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Building consensus is crucial for data governance success. These principles can help.

One of the hardest parts of starting a data governance program is building consensus around the policies that will guide your approach. There’s no set checklist that applies for every organization, since every company is structured differently  and  has varied objectives.

That said, MDM is a great place to start, improve or re-energize your data governance initiative. And as my colleague Ian Stahl wrote recently, one of the best ways to build consensus around your DG program is with a quick win.

MDM and data governance work together to achieve several corporate initiatives, including a single view of customers, products and other master entities or relationships.

Plus, MDM lends itself well to establishing the metrics and processes essential to data governance.

When trying to build consensus, keep these three calls to action at your core:

1. Identify business areas where a laser focus and an agile approach can be applied to data governance
2. Success is better than perfection! Find the area where business leaders can be involved and successful. Again, a quick win can go a long way.
3. Less is more! What areas can you actually solve a meaningful data quality problem in less than nine months? That should be your focus, rather than trying to boil the ocean.

Over time, these three calls to action have morphed into this list of principles about actual data governance policies:

• Less is More – not everything needs a policy – follow the 80/20 rule.
• Examples are good
• Policies should state ‘business’ principles more than system and program/initiative names that will change over time
• Policies have to be ‘actionable’ – if they can’t be implemented, measured, and managed, they won’t work
• Policies should not be ‘shelfware’ – if they’re not living documents they won’t get used (Wikis are ideal for creating, evolving, and communicating data quality policies)
• Avoid the temptation to create a massive data model; model only what you must to understand the data problems and bring clarity
• Context is key: Although other functional areas are out of scope, take a quick look to ensure the policies and rules won’t break anything in future increments
• Reuse industry standards for reference data quality – don’t reinvent the wheel!
• Only mandate what’s broken
• An iteration should not produce more than 25-35 policies

Is there anything I’ve missed? Add it in the comments.