Enterprise Data Governance Reference Master Data Management, Semantic Modeling (Pierre Bonnet(auth.))（Z-Library）
Data
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. Content: Chapter 1 A Company and its Data (pages 1–36): Chapter 2 Strategic Aspects (pages 37–56): Chapter 3 Taking Software Packages into Account (pages 57–67): Chapter 4 Return on Investment (pages 69–85): Chapter 5 MDM Maturity Levels and Model?Driven MDM (pages
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Enterprise Data Governance
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Enterprise Data Governance Reference & Master Data Management, Semantic Modeling Pierre Bonnet
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First published 2010 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc. Adapted and updated from Management des données de l’entreprise. Master Data Management et modélisation sémantique published 2009 in France by Hermes Science/Lavoisier © LAVOISIER 2009 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address: ISTE Ltd John Wiley & Sons, Inc. 27-37 St George’s Road 111 River Street London SW19 4EU Hoboken, NJ 07030 UK USA www.iste.co.uk www.wiley.com © ISTE Ltd 2010 The rights of Pierre Bonnet to be identified as the author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988. Library of Congress Cataloging-in-Publication Data Bonnet, Pierre. Enterprise data governance : reference and master data management, semantic modeling / Pierre Bonnet. p. cm. Includes bibliographical references and index. ISBN 978-1-84821-182-7 1. Data protection. I. Title. HF5548.37.B666 2010 658.4'78--dc22 2010014839 British Library Cataloguing-in-Publication Data A CIP record for this book is available from the British Library ISBN: 978-1-84821-182-7 Printed and bound in Great Britain by CPI Antony Rowe, Chippenham and Eastbourne.
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Table of Contents Testimonials from the MDM Alliance Group . . . . . . . . xiii Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxv Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxix Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . xxxix Introduction to MDM . . . . . . . . . . . . . . . . . . . . . . . . . xli 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
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vi Enterprise Data Governance 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
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Table of Contents vii 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
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viii Enterprise Data Governance 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
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Table of Contents ix 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
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x Enterprise Data Governance 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
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Table of Contents xi 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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Testimonials from the MDM Alliance Group “Master Data Management and Information Management are key disciplines in any Company Architecture and Service Oriented Architecture initiatives. The MDM Alliance Group is delivering some solid added value in these areas by releasing these procedures in the public domain. Excellent work.” Didier Boulet Director SOA - THALES Corporate - KTD/Software “Pierre Bonnet’s book on Master Data Management and Semantic modeling is a timely and comprehensive guide to creating solid foundations for Master Data Management within the company. The semantic approach to modeling master data represents an important step toward building industry specific standards and therefore massively reducing the risk and cost of Master Data Management projects. To have Pierre’s extensive architectural knowledge and vision within this area in print is a must for anyone embarking on a Master Data Management initiative.” Owen Lewis Director Product Development - Agile Solutions Ltd
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xiv Enterprise Data Governance “The data explosion in terms of volume and transactions has crystallized new paradigms such Event Driven Architecture (EDA), Cloud Computing and other models such SaaS. In such a massively distributed environment, completely virtualized and ubiquitous, the quality of data, their localization and transactional integrity will be dramatically critical. Another dimension of business that is magnifying the effect of the vectors described above is the breathtaking acceleration in the swiftness of business change that is forcing companies to rethink the manner in which they manage their critical data and information.” “Rethink it means making a conceptual leap into this new economic model which means that companies will need to be guided in order to successfully achieve their transformation, thus increasing their competitive advantage. The MDM Alliance Group is an initiative that will guide any company of any size to go through the transition phase in order to reach their goal. The modeling coupled with the semantic approach is an extremely powerful tool to increase the transparency of critical and valuable data while at the same time reducing the complexity of the architecture and multiplicity of interactions. The MDM alliance also provides tools and business templates for accelerating the learning and the operational effectiveness of companies. Bear in mind that data, since the beginning of mankind, has made and broken up empires and, from that view point, nothing has changed except that today data is more important than ever due to the speed of business change, which means that any company must safeguard its highest competitive asset: data.” Didier Mamma Director EMEA Strategy & Development - Progress Software S.A.S.
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Testimonials xv “The majority of companies have taken steps towards a better management of their data capital or reference data. They are all looking for reassurance and want to benefit from the thoughts of others. The MDM Alliance Group therefore brings an essential networking aspect, which is at the base of all good business practice for companies wishing to start a reference based management project. Atos Origin firmly supports the MDM Alliance Group.” Laurent Schapira BI, CRM & MDM Solutions Manager - Partner Atos Consulting “The MDM Alliance Group is a debating forum, a think tank that prevents us from re-inventing the wheel. The modeling procedures and the ready to use data model help progress from state of the art to the reality of company projects. The MDM Alliance Group is an accelerator helping to free the players involved be they users in industry or IT. Its contribution to the maturing process in the MDM market is well and truly established.” Clément Roudeix Director, BI & MDM, Financial Services – SOPRA group “I have come from a traditional data modeling background where languages were not commonly used, so I have been trying to find some unbiased guidelines that would enable me to express my intentions in a more formal and universal way. The explanation and examples given within the MDM Alliance Group documents have greatly helped me in this
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xvi Enterprise Data Governance goal. They have enabled me understand just how potentially wide ranging and valuable MDM is.” Graham Chapman Senior Enterprise Domain Architect: Information Enterprise Architecture at Inland Revenue, New Zealand “Time (or money) to market a solution seems to shorten as we age. Should we waste in modeling? Think of the data silos “galaxy” accumulated in less than 20 or 30 years of data processing within your own organization, and ask why? It has been disorganized to such extent because nobody wanted to pay the bill for modeling upfront...so, everybody had to pay anyway, but afterwards (data transformation, data migration, data redesign etc.). Data (and even more master data) is your asset. It is like your money in the bank. You may not have plenty initially but you most probably want it there for as long as possible. If you leave your safe on weak foundations, you will keep rebuilding and soon discover that everyone around preferred to use the bank as a service instead. Can you still afford to remain alone in your data mastering? Standards bodies like OASIS and others progressively deliver prebuilt vertical models like UBL, CIQ, ACORD, HL7 etc. However, those are intended for data flows and do not fit for data implementation. Packaged solutions (like SAP, Oracle Application, etc.) also provide out-of-the-box configurable data implementation schemas. However those implementations tend to embed most of your master data (from the package prospective only), thus preventing us from keeping an homogeneous level on data quality, control and governance in the overall IT eco- systems. The same pitfalls apply if your try Corporate Services in SOA before data mastering. This book is a great opportunity for re-using from past experiences and
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Testimonials xvii capitalizing on state-of-the-art pragmatic modeling techniques. Beyond is the MAG initiative which is a way to not only avoid the blank page but mostly to provide added value from all of us, as long as we all play the fair game of an open collaborative effort.” Xavier Fournier-Morel Co-author of the SOA Architecture Guidelines “MAG is a community Pierre Bonnet founded to share MDM Modeling procedures and pre-built data models. The MDM Alliance Group publishes a set of pre-built data models that include the usual concepts (Location, Asset, Party, Party Relationship, Party Role, Event, Period [Date, Time, Condition]) downloadable from the website. And some more interesting models like Classification (Taxonomy) and Thesaurus organized across three domains. Although we may disagree about the semantics I do agree with him that adopting this approach can help us avoid setting up siloed master databases…unfortunately often evident when using specific functional approaches such as PIM (Product Information Management) and CDI (Customer Data Integration) modeling.” James Parnitzke-James James is a hands-on technology executive, trusted partner, advisor, software publisher and widely recognized database management and company architecture thought leader “The MDM apparatus (it’s a lot more than just technologies), is a fundamental component to guarantee that a Company Architecture is translated to an efficient IT system. Moreover, without a correct MDM vision implemented at the four levels (semantic, logical, organizational and technological, see the Praxeme aspects) a
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xviii Enterprise Data Governance Company Architecture may be a complete failure and a total waste of money. The MAG work (method and pre-built models) is an invaluable step in our search for rationality for the IT system we are building for the near future.” Fabien Villard Secretary of the Praxeme Institute “The Praxeme methodology, “Sustainable IT Architecture” and especially Master Data Management have helped me in consulting with various organizations in service-oriented architecture, as well as in implementing the process of IT system overhaul. I recognize the MDM Alliance Group as a precious source of information and exchange platform for methodology.” Jay Zawar Independent Consultant in SOA “The correct management of a unique set of data repository is key to the company’s agility and financial performance. The MDM Alliance Group clearly demonstrates that this is a business opportunity.” Emmanuel Laignelet Director of Evolan Solutions – SOPRA Group “A reference book, that gives new found importance to companies’ IT heritage. In this book, Pierre Bonnet answers the concerns of IT architects giving a strong methodological framework for the management of data repository. He puts the data at the heart of the business in perspective and gives an efficient and pragmatic approach resting on a sure-footed base, the corner-stone of business data repositories.
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Testimonials xix In this book IT architects will find, the keys, a guide in the analysis and structuring in four layers (semantic, pragmatic, logic and software) and a collaborative approach (business/IT) which will help make their first MDM project a ‘success story’”. Olivier Sommerard Technical Director – KHIPLUS “Today a large amount of energy is expended to maintain, for better or worse, the quality of data. Whether this be in terms of data cleansing or data integration. But it is also necessary to take into account from the SOA point of view the wasted effort in the access management to the data. One can only fear that duplications of codes on the lower layers of SOA can only lead to the same in the upper layers. From the point of view of reference data (data used or produced by several applications), from the moment a drive is made to increase quality and reduce costs, standardization is mandatory. Time and time again this principle has been shown to be true in many industries. Without doubt it is time that the software industry takes into account this standardization at the level of reference and master data management. The logical consequence of this statement is that we can only wish for the normalization of reference and master data. In this framework, the processes of the MDM Alliance Group, that is to say the semantic modeling of the MDM and the pre-built data models, will almost certainly become, progressively, a must. An ambitious goal, certainly difficult but promising, being that it offers off-the-shelf models. Even supposing that these pre-built models cannot be used as they are with our respective IT systems, they do have the advantage of giving us the opportunity to
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xx Enterprise Data Governance personalize our model without having to start from scratch, an approach which is often costly and reliant on a knowledge base for re-use. The approach certainly merits a trial.” Jean-Pierre Latour Company Architect – SMALS “There is an intense worry about the reliability of the KPIs and the reliability of the risk indicators. Because of this, it is not unusual that the same KPI on the one hand supplied from the production data base and on the other from BI do not have the same value. Which one is correct? The explanations by Pierre, on the quality of the data repository and by extension on the quality of operational data are totally convincing: It is possible to correct this state of affairs thanks to MDM. Starting an MDM project is attainable: Both methods and tools exist. Pierre explains them very clearly. It is clear that this work is a contribution that cannot be ignored and which is of great value, to the good management of an MDM project.” Antoine Clave Information Systems Consultant – FIABILIS “Master Data Management is a key technology to ensure the consistency and integrity of IT systems. Pierre Bonnet, who is a renowned expert in this field, provides a global view within the company and its IS. In his book “MDM and semantic modeling“, he introduces modeling techniques in order to define the best architecture and MDM usage for each IS, thus paving the way to IS maturity improvement.” Philippe Desfray Director R&D – SOFTEAM
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Testimonials xxi “I have met Pierre Bonnet “virtually” on an MDM-related Linkedin group. This allowed me to discover and join the MAG initiative wholeheartedly. Although a long time database professional (from DBA to Data-base Architect), my exposure to MDM started only a few years ago, with an IBM MDM “draft” solution, while I was involved in “architecting” an analytical DWH model, based on an operational ODS/ETL model, integrating a 3rd party CRM package, and complying with global, corporate wide reporting requirements. My first impression, outside the “siloed” legacy (mainframe) world, was the semantic “chaos” introduced by the brave, new and open, distributed platforms, applications and database management systems. Beyond the versatile XML, the MDM (metadata management) was not standard, not accepted, but even had a lukewarm reception from the majors (IBM, MS & Oracle). It was clear that MDM was something else, a few abstraction layers higher, definitely aimed at the business alignment of the data and application semantics. SOA was hot and it promised relief to IT of all the legacy (mainframe, that is) pains. It is less hot now, but it is more mature and it has become absolutely clear there is no IT alternative to it (sic!). While becoming an architect (an alliteration for a seasoned systems engineer, with the stress on both terms) entering the marvelous Company Architecture (Zakman’s) World I've realized that architects have missed one point: legacy (including new technologies, from MS, Oracle and IBM, among others) was not present explicitly, while (passive) data or (actionable) information were persisted tremendously, all over the place, like in a (flat) Babel Tower. The (world of) IT was (is) flat (courtesy of T. Friedman). The answer to that lack of “dimensions” was (already) there: Master Data Management - that business's own lingua franca, that IT should translate into local platform MDM “dialects“, ensuring the long-time aimed “integration” and
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xxii Enterprise Data Governance “interoperability” of heterogeneous databases and applications. To my “beginners” experience, Pierre Bonnet has provided the SOA “basics” and he has promised the next and complementary book on MDM “all-you-can-eat”. The MDM Alliance Group (MAG) is the “place to be” and to discuss the future and mature MDM, tied to help business to seamlessly integrate and inter-operate data and information. Thank you Pierre!” Nick Manu Architect and DBA DB2 zOS & Linux, Crossroad Bank for Social Security, Belgium (eGovernment) “Pierre is an expert in the MDM domain and understands well the intersection of SOA and MDM which is a rapidly emerging topic in Company Architecture. His work on “Sustainable IT Architecture” is an important contribution to the field. As more companies seek to extract the maximum business value of the existing and ongoing investments in IT, the sustainability model helps to coordinate stakeholders and to establish a higher level of functioning for today’s much maligned IT department. The integration of MDM into the SOA conversation reflects a mature understanding of the reality of Company complexity, but also provides a path forward for Architects and Practitioners alike.” Miko Matsumura Vice President and Deputy CTO at Software AG – author of SOA Adoption for Dummies “Pierre has delivered, over a few short years, an impressive amount of guidance and best practices, be it with his colleagues at the Praxeme Institute, or by founding the Sustainable IT Architecture and MDM Alliance Group communities.
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AI Reading Assistant
Whole-book reading guide from stratified index samples; jump to passages in the text
AI guide
【One-Line Pitch】
A management-level guide to treating reference and master data as a strategic asset: it explains how governance, semantic modeling, and MDM architecture can restore data quality and regulatory traceability. Best for executives, IT managers, and architects who must align business rules with an increasingly regulated information system.
【Book Arc】
- **Opening (~0%–10%)**: Frames the problem — digital-economy regulation (Sarbanes Oxley, Basel II, Solvency II, IAS-IFRS) demands transparency and traceability, while legacy IT systems are complex, badly adapted, and fragile. Introduces MDM and semantic modeling as the forward-looking remedy.
- **Early (~10%–35%)**: Defines the company-and-data foundation: why scattered integration layers lack real models, what a "rich data model" is, and how reference/master data differ from transactional data. Distinguishes MDM from CDI, PIM/PLM, and LDAP repositories.
- **Middle (~35%–55%)**: Moves to strategy and economics — regulatory pressure on boards, the three-stage transformation (master data → business rules → business processes), and ROI/intangible-asset accounting for data and software.
- **Late (~55%–80%)**: Covers MDM maturity levels (virtual → static → semantic, model-driven), governance functions (versioning, validation rules, approval, access rights, hierarchies), and organizational aspects for enterprise-wide adoption.
- **Ending (~80%–100%)**: Shifts to the IT department view — semantic modeling framework, logical data modeling, organization modeling, and technical integration of the MDM system.
【Key Takeaways】
- **Data quality is now a governance obligation, not an IT preference** (Opening): regulations force transparency and traceability onto the board, making data a corporate-governance matter rather than a back-office concern.
- **A rich data model beats a data dictionary** (Early): meaning comes from relationships between data elements; those relationships reveal validation rules and integrity constraints that a flat dictionary cannot express.
- **MDM must precede rules and process repositories** (Middle): the book argues for regaining control of reference/master data first, then business rules (BRMS), then processes (BPM) — reversing this order is described as a considerable IT failure.
- **Maturity progresses from virtual to semantic MDM** (Late): virtual and static MDM carry risks; only model-driven, semantic MDM aligns the solution directly with the semantic model to reach "total quality."
- **Governance functions are business-facing tools** (Late): version management, use-context data entry, validation rules, approval workflows, access rights, and hierarchy management are designed for business users, not just IT consoles.
- **Software packages create silos that MDM can neutralize** (Middle): PIM/PLM handle products only and hard-code validation rules; an enterprise MDM on a neutral platform can synchronize across PLM, ERP, and supply chain without replacing them.
- **Data and rules are intangible assets to be capitalized** (Middle): ROI should account for intangible IT assets, with performance indicators jointly specified by business and IT.
- **Context changes validation** (Early): the same data may be validated differently depending on whether it is used by headquarters or affiliates, so use-context management is essential.
【Reading Tips】
- Read Part 1 (Chapters 1–4) carefully if you need the business case and ROI argument; skim if you already accept the premise.
- Deep-read the maturity-level and governance-function chapters — they are the practical core for planning an MDM program.
- Treat the semantic modeling and logical data modeling chapters as the technical hard spot; they reward slow reading with architects or modelers.
- Use the three-stage transformation logic (data → rules → processes) as your implementation checklist before starting any MDM project.
- Note the case study on semiconductor product configuration as a concrete example of MDM coexisting with ERP/PLM rather than replacing them.
【Coverage Limits】
The excerpts cover the book's structure, opening argument, strategic framing, and several middle chapters, but do not provide full detail on later technical chapters (semantic modeling procedures, logical data modeling, organization modeling, technical integration). Specific figures, ROI calculations, and complete chapter contents are not fully represented.
Passage locations
Excerpt 1
ta (pages 1–36): Chapter 2 Strategic Aspects (pages 37–56): Chapter 3 Taking Software Packages into Account (pages 57–67): Chapter 4 Return on Investment (pa...
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Excerpt 2
anizational aspects packages Chapter 4 Return on investment Part 3 – MDM from the IT Department Perspective Chapter 8 The semantic modeling Chapter 11 framew...
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Excerpt 3
of different management contexts, also called use contexts. From then on, an IT system cannot limit itself to working in a fixed context at the time of its d...
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Excerpt 4
ten for this control of management and must justify their contribution to the assets. Software is no longer seen as a cost but rather as a contributor to int...
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