What is Data Governance? A Practical Guide to Building Trustworthy Data in the Age of AI

From unclear ownership to missing standards, Charlotte Ledoux breaks down the simple governance practices that help organisations trust their data and ship faster.

Fellow Data Tinkerers

Today I will be talking with Charlotte Ledoux who writes the The Data Governance Playbook newsletter and works with companies on implementing data governance.

I discovered her work through the CDO game (worth trying if you haven’t!). It reminded me how often data governance is misunderstood, despite becoming essential as AI takes off.

We talked about her move from analytics to governance, how the real value comes from clarity and ownership rather than tools and why the smartest governance programs start with listening long before they start with policies.

So without further ado, let’s get into it!

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Can you tell us about your role?

I’m a Data & AI Governance expert. In practice, that means I make sure an organisation’s data is trustworthy, secure and responsibly used, especially as AI adoption accelerates. I help define the roles, responsibilities, processes and tools that state how data is collected, shared, protected and used so that teams can innovate with confidence rather than chaos.

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How did you break into data governance?

Before specializing in data governance, I worked more hands-on in the data ecosystem : collaborating with data teams on data science, analytics and data strategy. Over time, I realized that the biggest blockers to effective data use weren’t tools or skills but rather unclear ownership, missing standards and a lack of trust.

Governance drew me in because it sits at the intersection of strategy, quality, ethics and business value. It’s the discipline that creates the structure needed for data to actually deliver impact.

Charlotte’s path

data analytics → data strategy → data governance

So what is data governance? How do you explain it simply?

Data governance is the framework that ensures data is reliable, secure and used appropriately. It defines the rules, responsibilities and processes that allow an organization to manage data (and now AI!) in a controlled and value-driven way.

A simpler version I often use: it’s about enabling people to do great things with data.

It defines the rules, responsibilities and processes that allow an organization to manage data in a controlled and value-driven way.

What’s a common misunderstanding about data governance?