How to Improve Data Governance in Companies

Data governance is hitting a critical tipping point - and there are three big problems (and solutions) you can’t ignore: 1️⃣ Governance is Always an Afterthought: Often, governance only becomes important once it's too late. Fix: Embed governance from the start. Show quick wins so it's viewed as an enabler, not just cleanup. 2️⃣ AI Exposes - and Amplifies - Flaws: AI governance introduces exponential complexity. Fix: Proactively manage risks such as bias and black-box decisions. Automate data lineage and compliance checks. 3️⃣ Nobody Wants to ‘Do’ Governance: Mention "governance" and expect resistance. Fix: Make it invisible. Leverage AI to auto-document metadata and embed policies directly into everyday workflows, allowing teams to confidently consume data without friction. Bottom Line: → Plan governance early - late-stage fixes cost significantly more. → Use AI to do the heavy lifting - ditch manual spreadsheets. → Tie governance clearly to business outcomes like revenue growth and risk mitigation so it’s championed by leaders. Governance done right isn’t just compliance; it’s your strategic advantage in the AI era.

Over the past 10+ years, I’ve had the opportunity to author or contribute to over 100 #datagovernance strategies and frameworks across all kinds of industries and organizations. Every one of them had its own challenges, but I started to notice something: there’s actually a consistent way to approach #data governance that seems to work as a starting point, no matter the region or the sector. I’ve put that into a single framework I now reuse and adapt again and again. Why does it matter? Getting this framework in place early is one of the most important things you can do. It helps people understand what data governance is (and what it isn’t), sets clear expectations, and makes it way easier to drive adoption across teams. A well-structured framework provides a simple, repeatable visual that you can use over and over again to explain data governance and how you plan to implement it across the organization. You’ll find the visual attached. I broke it down into five core components: 🔹 #Strategy – This is the foundation. It defines why data governance matters in your org and what you’re trying to achieve. Without it, governance will be or become reactive and fragmented. 🔹 #Capability areas – These are the core disciplines like policies & standards, data quality, metadata, architecture, and more. They serve as the building blocks of governance, making sure that all the essential topics are covered in a clear and structured way. 🔹 #Implementation – This one is a bit unique because most high-level frameworks leave it out. It’s where things actually come to life. It’s about defining who’s doing what (roles) and where they’re doing it (domains), so governance is actually embedded in the business, not just talked about. This is where your key levers of adoption sit. 🔹 #Technology enablement – The tools and platforms that bring governance to life. From catalogs to stewardship platforms, these help you scale governance across teams, systems, and geographies. 🔹 #Governance of governance – Sounds meta, but it’s essential. This is how you make sure the rest of the framework is actually covered and tracked — with the right coordination, forums, metrics, and accountability to keep things moving and keep each other honest. In next weeks, I’ll go a bit deeper into one or two of these. For the full article ➡️ https://lnkd.in/ek5Yue_H

🚨 #HonestNoBS: Data governance has a branding problem. It’s been labeled as boring, bureaucratic, and the team of “No. But here’s the truth: Governance is finally exciting because it’s the carrot for AI. It’s not about slowing things down, it’s about safe speed. If your data isn’t: ✅ Understood (semantics) ✅ Trusted (business value) ✅ Usable (data products with clear context) ✅ Delivering fast wins (iterative, targeted effort) …then it’s not ready for AI. Here’s how to make governance actually work: 🔥 1. Minimum Valuable Governance Just enough governance to unlock value quickly. No overkill. What this looks like: • Start with the use case, not the policy manual. • Define only what’s necessary (clear terms, roles, semantics). • Engage the right stakeholders early, not everyone all at once. • Allow just enough access and quality to meet the goal. • Use an iterative approach — show quick wins, improve from there. 🛑 No more “boil the ocean” governance programs. ✅ Yes to fit-for-purpose, low-friction, value-first moves. 💡 2. Embedded Governance Built into how people already work, not a separate compliance layer. What this looks like: • Co-design with the business. Front office defines the “what & why,” back office enables the “how.” • Think like an energy company: governance is safety, and everyone owns it. • Governance pioneers = internal personal trainers. Empower, don’t enforce. • Bake governance into tools, workflows, and daily habits — not just into frameworks. Governance isn’t a team: it’s a culture. 📦 3. Data Products & Marketplace Reusable, governed assets people can actually find and use. What this looks like: • Define clear product boundaries and ownership. • Wrap data in contracts: semantics, SLAs, and accountability. • Focus on usability, build for the consumer, not the committee. • Measure impact: usage, satisfaction, business value. And at the center of it all? Metadata. But not stale, siloed metadata. We’re talking: Graphs. Context. Shareability. Here’s what kills governance efforts: ❌ Overengineering & scope creep ❌ Weak communication ❌ No ownership or accountability This is the talk that Tim Gasper and I will be giving at Snowflake today. Our thinking and POV comes from talking to hundreds of day leaders and practitioners, our Catalog & Cocktails Podcast guests (special shoutout to Rebecca O'Kill and Winfried Adalbert Etzel )