Seven key components to include in your AI data governance strategy

At this point, it’s rare, that organizations have a well-tuned AI factory of classifying, profiling and certifying AI data, and producing AI insights that are advancing — let alone, disrupting the business. But the intent to put their data house in order and define their AI strategy is in motion for the majority of the data leaders I meet with weekly. At the end of the day, AI offers a creative new world of endless possibilities, and we have the opportunity to shape that picture versus letting AI shape us.

Organizations who have already implemented a data catalog of some sort are ahead of the AI train for sure. Their data has been modeled, classified, curated and diagrammed. Catalog vendors are racing to the forefront with many new capabilities that go beyond governing data but make no mistake, these core capabilities are the main ingredients to a well-baked cake sure to delight your AI insights and outcomes. New catalog use cases like observing data pipelines, scoring data, monetizing and marketplace capabilities are creating savvy data communities and data leaders who can focus on taking next steps with the AI results and outcomes versus data clean up and mammoth migration efforts. At the end of the day, data performs best on well-defined, accurate, structured data.

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