The Influence of Business Intelligence in SMEs

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The Influence of Business Intelligence in SMEs

By

Sara Chambel Pinheiro

Student Number 10652329

Submitted in partial fulfillment of the requirements for the degree of

Master of Science

in

Business Information Systems

at

“Information is not Knowledge” – Albert Einstein

Dissertation 2014

Supervisors

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2 | P a g e T h e I n f l u e n c e o f B u s i n e s s I n t e l l i g e n c e i n S ME s

Abstract

This thesis examines the relationship between small and medium sized enterprises, and the corresponding Business Intelligence solutions. BI has tremendously impacted large companies‟ business, and SMEs have the best environment to take advantage of these benefits. However, SMEs are still lagging behind in the creation of BI. There are numerous reasons behind it, and this study will discuss some of these factors.

Competitive advantage is created through better and deeper understanding of data. This research intends to propose a framework that allows SMEs to implement successfully BI solutions. This adoption creates better understanding and analysis of data in order to support a more efficient and accurate decision making process. This research also seeks to define a set of Critical Success Factors that influence the BI implementation success.

This study develops a Theoretical Framework for BI systems implementation, based on literature. The framework is discussed and validated through a series of interviews with BI experts and then, a new Theoretical Framework is proposed with some changes according to the insights gained. Afterwards, the framework is re-checked with an expert interview. It is expected that this study makes a contribution to theory and practice on the implementation of BI systems in SMEs.

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Acknowledgements

I would like to express my gratitude to my supervisors, Ronald Kleijn and Dr. Guus Delen, for their guidance and support throughout the entire project. Also, I would like to thank the company where I intern for the last 2 months SURFsara, and more particularly, to Frank Heere.

In addition, I would like to thank my parents, sister and boyfriend for their unconditional support, patience and faith in me, without them this MSc would not have been possible.

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Chapter 1 - Introduction

Small and medium-sized enterprises represent the vast majority of all enterprises. These contribute to economic growth, job creation and innovation of a country (Audretsch and Keilbach, 2004; Van Stel et al., 2005). Following Van Gils (2005), small and medium-sized enterprises are important engines to stimulate the economic development of a country. The increasing complexity of the environment in which organizations currently operate generates many complications imposed by economic, social, environmental and technological factors (Rodrigues et al., 2012). This also creates new demands and new business opportunities. Thus, entrepreneurs need to increase their level of innovation and adapt their business models continuously. These aspects require from organizations agility of response and reliable information for decision-making. The amount of data that is now available for further analysis is growing at a rapid pace and IBM estimates 90% of the data in the world has been created in the last few two years (IBM research, 2011). More and more businesses are now realizing the importance that lies in their data. This data can be leverage to provide information to make better decisions, offer value and discover patterns (Scholz et al., 2010). Also, according to the same authors, “Leveraging information is a key success factor for companies. Over the last two decades Business Intelligence (BI) has evolved to become a foundational cornerstone of enterprise decision support (p.1).” These factors lead a increasing number of organizations to incorporate systems and Business Intelligence units in their operations to assist them in defining competences needed to optimally and more efficiently exploit their resources and capabilities.

Although competitive advantage is mostly related with innovation in products and markets, storing, collecting and analyzing information have become a new way for innovation and competitiveness; also some researchers believe that data may become a new “corporate asset” (Brown et al. 2011). The business activities in any organization, regardless of its size, involve management of large amounts of information. The continuous development of organizations in a growing competitive environment requires appropriate decisions to be made. These decisions are often made based on a set technologies and processes to make it as reliable and fast as possible (Tutunea et al., 2012). Gartner believes that BI revenue is established to reach more than $17 billion worldwide by 2016.

Nowadays, the need to convert raw data into intelligence and knowledge has become an important demand across all different industries. Small and medium sized enterprises (SMEs) need to upsurge to this occasion and use the opportunities that BI enables. “They cannot afford to be reactive because their competitors will step in and take control of the markets they play in. To improve competitiveness, they need to enable their people to make better quality decisions – that is what BI is all about. It makes a business predictive, proactive and informed. BI deployments aid revenue growth and foster competitive advantage” (Nick Bell, 201).

SMEs play a major economic and social role and, therefore, are a source of economic development (European Commission, 2011; Olszak and Ziemba, 2008). The importance of SMEs for economy has increased, “Small and medium-sized enterprises (SMEs) are the spine of the world‟s economy” (Scholz et al., 2010, p.2). Small and mid-sized enterprises start to gain the intelligent edge. These businesses are often more agile which gives them advantages when implementing BI solutions. Howard Dresner stated that “SMEs have the advantage of agility and the ability to use BI as a competitive differentiator (…) because of the closeness of executives to the technology, business and customers; they have an edge against larger competitors. However, larger organizations have many more resources -- people and money -- enabling them to invest in more long-term endeavors" (Wilson, 2013). However, literature reveals that particularly SMEs are obviously lagging behind in the creation of BI, which is a quite interesting factor as some studies indicate SMEs may gain benefits from adopting ICT in terms of better understanding their customers and to develop closer relations. This is based on the fact that SMEs are more flexible and informal than large companies (Simmons et al., 2011). A possible explanation for such an event could be the fact that BI projects often require large investments that bigger organizations are more likely to have (Hwang et al., 2004). Also, Bergeron (2000) suggests that conventional BI solutions are generally more focused on large organizations and would not meet the needs of SMEs. This happens because most SMEs do not have the necessary resources to implement a traditional BI (Hwang et al., 2004).

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Even though much is done concerning Business Intelligence, a gap exists between enabling Business Intelligence in small and medium-sized enterprises. This study will therefore investigate the benefits of implementing Business Intelligence solutions in SMEs. This research is both aimed at introducing new academic insights to the Business Intelligence adoption in SMEs literature, as well as provide valuable advice for SMEs when implementing BI solutions. Therefore, the research question that this study will address is the following:

“How can Business Intelligence solutions enhance small and medium enterprises competitiveness?”

Furthermore, the above assessment should make it possible to more clearly support the decision of SMEs to implement Business Intelligence systems. Subsequently, it is aimed to answer tree additional research questions. However, many of these decisions are context specific. The additional questions that will be considered in both a general and case specific way are:

1) How crucial is business intelligence to create new opportunities?

2) What is the business value for SME‟s when using Business Intelligence platforms? 3) Which are the Critical Success Factors (CSF) for the implementation of BI platforms?

The CSFs identified will be consolidated into a model to provide a comprehensive picture for BI managers, and hence allowing them to optimize their resources and efforts on those critical success factors that are most likely to have an impact on the system implementation.

Chapter 2 - Theoretical Background

In order to understand the connection between SMEs competiveness and Business Intelligence implementation one must better understand the understanding of the underlying factors that constitute SMEs and their importance to economy. Furthermore, the demand for SMEs to become more competitive will be clarified and a brief introduction to Business Intelligence will be given, its value and finally the critical success factors. An explanation of Business Value and the business value attached to the implementation of BI solutions will be developed.

2.1. SMEs and Role of SMEs in economy

According to Welsh and White (1981), a small firm is “not a little big business”. Large organizations are usually different from SMEs. Information systems and organizational theories applicable to large firms may not be appropriate to SMEs (Bharati and Chaudhury, 2006).

SMEs play a vital role in the European economy. They are a great source of entrepreneurial skills, innovation and employment. There are 20 million SMEs in Europe and these are responsible for around 86.8 million jobs. Of all the existing enterprises, 99,8% are SMEs (Figure 1, Appendix I). The employment scope of SMEs is significant in the US and EU countries (Brown and Lockett, 2004). SMEs employ half of all private-sector employees and have generated roughly 60-80% of the new jobs annually over the last decade. Also, SMEs play a crucial role in enhancing the competitiveness of economy through the process of economic renewal: birth, death and re-structuring of economic sectors (Bharati and Chaudhury, 2009). Following Olszak and Ziemba (2012), “SMEs are often referred to as the backbone of the European economy, providing a potential source for jobs and economic growth” (p.10).

This category of micro, small and medium-sized enterprises (SMEs) is defined by the following factors: employ fewer than 250 persons, an annual turnover not exceeding 50 million euro, and an annual balance sheet total not above 43 million euro. Within this category: small enterprises are defined as an enterprise that employs less than 50 persons and with an annual turnover does not exceed 10 million euros. Also, micro enterprises are those which employ less than 10 people and whose annual turnover does not exceed 2 million euro (European Commission, 2005).

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2.2. Why must we improve SMEs competitiveness worldwide?

“Micro, small and medium-sized enterprises (SMEs) are the engine of the European economy. They are an essential source of jobs, create entrepreneurial spirit and innovation in the EU and are thus crucial for fostering competitiveness and employment. The new SME definition, which entered into force on 1 January 2005, represents a major step towards an improved business environment for SMEs and aims at promoting entrepreneurship, investments and growth. This definition has been elaborated after broad consultations with the stakeholders involved which proves that listening to SMEs is a key towards the successful implementation of the Lisbon goals”(European Commission, 2005, p.3).

While new technologies have reduced the importance of economies of scale in many activities and enhanced the potential contribution of small and medium enterprises, the productivity growth is not following this trend. SMEs have hard time dealing with such problems. Thus, enhancing their competitiveness is crucial for their survival. The importance of innovative SMEs has been recognized by many international institutions, such as World Bank and United Nations (Country-Coordinator Republic of Turkey, 2014). In this context, SMEs should recognize the importance of ICT as an enable to innovation. In this regards, some policies and approaches to enhance their competitiveness have been deployed (European Commission, 2013).

2.2.1. SMEs business in EU

The SMEs business environment has changed. There are 20 million European SMEs which currently play an important role in economy. In 2012, the SMEs sector in total delivered 55, 6% of the gross total value added. This sector plays a major role in the European economic recovery as their number of companies, staff employed and added value constitute a large share of European economy. By providing the right tools and conditions to SMEs they can flourish to ensure a sustained recovery and achieve prosperity to the EU citizens. Recent studies show that the condition within SMEs operates and the entrepreneurial culture are key factors to determine the SMEs performance and therefore their contribution for economic growth (European Commission, 2013).

Overall, the importance of innovative SMEs is globally recognized. In this context, due to the considerable importance of SMEs in job creation as well as economic growth and development, entrepreneurship, competitiveness, innovation and employment, new policies and approaches are being deployed in order to enhance their competitiveness. The promotion of these organizations is an effective strategy to enable entrepreneurship, reduce poverty, lower the difference of income and stimulate economic growth in multiple sectors, on emerging and developing economies.

Some studies show that the limitation for SMEs development relies on the difficulty in IT implementation (Olszak and Ziemba, 2010). The European Union account for approximately 20,399,291 enterprises, of which 99,8% are SMEs (Figure 1, Appendix I) , therefore this sector plays a crucial role in economy (European Commission, 2013). In this regard, the development of SME market is acknowledged as one of the main targets of the government. By managing and implementing the right and appropriate tools to deal with information, a better exploration of their needs and goal is enhanced in this sector. Despite their importance, SMEs have several weaknesses and constrains, such as the poor grasp of technology. Usually when managers select IT equipment they are more concerned with its cost rather than its capabilities or resources necessary for its adoption. In order to make up for these weaknesses and constraints, government agencies are implementing various interventions and programs in areas in need of improvement. SMEs need a good business environment, the right know-how and the right level of technology in order to become more competitive.

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2.3. Business Intelligence Systems

This subject has been extensively studied and discussed in literature. BI systems are a very important component of the new information infrastructures because they contribute for both, success and competitiveness (Davenport et al., 2010). These are mainly used by managers while introducing new tools to manage enterprises in a better and more focused way (Wixom and Watson, 2010).

The role of BI systems and their influence over organizations has been a subject that changes continuously. From simple, static analytical systems they have developed into solutions that can be used in strategic planning, customer relationship management, monitoring operations and to study the profitability of products (Negash and Gray, 2008). They are a new approach to manage an organization and a new way of collecting, storing, processing, analyzing, and using information (Williams and Williams, 2007). The BI term is frequently used to describe the technologies, applications, and processes for gathering, storing, accessing and analyzing data to help users to make better decisions (Davenport et al., 2010; Wixom and Watson, 2010).

These systems refer to decision making, information analysis and knowledge management. According to Azvine et al. (2006), BI is all about the capture, access, understanding and the analysis of raw data into information/knowledge in order to improve business. Wells (2003) recognizes BI as the capability of an organization to explain, plan, predict and solve problems, think more abstractly, understand, invent, and learn so that organizational knowledge can increase, provide information for the decision-making process, enable effective actions, and support establishing and achieving business goals. Fundamentally, BI means to have access to right information at the right time, in order to make the right decision. Understanding the data that is generated through the day to day business of a company plays a major role of the business strategy for creating competitive SMEs. This need became more realistic with the complexity felt in today‟s business environment. All companies, not just SMEs, need to be agile and proactive in reaction to the market. They need to make decisions more quickly and for this matter it is necessary to understand the information and to track the history of future events that may occur. This factor is what mainly leads organizations to adopt BI solutions in its business processes.

2.3.1. Value of Business Intelligence

Williams and Williams (2010) stated that BI “It's not just a technology. It's not just a methodology. It's a powerful new management approach that – when done right – can deliver knowledge, efficiency, better decisions, and profit to almost any organization that uses it”.

The adoption of BI solution has become really important in today‟s hyper-competitive markets where organizations are seeking to become more efficient, agile and proactive in the decision-making processes. The necessity that has been created in the last few years about incorporating IT solutions for helping in the decision making process and the usage of BI tools is recognized by most entrepreneurs. Nowadays, it is clear that Information Systems play a key role in enabling SMEs to become more competitive. Literature reveals some relevant information of the adoption of Business Intelligence systems by SMEs. According to Lönnqvist et al. (2006), the BI tools have a number of advantages for businesses, with emphasis on the following, decrease of the distribution of information, increase the interaction between users, ease the access to information, the information is available in real time, versatility and flexibility in adapting to the reality of the company and is useful in the process of decision making. Also, Guarda et al. (2012) states that BI bridges different systems and users that have to access information, providing an environment that facilitates access to information needed for daily activities and by doing so this allows organization to analyze business performance.

BI can drive organizations to attain tangible benefits such as ROI and cost savings and intangible benefits, which are just as important (Gibson et al, 2004). BI can reduce decision latency by consolidating and integrating information from different sectors, also stores this information is structures which are easy to

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access and analyze. The top five intangible benefits identified included; better information, better strategies, improved decisions, and more efficient processes.

More precisely, for SMEs, Guarda et al. (2013) believe that an adequate and integrated BI can create the competitive advantage necessary for SMEs to be successful. Also, SMEs that have already implemented a BI tool realized that are now more able to face the competitive market environment and can compete more effectively. The successful SMEs businesses manage carefully the bottom line of the business and are always conscious of the value of a business investment. In order to become more competitive, organizations need robust planning tools, such as Business Intelligence to derive accurate, effective business decisions. SMEs can improve key performance indicators and become more successful and competitive in the market. These tools will also increase profitability in the long term and ensure that important and crucial information is not ignored and overlooked (ElegantJ BI, 2008).

2.4. Critical success factors for implementation of BI in SMEs

Many BI projects fail and some reasons behind this include a relatively low level of knowledge in organizations (particularly in SMEs) about the opportunities and benefits of BI systems, as well as their critical success factors. The theory behind the critical success factors gives a good foundation for starting which criteria should be followed during the implementation of such systems (Olszak and Ziemba, 2012). Several researchers have attempted to identify these factors in order to facilitate the success of BI solutions.

The use of CSFs is important in the field of information systems as these factors determine whether business objectives are met and why these should be met. Following Leidecker and Bruno (1987), CSFs are responsible for the properties that can influence the success of an enterprise that is creating its position in a specific industry supposed that the variables and properties of such an industry are preserved, sustained or managed. Also, the use of CSFs can help the identification of characteristics and the resources that should be at the disposal of a project team to focus on primary matters (Greene and Loughridge, 1996). Following Rockart (1979 p.85), “Critical success factors are (…) the few key areas where things must go right for the business to flourish. (…) As a result, the critical success factors are areas of activity that should receive constant and careful attention from management”. Essentially, there is a set of factors that influence the success of BI systems. These factors are called CSFs and these help in the alignment of the organization with the BI solution.

Nowadays the increasing volume of data demands an efficient way to manage it, especially for an SME where the use of Information Technology consistently lags behind (Rath et al., 2012). BI systems have been mainly adopted by large organizations as the cost required to adopt this kind of systems is very high. If SMEs can find a way to successfully deploy BI systems it is clear that those solutions will increase their competitiveness and provide means to manage the information more efficiently and correctly. In the last few years the market has changed dramatically. The recent speed of globalization and the increase in data volume that needs processing, requires companies, small and large ones, to evaluate the use of BI tools to manage more efficiently information and knowledge within an organization (Olszak and Ziemba, 2012). The last authors held a study on business-owners and managers of SMEs which confirmed that analyzing data in small organizations is just as important as in large ones.

Some research has been made in this particular topic. The research conducted on the critical success factors impacting the implementation of BI tools has several contributions (Eckerson, 2005; Yeoh and Koronios, 2010; Olszak and Ziemba, 2012). CSFs could be considered as a set of tasks that should be addressed in order to ensure BI systems success (Olszak and Ziemba, 2012). However, some of the results might not be adequate for the special case of SMEs (Hwang et al., 2004; Scholz et al., 2010). The implementation of BI tools is not the same as the implementation of other IT systems. That is, implementing BI systems is not a simple activity of just buying the application/tool; rather is a complex activity and requires an appropriate infrastructure and resource over a long period of time (Yeoh and Koronios, 2010).

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Identification of CSFs is important in the process of IT implementation and management, especially in the case of Business Intelligence. By ensuring that some particular events occur that affect the success of the project and by minimizing negative impacts, this contributes to the success of the project. The knowledge of the CSFs is important in planning activities and events as to achieve the objective/goal of the project.

The following table summarizes the critical success factors of BI tools implementation which are found in literature.

Table 1 - Summary of CSF found in literature. Sources: (Watson et al., 2004; Salmeron and Herrero, 2005; Yeoh, Gao, and Koronios (2007); Yeoh and Koronios 2010, Ereckson (2005))

Yeoh, Gao, and Koronios (2007)

Eckerson (2005) Yeoh and Koronios (2010) Others

Committed

management support and sponsorship Business user-oriented change management Clear business vision and well established case Business driven methodology and project management Business-centric championship and balanced project team composition

Strategic and extensible technical framework

Support all users via integrated BI suites Conforms to the way users work

Integrated with desktop and operational applications Delivers actionable information Foster rapid development Provide a robust extensible platform Committed management support and sponsorship Clear vision and well established business case Business-centric

championship and balanced team composition

Business-driven and iterative development approach User-oriented change management

Business-driven, scalable and flexible technical framework Sustainable data quality and integrity

The problem has to be clearly defined (Watson et al., 2004) The problems of users have to be identified (Watson et al., 2004) Flexibility and responsiveness- be aware of the changes that the users will require (Salmeron and Herrero, 2005)

2.5. What is IT Business Value?

There are various definitions of value. The Institute of Value Management defines value as the relationship between satisfying needs and expectations and the resources required to achieve them. According to Gerald Weinberg (1991), quality is reflected as value to someone. Also, Goldsmith (2004) defines that a requirement describes value that we need to deliver to someone. To sum up, to deliver value we have to satisfy a need that someone is willing to pay for.

A more particular case of value is the business value is the business value of Information Technology (IT) which has been a major concern for organizations and has proven a difficult and controversial task. Some studies on this area have focused on the return of investment in hardware and software as the definition for business value. However, different approaches of the IT business value have been undertaken by examining whether organizations that deploy IT enjoy better business value efficiencies. The term “IS business value” is recent: value IS adds to business (Kauffman, 1993), purchases (Strassmann, 1990); ability that IS enables the organization to gain competitive advantage (Brynjolfsson and Hitt, 1994).

A BI investment usually creates an asset that is used to generate incremental cash flows. Therefore, BI investments should be evaluated in order to assess how the investment will result. This particular case of business value lies in its use within management processes and its impact on operations that drive more revenue and fewer costs in its use within the operations. The operational impact of a BI is directly related to the business value of BI. Capturing BI value requires organizations to go beyond the technical implementations, the organizations must engage in effective processes of engineering and change management to ensure that BI is integrated in management and operational processes (Williams and Williams, 2003).

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Chapter 3 - Methodology

The aim of this research is to explore the critical success factors in BI implementation and construct a model that can help to a successful BI implementation in SMEs and enable them to gather business value to become more competitive in today‟s market. This chapter discusses the research approach adopted to investigate the research questions developed and proposed in the introduction chapter. A theoretical framework is proposed and validated trough a qualitative and quantitative study. This framework was developed from a literature review and after validated through semi-structured interviews to BI experts. Researchers find interviews as one of the most important and common way to gather data in qualitative studies (Rubin and Rubin, 2005). From the various interview techniques, the one chosen in this study was outlined by Fontana and Frey (2000), semi-structures or unstructured interview. This type of interview involves a script that act as a guide. Some questions are prepared beforehand but there is always opportunity to develop further and new questions pertinent to the interview process.

This research follows a two stage approach. The first stage is based on a literature survey, the classification of the CSFs for SMEs when implementing BI infrastructures, the proposed theoretical framework and associated interviews. The second stage and last step is the validation of the revised theoretical framework after the first round of interviews which gave the basis for some alterations and modifications based on the framework according to the feedback from experts. This consists of semi-structured interviews to BI experts regarding the Revised Theoretical Framework. It is expected that this study will make contribution to theory and practice on the implementation of BI systems in SMEs.

Stage one: literature survey and proposed model

This stage seeks to define and analyze the previous literature, gaining insights and studying the previous literature on SMEs, BI implementation and the value of BI in SMEs. The analysis of the literature identified a gap in research resulting in the formulation of the Theoretical Framework and associated research question and sub-questions. This process is complete through interviews (assuming that experts experience can add important value in situations where theory is incomplete) which support the model and some additional feedback for improvements.

The first approach to collect data was an online survey distributed to several SMEs. However, no conclusions were gathered from this as only 9 SMEs answered. The rest of the contacts were either info@ addressed, phone number and e-forms compilation which were not answered by the respondents. The contacts were made through an email that included the link to the questionnaire; this was sent to 40 addresses and a time limit of 30 days. From these only 9 were answered generating a 22,5% response rate. The response rate was already limited from the beginning as this study was only targeting SMEs that have BI systems already implemented or in the process of implementing.

Therefore, a different approach was required. The data was collected by means of semi-structured interviews conducted to a sample of 20 BI experts, retrieved between 13 of May and 13 June, which consisted on a series of questions based on the 4 stages of the model. These questions (Appendix II) were used as guide rather than for a formal interview. This unstructured approach allowed the conversation to flow and gather more insights on the experts‟ opinion and feedback on the framework. This survey was analyzed for the identification of Business Intelligence critical factors for SMEs and for the validation of the model and its different phases. Both open-ended and quantitative questions were held. In the latter, participants rated the questions using a 7 point Likert scale. According to Munshi (2014) research on the “Method for constructing Likert Scales”, the author suggests that the respondents make a small but unambiguous distinction between strong agreement and complete agreement. This aspect is missing in the 5-point Likert scales. Therefore, the choice for the use of a 7-point Likert scale in this closed question. After the conclusion of the interview, an online survey was conducted, concerning the CSF. All experts ranked and identified critical success factors relevant to Business Intelligence.

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Regarding the sample, there were some limitations; the RQ and the framework represent the most delimitation criteria. The subject of BI in SMEs restricted the sampling from the beginning, as SMEs still do not use BI systems and some of them are not aware of the benefits of its use. Bryman and Bell (2011) affirm that the dimension of the sample carries the most weight. Considering the results achieved by this study and considering that the SMEs market using BI solutions is still very small and also the time considered to the completion of this study is restrictive, the available representative sample was really small but met the expectations.

Stage two: validity of the Revised Theoretical Framework

To deepen the understanding and assure a certain level of reliability, a second round of expert interviews was conducted in order to validate the Revised Theoretical Framework. This second phase of data collection was important to ensure that the model after the first set of interviews was valid. A series of questions were developed based on the Revised Theoretical Framework. Once again, these questions were used as a guide not as a formal interview.

It should be noted that, even though this study is primarily focused on EU companies, the conclusions from this study may have relevance and can also be applied in general. This study used an analysis of static data and literature, semi-structured interviews and for the construction of the model some critical thinking and reasoning on the subject of matter.

Following are presented the models and theories in which the Theoretical Framework is based and argued.

The DeLone and McLean Model of Information Systems Success (2003)

Figure 2 – DeLone and McLean Model of IS success

DeLone and McLean (1992; 2003) argued that when measuring IS success researchers should combine measures from their six Information System success categories. This model has been critically argued as it did not recognize the different stakeholders within an organization involved with the system. This model has been updated since the original paper in 1992 and the new proposed model is the one presented in the Figure 2, presented above. A new dimension was added, “service quality” and the two single variables “individual” and organizational impacts” are now combined into a single one “net benefits”. The inclusion of this new term is important “because no outcome is wholly positive, without any negative consequences” (DeLone et al., 2003, p.14). This success measure captures the balance between the positive and negative impacts of the system.

The model defines IS success in terms of system use, user satisfaction and net benefits whereas the factors leading to the success incorporate information quality, system quality and service quality. BI tools are

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built on actual data driven form daily business activities which ease the analysis; consequently, data quality is possibly the most important factor. From the “second variables” which encompass intention to use and user satisfaction are important in Business Intelligence as these systems are generally used by the entire organization; users are obliged to use the solution and not their own data sources. Therefore, the use of these systems by the end-users plays a major role.

Critical Success Factor Framework for Implementing Business Intelligence Systems (Yeoh, Gao, and Koronios, 2007)

The paper by Yeoh et al. (2007) explains critical success factors of BI. The aim of this research was to develop a CSF framework for BI systems and implementation. The authors identified a series of CSFs which were consolidated into a framework in order to provide a clear and comprehensible image to the stakeholders of these projects. The framework proposed is organized in seven dimension covering 22 factors. This research provides insights into multi-dimensional CSFs that influence the BI systems implementation and hence allow BI stakeholders to optimize resources and efforts. The contextual elements combined with the critical factors and consolidated with the CSFs framework provide a complete understanding about CSFs.

This framework outlines how a set of factors contribute to the success of a BI system implementation. The last part of the model “BI system implementation success measure” is based on the classification performed by Ariyachandra and Watson (2006). These set of CSFs influence the implementation success that takes into account two key measures: infrastructure performance and process performance. The infrastructure performance matches with the three variables associated with the study of DeLone and McLean (1992; 2003) on IS success. These variables are the following, system quality, information quality, and system use. Process performance can be measured in terms of time schedule and budget. Infrastructure performance is

Figure 3 - Critical Success Factor Framework for Implementing Business Intelligence Systems

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related with the quality of the system and the standard of output and process performance, how well the process of a BI system implementation went. To summarize, infrastructure performance is connected with the quality of the system and information as well as the use of the system. Process performance is assessed by the time schedule and the budget considered for the project.

Yeoh and Koronios model of success in BI (2010)

“An understanding of the CSFs enables BI stakeholders to optimize their scarce resources and efforts by focusing on those significant factors that are most likely to aid successful system implementation” (Yeoh and Koronios, 2010). Having noted that, the authors proposed a model that included some of the critical success factors or success variables from DeLone and McLean (1992).

The authors divided the model in different three dimensions: organization, which includes vision and business case related factors, management and championship related factors; process, team related factors, project management and methodology related factors, change management related factors and technology with data related factors, infrastructure related factors. All those factors lead to business orientation, that when organized with implementation success drive business benefits.

Critical Success Factors for Implementing Business Intelligence Systems in Small and Medium Enterprises (Olszak and Ziemba, 2012)

The main objective of this study was to identify the CSFs for Business Intelligence (BI) systems implementation in small and medium enterprises. This study assesses the determinants and barriers to the BI Implementation in SMEs. Within the results of the determinants that are crucial in the SMEs market authors identified that price of BI system and its implementation plays the largest role. When analyzing the results of the barriers to BI system implementation in SMEs authors found that for the most part the implementation of BI systems have a business and organizational character. Among the business barriers, the most frequently mentioned were the lack of well-defined business problems, not determining the expectation of BI and the lack of relations between business and BI vision system. Between the organizational barriers, the lack of manager‟s support comes in first, the lack of knowledge about the BI systems and its capabilities are some of the most mentioned barriers. This research enabled them to obtain the basis for the most important CSFs for the BI system implementation in SMEs; these CSFs are present in the Theoretical Framework.

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Chapter 4 - Theoretical Framework for Successful Business

Intelligence Implementation in SMEs

In brief this framework reflects the critical success factors required to meet when deciding to implement a BI tool in a SME (Figure 4). It is based in the framework that Yeoh and Koronios (2010) proposed but with particular changes to align this framework with SMEs needs and its CSFs. It is important to mention that a list of key success factor does not automatically imply the success of the project (Adamala and Cidrin, 2011). However, the absence of these CSFs could lead to failure (Yeoh and Koronios, 2010). The key success factors mentioned are only a part of the tasks that implies the necessary conditions and factors for the development and ensuring the project‟s completion. These critical success factors represent areas that, if and when effectively managed, increase the possibility of a successful implementation.

This framework views the BI system implementation as a continuous cycle, which evolves over time. It is similar to a circuit that through time requires evaluation and re-evaluation of the BI tools implemented. Thereafter, the modifications and additions are deployed where and when needed. Also, it was employed a division of the phases in which a BI implementation passes through to make a more clear vision of the process.

Critical Success Factors Organization Dimension Adequate Budget

Support from senior management Competent BI project manager Skilled staff/team/managers Clear business vision and plan Past experience and cooperation with BI supplier

Process Dimension

Effective change management Well defined business problem and processes

Well defined users‟ expectation Adjusting the BI solution to users‟ business expectation

Technology Dimension Data quality

Integration between BI system and other systems

Appropriate technology and tools “User friendly” BI system The lack of BI flexibility

Orientation of Business, Processes and Staff BI system implementation success Perceived business value Process Performance Budget Time schedule Infrastructure Performance System Quality Information Quality System Use Align business with

new objectives

Figure 5 - Theoretical Framework for Successful Business Intelligence Implementation in SMEs

Set-up phase Implementation Phase Evaluation Phase Initiation Phase

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4.1. Initiation Phase

The CSFs are presented according to the dimensions proposed by Wixom and Watson (2001): organization, process and technology. Organization dimension means that unless the new solution is accepted into the organization and integrated in the day to day business with success the implementation will not be a successful one. Generally, this happens as an information system implementation, more particularly BI, can cause significant changes that employees tend to resist. This resistance intensifies with the magnitude of the changes that the new solution entails. Process dimension relates to the need of attention on the critical success factors and issues that may place the project in risk. Lastly, technical dimension is associated with the fact that a high number of diverse and different systems are used in an organization. Usually those need to be clearly understood and coordinated to be joined in one single system, the BI system in this case.

These CSF were first assessed through the research conducted by Olszak and Ziemba (2012) on the Critical Success Factors for Implementing Business Intelligence Systems in Small and Medium sized Enterprises on the Example of Upper Silesia, Poland. The model is adapted to BI implementation in SMEs as the CSFs proposed are associated to small companies.

Organization Dimension

Adequate Budget. This CSF mainly drives the rest of the project. This is important to understand which

type of tool is going to be used and to determine the rest of the resources that are going to be used during such implementation (Olszak and Ziemba, 2012).

Support from senior management. The support from the senior management is really important at this

point of BI implementation. This makes easier the support of resources for the project (Olszak and Ziemba, 2012).

Competent BI project manager. Having an adequate and competent BI project manager is deemed as

important throughout the literature as an important CSF as the one that drive and lead the team should be more skilled that the others. A competent and skilled manager is crucial for a good development of the project (Olszak and Ziemba, 2012).

Skilled staff/team/managers. The BI team should have the appropriate knowledge and skills to ensure

that the right decisions are made during the BI implementation. This team is responsible for the process of change management and implementation of BI is clear. Therefore, a skilled project team, consisting of employees, managers and BI experts, is essential; (Yeoh and Koronios, 2010; Olszak and Ziemba, 2012).

Clear business vision and plan. A BI initiative is driven by business, so a strategic vision is crucial to

determine the direction that the implementation should follow. The business should be aligned with the corporate side of the company to ensure a successful implementation. (Yeoh et al. 2008)

Past experience and cooperation with BI supplier. This CSF refers to the cooperation and experience

that the BI supplier had previously to this project. If an organization worked with a BI supplier should be more willing to work with it again if previous work was successful. Also, the experience is important within these projects (Olszak and Ziemba, 2012).

Process Dimension

Effective change management. Implementations such as BI place the foundations for change

management. This process lies in the preparation of the various stakeholders for the changes that are to come and to assist them coping with this and with the adaptation process. The processes that undertake this process drive changes in individual and organizational behavior. Some BI project failures are attributed to an

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ineffective change management. These activities are sometimes ignored and consequently, drive to failure (Olszak and Ziemba, 2012).

Well defined business problem and processes. BI systems must have the business problem and

processes clearly defined as this solutions support key processes and business decisions within the whole company (Olszak and Ziemba, 2012).

Well defined users’ expectation. The success of BI system is defined before it started. A good definition

of the expectation of the users may benefit the success of the BI solution. BI system must account the real needs of the users (Olszak and Ziemba, 2012).

Adjusting the BI solution to users’ business expectation. BI systems are in permanent changing and

adaption, mainly driven by the changes on the users‟ need and expectations. Thus, adjusting the BI to the new expectations and needs is mandatory. The consequence of BI systems that do not apply these changes is depreciation and withdrawal (Olszak and Ziemba, 2012).

Technology Dimension

Data quality. According to Yeoh and Koronios (2010) the quality of the data is crucial for a successful

BI implementation. Many data related issues are often discovered when the data is populated and queried within the BI system. The data quality will then affect the quality of the reports with directly influence the decision outcome.

Integration between BI system and other systems. The technical framework of BI systems must be

able to accommodate all the in-house systems that existed before. Also, should be scalable and flexible to deal with dynamic business needs (Yeoh and Koronios, 2010).

Appropriate technology and tools. Also, highly important for BI implementation in SMEs is the

technology and tools used in the project. As the investment that these enterprises are capable to make is not as big as that of large companies, the choice of the tool to use is critical (Olszak and Ziemba, 2012).

“User friendly” BI system. When implementing such systems in SMEs these require more usability of

the system, which leads to a more efficient utilization (Olszak and Ziemba, 2012).

The lack of BI flexibility. The problem with some BI tools is the way they force users to conform to

their tools, rather than customize the tool to the way users work and prefer to interact with information. Therefore, the flexibility, customization and personalization of BI platforms is important to enable users to configure dashboards, personalization of the BI page, portal and dashboard screen and flexible in the sense of enabling users to access information in the right and necessary way (Yeoh and Koronios, 2010).

4.2. Set-up phase

The implementation of a BI system is a financially large and complex undertaking activity (Watson et al., 2004). The implementation of a system like this is a main event and is normal to cause organizational perturbations (Ang and Teo, 2000). The BI team needs to take account of issues others than the ones related with operational systems implementation, such as cross-functional needs, poor data quality, organizational politics, and integration of the enterprise and challenges on consistency (Shin, 2003). BI implementation involves technology and this is able to influence the structure, the strategy and the people in the organization, as well as they are able to influence the technology (Nielsen et al 2002). The implementation of a BI system is considerably different from a traditional operational system and involves an infrastructure project which has an impact on business, processes and on the employees of the company. This is the reason behind the change of the “business orientation” proposed by Yeoh and Koronios (2010) into “Orientation of business, processes and staff”.

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4.3. Implementation phase

Following Ariyachandra and Watson (2006), the BI system implementation success is split into two key dimensions, “infrastructure performance” that gathers system quality, information quality and system use. This last dimension follows the study of DeLone and McLean which describes the three major IS success variables. The second dimension “process performance”, was proposed to meet classical project variables as budget and time schedule. The success of a BI implementation can be measured on how well the team meets the time schedule, budget and functional goals as system quality, information quality and system use. By meeting these goals, the probability of a successful implementation will rise.

More precisely, system quality refers to the performance characteristics of processing information, in which the system should be flexible, scalable and capable to integrate data (Ariyachandra and Watson (2006), Delone and McLean (1992, 2003)). Information quality discusses the precision of the information generated through the system, also the completeness, relevance, consistency, and usefulness of the latter (Ariyachandra and Watson (2006), DeLone and McLean (1992, 2003)). To finalize, system use is defined by the consumption of the output of the information system (DeLone and McLean, 1992, 2003). A BI implementation presenting high infrastructure and process performance can lead to the next step of the model, the assessment of the benefits of the BI implementation (Hwang and Xu, 2008).

4.4. Evaluation Phase

This phase is the where the benefits from BI implementation are assessed. This perception of benefits as explained by Yeoh and Koronios (2010) becomes part of an interactive and dynamic business-driven cycle to support the business needs that evolved in such implementations in order to improve the BI system. This way, one should consider BI implementation as a continuous and evolving cycle, always in constant evaluation and evolution of the information and user feedback. In each cycle is carried out evaluation of the existing BI solutions and modification/optimization according to the latter. So to speak, once the implementation is complete, it does not mean that the problems with the implementation ended. The success of a BI system undergoes monitoring and identification of problems by the end-users as well as modifications where and when needed.

The failure of some ICT projects is due to the lack of a strategic framework for the implementation (Raymond et al., 2005). Different SMEs clearly have different attitudes towards the adoption of BI. SMEs should look at BI as a way to maximize their potential. In order to minimize the risk of BI adoption they should develop appropriate strategies towards this implementation and have a structured framework for the implementation and utilization of BI solutions. There is no generic model for BI implementation success and, on the whole, the implementation of IT systems is filled with conflicting results. One reason for these results is that different implementation processes are unique in their own way. Nevertheless, the aim of this theoretical framework is to guide SMEs in the process of BI implementation. In so doing, it contributes to increase the so far limited literature on SMEs BI adoption. This theoretical framework should be followed for implementing BI in SMEs as an enabler for their competitiveness.

Chapter 5 - Analysis and Results

5.1. Validation Approach

This chapter discusses the outcomes of the interviews conducted during this study. The purpose of these interviews was to validate the Theoretical Framework and to determine which CSFs are the most important for SMEs. The data gathered during this study was collected through interviews with BI experts from different organizations with the aim to validate the proposed theoretical framework. BI practitioners and experts from the Netherlands, Portugal and Spain were interview, as my contact network covered only those

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countries (Figure 6). All the interviews were documented in terms of BI, the company background, critical success factors, the four stages of the framework and overall analysis.

Figure 6 - Responses by location

The practitioners and experts were interviewed in regards to their experience of BI and their opinion on the model. They demonstrated different opinions/feedback on the framework. Also, some quantitative questions were asked during the interview in order to gather more clear measures on the importance of the different stages and the most important characteristics in this process. The Revised Theoretical Framework was updated to reflect these findings.

5.2. Analysis and Results

The organizations in which the respondents are inserted are organizations which particularly work in the BI market, which gives credibility to the results. From the 12 different organizations, 5 were SMEs which improve the results obtained, as these practitioners know how this process unfolds. The rest of the interviewees are from renowned organizations such as Capgemnini, SAS, Nimble and CGD (Table 2, Appendix II). The sample interview questions used during these interviews is on Appendix II, as well as the interview‟s minutes. The interviews were structured in order to follow all the phases of the model. First some general questions on the model were asked and then more specifically for each stage of the framework.

5.2.1. Initiation Phase

Initially, during the interview, the respondents were asked on the three different perspectives and also, to rank the importance of the three according to a Likert scale (1- Absolutely disagree to 7- Absolutely agree). Before initiating further analysis on these 3 dimensions, a reliability test was performed Cronbach‟s Alpha which is an indicator of the internal consistency among the 3 variables. In this study, the Cronbach Alpha was 0,752 (Table 3), meaning a good internal consistency. Through the analysis of Table 4 one can understand that Process Perspective was considered the most import by the BI experts.

Table 3- Reliability statistics for the three dimensions on the Initiation Phase

Cronbach's Alpha Cronbach's Alpha Based on Standardized Items

N of Items

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21 | P a g e T h e I n f l u e n c e o f B u s i n e s s I n t e l l i g e n c e i n S ME s Table 4 - Average of each dimension

Organization Perspective Process Perspective Technology Perspective Mean 6,22 6,33 6,00

To further deepen the study on the critical success factors among the 3 dimensions (Organizational, Processes, and Technology) present at this phase, the interviewees were asked to rank the CSFs of each dimension according to their importance in the initiation of a BI process implementation, on an online survey (Online surveys platform: KwikSurveys.com). CSFs were identified from a study in literature, conducted by Olszak and Ziemba (2012) and some critical thinking and reasoning on the subject was carried. All experts identified critical success factors relevant to Business Intelligence. The semi-structured interview approach assisted in the identification and discussion on the CSFs.

The tool used to drive this part of the research set as default the most important CSF to the value of 1 and the least important to 6. This way, when evaluating the results, the CSF that has the lowest score (nearest to 1) is the most important one. This is only valid when interpreting the results from the online survey.

The following tables present the weighted average of the CSF presented at each dimension. The weighted average was calculated taking the quotient between the sum of the multiplication of each scale value by its frequency of choice, and the sum of all the weights (the sum of all values in the scale).

Organization Dimension

Table 5 shows the weighted average of each CSF presented in this dimension. The data that originated this table is in the Appendix III, Table 6, and here the results are shown according to the importance of CSF. One can easily understand that BI experts acknowledge the CSF “Clear business vision and plan” as the most important in the Organization Dimension to achieve a successful BI implementation, with the weighted average of 2,32. On the other hand, “Past experience and cooperation with BI supplier” was perceived as the less important CSF in a BI implementation, with a total of 5,74 out of 6. The interviewees were asked to rank the most import factors from the framework and then prompted to identify other CSF associated. Accordingly, newly Business Intelligence critical success factors were identified and included in the Revised Theoretical Framework. These newly identified CSF include the following: committed senior management and sponsorship support, support from business and employees, powered skilled staff/team/managers and balanced team composition.

Table 5 - Organization Dimension

Committed senior management support and sponsorship. This CSF has been acknowledged as one of

the most important factors for a successful BI implementation. Previously called “Support from senior management”, it was renamed after the first round of interviews, because it was pointed out by some interviewees that the sponsorship is also a key factor. Beyond the consisted and committed support from senior management, the interviewees mentioned the importance of considering sponsorship (also provided by the senior management), hence the addition of sponsorship to this CSF. The consistent support and sponsorship by the senior managers makes the process flow more gradually and more securely as the necessary resources are guaranteed by the sponsor. Also, there were some suggestions to have sponsors from

Critical Success Factors Weighted Average

(1- most important, 6 – less important)

1- Clear business vision and plan 2,32

2- Support from senior management 2,37

3- Adequate Budget 3,47

4- Competent BI project manager 3,53

5- Skilled staff/team/managers 3,58

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both IT and Business, in order to have an ideal team. The sponsor should be someone from senior/top management who has interest and need to see this project pursued.

Support from business and employees. Throughout the interviews was stated that during a process of

BI implementation may arise through this transaction. For instance, in general there is a transactional team and a BI team, which are separated. The transactional team is usually composed by people from the business side and averse to the changes that a BI implementation undertake. The interviewees expressed that support from business side and every employee is critical to ensure that the strategic vision is directing the implementation. Also, a BI initiative should involve the organization as a whole and not only the IT staff. Recommendations on this point were made to include different business domains.

Powered skilled staff/team/managers. This CSF is based on one presented in the last chapter called

“Skilled staff/team/managers”. Most participants believe that having the right team to make the right decisions is always important. More importantly, this staff should be powerful enough to make the decisions. This team of powerful and skilled employees should ensure that the process of change management and implementation of BI is clear.

Balanced team composition. Most experts recommended that a balanced team composition is a key

factor to a successful implementation. This way is possible to conduct the implementation according to business needs. This enables the end-users, which are mostly from the business side to be closely related with the business objectives and processes incorporated in the implementation.

Process Dimension

In order to analyze the degree of importance of the different CSF present on the Process Dimension, the respondents were asked to rank the CSF identified from literature. In this sense, Table 7 presents the results obtained from this sort. The data that originated this table is in the Appendix III, Table 8, and here the results are shown with the critical success factors ordered according to their importance. From this we can infer that “Well defined business problem and processes” is clearly identified as the most important CSF, with 1,84 (out of 4); followed by “Well defined users‟ expectations” and “Adjusting the BI solution to users‟ business expectation”. The least important CSF in this dimension is the “Effective change management” with 3,05 in 4. The newly CSFs suggested for this dimension were the following: Good risk management, Communication, related with the study conducted by Prof. Kooter in the field of change management and will be further explained. Also, Effective participation of the final users of the BI system (important in the conception phase, testing and roll-out phases of the project) was mentioned as a remark to this phase.

Table 7 - Process Dimension

Critical Success Factors Weighted average

(1- most important, 4 – less important) 1-Well defined business problem and processes 1,84

2-Well defined users‟ expectations 2,58

3-Adjusting the BI solution to users‟ business expectation

2,58

4-Effective change management 3,05

Good risk management. The management of the risks inherent to such implementations is really

important as this type of implementation as it entails huge risks, for instance, the resistance of employees to change. This usually happens when their importance and usefulness is not clearly communicated. Also, when the quality of the data or user adoption is poor, et cetera. Implementing and managing BI tools effectively can be risky. A suitable preparation for this type of events can save large amounts of time and money.

Communication. This CSF was suggested during the interviews and supported by the study of Kotter

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These take years to be fully accomplished. The communication in this process of change is really important for employees to understand their importance. For this reason, the manager should use every possible way to communicate the new vision and strategies to follow. Also, managers need to teach new behaviors by example.

Effective participation of the final users of the BI system. Having the participation of the end users in

the process of implementation may ease their resistance of the end users to the new tool. Many experts stated that there is a lot of resistance by the employees to new systems. Typically, the old system is not good but the new one is even worse. This happens because employees do not know how to use it; therefore, including the final users in this process may ease the change.

Technology Dimension

In order to analyze the Technology Dimension several CSFs were used; the respondents were asked to rank these. Table 9 summarizes the results of Table 10 Appendix III, with the critical success factors ordered according to their importance. In Table 9 a clear view on the level of importance from each of these CSF is presented. “Data Quality” was appointed as the most important CSF in this perspective, followed by “Integration between BI system and other systems” with a weighted average of 2,58 out of 5. The experts believe that “The lack of BI flexibility” is the least important CSFs to consider, with 4,26 in 5. In this dimensions, no expert suggested new CSFs.

Table 9 - Technology Dimension

5.2.2. Set-up Phase

The aim of this step is to orient the solution of BI with the business, with the processes that were previously implemented in the organization, and with the staff/employee. The latter are the most directly affected by such solutions, since although the decision is made by top managers, the operational part and all departments are affected and influenced by it.

A reliability test, Cronbach‟s Alpha, was also conducted for this stage on these 3 factors. In this study, the Cronbach‟s Alpha was 0,683 (Table 11), meaning an adequate consistency. Through the analysis of Table 12, one can find that business was considered the most important (6,21 out of 7) by the BI experts. However, the difference between the 3 factors is not significant and all of the three were considered important, with a mean higher than 6.

Table 11 - Reliability statistics for Set-up Phase Cronbach's Alpha Cronbach's Alpha Based on

Standardized Items

N of Items

0,683 0,711 3

Critical Success Factors Weighted average

(1- most important, 5 – less important)

1- Data quality 1,84

2- Integration between BI system and other systems 2,58

3- Appropriate technology and tools 3,00

4- “User friendly” BI system 3,32

The Influence of Business Intelligence in SMEs