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MASTER DATA MANAGEMENT AND DATA GOVERNANCE, 2/E

The latest techniques for building a customer-focused enterprise environment

"The authors have appreciated that MDM is a complex multidimensional area, and have set out to cover each of these dimensions in sufficient detail to provide adequate practical guidance to anyone implementing MDM. While this necessarily makes the book rather long, it means that the authors achieve a comprehensive treatment of MDM that is lacking in previous works." -- Malcolm Chisholm, Ph.D., President, AskGet.com Consulting, Inc.

Regain control of your master data and maintain a master-entity-centric enterprise data framework using the detailed information in this authoritative guide. Master Data Management and Data Governance, Second Edition provides up-to-date coverage of the most current architecture and technology views and system development and management methods. Discover how to construct an MDM business case and roadmap, build accurate models, deploy data hubs, and implement layered security policies. Legacy system integration, cross-industry challenges, and regulatory compliance are also covered in this comprehensive volume.

  • Plan and implement enterprise-scale MDM and Data Governance solutions
  • Develop master data model
  • Identify, match, and link master records for various domains through entity resolution
  • Improve efficiency and maximize integration using SOA and Web services
  • Ensure compliance with local, state, federal, and international regulations
  • Handle security using authentication, authorization, roles, entitlements, and encryption
  • Defend against identity theft, data compromise, spyware attack, and worm infection
  • Synchronize components and test data quality and system performance



Data Modeling Made Simple: A Practical Guide for Business and IT Professionals, 2nd Edition

The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK) Print Edition

Star Schema The Complete Reference

Master Data Management (The MK/OMG Press)

Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM)
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