Home / Component #2: Goals, Governance Metrics / Success Measures, Funding Strategies

Component #2: Goals, Governance Metrics / Success Measures, Funding Strategies

Some of your program’s goals may result in “soft” results that are anecdotal, or hard to measure. Others should be SMART: Specific, Measurable, Actionable, Relevant, and Timely.

How do you decide which goals you should pursue? Start by anticipating the effect of governance efforts on the “4 Ps”: Programs, Projects, Professional Disciplines, and People as individuals. Ask how your efforts could help enterprise programs (or high-profile projects)

        • Increase revenue and value
        • Manage cost and complexity
        • Ensure survival through attention to risk and vulnerabilities: compliance, security, privacy, etc.

Ask how the program could support the efforts of Architecture, Quality, Application Development, or other professional disciplines. Ask yourself what pains or wished-for gains of key individuals could be addressed by a strong Data Governance program.

And, don’t forget to look at the data itself. How can you affect the amount of or quality of or protection of data and metadata? Ask how you can measure that effect.

Metrics – just like goals – should be SMART. Everyone involved in Data Governance should know what success looks like, and how it’s being measured. Consider creating value statements with the following formula:

If we do A, then we should expect B, with a result of C;
otherwise, we should expect D, with a result of E.


Such clarity around value helps as you consider funding options available for your program. With your key stakeholders, you’ll want to explore

      • How you could fund your Data Governance Office (or its equivalent)
      • How you could fund Data Analyst/Architecture time needed to help define rules, define data, and research issues that must be resolved
      • How you could fund Stewardship activities

What protocols need to be established for Business  and IT staff who

      • Help define data
      • Analyze data issues
      • Help resolve data issues


Next: Component #3 – Rules, Definitions, Policies