Description
Purpose- This paper aims to identify the effective components of Business Intelligence (BI), in order to facilitate the evaluation of the Business Intelligence in an IT- based Company in Iran.
World Applied Sciences Journal 22 (9): 1344-1351, 2013
ISSN 1818-4952
© IDOSI Publications, 2013
DOI: 10.5829/idosi.wasj.2013.22.09.364
Corresponding Author: Peyman Akhavan, Department of Industrial Engineering,
Iran University of Science and Technology, Tehran, Iran.
1344
Critical Factors of Business Intelligence:
(Case of an It-Based Company)
Peyman Akhavan and Sima Salehi
Department of Industrial Engineering,
Iran University of Science and Technology, Tehran, Iran
Abstract: Purpose- This paper aims to identify the effective components of Business Intelligence (BI), in
order to facilitate the evaluation of the Business Intelligence in an IT- based Company in Iran.
Design/methodology/approach- This paper has identified the effective components of business Intelligence
through the case study Company based on a comprehensive review of recent literature. For this purpose, a
questionnaire was designed, validated by some BI experts and then analyzed by some statistical methods.
The results discussed various perspectives from the business Intelligence point of view to provide some
effective and essential components of BI. Findings-The statistical analysis determined five components as
effective issues in this case study. These components are "strategy", "Information Technology",
"environmental analysis", "Human resource" and " supporting-executive factors" and at least a conceptual
pattern of BI is introduced. The overall results from the case study were positive as well, thus reflecting the
appropriateness of the experts and managers of the case study Company. Research limitations/implications-
The extracted components can act as a guideline for BI adoption in the case study Company. This helps to
ensure that the essential issues are covered during design and implementation phase of BI program.
For academics, it provides a common language to discuss and study the components crucial for business
Intelligence .Originality/value- The paper may present high value to researchers in the business Intelligence
field and to practitioners involved with BI program in the case study Company, this paper gives valuable
information and guidelines that hopefully will help the leaders to consider the important issues during
business Intelligence establishment in the organization. Paper type: Case study
Key words: Business environment Business Intelligence Factor analysis BI components
INTRODUCTION establishing the BI for itself and its customers, so it's
In today's rapidly changing business environment, components in the related companies.
the need for timely and effective business information is For managing the business Intelligence, it's necessary
recognized as essential for organizations not only to to evaluate the BI's process, As there are so many
succeed, but even to survive. Business intelligence (BI) methods to evaluate the BI, in this paper by using
aims to support better business decision-making [1]. Thus extensive literature review, the essential factors of
a BI system can be called a decision support system [2]. business Intelligence were gathered and by having a
1
The Company which is worked on in this paper as a survey in the case study Company, the importance of
case study is working on IT field; This Company is one of the factors were questioned, then by using the statistical
the successful companies that focus on software projects, methods such as factor analysis, the main components
even military or nonmilitary. One of the main departments were determined in five items, as: "strategy", "Information
in this company is working on business Intelligence Technology", "environmental analysis", "Human
projects , this department needs to make sure about resource" and "supporting- executive factors", at least by
necessary to evaluate and therefore measure the BI
World Appl. Sci. J., 22 (9): 1344-1351, 2013
1345
using the components and related factors, a conceptual difficult to carry out [7-9]. According to a recent survey,
pattern of BI is introduced and validated by the BI only a few organizations have any metrics in place to
experts. measure the value of BI [10].
Business Intelligence : Wikipedia list BI as “Business measure knows the purpose of the measurement [11, 12].
intelligence", it refers to skills, technologies, applications According to [13], performance measurement can be used
and practices used to help a business acquire a better for the following purposes: decision making, control,
understanding of its commercial context. The term BI can guidance, education and learning and external
be used to refer to: communication. The user of the measures should also be
Relevant information and knowledge describing the According to the literature, BI measurements serve
business environment, the organization itself and its two main purposes. The first and most common reason for
situation in relation to its markets, customers, measuring BI is to prove that it is worth the investment
competitors and economic issues [14].
An organized and systematic process by which [15] Points out that CI manager need measures to
organizations acquire, analyze and disseminate justify their department’s existence. Similarly, executives
information from both internal and external need to know whether it is rational for them to invest in
information sources significant for their business BI, because it is still a rather new managerial discipline.
activities and for decision making. Moreover, the BI literature includes a lot of unverified
The purpose of BI is to aid in controlling the vast obtained empirical evidence regarding the value of BI as
stocks and flow of business information around and estimated by practitioners. According to his study, the
within the organization by first identifying and then estimated average payback of all BI projects is 310
processing the information into condensed and useful percent of cost, which seems quite high.
managerial knowledge and intelligence. As such, the BI The second main purpose for the measurement of BI
task includes little that is new and addresses very old activities is to help manage the BI process; that is, to
managerial problems; it is one of the basic tasks of many ensure that the BI products satisfy the users’ needs and
management tools; that is, analyzing the complex that the process is efficient [17]. Namely, a BI process can
business environment in order to make better decisions. be costly if the information gathered is not accurate or
As [3] have stated, organizations have: Collected does not match the information needs. The users of a BI
information about their competitors since the dawn of process measurement are likely to be the BI professionals
capitalism. The real revolution is in the efforts to in- in an organization and the typical measurement intent
stitutionalize intelligence activities. (e.g., guiding activities and learning) is to continually
BI presents business information in a timely and improve the BI products and services.
easily consumed way and provides the ability to reason Table 1 provides a summary comparison of BI
and understand the meaning behind business information measurement for these two different purposes [18].
through, for example, discovery, analysis and ad hoc As the Table 1- shows, for managing the BI
querying [4]. processes, it's necessary to evaluate and measure the
The BI literature suggests that much benefit can be factors of Business Intelligence. In the next sessions by
derived from using BI [5], however, applying BI takes using literature review, the recent factors that have more
resources and the benefits actually occurring in practice effects on BI are being shown.
are not always clear. There are so many articles that
examine the measurement of BI for assessing the effects Effective Factors of Business Intelligence: By using the
of BI activities as well as for assessing an organization's famous papers on the effective, important and critical
BI process. success factors of BI, about 70 factors were recognized,
Measurement of Business Intelligence: The reviews, the authors tried to merge them as possible;
measurement of business performance has long a t least by using Delphi method (with the experts of
traditions in organizations. In the BI literature, authors BI in the case study company), 25 applicable factors
have identified BI measurement as an important task are listed in the Table 2-with referring to the names of the
[6], but a common view among scholars is that it is authors.
An important issue in determining how and what to
taken into account.
assumptions about the effects of BI. For example, [16]
as there were so many repeated factors in these
World Appl. Sci. J., 22 (9): 1344-1351, 2013
1346
Table 1: Two Types of BI Measurement
Purpose for Measurement Main Users of Measurement Information Expected Benefits
Determining the value of BI Executives justifying BI investments Ability to cost-justify BI services and demonstrate the actual effects of BI
BI professionals
BI service providers Increased credibility of BI as a managerial tool
Researchers Improved rigor in BI research
Managing the BI process BI professionals
BI service providers Continuous improvement of BI products and services
Table 2: effective factors on BI
Author
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
NO. Factor [19] [20] [21] [22] [23] [24] [25] [26] [27]
1 Clear link with business objectives, vision and strategies * * * * *
2 BI projects as cross-organizational business initiatives *
3 Available or willing business representatives. *
4 choosing the areas of services *
5 Functioning IT/ business partnership * *
6 Widespread management support * * *
7 Providing adequate governance for the BI program
management * * * *
8 Decision making by using Appropriate information *
9 A strong evaluation plan with measurement of
outcomes at major milestones. *
10 Aware of the degree of uncertainty, and risk *
11 Integrates with Desktop and Operational Applications *
12 Foster Rapid Development *
13 Analytical Capabilities * * *
14 Acceptance of a phased and iterative development
approach. * * *
15 Appropriate team skills * * *
16 sufficient resources (funding, information,
human beings, …) * * * * *
17 Acceptance of a set of standardized BI tools, policies,
and procedures to support the BI Initiative. * *
18 Effective data management * * * * * *
19 Well-defined information and systems requirements * * *
20 Content quality (relevance, soundness) * * * *
21 A strong communication plan which is phased and
targeted to specific groups of users. * *
22 Conforms to The Way Users Work * *
23 Engaged business sponsors * * *
24 Restrictions or rules imposed on the BI application
which define boundaries or conformance standards
for the application * *
25 A sustainable BI environment with the ability to
adapt to future requirements. *
MATERIALS AND METHODS consisted of critical dimensions of business Intelligence
In this paper the authors had a random sampling for to evaluate the BI effect on organizational success.
the survey on BI factors, for this purpose a questionnaire The statistical society in this paper is the IT-based
was designed in three main parts: first section of the Company's members that are about 250 people, by using
questionnaire consisted of some questions about the the related formula (sample number of restricted society)
characteristics of the interviewees, The second section that is shown in relation 1-the number of statistical sample
was for reviewing main concept of business Intelligence is calculated that is 102 people, for more confidence, 170
and finally the third section of the questionnaire validated samples were choused.
listed in Table 2 which were asked as the important factors
( )
2 2
2
2 2 2
2
1
N Z
n
N Z
× ×
=
? + ×
( )
250 3.84 0.44
102
0.01 249 3.84 0.44
n
× ×
= =
× + ×
World Appl. Sci. J., 22 (9): 1344-1351, 2013
1347
Relation 1- sample number of restricted society
At least 102 questionnaires was completed, in these
questionnaires, the importance of 25 factor of BI are
Questioned through the Likert Spectrum that is shown in
Table 3.
Validity and Reliability Analysis: Validity analysis tries
to define the measuring tool's ability to measure the
intended characteristics, in this paper for validity
analysis the authors had some consulting sessions with
BI professors and then, after designing the questionnaire
(as a measuring tool) they checked it several times with
more than ten experts of BI department in the case study
Company.
With reliability analysis, you can get an overall index
of the repeatability or internal consistency of the
measurement scale as a whole and you can identify
problem items that should be excluded from the scale.
The Cronbach's is a model of internal consistency,
based on the average inter-item correlation. By using
SPSS software The Cronbach's [28] calculated from the
25 variables of this research that was 0.676 (67 percent),
which showed high reliability for designed measurement
scale.
Demographic Profiles of Interviewees: In this section, the
generic and demographic characteristics of interviewees
are analyzed; the demographic profile of employees who
participate in the survey has been summarized in Table 4.
As it shows:
Most of the members (56.5 percent) had Master
of Science (MS) or higher educations. About the
job title point of view, 60.6 percent of the participants
were expert, 32.9 percent were supervisors and the
others were managers in different levels. Table 4.
Also shows the seniority of the participants. As it
can be seen, 10.5 percent had over 20 years
seniority, 18.7 percent had 12-20 years, 39.1 percent had
4-12 years and the others had less than four years
seniority.
Table 3: Likert Spectrum
Very Low Low Middle High Very High
1 2 3 4 5
Table 4: Demographic characteristics of the interviewees
Characteristics Grouping Number Percent (%)
Education Diploma 2 1.2
Bachelor of Science (BS) 67 39.4
Master of Science (MS) 96 56.5
PHD 5 2.9
Total 170 100
Job title Expert 103 60.6
supervisor 56 32.9
Top management 8 4.7
Director 3 1.8
Total 170 100
Seniority 0-4 years 54 31.6
4-12 years 66 39.2
12-20 years 32 18.7
Over 20 years 18 10.5
Total 170 100
Age 25-30 years 63 37.1
30-35 years 45 26.5
35-40 years 39 22.4
Over 40 years 23 14.1
Total 170 100
Gender Male 99 0.58
Female 71 0.42
Total 170 100
DISCUSSION
In this paper, 25 factors of business Intelligence are
reviewed , for having more proper management on these
factors in the case study company, it's essential to reduce
the factors to some main components. For this purpose,
the Factor Analysis method is used to reduce the factors
and then group them in some components.
With factor analysis, the researcher can first identify
the separate factors of the structure and then determine
the extent to which each variable is explained by each
factor. Once these factors and the explanation of each
variable are determined, the two primary uses for factor
analysis-summarization and data reduction-can be
achieved. In summarizing the data, factor analysis derives
underlying factors that, when interpreted and understood,
describe the data in a much smaller number of concepts
than the original individual variables. Data reduction can
be achieved by calculating scores for each underlying
factors and substituting them for the original variables
[29].
In order to determine whether the partial correlation
of the variables is small, the authors used the
Kaiser-Meyer-Olkin measure of sampling adequacy and
World Appl. Sci. J., 22 (9): 1344-1351, 2013
1348
Bartlett's x test of sphericity [30] before starting the
2
factor analysis. The result was a KMO of 0.771 and less
than 0.05 for Bartlett test, which showed good
correlation as depicted in Table 5.
Factor analysis is a technique particularly suitable for
analyzing the patterns of complex, multidimensional
relationships encountered by researchers. It defines and
explains in broad, conceptual terms the fundamental
aspects of factor analytic techniques. Factor analysis can
be utilized to examine the underlying patterns or
relationships for a large number of variables and to
determine whether the information can be condensed or
summarized in a smaller set of factors or components. To
further clarify the methodological concepts, basic
guidelines for presenting and interpreting the results of
these techniques are also included. Factor analysis
provides direct insight into the interrelationships among
variables or respondents and empirical support for
addressing conceptual issues relating to the underlying
structure of the data. It also plays an important
complementary role with other multivariate techniques
through both data summarization and data reduction [29].
An important tool in interpreting factors is factor
rotation.
The term rotation means exactly what it implies.
Specifically, the reference axes of the factors are turned
about the origin until some other position has been
reached. The un-rotated factor solutions extract factors
in the order of their importance. The first factor tends to
be a general factor with almost every variable loading
significantly and it accounts for the largest amount of
variance. The second and subsequent factors are then
based on the residual amount of variance. Each accounts
for successively smaller portions of variance. The ultimate
effect of rotating the factor matrix is to redistribute the
variance from earlier factors to later ones to achieve a
simpler, theoretically more meaningful factor pattern.
The simplest case of rotation is an orthogonal rotation,
in which the axes are maintained at 90° [29].
Table 5: KMO indicator and Bartlet test
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .771
Bartlett's Test of Sphericity Approx. Chi-Square 2.872E3
df 351
Sig. .000
In this research, the factor analysis method is
"principle component analysis" which was developed by
Hotteling [31].
Table 7. Shows the rotated matrix of the variables,
each variable should have significant factor loading
(grater than 0.5) only on one component.
The condition for selecting factors was based on the
principle proposed by Kaiser [32]: Eigen value larger
than one and an absolute value of factor loading greater
than 0.5. The 25 factors were grouped into five
components. The results can be seen in Table 6.
Five components had an Eigen value greater than one
and the interpretation variable was 62.155 percent.
The factors were rotated according to Varimax.
The authors attempted to name the factors briefly
without losing contents of components. In this way, the
names and content of the five components are as bellow:
"Information Technology", "Executive and
Supporting variables", "Environmental Analysis",
"Human resource" and "Strategy" are the names of first,
second, third, fourth and fifth components of business
Intelligence. These components with the related variables
are shown in Table 8.
Finally, Correlation analysis between organizational
features and the BI components was performed; the
organizational features discussed here include
interviewee's age, gender, seniority, job titles and
educational degree.
The correlation analysis showed that Job title had an
extremely positive correlation with theses BI components:
"Strategy", "Executive and supporting variables" and
"Environmental analysis". This may be resulted from the
understanding level of personnel about management area
Table 6: Factor analysis results
Rotation Sums of Squared Loadings
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Component Initial eigen values Total % of Variance Cumulative %
1 4.937 4.613 17.085 17.085
2 3.877 3.819 14.143 31.228
3 3.231 3.485 12.908 44.136
4 2.433 2.461 9.114 53.250
5 2.304 2.404 8.905 62.155
World Appl. Sci. J., 22 (9): 1344-1351, 2013
1349
Table 7: Rotated component matrix
Executive and Supporting Environmental Strategy and
No Factors Information Technology variables Analysis Human resource Management
1 strategy .065 .013 -.041 -.047 .956
2 KM .021 -.084 .666 -.022 -.042
3 agent .022 .043 .586 -.035 -.024
4 service_area -.106 .697 .130 -.218 -.032
5 IT_BI -.079 .742 -.077 .087 -.031
6 management .008 .037 -.005 -.077 .824
7 planning .041 .691 -.072 .023 .168
8 information_usage .061 -.030 .035 .063 .846
9 evaluation -.098 .830 .043 -.074 .013
10 risk .760 -.142 -.015 .016 -.009
11 software_intigration .733 -.067 -.098 .020 .038
12 platform .726 .010 -.015 -.121 .045
13 analysis_software .723 -.007 .008 .093 .037
14 developement .655 -.024 -.011 -.127 .008
15 users_skill .054 -.070 -.030 .901 -.009
16 resources -.081 .831 -.168 .005 -.076
17 tools -.032 .913 -.054 -.042 -.026
18 date_management .724 -.024 .086 -.004 -.079
19 requirement_definition .737 -.058 -.071 .057 .129
20 information_quality .944 -.069 -.014 .018 .007
21 users_relation -.066 -.073 .003 .858 .034
22 users_satisfaction -.040 .001 .024 .888 -.088
23 sponsers -.086 -.017 .579 -.013 .064
24 rules -.013 .085 .713 .042 .070
25 future_enviroment -.010 .012 .925 .021 .000
Table 8: the main components with related factors
NO. Main Components Factors (variables)
1 Information Technology Aware of the degree of uncertainty,
Integrates with Desktop and
Foster Rapid Development
Analytical Capabilities
Acceptance of a phased and iterative development approach.
Effective data management
Well-defined information and systems requirements
Content quality (relevance, soundness)
2 Executive andSupporting variables choosing the areas of services
Functioning IT/ business partnership
Providing adequate governance for the BI program management
A strong evaluation plan with measurement of outcomes at major milestones.
sufficient resources (funding, information, human beings, …)
Acceptance of a set of standardized BI tools, policies and procedures to support the BI Initiative.
3 Environmental Analysis Restrictions or rules imposed on the BI application which define boundaries or conformance
standards for the application
A sustainable BI environment with the ability to adapt to future
4 Human resource Appropriate team skills
A strong communication plan which is phased and targeted to specific groups of users.
Conforms to The Way Users Work
5 Strategy and Management Clear link with business objectives, vision and strategies
Widespread management support
Decision making by using
World Appl. Sci. J., 22 (9): 1344-1351, 2013
1350
and different features of organization; it means that REFERENCES
people with higher ranks in the organization believe that
these topics are crucial for successful BI adoption.
This may be resulted from their understanding about the
situation and their familiarity about the importance of
these subjects.
Seniority and age have the positive correlation
with "Environmental analysis"; it would be interpret
in the way that people with more experience will
be more familiar with the environmental changes, so
they have more sense about the importance of
environment.
Educational degree of interviewees was
positively correlated with "Information Technology"
Since, they already had a thorough knowledge of BI
related science and their cognition increases with higher
educations.
Also there is a positive correlation between gender
and the component of "human resource"; it may be
because of the sensitivity of females that make them to
pay more attention than males to the communicational
and motivational factors of human beings.
CONCLUSION
In this paper from a comprehensive literature review,
25 critical dimensions of business Intelligence were
distinguished. Therefore, the interviewees selected more
important dimensions from these 25 variables by
assigning the ranks to them.
The study then used factor analysis to extract critical
factors of business Intelligence in the case study
Company through 25 variables. The result of factor
analysis was extracting five main component of business
Intelligence that are: "Information Technology",
"Executive and Supporting variables", "Environmental
Analysis", "Human resource" and "Strategy". Then by
using correlation analysis, the relationship between
demographic profiles of interviewees and extracted
components of BI was analyzed, that concluded to some
appropriate and logical results for the related company.
The authors believe that after this research, the BI
managers of the case study Company can decide in a
better way for establishing the business intelligence
systems. For further research the authors suggest other
organizations to recognize their own business
Intelligence 's factors for designing a suitable pattern of
BI evaluation.
1. Luhn, H.P., 1958. "A Business Intelligence System".
IBM Journal. Available at: http:// www.
research.ibm.com/ journal/ rd/ 024/ ibmrd0204H.pdf.
2. Power, D.J., 2007. A Brief History of Decision
Support Systems, version 4.0". DSSResources.COM.
available at: http:// dssresources. com/history/
dsshistory.html.
3. Gilad, B. and T. Gilad, 1986. SMR Forum: Business
Intelligence-The Quiet Revolution, Sloan
Management Review, 27(4): 53-61.
4. Azoff, M. and 1. Charlesworth, 2004. The New
Business Intelligence.A European Perspective, Butler
Group, White Paper.
5. Thomas, J.R.H., 2001. Business Intelligence Why?
eAI Journal, pp: 47-49.
6. Solomon, 1996. Viva Business Intelligence Inc., (2000)
7. Gartz, U., 2004. Enterprise Information Management,
in Raisinghani, M. (Ed.), Business Intelligence in the
Digital Economy: Opportunities, Limitations and
Risks, Idea Group Publishing, Hershey, PA.
8. Hannula, M. and V. Pirttimaki, 2003. Business
Intelligence - Empirical Study on the Top 50 Finnish
Companies,journal of American Academy of
Business, Cambridge, 2(2): 593-599.
9. Simon, N.J., 1998. Determining Measures of Success,
Competitive Intelligence Magazine, 1(2): 45-48.
10. Marin, J. and A. Poulter, 2004. Dissemination of
Competitive Intelligence,journal of Information
Science, 30(2): 165180.
11. Brooking, A., 1996. Intellectual Capital.Core Assets
for the Third Millennium Enterprise, International
Thomson Business Press, London.
12. Sveiby, K.E., 1997. The New Organizational Wealth:
Managing and Measuring KnowledgeBased Assets,
Berrett-Koehler Publishers Inc., San Francisco.
13. Simons, R., 2000. Performance Measurement and
Control Systems for Implementing Strategy, Prentice
Hall, New Jersey.
14. Sawka, K., 2000. Are We Valuable? Competitive
Intelligence Magazine, 3: 2.
15. Davison, 1., 2001. Measuring Competitive
Intelligence Effectiveness: Insights from the
Advertising Industry, Competitive Intelligence
Review, Voi, 12: 4.
16. Kelly¡, M., 1993. ¡ Assessing the Value of
Competitive Intelligence¡ Journal of AGSI¡
November.
World Appl. Sci. J., 22 (9): 1344-1351, 2013
1351
17. Herring¡, J., 1996.¡ Measuring the Value of 25. Yeoh, W., J. Gao and A. Koronios, 2007. Towards a
Competitive Intelligence: Accessing and Critical Success Factor framework for implementing
Communicating CI’s Value to Your Organization¡ business Intelligaance systems. IFIP International
SCIP Monograph Series¡ Alexandria¡ VA. Federation for Information Pocessing, Volume 255,
18. Lönnqvist, A. and V. Pirttimäki, 2008. Research and Practical Issues of Enterprise
The measurement of business intelligence. Information Systems II Volume 2, eds. L. Xu, Tjoa A.,
Universite Rene Descartes Paris. Chaudhry S. (Boston: Springer), pp: 1353-1367.
19. Atre, S.H., 2003. The Top 10 Critical Challenges for BI 26. IU Business Intelligence Roadmap, 2009.
success. Atre Group Inc., available at: www.atre.com India University Business Intelligence Initiative.
20. De Henry, F., 2007. Assesing Business intelligance vailable at: www.indiana.edu/~iubi/roadmap.shtml
readiness in your organization. FMT aystems 27. Rapid Requirements Discovery for Business
Inc.,available at: www.norcaloaug.com/ Intelligence, 2004. Portland, Professional Services
seminar_archive Inc.,White Paper, Available at: www.csgpro.com/
21. Popovic, A., 2010. Business Intelligence maturity. papers
University of Ljubljana.. Available at: http:// 28. Likert, R., 1974. The method of constructing an
miha.ef.uni-lj.si attitude scale. In: Marannell, G.M. (Ed.), Scales: A
22. Williams, S. and N. Williams, 2007. Critical Success Sourcebook for behavioral scientist. Aldine
Factors for Establishing and Managing a BI Program. Publishing Company, Chicago, IL, pp: 21-43.
excerpted from “The Profit Impact of Business 29. Hair, J.F. anderson, R.E., Tatham, R.L. and
Intelligence”, Elsevier Inc. available at: W.C. Black, 1998. Multivariate Data Analysis,
www.decisionpath.com Prentice-Hall, Upper Saddle River, NJ, pp: 7-232.
23. Eckerson, W., 2005. The Keys to Enterprise Business 30. Bartlett, M.S. 1950. Tests of significance in factor
Intelligence. The Data Warehousing Istitute, analysis. The British Journal of Psychology, 3
available at: http://knut.hinkelmann.ch (Part II), pp: 77-85.
24. Arnott, D., 2008. Success Factors for Data 31. Hotteling, H., 1935. The most predictable criterion,
Warehouse and Business Intelligence Systems. J.Ed.Psych, 26: 139-142.
19th Australasian Conference on Information 32. Kaiser, H.F., 1958. The varimax criterion for
Systems, 3-5 Dec 2008, Christchurch. Monash analytic rotation in factor analysis. Psychometrika,
University, Centre for Decision Support and 23(3): 187-200.
Enterprise Systems Research, Melbourne, Australia
doc_980731896.pdf
Purpose- This paper aims to identify the effective components of Business Intelligence (BI), in order to facilitate the evaluation of the Business Intelligence in an IT- based Company in Iran.
World Applied Sciences Journal 22 (9): 1344-1351, 2013
ISSN 1818-4952
© IDOSI Publications, 2013
DOI: 10.5829/idosi.wasj.2013.22.09.364
Corresponding Author: Peyman Akhavan, Department of Industrial Engineering,
Iran University of Science and Technology, Tehran, Iran.
1344
Critical Factors of Business Intelligence:
(Case of an It-Based Company)
Peyman Akhavan and Sima Salehi
Department of Industrial Engineering,
Iran University of Science and Technology, Tehran, Iran
Abstract: Purpose- This paper aims to identify the effective components of Business Intelligence (BI), in
order to facilitate the evaluation of the Business Intelligence in an IT- based Company in Iran.
Design/methodology/approach- This paper has identified the effective components of business Intelligence
through the case study Company based on a comprehensive review of recent literature. For this purpose, a
questionnaire was designed, validated by some BI experts and then analyzed by some statistical methods.
The results discussed various perspectives from the business Intelligence point of view to provide some
effective and essential components of BI. Findings-The statistical analysis determined five components as
effective issues in this case study. These components are "strategy", "Information Technology",
"environmental analysis", "Human resource" and " supporting-executive factors" and at least a conceptual
pattern of BI is introduced. The overall results from the case study were positive as well, thus reflecting the
appropriateness of the experts and managers of the case study Company. Research limitations/implications-
The extracted components can act as a guideline for BI adoption in the case study Company. This helps to
ensure that the essential issues are covered during design and implementation phase of BI program.
For academics, it provides a common language to discuss and study the components crucial for business
Intelligence .Originality/value- The paper may present high value to researchers in the business Intelligence
field and to practitioners involved with BI program in the case study Company, this paper gives valuable
information and guidelines that hopefully will help the leaders to consider the important issues during
business Intelligence establishment in the organization. Paper type: Case study
Key words: Business environment Business Intelligence Factor analysis BI components
INTRODUCTION establishing the BI for itself and its customers, so it's
In today's rapidly changing business environment, components in the related companies.
the need for timely and effective business information is For managing the business Intelligence, it's necessary
recognized as essential for organizations not only to to evaluate the BI's process, As there are so many
succeed, but even to survive. Business intelligence (BI) methods to evaluate the BI, in this paper by using
aims to support better business decision-making [1]. Thus extensive literature review, the essential factors of
a BI system can be called a decision support system [2]. business Intelligence were gathered and by having a
1
The Company which is worked on in this paper as a survey in the case study Company, the importance of
case study is working on IT field; This Company is one of the factors were questioned, then by using the statistical
the successful companies that focus on software projects, methods such as factor analysis, the main components
even military or nonmilitary. One of the main departments were determined in five items, as: "strategy", "Information
in this company is working on business Intelligence Technology", "environmental analysis", "Human
projects , this department needs to make sure about resource" and "supporting- executive factors", at least by
necessary to evaluate and therefore measure the BI
World Appl. Sci. J., 22 (9): 1344-1351, 2013
1345
using the components and related factors, a conceptual difficult to carry out [7-9]. According to a recent survey,
pattern of BI is introduced and validated by the BI only a few organizations have any metrics in place to
experts. measure the value of BI [10].
Business Intelligence : Wikipedia list BI as “Business measure knows the purpose of the measurement [11, 12].
intelligence", it refers to skills, technologies, applications According to [13], performance measurement can be used
and practices used to help a business acquire a better for the following purposes: decision making, control,
understanding of its commercial context. The term BI can guidance, education and learning and external
be used to refer to: communication. The user of the measures should also be
Relevant information and knowledge describing the According to the literature, BI measurements serve
business environment, the organization itself and its two main purposes. The first and most common reason for
situation in relation to its markets, customers, measuring BI is to prove that it is worth the investment
competitors and economic issues [14].
An organized and systematic process by which [15] Points out that CI manager need measures to
organizations acquire, analyze and disseminate justify their department’s existence. Similarly, executives
information from both internal and external need to know whether it is rational for them to invest in
information sources significant for their business BI, because it is still a rather new managerial discipline.
activities and for decision making. Moreover, the BI literature includes a lot of unverified
The purpose of BI is to aid in controlling the vast obtained empirical evidence regarding the value of BI as
stocks and flow of business information around and estimated by practitioners. According to his study, the
within the organization by first identifying and then estimated average payback of all BI projects is 310
processing the information into condensed and useful percent of cost, which seems quite high.
managerial knowledge and intelligence. As such, the BI The second main purpose for the measurement of BI
task includes little that is new and addresses very old activities is to help manage the BI process; that is, to
managerial problems; it is one of the basic tasks of many ensure that the BI products satisfy the users’ needs and
management tools; that is, analyzing the complex that the process is efficient [17]. Namely, a BI process can
business environment in order to make better decisions. be costly if the information gathered is not accurate or
As [3] have stated, organizations have: Collected does not match the information needs. The users of a BI
information about their competitors since the dawn of process measurement are likely to be the BI professionals
capitalism. The real revolution is in the efforts to in- in an organization and the typical measurement intent
stitutionalize intelligence activities. (e.g., guiding activities and learning) is to continually
BI presents business information in a timely and improve the BI products and services.
easily consumed way and provides the ability to reason Table 1 provides a summary comparison of BI
and understand the meaning behind business information measurement for these two different purposes [18].
through, for example, discovery, analysis and ad hoc As the Table 1- shows, for managing the BI
querying [4]. processes, it's necessary to evaluate and measure the
The BI literature suggests that much benefit can be factors of Business Intelligence. In the next sessions by
derived from using BI [5], however, applying BI takes using literature review, the recent factors that have more
resources and the benefits actually occurring in practice effects on BI are being shown.
are not always clear. There are so many articles that
examine the measurement of BI for assessing the effects Effective Factors of Business Intelligence: By using the
of BI activities as well as for assessing an organization's famous papers on the effective, important and critical
BI process. success factors of BI, about 70 factors were recognized,
Measurement of Business Intelligence: The reviews, the authors tried to merge them as possible;
measurement of business performance has long a t least by using Delphi method (with the experts of
traditions in organizations. In the BI literature, authors BI in the case study company), 25 applicable factors
have identified BI measurement as an important task are listed in the Table 2-with referring to the names of the
[6], but a common view among scholars is that it is authors.
An important issue in determining how and what to
taken into account.
assumptions about the effects of BI. For example, [16]
as there were so many repeated factors in these
World Appl. Sci. J., 22 (9): 1344-1351, 2013
1346
Table 1: Two Types of BI Measurement
Purpose for Measurement Main Users of Measurement Information Expected Benefits
Determining the value of BI Executives justifying BI investments Ability to cost-justify BI services and demonstrate the actual effects of BI
BI professionals
BI service providers Increased credibility of BI as a managerial tool
Researchers Improved rigor in BI research
Managing the BI process BI professionals
BI service providers Continuous improvement of BI products and services
Table 2: effective factors on BI
Author
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
NO. Factor [19] [20] [21] [22] [23] [24] [25] [26] [27]
1 Clear link with business objectives, vision and strategies * * * * *
2 BI projects as cross-organizational business initiatives *
3 Available or willing business representatives. *
4 choosing the areas of services *
5 Functioning IT/ business partnership * *
6 Widespread management support * * *
7 Providing adequate governance for the BI program
management * * * *
8 Decision making by using Appropriate information *
9 A strong evaluation plan with measurement of
outcomes at major milestones. *
10 Aware of the degree of uncertainty, and risk *
11 Integrates with Desktop and Operational Applications *
12 Foster Rapid Development *
13 Analytical Capabilities * * *
14 Acceptance of a phased and iterative development
approach. * * *
15 Appropriate team skills * * *
16 sufficient resources (funding, information,
human beings, …) * * * * *
17 Acceptance of a set of standardized BI tools, policies,
and procedures to support the BI Initiative. * *
18 Effective data management * * * * * *
19 Well-defined information and systems requirements * * *
20 Content quality (relevance, soundness) * * * *
21 A strong communication plan which is phased and
targeted to specific groups of users. * *
22 Conforms to The Way Users Work * *
23 Engaged business sponsors * * *
24 Restrictions or rules imposed on the BI application
which define boundaries or conformance standards
for the application * *
25 A sustainable BI environment with the ability to
adapt to future requirements. *
MATERIALS AND METHODS consisted of critical dimensions of business Intelligence
In this paper the authors had a random sampling for to evaluate the BI effect on organizational success.
the survey on BI factors, for this purpose a questionnaire The statistical society in this paper is the IT-based
was designed in three main parts: first section of the Company's members that are about 250 people, by using
questionnaire consisted of some questions about the the related formula (sample number of restricted society)
characteristics of the interviewees, The second section that is shown in relation 1-the number of statistical sample
was for reviewing main concept of business Intelligence is calculated that is 102 people, for more confidence, 170
and finally the third section of the questionnaire validated samples were choused.
listed in Table 2 which were asked as the important factors
( )
2 2
2
2 2 2
2
1
N Z
n
N Z
× ×
=
? + ×
( )
250 3.84 0.44
102
0.01 249 3.84 0.44
n
× ×
= =
× + ×
World Appl. Sci. J., 22 (9): 1344-1351, 2013
1347
Relation 1- sample number of restricted society
At least 102 questionnaires was completed, in these
questionnaires, the importance of 25 factor of BI are
Questioned through the Likert Spectrum that is shown in
Table 3.
Validity and Reliability Analysis: Validity analysis tries
to define the measuring tool's ability to measure the
intended characteristics, in this paper for validity
analysis the authors had some consulting sessions with
BI professors and then, after designing the questionnaire
(as a measuring tool) they checked it several times with
more than ten experts of BI department in the case study
Company.
With reliability analysis, you can get an overall index
of the repeatability or internal consistency of the
measurement scale as a whole and you can identify
problem items that should be excluded from the scale.
The Cronbach's is a model of internal consistency,
based on the average inter-item correlation. By using
SPSS software The Cronbach's [28] calculated from the
25 variables of this research that was 0.676 (67 percent),
which showed high reliability for designed measurement
scale.
Demographic Profiles of Interviewees: In this section, the
generic and demographic characteristics of interviewees
are analyzed; the demographic profile of employees who
participate in the survey has been summarized in Table 4.
As it shows:
Most of the members (56.5 percent) had Master
of Science (MS) or higher educations. About the
job title point of view, 60.6 percent of the participants
were expert, 32.9 percent were supervisors and the
others were managers in different levels. Table 4.
Also shows the seniority of the participants. As it
can be seen, 10.5 percent had over 20 years
seniority, 18.7 percent had 12-20 years, 39.1 percent had
4-12 years and the others had less than four years
seniority.
Table 3: Likert Spectrum
Very Low Low Middle High Very High
1 2 3 4 5
Table 4: Demographic characteristics of the interviewees
Characteristics Grouping Number Percent (%)
Education Diploma 2 1.2
Bachelor of Science (BS) 67 39.4
Master of Science (MS) 96 56.5
PHD 5 2.9
Total 170 100
Job title Expert 103 60.6
supervisor 56 32.9
Top management 8 4.7
Director 3 1.8
Total 170 100
Seniority 0-4 years 54 31.6
4-12 years 66 39.2
12-20 years 32 18.7
Over 20 years 18 10.5
Total 170 100
Age 25-30 years 63 37.1
30-35 years 45 26.5
35-40 years 39 22.4
Over 40 years 23 14.1
Total 170 100
Gender Male 99 0.58
Female 71 0.42
Total 170 100
DISCUSSION
In this paper, 25 factors of business Intelligence are
reviewed , for having more proper management on these
factors in the case study company, it's essential to reduce
the factors to some main components. For this purpose,
the Factor Analysis method is used to reduce the factors
and then group them in some components.
With factor analysis, the researcher can first identify
the separate factors of the structure and then determine
the extent to which each variable is explained by each
factor. Once these factors and the explanation of each
variable are determined, the two primary uses for factor
analysis-summarization and data reduction-can be
achieved. In summarizing the data, factor analysis derives
underlying factors that, when interpreted and understood,
describe the data in a much smaller number of concepts
than the original individual variables. Data reduction can
be achieved by calculating scores for each underlying
factors and substituting them for the original variables
[29].
In order to determine whether the partial correlation
of the variables is small, the authors used the
Kaiser-Meyer-Olkin measure of sampling adequacy and
World Appl. Sci. J., 22 (9): 1344-1351, 2013
1348
Bartlett's x test of sphericity [30] before starting the
2
factor analysis. The result was a KMO of 0.771 and less
than 0.05 for Bartlett test, which showed good
correlation as depicted in Table 5.
Factor analysis is a technique particularly suitable for
analyzing the patterns of complex, multidimensional
relationships encountered by researchers. It defines and
explains in broad, conceptual terms the fundamental
aspects of factor analytic techniques. Factor analysis can
be utilized to examine the underlying patterns or
relationships for a large number of variables and to
determine whether the information can be condensed or
summarized in a smaller set of factors or components. To
further clarify the methodological concepts, basic
guidelines for presenting and interpreting the results of
these techniques are also included. Factor analysis
provides direct insight into the interrelationships among
variables or respondents and empirical support for
addressing conceptual issues relating to the underlying
structure of the data. It also plays an important
complementary role with other multivariate techniques
through both data summarization and data reduction [29].
An important tool in interpreting factors is factor
rotation.
The term rotation means exactly what it implies.
Specifically, the reference axes of the factors are turned
about the origin until some other position has been
reached. The un-rotated factor solutions extract factors
in the order of their importance. The first factor tends to
be a general factor with almost every variable loading
significantly and it accounts for the largest amount of
variance. The second and subsequent factors are then
based on the residual amount of variance. Each accounts
for successively smaller portions of variance. The ultimate
effect of rotating the factor matrix is to redistribute the
variance from earlier factors to later ones to achieve a
simpler, theoretically more meaningful factor pattern.
The simplest case of rotation is an orthogonal rotation,
in which the axes are maintained at 90° [29].
Table 5: KMO indicator and Bartlet test
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .771
Bartlett's Test of Sphericity Approx. Chi-Square 2.872E3
df 351
Sig. .000
In this research, the factor analysis method is
"principle component analysis" which was developed by
Hotteling [31].
Table 7. Shows the rotated matrix of the variables,
each variable should have significant factor loading
(grater than 0.5) only on one component.
The condition for selecting factors was based on the
principle proposed by Kaiser [32]: Eigen value larger
than one and an absolute value of factor loading greater
than 0.5. The 25 factors were grouped into five
components. The results can be seen in Table 6.
Five components had an Eigen value greater than one
and the interpretation variable was 62.155 percent.
The factors were rotated according to Varimax.
The authors attempted to name the factors briefly
without losing contents of components. In this way, the
names and content of the five components are as bellow:
"Information Technology", "Executive and
Supporting variables", "Environmental Analysis",
"Human resource" and "Strategy" are the names of first,
second, third, fourth and fifth components of business
Intelligence. These components with the related variables
are shown in Table 8.
Finally, Correlation analysis between organizational
features and the BI components was performed; the
organizational features discussed here include
interviewee's age, gender, seniority, job titles and
educational degree.
The correlation analysis showed that Job title had an
extremely positive correlation with theses BI components:
"Strategy", "Executive and supporting variables" and
"Environmental analysis". This may be resulted from the
understanding level of personnel about management area
Table 6: Factor analysis results
Rotation Sums of Squared Loadings
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Component Initial eigen values Total % of Variance Cumulative %
1 4.937 4.613 17.085 17.085
2 3.877 3.819 14.143 31.228
3 3.231 3.485 12.908 44.136
4 2.433 2.461 9.114 53.250
5 2.304 2.404 8.905 62.155
World Appl. Sci. J., 22 (9): 1344-1351, 2013
1349
Table 7: Rotated component matrix
Executive and Supporting Environmental Strategy and
No Factors Information Technology variables Analysis Human resource Management
1 strategy .065 .013 -.041 -.047 .956
2 KM .021 -.084 .666 -.022 -.042
3 agent .022 .043 .586 -.035 -.024
4 service_area -.106 .697 .130 -.218 -.032
5 IT_BI -.079 .742 -.077 .087 -.031
6 management .008 .037 -.005 -.077 .824
7 planning .041 .691 -.072 .023 .168
8 information_usage .061 -.030 .035 .063 .846
9 evaluation -.098 .830 .043 -.074 .013
10 risk .760 -.142 -.015 .016 -.009
11 software_intigration .733 -.067 -.098 .020 .038
12 platform .726 .010 -.015 -.121 .045
13 analysis_software .723 -.007 .008 .093 .037
14 developement .655 -.024 -.011 -.127 .008
15 users_skill .054 -.070 -.030 .901 -.009
16 resources -.081 .831 -.168 .005 -.076
17 tools -.032 .913 -.054 -.042 -.026
18 date_management .724 -.024 .086 -.004 -.079
19 requirement_definition .737 -.058 -.071 .057 .129
20 information_quality .944 -.069 -.014 .018 .007
21 users_relation -.066 -.073 .003 .858 .034
22 users_satisfaction -.040 .001 .024 .888 -.088
23 sponsers -.086 -.017 .579 -.013 .064
24 rules -.013 .085 .713 .042 .070
25 future_enviroment -.010 .012 .925 .021 .000
Table 8: the main components with related factors
NO. Main Components Factors (variables)
1 Information Technology Aware of the degree of uncertainty,
Integrates with Desktop and
Foster Rapid Development
Analytical Capabilities
Acceptance of a phased and iterative development approach.
Effective data management
Well-defined information and systems requirements
Content quality (relevance, soundness)
2 Executive andSupporting variables choosing the areas of services
Functioning IT/ business partnership
Providing adequate governance for the BI program management
A strong evaluation plan with measurement of outcomes at major milestones.
sufficient resources (funding, information, human beings, …)
Acceptance of a set of standardized BI tools, policies and procedures to support the BI Initiative.
3 Environmental Analysis Restrictions or rules imposed on the BI application which define boundaries or conformance
standards for the application
A sustainable BI environment with the ability to adapt to future
4 Human resource Appropriate team skills
A strong communication plan which is phased and targeted to specific groups of users.
Conforms to The Way Users Work
5 Strategy and Management Clear link with business objectives, vision and strategies
Widespread management support
Decision making by using
World Appl. Sci. J., 22 (9): 1344-1351, 2013
1350
and different features of organization; it means that REFERENCES
people with higher ranks in the organization believe that
these topics are crucial for successful BI adoption.
This may be resulted from their understanding about the
situation and their familiarity about the importance of
these subjects.
Seniority and age have the positive correlation
with "Environmental analysis"; it would be interpret
in the way that people with more experience will
be more familiar with the environmental changes, so
they have more sense about the importance of
environment.
Educational degree of interviewees was
positively correlated with "Information Technology"
Since, they already had a thorough knowledge of BI
related science and their cognition increases with higher
educations.
Also there is a positive correlation between gender
and the component of "human resource"; it may be
because of the sensitivity of females that make them to
pay more attention than males to the communicational
and motivational factors of human beings.
CONCLUSION
In this paper from a comprehensive literature review,
25 critical dimensions of business Intelligence were
distinguished. Therefore, the interviewees selected more
important dimensions from these 25 variables by
assigning the ranks to them.
The study then used factor analysis to extract critical
factors of business Intelligence in the case study
Company through 25 variables. The result of factor
analysis was extracting five main component of business
Intelligence that are: "Information Technology",
"Executive and Supporting variables", "Environmental
Analysis", "Human resource" and "Strategy". Then by
using correlation analysis, the relationship between
demographic profiles of interviewees and extracted
components of BI was analyzed, that concluded to some
appropriate and logical results for the related company.
The authors believe that after this research, the BI
managers of the case study Company can decide in a
better way for establishing the business intelligence
systems. For further research the authors suggest other
organizations to recognize their own business
Intelligence 's factors for designing a suitable pattern of
BI evaluation.
1. Luhn, H.P., 1958. "A Business Intelligence System".
IBM Journal. Available at: http:// www.
research.ibm.com/ journal/ rd/ 024/ ibmrd0204H.pdf.
2. Power, D.J., 2007. A Brief History of Decision
Support Systems, version 4.0". DSSResources.COM.
available at: http:// dssresources. com/history/
dsshistory.html.
3. Gilad, B. and T. Gilad, 1986. SMR Forum: Business
Intelligence-The Quiet Revolution, Sloan
Management Review, 27(4): 53-61.
4. Azoff, M. and 1. Charlesworth, 2004. The New
Business Intelligence.A European Perspective, Butler
Group, White Paper.
5. Thomas, J.R.H., 2001. Business Intelligence Why?
eAI Journal, pp: 47-49.
6. Solomon, 1996. Viva Business Intelligence Inc., (2000)
7. Gartz, U., 2004. Enterprise Information Management,
in Raisinghani, M. (Ed.), Business Intelligence in the
Digital Economy: Opportunities, Limitations and
Risks, Idea Group Publishing, Hershey, PA.
8. Hannula, M. and V. Pirttimaki, 2003. Business
Intelligence - Empirical Study on the Top 50 Finnish
Companies,journal of American Academy of
Business, Cambridge, 2(2): 593-599.
9. Simon, N.J., 1998. Determining Measures of Success,
Competitive Intelligence Magazine, 1(2): 45-48.
10. Marin, J. and A. Poulter, 2004. Dissemination of
Competitive Intelligence,journal of Information
Science, 30(2): 165180.
11. Brooking, A., 1996. Intellectual Capital.Core Assets
for the Third Millennium Enterprise, International
Thomson Business Press, London.
12. Sveiby, K.E., 1997. The New Organizational Wealth:
Managing and Measuring KnowledgeBased Assets,
Berrett-Koehler Publishers Inc., San Francisco.
13. Simons, R., 2000. Performance Measurement and
Control Systems for Implementing Strategy, Prentice
Hall, New Jersey.
14. Sawka, K., 2000. Are We Valuable? Competitive
Intelligence Magazine, 3: 2.
15. Davison, 1., 2001. Measuring Competitive
Intelligence Effectiveness: Insights from the
Advertising Industry, Competitive Intelligence
Review, Voi, 12: 4.
16. Kelly¡, M., 1993. ¡ Assessing the Value of
Competitive Intelligence¡ Journal of AGSI¡
November.
World Appl. Sci. J., 22 (9): 1344-1351, 2013
1351
17. Herring¡, J., 1996.¡ Measuring the Value of 25. Yeoh, W., J. Gao and A. Koronios, 2007. Towards a
Competitive Intelligence: Accessing and Critical Success Factor framework for implementing
Communicating CI’s Value to Your Organization¡ business Intelligaance systems. IFIP International
SCIP Monograph Series¡ Alexandria¡ VA. Federation for Information Pocessing, Volume 255,
18. Lönnqvist, A. and V. Pirttimäki, 2008. Research and Practical Issues of Enterprise
The measurement of business intelligence. Information Systems II Volume 2, eds. L. Xu, Tjoa A.,
Universite Rene Descartes Paris. Chaudhry S. (Boston: Springer), pp: 1353-1367.
19. Atre, S.H., 2003. The Top 10 Critical Challenges for BI 26. IU Business Intelligence Roadmap, 2009.
success. Atre Group Inc., available at: www.atre.com India University Business Intelligence Initiative.
20. De Henry, F., 2007. Assesing Business intelligance vailable at: www.indiana.edu/~iubi/roadmap.shtml
readiness in your organization. FMT aystems 27. Rapid Requirements Discovery for Business
Inc.,available at: www.norcaloaug.com/ Intelligence, 2004. Portland, Professional Services
seminar_archive Inc.,White Paper, Available at: www.csgpro.com/
21. Popovic, A., 2010. Business Intelligence maturity. papers
University of Ljubljana.. Available at: http:// 28. Likert, R., 1974. The method of constructing an
miha.ef.uni-lj.si attitude scale. In: Marannell, G.M. (Ed.), Scales: A
22. Williams, S. and N. Williams, 2007. Critical Success Sourcebook for behavioral scientist. Aldine
Factors for Establishing and Managing a BI Program. Publishing Company, Chicago, IL, pp: 21-43.
excerpted from “The Profit Impact of Business 29. Hair, J.F. anderson, R.E., Tatham, R.L. and
Intelligence”, Elsevier Inc. available at: W.C. Black, 1998. Multivariate Data Analysis,
www.decisionpath.com Prentice-Hall, Upper Saddle River, NJ, pp: 7-232.
23. Eckerson, W., 2005. The Keys to Enterprise Business 30. Bartlett, M.S. 1950. Tests of significance in factor
Intelligence. The Data Warehousing Istitute, analysis. The British Journal of Psychology, 3
available at: http://knut.hinkelmann.ch (Part II), pp: 77-85.
24. Arnott, D., 2008. Success Factors for Data 31. Hotteling, H., 1935. The most predictable criterion,
Warehouse and Business Intelligence Systems. J.Ed.Psych, 26: 139-142.
19th Australasian Conference on Information 32. Kaiser, H.F., 1958. The varimax criterion for
Systems, 3-5 Dec 2008, Christchurch. Monash analytic rotation in factor analysis. Psychometrika,
University, Centre for Decision Support and 23(3): 187-200.
Enterprise Systems Research, Melbourne, Australia
doc_980731896.pdf