How Data Governance Is Changing in Response to AI Analytics

As enterprises rush to bring AI into business processes, data analytics is near the top of most of their wish lists. Injecting AI into data analytics promises benefits that range from improved cost savings to increased customer satisfaction and an enhanced competitive edge, bonuses that every enterprise wants to achieve.

This surge towards AI-powered data analytics is driving a number of consequences, most notably a renewed interest in data governance. Solid data governance is the foundation for all trustworthy analytics, but it's table stakes for organizations that adopt AI-powered analytics.

We spoke with Omri Kohl, Co-Founder and CEO at Pyramid Analytics, a unified analytics platform that allows teams to merge and manage data from myriad sources and supports analysis using natural language to make data insights accessible for all line-of-business users.

Kohl's years of experience in the trenches of data analytics have given him a valuable understanding of how it works behind the scenes and what's needed to ensure that analytics run smoothly. We spoke to him about how data governance roles and responsibilities are changing amid surging AI adoption, and what that means for enterprise functions.

The Connection between AI and Data Governance

In Kohl's words, AI "pushed data governance into the spotlight in a way we have not seen before. AI systems need high-quality, well-understood data at all times, and they need it with the business context baked in."

As he explains, AI systems, and particularly AI agents, will fail without that context. "If the AI does not understand your definitions and your logic, the output is not reliable," he says.

Of course, governance always mattered, but it used to sit "behind the scenes." Now the rise of AI "has forced organizations to rethink who owns governance and how it gets done. Suddenly, every team needs clean, consistent, well-defined data," observes Kohl. He sums it up by saying that "the old model of central governance is unsustainable."

Data Governance's New Look

Kohl outlines three pillars for the new data governance regime: unified governance, automation, and closer relations between governance and data.

He describes the need for unified governance as essential. "When your data prep lives in one system, your modeling in another, and your reporting somewhere else, it becomes impossible to maintain consistent policies," he emphasizes. More teams are consolidating their analytics stacks into a single, scalable analytics platform. This bakes governance in from the start, he notes with approval, instead of spreading it across multiple contexts.

Automation is just as important, says Kohl, who is glad to see that AI is taking on a bigger role in classifying and preparing data, checking quality, spotting anomalies, and enforcing policies. "Instead of relying on manual effort, governance becomes part of the workflow," he says. "When it is automated and embedded in the platform, you get safety and scale without slowing people down."

Additionally, Kohl points out the importance of governance policies and procedures residing in the same location as the data itself. Instead of divorcing governance from the data that it governs, he says, teams benefit when "governance responsibilities are pushed closer to where data is created and used."

New Roles as Stewards and Enablers

The ripples in data governance extend beyond procedural issues. Kohl remarks that "since 2020, the biggest change has been the expansion of governance roles across the enterprise. It's no longer something IT can do alone."

Some organizations are adding roles that focus on AI governance and data ethics, he notes, but even more are sharing the responsibility with existing positions.

"Business teams now play a central role, because they understand how the organization actually operates," Kohl says. "They bring the context AI needs to be effective."

As this shift cultivates shared responsibility for data governance, the data steward role is evolving from gatekeeping to enabling. "Data stewards are now focused less on checking individual datasets and more on enabling people with clear definitions and standards," points out Kohl. "IT still controls infrastructure and access, but it is far more connected to analytics teams than it was a few years ago."

Improved Agility and Confidence

According to Kohl, the changes in data governance have been wholly positive, bringing clarity to decision-making and agility to business processes. "The impact has been significant. Companies that have embraced this shift are more agile and more confident bringing AI into production," Kohl emphasizes. "Efficiency and protection used to compete with each other. Now they support each other."

Unified, automated governance saves time, reduces errors, and improves alignment across the organization. "When governance lives in one environment, everyone benefits from the same rules and the same version of the truth. It helps organizations avoid the fragmentation that often slows innovation," says Kohl.

With improved governance, Kohl explains, teams can move faster because they trust the data they are working with, and that trust is the bedrock for faster, more confident decision-making. Better collaboration between IT, analytics teams, and business users is also "raising the bar on transparency and trust, which is healthy for any enterprise adopting AI at scale."

Stronger Data Governance Means a Stronger Business

Overall, Kohl is enthusiastic about the way that data governance has evolved in response to the rise of AI-powered data analytics. As he sees it, when governance is unified, automated, and seen as the shared concern of the entire organization, it boosts trust, transparency, and ultimately business growth. The outcome is simple. "Organizations that get this right make better decisions, faster," he concludes.