We surveyed hundreds of data leaders at the State of Data Governance webinar to better understand how organizations are approaching data governance in 2025. The results highlight a shift toward operationalizing governance—embedding it into workflows, balancing control with agility, and tackling cultural roadblocks that slow progress.

Read on to see what the polls reveal.

The biggest challenges to governing AI-ready data

With 64% of responses, embedding governance seamlessly into workflows stands out as the primary challenge. This reinforces the need for governance to evolve from a rigid, compliance-driven function into an integrated, scalable framework that supports AI.

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Interestingly, gaining executive buy-in and scaling governance across teams both received just 18%, suggesting that while leadership support is important, execution is the real challenge.

One striking finding: ensuring data security and compliance received zero votes. This suggests organizations view security and compliance as table stakes, shifting their focus to AI-specific risks—like model bias, data drift, and ethical concerns.

Governance priorities for 2025

Governance in 2025 is defined by data quality—61% of respondents selected improving data quality and trust as their top priority. This overwhelming consensus signals that poor data quality remains the root cause of governance failures, AI risks, and decision-making inefficiencies.

Far behind but still significant, aligning governance with business strategy (18%) and automation and accountability (both at 11%) show that governance leaders are thinking beyond compliance, focusing on governance as a driver of business impact.

This trend underscores a shift toward governance as an enabler of business success. Without reliable data, compliance measures are difficult to enforce, AI models produce unreliable insights, and organizations struggle to extract value from their data investments.

Roadblocks to scalable data governance

The biggest challenge to scaling governance is not technical—47% of responses cited unclear ownership and accountability as the primary blocker. This reflects concerns that traditional governance models, reliant on siloed ownership, are breaking down in modern data environments. Governance expert Morgan Templar argues that rigid business ownership structures need an overhaul to support more flexible, cross-functional governance.

Resistance to change (27%) came in second, reinforcing that governance is as much a people problem as a data problem. Even the best frameworks fail without cultural adoption. 

Meanwhile, complexity of modern data ecosystems (20%) highlights the difficulties organizations face in applying governance principles across increasingly fragmented data environments. Yet, notably, only 7% cited lack of automation, suggesting that automation is less of a bottleneck than the human and organizational factors at play.

Challenges with fostering data culture

Two issues tied for first place:

• Building data literacy across teams (26%)
• Breaking down data silos (26%)

These responses reinforce the dual nature of governance challenges—technical and human. Even when governance frameworks are in place, they fail without team-wide literacy. Likewise, governance cannot scale if data remains trapped in silos across tools and teams. 

Following closely behind, promoting data ownership mindset (23%) again reflects the accountability concerns raised in the previous question. Many organizations struggle to instill ownership beyond technical teams, leading to governance programs that stall due to a lack of shared responsibility.

Notably, balancing governance with access (16%) and demonstrating governance ROI (10%) suggest that while access control remains an issue, proving the tangible benefits of governance is an even bigger challenge. This aligns with the growing industry conversation around measuring governance effectiveness in terms of business outcomes rather than compliance checklists.

The relationship between data governance and AI

The responses paint a picture of governance as both an enabler and a constraint in AI adoption:

• 34% said somewhat, but it still slows us down
• 31% said we haven’t tackled AI governance yet
• Only 17% said governance is foundational to AI success

This split indicates that AI governance maturity is still evolving. While some organizations have embedded governance into AI workflows, many still see it as a blocker. If governance is perceived as an obstacle, teams will work around it.

Measuring success of data governance programs

Once again, data quality emerged as the top success metric, with 38% selecting data quality improvements.

However, compliance remains a key focus—28% chose compliance metrics/risk reduction, showing that governance is still closely tied to regulatory needs.

Notably, 19% selected business value generated, suggesting a growing shift toward governance as a value driver rather than just a risk management function. This aligns with a broader industry trend where governance is increasingly being measured by its impact on revenue, operational efficiency, and AI success.

Meanwhile, time-to-access for governed data (13%) highlights an emerging focus on usability—how quickly teams can access governed data. This suggests that governance leaders are recognizing that frictionless access is just as important as control.

Key insights

These poll results tell a clear story about the state of data governance in 2025:

🔹 Quality is paramount – It’s both the top priority and the most common success metric. Organizations that don’t prioritize data quality will struggle with compliance, AI adoption, and decision-making.

🔹 Ownership remains unsolved – Accountability is the biggest blocker to scaling governance. Traditional business ownership structures need to evolve.

🔹 AI governance is still maturing – Most organizations haven’t fully integrated governance into AI workflows and still see it as a constraint.

🔹 Culture matters as much as technology – Data literacy and breaking down silos are just as critical as automation and tooling.

🔹 Workflow integration is critical – Governance must be embedded into workflows to scale AI successfully.

Organizations that tackle these five key areas will be well-positioned to make what Morgan Templar calls “The Great Pivot”—moving from reactive, partial governance to comprehensive, proactive governance that doesn’t just protect data but actively enables AI-driven innovation.

Register for our upcoming Data Leaders Forum to hear how industry experts in healthcare, finance and energy are addressing these key governance areas.