Rethinking Data Control Frameworks

In regulated environments like banking, insurance, and healthcare, trust in data is essential — it directly influences your license to operate. But gaining trust in data is not easy; it requires data to be accurate, protected and compliant.

When a regulator walks in your office, they are not interested in knowing how fancy your controls are, how many risks have you identified, how many bells and whistles have you put in place. All they need to know is, are you in control and can you demonstrate this?

This is where a Data Control Framework (DCF) steps in. This should be risk aware, scalable and measurable. And most importantly, this should not just be built for compliance but should also cater to the operational reality.

Risk or Controls, who leads?

Determining the leadership approach in DCFs is crucial for effective implementation. Traditionally, highly regulated environments have adopted a risk-centric (focuses on identifying and mitigating risks first) approach, formulating a risk appetite and then establishing controls to mitigate or eliminate risks. However, this can lead to over-engineering where everything is viewed as a risk. A control driven approach places more emphasis on establishing controls to guide operations and failure of a control, leads to a risk exposure. If we are totally in control, we cover all our identified risks.

Risk centric- but control driven

In my opinion, risks should only be used to scope and priortise controls, but the controls should be leading and should anchor execution. A control driven approach gives you scalability, but a risk first approach gives you acceptability. If your framework is not scalable, it is not adequate. If you cannot get people to accept a framework, it’s useless. The key is to strike the balance and providing a view which everyone understands. So, design your framework as control driven but you flip the view (control leads or risk leads), based upon the audience. After all, getting acceptance is as critical, if not more than robustness of a framework.

Is the Framework Top-Down?

The Data Control Framework should not be a top-down structure — like a rigid framework that flows from data strategy to policy to risk to control. That’s a limited view. A Data Control Framework is more of a standardized method to define, manage, and scale controls across diverse data activities. It’s about consistency and completeness, not just hierarchy.

Control Design Principles

Typically, each control should answer the following questions:

· What does it prevent, detect, correct, or direct?

· Which risk(s) does it address?

· Which policy or regulatory clause does it satisfy?

· How is it measured (KPI)?

Every control that is created should fall in one of the categories

• Preventive — stops issues before they occur (e.g. access restrictions)

• Detective — flags issues after they occur (e.g. data quality alerts)

• Corrective — resolves issues (e.g. restoring lost records)

• Directive — guides behavior (e.g. policies, training)

A robust Data Control Framework balances all four.

KPIs over Narratives

If a control exists but can’t be measured, it might as well not exist. That’s why every control should have a KPI. These don’t have to be overengineered — start simple. One control = one KPI. Eventually you may have many to many relationships, but do not treat complexity as a proof of robustness.

Some tips for KPIs:

• Group similar KPIs to form aggregated control metrics

• Align these to risk tolerance thresholds

• Feed this into dashboards for real-time monitoring and audit defense

KPI-driven controls shift conversations from “Do we have controls?” to “Are they working?”

Traceability: The binding agent

The DCF should not be a standalone initiative but should be integrated into the ecosystem of an organisation. This integration would ensure relatability and awareness within the organisation.

A well-structured DCF shows:

· What risk are we addressing?

· What policy mandates this control?

· Which KPIs are used to monitor it?

· What evidence is there to prove it?

· Who owns the control?

Conclusion

In regulated industries, Data Control Frameworks (DCF) are crucial not only for compliance but for ensuring data accuracy, protection, and operational effectiveness. Balancing risk-centric and control-driven approaches allows for scalability and acceptance across the organization. By focusing on measurable controls and integrating DCFs into daily operations, organizations improve traceability and accountability. Ultimately, placing data control at the center transforms strategies into actionable realities, strengthening governance, quality, privacy, security, and architecture.

How does your organization demonstrate control and turn strategies into action? Share your thoughts or reach out for discussion!