Description
For both academics and practitioners concerned with Business Intelligence (BI) systems, one of important issues is to identify the factors which are vital for successful implementation of BI projects.
Abstract—For both academics and practitioners concerned
with Business Intelligence (BI) systems, one of important
issues is to identify the factors which are vital for successful
implementation of BI projects. Hence, this paper offers a broad
summary of the most common and impact factors which can be
influenced in implementing BI projects. We believe it is
valuable to determine these factors, particularly for managers of
those companies are involved in implementing BI projects and
they face to evaluate readiness of their organizations before
launching the project in pre-implementation stage.
The objective of this paper is to provide a better
understanding of the important and critical success factors and
it is to conduct a survey and comprehensive study of the critical
factors in evaluation phase of the readiness by classifying the
factors into two main categories; organizational and technical.
It is obvious that each category has its own characteristics and a
brief description of each factor is discussed.
Keywords—Business Intelligence, Critical Success Factors,
Readiness Evaluation.
I. INTRODUCTION
N today's rapid technological and dynamic and
unpredictable business environment, BI solutions can
be assisted the managers in decision making process. The
interest in this subject has increased significantly when
the opinions began to appear indicating that BI systems
are an important component of a modern enterprise's
information infrastructure, as they contribute to its
success and competitiveness [1]. A successful
implementation of BI project enables experts and
managers of companies make and take better decisions.
But according to Farrokhi and Pokoràdi [2], risk of
failure is high in implementing BI projects.
To success in implementing a BI project and gain the
associated benefits, we need to identify the factors which
contribute to the success. These factors must be received
careful attention by top managers and BI project
managers of those companies are evaluating the readiness
of their organizations. These prerequisites can be
grouped in organizational factors and technical factors
for better understanding and concentrating. Most of
authors are often named these factors as Critical Success
Factors (CSF). Every BI project includes multiple stages
and each stage has its characteristic with specific
activities.
The main aim of this study is to find and categorized
CSFs in setting-up stage of implementing a BI project by
related literature review. In this way, the authors with
assisting of an experienced BI project manager provided
their best judgments based on their studies and
experiences in determining and categorizing CSFs.
This paper is organized as follows: In the next section,
we show an overview of a BI project and its components
in the form of a high-level architecture of BI. In the
Section 3, we express some reasons in importance and
necessity for implementing BI projects in companies. The
critical organizational and technical factors are depicted
in the Section 4. Finally, the Section 5 presents the
conclusions and future works of the authors.
II. BUSINESS INTELLIGENCE PROJECTS
In 1989, Howard Drenser of the Gartner Group
introduced BI to describe a set of concepts and methods
for improving business decision making by using fact-
based, computerized support systems [3]. The goal of BI
systems [4] is to capture (data, information, knowledge)
and respond to business events and needs better, more
informed, and faster, as decisions. One of the best ways
for the information in BI and its components to be
understandable is to describe it in form of architecture.
Hence, we have tried to express components of the new-
generation architecture which is introduced by W.
Eckerson [5]. This BI architecture is more analytical,
giving power users greater options to access and mix
corporate data with their own data via various types of
analytical sandboxes. It also brings unstructured and
semi-structured data fully into the mix using Hadoop and
nonrelational databases. This architecture is illustrated in
Fig. 1.
ORGANIZATIONAL AND TECHNICAL
FACTORS FOR IMPLEMENTING BUSINESS
INTELLIGENCE
Vahid FARROKHI
1
and Laszlo POKORADI
2
1
Doctoral School of Informatics, University of Debrecen, Debrecen, Hungary, [email protected]
2
Doctoral School of Informatics, University of Debrecen, Debrecen, Hungary, [email protected]
I
ANNALS OF THE ORADEA UNIVERSITY
Fascicle of Management and Technological Engineering
ISSUE #1, MAY 2013, http://www.imtuoradea.ro/auo.fmte/
75
Fig. 1. The new-generation BI architecture (Source: [5])
The top half of the figure indicates to classic top-down
architecture which data warehousing delivers interactive
reports and dashboards to casual users and the bottom
half represents a bottom-up analytical architecture with
analytical sandboxes and new type of data sources. Of
course, from the business (organizational) perspective, BI
systems mean specific philosophy and methodology that
refer to working with information and knowledge, open
communication, and knowledge sharing along with the
holistic and analytic approach to business processes in
organizations [6].
III. IMPORTANCE OF IMPLEMENTATION OF BI PROJECTS
IN COMPANIES
In recent years, implementing BI projects have been
rated as one of the highest priorities of information
systems and a significant portion of companies’ IT
budgets are spent on BI and related technology. A BI
system can correspond to the needs of users in different
levels of organizations, specially managers, with
supporting key processes and business decisions. It
utilizes a substantial amount of collected data during the
daily operational processes, and transforms the data into
information and knowledge to avoid the supposition and
ignorance of the enterprises [7]. A successful
implemented BI project plays an important role in
understanding business status, measuring organization
performance, improving relationship with stakeholders
and making profitable opportunities. The demands for a
range of capabilities to satisfy a diverse set of user needs
have enforced BI software companies to develop better
and more suitable BI applications. This concept is shown
in Fig. 2.
Based on the levels of ambitions of the various
stakeholders, a BI system can improve efficiency by on
time saving in developing reports and validating data,
reduced number of toolsets and maintenance/integration
costs, time saving in managing report security, etc. Also,
it can be make business more effective by increasing
conversion rate in marketing, cross-sell opportunities in
the call center, fraud detection, a better matching of
available human resources and internal job opportunities,
etc. BI initiatives can also be transformational by
enabling new business models, using a new pricing
model, providing BI to the customers and offering
relevant information as part of the service. Efficiency is
the starting point and it is hard to create a sustainable
business case aimed at effectiveness or transformation, if
there is no basic level of efficiency [8].
Fig. 2. The capabilities of BI applications (Source: [9])
IV. THE CRITICAL ORGANIZATIONAL AND TECHNICAL
FACTORS
The study of critical organizational and technical
factors helps us to extract the core activities that are
essential for successful implementing a BI project. These
critical success factors as Rockart [10] defined are “the
limited number of areas in which results, if they are
satisfactory, will ensure successful competitive
performance for the organization”. In fact, they are the
few key areas where “things must go right” for the
company to success in implementing its BI project. In our
literature review, as we mentioned previously, we
categorized these factors as organizational and technical
factors for better understanding and concentrating. Of
course, nature of these factors is also led us to this
categorization. For applying a BI system, an organization
needs to have capability in both organizational and
technical factors.
Based on related studies in the literature [11]–[25], the
organizational factors which influence in a BI project
success are management support, decision-making
structure, management style, managerial IT knowledge,
goal alignment, and resources allocation, user
participation, balanced and skilled project team, and agile
project management. The related and critical technical
factors are system quality, information quality, reliable
back-end system, metadata management, technical
framework, and agile methodology. Table 1 provides a
summary of these factors. Of course, the names of factors
are selected based on their similarities in concepts and
definitions.
ANNALS OF THE ORADEA UNIVERSITY
Fascicle of Management and Technological Engineering
ISSUE #1, MAY 2013, http://www.imtuoradea.ro/auo.fmte/
76
TABLE I
CRITICAL ORGANIZATIONAL AND TECHNICAL FACTORS FOR SUCCESSFUL
BI IMPLEMENTATION
Critical Success Factor Description
Management support
(Organizational)
The managers of organization involve
and participate in the activities of BI
project.
Organizational culture
(Organizational)
A corporate culture which emphasizes
on the value of sharing common goals
over individual pursuits and the value
of trust between partners, employees,
managers and corporations.
Decision-making structure
(Organizational)
The type of control or delegation of
decision-making authority throughout
the organization and the extent of
participation by organizational
members in decision-making
pertaining to BI.
Goal alignment
(Organizational)
The linking together of the business
goals and the BI goals.
Managerial IT
knowledge(Organizational)
Knowledge and experience of senior
management about IT.
Management style
(Organizational)
The way in which management tends
to influence, coordinate, and direct
people’s activities towards a group’s
objectives.
Resource allocation
(Organizational)
Allocating adequate resources of
money, people, and time.
User participation
(Organizational)
Involving and participating user in BI
development process.
Balanced and skilled
project team
(Organizational)
The composition and skills of a BI
team have a major influence on the
success of the systems
implementation.
Agile project management
(Organizational)
Managing team members work
together in the most effective manner
possible.
System quality (Technical) The performance characteristics of the
BI system itself, which includes ease-
of-use, functionality, reliability,
flexibility, integration, and response
time.
Information quality
(Technical)
It refers to accuracy, timeliness,
completeness, relevance, consistency,
and usefulness of information
generated by the system.
Reliable back-end system
(Technical)
It is critical to ensure that the updating
of data works well for the extraction,
transformation and loading (ETL).
Metadata management
(Technical)
It is an end-to-end process for
creating, enhancing and maintain
meta-data repository and associated
processes.
Technical framework
(Technical)
It must be business-driven, scalable
and flexible framework.
Agile methodology
(Technical)
The purpose of agile BI is to get the
development done faster, and react
more quickly to changing business
requirements.
Similarity of implementing BI projects with other
infrastructural projects like Enterprise Resource Planning
(ERP) projects implementation shows that this kind of
projects need to consider many aspects of the project
before deployment. Implementing a BI system is not a
simple activity entailing merely the purchase of a
combination of software and hardware; rather, it is a
complex undertaking requiring appropriate infrastructure
and resources over a lengthy period [22], [26]. Good
performance of the CSFs requires that their elements (or
constituents) be known so that management can
formulate appropriate policies and strategies to ensure
that the elements are constantly and carefully being
managed and monitored [27].
It is generally believed that the organizational factors
are more important than the technical factors, and
identifying these factors can help us to find the
organizational strength and weakness of the company
with regard to implementation of BI. Burton et al. [28]
pointed out that organizational dynamics are the most
significant challenge to the success of business
intelligence initiatives and implementations. The results
from the recent survey show clearly that non-technical
factors were the hardest to solve and indicate that these
CSFs play a dominant role in BI initiatives’ success in
large enterprises [29]. In the organizational factors,
management support has been widely acknowledged as
the most important factor in implementing a BI project.
The managers must consider the BI project as a top
priority and fund it and take an active role in leading the
change by involving in every step of the BI
implementation.
In spite of having second role of technical factors, they
must completely be concerned. Because without these
elements, implementing a BI project is impossible and
lack of each element can be lead to fail of the project. It
is assumed that the main tasks to be faced by BI systems
include intelligent exploration, integration, aggregation,
and a multidimensional analysis of data originating from
various information resources [30]. System and
information quality are the most important factors in the
technical factors because each BI system needs to
integrate right data and information from various source
systems. Hence, having a system approach for BI project
managers is a necessity and they should make a balance
in considering between organizational and technical
factors.
V. CONCLUSIONS AND FUTURE WORKS
An important step in pre-implementation stage of a BI
project is to identify critical factors which influence in
the project success. First, in this paper an attempt has
been made to depict an overview of BI from architectural
perspective. Then, the necessities for implementing BI
project for companies were described and finally, based
on the literature survey, vital and critical factors in both
of organizational and technical aspects which impact on
ANNALS OF THE ORADEA UNIVERSITY
Fascicle of Management and Technological Engineering
ISSUE #1, MAY 2013, http://www.imtuoradea.ro/auo.fmte/
77
the success were determined. We believe that both the
organizational and technical dimensions are important
and they should be concerned together and interact with
each other for leading to BI success. It can be useable for
assisting managers who are decision makers in
implementing BI projects by optimizing their scarce
resources on these CSFs and concentrate their
commitment to monitor, control and support only these
factors. Of course, ranking of these CSFs with the use of
a formal method like the analytic hierarchy process
(AHP) can be proposed for future work for those scholars
and practitioners who want to concentrate more attention
on the CSFs. Also, these CSFs will be applied in building
a model to assess readiness of organizations before
launching BI projects by the authors.
ACKNOWLEDGEMENT
The work/publication is supported by the
TÁMOP-4.2.2/B-10/1-2010-0024
project. The project is co-financed by the European Uni-
on and the European Social Fund.
REFERENCES
[1] T. H. Davenport, Harris, J.G., & Morison, R., Analytics at works:
Smarter decisions, better results. Boston: Harvard Business Press,
2010.
[2] V. Farrokhi, and Pokorádi, L., "The necessities for building a
model to evaluate Business Intelligence projects- Literature
Review," International Journal of Computer Science &
Engineering Survey (IJCSES), vol. 3, pp. 1-10, 2012.
[3] A. Nylund, "Tracing the BI family tree," Knowledge
Management, 1999.
[4] M. Guran, Mehanna, A., Hussein, B., "Real Time On-Line
Analytical Processing for Business Intelligence," U.P.B. Sci.
Bull., Series C, vol. 7, 2009.
[5] W. Eckerson. (2012). A Practical Guide to Advanced Analytics
[Pdf]. Available:
http://www.bileader.com/A_Practical_Guide_to_Analytics_E-
book.pdf
[6] C. M. Olszak, Ziemba, E., "Critical Success Factors for
implementing Business Intelligence Systems in Small and
Medium Enterprises on the example of Upper Silesia, Poland,"
Interdisciplinary Journal of Information, Knowledge, and
Management, vol. 7, pp. 130-150, 2012.
[7] Z. Wang. (2005) Business intelligence. Taiwan: DrMater Culture
Limited Company.
[8] F. Buytendijk, Landry, D., "BI Optimization: Building A Better
Business Case for Business Intelligence," ed. 500 Oracle
Parkway, Redwood Shores, CA 94065, U.S.A.: Oracle
Corporation, 2009.
[9] MicroStrategy. (2013). Architecture for Enterprise Business
Intelligence. Available: www.microstrategy.com
[10] J. Rockart. (1979) Chief executives define their own information
needs. Harvard Business Review. 81-92.
[11] R. Hussein, Abdul Karim, N., Mohamed, N., Ahlan, A., “The
Iinfluence of Organizational Factors on Information Ssystems
Success in E-Government Agencies in Malaysia" EJISDC, vol.
29, pp. 1-17, 2007.
[12] J. Farley, "Keeping the Data Warehouse of the Rocks" Measuring
Business Excellence, vol. 2, pp. 14-15, 1998.
[13] H. J. Watson, Haley, B. J. (1998) Managerial considerations.
Communications of the ACM. 32-37.
[14] L. D. Chen, Khalid S. Soliman, K. S., Mao, E., Frolick, M. N.,
"Measuring user satisfaction with data warehouses: an
exploratory study," Information & Management, vol. 37, pp. 103-
110, 2000.
[15] D. Sammon, Finnegan, P., "The ten commandments of data
warehousing," ACM SIGMIS Database, vol. 31, pp. 82-91, 2000.
[16] R. G. Little, Gibson, M. L., "Perceived influences on
implementing data warehousing," IEEE Transactions on Software
Engineering, vol. 29, pp. 290-296, 2003.
[17] D. Mukherjee, D'Souza, D., "Think phased implementation for
successful data warehousing," Information Systems Management,
vol. 20, pp. 82-90, 2003.
[18] A. Rudra, Yeo, E., "Issues in User Perceptions of Data Quality
and Satisfaction in using a Data Warehouse - An Australian
Experience," in 33rd Annual Hawaii International Conference on
System Sciences (HICSS), Hawaii, 2000.
[19] K. Joshi, Curtis, M., "Issues in building a successful data
warehouse," Information Strategy, vol. 15, pp. 28-36, 1999.
[20] B. H. Wixom, Watson, H. J., "An Empirical Investigation of the
Factors Affecting Data Warehousing Success," MIS Quarterly,
vol. 25, pp. 17-41, 2001.
[21] T. Chenoweth, Corral, K., Demirkan, H. (2006) Seven key
interventions for data warehouse success. Communications of the
ACM. 114-119.
[22] W. Yeoh, Koronios, A., "Critical Success Factors for Business
Intelligence Systems," Journal of Computer Information Systems,
pp. 23-32, 2010.
[23] J. Terry, Standing, C., "The Value of User Participation in E-
Commerce Systems Development," Informing Science Journal,
vol. 7, pp. 31-45, 2004.
[24] C. Stefanou, "Supply chain management (SCM) and
organizational key factors for successful implementation of
enterprise resource planning (ERP) systems," in AMCIS 1999,
Milwaukee, WI, 1999.
[25] Execution-MiH. (2013). Metadata Management definition - What
is metadata? Available:
http://www.executionmih.com/metadata/definition-concept.php
[26] L. T. Moss, Atre, S., Business intelligence roadmap. The
complete lifecycle for decision-support applications. Boston:
Addison-Wesley, 2003.
[27] W. Yeoh, Koronios, A., Gao, J., "Managing the Implementation
of Business Intelligence Systems: A Critical Success Factors
Framework," International Journal of Enterprise Information
Systems, pp. 79-94, 2008.
[28] B. Burton, Geishecker, L., Hostmann, B., “Organizational
structure: Business intelligence and information management,”
Gartner Research, 2006.
[29] S. Adamala, Cidrin, L., "Key Success Factors in Business
Intelligence," Journal of Intelligence Studies in Business, vol. 1,
pp. 107-127, 2011.
[30] V. L. Sauter, Decision support systems for business intelligence.
New Jersey: Wiley, 2010.
ANNALS OF THE ORADEA UNIVERSITY
Fascicle of Management and Technological Engineering
ISSUE #1, MAY 2013, http://www.imtuoradea.ro/auo.fmte/
78
doc_513021043.pdf
For both academics and practitioners concerned with Business Intelligence (BI) systems, one of important issues is to identify the factors which are vital for successful implementation of BI projects.
Abstract—For both academics and practitioners concerned
with Business Intelligence (BI) systems, one of important
issues is to identify the factors which are vital for successful
implementation of BI projects. Hence, this paper offers a broad
summary of the most common and impact factors which can be
influenced in implementing BI projects. We believe it is
valuable to determine these factors, particularly for managers of
those companies are involved in implementing BI projects and
they face to evaluate readiness of their organizations before
launching the project in pre-implementation stage.
The objective of this paper is to provide a better
understanding of the important and critical success factors and
it is to conduct a survey and comprehensive study of the critical
factors in evaluation phase of the readiness by classifying the
factors into two main categories; organizational and technical.
It is obvious that each category has its own characteristics and a
brief description of each factor is discussed.
Keywords—Business Intelligence, Critical Success Factors,
Readiness Evaluation.
I. INTRODUCTION
N today's rapid technological and dynamic and
unpredictable business environment, BI solutions can
be assisted the managers in decision making process. The
interest in this subject has increased significantly when
the opinions began to appear indicating that BI systems
are an important component of a modern enterprise's
information infrastructure, as they contribute to its
success and competitiveness [1]. A successful
implementation of BI project enables experts and
managers of companies make and take better decisions.
But according to Farrokhi and Pokoràdi [2], risk of
failure is high in implementing BI projects.
To success in implementing a BI project and gain the
associated benefits, we need to identify the factors which
contribute to the success. These factors must be received
careful attention by top managers and BI project
managers of those companies are evaluating the readiness
of their organizations. These prerequisites can be
grouped in organizational factors and technical factors
for better understanding and concentrating. Most of
authors are often named these factors as Critical Success
Factors (CSF). Every BI project includes multiple stages
and each stage has its characteristic with specific
activities.
The main aim of this study is to find and categorized
CSFs in setting-up stage of implementing a BI project by
related literature review. In this way, the authors with
assisting of an experienced BI project manager provided
their best judgments based on their studies and
experiences in determining and categorizing CSFs.
This paper is organized as follows: In the next section,
we show an overview of a BI project and its components
in the form of a high-level architecture of BI. In the
Section 3, we express some reasons in importance and
necessity for implementing BI projects in companies. The
critical organizational and technical factors are depicted
in the Section 4. Finally, the Section 5 presents the
conclusions and future works of the authors.
II. BUSINESS INTELLIGENCE PROJECTS
In 1989, Howard Drenser of the Gartner Group
introduced BI to describe a set of concepts and methods
for improving business decision making by using fact-
based, computerized support systems [3]. The goal of BI
systems [4] is to capture (data, information, knowledge)
and respond to business events and needs better, more
informed, and faster, as decisions. One of the best ways
for the information in BI and its components to be
understandable is to describe it in form of architecture.
Hence, we have tried to express components of the new-
generation architecture which is introduced by W.
Eckerson [5]. This BI architecture is more analytical,
giving power users greater options to access and mix
corporate data with their own data via various types of
analytical sandboxes. It also brings unstructured and
semi-structured data fully into the mix using Hadoop and
nonrelational databases. This architecture is illustrated in
Fig. 1.
ORGANIZATIONAL AND TECHNICAL
FACTORS FOR IMPLEMENTING BUSINESS
INTELLIGENCE
Vahid FARROKHI
1
and Laszlo POKORADI
2
1
Doctoral School of Informatics, University of Debrecen, Debrecen, Hungary, [email protected]
2
Doctoral School of Informatics, University of Debrecen, Debrecen, Hungary, [email protected]
I
ANNALS OF THE ORADEA UNIVERSITY
Fascicle of Management and Technological Engineering
ISSUE #1, MAY 2013, http://www.imtuoradea.ro/auo.fmte/
75
Fig. 1. The new-generation BI architecture (Source: [5])
The top half of the figure indicates to classic top-down
architecture which data warehousing delivers interactive
reports and dashboards to casual users and the bottom
half represents a bottom-up analytical architecture with
analytical sandboxes and new type of data sources. Of
course, from the business (organizational) perspective, BI
systems mean specific philosophy and methodology that
refer to working with information and knowledge, open
communication, and knowledge sharing along with the
holistic and analytic approach to business processes in
organizations [6].
III. IMPORTANCE OF IMPLEMENTATION OF BI PROJECTS
IN COMPANIES
In recent years, implementing BI projects have been
rated as one of the highest priorities of information
systems and a significant portion of companies’ IT
budgets are spent on BI and related technology. A BI
system can correspond to the needs of users in different
levels of organizations, specially managers, with
supporting key processes and business decisions. It
utilizes a substantial amount of collected data during the
daily operational processes, and transforms the data into
information and knowledge to avoid the supposition and
ignorance of the enterprises [7]. A successful
implemented BI project plays an important role in
understanding business status, measuring organization
performance, improving relationship with stakeholders
and making profitable opportunities. The demands for a
range of capabilities to satisfy a diverse set of user needs
have enforced BI software companies to develop better
and more suitable BI applications. This concept is shown
in Fig. 2.
Based on the levels of ambitions of the various
stakeholders, a BI system can improve efficiency by on
time saving in developing reports and validating data,
reduced number of toolsets and maintenance/integration
costs, time saving in managing report security, etc. Also,
it can be make business more effective by increasing
conversion rate in marketing, cross-sell opportunities in
the call center, fraud detection, a better matching of
available human resources and internal job opportunities,
etc. BI initiatives can also be transformational by
enabling new business models, using a new pricing
model, providing BI to the customers and offering
relevant information as part of the service. Efficiency is
the starting point and it is hard to create a sustainable
business case aimed at effectiveness or transformation, if
there is no basic level of efficiency [8].
Fig. 2. The capabilities of BI applications (Source: [9])
IV. THE CRITICAL ORGANIZATIONAL AND TECHNICAL
FACTORS
The study of critical organizational and technical
factors helps us to extract the core activities that are
essential for successful implementing a BI project. These
critical success factors as Rockart [10] defined are “the
limited number of areas in which results, if they are
satisfactory, will ensure successful competitive
performance for the organization”. In fact, they are the
few key areas where “things must go right” for the
company to success in implementing its BI project. In our
literature review, as we mentioned previously, we
categorized these factors as organizational and technical
factors for better understanding and concentrating. Of
course, nature of these factors is also led us to this
categorization. For applying a BI system, an organization
needs to have capability in both organizational and
technical factors.
Based on related studies in the literature [11]–[25], the
organizational factors which influence in a BI project
success are management support, decision-making
structure, management style, managerial IT knowledge,
goal alignment, and resources allocation, user
participation, balanced and skilled project team, and agile
project management. The related and critical technical
factors are system quality, information quality, reliable
back-end system, metadata management, technical
framework, and agile methodology. Table 1 provides a
summary of these factors. Of course, the names of factors
are selected based on their similarities in concepts and
definitions.
ANNALS OF THE ORADEA UNIVERSITY
Fascicle of Management and Technological Engineering
ISSUE #1, MAY 2013, http://www.imtuoradea.ro/auo.fmte/
76
TABLE I
CRITICAL ORGANIZATIONAL AND TECHNICAL FACTORS FOR SUCCESSFUL
BI IMPLEMENTATION
Critical Success Factor Description
Management support
(Organizational)
The managers of organization involve
and participate in the activities of BI
project.
Organizational culture
(Organizational)
A corporate culture which emphasizes
on the value of sharing common goals
over individual pursuits and the value
of trust between partners, employees,
managers and corporations.
Decision-making structure
(Organizational)
The type of control or delegation of
decision-making authority throughout
the organization and the extent of
participation by organizational
members in decision-making
pertaining to BI.
Goal alignment
(Organizational)
The linking together of the business
goals and the BI goals.
Managerial IT
knowledge(Organizational)
Knowledge and experience of senior
management about IT.
Management style
(Organizational)
The way in which management tends
to influence, coordinate, and direct
people’s activities towards a group’s
objectives.
Resource allocation
(Organizational)
Allocating adequate resources of
money, people, and time.
User participation
(Organizational)
Involving and participating user in BI
development process.
Balanced and skilled
project team
(Organizational)
The composition and skills of a BI
team have a major influence on the
success of the systems
implementation.
Agile project management
(Organizational)
Managing team members work
together in the most effective manner
possible.
System quality (Technical) The performance characteristics of the
BI system itself, which includes ease-
of-use, functionality, reliability,
flexibility, integration, and response
time.
Information quality
(Technical)
It refers to accuracy, timeliness,
completeness, relevance, consistency,
and usefulness of information
generated by the system.
Reliable back-end system
(Technical)
It is critical to ensure that the updating
of data works well for the extraction,
transformation and loading (ETL).
Metadata management
(Technical)
It is an end-to-end process for
creating, enhancing and maintain
meta-data repository and associated
processes.
Technical framework
(Technical)
It must be business-driven, scalable
and flexible framework.
Agile methodology
(Technical)
The purpose of agile BI is to get the
development done faster, and react
more quickly to changing business
requirements.
Similarity of implementing BI projects with other
infrastructural projects like Enterprise Resource Planning
(ERP) projects implementation shows that this kind of
projects need to consider many aspects of the project
before deployment. Implementing a BI system is not a
simple activity entailing merely the purchase of a
combination of software and hardware; rather, it is a
complex undertaking requiring appropriate infrastructure
and resources over a lengthy period [22], [26]. Good
performance of the CSFs requires that their elements (or
constituents) be known so that management can
formulate appropriate policies and strategies to ensure
that the elements are constantly and carefully being
managed and monitored [27].
It is generally believed that the organizational factors
are more important than the technical factors, and
identifying these factors can help us to find the
organizational strength and weakness of the company
with regard to implementation of BI. Burton et al. [28]
pointed out that organizational dynamics are the most
significant challenge to the success of business
intelligence initiatives and implementations. The results
from the recent survey show clearly that non-technical
factors were the hardest to solve and indicate that these
CSFs play a dominant role in BI initiatives’ success in
large enterprises [29]. In the organizational factors,
management support has been widely acknowledged as
the most important factor in implementing a BI project.
The managers must consider the BI project as a top
priority and fund it and take an active role in leading the
change by involving in every step of the BI
implementation.
In spite of having second role of technical factors, they
must completely be concerned. Because without these
elements, implementing a BI project is impossible and
lack of each element can be lead to fail of the project. It
is assumed that the main tasks to be faced by BI systems
include intelligent exploration, integration, aggregation,
and a multidimensional analysis of data originating from
various information resources [30]. System and
information quality are the most important factors in the
technical factors because each BI system needs to
integrate right data and information from various source
systems. Hence, having a system approach for BI project
managers is a necessity and they should make a balance
in considering between organizational and technical
factors.
V. CONCLUSIONS AND FUTURE WORKS
An important step in pre-implementation stage of a BI
project is to identify critical factors which influence in
the project success. First, in this paper an attempt has
been made to depict an overview of BI from architectural
perspective. Then, the necessities for implementing BI
project for companies were described and finally, based
on the literature survey, vital and critical factors in both
of organizational and technical aspects which impact on
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the success were determined. We believe that both the
organizational and technical dimensions are important
and they should be concerned together and interact with
each other for leading to BI success. It can be useable for
assisting managers who are decision makers in
implementing BI projects by optimizing their scarce
resources on these CSFs and concentrate their
commitment to monitor, control and support only these
factors. Of course, ranking of these CSFs with the use of
a formal method like the analytic hierarchy process
(AHP) can be proposed for future work for those scholars
and practitioners who want to concentrate more attention
on the CSFs. Also, these CSFs will be applied in building
a model to assess readiness of organizations before
launching BI projects by the authors.
ACKNOWLEDGEMENT
The work/publication is supported by the
TÁMOP-4.2.2/B-10/1-2010-0024
project. The project is co-financed by the European Uni-
on and the European Social Fund.
REFERENCES
[1] T. H. Davenport, Harris, J.G., & Morison, R., Analytics at works:
Smarter decisions, better results. Boston: Harvard Business Press,
2010.
[2] V. Farrokhi, and Pokorádi, L., "The necessities for building a
model to evaluate Business Intelligence projects- Literature
Review," International Journal of Computer Science &
Engineering Survey (IJCSES), vol. 3, pp. 1-10, 2012.
[3] A. Nylund, "Tracing the BI family tree," Knowledge
Management, 1999.
[4] M. Guran, Mehanna, A., Hussein, B., "Real Time On-Line
Analytical Processing for Business Intelligence," U.P.B. Sci.
Bull., Series C, vol. 7, 2009.
[5] W. Eckerson. (2012). A Practical Guide to Advanced Analytics
[Pdf]. Available:
http://www.bileader.com/A_Practical_Guide_to_Analytics_E-
book.pdf
[6] C. M. Olszak, Ziemba, E., "Critical Success Factors for
implementing Business Intelligence Systems in Small and
Medium Enterprises on the example of Upper Silesia, Poland,"
Interdisciplinary Journal of Information, Knowledge, and
Management, vol. 7, pp. 130-150, 2012.
[7] Z. Wang. (2005) Business intelligence. Taiwan: DrMater Culture
Limited Company.
[8] F. Buytendijk, Landry, D., "BI Optimization: Building A Better
Business Case for Business Intelligence," ed. 500 Oracle
Parkway, Redwood Shores, CA 94065, U.S.A.: Oracle
Corporation, 2009.
[9] MicroStrategy. (2013). Architecture for Enterprise Business
Intelligence. Available: www.microstrategy.com
[10] J. Rockart. (1979) Chief executives define their own information
needs. Harvard Business Review. 81-92.
[11] R. Hussein, Abdul Karim, N., Mohamed, N., Ahlan, A., “The
Iinfluence of Organizational Factors on Information Ssystems
Success in E-Government Agencies in Malaysia" EJISDC, vol.
29, pp. 1-17, 2007.
[12] J. Farley, "Keeping the Data Warehouse of the Rocks" Measuring
Business Excellence, vol. 2, pp. 14-15, 1998.
[13] H. J. Watson, Haley, B. J. (1998) Managerial considerations.
Communications of the ACM. 32-37.
[14] L. D. Chen, Khalid S. Soliman, K. S., Mao, E., Frolick, M. N.,
"Measuring user satisfaction with data warehouses: an
exploratory study," Information & Management, vol. 37, pp. 103-
110, 2000.
[15] D. Sammon, Finnegan, P., "The ten commandments of data
warehousing," ACM SIGMIS Database, vol. 31, pp. 82-91, 2000.
[16] R. G. Little, Gibson, M. L., "Perceived influences on
implementing data warehousing," IEEE Transactions on Software
Engineering, vol. 29, pp. 290-296, 2003.
[17] D. Mukherjee, D'Souza, D., "Think phased implementation for
successful data warehousing," Information Systems Management,
vol. 20, pp. 82-90, 2003.
[18] A. Rudra, Yeo, E., "Issues in User Perceptions of Data Quality
and Satisfaction in using a Data Warehouse - An Australian
Experience," in 33rd Annual Hawaii International Conference on
System Sciences (HICSS), Hawaii, 2000.
[19] K. Joshi, Curtis, M., "Issues in building a successful data
warehouse," Information Strategy, vol. 15, pp. 28-36, 1999.
[20] B. H. Wixom, Watson, H. J., "An Empirical Investigation of the
Factors Affecting Data Warehousing Success," MIS Quarterly,
vol. 25, pp. 17-41, 2001.
[21] T. Chenoweth, Corral, K., Demirkan, H. (2006) Seven key
interventions for data warehouse success. Communications of the
ACM. 114-119.
[22] W. Yeoh, Koronios, A., "Critical Success Factors for Business
Intelligence Systems," Journal of Computer Information Systems,
pp. 23-32, 2010.
[23] J. Terry, Standing, C., "The Value of User Participation in E-
Commerce Systems Development," Informing Science Journal,
vol. 7, pp. 31-45, 2004.
[24] C. Stefanou, "Supply chain management (SCM) and
organizational key factors for successful implementation of
enterprise resource planning (ERP) systems," in AMCIS 1999,
Milwaukee, WI, 1999.
[25] Execution-MiH. (2013). Metadata Management definition - What
is metadata? Available:
http://www.executionmih.com/metadata/definition-concept.php
[26] L. T. Moss, Atre, S., Business intelligence roadmap. The
complete lifecycle for decision-support applications. Boston:
Addison-Wesley, 2003.
[27] W. Yeoh, Koronios, A., Gao, J., "Managing the Implementation
of Business Intelligence Systems: A Critical Success Factors
Framework," International Journal of Enterprise Information
Systems, pp. 79-94, 2008.
[28] B. Burton, Geishecker, L., Hostmann, B., “Organizational
structure: Business intelligence and information management,”
Gartner Research, 2006.
[29] S. Adamala, Cidrin, L., "Key Success Factors in Business
Intelligence," Journal of Intelligence Studies in Business, vol. 1,
pp. 107-127, 2011.
[30] V. L. Sauter, Decision support systems for business intelligence.
New Jersey: Wiley, 2010.
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ISSUE #1, MAY 2013, http://www.imtuoradea.ro/auo.fmte/
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