ENSURING THE EFFICIENT UTILIZATION OF BUSINESS INTELLIGENCE

Transcription

1 LAPPEENRANTA UNIVERSITY OF TECHNOLOGY School of Industrial Engineering and Management Faculty of Innovation Management Innovation and Technology Management ENSURING THE EFFICIENT UTILIZATION OF BUSINESS INTELLIGENCE Examiner (1st) Examiner (2nd) Professor Tuomo Kässi Associate Professor Kalle Elfvengren Instructor M.Sc. (Tech) H. H. Helsinki Joel Friman

2 ABSTRACT Author: Joel Friman Subject: Ensuring the efficient utilization of Business Intelligence Department: Department of industrial management, program of Innovation and technology management Year: 2014 Place: Helsinki Master s Thesis. Lappeenranta University of Technology pages, 25 tables, 18 figures and 3 appendices Examiners: Prof. Tuomo Kässi (1 st ), Associate Prof. Kalle Elfvengren (2 nd ) Instructor: M.Sc. (Tech.) H. H. Keywords: Enterprise performance management, business intelligence, data warehouse, data quality, enterprise metrics framework, performance measurement Hakusanat: Yrityksen suorituskyvyn johtaminen, liiketoimintatiedon hallinta, tietovarastointi, tiedon laatu, suorituskyvyn mittaamisen viitekehys, suorituskyvyn mittaaminen In recent years, chief information officers (CIOs) around the world have identified Business Intelligence (BI) as their top priority and as the best way to enhance their enterprises competitiveness. Yet, many enterprises are struggling to realize the business value that BI promises. This discrepancy causes important questions, for example: what are the critical success factors of Business Intelligence and, more importantly, how it can be ensured that a Business Intelligence program enhances enterprises competitiveness. The main objective of the study is to find out how it can be ensured that a BI program meets its goals in providing competitive advantage to an enterprise. The objective is approached with a literature review and a qualitative case study. For the literature review the main objective populates three research questions (RQs); RQ1: What is Business Intelligence and why is it important for modern enterprises? RQ2: What are the critical success factors of Business Intelligence programs? RQ3: How it can be ensured that CSFs are met? The qualitative case study covers the BI program of a Finnish global manufacturer company. The research questions for the case study are as follows; RQ4: What is the current state of the case company s BI program and what are the key areas for improvement? RQ5: In what ways the case company s Business Intelligence program could be improved? The case company s BI program is researched using the following methods; action research, semi-structured interviews, maturity assessment and benchmarking. The literature review shows that Business Intelligence is a technology-based information process that contains a series of systematic activities, which are driven by the specific information needs of decisionmakers. The objective of BI is to provide accurate, timely, fact-based information, which enables taking actions that lead to achieving competitive advantage. There are many reasons for the importance of Business Intelligence, two of the most important being; 1) It helps to bridge the gap between an enterprise s current and its desired performance, and 2) It helps enterprises to be in alignment with key performance indicators meaning it helps an enterprise to align towards its key objectives. The literature review also shows that there are known critical success factors (CSFs) for Business Intelligence programs which have to be met if the above mentioned value is wanted to be achieved, for example; committed management support and sponsorship, business-driven development approach and sustainable data quality. The literature review shows that the most common challenges are related to these CSFs and, more importantly, that overcoming these challenges requires a more comprehensive form of BI, called Enterprise Performance Management (EPM). EPM links measurement to strategy by focusing on what is measured and why. The case study shows that many of the challenges faced in the case company s BI program are related to the above-mentioned CSFs. The main challenges are; lack of support and sponsorship from business, lack of visibility to overall business performance, lack of rigid BI development process, lack of clear purpose for the BI program and poor data quality. To overcome these challenges the case company should define and design an enterprise metrics framework, make sure that BI development requirements are gathered and prioritized by business, focus on data quality and ownership, and finally define clear goals for the BI program and then support and sponsor these goals.

3 TIIVISTELMÄ Tekijä: Joel Friman Työn nimi: Ensuring the efficient utilization of Business Intelligence Tiedekunta: Tuotantotalouden tiedekunta, Innovaatio- ja teknologiajohtamisen pääaine Vuosi: 2014 Paikka: Helsinki Diplomityö. Lappeenrannan teknillinen yliopisto (LUT) sivua, 25 taulukkoa, 18 kuvaa and 3 liitettä Tarkastajat: Prof. Tuomo Kässi, Tutkijaopettaja Kalle Elfvengren Ohjaaja: DI H.H. Keywords: Performance management, business intelligence, data warehouse, data quality, enterprise metrics framework Hakusanat: Suorituskyvyn johtaminen, liiketoimintatiedon hallinta, tietovarastointi, tiedon laatu, suorituskyvyn mittaamisen viitekehys Liiketoimintatiedon hallinta (Business Intelligence, BI) on tietohallintojohtajille (CIO) tärkeää ja he ovat tunnistaneet sen yhdeksi parhaista keinoista parantaa yrityksen kilpailukykyä. Monet yritykset kuitenkin epäonnistuvat realisoimaan BI:n lupaama arvoa. Tämä ristiriita luo tärkeitä kysymyksiä, joista yksi on: mitkä ovat BI:n kriittiset menestystekijät ja millä tavoin voidaan varmistaa, että yrityksen BI ohjelma parantaa yrityksen kilpailukykyä. Tämän tutkimuksen päätavoite on selvittää, miten voidaan varmistaa, että BI ohjelma täyttää tavoitteensa ja parantaa yrityksen kilpailukykyä. Tavoitetta lähestytään kirjallisuuskatsauksella sekä laadullisella casetutkimuksella. Kirjallisuuskatsauksen tavoitteena on vastata kolmeen tutkimuskysymykseen (RQs); RQ1: Mitä on BI ja miksi se on tärkeää moderneille yrityksille, RQ2: Mitkä ovat BI ohjelman kriittiset menestystekijät (CSFs), RQ3: Miten varmistetaan että nämä CSF:t täytetään? Laadullinen case-tutkimus käsittelee suomalaisen globaalin teollisuusyrityksen BI ohjelmaa. Case-tutkimus vastaa kahteen tutkimuskysymykseen: RQ4: Mikä on case-yrityksen BI ohjelman nykytila ja mitkä ovat sen keskeisiä kehittämisalueita, RQ5: Millä tavoin case-yrityksen BI ohjelmaa voisi parantaa? Case-yrityksen BI ohjelmaa tutkitaan seuraavilla menetelmillä; toimintatutkimus, haastattelut, maturiteetti-analyysi ja benchmark-tutkimus. Kirjallisuuskatsaus osoittaa, että BI on teknologiapohjainen informaatioprosessi, joka sisältää joukon systemaattisia aktiviteetteja, joita ohjaa yrityksen päätöksentekijöiden tietotarpeet. BI:n tavoite on tuottaa tarkkaa, oikea-aikaista ja faktoihin perustuvaa informaatiota, joka mahdollistaa ryhtymään toimiin yrityksen kilpailukyvyn parantamiseksi. BI on tärkeää esimerkiksi siksi, että se mahdollistaa kuromaan kuilua yrityksen halutuan ja nykyisen suorituskyvyn välillä. Kirjallisuuskatsaus osoittaa myös, että BI ohjelmalla on tietty joukko kriittisiä menestystekijöitä, jotka täytyy saavuttaa jos BI ohjelman halutaan tuottavan arvoa. Näitä ovat esimerkiksi johdon sitoutuminen ja tuki, liiketoimintalähtöinen kehitys sekä luottetava datan laatu. Kirjallisuuskatsaus osoittaa myös, että näiden kriittisten menestystekijöiden saavuttaminen ja haasteiden voittaminen vaatii kokonaisvaltaisempaa näkemystä liiketoimintatiedon hallintaan ja tätä konseptia kutsutaan nimellä moderni suorituskyvyn johtaminen (Enterprise Performance Management, EPM). Case-tutkimus osoittaa, että monet case-yrityksen kohtaamista haasteista liittyvät kirjallisuudesta löydettyihin kriittisiin menestystekijöihin. Tärkeimmät haasteet ovat: vähäinen tuki liiketoiminnan puolelta, huono datan laatu, kunnollisen BI kehitysprosessin puute ja se, että näkyvyys koko yrityksen suorituskykyyn on heikko. Näiden haasteiden voittamiseksi case-yrityksen tulisi määritellä ja tuottaa suorituskyvyn mittaamisen viitekehys, sekä keskittyä siihen, että BI ohjelman kehitysvaatimukset kerätään ja priorisoidaan liiketoiminnan johdosta. Myös datan laatun ja omistajuuteen, sekä BI ohjelmaan tarkoitukeen ja tarkoituksen kommunikointiin tulee kiinnittää erityistä huomiota.

4 ACKNOWLEDGEMENTS This research was an interesting journey to the complex world of Business Intelligence and the thesis work proved to be fun but challenging. Master s thesis is just one part of anyone s degree although usually the last and therefore I want to thank Lappeenranta University of Technology for valuable lessons and all my student friends for good times. I want to thank Professor Tuomo Kässi for valuable guidance and comments especially in the final stages of the thesis work. I also want to thank Associate Professor Kalle Elfvengren for the guidance and comments - and also for the books that I almost forgot to return. I want to thank the case company and especially my instructor Heikki for the thesis work opportunity, support, guidance and valuable comments throughout the thesis work. I also want to thank Juha for all support and ideas. And, of course, thank you goes to all the interviewees and other persons that I met during the thesis work. I want to thank my whole family and friends for support. Last but certainly not least, I want to thank my love Satu for supporting me during the thesis work and giving me strength whenever I was worn down by the work.

5 TABLE OF CONTENTS PART I INTRODUCTION INTRODUCTION BACKGROUND TARGETS AND LIMITATIONS IMPLEMENTATION OF THE RESEARCH Action research Maturity assessment Semi-structured interviews Benchmark RESEARCH OBJECT IN THE CASE COMPANY The case company Research object STRUCTURE OF THE REPORT... 8 PART II LITERATURE REVIEW BUSINESS INTELLIGENCE OVERVIEW OF BUSINESS INTELLIGENCE Background Definition Components and architecture Intelligence creation and use Viewpoint and level of information IMPORTANCE AND CRITICAL SUCCESS FACTORS Objectives & importance Critical success factors ENTERPRISE PERFORMANCE MANAGEMENT Overview and linkage to Business Intelligence Performance dashboards Known challenges of EPM PART III EMPIRICAL PART BUSINESS INTELLIGENCE IN THE CASE COMPANY BUSINESS INTELLIGENCE IN EIM History & overview Current state of the Business Intelligence program... 44

6 3.1.3 Challenges of the current Business Intelligence program MATURITY OF EIM S BUSINESS INTELLIGENCE PROGRAM The assessment in general Results of the assessment Summary of the assessment BUSINESS INTELLIGENCE IN OPERATIONS The interviews in general Key interview findings BENCHMARK The benchmark in general Key benchmark findings PART IV FINDINGS, CONCLUSIONS AND DISCUSSION KEY FINDINGS AND PROPOSED ACTIONS KEY FINDINGS OF THE LITERATURE REVIEW KEY FINDINGS OF THE EMPIRICAL RESEARCH CONCLUSIONS AND DISCUSSION GENERALIZATION AND LIMITATIONS OF THE RESEARCH FURTHER RESEARCH LIST OF REFERENCES APPENDICES APPENDICES APPENDIX 1. The interview themes and questions for the semi-structured theme interview. APPENDIX 2. TDWI s BI Maturity Model online questionnaire & answers. APPENDIX 3. Theoretical background for the maturity assessment.

7 LIST OF FIGURES FIGURE 1. ORGANIZATION OF THE CASE COMPANY (EIM)... 6 FIGURE 2. STRUCTURE OF THE LITERATURE REVIEW... 8 FIGURE 3. STRUCTURE OF THE EMPIRICAL PART FIGURE 4. SYNTHESIS OF THE LITERATURE REVIEW AND EMPIRICAL RESEARCH FIGURE 5. THE BPS MODEL (TURBAN ET AL. 2010, 6) FIGURE 6. TYPICAL COMPONENTS OF BI (PIRTTIMÄKI 2007, 91) FIGURE 7. TECHNICAL ARCHITECTURE OF BI (CHAUDHURI ET AL. 2011, 2 & VICKERS 2013, 3) FIGURE 8. INTELLIGENCE CREATION AND USE (KRIZAN 1999) FIGURE 9. INTELLIGENCE CONCEPTS (ADAPTED FROM PIRTTIMÄKI 2007, 91) FIGURE 10. CRITICAL SUCCESS FACTORS OF BUSINESS INTELLIGENCE (YEOH ET AL. 2009, 25) FIGURE 11. CLOSED-LOOP APPROACH TO EPM (TURBAN ET AL. 2010, 86) FIGURE 12. COMPONENTS OF MODERN EPM FIGURE 13. EPM OPTIMIZATION CYCLE (BALLARD ET AL. 2005, 6) FIGURE 14. A TYPICAL DASHBOARD (ORACLE 2014) FIGURE 15. TECHNICAL ARCHITECTURE OF EIM S BUSINESS INTELLIGENCE FIGURE 16. AVERAGE USAGE OF THE BI TOOL FIGURE 17. EIM IN TDWI S MATURITY MODEL FIGURE 18. EIM S BI MATURITY BY INTERVIEWEE

8 LIST OF TABLES TABLE 1. DEFINITIONS OF BUSINESS INTELLIGENCE TABLE 2. STEPS OF INTELLIGENCE CREATION AND USE EXPLAINED (KRIZAN 1999, 13 40) TABLE 3. THREE LEVELS OF BUSINESS INTELLIGENCE (WHITE 2006, 1) TABLE 4. CSFS FOR THE ORGANIZATIONAL DIMENSION (YEOH ET AL. 2009, 24-28) TABLE 5. CSFS FOR THE PROCESS DIMENSION (YEOH ET AL. 2009, 24-28) TABLE 6. CSFS FOR THE TECHNOLOGY DIMENSION (YEOH ET AL. 2009, 24-28) TABLE 7. THE KEY PHASES OF THE EPM CYCLE EXPLAINED (TURBAN ET AL. 2010, 87 98) TABLE 8. COMPARISON OF BI WITHOUT EPM AND BI WITH EPM (BALLARD ET AL. 2005, 27).. 34 TABLE 9. CHARACTERISTICS OF A DASHBOARD (FEW 2006, 35) TABLE 10. THREE CATEGORIES OF REASONS TO UNSUCCESSFUL EPM TABLE 11. THE EPM GAPS EXPLAINED (NEELY ET AL. 2008, 6-10) TABLE 12. PROBLEMS IN CURRENT EPM PROGRAMS (CHANDLER ET AL. 2011, 1-13) TABLE 13. POSSIBLE WAYS TO TACKLE PROBLEMS IN EPM (NEELY ET AL. 2008B, 8) TABLE 14. STRUCTURE OF THE CHAPTER TABLE 15. COMPONENTS OF ORACLE BI (ORACLE 2013) TABLE 16. CATEGORIES AND THEIR DESCRIPTION IN THE TDWI S BI MATURITY ASSESSMENT TABLE 17. MATURITY LEVELS AND THEIR CORRESPONDING SCORE TABLE 18. THE KEY CHARACTERISTICS OF EACH MATURITY LEVEL BY ASSESSMENT CATEGORY TABLE 19. MATURITY ASSESSMENT SCORES BY CATEGORY TABLE 20. KEY REASONS EXPLAINING THE SCORES TABLE 21. SUB-UNIT SPECIFIC PERFORMANCE MEASURES TABLE 22. USAGE BY DASHBOARD PAGE IN 2013 (YEAR TO DATE*) TABLE 23. INTERVIEWEES, THEIR ROLES AND THE INTERVIEW DATE TABLE 24. GENERAL INFORMATION ABOUT BMC TABLE 25. KEY FINDINGS

9 LIST OF ABBREVATIONS BI BI DW BICC BIT BMC EIM EMF EPM ETL KPI PM PMs Program Business Intelligence Business Intelligence Data Warehouse Business Intelligence Competency Center Abbreviation for the case company s BI Team Abbreviation for the benchmarked company Abbreviation for the case company Enterprise metrics framework Enterprise Performance Management Extract-Transform-Load Key performance indicator Performance Management Performance Measures A program is a portfolio comprised of multiple projects that are managed and coordinated as one unit with the objective of achieving (often intangible) outcomes and benefits for the organization.

10 PART I Introduction 1 INTRODUCTION 1.1 Background To remain competitive in a volatile and ever-changing business environment, enterprises are seeking ways to work more efficiently, and to respond more quickly to opportunities and threats. Business Intelligence (BI) can provide competitive advantage by enabling fact-based and precise decision-making, and by linking actions and measurement to strategy. In 2009 and in 2011, chief information officers (CIOs) around the world identified Business Intelligence as their top priority and as the best way to enhance their enterprise s competitiveness (IBM 2011, 6 & IBM 2009, 15). Yet, according to Boyer et al. (2010, 1) many enterprises are struggling to implement strategic BI programs and therefore are not gaining the competitive advantage - and most of these enterprises claim that there is a lack of time, resources and budget applied to Business Intelligence efforts. The journey to world class Business Intelligence & Enterprise Performance Management system is neither simple nor straightforward, and it is a journey enterprises are likely to pursue for some years to come (Neely et. al 2008a, 1). The idea of this study is based on the experiences of the researcher while working in a global Finnish manufacturing company s Business Intelligence team (BIT) from the end of 2011 to present day. The researcher and the case company s Business Intelligence manager both feel that the Business Intelligence program faces challenges and that there are areas where it could be improved. 1

11 1.2 Targets and limitations The main objective of the study is to find out, how it can be ensured that a BI program provides value and meets its goals in providing competitive advantage to an enterprise. The objective is approached with a literature review and a qualitative case study. For the literature review, the main objective populates three research questions (RQs); RQ1: What is Business Intelligence and why is it important for modern enterprises? RQ2: What are the critical success factors (CSFs) for Business Intelligence programs? RQ3: How it can be ensured that these CSFs are met? The qualitative case study covers the BI program of a Finnish global manufacturer company. The research questions for the case study are as follows: RQ4: What is the current state of the case company s BI program and what are the key areas for improvement? RQ5: In what ways the case company s Business Intelligence program could be improved? The case company s BI program is researched using the following methods; action research, semi-structured interviews, maturity assessment and benchmarking. Business Intelligence is a broad term including several components and it has both a technological as a non-technological aspect. This study will mainly focus on the nontechnological aspect of Business Intelligence as most of the problems faced in Business Intelligence programs tend to be non-technological in nature. However, the technological aspect is still reviewed & discussed in such detail that it is possible to gain a high-level understanding of the overall concept of Business Intelligence. 2

12 1.3 Implementation of the research The implementation of the thesis research is traditional consisting of the literature review (Part Two: Literature review) and the empirical research (Part Three: Empirical part). The literature review focuses on two linked concepts: Business Intelligence (BI) and Enterprise Performance Management (EPM). As both of the concepts are relatively new, the researcher has to the best of his ability tried to use as up-to-date literature as possible. The empirical part of the study contains the findings of the qualitative case study. The case company s BI program has been researched using the following research methods: Action research Maturity assessment Semi-structured interviews Benchmark There are two main parts in the empirical part of the study. In the first part, the researcher (Chapter 3) focuses on understanding and assessing the current state of the Business Intelligence program in EIM with the goal of finding out the key areas of improvement. In the second part (Chapter 4) the researcher highlights the key areas of improvement and proposes actions which would help EIM gain more value from its Business Intelligence program. These proposed actions are a synthesis of the literature review, benchmark and the researcher s own experience Action research The biggest contribution to the empirical part of the study has been collected using a method called action research, which is one area of qualitative research. In action research, the researcher tries to develop the object of the study, usually an organization, by participating in the daily activities of the organization by acting and researching. Usually action research means working in the organization / company, which is the object of study. (Saaranen-Kauppinen et al. 2006) 3

13 In this study, action research means that the researcher worked in EIM s BI team before and during the thesis work. The researcher s work consisted of end-user support & training, as well as new development and general BI development Maturity assessment A maturity model can be used to assess an organizations maturity to understand where the organization is currently, where it needs to go and what needs to be done in order to get there. (Chandler et al & Gonzales et al. 2012) The researcher, EIM s Business Intelligence manager, as well as a technical BI / DW expert assessed the maturity of EIM s Business Intelligence. The maturity assessment was done in the form of an online questionnaire provided by TDWI. The maturity assessment questions and answers are presented in Appendix 2 and theoretical background for the maturity assessment can be found from Appendix Semi-structured interviews Interviews can be used as a primary data gathering method to collect information from individuals about their own practices, beliefs, or opinions. They can be used to gather information on past or present behaviors or experiences. Interviews can further be used to gather background information or to tap into the expert knowledge of an individual. In semi-structured interviewing, a guide is used, with questions and topics that must be covered. The interviewer has some discretion about the order in which questions are asked, but the questions are standardized, and probes may be provided to ensure that the researcher covers the correct material. This kind of interview collects detailed information in a style that is somewhat conversational. Semistructured interviews are often used when the researcher wants to delve deeply into a topic and to understand thoroughly the answers provided. (RAND 2009) As part of the thesis work, the researcher conducted a semi-structured interview to gain insight on the question of how business users perceive EIM s Business Intelligence. Altogether eight (8) interviews were held and the interviewees were 4

14 manager level employees working in the case company s OPERATIONS function. The researcher interviewed all of the interviewees personally, six of them in EIM s headquarters and two using online tools. The researcher contacted the interviewees by sending a meeting request, which contained a short introduction to the topic of the thesis study as well as the five main themes of the interview. The interviews were held between June 2013 and July The duration of the interviews ranged from approximately one hour to almost two hours and all the interviews were recorded by the interviewee s permission. The interviews were transcribed so that the researcher listened to the recordings and made notes. The researcher has picked information from the interviews relevant to the study. The researcher has to the best of his ability tried to maintain the original message of each interviewee when transcribing the interviews Benchmark In short, benchmarking is a process of studying industry processes, practices, functions or products of another organization to find ways to improve in the benchmarked area. Benchmarking means comparing ones organization to another organization using some reference points, which help to understand the distance between the benchmarking organization and the benchmarked organization, in relation to these reference points. (Zink 1998) The researcher and EIM s Business Intelligence manager benchmarked another Finnish manufacturer company. Benchmarking was seen as a useful way to understand EIM s current situation better, as well as provide balance when mirroring EIM s Business Intelligence against the literature review. The benchmarked company (BMC) was visited on 21st of October 2013 and the benchmark took place in BMC s headquarters. BMC s Business Intelligence was discussed with the BMC s Strategy Development Director, as well as with the Corporate Performance Management & Finance Development Director. 5

15 The presented benchmark results are based on an open discussion between the attendees. BMC s representatives have reviewed the results. 1.4 Research object in the case company The case company The case company is a global Finnish manufacturer company. It was founded in 1940s and today it serves customers globally and employs roughly professionals worldwide. In this study, the case company is referred as EIM. EIM is a medium-sized company with one parent company and several subsidiaries around the world. EIM s headquarter is in Finland as well as most of its operations. EIM s organization is a traditional matrix organization, with two business areas, Business Area 1 (BA1) and Business Area 2 (BA2) and three group-wide functions (OPERATIONS, SERVICES and SUPPORT). EIM s organizational structure is shown below in Figure 1. Figure 1. Organization of the case company (EIM). 6

16 The Business Areas with their related market segments drive, grow and develop their businesses according to the strategy. They also carry profit and loss responsibility. The group-wide functions provide business areas with the resources and competencies to reach set goals in a cost-effective manner. To ensure an efficient way of working and two-way information sharing in this matrix model, the organization includes several dotted line roles that link the business areas and functions together Research object The research object, in the case company, is the case company s Business Intelligence program. The main goal of the research is to propose actions that would help the case company to gain more value from its Business Intelligence program. This goal populates the following research questions: RQ4: What is the current state of the case company s Business Intelligence and what are the key areas for improvement? RQ5: In what ways the case company s Business Intelligence could be improved? The Business Intelligence Manager manages the case company s Business Intelligence program and the Business Intelligence Team (BIT). BIT is one sub-unit of the SUPPORT function and it is responsible for managing & developing Business Intelligence in EIM and supporting the users of the Business Intelligence tool (Oracle BI). To simplify, the BIT is part of EIM s Group-wide IT sub-function and the business areas as well as the business functions are its customers. The researcher has worked in the BIT from the end of 2011, contributing to new Business Intelligence development, as well as training and supporting the users of the BI tool. 7

17 1.5 Structure of the report This report consists of the following four parts: Part I Introduction Part II Literature review Part III Empirical part Part IV Key findings, conclusions and discussion Literature review (Part II) contains the relevant findings from literature and the Empirical part (Part III) contains the findings of the empirical research. The structure of the Literature review (Part II) is shown below in Figure 2 and the structure of the Empirical part (Part III) is shown in Figure 3 on the next page. Figure 2. Structure of the literature review. The literature review focuses on two related concepts; Business Intelligence (BI) and Enterprise Performance Management (EPM). To highlight the linkage both concepts are discussed in Chapter 2: Business Intelligence. As will be later shown, Business 8

18 Intelligence is an essential part of Enterprise Performance Management and BI needs EPM to be truly successful. On the other hand, modern EPM is incomplete without Business Intelligence. Therefore, it is justified to review: 1. What is Business Intelligence (2.1) 2. What is the value of Business Intelligence and why is it important (2.2) 3. What is Enterprise Performance Management (2.3) 4. How are BI and EPM linked and why they both need each other to be truly successful (2.3) Figure 3. Structure of the empirical part. The Part III Empirical part contains the findings of the empirical research conducted in the case company (EIM). Part III contains the following chapters: Business Intelligence in the case company (Chapter 3) The key findings of the study as well as the proposed actions for the case company are presented in Part IV Key findings, conclusions and discussion as depicted in Figure 4. 9

19 Figure 4. Synthesis of the literature review and empirical research. 10

20 PART II Literature review 2 BUSINESS INTELLIGENCE Business Intelligence is a broad term with many definitions. Some see Business Intelligence as a concept related to performance management and to others Business Intelligence is a technological term, usually referring to technologies and tools used to refine information. This chapter is divided to the following sub-chapters: Overview of Business Intelligence (2.1) Importance and critical success factors (CSFs) (2.2) Enterprise Performance Management (2.3) 2.1 Overview of Business Intelligence Background To understand the next chapters, a brief background of computerized decision support is justified. According to Turban et al. (2010, 6) to understand why companies are embracing computerized support, such as BI, they have developed a model called the Business Pressures-Responses-Support (BPS) Model, which has the following components; business pressures, organizational responses and computerized support. The Business-Pressures-Responses-Support model is shown in Figure 5. 11

21 Figure 5. The BPS Model (Turban et al. 2010, 6) The business environment in which enterprises operate today is becoming more and more complex and this complexity creates opportunities, as well as problems. The intensity increases with time, leading to more pressure and more competition. In addition, functions within enterprises face decreased budgets and amplified pressures from top management to increase performance and profit. In this kind of environment, managers must respond quickly, be agile and innovate. (Turban et al. 2010, 6) Organizational responses are the actions taken to counter the pressures of business environment. Many, if not all, of these actions require some form of computerized support (information). (Turban et al. 2010, 6) Closing the strategy gap is one key objective of computerized decision support. It means facilitating closing the gap between the current performance of an organization and its desired performance. The desired performance is expressed in the enterprises mission, objectives and goals, and the strategy to achieve them. Business Intelligence is one way to provide this computerized decision support. (Turban et al. 2010, 8) 12

22 2.1.2 Definition The term Business Intelligence was first used by the Gartner Group in the mid- 1990s (Turban et al. 2010, 8) and according to Rausch et al. (2013, 4) it was used to describe tools that enable data analysis, reporting and query from the sea of data to help business users synthesize valuable information. Today, Business Intelligence can be seen as a form of computerized decision support with the key objective of closing the gap between the current performance of an enterprise and its desired performance (Turban et al. 2010, 8). According to Turban et al. (2010, 8), different decision support concepts have been implemented incrementally, under different names, by many vendors who have created tools and methodologies for decision support. Systems, which were generally called executive information systems (EIS), began to offer additional visualizations, alerts and performance measurement capabilities and by 2006, the major commercial products and services appeared under the umbrella term Business Intelligence (BI) (Turban et al. 2010, 8). In 2011, Chandler et al. (2011, 11) defined Business Intelligence as an umbrella term that spans people, processes and tools to organize information, enable access to it, and analyze it to improve decisions and manage performance. Chandler et al. (2011, 11) also stress that Business Intelligence focuses on locating and accessing the information that is most relevant to its users who handle the enterprise s analytical-, business- and decisions processes. In 2004 Negash et al. (2004, 178) defined Business Intelligence as systems that combine data gathering, data storage and knowledge management with analytical tools to present complex internal and competitive information to planners and decision makers. Four year later Negash et al. (2008, 175) defined Business Intelligence as data-driven decision support system (DSS) that combines datagathering, data-storage and knowledge management with analysis, to provide input for the decision makers. In 2010, Clark (2010, 1) refined the definition when saying 13

23 that Business Intelligence is a decision-support system (DSS) discipline aimed at providing timely, accurate information and analytical capabilities to the support the decision making of business. From the above definitions it is clear that Business Intelligence has something to do with data gathering, data storage and presenting information, but according to Vitt et al. (2002, 13) Business Intelligence can also be seen as a management philosophy and it can be considered to be an on-going performance management cycle via which a company can set goals, analyze development, gain insight, take action, measure success and begin all over again. Vitt et al. (2002, 13) define Business Intelligence cycle as a progression from analysis to insight and from insight to action. The above view is shared by Pirttimäki (2007, 92) as she defines Business Intelligence as an information process that contains a series of systematic activities which are driven by the specific information needs of decision-makers and the objective on achieving competitive advantage. Ranjan (2008, 461) defines Business Intelligence as the conscious, methodological transformation of data from any and all data sources into new forms to provide information that is business-driven and results-oriented. According to Chandler et al. (2011, 3) Business Intelligence can also be seen as the general ability to organize, access and analyze information in order to learn and understand business. The various definitions for Business Intelligence are summarized in Table 1 below. Table 1. Definitions of Business Intelligence. Year & author Definition 2002 Vitt et al. Business Intelligence is an on-going performance cycle via which a company can set goals, analyze development, gain insight, take action, measure success and begin all over again Negash et al. Business Intelligence is the systems that combine data gathering, data storage and knowledge management with analytical tools to present complex internal and competitive information to planners and decision makers Pirttimäki Business Intelligence is an information process that contains a series of systematic activities, which are driven by the specific information needs of decision-makers and the objective on achieving competitive advantage Negash et al. Business Intelligence is a data-driven decision support system (DSS) that combines data-gathering, data-storage and knowledge management with analysis, to provide input for the decision makers. 14

24 2008 Ranjan Business Intelligence is the conscious, methodological transformation of data from any and all data sources into new forms to provide information that is business-driven and results-oriented Turban et al. Business Intelligence can be seen as a form of computerized decision support with the key objective of closing the gap between the current performance of an enterprise and its desired performance Clark et al. Business Intelligence is a decision-support system (DSS) discipline aimed at providing timely, accurate information and analytical capabilities to the support the decision making of business Chandler et al. Business Intelligence is an umbrella term that spans people, processes and tools to organize information, enable access to it, and analyze it to improve decisions and manage performance Gartner Business Intelligence (BI) is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance TWDI Business Intelligence (BI) unites data, technology, analytics, and human knowledge to optimize business decisions and ultimately drive an enterprise s success. Based on the above definitions, the researcher understands that Business Intelligence has both a technological and a non-technological part. The technological side of BI is the architecture & tools used to refine information. The non-technological side of BI is related to providing timely, fact-based information to decision makers, with the ultimate goal of closing the strategy gap, which is the difference in an enterprises current performance and its desired performance. 15

25 2.1.3 Components and architecture Based on the definitions it is clear that Business Intelligence has many forms and components and that each author highlights the components, which are relevant to their subject of study. However, according to Pirttimäki (2007, 91) although there is no precise or universally shared conception of what BI is, there are some static components which are: philosophy, technology, process, managerial tool and refined form of information. These typical components of Business Intelligence are depicted in the Figure 6 below. Figure 6. Typical components of BI (Pirttimäki 2007, 91). The above components strengthen the view that Business Intelligence is more than a technology the philosophy component, refers to the performance management 16

26 methodologies and the process component to the fact, that BI is an on-going cycle. However, as the definitions show, providing information is essential to BI and therefore the typical technical architecture is reviewed next. A typical technical architecture for supporting enterprise Business Intelligence is shown in Figure 7 below. Figure 7. Technical architecture of BI (Chaudhuri et al. 2011, 2 & Vickers 2013, 3) According to Chaudhuri et al. (2011, 2) a typical technical architecture for Business Intelligence in an enterprise contains five different layers; data sources, data movement & streaming engines, data warehouse servers, mid-tier servers and frontend applications. Vickers (2013, 3) divides the technical architecture to four layers; data sources, data integration, data storage and data presentation. Data sources. Business Intelligence is based on data which according to Chaudhuri et al. (2011, 2) usually comes from multiple data sources typically some of these sources are operational databases across departments within the enterprise and some are external sources. According to Chaudhuri et al (2011, 2), an operational database contains the data that is generated from the daily use of the actual operational system in business processes. For example, the daily use can mean creating sales orders in 17

27 the operational system. These sales orders contain information like the sales order number, sales order value, customer name, customer country and created month. Data integration. The sources of the data are various and therefore according to Chaudhuri et al. (2011, 2) they contain data of varying quality - therefore the data is integrated, cleansed and standardized. This process of extracting and cleansing the data is referred to as Extract-Transform-Load (ETL) (Chaudhuri et al. 2012,2). In this step the sales orders created in the operational system are loaded in to the BI data warehouse and possibly, for example, transformed into the same currency. Data storage. According to Turban et al. (2010, 10) the BI data warehouse is the cornerstone of any Business Intelligence system the data in the BI DW is originated from operational systems and it is usually organized, summarized and standardized before it reaches the Data Warehouse where it is stored for a selected time period (e.g. five years). For example, the BI DW can contain all the sales order created during previous five years and to analyze and present information about them, a front-end BI tool is needed. Data presentation. Once the data is loaded to the BI DW it can be worked with using front-end BI applications and according to Turban et al. (2010, 10) this is called the business analytics environment in which, through the user interface, business users can access and work with the data in BI DW by creating reports and queries - these reports and queries include static and dynamic reporting, discovery of information and drill-down to details. For example, in the front-end BI tool the value of all the sales orders from previous year can be viewed by their creation month and/ or by the customer country Intelligence creation and use The BI architecture is the how of any information provided by Business Intelligence, but the what and why are not related to the technological aspects. There must be a need and a purpose for information provided by BI. The what and 18

28 why can be called intelligence creation and use which according to Turban et al (10), is usually a cyclical process with a series of interrelated steps. A typical process for intelligence creation and use is shown in Figure 8 below. Figure 8. Intelligence creation and use (Krizan 1999). According to Krizan (1999, 7) the intelligence process is complex and dynamic, but several steps can be distinguished from the whole. These seven steps are presented and explained in Table 2 on the next page. According to Turban et al. (2010, 16), the main step in converting raw data to decision supporting information is analysis, and that accurate and reliable analysis isn t possible unless the other steps of intelligence creation and use cycle have been properly addressed. 19

29 Table 2. Steps of intelligence creation and use explained (Krizan 1999, 13 40). Step Description 1 - Requirements The information requirements are collected from the customer. The requirements are often complex and time-sensitive. The information requirements require interpretation or analysis before they can be expressed as intelligence requirements that drive the production process. 2 - Collection When the intelligence requirements are understood, they must be validate against available sources of information. 3 - Processing Before the raw information can be used in the production of intelligence, it must be packaged meaningfully. Processing methods will vary depending on the form of the collected information and its intended use, but they include everything done to make the results usable by the customer. 4 - Analysis Analysis is the breaking down of a large problem into a number of smaller problems and performing mental operations on the data in order to arrive at a conclusion or a generalization. 5 Intelligence Simply put, intelligence creation means delivering briefings or reports for creation other analysts or for decision makers in the form of intelligence, that is, value-added actionable information tailored to a specific customer. 6 Intelligence use The customers use the created intelligence to give answers to problems or to gain understanding. 7 - Feedback Feedback from the customers is collected and some feedback may generate new intelligence requirements and the cycle start all over again. To continue the sales orders example, it could be that the BI DW does not contain any sales-orders-related data and therefore no information about sales orders can be viewed with the front-end BI tool. Naturally, the need for having sales orders related information would soon exist. The customer requirement could be to be able to view the value of all sales orders by creation month. From there, the creation of sales order related intelligence would typically follow the steps in Table 2. The architecture is responsible for turning data into information and the business user is responsible for turning information into Business Intelligence (IBM 2011, 44). 20

30 2.1.5 Viewpoint and level of information Sales orders are related to customers and customer information is external information. According to Pirttimäki (2007, 61) there are also many other intelligence concepts which are related to Business Intelligence, such as competitive intelligence, competitor intelligence, customer intelligence, market intelligence, strategic intelligence and environmental intelligence and that several of these concepts are used in the context of BI, but most of them focus mainly on the external environment and gather information from external sources. According to Choo (2002, 86) Business Intelligence has the broadest scope among intelligence concepts, and that BI needs information from various sources and its uses are various most importantly, strategic, long-term decisions are based on information provided by Business Intelligence. Pirttimäki explains (2007, 61) that the difference between BI and related intelligence concepts is there because of the way intelligence is managed and enriched stays mainly the same and the term applied is referring to the specific type of intelligence which is required in a particular company or even situation. According to Pirttimäki (2007, 64) BI can be understood as an intelligence process that includes a series of systematic activities, being driven by the specific information needs of business decision-makers and the objective of achieving competitive advantage. Pirttimäki continues (2007, 64) that through BI a company can gather, analyze, store and share accurate and timely information that is essential for its business activities and decision-making. BI can be then considered to be a comprehensive concept including internal and external intelligence and the whole operating environment besides a company itself, other intelligence concepts are therefore considered as components of BI. This relationship of intelligence concepts is depicted in Figure 9 on the next page. 21

31 Figure 9. Intelligence concepts (adapted from Pirttimäki 2007, 91) Business Intelligence can provide both external and internal information, but there are also different levels of information. Typically, Business Intelligence can be applied on three levels: strategic, tactical and operational. Strategic Business Intelligence is the providing of performance measures to management and executives often with in conjunction with a management methodology such as Balanced Scorecard (BSC) or Six Sigma. Strategic Business Intelligence can also be referred to as Enterprise Performance Management. Tactical Business Intelligence is the application of Business Intelligence to analyze business trends by comparing a specific measure to the same measure from a previous year or month. In most enterprises, there are usually few analysts in each department who perform these tasks. Operational Business Intelligence refers to delivering information to the front lines of business where information is used as a part of an operational process. (White 2006, 1 & Quinn 2006, 1) These levels are summarized in Table 3 on the next page. 22

32 Table 3. Three levels of Business Intelligence (White 2006, 1). Business focus Primary users STRATEGIC TACTICAL OPERATIONAL Achieve long-term business goals Executives and business analysts Manage tactical programs to achieve the long-term business goals Senior managers, business analysts and line-of-business managers Time-frame Months to years Days to weeks to months Data Historical metrics (key Historical metrics performance indicators) Manage and optimize daily business operations Line-of-business managers Intra-day Right-time measures To summarize, Business Intelligence is the ability to organize, store, access and analyze information with the goal of providing timely, fact-based information to decision makers, as well as closing the strategy gap between the enterprise s current performance and the desired performance. Business Intelligence is based on data from operational source systems, which is turned into information by the BI architecture based on the requirements from business customers. The viewpoint of Business Intelligence information can be both external and internal and it can be applied on three levels based on the business focus. 2.2 Importance and critical success factors According to the 2009 IBM Global CIO study (2009), Business Intelligence and analytics is the number one priority for chief information officers and according 2011 IBM Global CIO study (2011) Business Intelligence is of utmost importance as CIOs top visionary plan to increase competitiveness over the next three to five years. Next, the properties that make Business Intelligence important are discussed Objectives & importance According to Hovi et. al (2008, 80) Business Intelligence has the following objectives: 23

33 1. Speeding up and improving the enterprise s ability to make decisions. 2. Meeting the users information needs in a timely manner. 3. Supporting the enterprise s strategy and its goals. 4. Improving the user s independency regarding information-needs. 5. Lowering costs and improving operational efficiency. According to Nazier et al. (2013, 8) Business Intelligence is important for modern enterprises for the following reasons: 1. It helps enterprises to be in alignment of key performance indicators (KPIs) meaning it helps an enterprise to align towards its key objectives. 2. It enables taking sound fact-based decisions, in correct time, with correct manner. 3. It can provide information, which is mixed from different sources of data, which is relevant because often decisions made in an enterprise impact more than one aspect of an enterprise and therefore require data and measures from different aspects of an enterprise. Boyer et al. (2010, 3) as well as Hovi et al. (2008, 80) recognize the above 1 st reason for importance also when stating that one clear advantage of BI is the ability to measure and monitor how enterprises are executing against corporate goals, to understand whether the enterprise is on track or off track and why, and the ability to change direction when necessary. Nazier et al. (2013, 8) continue that to realize this value the enterprise must first design its KPIs in a way that they suit is style and strategies. The KPIs should be designed for each of the level in the organization, starting from the highest and moving towards the lowest. Hovi et al. (2008, 80) explain that BI is important because it helps to find and understand the linkage between enterprise s strategic goals and the lower level objectives. Boyer et al. (2010, 3) adds that a strategic, enterprise-wide Business Intelligence program offers more value than various tactical implementations. According to Boyer et al. (2010, 3) a successful strategic enterprise-wide BI program has the following outcomes: 24

34 It increases collaboration and leverages the decision-support structure across the enterprise to increase overall business effectiveness. This includes; better utilization of resources, consistent view of reliable data across the enterprise and the implementation of measures to measure the progress of key decision areas. It gives business users access to enterprise-wide information so they can make critical fact-based decisions based on data, which increases overall productivity and business efficiency Critical success factors In order for Business Intelligence to reach it objectives and provide the value that makes it important there are matters that need to be in order. According to Boyer et al. (2010, 5) a successful strategic Business Intelligence program is no overnight endeavor. Business Intelligence excellence is achieved when organizations have in place the strategy, people, process and technology approaches that result in business impact, value and effectiveness. Business impact and value are best achieved when the use of Business Intelligence, performance management and analytics spans department and silos to provide an enterprise view of information and a collaborative team approach to organizationally achieving goals. (Boyer at al. 2012, 7) These matters can be called the critical success factors (CSFs) of Business Intelligence. According to Yeoh et al. (2009, 31) and Nadini (2012) there are seven categories of critical success factors (CSFs) which are critical for Business Intelligence success. These CSFs are (Yeoh et al. 2009, 31 and Nadini 2012): 1. Committed management support and sponsorship 2. Clear vision and well established business case 3. Business centric championship and balanced team composition 4. Business-driven and iterative development approach 5. User oriented change management 6. Business-driven, scalable and flexible technical framework 7. Sustainable data quality and integrity 25

35 Yeoh et al. (2009, 31) also stress that non-technical factors, including organizational and process-related factors are more influential and important than technological and data-related factors. According to Adamala et al. (2011, 125), there are five CSFs for successful Business Intelligence: 1. The BI solution must be built with the end users in mind, as they need to use it 2. The BI program needs to be closely tied to an enterprise s strategic vision 3. The BI development projects need to be properly scoped and prioritized to concentrate on the best opportunities first 4. Although technological issues are encountered, all of them need to be solved 5. Non-technological issues should be avoided as they can hinder the success of the BI program. According to Boyer et al. (2012, 7) reaching Business Intelligence success requires a defined approach that considers the following: 1. Business strategy alignment, vision and business case. 2. Cultural and organizational behavior. 3. Technology and tools strategy. According to Yeoh et al. (2009, 31) the CSFs can be divided into three dimensions which are organization, process and technology. The CSFs and their dimension are depicted in Figure 10 on the next page. The CSFs are explained in in Tables 4, 5 and 6 on the following pages. 26

36 Figure 10. Critical success factors of Business Intelligence (Yeoh et al. 2009, 25) The below table (Table 4) contains the critical success factors of the Organizational dimension. These CSFs must be met to have a business-driven Business Intelligence program, which evolves as the organization evolves. Table 4. CSFs for the Organizational dimension (Yeoh et al. 2009, 24-28) Critical success factor Committed management support and sponsorship Clear vision and well established business case Description The most important factor for a successful BI program. The purpose of the BI program needs to be decided by and then supported and sponsored by business executives and senior management. The short-term and long-term goals of BI must be aligned with the strategic goals of the organization. 27

37 The below table (Table 5) contains the critical success factors of the Process dimension. These CSFs must be met to have a Business Intelligence program, which is developed in alignment with business. Table 5. CSFs for the Process dimension (Yeoh et al. 2009, 24-28) Critical success factor Business-centric championship and balanced team composition Business-driven and iterative development approach User oriented change management Description A successful BI program needs a balanced BI team and a champion from the business side of the organization. The champion understands the value of BI, and views the BI system primarily in strategic and organizational perspectives. The BI team should consist of both technical and business personnel to enable designing systems that are business driven. BI development must be business-driven and the development items need to be prioritized by business so that the BI team can concentrate on the best opportunities for improvement. User-oriented change management effort is critical and the users should be an important partner in building and delivering the right system. The below table (Table 6) contains the critical success factors of the Technology dimension. These CSFs must be met to have a Business Intelligence program, which is based on a flexible architecture and provides data of the highest quality. Table 6. CSFs for the Technology dimension (Yeoh et al. 2009, 24-28) Critical success factor Business-driven, scalable and flexible technical framework Sustainable data quality and integrity Description The technical architecture of a BI system must be able to change when the organization or business needs change. This means that the technical architecture needs to be business-driven, scalable and flexible. The quality of the data, particularly in the source systems is crucial for a successful BI program. Often, many data-related issues within the back-end systems are not discovered until that data is populated and queried within the BI system. The data quality at sources will affect the quality of management reports, which in turn influence decision outcomes. 28

38 2.3 Enterprise Performance Management Modern Enterprise Performance Management (EPM) can be seen as an outgrowth of Business Intelligence. The purpose of this chapter is to find out what is modern Enterprise Performance Management, how Business Intelligence is linked to it, and why both BI and EPM need each other to be truly successful Overview and linkage to Business Intelligence According to Turban et al. (2010, 86) Enterprise Performance Management (EPM) is the outgrowth of Business Intelligence and incorporates many of its technologies, applications, and techniques. Turban et al. (2010, 86) stress that Business Intelligence is a crucial part of EPM. According to Ballard et al. (2005, 13), Enterprise Performance Management is a process that enables organizations to meet their business performance measurements and objectives, by enabling proactive monitoring, managing of business processes and taking appropriate actions that result in meeting the enterprise s objectives. Turban et al. (2010, 81) share the above view when stating that Enterprise Performance Management is an integrated set of processes, methodologies, metrics and applications designed to drive the overall financial and operational performance of an enterprise it helps enterprises to translate their strategy and objectives into plans, monitor performance against those plans, analyze variations between actual results and planned results and, adjust the enterprises objectives and actions in response to these analyses. There has been discussion about the differences between EPM and BI, is EPM part of BI or BI part of EPM or are they the same? Simply put, EPM can be characterized as BI + planning. The term BI now describes the technology used to access, analyze and report on data relevant to an enterprise. EPM is the convergence of BI and planning on a unified platform. (Turban et. al 2011, 378) 29

39 According to Rausch et al. (2013, 7) Enterprise Performance Management is a closed-loop approach which helps to bridge the gap between the strategic and operational performance. Turban et al. (2010, 86) share the above view when stating that EPM is a strategy driven, closed-loop set of processes that link strategy to execution in order to optimize business performance. This cycle is shown in Figure 11 and it implies that optimum performance is achieved by (Turban et al. 2010, 86): 1. Setting goals and objectives (strategize), 2. establishing programs and plans to achieve those goals (plan), 3. monitoring actual performance against the goals and objectives (monitor), 4. and taking corrective action (act & adjust). Figure 11. Closed-loop approach to EPM (Turban et al. 2010, 86) The above cycle, a typical closed-loop approach, contains the key phases of modern Enterprise Performance Management. The Integrated Data and Measures is in the core of the cycle to highlight the fact that data can be used in every phase of the cycle. These key phases are explained next in Table 7 on the next page. 30

40 Table 7. The key phases of the EPM cycle explained (Turban et al. 2010, 87 98) EPM cycle phase Highlights 1 Strategize Answers the question: where do we want go in the future? Most enterprises answer this question in the form of a strategic plan, e.g. a strategy map which details a course for moving an enterprise from its current state to its future vision. The outputs of strategic planning are strategic objectives and goals. Strategic objectives and goals are high-level objectives that have to be met in order to move to the wanted direction. 2 Plan Answers the question: how do we get there? The strategic objectives are translated into a set of well-defined tactics and programs, resource requirements, and expected results for some future time period (e.g. a year). In this phase a more detailed plan (operational plan) is designed to ensure that an enterprise s strategy is realized. 3 Monitor & Analyze Answers the question: how are we doing? The execution of strategic and operational plans needs to be monitored (measured). A framework for monitoring should address two important issues, first being what to monitor and second being how to monitor. 4 Act & Answers the question: what do we need to do differently? It is critical for an Adjust enterprise to continually monitor its results, analyze what has happened, determine why it has happened, and adjust its actions accordingly. Modern Enterprise Performance Management can be seen to consist of three main components, which are performance measurement (PM), performance measurement methodologies (such as Balanced Scorecard or Six Sigma) and Business Intelligence. Sometimes EPM is characterized as BI + planning. (Turban et al. 2011, 378) The relation of the components is shown in Figure 12. Figure 12. Components of modern EPM. The Figure 12 highlights the fact that modern Enterprise Performance Management can be seen to consist of Performance Management methodologies, Performance 31

41 Measurement (PM) and Business Intelligence. The PM methodologies (such as Balanced Scorecard) help enterprises to decide what to measure and why, Performance Measurement is the actual performance measures and Business Intelligence is the way to provide these measures. Performance measurement (PM) Turban et al. (2011, 392) state that there are many books that provide recipes for determining whether a collection of performance measures is good or bad. Basic ingredients of a good collection are: 1. Measures should focus key factors. 2. Measures should be a mix of past, present and future. 3. Measures should balance the needs of shareholders, employees, partners, suppliers and other stakeholders. 4. Measures should flow from top to bottom. 5. Measures should have reachable targets (based on benchmarks or baselines). Measures need to be derived from corporate or business unit strategies and from an analysis of the key business processes required to achieve those strategies. Easier said than done otherwise all companies would have effective performance measurement systems in place (Turban et al. 2011, 393). Business Intelligence gathers information about business processes and activities to make it available to business users, enabling them to make decisions that are more informed and take actions that are more effective. According to Ballard et al. (2005, 27) it is the combination of Business Intelligence and Enterprise Performance Management that provides significant benefits to enterprises. The EPM can also be seen as a cycle for optimizing business performance (Ballard et al. 2005, 6). An EPM optimization cycle is shown in Figure 13 on the next page. 32

42 Figure 13. EPM optimization cycle (Ballard et al. 2005, 6) The above figure represents a cycle of integrated processes and it is through the execution and refinement of this process cycle that an enterprise can optimize its business performance. The core processes of model, deploy, monitor, analyze, and act define the methodology to enable performance optimization. (Ballard et al. 2005, 14) According to Ballard et al. (2005, 5) EPM brings value in the following ways Aligning strategy horizontally and vertically throughout the enterprise Enabling proactive and directed action Providing timely, contextual insight Delivering visibility into business operations Improving team productivity and effectiveness In short, when Business Intelligence (BI) applications gather information about business processes and activities to make it available to business users, enabling them to make more informed decisions and take more effective action, EPM helps BI to 33

43 cause operational decision making to become more proactive and timely, as well as support a wide range of business users. (Ballard et al. 2005, 28) The Table 8 shows a comparison of BI without EPM and BI with EPM. Table 8. Comparison of BI without EPM and BI with EPM (Ballard et al. 2005, 27). CATEGORY BI without EPM BI with EPM Implementation Departmental Enterprise-wide Focus Historical Timely (right-time or real-time) Decisions Strategic and tactical Strategic, tactical and operational Users Business analysts Everyone Orientation Reactive Proactive Output Analyses Recommendations and actions Measures Metrics Key performance indicators (KPIs) and actionable (incontext) metrics Views Generic Personalized Visuals Tables, charts and Dashboard and scorecards reports Collaboration Informal Built-in Interaction Pull (ad-hoc queries) Push (events and alerts) Analysis Trends Exceptions Data Structured Structured and unstructured Performance dashboards According to Turban et al. (2010, 117) scorecards and dashboards, visual displays of important information that is consolidated and arranged to a single screen, are the most common component of most, if not all, Enterprise Performance Management systems. A typical dashboard is shown in Figure 14 on the next page. The figure depicts an Interactive Dashboard, which can be accessed using a browser. The dashboard visualizes information about sales and related costs. The business user is able to interact with the data using the filters (e.g. Customer, Year). 34

44 Figure 14. A typical dashboard (Oracle 2014). According to Turban et. al (2011, 409) and Eckerson (2011, 5), the most distinctive feature of a dashboard is its three layers of information: 1. Monitoring Graphical, abstracted data to monitor key performance metrics (KPIs) 2. Analysis Summarized dimensional data to analyze the root cause of problems. 3. Management Detailed operational data that identifies what actions to take to resolve a problem. According to Few (2006, 2) a dashboard is a visual display of the most important information needed to achieve one or more objectives the information is consolidated and arranged on a single screen so that the information can be monitored at a glance. The characteristics of a typical dashboard are explained in Table 9 on the next page. 35

45 Table 9. Characteristics of a dashboard (Few 2006, 35) Characteristic Visual Display Important information Single screen Monitoring Description Dashboard presents the information visually, usually combining text, graphics and numbers. Dashboards are graphical because graphical presentation, when handled expertly, can often communicate with greater efficiency and richer meaning than text alone. Dashboards display the information needed to achieve specific objectives. To achieve a single objective often requires access to a collection of information, which is often coming from different sources related to various business functions. It is not a specific type of information, but information of whatever type that is needed to do the job. The information must fit in on a single screen. The objective is to have the most important information readily and effortlessly available so it can be quickly absorbed. A dashboard must be able to quickly point out that something deserves attention and might require action. The dashboard does its primary job it tells with one glance that action is needed. To summarize, modern Enterprise Performance Management is an on-going cycle aimed at optimizing business performance by setting goals, establishing programs and plans to achieve these goals, monitoring actual performance against the goals and objectives and taking corrective actions. Business Intelligence is linked to EPM and EPM needs BI as it provides the architecture, technology and tools for timely and accurate performance measures (PMs), as well as a place, the dashboard, for these PMs. BI needs EPM, as EPM brings clarity to what to display in the performance dashboards and scorecards Known challenges of EPM According to Cranfield University (2013), The Global Enterprise Performance Management Study shows there is a number of common issues which were identified in the analysis of the questionnaires from over 600 companies in the UK, USA, China, Japan and Australia while there is evidence that EPM, when designed appropriately, delivers significant value; many companies report an execution gap. 36

46 According to Neely et al. (2008a, 6), there are eight specific reasons (gaps) as to why enterprises have trouble executing EPM and that these eight gaps can be divided to three categories. These three categories are shown and described below in Table 10 and the eight gaps are shown and described in Table 11 on the next page. Table 10. Three categories of reasons to unsuccessful EPM. Category Advocacy and trust gaps Credibility, technology and alignment gaps Perception, insight and performance gaps Description Creating the passion shown by senior management to deliver EPM across all parts of the enterprise. Creating an enabling infrastructure. Knowing what success is. In the summary of The Global Enterprise Performance Management Study, Neely et al. (2008a, 6) highlight two important findings before discussing the eight gaps that are the reasons for the execution gap in EPM. These two important findings are as follows: 1. Measurement is still tactical not strategic. Despite all the rhetoric about the importance of aligning measures and strategy, enterprises still see measurement as tactical. The most important roles of measurement were identified as assessing performance, aligning employee behavior and improving operational efficiency, while the least important roles were identified as external reporting, validating strategy and strategic planning. The above view is shared by Krakauer as he stresses (2012, 3) that an enterprise needs to clearly understand the strategic goals of the organization before it can align with them for example, if the enterprises goal is to deliver supreme customer service and the focus of measurement and operations is on cost reductions, they goal will likely be compromised. 37

47 2. Financial measures still dominate. Researchers have highlighted the shortcomings of financial measures for decades. Yet, financial measures still dominate, in spite of all of the investment in performance measurement frameworks. In every country, financial measures are the most frequently measured and over 50 percent of surveyed reported that over 50 percent of their measures are financial. According to Neely et al. (2008a, 7) many enterprises report that there is a gap between the vision EPM and the execution of EPM and that there are eight specific reasons for this. These eight reasons are explained below in Table 11. Table 11. The EPM gaps explained (Neely et al. 2008, 6-10) Gap The advocacy gap The trust gap The credibility gap The technology gap The alignment gap The insight gap The performance gap The perception gap Description Measurement is still seen as a top-down process and senior management as the primary audience for measurement data. The level of advocacy for measurement decreases the further down the organizational hierarchy is went. This is called the advocacy gap, and while senior managers are the advocates of EPM they find it difficult to garner the same level of advocacy across the organization. The trust gap means that there is a gap between senior managers and middle managers. The passion to deliver EPM exists at the most senior levels, but necessarily throughout the organization. The credibility gap might explain the trust gap. 40 percent of the surveyed enterprises do not think that their performance measures are based on good quality data. Still, the spreadsheet is the most widely used performance management application. There is a lack of integrated technology. Organizations are struggling to integrate their various management systems (e.g. planning & budgeting, financial consolidation). This alignment gap results in shortcomings to the enabling infrastructure for EPM. The organizations are not sure if their performance measures deliver insight there is an incomplete understanding of the causal relationships between different measures within an organization. EPM systems are seen to have the biggest impact on operational performance and key performance indicators, and less impact on strategic performance. Relatively few surveyed organization perceived that their organization is worse than their competitors. One explanation for 38

48 this is an overly internal focus in organizations, lacking nonfinancial measures and use of benchmarking. According to Neely et al. (2008b, 5) there are seven key reasons why enterprises fail to use their Enterprise Performance Management systems strategically: 1. Tactical use of measurement 2. Questions of buy-in and engagement 3. Too much internal focus and over optimism 4. Concerns about quality of measures, data, and infrastructure 5. Challenges of integration 6. Lack of expert support 7. Lack of proper automation. Chandler et al. recognizes (2011, 3) problems in four different aspects of an EPM program; performance, people, processes and platform. These aspects are shown and described in Table 12 below. Table 12. Problems in current EPM programs (Chandler et al. 2011, 1-13). Aspect Performance People Processes Platform Problem description The most overlooked aspect of EPM programs. Most enterprises lack a framework for performance measures and there are multiple separate performance management programs in one organization. Many enterprises lack a rigid development process and/ or communication between the IT and business. Investment in business applications (e.g. ERP) has focused on automating business processes, which are now increasingly viewed as end-to-end process that span functional silos. However, often decision points and performance indicators are not modeled into the business processes. The IT organizations tend to over-focus on the technology of EPM/ BI tools and fail to get the business users adopt these tools. Chandler et al. stress (2011, 1) that the performance aspect of EPM is usually the most overlooked and least mature and according to Chandler et al. (2011, 4) to succeed in executing developed strategy, the enterprise first needs an enterprise metrics framework that links strategic goals with operational activities. This is supported by Neely et al. (2008, 8 UK) as they advise organizations to seek 39

49 understand the links between their measures by building cause-and-effect models that highlight how different dimensions of performance relate to one another. Neely et al. (2008, 8UK) add that without an explicit cause-and-effect model it is almost impossible to use EPM strategically and gain insight. Deploying an enterprise performance measurement framework (EMF) will benefit the enterprise as follows (Chandler et al and Neely et al. 2008): The EMF enables having a holistic view on performance measurement in an enterprise. The EMF enables visually communicating cause-and-effect relationships of business actions and their measures. The EMF can be used together with a strategy map to clarify common goals and enable people to see where they fit in the bigger picture and how they can contribute. To make communication between IT and business more continuous and prioritizing development efficient Chandler et al. suggest (2011, 6-12) to form a Business Intelligence Competency Center (BICC). BICC is also supported by Turban et al. (2010, 19) as they state that if the company s strategy is properly aligned with the reasons for BI program it is wise to establish a Business Intelligence Competency Center (BICC). According to Chandler et al. (2011, 13) BICC is also the owner and developer of the EPM framework. The BICC should also help users interpret and apply insight to business decisions and processes as well as define and measure the business impact that insight, analysis and resulting decisions have on improving the performance of the associated processes and the business overall. BICC is beneficial for the following reasons: 1. A BICC can transform BI from siloed efforts driven by the IT organization into business-driven, cross-organization program in another words to EPM (Hostmann 2010 & Hewlett-Packard 2012) 40

50 2. A BICC can maximize the value of enterprise investments in EPM by program management and coordination, as well as bridge the gap between the IT and the business (Hostmann 2010 & Krakauer 2012). 3. The BICC provides a visible, structured approach to analytics and fact-based decision support (Krakauer 2012) Neely et al. (2008b, 8) provide solutions to the seven key problems discussed earlier. These solutions are shown and described in Table 14 below. Table 13. Possible ways to tackle problems in EPM (Neely et al. 2008b, 8) Solution 1 Stop using measurement tactically 2 Focus on generating buy-in and engagement 3 Get out more 4 Crack the basics 5 Ensure integration 6 Engage expertise 7 Create the infrastructure Description Organizations should seek to understand the links between their measures by building cause-and-effect models that highlight how different dimensions of performance relate to one another. Without an explicit cause-and-effect model it is almost impossible to use EPM strategically and gain insight. Employees are key to the successful implementation of strategy. Creating a shared vision of the future and enabling people to see where they fit in the bigger picture and how they can contribute is essential. A success map can be used to clarify & communicate the organizations strategy. Accompanied with the appropriate performance measures a success map also enables people to see how much progress is being made toward delivering the strategy. Strategy can be seen as an organization s theory of competition. Without benchmarking and a stronger external focus, it is hard to assess whether this theory of competition is valid. Many organizations have significant concerns regarding their measurement systems the measures, data and infrastructure. Performance measurement should shift from being a control system to being a learning system. This would mean that performance measurement would be seen as a way to test hypothesis about the cause-and-effect relationships of a actions in a certain process. EPM system needs to align with other organizational systems and procedures, such as planning and budgeting, financial reporting, management reporting, risk management, reward system, forecasting, project management, and customer relationship management. Engaging experts can save significant time and cost for organizations seeking to develop and deploy EPM systems. Spreadsheets are still the most widely used tool to implement EPM or performance measures. Spreadsheets have numerous disadvantages; they are not scalable, they are time-consuming to update and they offer limited capability for collaboration. Organizations should move towards more automated systems for performance measuring. 41

51 PART III Empirical part 3 BUSINESS INTELLIGENCE IN THE CASE COMPANY This chapter contains the empirical findings of the qualitative case study, which covers the BI program a global Finnish manufacturer company (referred to as EIM ). The research questions for the case study are as follows: RQ4: What is the current state of the case company s BI program and what are the key areas for improvement? RQ5: In what ways the case company s Business Intelligence program could be improved? The table below (Table 15) shows the structure of subchapters and the corresponding research methods, as well as the perspective, which is explained below the table. Table 14. Structure of the chapter. Subchapter Divided to Research method 3.1 Business History & overview Method: Action research Intelligence in EIM Current state of the BI program Perspective: Internal Challenges of the current state 3.2 Maturity of EIM s BI program The assessment in general Results of the assessment Summary of the assessment Method: Action research and maturity assessment Perspective: Internal 3.3 Business Intelligence in OPERATIONS The interviews in general Key interview findings Method: Action research and semistructured interviews Perspective: External 3.4 Benchmark The benchmark in general Key benchmark findings Benchmark Perspective: External & Internal The chapter discusses and assesses EIM s Business Intelligence program from two perspectives with the aim of getting a clear understanding of the most important areas for improvement in EIM s BI program. The perspectives are as follows: 42

52 1. Internal - Business Intelligence program from the perspective of EIM s Business Intelligence Team (BIT). 2. External - Business Intelligence from the perspective of business users. 3.1 Business Intelligence in EIM History & overview The current enterprise resource planning system (Oracle E-Business Suite, ERP) was taken in to use in the end of 2008, first in the parent company and gradually, over the years, in the subsidiaries as well. Today the ERP is used in seven (7) of EIM s subsidiaries, as well as in the parent company. The ERP system is provided by Oracle and it was specifically customized to EIM, meaning that the default substance areas were modified (tailored) to suit EIM s processes better. The ERP s core functionalities are accessed through a global system and database, and it contains the following substance areas: Financial Ledgers Sales & Order Management Inventory & Operations Service HR The above substance areas are used by business in their daily work. The ERP is the primary source for business data as it serves most of EIM s business operations and therefore it is the host of for the majority of transactional data. EIM implemented its Business Intelligence tool, Oracle BI, virtually simultaneously with the ERP system in the end of The tool contains a pre-built set of shared analyses and dashboards for areas like; Procurement and Spend, Inventory, Order Management, Supply Chain Management, Human Resources and Finance. The tool also contains custom-built solutions for Service, Projects and CRM. 43

53 As EIM s enterprise resource planning system is now global, it means that the BI tool, which uses the ERP as a source system, is capable of delivering a global view of the wanted information Current state of the Business Intelligence program Business Intelligence architecture The information available through the BI tool is based on operational data which can is gathered from multiple source systems. Currently, business data BI originates from the three main data sources: ERP, CRM -tool and Group Consolidation & Official Reporting tool. Some data in BI originates from flat files (spreadsheets). A simplified, high-level technical architecture of EIM s Business Intelligence tool is depicted in Figure 15. Figure 15. Technical architecture of EIM s Business Intelligence. 44

54 The technical architecture can be divided to three separate areas: data integration, data storage and data presentation. The information that is shared to business users through the Interactive Dashboards is based on operational data, which emerges from the daily use of the source systems, depicted by the three cylinders in Figure 16. This operational data is gathered from EIM s ERP system as well as other operational & semi-operational source systems (data integration) and loaded to the Business Intelligence data warehouse through a process called Extract-Transform-Load (ETL) (data storage), which basically means selecting the data, transforming the data and loading the data to the BI data warehouse. Data storage layer also includes star models which means combining data from multiple dimensions into a single place (fact table) this can mean combining a customer revenue table, which contains data from product sales, project and service contracts dimensions. The data in the BI data warehouse is then refined & categorized to a form better understood by business users before it is brought to the actual Business Intelligence tool (data presentation) which in the above figure is inside the dotted line. Data integration and data storage are referred to as back-end and data presentation to as front-end. To recap: 1. Data integration selecting (mapping) and loading the date from operational source systems to the BI DW. 2. Data storage storing the selected data in BI DW. 3. Data presentation presenting the data through OBIEE. Before the data can be presented in the Interactive Dashboards for business users, an analyst is needed to build the analysis, validate the analysis with the appropriate process owner & ERP process expert, and choose the most proper form (table, graph, pie chart) in which the data brings the most value. Business Intelligence tool EIM s Business Intelligence data presentation tool is Oracle Business Intelligence Enterprise Edition (OBIEE), which has the following components: Oracle Business Intelligence Answers Oracle Business Intelligence Interactive Dashboards 45

55 Oracle Business Intelligence Delivers From business users perspective the only visible component is the Interactive Dashboards, which is a web-based interface accessed through a web browser. A small group of business analysts as well as the BI team has access to Answers & Delivers functionality. These components are explained in more detail in Table 15 below. Table 15. Components of Oracle BI (Oracle 2013). COMPONENT Answers Interactive Dashboards Delivers DESCRIPTION Provides answers to business questions. The interface allows analysts to build and modify reports, called requests that let business users explore and interact with information. The report information can be presented and visualized using charts, pivot tables and reports. The results can be formatted, saved, organized and shared with others. Reports created with Oracle BI Answers can be displayed in the Interactive Dashboards. Oracle BI Interactive Dashboards provide points of access for analytics information (shared Answers requests). Dashboards are typically used to display reports that contain specific to the needs of individual users or groups (e.g. Operations personnel). A dashboard can contain multiple pages (e.g. one important answers request by one page). Oracle BI Delivers is the interface used to create Oracle BI Alerts on analytical results. Specified results can be detected within reports and the appropriate people notified immediately through web, wireless and mobile communication channels (e.g. by or text message). Contents of the BI tool For business users the most visible part of EIM s BI tool is the Interactive Dashboards (Dashboards). EIM s BI tool has separate dashboards for both of the business areas (BA1 & BA2), as well as dashboards for the functions (OPERATIONS, SERVICES) and sub-functions (e.g. Marketing and Sourcing). There is also a separate dashboard called for EIM s Management Group. There are no rules for what a business unit specific dashboard can or cannot contain and therefore the business unit specific dashboards are very different from each other. Some dashboards are easy to understand, some harder. Some contain many analyses that can be described as key performance indicators (e.g. On Time Delivery, Speed of 46

56 Resolution), but some contain less or no such analyses. None of the dashboards contains a single page for most important analyses / measures. Some of the dashboards are described better than the others. There are dashboard pages that are named unclearly or analyses that are very complex to understand or interact with. The dashboard for the Management Group was taken into use in the beginning of The dashboard contains EIM s group level financial figures. The dashboard is definitely a change for the better, but it is troubled by financial myopia as it contains monetary information about net sales, profits and costs. There is however one analysis which breaks new ground; the analysis which measures if EIM s deliveries on time or not, is present in the dashboard. Business Intelligence tool s users Access to the front-end of EIM s BI tool is licensed so a business user needs a BI license to access the tool. Roughly one third (1/3) of EIM s employees have access to the Interactive Dashboards functionality and from those roughly 20 percent have access to the Answers functionality, meaning that they have the possibility to develop analyses which give answers to problems specific to their process or unit. Figure 16 on the next page depicts how often a business user has accessed the BI tool in the past six months. The Figure is taken from EIM s BI tool and based on the figure it can be said that the majority of users use the BI tool very rarely (under 15 BI days in past six months). 47

57 Figure 16. Average usage of the BI tool. Business Intelligence team EIM s Business Intelligence Team (BIT), led by the Business Intelligence manager, is responsible for the following: Collecting BI development requirements from business users Prioritizing and scheduling the back-end development Prioritizing and scheduling the front-end development Delivering shared analyses using the Answers functionality (front-end) Fixing back-end related technical issues Supporting the BI tools users in front-end related matters Managing the BI tool and its users Currently the BIT consists of one permanent full-time EIM employee (BI manager) and one temporary full-time EIM employee (the researcher), as well as four external technical BI consultants. Two of the consultants are full-time and they are mainly responsible for the BI application management support & ETL. The other two are part-time, focusing on the technical, back-end, BI development creating the capability for new analyses. 48

58 3.1.3 Challenges of the current Business Intelligence program Purpose & ownership of Business Intelligence For the BIT it is unclear if the purpose of Business Intelligence is fully understood in EIM. It is felt that BI is perceived more as a tool for reporting some analyses than a performance management related concept. There are some business units that have Balanced Scorecard (BSC) in use, but none of the business units has their scorecard in the BI tool. Often, the BI tool provides the information & analyses, which are then downloaded to spreadsheets for further processing which is not optimal. Actually, the literature review shows that one of the benefits of Business Intelligence is that the amount of spreadsheets can be reduced and therefore manual work also. Back-end development of BI One of the biggest challenges in EIM s Business Intelligence lies in the way how BI development needs are managed and requested by the business. The back-end BI development itself is working quite well, but prioritizing, scheduling and managing new back-end development items has proved to be a challenging task. The BIT feels that the reasons for the challenges in BI development are as follows: 1. Different back-end development items are not prioritized by business, but by the BIT itself. There are multiple parallel priority 1 development items and each one is seen as the most important by its business owner. The BIT feels that business shares little information regarding their Business Intelligence requirements with each other. 2. BI development is siloed and the development is done piece by piece. There can be multiple development requirements from a single business unit/ subunit, but the requirements are lacking the overall view to a certain business problem. 49

59 For example, it s common that the BIT gets a request: We would like to have these two information fields related to Suppliers brought from the ERP to the BI tool a relatively simple development task which gets completed, but then a few weeks later: We would also like to have this one information field also. If the BIT would serve only one business unit/ sub-unit in EIM, this might work, but the BIT is serving whole EIM. This leads to smalls enhancements delivered parallel in different areas to different functions. The BIT feels that BI development would work better if the requirements would be more focusing on the whole business problem: These are all the Supplier related information fields in the ERP system which we would like to have in the BI tool. We need these fields because we want to analyze and measure Supplier related information from different kinds of business angles and from the viewpoint of different sub-units e.g. buyers need to see this kind of information, F&C needs to see this kind of information and Supplier owners need to see this kind of information. 3. Cooperation between the BI developers, business process owners, business- BI-analysts and ERP process experts has also proven to be a challenging task. There have been situations where the back-end development stalls because there requirements are unclear or because some required data fields (e.g. in ERP) can t be found from the ERP. Front-end development of BI The technical BI development is something that should be handled by a group of experts from the BIT, as it is now. The situation is somewhat different for the frontend development of BI analyses (built in the Answers functionality of the BI tool). Large part of the analyses that give answers to business problems are still developed by the researcher and EIM s BI manager and the demand exceeds the supply. It is a positive challenge, but inevitably leads to dissatisfaction in some business user 50

60 groups. Luckily, the situation is for the better as the number of business-bi-analysts has increased from the past. BIT recognizes that training is essential for having more of skilled business-bi-analysts. BIT understands the BI tool very well, but it cannot have as detailed business or process understanding as the business counterparts and therefore can only provide the analyses which are requested. Business counterparts understand their business or process very well, but lack understanding of the capabilities of the BI tool and therefore don t necessarily know what kinds of analyses could actually be done with it. As the organization or business processes change, the analyses naturally also change. The analyses also naturally evolve and need improvements over time and therefore need maintenance and further development and a large part of this is handled by the BIT. Currently, the BIT is delivering new analyses when requested as well as delivering changes / improvements to existing analyses. The BIT is also delivering ad hoc analyses when requested. Back-end and front-end BI support The BIT is also responsible for supporting the BI tool s users in back-end, as well as front-end related issues. Business users can post their issues related to the BI tool through an internal ticketing system. The issues can be roughly categorized as follows: Issues related to using or accessing the BI tool Information requests Required improvements / changes to existing dashboard analyses Issues related to the back-end BI architecture (data quality) Requirements for new back-end development The BIT recognizes that every single issue is equally important to its owner, but it is the support, which often stays in the shade of back-end, and front-end BI development and the resolution time of these issues is not on an acceptable level. Some part of the issues are also such that could be handled within business and again, training is essential for this to happen. 51

61 3.2 Maturity of EIM s Business Intelligence program A BI maturity model shows the path that most organizations follow when developing their Business Intelligence from a low value, cost-center operation, to a high value, strategic asset. The maturity model can be used to determine where the organization is currently, where it needs to go and how it can get there. To gain a better understanding of the current state of EIM s Business Intelligence, a maturity assessment was conducted. The maturity assessment clarifies the areas of improvement and helps to focus the improvement efforts. The theoretical background for the maturity assessment can be found from APPENDIX The assessment in general EIM s Business Intelligence maturity was assessed with a maturity assessment tool provided by The Data Warehousing Institute for Business Intelligence and Data Warehousing (TDWI). The tool (link) is called The Benchmark Survey and it is an online survey consisting of 40 questions. The survey was done on 25 th October 2013 by the researcher together with EIM s BI manager & external BI / DW technical expert. The assessment questions and answers can be found from Appendix 2. Each of the 40 questions in the eight categories has five answer choices and each answer represents a different level in the TDWI s BI Maturity Model (discussed Appendix 3). Each answer is weighted from 1 (corresponding to the Nonexistent level) to 5 (corresponding to the Optimized level). The tool provides a score for each category as well as an overall BI maturity score. The overall score is calculated as a weighted average from the category scores. The eight categories and their area of assessment are presented below in Table

62 Table 16. Categories and their description in the TDWI s BI maturity assessment. Category in TWDI s maturity assessment Description 1. Scope Assessing to what extent does the BI & DW program support all parts of the organization and all potential users. 2. Sponsorship Assessing to what degree are BI & DW sponsors engaged and committed to the program. 3. Funding Assessing how successful is the BI & DW team in securing funding to meet business requirements. 4. Value Assessing how effectively does the BI & DW solution meet business requirements. 5. Architecture Assessing how advanced the BI & DW architecture is. 6. Data Assessing to what degree does the data provided by the BI & DW environment meet business requirements. 7. Development Assessing how effective is the BI & DW team s approach to managing projects and developing solutions. 8. Delivery Assessing how aligned are reporting & analysis capabilities with user requirements and what is the extent of usage. The table below, Table 17, contains the TDWI s BI maturity levels and the corresponding score range. The table also contains the ranges for the two obstacles Gulf and Chasm, presented in Appendix III. Table 17. Maturity levels and their corresponding score. Maturity level Score Nonexistent 5 to 7 Preliminary 8 to 12 (Gulf between 6 to 9) Repeatable 13 to 17 Managed 18 to 22 (Chasm between 15 to 19) Optimized 23 to 25 The key characteristics of each maturity level by assessment category are presented in Table

63 Table 18. The key characteristics of each maturity level by assessment category. Category / Level Nonexistent Preliminary Repeatable Managed Optimized Scope Individual Department Division Enterprise Inter-enterprise Sponsorship Funding Non-existent or uncommitted None Departmental budget Somewhat committed & accountable Divisional budget Corporate IT budget Value Cost Center Tactical Mission critical Strategic Architecture Data Development Delivery Spreadmarts Not trustworthy, not timely, not comprehensive Non-standardized processes View static reports Non-integrated data marts Analyze trends and issues Non-integrated data warehouses Somewhat trustworthy, timely, and comprehensive Somewhat standardized processes Monitor processes Central DW with or without data marts Predict outcomes Very committed & accountable Self-funding Competitive differentiator BI or data service via service-oriented architecture Fully trustworthy, timely, and comprehensive Fully standardized processes Automate processes Results of the assessment The assessment categories, and the key factors explaining each category s score, are discussed separately. (The full survey and the answers are presented in Appendix 2) Overall Score EIM s Business Intelligence got an overall score of out of 25, which corresponds to the second lowest maturity level called Preliminary the characteristics of the Preliminary level, as well as the next level (Repeatable) are recapped on in this page. As an overall note, it can be said that EIM s BI team feels that the TDWI s overall score depicts the current situation of Business Intelligence quite well. The key characteristics of the Preliminary (2 nd ) level as well as the Repeatable (3 rd ) level are as follows: The key characteristics of the 2 nd level of maturity are: 54

64 Siloed skills, applications and information. Inconsistent or conflicting data from the various system and metrics. There is no attempt to align BI program with other programs in the organization. The BI program is departmental in scope. The key characteristics of the 3 rd level of maturity are: People, processes and technologies start to become more coordinated across the enterprise, however little sharing of analytic and decision processes, components and resources occur. A senior executive from the business side becomes BI sponsor. A cross-organizational BI team, led by the BI manager, is implemented by most organizations. The organization creates a standard set of parameterized reports or dashboards, tailored to different groups of users and these reports are refreshed daily and contain KPIs that visually depict performance against plans. The table below (Table 19) presents EIM s overall BI maturity score as well as the score of each category, and the corresponding maturity level. These assessment categories and EIM s results are discussed in more detail on the following pages. Table 19. Maturity assessment scores by category. Category Score (out of 25) Corresponding maturity level 1. Scope 11 Preliminary 2. Sponsorship 7 Gulf (Nonexistent) 3. Funding 13 Repeatable 4. Value 13 Repeatable 5. Architecture 9 Gulf (Preliminary) 6. Data 8 Gulf (Preliminary) 7. Development 12 Repeatable 8. Delivery 8 Nonexistent Overall Score Preliminary 55

65 Scope EIM got a score of 11/25 in the Scope category, which is above the overall score. The score corresponds to the Preliminary level, therefore being characterized as departmental. The Scope category assessed how well the requirements for a system are defined before building it, how well the BI strategy is aligned with the organizations strategy and if BI adapts to the changing objectives of the organization. It was assessed that BI s objectives adapt to the changing objectives of the organization, which has a positive impact on the score. However, the score isn t good and there are two issues clearly explaining the low score. The first one is that BI strategy is not really aligned with the strategic plan of the organization. It can be said that there is no BI strategy or that in the current situation there is no room for BI strategy. The second issue is that the business counterparts (users) are not assigned full-time tasks / roles to BI development (projects) which is problematic because it slows down development and complicates data validation as the developers naturally need continuous input from business side. It is also common that some new development (data models & analyses) is completed and then altered after a few weeks or months, as the business users notice that they actually do not get all the required information. It was assessed that both of these problems would see some relief if business users were able to participate more actively to the BI development projects. Sponsorship EIM got a score of 7/25 in the Sponsorship category, which is significantly less than the overall score. The score corresponds to the Nonexistent level, but is inside the limits of Gulf. The score of 7/25 means that the sponsorship is non-existent and/or uncommitted. The category assessed how executives perceive the purpose of BI, who sponsors BI and is the sponsor accountable for BI s outcome and committed to the BI program. This category got the lowest score of all categories. As learned from literature review, the sponsor for BI should be a C-level executive from the business side such as a CFO or COO (in a perfect world possibly even the 56

66 CEO). In EIM the BI sponsor is the CIO, which is positive, but according to the literature review and both maturity models the optimal sponsor for a BI program should come from business-side and the sponsor should be in serious need of the BI capabilities for a specific business purpose. After fulfilling these needs, the sponsor could drive and communicate the business value of BI. In short, a BI sponsor needs to be committed and accountable for the BI as well as have the power to prioritize and drive BI requirements in a way that it supports the organization s strategy. Funding EIM got a score of 13/25 in the Funding category, which is significantly higher than the overall score. The score corresponds to the Repeatable level. Generally it is felt that there are no major problems with the funding of BI, although it was assessed that the BI team has sufficient funds to deliver and support some but not all requested business projects, simply because the demand exceeds the supply. Part of this is a matter of prioritization, but part is due to scarce resources. Value EIM got a score of 13/25 in the Value category, which corresponds to the Repeatable level, is higher than overall score and is characterized as mission critical. According to the assessment, EIM has moved from providing tactical value, to providing mission critical value, however not yet providing strategic value. It was assessed that BI helps some user groups in identifying the most appropriate customers for EIM and assists in monetizing customer & supplier information, which affected positively to the score. The assessors were uncertain if BI reduces costs for many business processes or enhances the value of EIM s products & services, which lowered the score. This uncertainty raises the question if all of the necessary things have been developed. Architecture EIM got a score of 9/25 in the Architecture category, which corresponds to the Preliminary level, but is inside the limits of Gulf and lower than the overall score. The key characteristic of the Preliminary level is non-integrated 57

67 data marts which on an overall level doesn t depict EIM s architecture as there is a central BI data warehouse in place which affects positively to the score. It was assessed that few groups in EIM adhere to the technology and tool standards created and that team BI team has defined, documented and implemented only some definitions & rules for key terms and metrics. In addition, the users can access some but not all the data they need. All of these three factors lowered to score. Data The Data category got (together with Delivery) the score of 8/25 which is second lowest score of all categories. The score corresponds to the Preliminary level, but is inside the limits of Gulf and is characterized between not trustworthy and somewhat trustworthy. The category assessed the data quality and in what way and how often the data is updated. There are two clear reasons for the low score. Firstly and most importantly, it was assessed that the BI tools users do not really trust the data it is fairly common that the data provided by the tool is compared against its source system or questioned. The users reconcile the data with more trusted sources and the reasons for this are as follows: 1. In the history there have been such where some data has been false in the BI tool or missing from it. 2. The quality of the source systems data, especially the ERP s, is not perfect, mainly due to too flexible use. This has caused anomalies in the data provided by the BI tool. The data in the BI tool is updated either weekly or daily depending on the source system which is on a good level, but the degree of synchronization got a low score as most of the data models are manually synchronized. In addition, the fact that there is no unstructured data (text or documents) in the BI tool lowers the score. Development The Development category got a score of 12/25 which places it on the Repeatable level and above the overall score. The key characteristic of the 58

68 category at Repeatable level is that development consists of somewhat standardized processes. It was assessed that development itself is one of the categories, which is in better shape than many others. There are however two important findings; 1. It was assessed that although the BI team uses a common set of tools and techniques, the developed solutions are one-off solutions. This is clearly related to two issues sponsorship and prioritizing BI development, which is discussed below. 2. Managing BI development is difficult as there really is no process for prioritizing the development with business. There are important projects & programs throughout the organization, which can generate multiple very important BI development requirements, which are then taken under work (almost) in the order as they come. And, sometimes some development items are strongly related to each other or even have their roots in the same business unit or process it s is obvious that these kind of request should be developed at once and not in bits and pieces. The difficulties in BI development have their roots in how the BI program is sponsored, what is its scope and how the development itself is managed & prioritized. As the literature review shows, BI should be a true collaboration between the BI team & business users. It is also clear that BI development items should be (also) managed centrally from the business side and this is why the business side BI sponsor is needed. Delivery The last category is Delivery where EIM got a score of 8/25, which is lower than the overall score and corresponds to the Nonexistent level. The Delivery category assessed the business understanding of the BI users, availability & measurement of BI training, as well as availability of business training for the BI team. It was assessed that the regular users of BI have a strong understanding of business functions / processes. However, there is lack of well-organized (technical) BI training 59

69 for the business users, which the BIT recognizes. There is also lack of business training for the BI team both the business users as well as the BIT would benefit from training, as part of the BI team s work is to understand & discuss business problems before development and the users need to understand the limits & possibilities of the BI tool Summary of the assessment The results of the maturity assessment show that there is room for improvement in each category of EIM s Business Intelligence. Four categories received a higher score than the average and four below the average of The table on the next page (Table 20) recaps the categories and their assessment scores as well as the points out one key factor having either a positive or negative impact to the score in each category. From the assessment, it is clear that there are challenges the BI team needs to overcome, but that there are also challenges that business needs to address if EIM aims to withdraw more benefit from its Business Intelligence. The assessment reinforces the view that successful Business Intelligence is heavily dependent of the true collaboration of the business and IT. 60

70 Table 20. Key reasons explaining the scores. Category Score (out of 25) Positive impact 1. Scope 11 BI adapts to the changing business objectives. 2. Sponsorship 7 A CIO sponsors the program. 3. Funding 13 Funding is generally in order. 4. Value 13 BI assists in monetizing customer & supplier information. Negative impact Business counterparts are not assigned full-time tasks / roles to BI development projects No one sponsor the BI program from the business side. Not all BI development requirements can be met. Hard to point out if BI reduces costs for many business processes or enhances the value of EIM s products & services. 5. Architecture 9 A central BI DW exists. Users can t access all the data they need. 6. Data 8 Data retrieved from multiple source systems. The data provided by BI is not really trusted. 7. Development 12 Development is agile & Prioritizing development. quick. 8. Delivery 8 Regular BI users have a strong understanding of business functions. Availability of well-organized training for both business users and BI team. Overall Score (avg.) The average overall score, calculated based on the answers of all who have taken the survey, is 9.93 and the industry average (Manufacturing: Non-computer related) is The above table provides a high level understanding of the maturity assessment as well as displays the factors that have a negative or a positive impact on the overall score. The table does not propose actions, but focusing on these negatively impacting factors and addressing the challenges behind them will surely move EIM s Business Intelligence to a more strategic, competitive advantage providing direction. 61

71 As a summary, EIM got a maturity score of which places it firmly to the Preliminary level (score 8-12) and just over the first bigger obstacle called Gulf (score 6 9). The Figure 17 below is a representation of the five stages of the TDWI maturity model. The vertical axis (or bell curve) depicts the percentage of organizations in any given stage. According to TDWI (2012), in 2004 most of the organizations were stuck between Gulf and Chasm. The proliferation line represents EIM s position in the maturity model (overall score of 10.12) and the dash-lined box represents the area in the maturity model where most organizations are today according to TDWI (2012). Figure 17. EIM in TDWI s maturity model. 62

72 3.3 Business Intelligence in OPERATIONS The OPERATIONS function is responsible for the order fulfillment to EIM s customers. OPERATIONS does this by managing the entire supply chain including the company s material suppliers, contract manufacturers, internal production, logistics and quality assurance. OPERATIONS is also responsible of the life cycle management (LCM) of the company s product portfolio including new product introduction, tester development, maintenance and product ramp-down. Strategy The strategy of OPERATIONS is defined and communicated using a strategy map, which clearly defines the four key objectives which are as follows: Deliver on time Shorten lead-times Manufacture defect-free products Lower costs Strategy measurement There are defined performance metrics which are linked to bonuses. For the whole OPERATIONS the bonus measures are related to delivering on time and reducing costs. There are also other performance measures that are specific for some sub-unit. The sub-unit specific measures are presented in Table 21. Table 21. Sub-unit specific performance measures. SUBFUNCTION 1. OPERATIONS Management 2. Factory 1 3. Factory 2 4. Factory 3 5. OPERATIONS BO 6. Sourcing & SCM 7. S&OP 8. LCM 9. Quality PERFORMANCE METRIC 1. Manufacturing quality & Supplier number reduction 2. Manufacturing quality 3. Manufacturing quality 4. Scrap (direct material) 5. DOS & Manufacturing quality 6. Sourcing Savings, Reduction of Suppliers, Inventory Accuracy, Buy-items DOS and Supplier OTD 7. OPS DOS, Minimizing planning exceptions & SO entry errors corrected 8. ECO throughput time & Supplier number reduction 9. Individual targets 63

73 The above performance measures are monitored, but not necessarily in the BI tool. Some of them get the data from the BI tool, but are managed in spreadsheets. In EIM s BI solution the OPERATIONS function has three (3) dashboards; OPS Management, Sourcing and Manufacturing. The contents of the dashboards and the corresponding usage rates are depicted in Table 22 below. The information has been gathered using EIM s BI tool. The RATIO is calculated from how many hits a certain amount of users have created during a nine month period. Table 22. Usage by dashboard page in 2013 (year to date*). DASHBOARD PAGE USAGE (hits / users) in 2013 OPS Management 1. On-Time Delivery / Late Deliveries / DSB / Inventory value monthly / 43 trend by Sub inventory 5. Forthcoming deliveries / OPS Costs / OPS costs drill down / 8 8. Global Inventory Trend / 4 (GIT) or 90 / 3 (IWO) Sourcing 1. (Supplier) OTD 2. Schedules 3. VMI 4. Sub inventory Value by Planner 5. Inventory Monthly Value 6. Inventory Weekly Value 7. DOS 8. ABC Category 9. Supplier base 10. Supplier Spend 11. Material Consumption Manufacturing 1. PMO Shipping 2. Boulder 3. Material Usage 4. Inventory Report for Planners 5. HEL EBS Project Inventories *Usage Tracking Operations dashboards data taken on / / / / / / / / / / / / / / / / 11 RATIO Based on the ratios in the above table there are some measures which are visited often, but there are also measures which have been barely visited which raises the 64

74 question if they are relevant to users or if there is some development & improvements needed for them to be used more often The interviews in general As literature review shows, one way to assess the state of Business Intelligence is to interview the business users who use the information provided by it. To get a better understanding on how the users perceive EIM s Business Intelligence, the researcher interviewed eight manager-level employees from the Operations business unit. The table below (Table 23) contains the interviewees, their roles and interview dates. The table also contains their usage-level of the BI tool. Table 23. Interviewees, their roles and the interview date. INTERVIEWEE ROLE DATE USAGE Interviewee 1 Senior Procurement Manager 29 th May 2013 Daily Interviewee 2 Logistics Manager 3 rd June 2013 Monthly or less Interviewee 3 Sales & Operations Planning 4 th June 2013 Weekly Manager Interviewee 4 Head of Sourcing and SCM 7 th June 2013 Daily Interviewee 5 Head of Factory 1 18 th June 2013 Monthly or less Interviewee 6 Head of B. Operations 25 th June 2013 Weekly Interviewee 7 Head of Factory 2 28 th June 2013 Monthly or less Interviewee 8 Head of Factory 3 23 rd July 2013 Weekly The interviews were semi-structured and the interviewees were asked questions which were grouped under the following themes: 1. Business Intelligence speeds up and improves the organizations ability to make decisions 2. Business Intelligence meets the users information needs in a timely manner 3. Business Intelligence improves the users independency regarding information needs 4. Business Intelligence supports the organizations strategy and goals 65

75 5. Business Intelligence lowers costs and improves the organizations operational efficiency. The detailed interview questions can be found from Appendix 1. The expected outcome of the interviews was to gain a better understanding on the following subjects: 1. How do business users perceive EIM s Business Intelligence in general? 2. From the perspective of business users, what are the key areas of improvement in EIM s Business Intelligence? The interviewees were also asked to assess the maturity of EIM s Business Intelligence. They were given time to view the Gartner s BI Maturity Model summary figure and then they gave EIM s BI a score. In the next section, the key takeaways of the interviews are discussed focusing on the areas of improvement Key interview findings A1: How do business users perceive EIM s Business Intelligence? Business Intelligence should give answers to relevant business questions and be adaptive, as what is relevant tends to change over time. The interviewees were asked to consider the purpose of Business Intelligence. The answers were almost unanimous as all interviewees thought that Business Intelligence is something that provides easy access to relevant information, which gives answers to relevant questions. The interviewees considered Business Intelligence to be as follows (e.g.): BI supports daily work by delivering easy access to needed measures. BI gives answers to relevant questions and therefore be flexible as what is relevant tends to change. 66

76 BI provides information & measures that support decision making as well as enables in-depth analyses. BI gives completely clear and transparent information about a specific topic. BI is for gathering information, which can then lead to better decisions. BI is for getting the overall picture of a certain topic. Can be used for further analyses also. It is also valuable to better understand how often and in what way is Business Intelligence used by the interviewees. From the total of eight interviewees, two described themselves as daily users meaning that they use information provided by the BI tool on daily basis, three interviewees described themselves as weekly users and the rest as monthly or bi-monthly users. Some of this variation in the usage is naturally explained by the different roles & responsibilities of the interviewees, but one thing was common for all interviewees. Except for the two daily users, they all would like to use information provided by Business Intelligence more often. The more the interviewees have important information in the BI tool the more they use it. The question of in what way Business Intelligence is used by its business users also varies by roles & responsibilities. A2: From the perspective of business users, what are the key areas of improvement in EIM s Business Intelligence? Strategy driven & cross-organizational BI We are missing the link between operational analyses and the most important, strategic, measures. Virtually all interviewees thought that EIM s BI should be more strategy driven. In another words, the measurement of strategy should be visible in the BI tool. One interviewee said it well when pointing out that the most important shared measures 67

77 should be first selected by management and then communicated to all in such detail that all understand the business logic behind the measure. This would help linking sub-unit objectives to higher level (strategic) objectives. Another interviewee supported this and added that the linkage of these strategic measures to different goals and business units should be visually depicted. The interviewee also added that: Now we have (visually) described and communicated EIM s strategy. Now we should start to measure it, as agreed. We should get to work. Another thing that was mentioned by many interviewees is the fact that currently there are no shared analyses in the BI tool. Having shared measures, would according to one interviewee, clarify the important matters in a matrix organization and also increase cross-organizational communication & collaboration. Another interviewee pointed out that not all measures should be selected by a specific business unit itself, but instead some measures should be forced. For example, Sales would have to view a measure from Operations business unit and vice versa. As EIM s strategy is visible for all, the measurement of the strategy should also be visible for all. Some interviewees also pointed out that it is odd, that EIM s strategy is visible for all, but the measurement is not. There are some measures shared in quarterly meetings, but these measures are not presented in the BI tool or somewhere else. Virtually every interviewee pointed out (in one way or another) that they would like to have a EIM level scorecard, possibly in the BI tool. And naturally their business unit s scorecard in the BI tool also. There are quite many analyses & measures in Operations interactive dashboards and still some relevant analyses are missing! 68

78 Another thing that many of the interviewees pointed out was that their business unit s dashboard(s) do have a lot of analyses, but still there are relevant analyses missing. There also seemed to be a lack of knowledge regarding what analyses is there in the BI tool on their business unit s level. Many interviewees also stressed that there are important analyses in the BI tool, but getting an overall picture of their business unit s performance is not possible and that there is a separate, spreadsheet-based scorecard which contains the important measures. The BI tool provides the data for many of this scorecard s measures, but their controller updates the spreadsheet semimanually some of the measures are altered from the existing analyses in the BI tool. One interviewee pointed out that selecting the most important measures would be beneficial for all, when saying: We have a huge amount of analyses already. Now we should select the most important ones and concentrate on those, strategically important analyses develop them and drive them further. Scarcity of resources also supports this. BI tool and its contents The interactive dashboards & analyses are easy to use if you know from where and what you are looking for. Without help, finding information would be hard. In general, the interviewees very satisfied with the BI tool. However, virtually all interviewees felt that the response time of some dashboard analyses could be faster. One interviewee pointed out that some analyses are performing so slowly (e.g. OTD) that one needs to book time from calendar. Almost all interviewees were happy with the daily refresh interval and in general, but all interviewees also stressed that it can t get any sparse. As a summary it can be said, that the BI tool itself did not get much objections, except for the performance. Most interviewees thought that the high-level contents of the BI tool should be documented and displayed to all EIM s employees. The documentation should contain the dashboards and their contents (pages, reports). 69

79 The dashboards / pages itself should be well-described, containing the page owner and a description of how the page s measure is calculated. The source data should also be described. Also, many interviewees thought that the analyses should be simple enough. Some interviewees also mentioned that the level of automation could be higher some important analyses could be automated and sent by when some alert level is triggered. Also, the dashboard analyses should contain more upper- and lower levels (or baseline and target levels). One interviewee stressed the importance of naming the data fields in the BI tool consistently with the source system (e.g. ERP). The data fields should be also described in a way that a business user understands what is the source system counterpart for a certain BI data field e.g. all data fields related to Purchase Orders should be named the same way as they are named in the ERP tool. Data quality I don t trust the data quality of the source system (ERP), but from decision-making point of view a good estimate is enough and it s not because of BI, but because of how we use the ERP system (that the data quality is not trusted). BI reflects the data quality of the source system (ERP) and currently the ERP is sometimes used almost in a criminal way. Almost all interviewees commented on the quality of the information/ data provided by the BI tool the information is not fully trusted, and it is usually checked from the source system. However, most of them, being heavy users of the ERP system, pointed out that it is the data quality of the ERP, which is causing most problems. One interviewee gave an alarming example: In the ERP system, when anything is changed in a Sales order, there is a field which basically asks why why the original order has been altered in some way. There 70

80 are different answers from which to choose, but at some point Sales has decided that always the same answer is chosen. And now we don t have the data that would tell that from where and why these changes to Sales orders come from. Back-end and front-end BI development It is not clearly communicated how new development is requested. Some interviewees felt that the BIT has not communicated clearly enough how new BI development is requested. Some interviewees also felt that it is always a small battle to have some new analyses developed or to have some new data brought to the BI tool. One of the interviewee stressed the importance of collaboration between the BIT and the business customer during the development. The developed BI analyses should also be thoroughly validated in a meeting with a process expert, BI technical expert, BI analyst and the business counterpart. (Validation means that the ERP system is compared against the results of the BI analysis.) BI Support One very alarming thing: if there is a bug in ERP system or BI tool, it seems to take 6 to 12 months to get it fixed this is not acceptable. A couple of the interviewees had poor experiences of the technical BI support and it was seen as a very clear area for improvement. Those interviewees that mentioned fixing back-end bugs stressed the fact that technical issues should be solved within weeks and not within months. Availability of training Training is essential if we want improve its users independency regarding information needs. 71

81 In general, all interviewees felt that there would be benefits from having more training regarding the BI tool, its contents and use. Some of the interviewees also expressed their interest towards being able to build analyses themselves using the Answers functionality. Many interviewees though that there should always be some sub-unit specific business user who would be able to use the Answers functionality and therefore be able to produce analyses from the BI tool. One of the interviewees stressed that training is essential if EIM is to fulfill the objective of BI supporting its strategy and goals. Maturity The interviewees were also asked to assess EIM s BI maturity. They were shown Gartner s Maturity Model and based on that model they gave EIM s BI a score. The Figure 18 below shows the answers by interviewee. Based on the maturity scores it can be said that the interviewees perceive the maturity differently, but none perceive the maturity to be higher than 3. It is interesting that the interviewees perceive EIM s BI to be somewhere between levels 2 and 3.This corresponds quite well to the maturity assessment conducted by the BIT. Figure 18. EIM s BI Maturity by Interviewee. 72

82 3.4 Benchmark As a part of the thesis research, the Business Intelligence of another Finnish global manufacturer company was benchmarked The benchmark in general The researcher and EIM s BI manager visited Benchmark Manufacturer Company (BMC) on 21 st of October The benchmark took place in BMC s headquarters and BMC s Business Intelligence was discussed with the BMC s Strategy Development Director and the Corporate Performance Management & Finance Development Director. General information about BMC is presented in the following table (Table 24). Table 24. General information about BMC. Information Details Business Five geographical business areas Business Lines (Areas) 1. Service Business 2. New Product Business Business Units / Processes 1. Marketing & Communications 2. Customer Experience 3. Strategic Alliances, M&As and Legal Affairs 4. Human Resources 5. Finance The idea of the benchmark was to gain a better understanding about the utilization of Business Intelligence by focusing on the following questions: Q1: How does BMC ensure that their Business Intelligence is aligned with strategy? Q2: How does BMC ensure that their BI development is strategy-driven? 73

83 Q3: Who uses BI and how it is linked to the goal setting of business units, teams or even individuals? The scope of the benchmark was to discuss the utilization of Business Intelligence from the utilization point of view, not focusing on the technical solutions itself Key benchmark findings A1 - How does BMC link Business Intelligence and strategy? The CPM director who reports to the Group Business Controller manages BMC s Business Intelligence program. In BMC, Business Intelligence is a synonym to Corporate Performance Management. Their CPM program is strongly supported by the CFO who is equally interested in financial and non-financial performance measures in fact, the CPM director pointed out that 75 % of measures followed by BMC s management, are non-financial and measure the performance of business processes. This is seen as a natural way, because these non-financial measures are leading indicators for the lagging financial measures. The CPM also pointed out that there are some measures, which have been the same for over 10 years. BMC s business is run by 2-year-planning and must-win-battles. Information from the business processes is heavily integrated to strategic development and objective setting. All strategic objectives have corresponding key performance indicators with targets. Past performance is reviewed through numbers and all objectives are agreed on a numeric level. In BMC, CPM is seen as an important part of the strategic planning cycle. A2 How is Business Intelligence development organized & managed? The fact that CPM (or BI) is directly linked to strategic planning is important. It is also important that the development of BI is organized and developed in a way that it provides the most important analyses / measures. According to the CPM director, when BMC started their CPM program they noticed the importance of BICC. Their 74

84 response was a team of analysts and technical experts managed by the CPM director. The CPM director acts as a link between business and the BICC. When asked about how BMC prioritizes their BI development the CPM Director explained that they have a monthly meeting where the BICC as well as all the Global Process Owners from the business-side meet. In this meeting, any BI development items are discussed and prioritized the business process owners handle final prioritizing. The CPM director said that this works well - especially when the business process owners really understands their processes and have enough power to change the processes. Both the CPM director and the Strategy Development director stressed that BMC is a process-oriented company, and that the process discipline is strict. The researcher and EIM s BI manager were left with the impression that BCM understands that the business processes are the ones, which generate the business data, which can then be presented by a BI tool. A3 - Who uses BI and how it is linked to the goal setting of business units, teams or even individuals In BMC, the key performance indicators are provided as a monthly summary to the senior and middle management. The senior and middle management also have access to the BI tool providing the KPIs. The monthly summary is sent by and according to the CPM director, contains almost too detailed information. These same KPIs that BMC s management follows are cascaded throughout the organization and linked to team-level objectives, and even to some individual objectives. If more information about the KPIs is needed then another BI tool (similar to EIM s Answer s functionality) can be used for analyses. BMC did not have Business Intelligence dashboards in use. 75

85 The below table (Table 25) summarizes the key findings from the benchmark. Table 25. Key findings. BI s link to strategy BI s link to processes BI s link to performance measurement BI s link to goals BI sponsorship BI development BMC s BI is heavily linked to strategic development and to the measurement of strategic objectives. BMC can be described as a process oriented company. Each process has an owner. In BMC Business Intelligence is seen as a synonym to Corporate Performance Management or at least as natural part of it. The CPM director stressed that everything that is set as a goal for a business unit, process, segment or team, need to be somehow measured. If a goal can t be measured, it s not a good goal. In BMC, Business Intelligence is sponsored by the CFO, who is equally interested in both financial and non-financial measurement. In BMC, Business Intelligence development is managed by the CPM director who acts as a link between business and the developers. Development is prioritized within business, by business process owners in a monthly meeting. 76

86 PART IV Findings, conclusions and discussion 4 KEY FINDINGS AND PROPOSED ACTIONS 4.1 Key findings of the literature review The purpose of the literature review was to answer research questions RQ1, RQ2 and RQ3. RQ1: What is Business Intelligence and why is it important for modern enterprises? Based on the literature review it is evident that there is no unanimous definition for Business Intelligence. The term was coined by the Gartner Group in the mid-1990s (Turban et al. 2010, 8) and today it is used in various meanings by academic researchers, university teachers, consultants, business persons and software vendors. Based on the reviewed definitions (p ), the researcher defines Business Intelligence as follows: Business Intelligence is a technology based information process that contains a series of systematic activities, which are driven by the specific information needs of decision-makers. The objective of BI is to provide accurate, timely, fact-based information, which enables taking actions that lead to achieving competitive advantage. This definition is refined from the definitions of Vitt et al. (2002), Pirttimäki (2007, Turban et al. (2010), Chandler et al. (2011), Gartner (2013) and TDWI (2013). Business Intelligence is important for modern enterprises (for example) for the following two reasons (p ): It gives the ability to measure and monitor how enterprises are executing against corporate goals, to understand whether the enterprise is on track or off track and why, and if a change in direction is needed. 77

87 It gives business users access to enterprise-wide information so they can make critical fact-based decisions based on data, which increases overall productivity and business efficiency. RQ2: What are the critical success factors (CSFs) for Business Intelligence programs? There are known critical success factors for Business Intelligence programs. Some of these CSFs are (p ): Management supports and is committed to the BI program. The purpose of the program is clearly defined and closely linked to the enterprises strategic vision. The output (information) of the BI program is based on business-driven iterative development approach. The BI solution is built with the business users in mind. The output data is of the highest quality and integrity. RQ3: How it can be ensured that these CSFs are met? As many of the identified CSFs and challenges are non-technological in nature a more holistic concept, which also focuses on these non-technological aspects, is needed. This concept is called Enterprise Performance Management (EPM) and it is a modern form of technology-based performance management incorporating Business Intelligence and focusing on why and what to monitor, while leaving the how for Business Intelligence. EPM is a strategy driven, closed-loop set of processes that links strategy to execution in order to optimize business performance through a cycle of strategize, plan, monitor and analyze, act and adjust. The literature review shows that BI needs EPM in order to be successful. 78

88 4.2 Key findings of the empirical research The purpose of the qualitative case study was to answer research questions RQ4 and RQ5. RQ4: What is the current state of the case company s BI program and what are the key areas for improvement? The literature review and the empirical research reveals that there are clear areas where EIM s Business Intelligence could be improved and developed towards a more business-driven, strategic and performance management oriented form of computerized decision support. Based on the literature, BI needs EPM in order to fulfill the ultimate goal of providing competitive advantage. According to the literature review, most of the challenges that EIM faces in its Business Intelligence programs are also faced by other organizations also. Some of the challenges are related to technological matters, but most of them are related to non-technological matters, which according to the literature review is common. To recap, EIM s Business Intelligence program is between the 2 nd and 3 rd level of maturity (p. 55, 64 and 78), and it faces the following challenges: EIM s Business Intelligence lacks a business centric champion and a sponsor from the business side. (p. 57) EIM s Business Intelligence lacks overview to EIM s business performance and a clear linkage to EIM s strategic goals, as well as little shared measures. (p ) The data quality of the source systems (especially ERP) is not trusted and therefore the business users do not fully trust the information provided by Business Intelligence. (p and 70-71) In many cases, the business users are not happy with the performance of the Business Intelligence tool. (p. 72) 79

89 Managing and prioritizing back-end BI development is challenging as the final prioritizing is done within the BIT. (p. 49 and 59) Fixing back-end related technical BI issues (bugs) is not on acceptable level. (p. 74) RQ5: In what ways the case company s Business Intelligence program could be improved? Based on the literature review and the qualitative case study the researcher proposes that EIM considers taking the following four actions: Enterprise Metrics Framework (EMF). To increase visibility to EIM s overall business performance, to understand how function-specific objectives depend on each other and link to strategic goals, and to communicate how these objectives are monitored, an Enterprise Metrics Framework is proposed to be defined and developed. The EMF could be a highlevel KPI tree which would visually depict the linkage of high-level actions in different functions and business processes and how these actions affect the achievement of EIM s strategic goals. The EMF would clarify the most important matters in a matrix organization, and also increase cross-organizational collaboration and communication. The EMF would also help individuals to understand how their contribution is linked to EIM s business performance. 80

90 Data quality and ownership. To get the business users trust the researcher proposes that the data quality is seen as value chain which starts from the business processes and their operational systems (e.g. ERP) and ends with the business users of the BI tool. If the data quality of the source systems is not on a sufficient level, it is time- and resource consuming (or maybe even impossible) to provide high-quality Business Intelligence information. It needs to be understood in EIM, that the business processes and the everyday use of operational systems generate the data which is used in Business Intelligence analyses. Clearly, data quality (or business master data) must be owned and managed, meaning that there is an employee whose responsibility is to make sure that the operational source system is used in a way it supports and enables generating Business Intelligence information. It is also important that the linkage between the attribute in the BI tool and in the corresponding source system is defined and documented the attributes should also be named consistently, meaning that a common term for the attribute exists. Gathering and prioritizing new BI development requirements. To make back-end and front-end BI development business driven and to ensure that the right things on EIM-level are developed in the right order and right time, and also to ensure that development is aligned with EIM s goals, the researcher suggests that the BI development process is refined in the following ways: o BI development requirements should come centrally from a group of business employees. These same employees should also take the responsibility of prioritizing these requirements. EIM could consider the BMC s model where all BI requirements are gathered and prioritized by the business process owners in a monthly meeting. o These persons would be ideal members for a virtual BICC and they would receive in-depth training about the BI tool, so that they understand the capabilities and limitations of the tool, and also the required level of detail for new development requirements. 81

91 The goals of BI need to be defined, and then supported. The goals of Business Intelligence in EIM need to be defined together with business. Basically, it s a matter of will if EIM wants to develop its BI to a more strategic, performance management oriented resource. If it is wanted, then it needs to be communicated, supported and sponsored from the business side. And then a business side BI sponsor is needed. The sponsor would support the goals of BI and highlight the importance of (e.g.) improving data quality or developing an EMF. The sponsor would be someone who is aware of the most important EIMlevel goals and EIM-level measurement needs, and be (e.g.) the final judge in prioritization issues. Neither of the actions proposed above are quick or simple tasks. It is likely that not all of them can be taken under consideration at the same time, but the literature review and the benchmark clearly show that taking the above actions would have a positive impact on the business value of the BI program. Therefore, all of the above actions are important and necessary. All of the above actions need true cooperation from business and the BI team, and all of them need to be wanted by EIM as a company. 82

92 5 CONCLUSIONS AND DISCUSSION The main objective of this study was to find out how it can be ensured that a Business Intelligence program meets its goals in providing competitive advantage to an enterprise. The objective was approached with a literature review and a qualitative case study. The literature review focused on two linked concepts; Business Intelligence and Enterprise Performance Management and provided answers to research questions RQ1, RQ2 and RQ3. The qualitative case study examined the BI program of a global Finnish manufacturing company and provided answers to research questions RQ4 and RQ5. The first two research questions, RQ1 and RQ2, concerned Business Intelligence as a technology-based performance management related concept, focusing on the common challenges and critical success factors (CSFs) of Business Intelligence programs. The third research question, RQ3, concerned how it can be ensured that these CSFs are met. Regarding RQ1, it was found out that Business Intelligence is the general ability to organize, store, access, analyze and provide information with the help of modern technology, and that it is an information process that contains a series of systematic activities which are driven by the specific needs of decision-makers and the objective of achieving competitive advantage. The ultimate goal of BI is to close the gap between the enterprise s current performance and its desired performance. BI enables doing this by providing the possibility to monitor and analyze the most important performance measures. Business Intelligence is based on data from operational source systems, which is turned into information by the BI architecture based on the requirements from business customers. The viewpoint of Business Intelligence information can be both external and internal and it can be applied on three levels based on the business focus. Business Intelligence is important for modern enterprises because it helps to bridge the strategy gap, which is the gap between an enterprise s current performance and its desired performance. 83

93 Regarding RQ2, it was found out that there are known critical success factors (CSFs) for Business Intelligence programs which have to be met if the program is expected to deliver the above value. It was found out that the most successful BI (and EPM) programs usually share the following characteristics: 1. Committed management support and sponsorship for the initiative. 2. Business centric championship and balanced BI team composition (BICC). 3. Business-driven and iterative development approach. 4. Business-driven change management. 5. Sustainable data quality and integrity. 6. Information is seen as a strategic asset. 7. A well-defined Enterprise Measurement Framework (EMF) exists. 8. Users can see the enterprises performance and the factors that contribute to it. Regarding RQ3, it was found out that meeting these CSFs requires a systematic and holistic view to Business Intelligence and performance management. As many of the challenges, that are related to meeting the CSFs, are non-technological in nature, a concept, which focuses on these non-technological aspects, is needed. This concept is called Enterprise Performance Management, and it focuses more on what information is used, how and why. The current state of EIM s Business Intelligence program was described and analyzed, giving answers to RQ4 and RQ5. EIM s BI program was researched using the following methods; action research, maturity assessment, interviews and benchmark. Regarding RQ4, it was found out that EIM s Business Intelligence program is somewhere between the second (2 nd ) and third (3 rd ) level of maturity (maturity assessment) and faces problems that are related to lack of management support and sponsorship, lack of visibility to overall business performance, lack of rigid BI development process, lack of clear purpose for the BI program and poor data quality 84

94 (maturity assessment, action research, interviews). It was also found out that EIM s Business Intelligence team and users of BI information perceive the current state in a very similar fashion the key interview findings were in line with the findings of the maturity assessment. This finding indicates that there is a positive possibility to improve EIM s BI program. Regarding RQ5, the researcher proposed that EIM takes certain actions to encounter the most important challenges faced in its BI program. The proposed actions were; 1) to start designing and defining an Enterprise Metrics framework, 2) to focus on business data quality and ownership of the data, 3) to refine and strengthen the process of gathering and prioritizing BI development requirements and 4) to define clear goals for the BI program and then support these goals. The findings of the case study are largely in line with the findings from the literature review. Many of the challenges faced in the case company s BI program are such that can be found from the literature. The literature also provides ways to tackle these challenges. 5.1 Generalization and limitations of the research The findings of the case study are largely in line with the findings from the literature review. Many of the challenges faced in the case company s BI program are such that can be found from the literature. The literature also provides ways to tackle these challenges. However, it must be noted that this study only covers the definition and assessment of one Finnish manufacturer company s Business Intelligence program. It is obvious that generalization possibilities based on a single case study are limited and that any understanding gained through a case study includes bias affected by the assumptions, opinions and perspectives of the researcher. On the other hand, concentrating only on one company and only on one BI program enabled the researcher to form a good understanding on the subject or at least of the way how the case company has understood the subject. The research object is complex and contains various technological and non-technological aspects, which are sure to be 85

95 different in every company - studying various enterprises in a similar fashion would be a time and resource consuming task. Another thing that needs to be noted is that the conclusions are based on qualitative research and therefore it is hard to point out any quantitative benefits occurring from the proposed actions or other changes done based on this study. The empirical data was collected by observing and participating (action research), by interviewing users of BI information and by benchmarking which are all the results are qualitative. The proposed actions are heavily based on the literature, but also based on the opinions of the interviewees and the case company s BI manager, which increases the validity and reliability. The researcher also tried to use as current literate sources as possible to increase the reliability of this study. 5.2 Further research During this study, it was noticed that as much as Business Intelligence has to do with performance management and measurement, there is very little literature that deals with how the performance of Business Intelligence itself could be monitored and measured. It would be interesting to know if it is possible to point out some quantitative measures that would follow the success of a BI program on (e.g.) monthly basis, or to know how the quality of the information (provided by BI) could be measured from the perspective of its user. 86

96 LIST OF REFERENCES Adamala, S. & Cidrin, L Key Success Factors in Business Intelligence. Journal of Intelligence Studies in Business. Vol. 1. pp Ballard, C., White, C., McDonald, S., Myllymaki, J., McDowell, S., Goerlich, O. & Neroda, A Business Performance Management Meets Business Intelligence. IBM Redbooks. Bitterer, A The BI(G) Discrepancy: Theory and Practice of Business Intelligence. Gartner Research Note G Boyer, J., Frank, B., Green, B., Harris, T. & Van De Vanter, K Business Intelligence Strategy: A Practical Guide for Achieving BI Excellence. First Edition. IBM Corporation. Chandler, N., Hostmann, B., Rayner, N. & Herschel, G Gartner s Business Analytics Framework. Gartner Research Note G Chaudhuri, S., Dayal, U. & Narasayya, V An Overview of Business Intelligence Technology. Vol. 54. No. 8. Choo, C.W Information Management for the Intelligent Organization: The Art of Scanning the Environment. 3 rd Edition. Information Today, Medford, NJ. Clark, J.W Business Intelligence and Decision Making: Understanding B.I. as a Theory-performing Discipline of Decision Improvement. Sprouts: Working Papers on Information Systems. [URL Eckerson, W Performance Dashboards: Measuring, monitoring and managing your business. Second Edition. John Wiley & Sons Inc. 87

97 Gonzales, M. & Eckerson, W TDWI Benchmark Guide: Interpreting Benchmark Scores Using TDWI s Maturity Model. The Data Warehousing Institute Research. Hagerty, J. & Hostmann, B ITScore for Business Intelligence and Performance Management. Gartner. Gartner IT Leaders Research Note G Hewlett-Packard Deliver the information business user need: Building the Business Intelligence Competency Center. Hewlett-Packard Development Company, L.P. Hostmann, B Business Intelligence Competency Center Key Program Overview. Gartner Research Note G Hovi, A., Hervonen, H., Koistinen, H Tietovarastot ja Business Intelligence. WSOY, Porvoo. 196 p. IBM Global Business Services The New Voice of the CIO: Insights from the Global Chief Information Officer Study. IBM Institute for Business Value. IBM Global Business Services The Essential CIO: Insights from the Global Chief Information Officer Study. IBM Institute for Business Value. Krakauer, J The Business Intelligence Competency Center: Enabling Continuous Improvement in Performance Management. Oracle Corporations. Krizan, L Intelligence essentials for everyone. Occasional Paper Number Six. Joint Military Intelligence College, Washington, DC. Nadini, M Global Business Intelligence Initiatives: 7 Critical Success Factors. [online] Available at < [Referred to at February 1 st 2014]. 88

98 Nazier, M.M, Khedr, A., Haggag, M Business Intelligence and its role to enhance Corporate Performance Management. International Journal of Management & Information Technology, Vol. 3, No. 3, pp Negash, Solomon & Gray, Paul Business Intelligence. Handbook on Decision Support Systems 2. Negash, Solomon Business Intelligence. Communications of AIS Vol. 13. p Organization, San Jose, CA, Pirttimäki, V Business Intelligence as a Managerial Tool in Large Finnish Companies. Tampere University of Technology, Publication 646. Thesis for the degree of Doctor of Technology. Rajteric, I Overview of Business Intelligence Maturity Models. Information Management, Vol. 15, 2010, 1, pp Ranjan, J Business justification with Business Intelligence. Institute of Management Technology, Ghaziabad, India. The journal of information and knowledge management systems, Vol. 38, No. 4, pp Emerald Group Publishing Limited. Rausch, P., Sheta, A.F. and Ayesh, A., Business Intelligence and Performance Management: Theory, Systems and Industrial Applications. Springer Publishing Company, Incorporated. 269 p. ISBN (online). ISBN (print). Saaranen-Kauppinen, A. & Puusniekka, A KvaliMOTV - Menetelmäopetuksen tietovaranto [internet]. Tampere: Yhteiskuntatieteellinen tietoarkisto [copyright]. < (Cited ) Vickers, Mike Discovery Report for the Future State of Business Intelligence Solution in EIM. Rittmanmead. 89

99 Vitt, E., Luckevich, M., and Milsner, S Business Intelligence: Making Better Decisions Faster. Microsoft Press, Washington. Yeoh, William & Koronos, Andy Critical success factors for Business Intelligence systems. Journal of Computer Information Systems, Spring Neely, A., Yaghi, B. & Youell, N. 2008a. Enterprise Performance Management: The Global State of the Art. Cranfield University, School of Management, Center for Business Performance. Neely, A. Yaghi, B. & Yonell, N. 2008b. Enterprise Performance Management: The UK State of the Art. Cranfield University, School of Management, Center for Business Performance. Quinn, K Strategic, Tactical and Operational Business Intelligence. Information Management. White, C The Next Generation of Business Intelligence: Operational BI. BI Research. Version 1. OTHER SOURCES The case company s internal information sharing and learning site (Intranet). 90

100 APPENDICES APPENDIX 1. The interview themes and questions for the semi-structured theme interview. 1. Business Intelligence speeds up and improves the organization s ability to make decisions. What are the typical decisions you make monthly / weekly? What kind of information you would need to make these decisions? What kind of information you currently use to make these decisions? Is this information available in EIM Operations BI? Do you use some other EIM BI dashboard/report for information and why? What kind of information do you request from your team members and how do they provide it? Do you share information from EIM BI internally (to team members, your supervisor, other EIM personnel). How and to whom? Do you think EIM BI Operations speeds up your decision making? Do you think EIM BI Operations improves your ability to make decisions? Keeping the first goal in mind, how should the EIM BI be improved? 2. Business Intelligence meets the users information needs in a timely manner. How often do you use EIM BI (e.g. times / week)? Is the information you need accessible when you need it? Are you satisfied with the response time of the dashboard pages / reports? Is the information in the right format or do you use the download functions and do further analysis e.g. in Excel? Is there some information you would need to explore (drill, navigate) but you are not able to? Keeping the first goal in mind, how should the EIM BI be improved? 3. Business Intelligence supports the organization s strategy and its goals. Are you able to get an overall picture from EIM BI of the performance of the processes / areas that you are responsible for? Is the performance compared to targets in EIM Operations BI? Do you think EIM Operations BI is aligned with Operations' strategy & goals? Do you think EIM Operations BI is aligned with the company's strategy & goals? Can the dependencies of different measures be found from the EIM Operations BI? To strategy and goals To other measures or upper level KPIs Do you think that the reports are interpreted in many ways?

101 Could you list few reports that support and/or do not support the strategy & goals of Operations? When using these supporting reports - what concrete actions do you take based on the results? Do you use EIM BI to support the goal setting and follow-up of targets in meetings or in supervisor-employee discussions? Keeping the first goal in mind, how should the EIM BI be improved? 4. Business Intelligence improves the users independency regarding information needs. Is the EIM Operations BI dashboard structure such that you are able to search for information independently? Are the reports easy to use, understandable and well described? Do you trust the quality of existing information (why / why not)? Do you know who is the owner of some specific dashboard / dashboard page? Do you report some EIM BI based information to your supervisor or other stakeholders? What kind of information? Do you repetatively request some measures or reports that are not in EIM Operations BI dashboards? Who provides this information and how? If you have questions related to the actual figures (business process) do you know who to contact? Keeping the first goal in mind, how should the EIM BI be improved? 5. Business Intelligence lowers costs and improves operational efficiency. Do you feel that EIM BI Operations has brought some cost reductions to Operations? Could you give me an example? Do you feel that EIM BI Operations has brought information that has led to improvements or changes in the underlying business processes? In general, how do you think that EIM BI is linked to the improvement of operational efficiency? Is using EIM (Operations) BI part of your daily work routines? Do you think EIM BI is part of your team members / Operations' personnel's daily routines? Keeping the first goal in mind, how should the EIM BI be improved?

102 APPENDIX 2. TDWI s BI Maturity Model online questionnaire & answers.

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106 APPENDIX 3. Theoretical background for the maturity assessment. As BI and EPM programs are complex in nature, many of the earlier mentioned problems can occur at the same time in an enterprise. One way to assess the current state of any BI/ EPM program is to use a tool called a Maturity Model. A BI maturity model usually offers a better understanding of the following questions (Rjateric 2010, Hagerty et al. 2010, Gonzales et al. 2010): What drives Business Intelligence in the enterprise? Which strategies for developing Business Intelligence are used today? What business value does Business Intelligence bring? According to Rajteric (2010, 49) maturity models are used to describe, explain and evaluate growth of life cycles - the fact that things change over time, and that most of these changes can be predicted and regulated, is the basis for the basic concept of all models. A typical maturity model is shown in Figure 14 below. In this case the evolution of a BI / EPM program is depicted using five levels (Unaware to Transformative). Gartner s BI and EPM maturity model (Hagerty et al. 2010)