Investigating The Implementation Of Business Intelligence Systems In SMEs In Zimbabwe
Tittle Investigating The Implementation Of Business Intelligence Systems In SMEs In Zimbabe
University Of Zimbabwe
Name Of Student: Program:
Forward Chigaro
Business Studies And Computing Science (BSCT)
Supervisor: Mr O. Kufandirimbwa
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Investigating The Implementation Of Business Intelligence Systems In SMEs In Zimbabwe
Abstract Implementation of a business intelligence (BI) system requires considerable resources. There is an imperative for a CSF approach to enable BI stakeholders to focus on the key issues that lead to successful BI systems implementation. Companies are increasingly focusing their information systems efforts around Business Intelligence (BI) solutions. The benefits realized from BI vary significantly from company to company. BI systems are now being used as extensions of Enterprise Resource Planning (ERP) systems as they consolidate, transform and analyze the vast amounts of data generated by the firm. The previous literature, focused on big firms and traditional implementation of Business Intelligence solutions, highlighted the importance of understanding the key factors in successful projects. In the past few years, a new delivery model for Business Intelligence software has taken place: the cloud computing. This research aims to identify and analyze the CSFs of BI in SMEs. I restricted the study to SMEs, since historically the business intelligence market has been driven by big companies who had the resources to purchase these expensive IT solutions.
ACKNOWLEDGEMENTS I would like to express my utmost gratitude to my supervisor Mr. O. Kufandirimbwa, who gave me an opportunity to work with him and develop a research in Business Intelligence Solutions. The field of Business Intelligence(BI) is truly an interesting one, that offers multiple paths to investigate. I chose my niche in two ways: from one side, I restricted the study to SMEs, since historically the business intelligence market has been driven by big companies who had the Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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resources to purchase these expensive IT solutions. Finally, I would wish to extend my deepest thanks to the following people Âľy classmates who gave me valuable suggestions during the seminars and the class discussion and all the companies that took the time to participate in this study. Without them the completion of this disserttation would have not been possible.
Table of Contents Chapter One.......................................................................................................................1 Introduction...............................................................................................................................2 Background...............................................................................................................................................3 Motivation Of The Project Problem Statement
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Significance Of The Project Research Aim
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Research Objective Justification
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Chapter Two: Literature Review....................................................................................10 Introduction.............................................................................................................................10 Literature Of Business Intelligence.........................................................................................11 Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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Investigating The Implementation Of Business Intelligence Systems In SMEs In Zimbabwe Chapter Three: Methodology Introduction
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Research Approach
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Sample Selection
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Data Collection 25 Chapter Four
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Data Analysis
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Chapter Five: Findings And Conclusions 40 Introduction
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Findings
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Discussion Of Findings 49 Implications And Recommendations
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Implications For Future Research
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References
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Appendix 1: Questionnaire For The Interviews 54
Chapter One Introduction Enterprise business intelligence is one of the most important keys to be able to outstand in today’s economy. However, success no longer means accumulating the most important information, but rather knowing how to effectively use and manage this information. The business intelligence (BI) market has been continuously experiencing growth as vendors keep on reporting substantial profits (Chen et al. 2012). In a survey of 1400 CIOs, BI featured in the list of top ten priorities(Gartner 2009). 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
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also seeks to define a set of Critical Success Factors that influence the BI implementation success for SMES in Zimbabwe.
Background 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 amount of data available for analyses is growing relentlessly and IBM estimates 90% of the data in the world has been created in the last three years (IBM research, 2013). In the last thirty years, storage space has been endlessly increasing here as its cost has followed the opposite trend (Storage Trend study). Nowadays, more and more businesses are realizing the massive potential that lies in their data. Potential that can be leveraged to make better decisions, offer more value to both customers and shareholders, and discover patterns that could be “disruptive” (Scholz et al., 2010). The discipline that specializes in turning data into useful information is called Business Intelligence (BI). Why is it important for the economy of a company to have better information? Answering this question is crucial, as remarked by other authors (Nyblom et al., 2012; Watson and Wixom,2007). Having access to the right information at the right time increases the likelihood of taking better decisions (Yeoh and Koronios, 2010). The importance of any IT solution can be measured in terms of how it affects, directly or indirectly, the two aforementioned metrics (Poston & Grabski, 2001). As a consequence, even the benefits of a BI system are measured in terms of “increased revenue” or “decreased costs”. Examples of benefits are the acquisition of new customers, upselling/cross-selling techniques, optimization of resource allocation, lowering operating costs and improve customer service (Vodapalli, 2009). Most of the benefits however, are difficult to quantify in terms of revenue or cost, since some of them are hidden behind the multiple activities characterizing an organization. All of these benefits are named “Non-Financial”. Due to the fast development of new technologies, the Business Intelligence market is changing rapidly, forcing vendors to adapt their offerings to the customers’ needs. As the amount of data available to companies has been substantially increasing in the past years, the need of suitable Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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software tools that perform the right analyses became essential, even in the small and medium sized business' environment. The previous literature, focused on big firms and traditional implementation of Business Intelligence solutions, highlighted the importance of understanding the key factors in successful projects. In the past few years, a new delivery model for Business Intelligence software is taking place: the cloud computing. To date, key factors for adopting cloud Business Intelligence in small and medium sized enterprises (SMEs) have not been systematically investigated. Existing studies have rarely considered these arguments and we lack of a proven framework. Historically, the BI systems have been mainly adopted in large and multinational enterprises (Wong, 2005) which could afford the hefty cost required in terms of money, expertise and capabilities. As remarked by other scholars (Hwang et al. 2004), the resources necessary to implement a traditional BI tool are not available in most SMEs. In Zimbabwe most of SMES are undercapitalised due to the harsh economic situation that the country is facing. Bergeron reports similar findings and suggests that conventional BI systems would not meet the needs of SMEs (Bergeron, 2000). Furthermore, despite all the precautions taken, the enormous failure rate that characterizes BI projects, over 50%, (Beal, 2005) does not encourage SMEs to invest in these risky activities. Although major organisations have led the way in introducing and implementing Business Intelligence solutions, the recent increase of globalization, competition and the amount of data to be processed has been forcing SMEs to evaluate the purchase of BI tools (Wong, 2005) and SMES in Zimbabwe are no exceptions since most of them are aiming to go global . These software applications do help a small business compete with larger ones, increase market share or provide insights and patterns that otherwise cannot be seen (Grabova et al., 2010).
Motivation of the project I have always been inclined to know more about business side of the technological aspects especially in IT. This project has provided me a wonderful opportunity to explore both sides of the coin namely business and IT. The idea of knowing more about these two aspects, especially how they can interlink, exchange and wonderfully overlap each other’s functionalities by mutual co-operation, overall, this has been a wonderful journey. Most IT systems are very expensive because they are performance oriented,
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Investigating The Implementation Of Business Intelligence Systems In SMEs In Zimbabwe they don’t focus much on customer needs. IT business alignment is a key motivation for the researcher since it helps developers to be customer oriented so as to make IT systems effective in the eyes of the users.
Problem Statement In the last few years, uncertain and turbulent economic conditions in Zimbabwe have forced companies, small as well as big ones, to find ways for streamlining operations and cutting costs in many areas. Moreover, the increase in data volume calls for an efficient way to manage the information within an organization, especially of a small and medium size where the use of Information Technology has consistently lag behind. The advent of cloud computing could represent a breakthrough for the IT segment, since the advantages brought in by this technology are particularly appealing to SMEs, and it potentially provides a solution for the issues abovemetioned. Business Intelligence market currently represents only 3% of the total BI turnover and the adoption rate among SMEs is still low (ICT EXPO 2014, HICC). A variety of factors might explain this poor result but the major one is a lack of knowledge on what happens specifically in the area of Business Intelligence for SMEs.
Significance of the project This project explores various aspects of BI implementation and the CSFs impacting the implementation process. This project is expected to contribute both in theory and in practice. In theoretical terms, this project is expected to: 1. Add and contribute to the already existing literature in the area of BI, in particular towards CSFs in the implementation process. 2. Identify the issues and criteria which determine the success of a BI implementation. 3. Examine previous literature, if any, available on CSFs of BI systems implementation and compare them with emerging CSFs obtained by first hand data available by means of interviews and thus extend significantly the literature on BI implementation. 4. Validate and extend knowledge of current CSFs in BI systems.
In practical terms, this project is expected to: Identify the CSFs and associated contextual elements that impact on BI systems
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Investigating The Implementation Of Business Intelligence Systems In SMEs In Zimbabwe implementation, so enabling stakeholders to better use their scarce resources by focusing on those key areas that are most likely to have a greater impact
Research Aim The objective of this project is to find out the factors which are very crucial in turning any business intelligence implementation into success in SMES in Zimbabwe thus forming success criteria ad to analyse theses CSFs. These criteria thus form base of any BI implementation and when handled properly can make it successful.
Research Objective •
To identify critical success factor for the implementation of BI in SMES in Zimbabwe
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To understand the real needs of SMEs in terms of managing the information, through the adoption of BI systems, in a more comprehensive way.
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To provide a solid ground for future research by providing a base of information about BI adoption in SMES in Zimbabwe.
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To develop a CSFs framework that can be used by BI practitioners in better using their scarce resources by focussing on those areas, which need more attention.
Justification This research is justified by the need to investigate the gap in SMES in Zimbabwe in adoption of BI since most of the firms are still lagging behind in implementation BI Solutions. This research is also justified by the need to understand why the SMES are standing at this position
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in this modern era of technology. Lastly, this research is necessary since it will establish the critical success factors necessary for the implementation of BI in SMES in Zimbabwe.
Chapter Two Literature Review Introduction This chapter presents literature relevant to this study. It reviews the existing evaluation criteria and Critical Success Factors proposed by various authors in the literature. More specifically, the chapter begins by giving an overview of Business Intelligence. It then goes on to give details of the works done before and related to this research and the basis of this research and other sources from which I developed my own research.
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Overview of Business Intelligence Evolution of business intelligence Business Intelligence is a term that has roots long back in the past. It is commonly agreed the term Intelligence has been coined the first time by IBM analyst H.P. Luhn. He defined it as “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal” (Luhn, 1958). Therefore, the first decision-support systems were born and further developed in the following years, becoming important IT solutions for supporting the decision-making process. In 1989, Howard Dresner proposed the widely accepted definition of Business Intelligence, still used today; “concepts and methods to improve business decision making by using fact-based support systems” (Watson and Wixon, 2007).
Business Intelligence Systems According to Olszak & Ziemba (2007), Business Intelligence Systems provide a proposal that faces needs of contemporary organizations. Main tasks that are to be faced by the BI systems include intelligent exploration, integration, aggregation and a multidimensional analysis of data originating from various information resources. Systems of a BI standard combine data from internal information systems of an organization and they integrate data coming from the particular environment e.g. statistics, financial and investment portals and miscellaneous databases. Such systems are meant to provide adequate and reliable up-to-date information on different aspects of enterprise activities. BI system when implemented gives the ability to access, use and share data and information in an efficient and relevant way that helps improve business performance. Business intelligence capabilities empower employees to: 1. Align day-to-day operations with overall company strategy and objectives 2. Identify and understand the relationship between business processes and their impact on performance Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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3. Access information relevant to specific user roles and responsibilities 4. Analyse data from documents and spread sheets in an easy way 5. Gain contextual insight into business drivers 6. Monitor the vital business indicators that are needed to move an organization forward such as: o Current status and trend of essential financial ratios o Effectiveness and profitability of sales channels o Crucial operational metrics. In short, business intelligence helps companies gain a comprehensive and integrated view of their business and facilitate better and more effective decision-making (NAV, 2007).
Drivers of BI Implementation Gone are the days when key decision makers in the business organizations used to rely mainly on their instincts or gut-feelings to take important decisions. Business Intelligence has now started to emerge and “is playing a key role in business decisions by providing proven solutions to help leverage best business practices to deliver the timely granular intelligence needed to create increasingly effective business strategies, continuously strengthen competitive positions, and steadily improve revenue streams” (Peter Callaghan, 2005). “BI has a reputation for being a resource sink that delivers reports almost no one reads. It doesn’t have to be that way. And you can no longer afford to let it be” (Gruman, 2007). Gruman also says, by treating BI as a set of technologies, most organizations veer off track, building ever-more-complex systems that fail to meet user needs – while what’s really needed is a better understanding of the underlying data and business requirements. Benefits of Business Intelligence The benefits of BI range from minimising risk to making smarter decisions, to saving time and money, to getting a jump on competition (Sharp 2004). BI can eliminate a lot of guesswork within an organization, enhancing communication among departments while coordinating Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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activities and enabling companies to respond quickly to changes in financial conditions, customer preferences and supply chain operations (Ranjan 2008; Atre 2008; Watson and Wixom 2007). Many authors are in agreement that BI saves time for the data supplier and users because of more efficient data delivery. As such, it creates better and more focused marketing as well as enhanced relationships with customers and suppliers (Ranjan 2008; Atre 2008; Watson and Wixom 2007). According to Ranjan (2008), firms have recognised the importance of business intelligence and that it has arrived for the masses. Some of them are listed below: • With business intelligence, organizations can track their most profitable customers and the underlying reasons for those customers’ loyalty. • Employees can now easily convert their business knowledge via the analytical tools to solve many business issues. • Analyse potential growth customer profitability. Determine what combination of products and service lines customers are likely to purchase and when.
Challenges Faced in Business Intelligence Increasingly organizations are realizing that there is more to BI than simply employing technology. Business intelligence requires many technologies, tools and needs to interoperate with many enterprise applications and systems. More to the point, there is a need for a comprehensive, strategic approach to BI that addresses human capital, knowledge processes and culture. There are many challenges to make them work together seamlessly. Several authors claim that the data required and time and effort necessary to acquire the necessary data to ensure their accuracy is often underestimated (Miller et al. 2006). This often results in user requirements not sufficiently met and thus the analysis is repeated with different parameters. Data issues are typically the leading cause of failure and the most costly element of BI implementations. Both Atre (2008) and Cui et al. (2005) state that it usually takes a lot of Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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time, resources and effort to identify, map and create necessary rules and processes to ensure that the data are used consistently and accurately across the organization, promoting a single version of the truth. Resolving data quality issues requires communication and working together with various groups and experts to resolve the root causes and underlying issues. Where BI is concerned, collaboration is not only restricted to departments within an organization, it requires integration of knowledge about customers, competition, market conditions, vendors, partners, products and employees at all level. Thus, it is very important to highlight that several authors have stressed on the importance of nurturing a cross organizational collaborative culture in which everyone grasps and works towards the strategic vision (Miller, Brautigam et al. 2006; Azvine, Cui et al. 2005; Atre 2008). Building on a research that was carried out by Microsoft, it has been found that due to the complex nature of IT projects, BI deployment timelines frequently far exceed initial expectations, often taking many months and in some cases, years to implement. As a result, longer implementations require more resources allocated and increased pressure on project budgets, not to mention the challenge of assessing the deployment’s return on investment (ROI).
Critical Success Factors Certain areas need to perform at satisfactory levels for a system or organization to run successfully in competitive conditions. These areas thus become important ingredients of a successful system and hence called Critical Success Factors. Most importantly these factors are not measurable. According to Rockart (1979), CSFs are the limited number of areas in which satisfactory results will ensure successful competitive performance for the individual, department or Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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organization. CSFs are the few areas where “things must go right” for the business to flourish and for a manger’s goals to be attained. CSFs are the characteristics, conditions or variables that can significantly impact on the success of a firm competing in particular industry given that the variables, conditions and characteristics are well sustained, maintained or managed (Leidecker & Bruno, 1987) and identifying them can help to clarify the nature and scope of resources that must be gathered to permit the project team to concentrate its efforts on priority issues rather than wasting time considering what the available technologies will allow (Greene & Loughridge, 1996). A set of CSFs identified for the development of any major information system, such as a BI system, is fundamentally different from the set of interlinked detailed tasks which must be accompanied to ensure a project’s completion (Dobbins, 2000; Yeoh, 2008). Hence, a mere ensuring of successful execution of CSFs may not guarantee success of a project implementation but surely it can give a prolonged run to the project. 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 decisionmaking 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 Guarda et al. (2012) 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 Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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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 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).
Critical success factors for implementation of BI Many BI projects fail and some reasons behind this include a relatively low level of knowledge in organizations about the opportunities and benefits of BI systems, as well as their critical success factors(CSFs). 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 Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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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, Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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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). 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. Critical Success Factors in BI have been treated by many authors (Eckerson, 2005 and they could be considered as “a set of tasks and procedures that should be addressed in order to ensure BI systems accomplishment” (Olszak and Ziemba, 2012). In this paragraph, those factors are reviewed and particular attention will be paid to the ones related to small and medium sized companies. Table 3 summarizes the literature on the argument: Organization perspective
Process perspective
Technology perspective
Adequate budget
Well defined business
Integration between BI system
Support from senior
processes
and other systems (Desktop
management
and issues
applications, software..)
Competent BI project manager Well defined users’
Data quality
Sufficient skilled staff/team
expectations
BI flexibility and
Clear business vision and plan
Adjusting the BI solution to
responsiveness on users’
Past experience and
users’ business expectations
requirements
cooperation with a BI supplier
Understanding how and when
Appropriate technology and
Rolling out training initiatives
data will be delivered
tools User-friendly BI system Delivers actionable Information
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Fig 1: Yeoh and Koronios model for CSFs for BI Implementation
The bulk of studies on Critical Success Factors have been heavily focused on large companies and it is believed that not all the factors can applicable to the small and medium sized enterprise environment (Wong, 2005). These studies analysed traditional and expensive BI projects, commonly characterized by long implementation periods; whereas the typology of BI systems I am going to focus on this study requires a minimal implementation effort (Sheikh, 2011). Given this premise, the use of past research on critical success factors seems inappropriate for the Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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purpose of this study. There are substantial differences between cloud and traditional BI implementation, in terms of resources, complexity, and architecture. In reality, those researches offered me valuable foundations applicable throughout the whole research. Certainly, not all factors present in the table 3 will appear in the final framework and some of them have been adjusted to fit the context of this investigation. Elements such as clear business vision and plan, support from senior management, well defined business processes and issues, sufficient skilled staff are typical of long IT projects, which require multiple interactions between the client and the vendor, given the amount of resources required to roll-out the initiative. The overall process of adopting a cloud Business Intelligence solution is less complex and these factors do not play a major role. Another example is Rolling out training initiatives, which represents a customer support activity 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 Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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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).
BI Architecture As per David Loshin (2008), the objective of the business information analysis was, and still is, the ability to evaluate how efficiently the organization operates and subsequently seek out opportunities for exploiting actionable knowledge. This notion of performance management and improvement through reporting and analytics has evolved into what is commonly referred to as “business intelligence.� While many front-end presentation, reporting, and visualization products help in communicating the results of this analysis, business intelligence remains inextricably linked to the technical infrastructure of the data warehouse. The tactical challenges for providing a framework for business intelligence involve collecting data from disparate distributed systems, consolidating that data into a centralized model, and organizing the data to feed the front-end applications for driving business analysis and reporting. As is seen in Figure 2.1, this requires data connectors to extract data from the sources, integration tools to assimilate and consolidate the data, servers and storage used to house the data warehouse and its database management system, all feeding the front-end application architecture for reporting, dashboards, data mining, and dimensional analysis (David Loshin, 2008).
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Investigating The Implementation Of Business Intelligence Systems In SMEs In Zimbabwe Components of BI Architecture The components of the above technical architecture are described as follows (kJube, 2002): 1. Source systems: It consists of all the data that an organization might require for the analysis. It may consists of data from a source database, external data feeds from the partners in XML format, data from the click-stream analysis captured from the organizational website based on customer behavior, a connection from application server level ERP connection, data from Excel files and many more sources. (kJube,2002). 2. ETL tools: ETL is a process to extract data, mostly from different types of system, transform it into a structure that’s more appropriate for reporting and analysis and finally load it into the database. Data Quality: According to Passionned Nederland B.V (2009), today ETL does much more than what it is known for. It also covers data profiling, data quality control, monitoring and cleansing, real-time and on-demand data integration in Service Oriented Architecture (SOA), and Metadata Management. Data mining: As per Kurt Thearling (2009), Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally consuming a lot of time to resolve. Data staging: In the data warehousing process, the data staging area is composed of the data staging server application and the data store archive (repository) of the results of extraction, transformation and loading activity. The data staging application server temporarily stores and transforms data extracted from OLTP1 (Online Transaction Processing) data sources and the archival repository stores cleaned, transformed records and attributes for later loading into data marts and data warehouses (Firestone,
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Investigating The Implementation Of Business Intelligence Systems In SMEs In Zimbabwe 2002). Operational Data Store (ODS): According to Inmon (1998), an ODS is an integrated, subject-oriented, volatile (including update), current-valued structure designed to serve operational users as they do high performance integrated processing. The essence of an ODS is the enablement of integrated, collective on-line processing. An ODS delivers consistent high transaction performance – two to three seconds. An ODS supports on-line update. An ODS is integrated across many applications. An ODS provides foundation for collective, up-to-the-second views of the enterprise. And, at the same time, the ODS supports decision support processing.
Multi-dimensional data warehouse: As per kJube (2002), the multi-dimensional data warehouse is the core of the business intelligence environment. Basically it is a large database containing all the data needed for performance management. The modeling techniques used to build up this database are crucial for the functioning of the BI solution. Typical characteristics of multi-dimensional data warehouse are that it contains invariant, integrated, and atomic data. A good understanding of multidimensional modeling techniques is necessary for better understanding of such data warehouse. Data mart: A data mart is a smaller version of a data warehouse, typically containing data related to one functional area of the firm or with limited scope in some other way. It can be a useful step to a full-scale data warehouse (Rai & Storey, 2001). OLAP: According to Rai & Storey (2001), Online Analytical Processing or OLAP as it is popularly known, is a technology for real-time ad-hoc data access and analysis. It is a vendor-driven solution to meet business needs. The OLAP software may require scheduling, queuing, monitoring capabilities to handle thousands of users. Semantic layer: According to Vezzosi (2009), Semantic layer reduces time to Business Intelligence. It provides a secured access to data with simple business terms. It can open access to any client and user, data type. It can combine with Data Federation for more agility. Semantic layers acts as a intermediary
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Investigating The Implementation Of Business Intelligence Systems In SMEs In Zimbabwe between presentation layer, which normally is the user interface for all end-users, business analysts, and administrators and the data storage area. Single point of information access: As a BI solution can generate various types of outputs such as reports, dashboards, scorecards etc. These outputs may sometimes contain tags, comments, interpretations etc which is in multiple forms and needs to be stored in a storage location which can be attached to these reports and hence may consume huge amounts of data storage space. So it is often advisable to have a single point of access through a portal solution, where it is also possible to attach a business user, scope of the report along with accessibility options (kJube, 2002).
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Chapter Three Methodology Introduction This chapter will offer a detailed explanation about the research process as a whole, the strategy and approach adopted throughout the study and the sample selection. Overall, the aim of this chapter would be to describe the measures adopted to assure reliability and validity. 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 in Zimbabwe and enable them to gather business value to become more competitive in today’s market. A theoretical framework is proposed and validated trough a qualitative and quantitative study.
Research Approach My approach towards this research has been mostly qualitative rather than quantitative. The researcher contacted professionals in the IT sector and conducted 7 personal interviews (questions in Appendix 1). Interview method was used as part of the research methodology for this project because that would assure me that the data obtained is of highest quality and is firsthand. That would also give me valuable insights into how the BI industry is functioning in Zimbabwe and at the same time get an impression of prospective employers. But due to many constraints (time, finance, availability of respondents etc) conducting interviews for many people was not feasible. Secondary data was gathered from publications of the Ministry of SMEs and other journals in the University Library. Internet was also used to access journals and publications from other sources and its citation is well acknowledged in this research. A basic Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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theoretical framework of CSFs of BI Implementation developed by Yeoh and Koronios in 2011(fig 1 in literature review) was used for the basis of the research from which the research developed the CSFs of BI Implementation for SMES in Zimbabwe.
Sample Selection Sample selection for this research has been randomly identified by searching on professional networking websites LinkedIn.com, Facebook.com and directories like yellowpages and requests for interviews has been sent to people after going through their profiles and on confirming that their current work location is in Harare. Out of ten requests sent randomly through the LinkedIn network and facebook since email address where hard to get, only 7 responded positively. Preference was given to firms with offices in Mt Pleasant and Emerald Hill so as to minimise my transport costs since these areas are a walking distance away from the campus.
Data Collection Method Stage 1 literature survey and proposed model
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. A deep understanding of the basic theoretical framework of CSFs of BI Implementation developed by Yeoh and Koronios in 2011 was established and all the data which was to be collected was referenced to this model before further analysis.
Stage 2
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Although time-consuming and challenging to secure appointments, overall interview process for this research has been very satisfactory. The researcher used a Delphi technique in which two rounds of interviews were conducted. The researcher through further consultancy of literature and the supervisor continually developed more understanding of the area of study. Question 21 was added later after the first round of the interviews so there was need to conduct a second round. All the interviews were taken by meeting the respondents personally at their office locations or in public places. The respondents responded well to questionnaires and it was very impressing that all questionnaires were usable i.e. no spoiled scripts. The data from interviews, questionnaires and secondary sources was then put together for analysis.
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CHAPTER FOUR
Data Analysis Analysis of the data recorded during the interviews could be used to understand the CSFs better and to analyze if there are any differences generated from this output to that of results obtained from the literature review. Here are some facts about the interviews taken for this research: •
Although, the researcher tried to have as many interviews as possible. I could only manage total 7 interviews for this research. Only 2 respondents have responded positively when asked whether I could mention their name and the organization they are working. These are Fuzzy Electronics, Kurima Gold and Makomo Resourses. 4 other respondents had no issues mentioning their name in this report but for some reasons did not wish to relate their views with the organization they are working for, hence wanted me to keep their company name anonymous. 1 respondent wanted to keep both the details as anonymous.
•
Average experience of the respondents in IT industry was 9 years. The minimum experience level recorded was of 5 years, while the maximum experience level was of 25 years.
•
Average experience of the respondents in BI field was 3 years. The minimum experience level recorded was of 1 years, while the maximum experience level was of 8 years.
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Αverage number of implementations done by the respondents was 2. While the minimum implementations recorded by a respondent were 1, maximum implementations were 4.
•
Out of the 7 respondents, 2 belonged to the health-care / pharmaceutical / medico technique (as respondent would like to call it) sector of the industry, 3 belonged to the financial services / banking, and 2 from the IT services.
•
Section 1 of the questionnaire is aimed at getting to know the person, his experience, business sector of the organization and the quality of experience on a whole. Section 2 of the questionnaire is aimed at gaining more insight into the respondent’s work related aspects, his views, definitions, type of applications, vendors, tools and architecture they deal in day-to-day work life. Section 3 of the questionnaire deals more with the CSFs trying to extract their views, opinions and priorities as viewed by them with respect to BI implementation. And thus more useful for our analysis. Two CSFs namely, ‘Involvement of top management’ and ‘Involvement of endusers’ have been added to the list of organization perspective as compared to that of list of CSFs from the literature review. As mentioned before, Section1 and Section2 is meant only for knowing and understanding the existing system in organizations of various respondents, I analyzed only Section3 for this research.
The table consists of all the CSFs I have used during my interview process from questions 17 through 21 and have been arranged beneath each perspective I believe they correspond to. The alphabets placed in front of the CSFs separating them by a hyphen correspond to individual CSF and have been used to refer them during further analysis and graphical representation.
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List of CSFs from the questionnaire Source: Developed for this research
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Investigating The Implementation Of Business Intelligence Systems In SMEs In Zimbabwe The researcher analysed the responses of section 3 only for this research as they are most relevant for this research. When the responses from all the respondents are observed for the questions 13 to 16, some factors mentioned by them like ‘Support from key personnel’, ‘Quality of the process’, ‘Project management’, ‘High maintainability’ etc. have already been considered and are placed under respective perspectives they belong to hence those data are ignored. And further questions beyond 16 have been chosen for analysis. While answering the questions from 17 to 19 respondents were asked to rate the CSFs in a range of 1 to 5, where, 1 corresponds to not critical and 5 corresponds to most critical. Hence, higher the total value of a particular CSF, more essential the CSF would be. Similarly, in graphical representation, higher the bars are placed more critical or essential that particular CSF could be considered. Analysis of question 17: Critical Scale Analysis of CSFs in the Organization Perspective Source: Developed for this research
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Critical Scale Analysis of Organizational Perspective Source: Developed for this research
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Analysis of question 18: Critical Scale Analysis of CSFs in the Process Perspective Source: Developed for this research
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Critical Scale Analysis of Process Perspective Source: Developed for this research Analysis of question 19: Critical Scale Analysis of CSFs in the Technology Perspective Source: Developed for this research
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Critical Scale Analysis of Technology Perspective Source: Developed for this research
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Investigating The Implementation Of Business Intelligence Systems In SMEs In Zimbabwe While answering the questions 20 and 21, respondents were asked to prioritize the CSFs in the order of its priority in implementation of a BI system. Hence, the lower value (i.e., 1) given to a particular CSF could mean a higher priority among the other relevant factors. Hence, lower the total value of a particular CSF, that particular CSF could be considered more essential. Similarly, in graphical representation, lower the bars are placed more critical or essential that particular CSF could be considered.
Analysis of question 20: Organization Perspective Priority Analysis of CSFs in the Organization Perspective Source: Developed for this research
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Priority Analysis of Organization Perspective Source: Developed for the research Process Perspective Priority Analysis of CSFs in the Process Perspective Source: Developed for this research
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Priority Analysis of Process Perspective Source: Developed for this research Technology Perspective Priority Analysis of CSFs in the Technology Perspective Source: Developed for this research
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Priority Analysis of Technology Perspective Source: Developed for this research Analysis of question 21:
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Table 4.8 Priority Analysis of all the CSFs with respect to BI Implementation Source: Developed for this research
Priority Analysis of all CSFs Source: Developed for this research
The responses to the above question 21 are for the priority order of the CSFs hence the lowest to highest total value in the previous table and the shortest bar to the longest in the previous Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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graph represent CSFs of decreasing priority order. The priority order for this data can be illustrated with the following Table: Priority Order of CSFs with respect to overall BI implementation Source: Developed for this research
Validity and Reliability As all the data gathered during this research is of first-hand, I consider all the data as valid and reliable to the best of my knowledge.
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CHAPTER FIVE FINDINGS AND CONCLUSIONS Introduction This chapter aims to present and discuss the findings of the researcher’s analysis. Further it discusses what these results could imply followed by the future research prospects in this area to conclude with.
Findings After going through the analysis of data done in Data Analysis, it is hard to understand the results of it by looking at that section. Hence, I have decided to make it easy for common people to understand it by joining the data in second, third and fourth tables for understanding the critical nature of all the CSFs. A table with list of CSFs in order of critical scale is prepared. The last three tables are joined to make it easy to understand the priority order of CSFs when considered with each perspective. After generating these tables an analysis of these tables with that of the priority table generated for the question 21, which is the priority table of CSFs with respect to overall BI implementation.
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Critical Scale Analysis of CSFs from the Perspectives Source: Developed for this research
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Table 5.2 Critical order of the CSFs Souce: Developed for this research
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Table 5.3: Priority Analysis of CSF from the perspectives Source: Developed for this research
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As this analysis is being done for the priority order of the CSFs, the lowest the total value in Table 5.2 has the highest priority order; similarly lower the bar level in the graphical representation gives highest priority to the CSF. The priority order in this case could be given by the following Table 5.4.
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Table 5.4: Priority order of the CSFs Source: Developed for this research
At this stage we have 3 Tables, Table5.2 representing Critical Scale order of the CSFs, whereas, 4.9, and 5.4 both representing Priority Order of the CSFs. Hence to avoid the confusion, the researcher would like to join the tables 4.9 and 5.4 to arrive at a single table for Priority Order of the CSFs. Table 5.5: Analysis of priority order of CSFs in tables 4.9 & 5.4 Source: Developed for this research
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From the data analysis of above table 5.5, given below is the priority order of the CSFs : Tabel 5.6: Priority order of CSFs Source: Developed for this research
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The graphical representation of the following table is illustrated in figure 5.3:
From the above data which is in order of decreasing priority, lowest total value in Table 5.5 represents CSF of highest priority and vice versa; at the same time in the Figure 5.3 bar lines with shortest length are CSFs of highest priority and vice versa. Thus, we can now generate an overall priority order of CSFs with the data we have gained through interview responses, it is tabulated in the following table:
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Table 5.8: Priority Order of CSFs developed from this Research Source: Developed for this research
From the above research, addition of two new CSFs ”Involvement of top management” and ”Involvement of end-users” has been justified as they are placed well above the CSFs like ”Map the solutions to the users” and ”Balanced team composition” which were placed much higher in the CSFs list obtained from the data analysis part of literature review.
Discussion of findings If we compare the above drafted tables we can observe that there is not much difference in the priority order of the CSFs listed in all the tables except that the CSFs ”Map the solutions to the users” and ”Balanced team composition” have been given more importance in the literature review, where as the respondents have placed them in the bottom of the list. Rather amusingly, the newly included CSFs in my interviews ”Involvement of top management” and 70 ”involvement of the end-users” were placed higher above them. Both the literature study and the interview process confirm placing the CSF ”Integrated BI applications” at the bottom of the list.
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Investigating The Implementation Of Business Intelligence Systems In SMEs In Zimbabwe CSFs are those areas of the project that are absolutely essential to its success. Whilst there is no hard and fast rule, it’s useful to limit the number of CSFs to absolute essentials. This helps to maintain the impact of your CSFs, and so give good direction and prioritization to other elements of your project strategy. If we observe the CSFs from literature review, the list is huge which means it becomes impossible to concentrate on the various aspects of the project. Hence, the researcher decided to take top 5 so that there would be a shorter list which could be more meaningful and hence allowing managers to focus more on the project. The list of CSFs thus evolved in alphabetical order is: Business driven methodology & project management Clear vision & planning Committed management support & sponsorship Data management & quality issues Map the solutions to the users Performance considerations and Robust & extensible framework The researcher therefore recommends the managers handling BI implementation aspects to focus on these above mentioned factors to have a prolonged run in keeping the project and process alive.
Implications and Recommendations There are numerous factors that could affect the implementation process of BI system. The choice of CSFs may actually vary depending on which perspective the manager is looking rom and hence the list of tables in data analysis each show credibility of CSFs with a critical scale order and they also show credibility of CSFs in their priority order from each perspective point of view. Table shows priority order of CSFs when viewed from overall perspective of the business. Table 5.1 shows critical scale order of CSFs when viewed from overall business perspective. Table 5.3 shows combined priority order of CSFs when viewed from individual perspective and Table 5.5 shows priority order of CSFs when tables 4.8 and 5.3 are combined together. To make it easy to understand, the researcher recommends managers to follow Table 5.1 when they want to view CSFs in critical scale order and Table 5.5when they want to view CSFs in priority order of CSFs. And from overall BI implementation point of view the researcher recommends them to visit the list of CSFs given in section 5.3 of this chapter. The scope of BIs from literature review shows that they collect information from past events and transactions, analyse the information and produce reports so that managers can make decisions. This means that decisions are made based on the information and
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Investigating The Implementation Of Business Intelligence Systems In SMEs In Zimbabwe data of past events and transactions and the current state of the economy. Information from BIs may fail to be helpful in a turbulent economy of Zimbabwe since information of the past and present may not be relevant in the future. To complete the goal of implementing BIs, developers should enable to predict the next state of the economy by considering the current trends. By considering the GDP, Inflation level, exchange rate, interest levels, industry capacity, poverty datum line, unemployment levels etc, the BI tools should be able to predict the next state of the economy so that managers make decisions that are relevant in the future.
Implications for Future Research During this project the researcher has only recorded the CSFs as observed from various literatures and have then analyzed them. This seems to be a common approach but it does not give practical insight into the implementation aspects which could be more meaningful for the topic. Participation in the implementation process while reading all the variables (both internal and external) would have been a perfect approach for this kind of studies. Hence further research in these lines is recommended. Future researchers can aim on how to expand the scope of BIs so as to enable it to predict the next state of the economy.
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References 1. Adelman, S., & Moss, L. (2000). Data Warehouse Project Management. Saddle River,
NJ: Addison-Wesley. 2. Dobbins, J. (2000). A Generalized CSF Process Model for Critical Success Factors
Identification and Analysis; Thesis. George Washington University. 3. Eckerson, W. W. (2005). The Keys to Enterprise Business Intelligence: Critical Success
Factors. The Data Warehousing Institute. 4. Gartner (2013). Magic Quadrants for Business Intelligence and Analytics platforms.
Accessed March 16, 2015 from http://www.gartner.com/technology/reprints.do?id=11DZLPEK&ct=130207&st=sb. 5. Greene, F., & Loughridge, B. (1996). Investigating the Management Information Needs
of Academic Heads of Department: A Critical Success Factor Approach. Information Research, Vol.1, no.3 , 1-3. 6. Gibson.M, Arnott.D, & Melbourne.A, J. &. (2004). Evaluating the Intangible Benefits of Business Intelligence: Review & Research Agenda. Proceedings of the 2004 IFIP International conference on Decision Support Systems (DSS2004): Decision Support in an Uncertain and Complex World, (pp. 295-305).
7. Gruman, G. (2007). Rethinking Business Intelligence. Infoworld.com. Business
Intelligence Scenario. paper presented at the Gartner Business Intelligence Summit. London: Gartner Research.
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Investigating The Implementation Of Business Intelligence Systems In SMEs In Zimbabwe 8. Koronios, A & ,.Yeoh, W .(2008) .Managing the Implementation of Business
Intelligence Systems: A Critical Success Factors framework .International Journal of Enterprise Information Systems, IGI Publishing , Vol.4, Issue 3 , 79-94. 9. Leidecker, J., & Bruno, A. (1987). CSF Analysis and the Strategy Development Process.
Strategic Planning and Management Handbook , 333-351. 10. Luhn, H.P. (1958). A business intelligence system. IBM Journal of Research and
Development, 2(4), 314-319. 11. Olszak, C. M. and Ziemba, E., (2007), “Approach to building and implementing
Business Intelligence Systems�, Interdisciplinary Journal of Information, Knowledge, and Management, 2, 135-148. 12. Olszak, C. M., & Ziemba, E. (2012). Critical Success Factors for Implementing
Business Intelligence Systems on the Example of Upper Silesia, Poland. Interdisciplinary Journal of Information, Knowledge, and Management, 7. 13. Peter Callaghan. (2005). Business Intelligence: the key to optimizing sales, marketing,
and bottom line results. Maximizer Software Inc.
14. Rockart, J. F. (1979). Chief Executives Define Their Own Data Needs. Harvard Business Review , 85.
15. Watson, H. J, & Wixom, B. H., (2001). An empirical investigation of the factors
affecting data warehousing success. MIS quarterly, 25(1), 17-32. 16. Watson, H. J., & Wixom, B. H. (2007). The current state of business intelligence.
Computer, 40(9), 96-99. 17. Williams, S., & Williams, N. (2007). Critical Success Factors for Establishing and
Managing a BI Program; Excerpted "The Profit Impact of Business Intelligence". DecisionPath Consulting. Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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Appendix 1: Questionaire for the interview
Interviewee’s name: ________________________ Organization: ________________________ Location: ________________________ Date: ________________________ Start time: ________________________ Finish Time: ________________________ Section 1: General Information Please tell me about yourself: 1. Your background a. In what industry do you work? (E.g. IT services, Healthcare, Public Sector, Banking, etc) b. Size of your organization (no. of employees / active users, approximately) c. What is your current position in the organization? d. Whom do you report to? e. Total number of years in the industry? Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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f. Number of years in BI area? g. Number of implementations done? Section 2: Business Intelligence in general 2. How do you define Business Intelligence? 3. Most often than not Business Intelligence is interchangeably used with Data Warehousing and Reporting (Generation of reports), How would you relate BI with Data warehousing and Reporting? 4. What are the primary motives for implementing BI in an organization? a. The problems before implementation b. The benefits perceived after implementation 5. Please describe your organization’s BI system a. Implementation duration and stage (E.g., in-use, partially in-use or planning stage?) b. Primary BI applications or tools used (E.g., Dashboards, Scorecards, Mashups etc) c. Types of business users d. Applied BI vendors e. BI architecture 6. How would you define success or failure of BI implementation? 7. Choose all you wish to include in defining success factors of an BI implementation a. Support to key stakeholders b. Cost savings c. Improved business performance d. Better access to data e. Return on Investment f. User perception g. Number of active users h. Further comments: 8. What is the success rate of your implementations? (optional) a. (Mostly) Failure b. Slightly Failure c. Moderately Successful Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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d. Successful 9. To what extent does the BI implementation contribute to your company’s performance? a. Not at all b. Slightly c. To some extent d. Significantly 10. For how many years has your BI deployment been available? 11. How would you categorize your BI implementation? a. Departmental implementation (E.g. Finance, Marketing, HR, Production etc) b. Unit wide (All Departments of the branch) c. Enterprise-wide implementation (all branches / all countries) 12. If data were available in tables, would you like to work on normalized form of data or data in imensional model and why? Section 3: Critical Success Factors 13. In terms of Organization or Business entity perspectives what do you think may influence implementation of BI system? 14. In terms of Process perspective what do you think may influence implementation of a BI system? 15. In terms of Technology perspective what do you think may influence implementation of a BI system? 16. Are there any other factors you feel important and why? Please rate the following BI perspectives using the scale: 1. Not important 2. Little importance 3. Important 4. Very important 5. Essential / critically important 17. Organization Perspective a. Clear vision and planning 1 2 3 4 5 b. Committed management support and sponsorship 1 2 3 4 5 c. Involvement of top management 1 2 3 4 5 d. Partnership between business community & IT 1 2 3 4 5
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e. Information governance via BI competency center 1 2 3 4 5 f. Involvement of end-users 1 2 3 4 5 18. Process Perspective a. Business driven methodology & project management 1 2 3 4 5 b. Balanced team composition 1 2 3 4 5 c. Usage of iterative prototyping to define requirements & scope 1 2 3 4 5 d. Map the solutions to the users 1 2 3 4 5 e. Change management 1 2 3 4 5 19. Technology perspective a. Robust & extensible framework 1 2 3 4 5 b. Data management & quality issues 1 2 3 4 5 c. Appropriate technology/tools 1 2 3 4 5 d. Integrated BI applications 1 2 3 4 5 e. Performance considerations 1 2 3 4 5 f. User training & support 1 2 3 4 5 20. Please prioritize the following factors with respect to each perspective Organization Perspective [ ] a. Clear Vision and planning [ ] b. Committed management support and sponsorship [ ] c. Involvement of top management [ ] d. Partnership between business community & IT [ ] e. Information governance via BI competency center [ ] f. Involvement of end-users Process Perspective [ ] a. Business driven methodology & project management [ ] b. Balanced team composition [ ] c. Usage of iterative prototyping to define requirements & scope [ ] d. Map the solutions to the users Forward Chigaro Business Studies And Computing Science-Dissertation 2015
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[ ] e. Change Management Technology Perspective [ ] a. Robust & extensible framework [ ] b. Data management & quality issues [ ] c. Appropriate technology / tools [ ] d. Integrated BI applications [ ] e. Performance considerations [ ] f. User training & support 21. Please organize the following Critical Success Factors in a priority order you would like to place them with respect to an BI implementation [ ] a. Clear Vision and planning [ ] b. Committed management support and sponsorship [ ] c. Involvement of top management [ ] d. Partnership between business community & IT [ ] e. Information governance via BI competency center [ ] f. Involvement of end-users [ ] g. Business driven methodology & project management [ ] h. Balanced team composition [ ] i. Usage of iterative prototyping to define requirements & scope [ ] j. Map the solutions to the users [ ] k. Change Management [ ] l. Robust & extensible framework [ ] m. Data management & quality issues [ ] n. Appropriate technology / tools [ ] o. Integrated BI applications [ ] p. Performance considerations [ ] q. User training & support
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