Key takeaways:
- The Measurement Gap: 39% of data leaders struggle to demonstrate governance impact to leadership, yet they’re still measuring success through operational metrics.
- No Industry Consensus: Data leaders are split on governance structure —36% centralized, 36% federated, 29% hybrid.
- Automation is Key: 54% of modernization efforts focus on embedding governance into workflows and increasing automation.
- AI Governance Blind Spot: While AI dominates business strategy, 31% of organizations are still in the early stages of AI governance policy development.
Despite growing investments in enterprise data governance, nearly 40% of senior data leaders at Fortune 1000 companies say their biggest challenge in modernizing governance is proving its impact to leadership.
That’s one of the key findings in the newly released 2025 State of Enterprise Data Governance Report from the Enterprise Data Strategy Board.
While governance programs have matured, many still rely on operational metrics, like data quality scores and user adoption rates, to measure success, which rarely resonate at the executive level.
This disconnect could signify a deeper challenge: a misalignment between how governance is measured and how its value is perceived across the enterprise.
To better understand how companies are evolving their approach to data governance, we surveyed senior data leaders at billion-dollar organizations. In this blog, we’ll preview some of the key findings, offering a snapshot of how today’s data leaders are structuring, scaling, and rethinking governance in 2025.
The Great Governance Divide: Federated vs. Centralized Models
One of the clearest takeaways from the report? There’s no universal approach to structuring enterprise data governance.
Surveyed data leaders were evenly split between centralized (36%) and federated (36%) models, while 29% are embracing hybrid structures that blend elements of both.
This near-even divide underscores a key point: each model brings trade-offs, and the best fit often depends on your organization’s culture, data maturity, and business structure. For some, centralization offers greater control and consistency. For others, federated models empower business units to take ownership and drive adoption.
The key takeaway from this finding might be that while structure matters, alignment with business goals matters more.
Automation Is the Future of Scalable Governance
While data leaders are split on governance structure, there’s surprising consensus on what comes next — automation.
A full third of surveyed leaders identified embedding governance into data workflows as their top modernization priority. Another 21% pointed to increased automation in enforcement, signaling that manual oversight is no longer viable at scale.
This perspective came through clearly in a recent confidential conversation among Enterprise Data Strategy Board members. A data leader from a global financial services company described how their governance strategy is evolving alongside a major cloud migration. Their goal is to build controls directly into both IT and business processes from the start.
Why? Because manual governance simply can’t keep up. To address this, they’ve assigned practice leads for each data domain — owners who make approval decisions, supported by expert data stewards. The result is a governance model that moves at the speed of business, not behind it.
The AI Governance Gap: Big Risks, Few Playbooks
While everyone talks about AI transformation, 31% of data leaders admit they’re still in the early stages of defining AI governance policies. Even more telling, AI governance ranked dead last when leaders prioritized their governance concerns — just 7% put it in their top focus areas.
This points to a critical disconnect between AI ambition and governance readiness, which could create major blind spots.
The frameworks that governed your data warehouse won’t apply cleanly to machine learning pipelines, and most organizations are still figuring out what the new frameworks should be.
Enterprise Data Strategy Board members recently came together to discuss scaling AI governance as part of our Data Leadership Speaker Series — an opportunity to hear from guest speakers who have data knowledge and expertise to share with the community.
This topic recently took center stage during a Data Leadership Speaker Series hosted by the Enterprise Data Strategy Board. A Chief Data Officer at a global financial institution shared lessons from his team’s early AI governance efforts.
His guidance: embed governance into the product development lifecycle and build on existing privacy and security protocols rather than starting from scratch. He also stressed scalability.
With hundreds of AI use cases in flight, and many more on the horizon, his team is focused on automation to keep governance from becoming a bottleneck. While some business partners worry that oversight slows innovation, his team is streamlining processes to enable speed without sacrificing control.
Governance is Evolving Fast. Stay Ahead of the Curve With Peer Insights.
The data leaders who are making real progress aren’t waiting for best practices to be codified. They’re testing, iterating, and sharing what actually works.
Our survey captures their approaches in real time, from the KPIs that resonate with leadership, to how teams are balancing centralized oversight with federated execution, to the automation strategies enabling governance at scale.
But more than that, these leaders are preparing for what’s next. They expect the next five years to bring sweeping changes: smarter automation, formalized AI governance frameworks, and the cultural shifts needed to make governance feel seamless.
Want the full picture? Download the 2025 State of Enterprise Data Governance Report for detailed findings, strategic benchmarks, and lessons from the Fortune 1000 leaders building the future of governance today.
Because in a field this fluid, the edge belongs to those with real insight, not just theory.
