One of the biggest stumbling blocks is getting business resources to govern data. We've all heard it:

"I don't have time for this."

"Do you really need a full time person?"

"That really isn't my job."

"Isn't that an IT thing?"

"Can we just get a tool or hire a service company to fix the data?"

Let's face it, resources are the data governance killer even in the face of organizations trying to take on enterprise lead data governance efforts.

What we need to do is rethink the data governance bottlenecks and start with the guiding principle that data can only be governed when you have the right culture throughout the organization. The point being, you need accountability with those that actually know something about the data, how it is used, and who feels the most pain. That's not IT, that's not the data steward. It's the customer care representative, the sales executive, the claims processor, the assessor, the CFO, and we can go on. Not really the people you would normally include regularly in your data governance program. Heck, they are busy!

But, the path to sustainable effective data governance is data citizenship - where everyone is a data steward. So, we have to strike the right balance between automation, manual governance, and scale. This is even more important as out data and system ecosystems are exploding in size, sophistication, and speed. In the world of MDM and data quality vendors are looking specifically at how to get around these challenges. There are five (5) areas of innovation:

  1. Social governance - infusing social capabilities into applications, analytic tools, mobile devices, etc. that allow users of the data to send in feedback, likes, dislikes, and sharing behavior to inform data governance policies and rule changes or data remediation.
  2. Semantic MDM - the ability to model master data in business terms rather than data systems structures that strip away context and meaning.
  3. Analytical MDM - the ability to use the MDM repository as an analytic data source and leverage visualization tools on top of the repository.
  4. Social style environment - providing a look at feel in a data steward's workspace that is intuitive and application like to review and govern data rather than living in a data development environment
  5. Intelligence MDM - leveraging unsupervised artificial intelligence and machine learning to speed up and automate more of the manual data governance processes, reduce the need to manually create rules and quickly incorporate new data sources.

Ultimately, business users want access to the data to use the data. Why slow them down with data governance? Speed them up these new capabilities and give them the tools and feedback channels to improve your data governance programs ability to keep up with changing ecosystems, data, and demands.
The new report Brief: Data Governance Disrupts The MDM Status Quo (available for purchase here) tells you more and provides examples of vendors and capabilities you can leverage to make governing master data simply a part of the business and not a business disruption.

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