2007) presented the notion of
Adaptive Business Intelligence, incorporating
Artificial Intelligence (AI) with BI. Owing to the
wide variety of concepts presented in the literature, a
framework is needed.
Being rooted in the Decision Support Systems
(DSS) discipline, BI has suffered a considerable
evolution over the last years and is, nowadays, an
area of DSS that attracts a great deal of interest from
both the industry and researchers (Arnott & Pervan,
2008; Clark et al., 2007; Hannula & Pirttimäki,
2003; Hoffman, 2009; Negash, 2004; Richardson et
al., 2008; Richardson et al., 2009). BI has strong
associations with Knowledge Management (KM)
and Competitive Intelligence (CI) (Clark et al.,
2007; Liebowitz, 2006; Negash, 2004; Turban et al.,
2008; Zeller, 2008). Despite being treated as
independent areas, there is the need to consider
looking into the intersections between them.
This paper contributes to the design of a
framework for BI. The paper contributes, moreover,
to a better understanding of BI roots and connected
areas. It also helps to get an insight on research on
BI, and on some trends and open issues.
The paper is organized as follows: business
intelligence roots and associations are presented in
section Two and an overview of research on BI is
presented in section Three; in section Four, a
framework for Business Intelligent is introduced; in
section Five, trends and open issues are pointed out;
in section Six, conclusions and future investigation
directions are mentioned.
2 BUSINESS INTELLIGENCE
ROOTS AND ASSOCIATIONS
The roots for Business Intelligence (BI) can be
found in the field of Decision Support Systems
(DSS) which “is the area of the information systems
(IS) discipline that is focused on supporting and
improving managerial decision-making” (Arnott &
Pervan, 2008). DSS can also be presented as a
computer-based solution that can be used to support
296
Azevedo A. and Filipe Santos M. (2009).
BUSINESS INTELLIGENCE - State of the Art, Trends, and Open Issues.
In Proceedings of the International Conference on Knowledge Management and Information Sharing, pages 296-300
DOI: 10.5220/0002303602960300
Copyright
c
 SciTePress

Figure 1: BI Associations.
complex decision making, and solving complex,
semi-structured, or ill-structured problems (Nemati
et al., 2002; Shim et al., 2002). The term BI has
replaced other terms such as executive information
systems and management information systems
(Negash, 2004; Turban et al., 2008). Nowadays it
can be said that BI is an area of DSS that attracts a
great deal of interest. BI refers to Information
Systems aimed at integrating structured and
unstructured data in order to convert it into useful
information and knowledge, upon which business
managers can make more informed and
consequently better decisions.
BI is associated with Competitive Intelligence (CI)
and Knowledge Management Systems (KMS) (Clark
et al., 2007; March & Hevner, 2007; Negash, 2004;
Thierauf, 2001; Turban et al., 2008). Negash
(Negash, 2004) presents CI as a branch of BI, and
refers to it as “a systematic and ethical program for
gathering, analyzing and managing external
information that can affect company’s plans,
decisions and operations” (Negash, 2004). KMS
refers to “IT-based systems developed to support
and enhance the organizational processes of
knowledge creation, storage/retrieval, transfer, and
application.” (Alavi & Leidner, 2001). It can be
argued that BI and KMS are not disparate systems,
but that they are complementary as they share
elements required to support managerial decision
making (Clark et al., 2007; Liebowitz, 2006).
Moreover, BI, KMS, CI, and AI should be
aggregated so as “to provide value-added
information and knowledge toward making
organizational strategic decisions” (Liebowitz,
2006), in order to achieve Strategic Intelligence for
businesses. These associations are depicted in Figure
1.
“Organizational performance often depends more on
an ability to turn knowledge into effective action and
less on knowledge itself” (Alavi & Leidner, 2001).
Deeper studies evolving the associations presented
could conduct to an understanding of how BI could
lead decision makers to attain this ability.
3 RESEARCH ON BUSINESS
INTELLIGENCE
Despite wide acceptance that the term BI was coined
by Gartner in 1989 (Power, 2007; Turban et al.,
2007; Turban et al., 2008; Zeller, 2007), the first
reference to Business Intelligence was made by
Lunh (Lunh, 1958), and several publications on BI
can be found between 1958 and 1989. Lately, the
use of the term BI has been growing (Arnott &
Pervan, 2008; Hannula & Pirttimäki, 2003), and
there can be found a significant number of
publications that focus on this subject, as well as
professional associations whose main goal is to
disseminate the use of BI by organizations. Software
vendors have defined positions on the market with
diversified BI software packages and open source
platforms are also available. As a result, the market
tends to stabilize (Richardson et al., 2008;
Richardson et al., 2009).
Negash refers, in 2004, that Information Systems
research in the BI field is, by that time, scarce
(Negash, 2004). Since then, scientific investigation
is growing at a significant rate, as can be confirmed
BUSINESS INTELLIGENCE - State of the Art, Trends, and Open Issues
297

Figure 2: A schematic view of BI approaches.
with a search in some of the most known scientific
sources. A great number of publications on BI
appears in diversified publications, but new journals
focused specifically on BI are arrising. The literature
presents research that explores several aspects of BI.
To mention just a few: (Arnott & Pervan, 2008;
Clark et al., 2007; March & Hevner, 2007) include
in their research references to the role of BI in the
DSS and IS disciplines, (Hannula & Pirttimäki,
2003) present an empirical study about BI activities
on Finnish Companies, (Pervan & Arnott, 2006)
present an analysis on research in data warehousing
and BI between 1990 and 2004, (Cheng et al., 2009;
Li et al., 2008) develop BI applications to specific
managerial problems, (Elbashir et al., 2008; Lin et
al., 2009) intend to develop models to evaluate BI
systems, (Hobek et al., 2009; Watson, 2009) are
concerned about the role that people play on a BI
project.
BI presents a vast area of research. Therefore, it
is difficult to be comprehensive on the presentation
of the research that is being done. Nevertheless, a
framework for BI, trends on BI, and several research
issues emerge from the literature.
4 A FRAMEWORK FOR BI
As pointed out above, BI refers to information
systems aimed at integrating structured and
unstructured data in order to convert it into useful
information and knowledge, upon which business
managers can make more informed and
consequently better decisions. There are different
approaches to BI. A schematic view of the main
approaches that are presented in the literature is
depicted in Figure 2.
The traditional approach of BI is concerned with,
data aggregation, business analytics and data
visualization (Kudyba & Hoptroff, 2001;
Raisinghani, 2004; Turban et al., 2008). According
to this approach, BI explores several technological
tools, producing reports and forecasts, in order to
improve the efficiency of the decision making. Such
tools include Data Warehouse (DW), Extract-
Transform and Load (ETL), Online Analytical
Processing (OLAP), Data Mining (DM), Text
Mining, Web Mining, Data Visualization,
Geographic Information Systems (GIS), and Web
Portals.
On the next level there is a concern with the
integration of business processes on BI (Eckerson,
2009; Golfarelli et al., 2004; Turban et al., 2008;
Wormus, 2008; Zeller, 2007). According to this
approach, “BI is a mechanism to bridge de gap
between the business process management to the
business strategy” (Zeller, 2008). In addition to all
the tools in traditional BI, tools such as Business
Performance Management (BPM), Business Activity
Monitoring (BAM), Service-Oriented Architecture
(SOA), Automatic Decision Systems (ADS), and
dashboards, are included.
Adaptive Business Intelligence is concerned with
self-learning adaptive systems, that can recommend
the best actions, and that could learn with previous
decisions, in order to improve continuously
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(Michalewicz et al., 2007). Artificial Intelligence is,
in this manner, incorporated on BI systems.
5 TRENDS AND OPEN ISSUES
It is difficult to be comprehensive on the coverage of
such a vast area hence a choice was made to
highlight the trends and research issues considered
most relevant.
One trend is Pervasive BI , or BI for the masses
(Eckerson, 2008; Lunger, 2008; Negash, 2004).
There is a concern on delivering BI to all levels of
an organization. Another trend is Real-time BI or
Operational BI, which pretends to deliver
information based on real time data, as opposed to
historical data (Brobst & Pareek, 2009; Klawans,
2008; Negash, 2004). Other point concerns on how
to deal with the increasing quantities of data
available for BI systems (Klawans, 2008; Strenger,
2008). Emphasis is also being placed on cultural
aspects and on the human side of BI (Hobek et al.,
2009; Lin et al., 2009; Watson, 2009).
Some research issues that have been identified in
the literature in DSS could also be explored in the BI
area, namely, integration issues, analysis of
usability, assessment, return on investment, and
technological issues. A research area could analyze
and evaluate technologies that are potentially
applicable to analysis and understanding (Nemati et
al., 2002). Powerful analytical tools, such as DM,
remain too complex and sophisticated for the
average consumer, therefore, another area of
research could be the development of more effective
human-computer interfaces (Clark et al., 2007;
March & Hevner, 2007).
6 CONCLUSIONS AND FUTURE
WORK
According to the present analysis, BI is an emergent
dynamic area. The presented framework can be used
as the basis for subsequent research, since it helps to
operationalize the actual state of the art. Research
could be developed along all the presented levels
(Figure 2), since there are open issues on all of them.
The associations with knowledge management,
competitive intelligence, and artificial intelligence,
have a great potential for development, and for
research.
Investigation areas on BI could include
integration issues, analysis of usability, assessment,
return on investment, and technological issues.
As future work the authors will explore the
usage of DM tools on BI, considering the
Knowledge Discovery on Databases (KDD) process,
as presented by (Fayyad et al., 1996). It is their
belief that only the full integration of the KDD
process on BI can conduct to an effective usage of
DM on BI.
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