Data Governance trends for 2026 that definitely weren’t written by AI

#40 The year governance gets real

Hello 😀

I loove this time of the year where I think I have time to reflect - when what’s really happening is a ton of emails and projects to finish before Christmas break. I even forgot to write this newsletter 😅

Last drops of sun in south of France during November, what a blast !

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Let’s see how to stay trendy

Agenda

1. Market feeling

2. Key trends

3. What to do in 2026

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Market feeling

I really hope that I haven’t lost my tone this year. Because, yes, I’ve been using AI to help with reformulation and brainstorming of the content of this newsletter.

For this one, let’s not use it.

Let’s just pour what I’ve seen, noticed and felt. It will be biased with my experiences. But AI is too, with other stuff. I tend to think it’s more interesting to read what a real human being has collected along a year in the field, but let me know !

What happened for those who started Data Governance

Thanks to GenAI massive noise, companies seem to have finally understood that working on their foundations first is a good idea. Data Governance has received a lot of attention this year. Buy-in got a little easier to get, as more and more have become aware of the issue. That’s good news !

Then, data domains have been mapped. Data Owners and Data Stewards have been identified. Committees were initiated. Data catalogs were bought and implemented. Good.

But… data catalogs are still …uhm… empty.

🤯 That’s the “dead catalog” trend. Many companies have been reaching out this year to tell me they have a data catalog, but it’s either an empty shell or a complete mess that makes no sense.

Wait, so AI didn’t really help to fill data catalogs?

Well, no, because it’s not magic. How is AI supposed to know the business meanings of data if there’s no existing documentation of it?

There’s a Reddit post that is really catching this issue :

So yes, AI helped but mostly by turning up the lights and showing that Data Governance is necessary.

I think in 2026 the real work will begin : activating those catalogs, empowering stewards, aligning processes, and turning governance from a checkbox into an engine of value.

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Key trends

Dead catalogs can come back to life. But it takes people, incentives, and sustained commitment - not just AI hype.

Acculturation above all

2026 will be the year of acculturation, finally ! We desperately need it.

Data teams began to understand not just what governance is, but why it matters for reliability, AI readiness, and business performance. In 2026, it will go further, with acculturation at scale from execs to operational team members - so excited for that !

The data / IT stewards duo

Another shift that I’ve noticed : the growing recognition that Data Stewards are not just “IT people”. They are actually Business Stewards bringing semantics, context, and actual usage patterns.

And guess what, Data Stewards need to work in pair with IT Stewards.

Because IT Stewards bring the technical and system knowledge, and will actually implement controls and execute policies and rules. Which is the whole point of governance. I think some organizations are calling them Data Custodians…

Towards a federated model

Many organizations moved towards a federated governance model with more distributed responsibilities, local ownership and domain-level accountability.

This is a healthy evolution, but it also revealed how much framework work remains.

Committees were formed. Councils were named. Charters were drafted.
But, in many cases, the “how” is still being defined : how do domains make decisions? what gets escalated vs. managed locally?

Focusing on ROI and high value embedded tasks

Organizations are becoming tired of governance “for the sake of governance.” They want measurable outcomes : faster data discovery, better AI performance, reduced compliance risk, improved operational efficiency, etc.

Data Governance proves its ROI when it removes friction from everyday work.

This means having terms defined before launching a data project, prioritizing new data quality issues depending on criticality, adding controls through data contracts for each interface, etc.

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What to do in 2026

Well, of course, it depends on your context, maturity… and if your budget is still up and running ! But here are some guidelines :

1️⃣ Refresh your framework

Refresh the list of owners and stewards, re-map responsibilities, and ensure everyone knows their place in the chain. Simple, explicit steps for decisions. You should also plan the committees for the year. It’s back to basics but with much higher maturity.

2️⃣ Craft a playbook, make it fun

You should focus on building a practical playbook for Data Stewards : clear examples, templates, before/after visuals, do’s and don’ts, quick wins, and even a bit of humor. Make it light, visual, and usable. The goal is to turn governance tasks into something approachable and intuitive, you can even have them play data governance simulations !

3️⃣ Build a semantic layer

A semantic layer will no longer be a nice-to-have : it will be essential. GenAI, self-service analytics, and federated governance all depend on a clear, consistent business vocabulary. 2026 must be the year organizations finally centralize meaning : shared terms, shared metrics, shared relations. To be started this year for sure.

4️⃣ Prepare AI Governance

AI Governance is coming fast, and most companies aren’t ready. The good news is that traditional Data Governance provides a huge head start. In 2026, you should define the pillars of your AI Governance : data sourcing requirements, documentation practices, human-oversight checkpoints, performance monitoring, etc.

The big question will also be : should the Data Governance team become the Data & AI Governance team? My answer is yes !

Finally, remember that the best governance is the governance people don’t notice.

In 2026 : think invisible by design.

Happy holidays 🎄

Charlotte

I’m Charlotte Ledoux, freelance in Data & AI Governance.

You can follow me on Linkedin !