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Nonlinear Theory of Elastic Plates

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Exterior Algebras

Elementary Tribute to Grassmann's Ideas

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From Pinch Methodology to Pinch-Exergy Integration of Flexible Systems

Thermodynamics – Energy, Environment, Economy Set

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Data Treatment in Environmental Sciences

Multivaried Approach

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Gas Hydrates 1

Fundamentals, Characterization and Modeling

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Smart Decisions in Complex Systems

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Chi-squared Goodness-of-fit Tests for Censored Data

Stochastic Models in Survival Analysis and Reliability Set – Volume 3

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Baidu SEO

Challenges and Intricacies of Marketing in China

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Supply Chain Management and Business Performance

The VASC Model

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Asymmetric Alliances and Information Systems

Issues and Prospects

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Enterprise Data Governance

Reference & Master Data Management, Semantic Modeling

Pierre Bonnet, Orchestra Networks, France

ISBN: 9781848211827

Publication Date: June 2010   Hardback   336 pp.

92.50 USD

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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.


Introduction to MDM.
Part 1. The MDM Approach
1. A Company and its Data.
2. Strategic Aspects.
3. Taking Software Packages into Account.
4. Return on Investment.
Part 2. MDM from a Business Perspective
5. MDM Maturity Levels and Model-driven MDM.
6. Data Governance Functions.
7. Organizational Aspects.
Part 3. MDM from the IT Department Perspective
8. The Semantic Modeling Framework.
9. Semantic Modeling Procedures.
10. Logical Data Modeling.
11. Organization Modeling.
12. Technical Integration of an MDM System.

About the Authors

Pierre Bonnet is the Co-founder of Orchestra Networks, a software editor specialized in Model-driven MDM. He is also the Founder of the “Sustainable IT Architecture” and “MDM Alliance Group” communities.


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