In a recent energy industry project focused on consumer data, the shortcoming in the data governance foundation became frighteningly clear. Data owners and data stewards alike hesitated to step up and take accountability. The reasons were as below:
- They were stretched thin, lacking time and specialized skills
- They worried they would be blamed if any issues surfaced later
- There was no clear roadmap of responsibilities and no proactive framework to anticipate problems
The result? We didn’t have a clear data ownership model, leaving room for every data problem to be handled reactively.
In many organizations, the question “Who owns the data?” gets asked over and over again with no confident answer. When role definitions and governance processes are vague, tasks fall through the cracks until a crisis forces action.
With data pouring in from multiple data systems, multiple geographies and from multiple external sources or before we rush to new AI projects into production, we must fix this foundation. Otherwise, the cracks will only widen under pressure. It’s a recipe for disaster if left unchecked.
Why Traditional Governance Can’t Keep Up
An underlying assumption with traditional governance exercises is that it could be handled with static policies and yearly audits. But can today’s digital, diverse and dynamic organizations do with only this snapshot-based governance activities?
Below are some of the findings:
- Old assumption: Data quality rules are written once and rarely change. New reality: Data flows change every day. Policies kept in spreadsheets or PDFs become outdated as soon as they’re created.
- Old assumption: A periodic audit or manual check is enough to catch problems. New reality: By the time a quarterly audit / DPM forum happens, ungoverned data is already in production putting us back to square one with the cleaning activities.
- Old assumption: Data access and quality are gatekept by humans. New reality: Manual approvals and stale metadata slow teams down. Business users often find workarounds, skipping governance altogether.
In practice, governance often exists on paper but not in daily operations. Too often we hear, “Our governance is just theoretical,” or “Data quality issues still pop up despite the rules.”
The traditional model is reactive: controls kick in after something goes wrong. Metadata catalogues collect dust. Human bottlenecks create frustration and bypasses.
In short, the data owners and stewards are in for exhaustion attending several data forum but are still unable to arrest the bad data in the system.
Enter AI: Making Governance Continuous, Scalable, and Shared
This is where AI changes the game. AI should not be feared as a replacement for the data owners or Stewards — instead, it should empower them. In a human-in-the-loop model, machines do the grunt work while people make the strategic decisions. This not only frees up the bandwidth of the data owners and stewards but also brings governance in real time. Now, the big question how to do it? Below is a 4 step sample model to empower data owners and stewards to practice data governance effectively with the help of AI :
- Observe Continuously: The foundation of AI-driven governance lies in continuous observation. This layer constantly monitors how data is accessed, used and transformed across systems. It helps a data owner to track usage patterns, detect schema changes and automatically discover metadata and lineage as data flows through the ecosystem.
- Anomaly detection: This step applies AI and machine learning to interpret what is happening within the data landscape. It identifies anomalies, predicts potential data quality degradation, and analyzes the downstream impact of policy or schema changes. It helps the owners & Stewards with actionable insights.
- Autonomous Governance: This step helps with informed decision making. This step provides AI-driven recommendations to data owners and stewards. Human oversight remains critical, but decisions are guided by evidence, patterns and predicted outcomes rather than manual judgement.
- Execution: The final layer operationalizes governance by executing decisions at scale. Controls are enforced in Realtime, remediation actions are triggered automatically when thresholds are breached, and alerts are generated with clear explanations of why action was taken. This ensures transparency, auditability and trust while significantly reducing manual intervention.
With these capabilities, data owners & stewards transform from reactive gatekeepers into proactive overseers.
They review AI-generated alerts and focus on high-level strategy instead of endless approval queues. Instead of manually approving every request, they work with AI to automate compliance checks and educate teams about exceptions. This changes governance from a checklist into an ongoing, adaptable practice.
Teams gain confidence. When AI catches a mislabeled data set or a potential privacy issue, stewards can fix problems before they spread. Over time, business users see governance as an enabler, not a roadblock.
As data policies stay up-to-date and violations get flagged immediately, trust in the data grows. Governance becomes a driver of efficiency and innovation, rather than an activity to close audit actions.
Conclusion: Fixing the Foundation, at the Speed of the Enterprise
If we want to scale our data and AI initiatives successfully, we must strengthen the foundation first. Investing in smart, AI-powered governance now means fewer crises later. This isn’t just about compliance checkboxes, nor is it about changing cataloguing tools time and again in hope to get better results.
It’s about trust — trust in our data, trust from our customers, and trust that we can move fast without falling apart. When policies and guardrails run continuously and proactively, teams spend less time firefighting and more time innovating.
The time to act is now ,Modern enterprises run on data, and without solid governance that data can become a liability. But with AI helping us stay ahead, governance can keep up with demand.
Governance powered by AI is not a threat, it is a modern enterprise enabler.
