Data Governance in the AI era: A roadmap for organisations

Data Governance in the AI Era

Why strong foundations matter more than ever in a world of generative AI.

As organisations rush to embrace the opportunities of Artificial Intelligence, data governance is becoming both more critical and more complex. Once seen as bureaucratic or IT-focused, data governance is now a key enabler of innovation—especially in an era where 80% of enterprise data is unstructured, shadowy, or outside traditional control mechanisms.

Whether you're automating decisions, building AI models, or simply trying to trust the data behind your dashboards, governance provides the clarity, accountability, and safeguards to do it responsibly.

Why Data Governance Still Matters (More Than Ever)

Data governance defines how data is collected, managed, and used. Done well, it:

• Unlocks siloed data for smarter decision-making
• Clarifies ownership and accountability
• Controls how data is shared inside and outside the business
• Reduces disputes about accuracy and reduces risk

Despite this, less than half of Australian organisations have a formal data governance framework. And only around one in three data leaders see governance as critical to business outcomes.

That needs to change—because generative AI is raising the stakes.

New Challenges in the Age of AI

AI isn’t just another tool. It creates new pressures on how data is governed, particularly across five areas:

1.  Unstructured and Shadow Data

Emails, documents, PDFs, social media posts—unstructured data accounts for most of what organisations collect, but it’s rarely governed well. Generative AI now makes it possible (and tempting) to use this data in powerful new ways.

At the same time, “shadow data” created or stored outside approved systems (think of personal drives, copied spreadsheets, or AI-generated content) raises serious concerns about data quality, security, and permission.

Consideration: Invest in metadata automation and discovery tools to locate and classify hidden data. Focus on the ethical and legal right to use that data—not just the technical ability.

2. Data Lineage and Explainability

AI models are only as good as the data they’re fed. If you don’t know where your data came from or how it was transformed, it’s hard to explain or trust AI-driven decisions—particularly in high-stakes fields like finance, health, or government.

Consideration: Link data governance with model governance. Track not just data sources, but also the transformations and assumptions made along the way.

3. Speed vs. Rigour

AI thrives on experimentation and rapid development. Governance is built on control and consistency. These cultures can clash—unless actively aligned.

Consideration: Create lightweight governance for early-stage AI exploration, then layer in more formal controls as models move toward production.

4. Ethics and Compliance

AI introduces new risks around fairness, bias, consent, and transparency. Just because a dataset is available doesn’t mean it should be used. And with new frameworks like the EU AI Act and Australia’s AI Guardrails on the horizon, compliance is quickly becoming non-negotiable.

Consideration: Align your data governance framework with ethical AI principles. Review models not just for performance, but for fairness, bias, and explainability.

5. Culture and Capability

Governance isn’t just a technical function—it’s a mindset. As AI tools reach more parts of the organisation, it’s critical that staff understand concepts like data quality, consent, and risk.

Consideration: Treat literacy as a journey, not a checklist. Run campaigns, build champions, and ensure teams are confident to challenge how AI uses data.

How to Establish a Scalable Governance Capability

If your organisation is still building its data governance foundations, here are some key principles to keep in mind:

• Start with Strategy: Align data governance efforts to your corporate goals. If compliance is urgent, start with policies for sharing and protecting sensitive data. If AI is a priority, focus on lineage and model accountability.
• Build Sustainable Processes: Focus on what’s essential. Prioritise business-critical data sources and automate repetitive tasks like cataloguing or quality checks.
• Secure Executive Buy-In: Clear leadership and accountability drive success. Appoint a senior sponsor or governance champion who understands the value of trusted data—and can influence others.
• Develop Frameworks and Policies: Establish clear principles around data ownership, lifecycle, and ethics. Use proven frameworks like the DAMA DMBOK or ISO 8000 as a foundation.
• Foster a Data-Driven Culture: Governance works best when business units take ownership. Identify internal champions—such as a marketing leader who improved campaign ROI through better data—and let them tell their success stories.
• Plan for Continuous Improvement: Mature governance doesn’t happen overnight. Start small, with achievable wins (like clarifying responsibilities), then evolve toward more sophisticated capabilities like automated monitoring or advanced metadata management.

How RSM Can Help

Whether you’re starting from scratch or looking to mature your capabilities, RSM can help your organisation unlock the full value of data by making it more accessible, trustworthy, and actionable.

We support organisations with:

• Data Governance Framework Development
• Data Strategy and Roadmap Design
• Capability Building and Change Support
• Maturity Assessments aligned to DAMA and ISO standards

Final Thought

Data governance is no longer just an IT concern—it’s a strategic priority. In an AI-driven world, the cost of inaction is rising. By investing in modern, agile governance, your organisation can move faster, stay compliant, and unlock the full value of its data assets—safely and responsibly.

 FOR MORE INFORMATION

Ready to take the next step? Contact RSM’s data governance experts today.