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Business Intelligence Implementation Readiness: A
Framework Development and Its Application to Small
Medium Enterprises (SMEs)
Achmad Nizar Hidayanto, Robertus Kristianto, Muhammad Rifki Shihab
Faculty of Computer Science
Universitas Indonesia
ABSTRACT
Purpose – This research aims to develop a framework for measuring readiness level of Business
Intelligence (BI) implementation.
Design/methodology/approach – We formulated our framework by using Critical Success Factors (CSFs)
of BI implementation. The weight of each dimension in the framework is determined by using Analytic
Hierarchy Process (AHP). The validation of the framework is performed by involving five (5) BI experts.
Findings – The proposed framework comprises of three categories: organizational, process and technology.
The number of aspects for organizational, process and technology are nine, four, and five respectively.
Among other aspects, strategic alignment, committed management support and sponsorship, clear vision
and well-established business case, as well as business-centric championship and balanced team
composition are considered the most important aspects to measure BI implementation readiness.
Practical implication – In order to gain success in BI implementation, it is necessary that organizations
conduct an assessment on its current condition, to identify their weaknesses.
Originality/value – We proposed a framework for measuring BI implementation readiness, which is
currently unavailable. Many current research in BI mainly focuses on the implementation aspects, whereas
our research focuses on pre-implementation aspects. We hope by considering our proposed framework,
organizations will be able to implement better BI services, and gain more value to help organization in
decision making.
Keywords – Business Intelligence, Business Intelligence readiness, BI readiness, Analytic Hierarchy
Process, AHP, Critical Success Factor, CSF, Delphi method
Paper type – Research paper
1. INTRODUCTION
The rapid development of Small Medium Enterprises (SMEs) in Indonesia as well as the enactment of the
ASEAN-China Free Trade Area (ACFTA) on January 1, 2010 makes the competition that must be faced
by SMEs becomes very tight (Dewitari, 2009; Wahyuni, 2011). To be able to survive in such competition,
requires fast, innovative, as well as agile planning and decision-making to run the company. For this
reason, SMEs need appropriate support from information technology, namely Business Intelligence (BI).
BI is a managerial concept consists of a set of patterns, tools or technologies that change the raw data into
meaningful information and knowledge, which are used for decision making to drive profitable business
action (Chuah, 2010; Najmi, Sepehri, & Hasherni, 2010).
The growing use of cloud computing, Software as a Service, as well as open source BI, open the
opportunities for SMEs to implement BI. Despite of this, it still carries high level of risk of failure and
consumes great resources (Hidayanto, Karnida, and Moerita, 2012). Related to this, we need a framework
that could help assess the readiness of BI implementation for SMEs, so that SMEs with BI
implementation plans can evaluate their level of readiness and identify aspects which are still considered
weak. Thus it is expected that this framework could further improve the success rate of BI
implementation in SMEs.
The objectives of this research are to:
•Explore the critical success factors in BI implementation
•Develop a framework for measuring the readiness level of BI implementation in an organization.
•Assess the applicability of the proposed framework through a case study at an SME
The rest of the paper is organized as follows. First, we present theoretical background for framework
development. Second, we describe the development of the framework. Third, we explain the application
of our proposed framework by using a real case. Finally, we discuss our findings for the case study we
explore.
2. ANALYTIC HIERARCHY PROCESS (AHP)
AHP is a method for ranking decision alternatives and selecting the best one when the decision maker has
multiple criteria (Taylor, 2005). Since 1977, Saaty (1980) proposed AHP as a decision aid to help solving
unstructured problems in economics, social and management sciences (Özdagoglu, 2007). AHP enables
decision-makers to structure a complex problem in the form of a simple hierarchy and to evaluate a large
number of quantitative and qualitative factors in a systematic manner under multiple criteria environment
in confliction (Cheng, 1996).
AHP helps decision makers to choose the best alternative by giving weight to each alternative
based on certain criteria. The best alternative is the one with the highest weight. In this research, AHP is
used to give weight to each readiness factors, in order to develop an assessment framework for BI
implementation readiness. The steps of AHP are explained in the following paragraphs.
Step 1: Decompose the problem into a model that consists of three levels, which are goal, criteria,
and alternatives. Goal is the aim, criteria include things to consider in selecting alternatives, and
alternatives are the choice to be chosen. The AHP model is shown in the Figure 1.
Figure 1. AHP Model
Figure 2. The scale of pairwise comparisons
Step 2: Make pairwise comparisons for criteria and alternatives. In this step, alternatives are
compared each other, so do the criteria. The comparisons are done in 1-9 scale, which can be seen in
Figure 2. The result of this step is a pairwise comparison matrix, for criteria and alternatives, which can
be seen in Figure 3.
Figure 3. Pairwise comparison matrix
Step 3: Find the weight for each alternative through AHP calculation.
In order to avoid invalid pairwise comparison, we need to check the consistency against pairwise
comparisons in the AHP framework, by computing consistency ratio (CR) of each paired comparison. If
the CR is valued at less than 0.1, then the pairwise comparisons are revealed as consistent and valid.
3. CRITICAL SUCCESS FACTORS (CSFs) OF BI IMPLEMENTATION
The concept of "success factors" was developed by Daniel (1961) of McKinsey & Company. The concept
was refined by Rockart (1981). Critical success factor refers to an element that is necessary for an
organization or project to achieve its mission. It is a critical factor or activity required for ensuring the
success of a company or an organization.
In terms of Business Intelligence, there numerous authors who have formulated CSFs, among of
them are Atre (2003), Williams & Williams (2004) and William & Koronios (2010). From these
literatures, we identified 18 factors that are considered having significant impacts to BI implementation,
which are:
•Committed management support and sponsorship: Committed management support and sponsorship
has been widely acknowledged as the most important factor for BI system implementation (William
& Koronios, 2010). Consistent support and sponsorship from business executives make it easier to
secure the necessary operating resources such as funding, human skills, and other requirements
(William & Koronios, 2010).
•Clear vision and well-established business case: As BI initiative is typically driven by the business, a
strategic business vision is needed to direct the implementation. A long-term vision, primarily in
strategic and organizational terms, is needed to establish a solid business case. The business case
must be aligned to the strategic vision, thereby meeting the business objectives and needs. If the
business vision is not thoroughly understood, it would eventually impact the adoption and outcome of
the BI system (William & Koronios, 2010).
•Strategic alignment: Much has been written about strategic alignment between business and IT
(Williams & Williams, 2004). The discussion is generally about consistency between business
strategy, business organization and processes, IT strategy, IT infrastructure, and IT organization and
processes (Cooper, Watson, Wixtom, & Goodhue, 2000).
•Effective business/IT partnership for BI: Organizations that have effectively used IT to improve
business results will be more capable of leveraging BI to create value than those who do not
(Williams & Williams, 2004).
•BI Portfolio Management: Organizations that have undertaken a comprehensive review of the major
BI opportunities for sales, marketing, manufacturing, distribution, customer service, quality, and so
forth are in the position to manage BI as a portfolio of investments, ranked by business impact and
risk. This is important in an environment where capital budgets for IT are constrained, as they almost
always are (Williams & Williams, 2004).
•Continuous Process Improvement Culture: If business users do not change the business processes to
leverage BI, then the investment in BI will have no impact on the economic well-being of the
organization. We want to assess whether an organization is ready to manage the process changes that
will be required to capture the business value of a BI initiative (Williams & Williams, 2004).
•Culture Surrounding the Use of Information and Analytical Applications: Organizations embracing
the use of information and analytical applications to improve profits (private sector) or productivity
and service (public sector) are better able to leverage investments in BI than organizations that do not
embrace and reward such approaches to creating business value (Williams & Williams, 2004).
•Cross-Organizational Collaboration: Where BI is concerned, collaboration is not limited to
departments within the organization; it requires integration of knowledge about customers,
competition, market conditions, vendors, partners, products and employees at all levels (Atre, 2003).
To succeed at BI, an enterprise must nurture a cross-organizational collaborative culture in which
everyone grasps and works toward the strategic vision (Atre, 2003).
•Decision Process Engineering Culture: Decision process engineering is a term we have coined to
convey the concept of using structured decision processes to increase the effectiveness of certain
decisions that organizations face on a recurring or semi-recurring basis. These structured decision
processes can incorporate the use of information, analytical applications, and/or quantitative methods
as appropriate for the type of decision to be made (Williams & Williams, 2004).
•Business-centric championship and balanced team composition: Having the right champion from the
business side of the organization is critical for implementation success. A champion who has
excellent business acumen is always important since he/she will be able to foresee the organizational
challenges and change course accordingly. More importantly, this business-centric champion would
view the BI system primarily in strategic and organizational perspectives, as opposed to one who
might over-focus on the technical issues. The composition and skills of a BI team have a major
influence on the success of the systems implementation. The BI team should be cross-functional and
composed of both technical and business personnel, so-called “best of both worlds” (William &
Koronios, 2010).
•Availability of Skilled Team Members: A BI project team lacking BI application implementation
experience will most likely fail to deliver desired results in the first iteration. Since most BI projects
have aggressive timelines and short delivery cycles, an inexperienced and unskilled team is a risk that
must be avoided (Atre, 2003).
•Business-driven Development Approach and Iterative Development Approach: Adequate business-
oriented project scoping and planning allow the BI team to concentrate on the best opportunities for
improvement. Thorough scoping and planning facilitate flexibility and adaptability to changing
requirements within the time frame and resources. Moreover, adequate scoping enables the project
team to focus on crucial milestones and pertinent issues while shielding them from becoming trapped
in unnecessary events. Many experts stated that it is advisable to start with small changes and
developments and then to adopt an incremental delivery, a so-called ‘iterative’ approach (William &
Koronios, 2010).
•User-oriented change management: Better user participation in the process of change can lead to
better communication of their needs, which in turn can help ensure successful introduction of the
system. Formal user participation can help meet the demands and expectations of various end users.
Users know what they need better than an architect or developer who lacks direct experience of the
product (William & Koronios, 2010).
•Business-driven, scalable and flexible technical framework: The technical framework of a BI system
must be able to accommodate scalability and flexibility requirements in line with dynamic business
needs (William & Koronios, 2010).
•Sustainable data quality and integrity: The quality of data, particularly in the source systems, is
crucial if a BI system is to be implemented successfully (William & Koronios, 2010).
•Importance of Metadata: Clean data is worthless to knowledge workers if they do not understand its
context. Valid business data, unless tied to its meaning, is still meaningless. Therefore, it is
imperative for all BI applications to consciously create and manage the meaning of each data element.
This data about data is known as meta-data, and its management is an essential activity in BI projects
(Atre, 2003).
•BI and DW Technical Readiness: Level of readiness of an organization in implementing business
intelligence is not only determined by non-technical aspects such as organizational culture, but also
by technical factors. Technical readiness of an organization, such as network infrastructure,
availability of adequate machinery for the implementation of data warehouse, ready for ETL
applications and analytic tools, is also greatly influence the success or failure of the application of
business intelligence in organizations.
•The Silver Bullet Syndrome: BI project teams must always consciously strive for the lowest possible
number of tools (Atre, 2003).
4. FRAMEWORK DEVELOPMENT
4.1. AHP Model Composition
The factors used in the assessment are identified after a thorough study of the critical success factors
(CSFs) of BI implementation. CSFs are important aspects that should be considered by organizations in
order to gain BI implementation success. We adopted the CSFs as our readiness factors for BI
implementation. By adopting such factors, we hope that our proposed framework can measure the extent
to which an organization has met those CSFs.
As mentioned earlier, AHP model consists of three levels, which are goal, criteria, and
alternatives. In our case, the goal (first level) for the AHP model is BI implementation readiness. The
criteria (second level) consists of readiness factors that we adopted from CFSs and are grouped into three
categories adopted from William & Koronios (2010) which comprise of organizational, process and
technology. The readiness factors itself are used as alternatives (third level) of the model. The complete
AHP model is depicted in Figure 4.
Figure 4. The composed AHP model
4.2. Delphi Method
To get a weight for each factor, pairwise comparisons that are achieved through a consensus among
several experts are needed. In this research, we used four experienced and credible BI experts in private
sector. The experts have numerous experience in developing and consulting for BI implementation.
In order to achieve consensus among involved experts, we choose to use Delphi method. Each
expert was asked to give values to four pairwise comparisons matrix, one for inter-category comparison,
one for organizational category, one for process category and one for technology category, by e-mail or
direct meeting. They were also asked to give their feedback via e-mail. The pairwise comparisons, as
result of the Delphi method are shown in Figure 5, 6, 7, 8.
Figure 5. Inter-category pairwise comparison
Figure 6. Pairwise comparison for organizational
category
Figure 7. Pairwise comparison for process
category Figure 8. Pairwise comparison for technology
category
4.3. AHP Calculation and Consistency Checking
The result of the AHP calculation, the weight of each factor and the AHP consistency check can be seen
in Table 1 and Table 2 respectively.
Table 1. The weight of each factor
Factors Weight Factors Weight
Committed management support and
sponsorship 0.1253 Business-centric championship and
balanced team composition 0.1039
Clear vision and well-established
business case 0.1253 Availability of skilled member team 0.0158
Strategic alignment 0.1392 Business-driven and iterative
development approach 0.0402
Effective business/IT partnership for BI 0.0245 User-oriented change management 0.0402
BI portfolio management 0.0256 Business-driven, scalable and
flexible technical framework 0.0232
Continuous process improvement culture 0.0495 Sustainable data quality and integrity 0.0904
Culture surrounding the use of
information and analytical applications 0.0403 Importance of metadata 0.0485
Cross-organizational collaboration
culture 0.0349 BI and DW technical readiness 0.0258
Decision process engineering culture 0.0355 The silver bullet syndrome 0.0121
Table 2. AHP consistency check result
Pairwise Comparison Consistency Ratio
Inter-category 0.0158
Organizational category 0.0499
Process category 0.0161
Technology category 0.0559
The result of AHP consistency check shows us that the consistency ratio of each pairwise
comparison is below 0.1. Therefore, all pairwise comparisons are consistent and valid.
4.4. Readiness Level Description
To be able to give a fair and clear judgment of each factor’s value in a company, a description about each
factor’s readiness level is made. We adopted assessment model of Electronic Government Procurement
(e-GP) Readiness Self Assessment (2004) as reference. The readiness level of each factor thus can be
evaluated by using description as depicted in Table 3.
Table 3. Readiness level
Level of Readiness Description
0. None There is no real evidence that components are being addressed and supported.
1. Small degree There is little evidence that components are being addressed and supported.
2. Some degree There is some evidence that components are being addressed and supported.
3. Adequate degree There is adequate evidence that components are being addressed and
supported.
5. FRAMEWORK APPLICATION
The assessment framework has been applied in Mode Fashion Group to measure the firm’s readiness to
implement BI. Mode Fashion Group is a medium scale enterprise built in 1984. Today, Mode Fashion
Group has 14 outlets in Indonesia and approximately 200 workers. To improve its performance, Mode
Fashion Group is planning to implement BI. We aim to help them assess their readiness in implementing
BI.
Data collection was carried out by using semi-structured interview. The interview results were
mapped to the readiness level in 0-3 scale by using the guideline in Table 3. The overall results of our
assessment to Mode Fashion Group are presented in Table 4.
In Table 4, values in organizational readiness column are obtained by multiplying the value in the
level column with its corresponding factor’s weight. The overall readiness score are obtained from the
total value of all factors’ organizational readiness value. The value 1.7415 from 0-3 scale, or 58.0512
from 0-100 scale, shows the intermediate level. These results indicate that there is still a fairly high risk
and significant possibility of impending obstacles and failures of BI implementation at Mode Fashion
Group.
Furthermore, the result shows us that effective business/IT partnership for business intelligence,
cross-organizational collaboration culture, business-driven and iterative development approach, user-
oriented changed management, sustainable data quality and integrity, importance of metadata and the
silver bullet syndrome factors have been defined very well. Factors clear vision and well-established
business case, strategic alignment, continuous process improvement culture, culture surrounding the use
of information and analytical applications are defined fairly well. However, major difficulties can be
found related to business intelligence portfolio management, business-centric championship and balanced
team composition, availability of skilled member team, business-driven, scalable and flexible technical
framework, BI and DW technical readiness, committed management support and sponsorship, and
decision process engineering culture.
Table 4. Assessment on Mode Fashion Group result
Factors Level Org.
Readiness Factors Level Org.
Readiness
Committed management
support and sponsorship 1 0.1253
Business-centric
championship and
Balanced team
Composition
0 0.0000
Clear vision and well-
established business case 2 0.2505 Availability of Skilled
Member Team 0 0.0000
Strategic Alignment 2 0.2783
Business-driven and
iterative development
approach
3 0.1205
Effective Business/IT
Partnership for BI 3 0.0735 User-oriented change
management 3 0.1205
BI Portfolio Management 0 0.0000
Business-driven, scalable
and flexible technical
framework
0 0.0000
Continuous Process
Improvement Culture 2 0.0990 Sustainable data quality
and integrity 3 0.2713
Culture Surrounding the Use
of Information and
Analytical Applications
2 0.0806 Importance of metadata 3 0.1454
Cross-Organizational
Collaboration culture 3 0.1048 BI and DW Technical
Readiness 0 0.0000
Decision Process
Engineering Culture 1 0.0355 The Silver Bullet
Syndrome 3 0.0363
Overall Readiness Score 1.7415
Finally, the company should act to enhance their readiness in the mentioned factors and prevent
obstacles that might arise. Such actions may include socialization concerning the BI system and its
benefits, training to enhance their human resource quality in BI implementation, outsourcing or new
member recruitments, create a portfolio management for BI, in particular, and IT, in general, implement a
business-driven, scalable and flexible technical framework, and hold an adequate infrastructure for BI and
DW.
6. IMPLICATION
One form of IT services that can be provided to management is a reporting service in the form of
Business Intelligence (BI). This service will offer benefits when it comes from valid data source, so that
the decisions taken by management are truly based on accurate data. With respect to this, organizations
need to provide a guide to assess their readiness to implement BI, so that it is expected to gain higher
success in BI implementation. Result of this research showed that there are three the most important
aspects to prepare BI implementation, which are: strategic alignment, committed management support
and sponsorship, as well as clear vision and well-established business case. Thus organizations seek to
implement BI services should be supported by their management, so that its implementation can be run
smoothly.
7. CONCLUSION
This study was conducted to develop a framework for measuring and analyzing an organization’s
readiness level to implement Business Intelligence. This framework was developed initially to analyze the
level of readiness for implementation of business intelligence in Mode Fashion Group, an SME
specializing in textiles. Our proposed framework for measuring Business Intelligence readiness comprises
of eighteen (18) factors which are derived from CSFs of BI implementation. We obtain weight of each
factor by experts’ judgments with the help of AHP method. From these eighteenth factors, strategic
alignment factor obtains the highest weight, while the silver bullet syndrome factor obtains the lowest
weight.
Our assessment to Mode Fashion Group using the proposed framework showed that their
readiness score for BI implementation is 1.7415 on a scale of 3 or 58.05 on a scale of 100. This means
that the Mode Fashion Group is considered not yet ready to implement Business Intelligence. We also
found that there are other aspects that may have to be improved by Mode Fashion Group to be able to
enhance the chances of a successful BI implementation, which are:. the use of portfolio management;
support, sponsorship, and championship from management, especially from a business side; human
resource skills in Mode fashion Group related to implementation of business intelligence; data warehouse
infrastructure and networks and other technical readiness; and business-oriented, flexible, and scalable
technical framework.
ACKNOWLEDGEMENTS
This research is supported by Universitas Indonesia through ‘Riset Madya’ grant No.
2139/H2.R12/HKP.05.00/2012. It is a pleasure to convey my gratitude to my university for their
continuous support, particularly for Research and Public Service Directorate for their excellent services.
We also thank to IMHERE project officers for their support in disseminating the results of this research.
We hope that we can produce high quality results from this research.
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AUTHOR BIOGRAPHIES
Dr. Achmad Nizar Hidayanto is Head of Information Systems / Information Technology Stream, Faculty
of Computer Science, Universitas Indonesia. He received his PhD in Computer Science from Universitas
Indonesia. His research interests are related to information systems/information technology, e-learning,
information systems security, change management, distributed systems and information retrieval. He can
be contacted at: nizar@cs.ui.ac.id
Robertus Kristianto obtained his bachelor degree in Information Systems, University of Indonesia in
2012. His interests include business intelligence as well as IS/IT audit. He can be contacted at:
robertus.kristianto@ui.ac.id
Muhammad Rifki Shihab, M.Sc. obtained his master’s degree from Temple University. Currently, he is
working as a lecturer at Faculty of Computer Science Universitas Indonesia. His research interests are
related to information systems. He can be contacted at: shihab@cs.ui.ac.id
