Designing a Fit-for-Purpose DM Framework : Reviewing and Tailoring Industry Frameworks

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About Course

Industry Data Management frameworks provide valuable guidance, but they differ considerably in structure, terminology, scope, and level of detail.

This course helps strategic and tactical leaders move from reviewing industry guidance to designing an organizational Data Management framework that fits their own situation.

You will compare major industry frameworks, evaluate whether to use, adapt, combine, or develop a framework, and apply three design principles: fit for purpose, feasibility, and coherence.

The course then introduces an output-based approach to framework design. Instead of allowing capability labels to determine scope, you identify the outputs, artifacts, and deliverables the organization actually requires and use them to shape one coherent framework.

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What Will You Learn?

  • Compare major Data Management industry frameworks.
  • Recognize differences in framework structures and terminology.
  • Choose an appropriate framework design route.
  • Apply fit for purpose, feasibility, and coherence.
  • Compare frameworks through required outputs and artifacts.
  • Tailor selected content into one organizational DM framework.

Course Content

Industry Framework Review
Industry frameworks approach Data Management from different viewpoints and organize their content in different ways. This topic reviews major industry sources and compares how they structure Data Management, governance, architecture, and metadata-related capabilities. The comparison prepares you to use these frameworks as reference sources rather than assuming that one structure provides a universal answer.

  • Comparing DM Industry Frameworks
    17:50
  • Conceptual Understanding
  • Analytical Thinking

Organizational DM Framework Design
Selecting an industry source is only the beginning of framework design. This topic examines the routes available to an organization and introduces three principles for judging them: fit for purpose, feasibility, and coherence. It then moves from framework labels to the outputs and artifacts the organization actually needs, providing a practical basis for tailoring one organizational Data Management framework.

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