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Unlocking Business Value with Data Governance, Data Architecture, and Metadata Management

By   /  December 5, 2012  /  No Comments

This White Paper is Sponsored by:

About the White Paper

This paper examines the root causes of data centralization failure and then reviews straightforward best practices that can help avoid such failures but are typically ignored when systems are designed in an ad hoc, organic manner (as in most organizations). Instituting data governance best practices will reduce the risks and increase trust in organizational information.

These include:

  • Data architecture and data modeling standards
  • Enterprise metadata management
  • Comprehensive data requirements analysis

Implementing these best practices requires the integration of processes and technology, specifically data requirements management, metadata management, and data modeling. However, these tools are employed most effectively when knowledge captured within any part of the technology can be shared across the entire application development lifecycle. When the tools and techniques provide a line of sight during the design phase from the requirements through to the implementation and the transition into production, a link can be made from concept to data instance. In this way, all system impacts can be identified for any adjustments or changes in semantics or
structure at all levels of data precision.


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