Data governance might become easier and more complete in the future

AI is challenging data engineering and software engineering – the future seems interesting, but unstable: is data + software engineering performed by humans still a viable career path in the decades to come, or will AI simply take over to such a degree that only very few humans will be needed? At this point, we don’t know. What we do know is that young people in those fields are faced with challenges that are way more difficult than the previous generations. I hope they will manage – and maybe, there is a new opportunity for them:

Because the opposite reality seems to be emerging for data governance. If you have read the books of Laura Madsen, Piethein Strengholt, Tiankai Feng, Malcolm Hawker and Amy Raygada, you know that data governance is an uphill battle. Getting executive buy in… And then, if obtained, you need to unleash a cacophony of manual tasks in a data management initiative across the enterprise: Assigning data ownership, tagging datasets with business terminology, assessing levels of confidentiality, sensitivity, and quality. It’s a project so enormous that many data governance initiatives fail exactly because of this: The manual effort is too big, no one has time to do it, no one is rewarded for doing it.

Or at least, it used to be like this.

Perhaps the AI revolution is about to become a data governance revolution.

Unlike data engineering and software engineering, where highly paid employees are perhaps getting replaced by AI, data governance was not materialized in big enough data management teams. Data governance has always suffered from insufficient funding and subsequently lacking results – it has always suffered from too many manual tasks and too few hands to perform them.

And AI is changing that. Applied correctly, the manual tasks prescribed from a data governance initiative may just be performed by AI. If we place the human right where the human needs to be, data governance can take a revolutionary step forward. The human needs to be a “semantic steward” , someone that analyses data in terms of origin, intent, consequence and usability. Someone that guides AI in performing the many thousands manual tasks that humans – let’s face it – never had time to do anyway. And just maybe, if they find themselves looking for new opportunities, that could be a role for data + software engineers in the future.

That’s how the AI revolution could be a data governance revolution as well.