Enterprise Data Governance : Reference and Master Data Management Semantic Modeling
Pierre. Bonnet
Bok Engelsk 2013 · Electronic books.
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Utgitt | Hoboken : : Wiley, , 2013.
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Omfang | 1 online resource (334 p.)
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Opplysninger | Description based upon print version of record.. - Cover; Title Page; Copyright Page; Table of Contents; Testimonials from the MDM Alliance Group; Foreword; Preface; Acknowledgements; Introduction to MDM; PART ONE: THE MDM APPROACH; Chapter 1. A Company and its Data; 1.1. The importance of data and rules repositories; 1.2. Back to basics; 1.2.1. Past differences; 1.2.2. The rich data model; 1.3. Reference/Master data definition; 1.3.1. Data initialized before use by transactional systems; 1.3.2. Duplicate data; 1.3.3. Data exchanged with third parties; 1.4. Searching for data quality; 1.4.1. Data quality; 1.4.2. The quality of data models. - 1.4.3. The level of maturity of data quality1.5. Different types of data repositories; 1.5.1. Technical classification; 1.5.2. Customer Data Integration (CDI); 1.5.3. Product Information Management (PIM) and Product Life Management (PLM); 1.5.4. Lightweight Directory Access Protocol (LDAP); Chapter 2. Strategic Aspects; 2.1. Corporate governance; 2.1.1. Forced against the wall by regulations; 2.1.2. The new scorecard; 2.2. The transformation stages of an IT system; 2.2.1. First stage: the data repository; 2.2.2. Second stage: the business rules repository is added to the data repository. - 2.2.3. Third stage: adding the business processes repository2.3. Sustainable IT Architecture; 2.3.1. The new management control; 2.3.2. Maintaining knowledge and the strategic break; Chapter 3. Taking Software Packages into Account; 3.1. The dead end of locked repositories; 3.2. Criteria for choosing software packages; 3.2.1. Availability of the data model; 3.2.2. Repository updates; 3.2.3. Neutralization of a locked MDM; 3.3. Impact for software vendors; 3.4. MDM is also a software package; Chapter 4. Return on Investment; 4.1. Financial gain from improved data quality. - 4.2. The financial gain of data reliability4.3. The financial gain of mastering operational risks; 4.3.1. An all too often inefficient control system; 4.3.2. MDM for the control of operational risks; 4.4. The financial gain of IS transformation; 4.4.1. The overlap of an Information System and IT; 4.4.2. Financial valuation of an Information System; 4.4.3. The MDM as a springboard for transformation of IS; 4.5. Summary of the return on investment of MDM; PART TWO: MDM FROM A BUSINESS PERSPECTIVE; Chapter 5. MDM Maturity Levels and Model-driven MDM; 5.1. Virtual MDM; 5.2. Static MDM. - 5.3. Semantic MDM5.3.1. Improved administration by business users; 5.3.2. A greater reliability in the data repository; 5.3.3. Preparation for MDM integration with the rest of a system; 5.4. The MDM maturity model; 5.5. A Model-driven MDM system; 5.5.1. Variants; 5.5.2. Hiding join tables; Chapter 6. Data Governance Functions; 6.1. Brief overview; 6.2. Ergonomics; 6.3. Version management; 6.4. The initialization and update of data by use context; 6.4.1. The affiliation of contexts; 6.4.2. The automatic detection of shared data; 6.5. Time management; 6.5.1. Data history tracking. - 6.5.2. Business transaction. - 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 answ
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ISBN | 9781848211827
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