Enterprise Data Governance: Reference & Master Data Management, Semantic Modeling

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 87–107):
Chapter 6 Data Governance Functions (pages 109–132):
Chapter 7 Organizational Aspects (pages 133–149):
Chapter 8 The Semantic Modeling Framework (pages 151–185):
Chapter 9 Semantic Modeling Procedures (pages 187–214):
Chapter 10 Logical Data Modeling (pages 215–231):
Chapter 11 Organization Modeling (pages 233–246):
Chapter 12 Technical Integration of an MDM system (pages 247–266):