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) Professor Tuomo Kässi
Examiner (2nd) Associate Professor Kalle Elfvengren Instructor M.Sc. (Tech) H. H.
Helsinki 6.3.2014 Joel Friman
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.
86 + 18 pages, 25 tables, 18 figures and 3 appendices
Examiners: Prof. Tuomo Kässi (1st), Associate Prof. Kalle Elfvengren (2nd) 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 decision- makers. 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.
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).
86 + 18 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 case- tutkimuksella. 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.
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.
TABLE OF CONTENTS
PART I INTRODUCTION ... 1
1 INTRODUCTION ... 1
1.1 BACKGROUND ... 1
1.2 TARGETS AND LIMITATIONS ... 2
1.3 IMPLEMENTATION OF THE RESEARCH ... 3
1.3.1 Action research ... 3
1.3.2 Maturity assessment ... 4
1.3.3 Semi-structured interviews ... 4
1.3.4 Benchmark ... 5
1.4 RESEARCH OBJECT IN THE CASE COMPANY ... 6
1.4.1 The case company ... 6
1.4.2 Research object ... 7
1.5 STRUCTURE OF THE REPORT ... 8
PART II LITERATURE REVIEW ... 11
2 BUSINESS INTELLIGENCE ... 11
2.1 OVERVIEW OF BUSINESS INTELLIGENCE... 11
2.1.1 Background ... 11
2.1.2 Definition ... 13
2.1.3 Components and architecture ... 16
2.1.4 Intelligence creation and use ... 18
2.1.5 Viewpoint and level of information ... 21
2.2 IMPORTANCE AND CRITICAL SUCCESS FACTORS ... 23
2.2.1 Objectives & importance ... 23
2.2.2 Critical success factors ... 25
2.3 ENTERPRISE PERFORMANCE MANAGEMENT... 29
2.3.1 Overview and linkage to Business Intelligence ... 29
2.3.2 Performance dashboards ... 34
2.3.3 Known challenges of EPM ... 36
PART III EMPIRICAL PART ... 42
3 BUSINESS INTELLIGENCE IN THE CASE COMPANY ... 42
3.1 BUSINESS INTELLIGENCE IN EIM ... 43
3.1.1 History & overview ... 43
3.1.2 Current state of the Business Intelligence program ... 44
3.1.3 Challenges of the current Business Intelligence program ... 49
3.2 MATURITY OF EIM’S BUSINESS INTELLIGENCE PROGRAM ... 52
3.2.1 The assessment in general ... 52
3.2.2 Results of the assessment ... 54
3.2.3 Summary of the assessment ... 60
3.3 BUSINESS INTELLIGENCE IN OPERATIONS ... 63
3.3.1 The interviews in general ... 65
3.3.2 Key interview findings ... 66
3.4 BENCHMARK ... 73
3.4.1 The benchmark in general ... 73
3.4.2 Key benchmark findings ... 74
PART IV FINDINGS, CONCLUSIONS AND DISCUSSION ... 77
4 KEY FINDINGS AND PROPOSED ACTIONS... 77
4.1 KEY FINDINGS OF THE LITERATURE REVIEW ... 77
4.2 KEY FINDINGS OF THE EMPIRICAL RESEARCH ... 79
5 CONCLUSIONS AND DISCUSSION ... 83
5.1 GENERALIZATION AND LIMITATIONS OF THE RESEARCH ... 85
5.2 FURTHER RESEARCH ... 86
LIST OF REFERENCES ... 87 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.
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. ... 9
FIGURE 4.SYNTHESIS OF THE LITERATURE REVIEW AND EMPIRICAL RESEARCH. ... 10
FIGURE 5.THE BPSMODEL (TURBAN ET AL.2010,6) ... 12
FIGURE 6.TYPICAL COMPONENTS OF BI(PIRTTIMÄKI 2007,91). ... 16
FIGURE 7.TECHNICAL ARCHITECTURE OF BI(CHAUDHURI ET AL.2011,2&VICKERS 2013,3) ... 17
FIGURE 8.INTELLIGENCE CREATION AND USE (KRIZAN 1999). ... 19
FIGURE 9.INTELLIGENCE CONCEPTS (ADAPTED FROM PIRTTIMÄKI 2007,91) ... 22
FIGURE 10.CRITICAL SUCCESS FACTORS OF BUSINESS INTELLIGENCE (YEOH ET AL.2009,25) ... 27
FIGURE 11.CLOSED-LOOP APPROACH TO EPM(TURBAN ET AL.2010,86) ... 30
FIGURE 12.COMPONENTS OF MODERN EPM. ... 31
FIGURE 13.EPM OPTIMIZATION CYCLE (BALLARD ET AL.2005,6) ... 33
FIGURE 14.A TYPICAL DASHBOARD (ORACLE 2014). ... 35
FIGURE 15.TECHNICAL ARCHITECTURE OF EIM’S BUSINESS INTELLIGENCE. ... 44
FIGURE 16.AVERAGE USAGE OF THE BI TOOL. ... 48
FIGURE 17.EIM IN TDWI’S MATURITY MODEL. ... 62
FIGURE 18.EIM’S BIMATURITY BY INTERVIEWEE. ... 72
LIST OF TABLES
TABLE 1.DEFINITIONS OF BUSINESS INTELLIGENCE. ... 14
TABLE 2.STEPS OF INTELLIGENCE CREATION AND USE EXPLAINED (KRIZAN 1999,13–40). ... 20
TABLE 3.THREE LEVELS OF BUSINESS INTELLIGENCE (WHITE 2006,1). ... 23
TABLE 4.CSFS FOR THE ORGANIZATIONAL DIMENSION (YEOH ET AL.2009,24-28) ... 27
TABLE 5.CSFS FOR THE PROCESS DIMENSION (YEOH ET AL.2009,24-28) ... 28
TABLE 6.CSFS FOR THE TECHNOLOGY DIMENSION (YEOH ET AL.2009,24-28) ... 28
TABLE 7.THE KEY PHASES OF THE EPM CYCLE EXPLAINED (TURBAN ET AL.2010,87–98) ... 31
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) ... 36
TABLE 10.THREE CATEGORIES OF REASONS TO UNSUCCESSFUL EPM... 37
TABLE 11.THE EPM GAPS EXPLAINED (NEELY ET AL.2008,6-10) ... 38
TABLE 12.PROBLEMS IN CURRENT EPM PROGRAMS (CHANDLER ET AL.2011,1-13). ... 39
TABLE 13.POSSIBLE WAYS TO TACKLE PROBLEMS IN EPM(NEELY ET AL.2008B,8) ... 41
TABLE 14.STRUCTURE OF THE CHAPTER. ... 42
TABLE 15.COMPONENTS OF ORACLE BI(ORACLE 2013). ... 46
TABLE 16.CATEGORIES AND THEIR DESCRIPTION IN THE TDWI’S BI MATURITY ASSESSMENT. ... 53
TABLE 17.MATURITY LEVELS AND THEIR CORRESPONDING SCORE. ... 53
TABLE 18.THE KEY CHARACTERISTICS OF EACH MATURITY LEVEL BY ASSESSMENT CATEGORY. ... 54
TABLE 19.MATURITY ASSESSMENT SCORES BY CATEGORY. ... 55
TABLE 20.KEY REASONS EXPLAINING THE SCORES. ... 61
TABLE 21.SUB-UNIT SPECIFIC PERFORMANCE MEASURES . ... 63
TABLE 22.USAGE BY DASHBOARD PAGE IN 2013(YEAR TO DATE*). ... 64
TABLE 23.INTERVIEWEES, THEIR ROLES AND THE INTERVIEW DATE. ... 65
TABLE 24.GENERAL INFORMATION ABOUT BMC. ... 73
TABLE 25.KEY FINDINGS. ... 76
LIST OF ABBREVATIONS
BI Business Intelligence
BI DW Business Intelligence Data Warehouse
BICC Business Intelligence Competency Center
BIT Abbreviation for the case company’s BI Team
BMC Abbreviation for the benchmarked company
EIM Abbreviation for the case company
EMF Enterprise metrics framework
EPM Enterprise Performance Management
ETL Extract-Transform-Load
KPI Key performance indicator
PM Performance Management
PMs Performance Measures
Program 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.
1
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.
2 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 non- technological 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’.
3 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.
1.3.1 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)
4 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.
1.3.2 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. 2010 & 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 3.
1.3.3 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. Semi- structured 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
5
‘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 2013.
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.
1.3.4 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.
6 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 1.4.1 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 1500 – 2000 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).
7 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.
1.4.2 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.
8 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
9 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.
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Figure 4. Synthesis of the literature review and empirical research.
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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 2.1.1 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.
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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)
13 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 data- gathering, data-storage and knowledge management with analysis, to provide input for the decision makers. In 2010, Clark (2010, 1) refined the definition when saying
14 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.
2004 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.
2007 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.
2008 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.
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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.
2010 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.
2010 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.
2011 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.
2013 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.
2013 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.
16 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
17 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 front- end 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
18 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.
2.1.4 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
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‘why’ can be called intelligence creation and use which according to Turban et al.
2010 (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.
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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 creation
Simply put, intelligence creation means delivering briefings or reports for 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).
21 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.
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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.
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Table 3. Three levels of Business Intelligence (White 2006, 1).
STRATEGIC TACTICAL OPERATIONAL
Business focus Achieve long-term business goals
Manage tactical programs to achieve the long-term business goals
Manage and optimize daily business operations
Primary users Executives and business analysts
Senior managers, business analysts and line-of-business managers
Line-of-business managers
Time-frame Months to years Days to weeks to months
Intra-day Data Historical metrics (key
performance indicators)
Historical metrics 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.
2.2.1 Objectives & importance
According to Hovi et. al (2008, 80) Business Intelligence has the following objectives:
24 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 1st 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:
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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.
2.2.2 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
26 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.
