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Developing an EA


Two aspects of quality will have significant impact on the success of an EA. First, the EA artifacts (e.g. purpose statements, data architecture models, strategic documents) can be measured in terms of consistency, clarity, cohesiveness, scope, granularity, breadth, timeliness, or other qualities. The quality of the overall EA program can also be measured based on factors such as stakeholder involvement and compliance. No single set of quality metrics is appropriate for all enterprises. Instead, EA practitioners should establish a set of metrics consistent with the organization’s maturity and the EA purpose and scope. Quality measurement should strike an appropriate balance between the costs of measurement and the insights derived from those measures. Ideally, a good quality measurement program should track a few key metrics that help the EA program to improve over time.

This topic area offers approaches for identifying quality metrics, techniques for implementing a measurement program within the enterprise, and suggestions for how to adapt the EA program based on quality measurement results. Also included under this topic area are tools and models that may facilitate the implementation of a quality measurement program.


 EABOK is an evolving knowledge base and more information will be released as available.

In addition to the EABOK Board members, the content is also contributed by the following MITRE employees:

  • Carla Kendrick
  • Brenda Yu
  • Eddie Wang
  • Rose Tykinski
  • Wakar Khan
  • Mike Russell




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