How to Design Data Governance Programs

Where do I start? This is arguably the question I’ve been asked the most by data leaders tasked with a large scale transformation initiative. The transformation could be a cloud migration, an ERP consolidation, or any large data-centric replatforming that involves a complex web of people, process, and technology. Quite often, many leaders have convinced themselves, or have been guided by a consultant, that taking a ‘bottoms up’ approach that starts with with an inventory of the data, often along with some form of a maturity assessment, is the right way to go. It’s not. The right way to go is to take an outcome-driven approach where you are rabidly focused on solving a very limited number of business problems. Each problem would have a well defined and limited scope, and would be accompanied by a business case where the financial benefits of that initiative are quantified, and aligned upon by your customers. Instead of focusing on all data, you’ll instead inventory, observe, govern, steward, master and integrate only the data needed to solve your immediate problem. Yes, some idea of the ‘future state’ must be defined and you need to ensure you’re building out an architecture that is scalable and flexible, but complete clarity on all aspects of every individual deliverable between now and that future state do not need to be defined. If you focus each of your phases around solving specifc problems, you will build the momentum and business support you need to get more funding, and slowly grow the program over time. Instead of taking a ‘framework driven’ approach that ensures your customers will have to wait 18+ months to see any value, your customers will get benefits now. Don’t be foooled into thinking that you need to catalog and govern everything in order to transform your data estate. You don’t. Focus on solving business problems and in time, you’ll catalog and govern what matters the most. What do you think? If you have different ideas on where to start, I would love to hear them? #cdo #datagovernance #datamanagement

🚨 #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 )

A while back, I shared a post introducing the #datagovernance framework I’ve used and refined over the course of 100+ #datastrategy and execution projects. It laid out the 5 components I believe every data governance approach should cover: #strategy, governance, capability areas, implementation, and #technology. I want to double click on #implementation because it’s less straightforward than the others. This part is often missing from high-level frameworks. Plenty of organizations do a great job defining a vision, specific strategic pillars, and detailed policies. But the part that actually determines whether your data governance takes hold or fizzles out, is execution. It comes down to two concepts: roles and domains. ROLES Roles may differ a little bit across organizations, but usually variations of a data owner, data steward, system or application owner, business process owner, data governance lead, data privacy officer, and more can be identified. For each of these, you can identify what they are generally responsible for. E.g., a data steward might be responsible for maintaining data definitions. DOMAINS Domains are how you divide up the organization into meaningful scopes. Usually, these domains correlate with the nature of the data (e.g., “customer” or “product”) as well as clusters of business processes (e.g., “supply chain” or “finance”). ROLES x DOMAINS When you have defined both roles and domains, something powerful gets unlocked: you can do domain-driven data governance. The roles and responsibilities give you a playbook for who does what, and the domains tell you where it can be applied. The last thing you’d want, is for any sub-domain in your org to reinvent the wheel around how to governance data. Indeed, domain-driven data governance allows you to drive both: > #acceleration, as you provide domain teams with a clear suggested set of actions, templates, and tools to get started > #consistency, as you make sure that across domains, similar best practices are leveraged In any data governance-related engagement I have been part of in the last few years, some form of domain-driven data governance became the desired approach. Every. Single. Time. Now, if you consider domain-driven data governance as a whole, that might be daunting. But just the two steps of (1) defining the key roles and their responsibilities and (2) defining the domains are not that complicated at all. In fact, with GenAI you can generate a really decent initial version for any organization, in any sector, with a few keystrokes. And once you have (1) and (2), you can do (1) x (2), and step through prioritized domains, activating governance. (Of course… with this, you have the who, what, and where, but not the “how,” or in other words, there are different ways in which specific responsibilities could be activated, and many of them might require tooling. But as you’ll see, solving for that just became a lot more actionable.) Link to article in the comments.

If you ask a data engineer what they think of data governance, they’ll probably say: "It’s just more paperwork." And they’re not wrong. People are told to follow policies but don't know why they should. And when things break, they will still get blamed. This is why so many policies don’t stick. They sound good in meetings but in real work, they slow people down. How can you design better governance programs then? ➨ Design governance with change management in mind. Start by listening: What makes it hard to follow policies today? Build with your team: Test new rules with data producers and consumers. Remove blockers: Automate checks and integrate the norms with existing tools. Share ownership: Make business teams part of the process with the data engineers. Governance works when it fits into how people already work, not when it’s pushed from the top. How are you making your governance easier for your team to follow?