Master Data Management and Semantic Modeling: MDM / Edition 1
by P. Bonnet
In an increasingly digital economy, mastering the quality of data is an increasingly vital yet still, in most organizations, a considerable task. The necessity of better governance and reinforcement of international rules and regulatory or oversight structures (Sarbanes Oxley, Basel II, Solvency II, IAS-IFRS, etc.) imposes on enterprises the need for greater
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In an increasingly digital economy, mastering the quality of data is an increasingly vital yet still, in most organizations, a considerable task. The necessity of better governance and reinforcement of international rules and regulatory or oversight structures (Sarbanes Oxley, Basel II, Solvency II, IAS-IFRS, etc.) imposes on enterprises the need for greater transparency and better traceability of their data.
All the stakeholders in a company have a role to play and great benefit to derive from the overall goals here, but will invariably turn towards their IT department in search of the answers. However, the majority of IT systems that have been developed within businesses are overly complex, badly adapted, and in many cases obsolete; these systems have often become a source of data or process fragility for the business. It is in this context that the management of ‘reference and master data’ or Master Data Management (MDM) and semantic modeling can intervene in order to straighten out the management of data in a forward-looking and sustainable manner.
This book shows how company executives and IT managers can take these new challenges, as well as the advantages of using reference and master data management, into account in answering questions such as: Which data governance functions are available? How can IT be better aligned with business regulations? What is the return on investment? How can we assess intangible IT assets and data? What are the principles of semantic modeling? What is the MDM technical architecture? In these ways they will be better able to deliver on their responsibilities to their organizations, and position them for growth and robust data management and integrity in the future.
Product Details
- ISBN-13:
- 9781848211827
- Publisher:
- Wiley
- Publication date:
- 06/28/2010
- Series:
- ISTE Series, #462
- Pages:
- 320
- Sales rank:
- 1,280,406
- Product dimensions:
- 6.30(w) x 9.30(h) x 0.90(d)
Table of Contents
Testimonials from the MDM Alliance Group
Foreword
Preface
Acknowledgements
Introduction to MDM
Part One The MDM Approach 1
Chapter 1 A Company and its Data 3
1.1 The importance of data and rules repositories 3
1.2 Back to basics 6
1.2.1 Past differences 9
1.2.2 The rich data model 11
1.3 Reference/Master data definition 12
1.3.1 Data initialized before use by transactional systems 14
1.3.2 Duplicate data 14
1.3.3 Data exchanged with third parties 19
1.4 Searching for data quality 19
1.4.1 Data quality 20
1.4.2 The quality of data models 23
1.4.3 The level of maturity of data quality 25
1.5 Different types of data repositories 27
1.5.1 Technical classification 28
1.5.2 Customer Data Integration (CDI) 31
1.5.3 Product Information Management (PIM) and Product Life Management (PLM) 33
1.5.4 Lightweight Directory Access Protocol (LDAP) 35
Chapter 2 Strategic Aspects 37
2.1 Corporate governance 37
2.1.1 Forced against the wall by regulations 38
2.1.2 The new scorecard 41
2.2 The transformation stages of an IT system 42
2.2.1 First stage: the data repository 43
2.2.2 Second stage: the business rules repository is added to the data repository 46
2.2.3 Third Stage: adding the business processes repository 49
2.3 Sustainable IT Architecture 51
2.3.1 The new management control 52
2.3.2 Maintaining knowledge and the strategic break 55
Chapter 3 Taking Software Packages into Account 57
3.1 The dead end of locked repositories 57
3.2 Criteria for choosing software packages 59
3.2.1 Availability of the data model 61
3.2.2 Repository updates 62
3.2.3 Neutralization of a locked MDM 62
3.3 Impact for software vendors 63
3.4 MDM is also a software package 65
Chapter 4 Return on Investment 69
4.1 Financial gain from improved data quality 69
4.2 The financial gain of data reliability 71
4.3 The financial gain of mastering operational risks 74
4.3.1 An all too often inefficient control system 74
4.3.2 MDM for the control of operational risks 76
4.4 The financial gain of IS transformation 77
4.4.1 The overlap of an Information System and IT 78
4.4.2 Financial valuation of an Information System 79
4.4.3 The MDM as a springboard for transformation of IS 81
4.5 Summary of the return on investment of MDM 83
Part Two MDM From a Business Perspective 87
Chapter 5 MDM Maturity Levels and Model-driven MDM 89
5.1 Virtual MDM 89
5.2 Static MDM 92
5.3 Semantic MDM 95
5.3.1 Improved administration by business users 97
5.3.2 A greater reliability in the data repository 97
5.3.3 Preparation for MDM integration with the rest of a system 98
5.4 The MDM maturity model 100
5.5 A Model-driven MDM system 103
5.5.1 Variants 103
5.5.2 Hiding join tables 104
Chapter 6 Data Governance Functions 109
6.1 Brief overview 109
6.2 Ergonomics 111
6.3 Version management 112
6.4 The initialization and update of data by use context 114
6.4.1 The affiliation of contexts 115
6.4.2 The automatic detection of shared data 117
6.5 Time management 118
6.5.1 Data history tracking 119
6.5.2 Business transaction 120
6.5.3 Validity period 121
6.6 Data validation rules 122
6.6.1 Facets 123
6.6.2 Integrity constraints 124
6.6.3 Business rules 126
6.7 The data approval process 128
6.8 Access rights management 129
6.9 Data hierarchy management 130
6.10 Conclusion 131
Chapter 7 Organizational Aspects 133
7.1 Organization for semantic modeling 133
7.1.1 The foundations of the organization 135
7.1.2 Data owners 136
7.1.3 The Enterprise Data Office 137
7.1.4 Does this organization involve risks? 139
7.2 The definition of roles 146
7.2.1 Data owner 146
7.2.2 Data analyst 146
7.2.3 Data architect 147
7.2.4 Data cost accountant 148
7.2.5 Data steward 148
7.3 Synthesis of the organization required to support the MDM 148
Part Three MDM From The IT Department Perspective 151
Chapter 8 The Semantic Modeling Framework 153
8.1 Establishing the framework of the method 153
8.1.1 The objectives of semantic modeling 154
8.1.2 The lifecycle of semantic modeling 157
8.2 Choosing the method 161
8.2.1 The Praxeme method 161
8.2.2 Choosing another method 170
8.3 The components of Enterprise Data Architecture 172
8.3.1 The business object 173
8.3.2 The data category 175
8.3.3 Business object domains 176
8.3.4 Data repository architecture 176
8.4 The drawbacks of semantic modeling 178
8.4.1 The lack of return on investment 178
8.4.2 Lack of competency 179
8.4.3 The blank page effect 180
8.5 Ready-to-use semantic models 180
8.5.1 Software packages 181
8.5.2 Industry specific models 182
8.5.3 Generic data models 183
Chapter 9 Semantic Modeling Procedures 187
9.1 A practical case of semantic modeling: the address 187
9.1.1 Non-compliant version of semantic modeling 188
9.1.2 First draft of semantic modeling 191
9.1.3 Modeling of the lifecycle of the address 192
9.1.4 Complete semantic modeling of the address 197
9.2 Example of Enterprise Data Architecture 199
9.3 Semantic modeling procedures 202
9.3.1 Extended business operation 202
9.3.2 Elementary business operation 207
9.3.3 Single-occurrence and multi-occurrence business operations 208
9.3.4 Fostering the upgradeability of data models 209
9.3.5 Other principles 213
Chapter 10 Logical Data Modeling 215
10.1 The objectives of logical modeling 215
10.2 The components of logical data modeling 216
10.3 The principle of loose-coupling data 217
10.4 The data architecture within categories 221
10.5 Derivation procedures 221
10.5.1 Derivation of the semantic classes 221
10.5.2 Examples of derivation of semantic classes 224
10.5.3 Derivation of elementary business operations 227
10.5.4 Derivation of extended business operations 228
10.5.5 Derivation of inheritance 228
10.5.6 Identifier management 229
10.5.7 Calculated information 229
10.6 Other logical modeling procedures 229
10.6.1 Enumeration data type 229
10.6.2 User message 230
10.6.3 User interface components 230
10.6.4 Data documentation 231
10.6.5 Naming rules 231
Chapter 11 Organization Modeling 233
11.1 The components of pragmatic modeling 234
11.2 Data approval processes 235
11.2.1 Process example 235
11.2.2 Synchronization of use cases with processes 238
11.2.3 The other processes 239
11.3 Use cases 239
11.3.1 Documentation of use cases 240
11.3.2 Elementary use case 242
11.3.3 Extended use case 243
11.4 Administrative objects 243
11.5 The derivation of pragmatic models to logical models 244
11.5.1 Use cases 244
11.5.2 Data approval process 245
11.5.3 Administrative object 245
11.5.4 Transaction 245
Chapter 12 Technical Integration of an MDM system 247
12.1 Integration models 248
12.1.1 Weak coupling 250
12.1.2 Tight coupling 251
12.1.3 Loose coupling 252
12.1.4 Consequences of integration models 253
12.2 Semantic integration 254
12.3 Data synchronization 258
12.3.1 Business event 260
12.3.2 Organizational event 260
12.3.3 Applicative event 260
12.4 Integration with the BRMS 261
12.5 Classification of databases and software development types 263
Conclusion 267
Appendix. Semantic Modeling of Address 271
A.1 The semantic model 272
A.2 Examples of screens generated by Model-driven MDM 277
A.2.1 Postal Code Patterns 277
A.2.2 Automatic calculation of postal code 279
A.2.3 Address input 281
A.3 Semantic modeling and data quality 282
A.4 Performance 282
A.5 Lifecycle of the Address business object 282
A.6 Insight into the XML schema 283
Bibliography 285
Index 287
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