A Pragmatic Approach to Building a Data Governance Framework

I’m Dr Joshua Depiver, and over the years, I’ve helped many organisations—spanning financial services, energy, healthcare, and local authorities—develop or refine their data governance strategies. From my experience, a robust data governance framework is the key to turning raw data into a reliable, strategic asset. It clarifies roles and responsibilities, instils accountability, and helps organisations harness data in ways that truly drive value.

Below, I’ll walk you through why data governance matters, how to set up a framework, the crucial pillars of governance readiness, and what it takes to measure success along the way.

1. Why Do I Need a Data Governance Framework?

For many businesses, data governance sounds like a compliance-driven chore. Yet, in practice, it delivers much more. A data governance framework enables you to:

• Define and document standards, norms, and processes so data is handled consistently.
• Clarify accountability, ownership, and roles—no more confusion about who’s responsible for what.
• Set key quality indicators (KQIs), key data elements (KDEs), key performance indicators (KPIs), and data risk/privacy metrics so you can measure success and spot problems early.
• Establish a shared business vocabulary, ensuring everyone speaks the same ‘data language’.
• Develop data quality rules for trustworthy, consistent data you can rely on.

The ultimate goal is to generate the greatest possible return on data while mitigating risks from poor or inappropriate data use. When everyone within the organisation understands how data is governed, you avoid potential pitfalls—like regulatory fines or reputational damage—and unlock opportunities to leverage data assets for real business value.

2. Bringing Order to Your Data Universe

A governance framework isn’t just about ticking policy boxes. It’s also about data discovery—achieving a unified data view across the enterprise. This involves:

• Data Relationships and Lineage: Tracking how data is created, transformed, and stored.
• Technical and Enterprise Metadata: Documenting data sources and structures ‘who, what, and how’.
• Data Profiling and Certification: Assessing the quality and suitability of data before it’s widely used.
• Data Classification: Labeling sensitive data so it’s protected appropriately.
• Data Engineering: Streamlining ETL/ELT pipelines to ensure accurate, timely data flows.
• Collaboration: Engaging IT, business units, compliance teams, data owners and stewards in continuous dialogue.

From my experience, taking the time to understand the broader data landscape is crucial. When organisations skip detailed discovery, they often end up with siloed systems, conflicting data definitions, and repeated firefighting. A unified approach ensures that data becomes a genuine asset rather than a fragmented liability.

3. Essential Process Components of a Data Governance Programme

Improving and Managing Data Quality

Data quality often gets overlooked until there’s a crisis—like inaccurate financial reports or a compliance gap. By setting clear metrics for accuracy, completeness, and timeliness, you can spot and fix issues before they cause major headaches.

Addressing Data Issues

When data errors or conflicts do arise, it’s vital to have a formal escalation path and a well-documented way to prioritise and resolve them. This prevents departmental finger-pointing and ensures issues are tackled swiftly.

Identifying Data Owners

Every data set should have a recognised Data Owner—typically a senior leader accountable for decisions around that data’s use and funding. That owner then works hand-in-hand with Data Stewards who manage the day-to-day data quality checks.

Building a Data Catalogue

A data catalogue acts like a library index for your enterprise, showing where data is stored, how it’s defined, and who’s responsible. This saves valuable time otherwise spent searching for the right data or duplicating work.

Creating Reference and Master Data

Organisations often stumble when multiple systems store different versions of ‘the same’ data. Reference data (like codes and lists) and master data (like customers or products) must be centralised and standardised to minimise chaos.

Protecting Data Privacy

Strong privacy measures are not optional in an era of GDPR and heightened public scrutiny. A data governance framework sets rules on how personal or sensitive data is gathered, stored, and shared, ensuring everyone is aligned with legal requirements.

Enforcing and Monitoring Data Policies

Governance policies need both teeth (the ability to enforce) and eyes (the ability to monitor compliance). Automated checks and regular audits can highlight policy breaches before they become systemic.

Driving Data Literacy

As data becomes increasingly central to decision-making, employees must understand how to interpret and use it responsibly. Training sessions and open data forums help embed a culture of data literacy throughout the organisation.

Provisioning and Delivering Data

Ultimately, data must be accessible to the right people at the right time—without over-exposing sensitive information. A governance framework defines who can access which data sets, under what circumstances, and via which tools.

4. What Are the Pillars of Data Governance Readiness?

A framework is only as strong as the people and processes behind it. Based on my observations, successful data governance hinges on four crucial pillars:

• People

Collaboration and Commitment: Do your teams genuinely believe in data governance? Roles and Responsibilities: Are these clearly defined?

Skills and Data Literacy: Do employees understand how to handle and interpret data? Change Management: Is there a clear plan—backed by senior sponsors—to drive cultural adoption?

• Processes

Realistic Data Rules and Goals: Are your definitions and quality standards practical and aligned with business needs?

Modernised Business Processes: Have you updated workflows to reflect current technology and governance policies?

Integration of Governance: Do your processes seamlessly incorporate governance steps to deliver real, measurable outcomes?

• Contributors

Subject Matter Experts (SMEs): Identify business leaders, process owners, data stewards, and IT architects who offer insights and context.

Stakeholder Mapping: Know who your data governance stakeholders are, where their expertise lies, and how they connect across the organisation.

• Technology

Tools and Platforms: Do you have solutions for data profiling, lineage, metadata management, and classification?

Automation and Scalability: Can your tools handle the volume and complexity of your data without overwhelming your teams?

Continuous Improvement: Even if some governance is in place, can platform technology help optimise outcomes?

5. Measuring and Monitoring Results

One of the biggest challenges I see in governance programmes is proving their value. A solid framework includes a mechanism for KPIs, metrics, and dashboards that shed light on:

• Data Quality and Proliferation: Are you meeting your accuracy and completeness targets? How widely is data spreading through the organisation?
• Data Privacy and Risk Exposure: Are you logging access properly, spotting anomalies, and safeguarding sensitive information?
• Auditing and Policy Management: Is there a clear record of who changed what and when? Do you know if policy breaches are detected and resolved quickly?

When these metrics are tied to strategic objectives—such as reducing operational costs, cutting the time spent on manual reconciliations, or boosting customer satisfaction—they help demonstrate tangible returns on your governance efforts.

6. Final Thoughts

From my experience, data governance works best when it’s seen as a practical enabler rather than a bureaucratic hurdle. It provides the guardrails and clarity your organisation needs to unlock data’s full potential, whether improving customer service, sharpening risk management, or identifying new revenue streams.

By establishing a comprehensive framework—one that covers discovery, quality, privacy, and the readiness pillars of people, processes, contributors, and technology—you lay the groundwork for a truly data-driven culture. In that culture, data isn’t just numbers in a system—it’s a strategic resource that informs everyday decisions, steers innovation, and drives measurable business impact.

About Dr Joshua Depiver

I’m a data management specialist passionate about making data governance accessible and effective. I’ve worked across industries—financial services, energy, healthcare, and local authorities—to help organisations build data strategies that yield real-world results. If you’d like to dive deeper into data governance or explore how to tailor a framework for your needs, feel free to connect. After all, with the right governance in place, data becomes your strongest ally for innovation and growth.

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