Data Management & Governance in the age AI

Rethinking an Overlooked Enabler

By Emmanuel Asimadi – Data & AI Leader

The rise of Artificial Intelligence is reshaping every domain it touches—and data management is no exception. At a recent Public Sector AI Week, I had the opportunity to reflect on how AI is not only disrupting traditional data practices but also opening new doors for value creation, governance, and competitive differentiation. In this piece I share this reflection and lessons from practice.

The Conundrum: When Data Governance and Analytics/AI Don’t Talk

Too often, there is a disconnect: AI and analytics professionals view data management as bureaucratic red tape, while data governance teams prioritize risk avoidance over innovation. This divergence hampers the true potential of data in any organization.

But what if we flipped this script?

Done right, data management becomes an enabler—not red tape! It unlocks safe, scalable, and strategic data use, empowering both humans and machines to drive business value. ensuring that we are treating data not as an after thought but central to the success of the organisation.

Why Rethink Data Management Now?

AI is forcing a rethink of traditional data management for several reasons:

  • Garbage In, Garbage Out: Poor-quality data undermines even the most sophisticated AI models, potentially causing reputational harm and wasted investment.

  • Unstructured Data Explosion: Multimodal AI can now consume audio, video, text, and code—pushing unstructured data to the forefront of management challenges.

  • AI Governance: We now face the task of governing not just data, but the AI models themselves—ensuring fairness, transparency, and compliance.

  • Machines as Consumers: With GenAI and intelligent agents on the rise, data must be machine-consumable, demanding richer metadata and contextualization.

Treat Data as an Asset—Because It Is One

Central to the success of data governance is operationalizing the idea of data as a strategic asset. Inheriting all the properties of an asset as captured in ISO55000. This means maintaining data inventories, lifecycle management, and clearly linking data initiatives to ROI/value.

But data isn’t just another asset—it’s a unique one:

  • Non-depletable and Non-tangible: Unlike money or materials, data doesn’t diminish with use and isn’t physical.

  • Multi-use and Durable: Data can serve many teams simultaneously and doesn’t wear out—but its value can decay (think half-life).

  • Easily stolen but not visibly lost: Data breaches don't deplete your databases—but they can devastate trust.

Understanding these nuances helps shape smarter governance, usage policies, and technology strategies.

Applying AI to Data Management

  • 🧾 Automated Metadata Creation

    Tools like Databricks Unity Catalog and Atlan can generate descriptive metadata, speeding up cataloging for human and machine consumption.

  • 🏷️ Data Classification & Tagging

    Automatically identify PII or sensitive content at scale, improving compliance and searchability.

  • ✅ AI Generated Quality Rules

    LLMs can draft quality expectations based on metadata—transforming manual, error-prone tasks into scalable processes.

  • 💬 Chatbots for Governance

    AI assistants can answer policy and governance FAQs, democratizing access and reducing bottlenecks.

  • 📜 AI-assisted Policy Drafting

    Governance frameworks and documentation that once took months can now be scaffolded in days.

  • 🔐 Data Redaction or Obfuscation

    Sensitive data can be masked intelligently in real time, enabling safe data sharing and analysis without breaching compliance or privacy.

  • 🧠 Personnel Productivity

    AI becomes a research and execution assistant, helping data governance professionals move faster and think bigger. Complex questions get answers in seconds, not days—enabling teams to focus on high-value work.

Key Trends to Watch

These are not hypothetical changes—they're happening now:

  • Data Products: Data is being packaged with lifecycle thinking, from discovery to retirement, like a real product.

  • AI & Data Governance Convergence: Managing AI and data in separate silos creates unnecessary complexity. These need integrated oversight.

  • Managing Data Derivatives: Features, embeddings, and AI outputs now require tracking and governance as first-class data assets.

The Business Case: Why This Matters

Done right, data management offers more than compliance. It delivers:

  • Faster time to insights

  • Higher-performing AI/ML models

  • Cost savings through operational efficiency

  • A measurable return on data investment

  • Competitive advantage in industries where most still struggle

Whether in the public or private sector, the message is clear: mature, modern data management isn’t just a hygiene factor—it’s a strategic differentiator.

Implementing AI-Driven Data Management: Strategies and Pitfalls

While the opportunities are exciting, turning them into real-world impact requires thoughtful execution. Here are key strategies and common pitfalls to be aware of when embedding AI into your data management practices:

🔧 Start with Clear Use Cases
Don’t deploy GenAI just because it’s trendy. Start with specific pain points—metadata gaps, slow policy creation, or poor data discoverability—and build tailored solutions.

🧩 Integrate AI into Existing Workflows
AI tools should enhance, not replace, your existing governance frameworks. Embed GenAI within data catalogs, data quality platforms, and stewardship processes—so it fits naturally into how people already work.

👥 Upskill and Involve Your People
AI won’t replace governance professionals—but those who know how to use AI will outpace those who don’t. Invest in training your team to become AI-literate and proactive partners in innovation.

🔍 Monitor for Bias and Hallucination
AI-generated metadata, rules, and policies must be validated. Don’t assume accuracy—establish human-in-the-loop review processes, especially in regulated industries.

🚧 Avoid "Tech-First" Traps
It’s tempting to chase tools, but effective data management starts with strategy. Align your GenAI initiatives with business goals, governance priorities, and your organization’s data maturity.

🛠️ Build for Scalability
Start small, but design for scale. Successful pilots should transition into repeatable patterns and platform-integrated capabilities—creating sustainable change across your data estate.

Final Thoughts

As AI becomes ubiquitous, the quality and governance of your data will increasingly define your success. We must evolve from reactive, siloed management to proactive, value-oriented practices.

In this new era, let’s do unto data what we do unto finance, people, and equipment—treat it with intention, intelligence, and strategic care.

📨 Want to collaborate or explore how AI and data can unlock value in your organization? Connect with me on LinkedIn