Governance strategies critical to turn data into actionable information

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While artificial intelligence and machine learning seem to hog the headlines this year, where many organizations are placing a lot of their focus is on data governance programs.

There is “a renewed emphasis on data governance and a clamor to clean up the incoherence between build and user communities around information-related projects,” says William McKnight, president at McKnight Consulting Group.

“Companies are realizing the only way to achieve enterprise data governance is to grow it step-by-step with application-by-application governance, and eventually try to gain synergy from the overlap,” McKnight says.

As a pillar in the management of what is fast becoming an important aspect of business—information—data governance “has never been pursued as much,” McKnight says. “Data governance is on the rise due to the plethora of data stores prevalent in an organization today.”

Unfortunately, those organizations that have been unable to build scalable systems need governance the most, and are the least able to achieve it, McKnight says.

Artificial intelligence (AI) will likely have an impact on data governance as it matures.

See Also How organizations can develop an AI governance strategy

“AI is starting out as incubated research inside of organizations, with some of the same thought process that early data warehousing and big data had,” McKnight says. “It is being taken as far as it can without major integration with the rest of the enterprise, and hence today there is not much taken under governance.”

This will change as AI becomes the “top calling for data in an organization,” McKnight says. “Some organizations are already placing a renewed emphasis on data for the AI need to come, and as a result, needing more data governance.”

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Data governance Data management Data strategy