How to Change Perceptions of Data Governance

🚨 #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 )

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.

What Does Data Governance Actually Do? Ask this question across different organizations and you’ll receive a range of answers. And that’s not a flaw — it reflects a fundamental truth: data governance is context-dependent. Its function and effectiveness are shaped by an organization’s size, culture, data maturity, operating model, tech stack, and strategic priorities. There is no universal blueprint. Can a Data Governance team simply join a company, roll out a familiar framework—like SCRUM applied to the Software Development Lifecycle—and immediately begin delivering measurable business value, such as cost savings or new revenue? In theory, perhaps. In reality, rarely. Yes, governance teams can author policies and define standards. But in decentralized organizations with fragmented ownership, expecting distributed data teams to read, interpret, and update their workflows and code to comply is overly optimistic. Without deeper alignment, most policies remain shelfware. So what actually works? Trust. Influence. Embedded engagement. Most governance teams don’t have armies of full-time stewards across the enterprise. Success requires embedding governance into existing roles. It must become a shared responsibility—adopted by teams who see it not as a burden, but as a business enabler. To get there, you must clearly articulate value: What does it unlock for them? How does it mitigate risk? Does it improve efficiency, accuracy, or trust in their data? Laying that foundation requires often-unseen work: Identifying and cultivating data champions and allies Launching internal communications and storytelling campaigns Establishing cross-functional governance working groups Creating learning paths to elevate data literacy and alignment These may seem “non-technical,” but they are foundational. Trying to implement governance without cultural awareness, feedback loops, or business justification leads to superficial compliance—or worse, disengagement. Building the Foundation Is a Surgical Process Standing up a governance function is not a checklist—it’s a diagnostic process. It requires listening before acting, and understanding before prescribing. You must assess what will work in your organization before defining where to focus. And what to focus on? That’s a topic of its own. Governance is often expected to solve everything—access, retention, quality, lineage, metrics, metadata, privacy, compliance, and more. But most teams are lean. Success depends on cross-functional adoption, not siloed ownership. The Best Governance Teams Are Multidisciplinary by Nature They wear many hats—spanning product management, program delivery, quality engineering, BI, and change management. They don’t just write standards—they influence behavior, broker alignment, and elevate operational clarity.

Are you an IT leader implementing a data governance function and struggling with where to start? I have some advice. Go talk with your customers. Even better - go 𝐥𝐢𝐬𝐭𝐞𝐧 𝐭𝐨 your customers. Not for an hour, and not for a day or two. To get a good idea of what the various governance needs are across your business, this process will likely take several weeks. The best thing you can do in an early-stage governance effort is to get some quick and valuable wins. And the only way you can do that, is to go find out exactly what data related problems your customers are having that could be resolved with better governance of data. Who is doing this listening? Optimally, it would be both the leader of the function, and any analysts or lead stewards from within IT who will document and implement these policies. These conversations are necessary, and extremely valuable because: ✅ You will quickly learn who is willing to work with you, and who is not (Spoiler alert, trying to force anyone in the business to work with you is a recipe for disaster) ✅ They put your focus on customer success, and not the implementation of a framework. ✅ They will help to establish a producer/consumer relationship between the governance team and the beneficiaries of governance efforts - something sorely lacking in most governance programs. ✅ They will drastically improve the likelihood you'll have customer engagement (and maybe even funding!) in your efforts. ✅ They are needed to develop your top priorities, roadmap, and to isolate your early stage wins. ✅ They will help your team develop better analytical, customer service, and problem solving skills. The long-term success of data governance depends on s𝐡𝐢𝐟𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐩𝐞𝐫𝐜𝐞𝐩𝐭𝐢𝐨𝐧 𝐨𝐟 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 from one of control, to one of enablement. A great first step in this journey is to sit down and listen to your customers. You might be surprised by what you learn. What tactics have helped you the most to establish your governance function? What additional advice would you provide? #datagovernance #governance #cdo

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?