An Industrial Engineering Perspective Of Business Intelligence

Description
An Industrial Engineering Perspective Of Business Intelligence

A AN N I IN ND DU US ST TR RI IA AL L E EN NG GI IN NE EE ER RI IN NG G P PE ER RS SP PE EC CT TI IV VE E O OF F

B BU US SI IN NE ES SS S I IN NT TE EL LL LI IG GE EN NC CE E

P PI IE ET TE ER R J JA AC CO OB BU US S C CO ON NR RA AD DI IE E

A A t th he es si is s s su ub bm mi it tt te ed d i in n p pa ar rt ti ia al l f fu ul lf fi il lm me en nt t o of f t th he e r re eq qu ui ir re em me en nt ts s f fo or r t th he e
d de eg gr re ee e

P PH HI IL LO OS SO OP PH HI IA AE E D DO OC CT TO OR R ( (I IN ND DU US ST TR RI IA AL L E EN NG GI IN NE EE ER RI IN NG G) )

I In n t th he e

F FA AC CU UL LT TY Y O OF F E EN NG GI IN NE EE ER RI IN NG G, , B BU UI IL LT T E EN NV VI IR RO ON NM ME EN NT T A AN ND D
I IN NF FO OR RM MA AT TI IO ON N T TE EC CH HN NO OL LO OG GY Y

U UN NI IV VE ER RS SI IT TY Y O OF F P PR RE ET TO OR RI IA A

O Oc ct to ob be er r 2 20 00 04 4

U Un ni iv ve er rs si it ty y o of f P Pr re et to or ri ia a e et td d – – C Co on nr ra ad di ie e, , P P J J ( (2 20 00 05 5) )
A AB BS ST TR RA AC CT T

A AN N I IN ND DU US ST TR RI IA AL L E EN NG GI IN NE EE ER RI IN NG G P PE ER RS SP PE EC CT TI IV VE E O OF F

B BU US SI IN NE ES SS S I IN NT TE EL LL LI IG GE EN NC CE E

P PI IE ET TE ER R J JA AC CO OB BU US S C CO ON NR RA AD DI IE E

Promoter: Professor PS Kruger
Co-promoter: Professor SJ Claasen
Department: Industrial and Systems Engineering
University: University of Pretoria
Degree: Philosophiae Doctor

Key words:

Business intelligence, strategy alignment, Balanced Scorecard, strategy map, enterprise
modelling, business process management, performance management, value chain, data
warehouse, dimensional modelling, key performance indicators.

Summary:

In this thesis the candidate explores the apparent gaps between strategy development and
strategy implementation (the strategy alignment question), and between business end-user
needs and the suppliers of information technology (IT) related products and services. With
business intelligence (BI) emerging as one of the fastest growing fields in IT, the candidate
develops a conceptual model in which BI is placed into context with other relevant subjects
such as strategy development, enterprise architecture and modelling and performance
measurement.

The emphasis is on the development of processes and templates that support a closed loop
control system with the following process steps:

- A business strategy is defined.
- The implication of the strategy on business processes, supporting IT resources
and organizational structure is formally documented according to enterprise
architecture principles.
- This documented blueprint of the organization helps to implement the selected
business strategy.
- A performance measurement system is developed and supported by a well-
designed data warehouse.
- On a regular basis the measurements that were defined to support the
implementation of the strategy, together with information from the external
environment are interpreted and this analysis leads to either a new strategy, or
refinement of the implementation of the existing strategy. Both options may lead
to changes in the enterprise architecture, the execution of business processes
and/or the performance measurement system.

Some of the individual components of the model are supported by existing theories, for
example the Zachman Framework for enterprise architecture and the Balanced Scorecard
from Kaplan and Norton. The contribution of the author was to position them in the bigger
picture to indicate how they can add value with regard to the establishment of business
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intelligence in organizations. Instead of packaging existing ideas slightly differently under a
new name, the author intentionally searched for existing theories to fulfil certain
requirements in the Bigger Picture BI Context Model.

Apart from a set of templates that were adapted from various other sources and packaged
into practical formats that can be used during facilitation sessions, the author has also
developed and described the Fourier Model and the Pots of Money Model. The Fourier Model
is a powerful conceptual model that helps a business to package solutions for market
related requirements through selections of previously defined building blocks (technical
components) that can be delivered through various business entities, depending on the
requirements of the opportunity. The Pots of Money Model is a quantitative model
embedded in a spreadsheet format to illustrate and communicate the effect of spending
decisions in one area of the business on other areas.

The candidate demonstrates the Bigger Picture BI Context Model in several case studies.
The thesis is accompanied by a CD ROM, which contains over 700 references to relevant
literature (most of them available in full text) and links to internet web sites, as well as
examples of the software templates that support some of the steps in the context model.

The following figure depicts the conceptual model in schematic format:

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a ac ck kn no ow wl le ed dg ge em me en nt ts s

Various people have assisted the author in many different ways during this learning
experience over the last number of years. A mere thank you is probably not enough to show
appreciation, but none the less the author wants to acknowledge and show gratitude for the
following contributions:

To an Almighty God who has given me the ability and persistence to travel this journey
to the end. A God that does not need any business intelligence to make his decisions,
but who gives us the talents to improve ours.
To Prof. Paul Kruger and Prof. Schalk Claasen, the promoters of this thesis, for their
patience, understanding and guidance during the whole process. Thank you also for the
necessary pressure to conclude the exercise.
To all colleagues at the industrial engineering department of the University of Pretoria
for their support and encouragement during the years.
To all colleagues at Fourier Approach for their willingness to experiment and explore, to
contribute and learn, to give constructive criticism when necessary and for being the
team that they are.
To Pierre Lombard for his creative role in the technical design and putting the finishing
touches to the CD ROM that accompanies the thesis, as well as his initial contributions to
start the documentation process.
To Lenie van der Merwe for taking professional care of the language aspects in the
thesis.
To many friends and family members who have been neglected for a number of years -
thank you for support, understanding and encouragement.
To my parents who have always supported me and who have gone out of their way to
assist my family when I was not there.
To my children, Leandri and Ansoné, who have shown maturity far beyond their age in
understanding why I could not always be there for them.
Last, but definitely not least, to my wife and friend Genie. Without your support and
understanding I would not have been able to finish this project - let our lives begin
again!

pieter conradie
an industrial engineering perspective
of business intelligence
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t ta ab bl le e o of f c co on nt te en nt ts s
1 INTRODUCTION 1
1.1 BACKGROUND .............................................................................. 1
1.2 MAJOR ROLE PLAYERS ................................................................... 1
1.2.1 Industrial engineers 1
1.2.2 Management science 2
1.2.3 Information and communication technology 2
1.3 THE GAP BETWEEN DIFFERENT WORLDS .......................................... 3
1.4 PROBLEM STATEMENT.................................................................... 4
1.5 RESEARCH METHODOLOGY............................................................. 5
1.6 ORGANIZATION OF THIS THESIS..................................................... 5
1.6.1 Document structure 5
1.6.2 CD-ROM 6
2 LITERATURE STUDY 7
2.1 INTRODUCTION ............................................................................ 7
2.2 INFORMATION .............................................................................. 9
2.2.1 Defining information 9
2.2.2 Types of information 11
2.2.3 Information in organizations 12
2.2.3.1 Sophistication of use of information .............................................12
2.2.3.2 Levels of corporate information focus ...........................................12
2.3 BUSINESS STRATEGY AND SCENARIO PLANNING .............................14
2.3.1 Life cycles 15
2.3.2 Innovation Matrix 18
2.3.3 Innovation in strategic planning 20
2.3.4 Strategy – an ongoing conversation 22
2.3.4.1 Creating the right context...........................................................22
2.3.4.2 Important business concepts.......................................................23
2.3.4.3 A strategy creating process.........................................................28
2.3.5 Scenario planning 34
2.4 ENTERPRISE INTEGRATION AND ARCHITECTURE..............................37
2.4.1 Overview 37
2.4.2 PERA 38
2.4.3 GERAM 41
2.4.4 The Zachman Framework 42
2.4.5 CuTS (culture, technology and skills) 45
2.4.6 Other architectures 47
2.4.6.1 GRAI-GIM ................................................................................47
2.4.6.2 CIMOSA...................................................................................50
2.4.6.3 ARIS .......................................................................................51
2.4.7 Summary 52
2.5 DATA WAREHOUSING ...................................................................53
2.5.1 The Corporate Information Factory (CIF) - Inmon 53
2.5.1.1 Information ecosystem...............................................................53
2.5.1.2 Visualizing the CIF.....................................................................54
2.5.1.3 Components of the CIF...............................................................55
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2.5.1.4 Migrating to the CIF...................................................................63
2.5.1.5 Enhanced CIF picture.................................................................65
2.5.2 The data warehouse - Kimball 67
2.5.2.1 Components of a data warehouse ................................................67
2.5.2.2 Implementing the components of the data warehouse ....................71
2.5.2.3 Business Dimensional Lifecycle....................................................72
2.5.2.4 Handling changes to dimensions..................................................73
2.5.2.5 Fact table types ........................................................................74
2.5.3 Comparing Inmon and Kimball 75
2.6 KNOWLEDGE MANAGEMENT...........................................................77
2.7 PERFORMANCE MEASUREMENT ......................................................78
2.7.1 Why do we need to measure performance? 78
2.7.2 Performance measurement or management? 78
2.7.3 Link between strategic management and performance management. 78
2.7.4 Cross-functional management 79
2.7.4.1 The organization level (I) ...........................................................80
2.7.4.2 The process level (II).................................................................82
2.7.4.3 The job/performer Level (III) ......................................................86
2.7.4.4 A holistic view of performance.....................................................86
2.7.5 The Balanced Scorecard (BSC) 88
2.7.5.1 Financial perspective..................................................................89
2.7.5.2 Customer perspective ................................................................90
2.7.5.3 The internal business process perspective.....................................91
2.7.5.4 The learning and growth perspective............................................92
2.7.5.5 Linking BSC measures to the business strategy .............................92
2.7.6 Key performance indicators (KPIs) 94
2.7.6.1 24 Ways by Richard Connelly et al. ..............................................94
2.7.6.2 PIs and MIs by Absolute Information............................................96
2.7.7 Summary 98
2.8 MERGING BUSINESS INTELLIGENCE (BI) WITH TECHNOLOGY ............99
2.8.1 Business intelligence 99
2.8.2 The decision-making process 99
2.8.3 Business intelligence tools 102
2.8.3.1 Views from Gartner Research.................................................... 102
2.8.3.2 Views from the OLAP Report ..................................................... 106
2.8.3.3 Views from Ventana Research ................................................... 106
2.8.4 The role of chief information officer 107
2.8.5 Summary 110
2.9 CONCLUSION OF LITERATURE STUDY ........................................... 111
3 BI IN CONTEXT – A CONCEPTUAL MODEL 113
3.1 INTRODUCTION ......................................................................... 113
3.2 OVERVIEW OF THE BIGGER PICTURE BI CONTEXT MODEL ............... 113
3.2.1 Strategy development 114
3.2.2 Enterprise architecture 117
3.2.2.1 Selection of methodology ......................................................... 117
3.2.2.2 Selection of a case tool ............................................................ 118
3.2.2.3 Process simulation modelling .................................................... 120
3.2.3 Strategy implementation and execution 121
3.2.3.1 The move from planning to doing .............................................. 121
3.2.3.2 Business processes management (BPM)...................................... 121
3.2.3.3 Workflow impact on business processes...................................... 122
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3.2.4 Performance measurement from a data warehouse 125
3.2.4.1 Rummler and Brache framework................................................ 125
3.2.4.2 Balanced Scorecard approach.................................................... 126
3.2.4.3 Data warehousing approach...................................................... 126
3.2.4.4 Business intelligence tools ........................................................ 127
3.2.5 Interpretation of business intelligence 129
3.2.6 Updating of the enterprise architecture 130
3.3 SUPPORTING TEMPLATES ............................................................ 131
3.4 CONCLUSION OF BI IN CONTEXT.................................................. 131
4 CASE STUDY – CONCEPTUAL MODEL DEMONSTRATED 134
4.1 INTRODUCTION ......................................................................... 134
4.2 BACKGROUND OF THE CONSULTING FIRM..................................... 134
4.3 STRATEGY DEVELOPMENT ........................................................... 137
4.4 ENTERPRISE ARCHITECTURE ....................................................... 141
4.5 IMPLEMENT AND EXECUTE STRATEGY ........................................... 148
4.5.1 Using the Balanced Scorecard 148
4.5.2 Using the Fourier Model 151
4.5.3 Using the Pots of Money Model 152
4.6 PERFORMANCE MEASUREMENT .................................................... 155
4.7 INTERPRET FEEDBACK ................................................................ 160
4.8 DISCUSSION OF OTHER CASE STUDIES ........................................ 161
4.8.1 Data warehousing in a facility management environment 161
4.8.2 Applying BI in a typical academic environment 166
4.9 CONCLUSION............................................................................. 167
5 THESIS SUMMARY 168
5.1 CONTRIBUTION TO THE BODY OF KNOWLEDGE.............................. 168
5.2 RETROSPECTION ON THE PROCESS .............................................. 169
5.3 MATERIAL FOR FURTHER INVESTIGATION ..................................... 170
6 BIBLIOGRAPHY 171

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l li is st t o of f f fi ig gu ur re es s

Figure 1. Translation gap between IT and business........................................................3
Figure 2. Strategic alignment .....................................................................................3
Figure 3. Attributes of information. (Adapted from Swanborough 2002) .......................... 11
Figure 4. The three financial management "absolutes" ................................................. 11
Figure 5. The four informational management "absolutes"............................................ 11
Figure 6. Levels of corporate information focus ........................................................... 13
Figure 7. Economic cycles (Kondratieff, as referred to by Grulke 2001) ................................ 15
Figure 8. Schumpeter's waves (as referred to by Grulke 2001) ..................................... 16
Figure 9. Business cycle (Grulke 2001) ........................................................................ 17
Figure 10. Innovation Matrix (Grulke 2001) ................................................................. 18
Figure 11. Learning from the future (As adapted from Grulke 2001)................................... 21
Figure 12. Hustling with a purpose (Manning 2001)....................................................... 23
Figure 13. Unaligned stakeholders (Manning 2001)........................................................ 25
Figure 14. Aligned stakeholders (Manning 2001) ........................................................... 25
Figure 15. Effect of human spirit on strategy (Manning 2001) ......................................... 26
Figure 16. Four steps to implement change (Manning 2001) ........................................... 27
Figure 17. Does the business logic add up? (Manning 2001)............................................ 29
Figure 18. Two frameworks to explore your business environment................................. 31
Figure 19. Five building blocks of a strategic plan (Manning 2001) ................................... 32
Figure 20. The 7 Ps Model (Manning 2001) ................................................................... 33
Figure 21. The Strategy Wheel to identify top priority issues......................................... 34
Figure 22. Foxy Matrix (Ilbury and Sunter 2001) ............................................................ 35
Figure 23. Purdue Enterprise Reference Architecture.................................................... 39
Figure 24. GERAM framework components (Adapted from Williams and Li 1998) .................. 42
Figure 25. Zachman Framework for enterprise architecture (Zachman 1987)..................... 43
Figure 26. Zachman Framework for enterprise architecture (Zachman and Sowa 1992) ....... 45
Figure 27. The CuTS model (Absolute Information 2001) ................................................. 46
Figure 28. Defining information needs (Absolute Information 2001)................................... 47
Figure 29. GRAI Global Model (http://www.atb-bremen.de/projects/prosme/Doku/oqim/GRAI.htm)
..................................................................................................................... 48
Figure 30. GRAI-GIM Enterprise Life Cycle (Adapted from Koorts 2000) ............................. 49
Figure 31. The Corporate Information Factory (Inmon et al. 2001) ................................... 55
Figure 32. Applications feed data into the I and T layer (Inmon et al. 2001) ...................... 56
Figure 33. The feeds into and out of the I and T layer (Inmon et al. 2001) ........................ 57
Figure 34. A data warehouse in the context of the CIF (Inmon et al. 2001)........................ 58
Figure 35. The data warehouse feeds to the data marts (Inmon et al. 2001)..................... 60
Figure 36. The essential components of the web and the CIF (Inmon et al. 2001)............... 62
Figure 37. First three steps to building the CIF (Inmon et al. 2001) .................................. 64
Figure 38. The next steps to building the CIF (Inmon et al. 2001) .................................... 65
Figure 39. Enhanced CIF picture (Inmon and Imhoff 2001) .............................................. 66
Figure 40. The basic elements of the data warehouse (Kimball et al. 1998)........................ 68
Figure 41. Star schema (Kimball et al. 1998) ................................................................ 69
Figure 42. The data mart matrix showing the Data Warehouse Bus Architecture (Adapted
from Kimball et al. 1998)...................................................................................... 71
Figure 43. Business Dimensional Lifecycle diagram (Kimball et al. 1998)........................... 72
Figure 44. Traditional (vertical) view of an organization ............................................... 79
Figure 45. The "silo" phenomenon (Rummler and Brache 1995)........................................ 80
Figure 46. Systems (horizontal) view of an organization............................................... 81
Figure 47. An organization as an adaptive system....................................................... 82
Figure 48. The organization level of performance ........................................................ 83
Figure 49. The process level of performance............................................................... 83
Figure 50. Computec order filling: "As-is" process map (Rummler and Brache 1995)............ 85
Figure 51. The job/performer level of performance...................................................... 86
Figure 52. The customer perspective........................................................................ 91
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Figure 53. The generic value model (Kaplan and Norton 1996) ......................................... 92
Figure 54. Cause-and-effect example (Kaplan and Norton 1996)....................................... 93
Figure 55. Typical current situation - old focus.......................................................... 100
Figure 56. Traditional approach - old focus............................................................... 101
Figure 57. Re-engineering approach - new focus....................................................... 101
Figure 58. Hype cycle for BI (Buytendijk et al. 2003)................................................ 103
Figure 59. EBIS Magic Quadrant August 2003 (Dresner et al. 2003)................................ 104
Figure 60. EBIS Magic Quadrant April 2004 (Dresner et al. 2004)................................... 104
Figure 61. BI Platform Magic Quadrant August 2003 (Dresner et al. 2003)....................... 105
Figure 62. BI Platform Magic Quadrant April 2004 (Dresner et al. 2004) .......................... 105
Figure 63. Evolution of information management ...................................................... 108
Figure 64. Traditional IT manager roles ................................................................... 108
Figure 65. The traditional MIS manager ................................................................... 109
Figure 66. The CIO structure (Absolute Information 2001) ............................................. 109
Figure 67. An overview of the Bigger Picture BI Context Model.................................... 114
Figure 68. The Fourier Model.................................................................................. 116
Figure 69. Logical ERD of the Fourier Model.............................................................. 117
Figure 70. Zachman Framework embedded in Casewise............................................. 119
Figure 71. Various formats to capture and associate entities in Casewise. ..................... 119
Figure 72. A typical generic process ........................................................................ 122
Figure 73. Typical paperwork during activities .......................................................... 123
Figure 74. Typical "hand-offs" between human resources ........................................... 123
Figure 75. Estimated time for the total process......................................................... 123
Figure 76. Improved system.................................................................................. 124
Figure 77. The Microsoft BI tool offering (Microsoft partner information 2004).................... 128
Figure 78. An overview of the Bigger Picture BI Context Model.................................... 131
Figure 79. Focus areas to bridge the gap ................................................................. 136
Figure 80. An example of the Foxy Matrix applied to Fourier Approach. ........................ 138
Figure 81. The 7Ps model applied to Fourier Approach. .............................................. 139
Figure 82. Innovative Matrix applied to Fourier Approach. .......................................... 140
Figure 83. Example of a Strategy Wheel for Fourier Approach ..................................... 140
Figure 84. Definition of strategic goals..................................................................... 142
Figure 85. Fourier external organizational context ..................................................... 143
Figure 86. Breakdown of Fourier related enterprise group........................................... 143
Figure 87. An object can be part of various hierarchies .............................................. 144
Figure 88. Application software associated with finance management........................... 144
Figure 89. Value chain of Fourier Approach .............................................................. 145
Figure 90. Hierarchy of financial processes............................................................... 146
Figure 91. Example of a business dynamic model ...................................................... 146
Figure 92. Example of a system dynamic model ........................................................ 147
Figure 93. Simplified version of the value chains....................................................... 149
Figure 94. Strategy map for Fourier Approach .......................................................... 150
Figure 95. The Fourier Model.................................................................................. 151
Figure 96. Overview of the Pots of Money Model ....................................................... 153
Figure 97. Detailed example of Pots of Money Model.................................................. 154
Figure 98. Context of the project management data marts ......................................... 155
Figure 99. Extract from Bus Matrix for Fourier data warehouse.................................... 156
Figure 100. Star scheme of the actual project transaction mart ................................... 157
Figure 101. An example of a typical Sagent ETL plan ................................................. 159
Figure 102. Typical overall robot screen................................................................... 162
Figure 103. Detail figures for a specific KPI .............................................................. 163
Figure 104. Typical trend report for a specific KPI ..................................................... 163
Figure 105. KPI definition and management application ............................................. 164
Figure 106. English definition versus SQL statement.................................................. 164
Figure 107. The Bigger Picture BI Context Model....................................................... 168

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l li is st t o of f t ta ab bl le es s

Table 1. Types of Information (Absolute Information 2001) .............................................. 12
Table 2. The sophistication of use of information......................................................... 12
Table 3. Creative destruction of job opportunities (Grulke 2001) ..................................... 16
Table 4. Enterprise entity life cycle (Adapted from Williams and Li 1998) ............................. 40
Table 5. CIMOSA - Dimension of genericity ................................................................ 51
Table 6. CIMOSA - Dimension of model ..................................................................... 51
Table 7. CIMOSA - Dimension of view........................................................................ 51
Table 8. Fact table type comparison (Adapted from Kimball and Ross 2002)......................... 74
Table 9. The Nine Performance Variables with questions (Rummler and Brache 1995).......... 87
Table 10. Selected functional goals based on Computec order-filling process goals (Rummler
and Brache 1995) ............................................................................................... 88
Table 11. Measuring strategic financial themes (Kaplan and Norton 1996) ......................... 90
Table 12. Growth in the OLAP market worldwide (www.olapreport.com 2004) ................... 106
Table 13. Definition of dimensions .......................................................................... 156
Table 14. Fact definitions for actual project transaction data mart ............................... 157
Table 15. Detailed specification of the client dimension .............................................. 158
Table 16. Expectations of KPIs from various subject areas.......................................... 165

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a ac cr ro on ny ym ms s

AIM Absolute information management
B2B Business to business
BAM Business activity monitoring
BP Business process
BPM Business performance management
BPM Business performance measurement
BPM Business process management
BI Business intelligence
BSC Balanced scorecard
CD Compact disk
CD ROM Compact disk read only memory
CIF Corporate information factory
CIM Computer integrated manufacturing
CORS Cognitive, operit, revit and synit
CRM Customer relationship management
CSF Critical success factor
CuTS Culture, technology and skills
DSS Decision support system
DW Data warehouse
EA Enterprise architecture
EAI Enterprise application integration
EBIS Enterprise business intelligence suite
EDW Enterprise data warehouse
EII Enterprise information integration
ER Entity relationship
ERP Enterprise resource planning
ETL Extraction, transformation, loading
FK Foreign key
GERAM Generalized enterprise reference architecture and methodology
IE Information ecosystem
IS Information system
I and T Layer Integration and transformation layer
IT Information technology
ICT Information and communication technology
JIT Just in time
KM Knowledge management
KPI Key performance indicator
MBO Management by objectives
MIS Management information system
MOLAP Multidimensional OLAP
OLAP Online analytical processing
OLTP Online transactional processing
ODS Operational data store
PERA Purdue enterprise reference architecture
PK Primary key
ROLAP Relational OLAP
RSA Republic of South Africa
SCM Supply chain management
SIG Swanborough information grid
SWOT Strengths, weaknesses, opportunities and threats
TQM Total quality management
UI User interface

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1 Introduction

“To win without fighting is best” – Sun Tzu
1.1 Background

During the last number of centuries wars have been fought not only on the
battlegrounds, but also in the boardrooms and corridors of businesses. Long
before the term business intelligence became fashionable, the military world was
talking about military intelligence (even though there are people who refer to it –
tongue in cheek – as an example of a contradiction in terms!)

Just as the military realised that pertinent, actionable information is necessary to
be successful, businesses also need information to base their decisions on. Just as
military generals need to develop and implement strategies to survive, the long-
term survival of businesses depends on the way in which they strategise and
adapt to changing business environments. Many of the principles and guidelines
that are discussed in The Art of War, by Sun Tzu (1991), are used successfully by
business leaders in their handling of organizations in conflict – the analogy
between martial art and business success is therefore not that far fetched.

Business intelligence (BI), according to the definition by Kimball and Ross (2002),
is a generic term to describe leveraging the internal and external information
assets of the organization to make better business decisions. Inmon, Imhoff and
Sousa (Inmon et al. 2001) see BI as representing those systems that help
companies understand what makes the wheels of the corporation turn and help
predict the future impact of current decisions. They also add that these systems
play a key role in the strategic planning process of the corporation.

Although the definitions will be further explored later on in the study, it is clear
that BI has to do with information and decision support.
1.2 Major role players
1.2.1 Industrial engineers

Traditionally, industrial engineers have been involved in decision support at
various levels in the organization. At first they focussed on the production
function of organizations, but during the last number of decades they have also
played an important role in the improvement of business processes in other
business functions, such as human resource management, financial management,
procurement and marketing. They are also playing an increasing role in the
streamlining of transactions between businesses. The process approach that
industrial engineers bring into the environment often enables different disciplines
in an organization to see their role in context of the bigger business picture for
the first time.

The deserved attention that supply chain management (SCM) has been receiving
since 1990 is proof of the potential value that can be unlocked by improving
inter-company activities and information flow – managing an even bigger picture
of interdependent businesses.

Other typical industrial engineering activities such as quality management,
simulation modelling, systems engineering and integration and enterprise
architecture also play a role in helping businesses to clarify their information
an industrial engineering perspective of business intelligence 1
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system needs.

Industrial engineers are, however, not the only players in the field. Various other
disciplines are also playing their parts and bringing specific expertise to the table.
Management science and information and communication technology (ICT) are
two other major players that are also involved.
1.2.2 Management science

Concepts like Management by Objectives (MBO), Total Quality Management
(TQM), Balanced Scorecard (BSC) and many more were originally developed by
people that entered the arena from the business management and operations
research point of view. These concepts are often qualitative of nature and need
some kind of quantitative support foundation to become practically usable.

Buys (2002) points out:

What managers need are new and improved theories and models (tools)
that can be applied in practice. Theories should be embodied in conceptual
models (graphical, mathematical or schematic descriptions or analogies)
or practical methods (procedures or techniques).

Currently, in the so-called “information era”, the necessary quantitative support
foundation for these theories very often involves information and communication
technology.
1.2.3 Information and communication technology

People operating in the ICT environment are producing enabling tools that are
potentially capable of supporting almost any conceptual curveball that the
management science people can throw at them through sophisticated hardware
and software products. The speed at which generic products are developed and
introduced into the market is extremely fast and provides in itself a challenge to
decide what to select and when to use it.

The reason why generic products are often developed instead of user specific
solutions is obvious – the potential market is much bigger and the development
cost can be recovered from various parties, making the tools also more affordable
to the buyers.

The implementation of acquired tools in the existing environment and
circumstances of a specific organization often proves to be a task beyond the IT
product/service provider (because of a lack of business knowledge), as well as
the business user (because of a lack of knowledge of the system and the way
systems are integrated).
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1.3 The gap between different worlds

Business Requirements - Management Concepts
Generic IT related products and services
Translation gap

Figure 1. Translation gap between IT and business

Given this background, it can be stated that various gaps still exist in the ideal
picture. First of all, there is the gap between business end-users and information
technology. As Jim Kanzler (2003) summarizes the situation in the title of one of
his internet articles, “IT is from Mars, End-Users are from Venus”. The struggle
between end-users and IT over reporting and data responsibility is far from over,
and each party has a valid case. Business intelligence tools have progressed over
time to empower end-users to generate their own reports, but they often still
need bits of data that are not provided for in the Enterprise Data Warehouse
(EDW). This leads to cutting and pasting into spreadsheets – a manual process
prone to error and open to criticism when the business user, who comes up with
a figure, cannot answer the common question: How did you get that number?

The traditional management gap between strategic planning and operational
execution (the strategic alignment question) is still haunting most organizations.
Various management models have been developed to address this issue, but they
are not always successfully implemented.

Implementing well thought
out strategies - strategic
alignment
Strategic
Tactical
Operational

Figure 2. Strategic alignment

The concept of identifying key performance indicators (KPIs) to guide people's
efforts in the right direction (strategically speaking) is an old technique, dating
back to the early days of Peter Drucker’s concept of Management by Objectives
(MBO). Combined with performance measurement, it can be a powerful
instrument. However, people often find it difficult to define the right KPIs and to
get objective measurements from the existing information sources.
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The use of a Balanced Scorecard approach, as proposed by Kaplan and Norton
(1996), goes a long way to ensure that the right KPIs are identified (instead of
just a list of measurements that sound good or are easy to measure). It also links
measurements from various business perspectives in a cause-and-effect manner
that supports the selected business strategy.

Various other less traditional approaches to identify innovative products, services
and total solutions (such as the “Lessons in radical Innovation” by Wolfgang
Grulke, 2001), and structured methods to do scenario planning that will give
strategic direction (such as the Foxy Matrix by Illbury and Sunter, 2001) will have
to find a place in the bigger picture framework.
1.4 Problem statement

This thesis explores the important role that industrial engineers may play in the
selection, implementation and integration of relevant IT solutions to meet
business requirements, when they position themselves on the side of businesses
instead of products and specific IT solutions. The goal is to develop a bigger
picture model, or framework, that will put a number of the existing theoretical
models into context and will provide a generic process for implementing BI in
organizations.

The roles of change agent, translator of user requirements into functional
specifications and integrator of various components in a total solution are not
really new to industrial engineers. The focus of this study, however, is on bridging
the gap between business requirements and the suppliers of ICT products and
services with special attention to

a structured approach to link business strategy to an information technology
strategy in such a way that the value stream and underlying business
processes of the organization are supported by appropriate transactional and
business intelligence information systems, which are in turn supported by
appropriate and flexible IT infrastructure;
data warehousing as the foundation for information needs;
performance measurement to support strategic, tactical and operational
goals;
management information systems (MIS) for decision support – the delivery
mechanisms of relevant information at the right time.

Having stated where the emphasis of the thesis lies, it is also appropriate to state
what is not included in the study:

Purely technical issues in the information technology arena such as specific
differences between various databases (e.g. SQL Server and Oracle, or the
differences between various versions of Oracle).
The differences between and detail algorithms used by various data mining
methods.
Detail comparisons between various BI related tools – for example Cognos vs.
Business Objects, or Datastage vs. Sagent. One reason for excluding such
comparisons is the fact that it is almost impossible to have thorough enough
knowledge of all the products at a certain point in time to compare them
effectively. Furthermore, all the products are constantly in a mode of
development with enhancement releases at least once a year and from time
to time products are acquired and packaged differently with other new or
existing products.

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1.5 Research methodology

Buys (2002) clearly distinguishes between pure management practice and
research. “To qualify as a research project, there must also be some generation
of new knowledge.” This new knowledge can be demonstrated in three different
ways:

Application of existing theories, models and methods to a new problem.
Testing of existing theories, models and methods.
Building of new or improved theories, models and methods.

The research methodology followed in this case was the following:

The identification of relevant existing theories, models and methods in the
fields of strategic management, enterprise architecture, performance
management, data warehousing and knowledge management through
literature studies, internet searches and practical exposure.
Critical testing and comparison of a number of these theories, models and
methods.
The integration of a number of these theories, models and methods into a
new framework of integrated theories, models and methods that can assist
businesses in bridging the gap between their requirements and information
technology offerings.
Testing the new integrated framework and parts thereof in a limited number
of case studies, which have led to further refinements of the framework and
supporting templates.

What makes the work different from pure management practice is the
integration of the various existing theories, models and methods and the
development of supporting templates to assist the user in various steps within
the bigger framework. The design of a set of data marts that support the value
chain of a typical consulting firm is a further deliverable that should have
reusability in similar environments.

1.6 Organization of this thesis
1.6.1 Document structure

Chapter 2, a literature study, provides insight into a number of subjects that form
the foundation for the bigger picture model that is later developed. The literature
study is presented along a number of themes:

Strategic positioning and scenario planning
Frameworks for enterprise architecture
Data warehousing
Knowledge management
Performance measurement
Business intelligence and technology tools

In Chapter 3 the various theories and conceptual models are analyzed and a new
contextual framework is developed where the existing theories, together with
some new inputs, are integrated in the Bigger Picture BI Context Model. Practical
and simplistic templates are developed and discussed.

In Chapter 4 this contextual framework is applied to a consulting company and
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the results are discussed. Other case studies where elements of the framework
were used are also discussed, as well as situations that were handled without the
framework.

Chapter 5 summarizes the thesis and evaluates the study. Various
recommendations regarding further enhancements are made.

1.6.2 CD-ROM

In addition to the thesis document a CD-ROM is provided with a rich collection of
current literature (mostly dated from 1999 to 2004), as well as electronic
versions of the templates that were developed. Many of the sources that are on
the CD have not been referenced directly in the document and do therefore not
appear in the bibliography.

Numerous references to web sites of relevant service providers are also included.
Some of the electronic articles on the CD have links to the internet and it is
recommended that one should be linked to the internet while browsing the CD.
However, since many of the internet links change sooner or later, the majority of
articles were captured in such a way that they will be usable off line.

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2 Literature study
2.1 Introduction

The challenging (and frustrating) part of a literature study is the decision what to
include in the final presentation and also in what manner or structure. During the
whole journey of discovery, which stretched over four to five years and included
literally hundreds of books, journal articles, white papers and internet articles,
many detours were taken on interesting, albeit slightly unrelated, paths.

Also, with the problem statement to develop a bigger picture framework, one is
tempted to try and accommodate everything. The main focus, however, is on
business intelligence (BI) and the process orientation that the industrial engineer
can offer to make the process of extracting BI from data more practical. It is
clear that business intelligence does not stand on its own – the what, why, who,
when, how, where and other relevant questions put it in a certain context. To
understand BI in this context it is necessary to explore a number of related
subjects.

The following figure illustrates the components of this literature study within the
context of an enterprise. It takes into account all aspects that influence business
intelligence in the author’s view.

The numbers indicate the section headings that will follow and the order in which
they will be addressed.

Merging
business with
technology
Information
Technology (Infrastructure for information)
Strategy
Company
direction
Enterprise
architecture
Align processes
with strategy
Data
warehouse
Store & retrieve
information
Performance
measurement
Are we on track?
2 3
4
5
6
1
Enterprise

1. Information

Defining information and its generic role in the enterprise.

2. Strategy and scenario planning

Establishing the mission and the strategy to accomplish the mission.

3. Enterprise architecture

Creating a blue print of all relevant aspects in the organization, linking strategic
direction to organizational structure, business processes, systems and
technological infrastructure.

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4. Data warehousing

Providing a central repository where various knowledge workers can extract
information in a user-friendly and consistent manner.

5. Utilizing information to measure performance

Identifying KPIs and measuring company performance to aid in decision-making.

6. Merging business with technology

This section explores other theories that seek to bring together all (or some) of
the above mentioned components. It aims to bring understanding of the
relationship between the above-mentioned topics and to align the utilization of
information with the company strategy.

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2.2 Information

It is common knowledge that the amount of information accessible to people has
increased enormously since the industrial age. The problem is no longer a lack of
information, but how to utilize that information effectively to aid in decision-
making. Business intelligence aims to achieve just that. However, merely
transforming information into knowledge, to aid decisions, is not the only purpose
of BI. To illustrate, consider the following example: If a business is focused on the
wrong processes, those that do not drive profit and strategy, information will be
gathered on how to improve those processes. The decisions made will at best
achieve only improvement of the current processes. Thus, the company will
remain on the wrong road. Also, if the company does have the right processes,
but the information gathered does not support the selected strategy, then the
decisions made will not necessarily support the successful implementation of the
strategy.

To be successful a company first has to establish a business strategy to
accomplish its mission. Then it must determine the processes required to support
the strategy and decide what information is required for the processes to run
smoothly. As soon as the processes are aligned the company can establish what
information is required to measure performance against the strategic objectives.
Finally the company must decide how to manage the information, perhaps
through a data warehouse, and how to retrieve it effectively. All of these actions
together help a company to be an intelligent business.

It is evident that information plays a major role within all activities of an
organization. But before the company can optimise the utilization of that
information, it must first understand what information is and in what forms it
manifests itself within the company. "The starting point for successful information
systems is not the definition of information needs, it is the definition of
information." (Absolute Information 2001) The following section will address this
issue.
2.2.1 Defining information

A typical dictionary definition of information would be “knowledge acquired
through experience or study; the meaning given to data by the way it is
interpreted”. (The Collins Concise Dictionary, 21
st
Century edition 2004) Often the
distinction between data and information is stated in the phrase that information
is processed data.

English (1999) also puts the relationship between data, information, knowledge
and wisdom into context by defining it as follows:

Simply stated, data are the representation of facts about things. Data are
only the raw material from which information may be produced.

Information is data in context. Information quality requires quality of
three components: clear definition or meaning of data, correct value(s),
and understandable presentation (the format represented to a knowledge
worker).
Information = f(Data + Definition + Presentation)

Knowledge is not just information known - it is information in context.
Knowledge means understanding the significance of the information.
Knowledge is applied information and may be represented as a formula:
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Knowledge = f(People + Information + Significance)

Wisdom is applied knowledge and may be expressed in the formula:
Wisdom = f(People + Knowledge + Action)

According to English (1999) “… it is in wisdom, or applied knowledge, that
information is exploited, and its value is realized”.

Swanborough (2002) pays a lot more attention to definitions. He argues that very
often objects or concepts are defined in terms of their uses and not their actual
characteristics. This narrows the perception of the subject. To introduce his
(somewhat eccentric) definition of information he starts off with the following
analogy: If a person were asked to define a chair, the answer would probably be
that it is something you sit on. This is true, but it does not answer the question.
The person’s answer states what a chair is used for, not what a chair is.

This analogy can be applied to information as well. The answer to the question
“What is information?” would probably be “Information is something I use that
tells me what happened, or what I should do, or what I base my decisions on.”
Again the answer is true, but still it addresses only what information is used for
and not what it is.

According to Swanborough (2002) the correct answer should be “Information is
signals of coherent content that pass within or between orgs”. He then further
explains the semantic content:

“Signals” means light-signals, sound-signals, flavour-signals, smell-
signals, or tactile-signals for humans and other living things, and
additionally electronic-signals or mechanical signals for machinery and
other non-living things (and thus being tangible and measurable in terms
of magnitude, time and/or direction), making a maximum of seven signal
types thereof.

“Coherent content” means “not noise” and therefore means four-, three-,
two- or one- dimensional content or abstract content relating to the
width, depth, height, time (including magnitudes) or the names of things,
or any combination thereof, making a maximum of five coherencies
thereof.

“Occur” means manifesting in one or more of the four linguistic contextual
constructs of “synit” (expectation), “revit” (reflection), “operit”
(instruction) or “cognitive” (identification) information, making a
maximum of four contexts thereof.

“Within” means not leaving the org, such as a stored memory, a personal
thought (organism) or an internal memo (organization).

“Orgs” means structured complexity in the form of “organizations” (non-
living) or “organisms” (living); organism or organization being two
destination types thereof.

“Between” means leaving one org and entering another org, such as a
verbal communication (organism to organism) or a personal invoice
(organization to organism) or an attention signal (organization to
organization).

Figure 3 shows the attributes of information in a schematic manner.
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Knowing / Amplification Getting / Movement
CONTENT CONTEXT Sight
4 Dimensional Expectational Sound
3 Dimensional Reflectional Smell
INTERNAL ORGANISM
2 Dimensional Instructional Taste
1 Dimensional Identificational Touch
Abstract Mechanical

X
Electrical
X
EXTERNAL
X
ORGANI-
ZATION
Information as intelligence,
knowledge and strategy - "THING"
>
Information as communication - "FLOW"
Figure 3. Attributes of information. (Adapted from Swanborough 2002)

2.2.2 Types of information

Swanborough bases his classification of information types on the principles of
financial management. A financial transaction is described by three absolutes,
being a Debit, Credit and the description of the content as in Figure 4.

Debit

Credit Currency
3
“absolutes”

Foundation for FINANCIAL
literacy
Lucas Pacioli
15
th
century
2 “Primary”
transaction types
Content
identification

Figure 4. The three financial management "absolutes"
(Absolute Information 2001)

For information, using the same concept as for financial management,
Swanborough introduces four absolutes, “Synoptic”, “Review”, “Operative” and
“Cognitive”. See Figure 5.

Review Operative Cognitive
4 “absolutes”

Foundation for INFORMATION
literacy
3 “Fundamental”
transaction types
Content
identification
Synoptic

Figure 5. The four informational management "absolutes"
(Absolute Information 2001)

Cognitive information has no time content and simply provides descriptive
information. The other three information types, in short Synit, Revit and Operit,
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do have time-content and apply to processes and the management of processes.

For simplicity and easy visual identification, each information type is denoted with
an arrow as indicated in Table 1. The table summarizes the types with their
description and shows which arrow represents it.

Table 1. Types of Information (Absolute Information 2001)

Type Arrow Description
Synit Long range forecasting information
Revit Summarized past performance
Operit Short range instructions and decisions made
Cognitive Description
2.2.3 Information in organizations
2.2.3.1 Sophistication of use of information

Information can be utilized at various levels of sophistication. Absolute
Information (2001) identified seven levels of sophistication of use of which
companies must aim to achieve the highest level possible. These levels are shown
in Table 2.
Table 2. The sophistication of use of information
(Absolute Information 2001)

Levels of sophistication
Level To Address Derive Use
7 Wisdom MAs Learning algorithms Management advices
6 Knowledge MDs Rules/Policies Management decisions
5 Effectiveness MIs SMIs Management indicators, synoptic
4 Efficiency MIs OMIs Management indicators, operative
3 Effort MIs RMIs Management indicators, review
2 Activity PIs RPIs Process indicators, review
High

Low
1 Description Detail Data Description

Many technologies address levels 1 to 5, but it is not common knowledge
that knowledge based systems or expert systems that aim to address
levels 6 and 7 have been implemented successfully. Knowledge based
systems combine the indicators of levels 3 to 5, policies and rules to
deliver management decisions (MDs). By learning from these MDs, the
system can automatically generate management advices (MAs). (Absolute
Information 2001)
2.2.3.2 Levels of corporate information focus

It is clear that information is utilized throughout the organization, the distinction
being in the different levels of sophistication. To visualize the different levels,
Absolute Information (2001) introduces the following “logical levels of corporate
information focus”:

Communication
System
Enterprise

Communication level

The communication level represents the infrastructure by which information is
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collected, processed, stored and distributed.

Systems level

The systems level represents the processes within the enterprise and their
relationships in order to establish the flow of information.

Enterprise level

This level represents the core level of functioning of the organization,
encompassing all systems and processes. Absolute Information (2001) identifies
four business domains:

Manpower
Money
Machinery
Material

The different information types (see Table 1) related to the four domains above
could be utilized to establish the required information content and attributes. The
three levels are illustrated in Figure 6. Note that the closer to the middle an item
is, the more closely it is related to the core business issues.

Systems
Communication
Enterprise

Figure 6. Levels of corporate information focus
(As adapted from Absolute Information 2001)

This concludes the literature section on information. Although there are many
other sources (perhaps with more of an information technology undertone), it is
felt that this slightly unorthodox view of information and the way in which it can
be defined is sufficient for purposes of this study. The classification of information
in an organization using the different types, levels of sophistication and business
domains will be discussed later.

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2.3 Business strategy and scenario planning

“Life is what happens when you’re busy making other plans” – John Lennon

Even though the reader may wonder why the literature journey of a thesis on
business intelligence incorporates business strategy, the motivation is found in
the following reasons:

Business intelligence implemented by an enterprise must support the strategy
to be effectively utilized.
The output from BI may improve or influence the business strategy process
when BI is effectively in place in an organization.
For organizations that are new in BI, the business strategy process may
provide some valuable pointers on how to start the BI implementation
process and what to concentrate on.

As it is (or should be) the aim of the industrial engineer to improve and
streamline all processes in an organization to add value in the long run, it would
be foolish to skip what should be the first and most important process of all
organizations, namely that of strategic management.

The popular view of business strategy is that it is an annual exercise done by top
management (preferably in the bush somewhere) where they take a long term
view of where the business is headed, do some SWOT (strengths, weaknesses,
opportunities and threats) analysis, reconfirm the vision, mission and values of
the organization and create an action plan.

Tony Manning (2001) puts it this way: “Strategy, it seems, is something that a
few smart and powerful people think about. Then they pass their wisdom down
the line in the form of instructions, and the drones get busy.”

During the early 1980s the process of strategic management was fairly sorted out
and various versions with approximately the same content were taught at
business schools. They all had the following elements:

Define the vision of the organization.
Define the mission (what do we do, for whom, with what technology).
Examine the macro environment (state of the economy, politics, legal issues,
demographics, and so forth).
Do the SWOT analysis – examining the microenvironment within the
organization, as well as the competition.
Derive a grand strategy (select from a number of options like high volume,
low price).
Develop a specific strategy with long-term goals, as well as tactical plans.
Pass this enterprise strategy on to the various lower levels in the
organizational hierarchy and let them develop divisional and departmental
strategies that are in line with the overall strategy, as well as tactical and
operational plans.

However, according to Manning (2001), “A lot (of corporate evolution) happens
way out at the edges, far from the planners, the scenarios, and the spreadsheets,
where ‘low-level people’ serve customers, make stuff, fix things, punch buttons,
sign documents, interpret events, and otherwise do their own thing. People at the
top don’t have ‘line of sight’ to the real world. The rest don’t have ‘line of sight’ to
the reasoning behind their organizations strategy. This blindness makes both
groups less effective than they might be.” Even in a large and diverse academic
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institution like the University of Pretoria, it is evident that aligning the activities of
the operational and academic staff with the vision of top management is a
challenging task.

To add to this dilemma of a gap between the strategy planners and the strategy
executers, the business world early in the twenty first century is a world of
accelerating change and increasing discontinuity. Thus, the processes and
methods that were used with some degree of success in the second half of the
previous century are not necessarily wrong – they are simply incomplete and
insufficient. The managers that were trained in that era are not necessarily
inefficient and incapable – they are unequipped to deal with the changed business
scenario.

To put the changing world in perspective, the following section will address the all
too familiar subject of life cycles. It is followed by a discussion on innovation and
scenario planning and the section concludes with the concise and “no-nonsense”
approach of Manning towards strategy.
2.3.1 Life cycles

Everything in life goes through cycles – people, weather patterns, the seasons,
economies, products and projects - even fashion. If one could anticipate the next
phase in a cycle you would definitely have a competitive advantage. Business
intelligence includes the identification of trends over time and therefore this brief
study of life cycles.

Wolfgang Grulke (2001) distinguishes between small cycles and big cycles. The
big cycles refer to long economic cycles as defined in 1922 by Kondratieff (who
was unpopular with his superiors and had to spend the rest of his life in Siberian
exile). His identified turning points are shown in Figure 7:

Figure 7. Economic cycles (Kondratieff, as referred to by Grulke 2001)

In 1939 Joseph Schumpeter published a book, Business Cycles, in which he
associated each of Kondratieff’s long waves with specific innovations in
technology and commerce. He believed that the driving force behind the waves
was innovation – not only new inventions, but also any change in the method of
supplying commodities. See Figure 8 for a chart that was taken from “The
Economist” of February 1999 (referred to by Grulke 2001) and that shows how
the waves accelerate.

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Figure 8. Schumpeter's waves (as referred to by Grulke 2001)

Schumpeter also coined the phrase “creative destruction” to describe the effect of
true innovation. Table 3 (data supplied by the US Bureau of Census) illustrates
the effect of creative destruction on job opportunities:

Table 3. Creative destruction of job opportunities (Grulke 2001)

Destruction! Today Yesterday
Railroad employees 231000 2076000 1920
Carriage, harness makers