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!
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.
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?
Many think governance is about control and restriction, slowing teams down with rules. In reality, good governance accelerates all projects using data. It creates clarity, improves data quality and makes it easier for people to use data and AI responsibly rather than reinventing processes or taking unnecessary risks.
You’ve talked about executives reacting faster to quantified waste than to better data hygiene. How do you translate governance issues into numbers leaders care about?
Leaders respond when governance is tied to measurable business impact. Instead of talking about “data quality,” I translate issues into cost, risk and revenue language:
• Time wasted searching, cleaning or reconciling data → hours × cost per hour
• Duplicate data or tools → direct spend and maintenance costs
• Regulatory exposure → fines or incidents avoided
• Delayed projects due to poor data → revenue or launch impact
This makes it easier to get senior business leaders on-board with data governance initiatives.
Can you share a real-life example of where weak governance caused a costly or painful problem?
Yes, and it’s more common than we like to admit. In one organization, different teams were reporting revenue with slightly different definitions. Each department owned its own dashboard, pulling data from different sources.
Each quarter, one analyst was spending 3 weeks reconciling numbers manually to get a result for the board. Decisions were delayed because no one trusted the data.
What about a governance win you’re proud of that changed a real business outcome?
A project I’m proud of involved building a simple, scalable data quality process for promotion data at a large FMCG company. Before this, teams spent a huge amount of time cleaning and reconciling data because errors were only discovered downstream : often too late, sometimes right before reporting.
We introduced early anomaly detection, ownership rules and validation checks at ingestion. Suddenly, issues were caught in days instead of weeks. Promotion data became cleaner, more complete and much easier to work with.
Why do data governance programs stall?
Most governance programs stall before they properly begin because buy-in wasn’t earned early enough. Governance must be framed as something that solve business problems.
Successful programs start with listening, mapping pain points, and showing potential ROI to the Top Management and Business Sponsors.
Governance must be framed as something that solve business problems.
How do you keep governance from becoming a blocker for data engineers and scientists who just want to ship things?
The key is to make governance help people do their job faster, not slower. Instead of policies first, create enablement, shortcuts and templates :
Bake standards into tooling instead of PDFs nobody reads
Automate quality checks with data engineers
Co-design rules so teams feel ownership
Start with access policies that make it easier, not harder, to use data
What are some underrated governance practices that give a lot of value without needing a big tool?
3 high-impact, low-cost moves:
Clear data ownership. One accountable owner per domain or dataset reduces confusion and speeds decision-making.
A shared vocabulary. Even a simple glossary at least for the top 5 metrics used or most important business terms.
Lightweight quality standards. A checklist for critical datasets (freshness, documentation, lineage, SLAs) prevents firefighting later.
None of this requires a platform : just alignment, a shared space and discipline to iterate.
If you join a company as the first data governance person, what are the first three conversations you have and with whom?
1. Business Leaders: to understand business goals and where data should create impact or reduce risk. Governance needs a purpose, not a template.
2. The CDO (or whoever owns the data strategy): to clarify priorities, risk appetite and what “good” looks like for the organization.
3. Data consumers (analytics, finance, operations, AI teams): to map which decisions depend on data and what trust issues or inefficiencies slow them down today.
Has the rise of AI changed how you think about governance?
Absolutely. AI moved governance from “somewhat important” to non-negotiable. With traditional data, quality issues were inconvenient, with AI, they become amplified and automated at scale. AI forces us to think beyond data assets to models, prompts, lineage of training data, human oversight and impact on decisions.
That’s why all Data Governance teams should rename themselves as Data & AI Governance teams.
One thing you wish you had known earlier about data governance?
That governance is 80% change management and communication and only 20% frameworks and policies. Early on, I focused heavily on standards, operating models, and processes but I learned quickly that none of it matters if people don’t understand the “why” or see what’s in it for them.
Success comes from building trust, telling a clear value story and bringing people along the journey.
Governance is 80% change management and communication and only 20% frameworks and policies.
If you enjoyed reading this and want to learn more about Data and AI Governance by playing games, check out Datagovia where Charlotte is gamifying data governance
Was there a question that you would like to ask?
Let me know your thoughts by replying to the email or leaving a comment below!
If you are already subscribed and enjoyed the article, please give it a like and/or share it others, really appreciate it 🙏
Keep reading
 | A guest post by
|
Really strong framing here. The 80/20 split between change management and policy work is underrated. Most data teams still think governance is about building frameworks when it's really about getting people to actually use them. The example about quantifying waste hours instead of just saying 'quality is bad' is the kind of translation that actualy gets budget and headcount. Curios how you handle the tension between early anomaly detection and alert fatigue though.