Enterprise Data Governance

Pierre Bonnet 지음

Wiley-ISTE 2013년 03월 04일 출간

작품소개

책소개

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 lsquo;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.

목차

Testimonials from the MDM Alliance Group xiiiForeword xxvPreface xxixAcknowledgements xxxixIntroduction to MDM xliPART ONE: THE MDM APPROACH 1Chapter 1. A Company and its Data 31.1. The importance of data and rules repositories 31.2. Back to basics 61.3. Reference/Master data definition 121.4. Searching for data quality 191.5. Different types of data repositories 27Chapter 2. Strategic Aspects 372.1. Corporate governance 372.2. The transformation stages of an IT system 422.3. Sustainable IT Architecture 51Chapter 3. Taking Software Packages into Account 573.1. The dead end of locked repositories 573.2. Criteria for choosing software packages 593.3. Impact for software vendors 633.4. MDM is also a software package 65Chapter 4. Return on Investment 694.1. Financial gain from improved data quality 694.2. The financial gain of data reliability 714.3. The financial gain of mastering operational risks 744.4. The financial gain of IS transformation 774.5. Summary of the return on investment of MDM 83PART TWO: MDM FROM A BUSINESS PERSPECTIVE 87Chapter 5. MDM Maturity Levels and Model-driven MDM 895.1. Virtual MDM 895.2. Static MDM 925.3. Semantic MDM 955.4. The MDM maturity model 1005.5. A Model-driven MDM system 103Chapter 6. Data Governance Functions 1096.1. Brief overview 1096.2. Ergonomics 1116.3. Version management 1126.4. The initialization and update of data by use context 1146.5. Time management 1186.6. Data validation rules 1226.7. The data approval process 1286.8. Access rights management 1296.9. Data hierarchy management 1306.10. Conclusion 131Chapter 7. Orga

작가정보

저자(글) Pierre Bonnet