Tips to Overcome Data Governance Challenges

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.

🚨 #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 )

The last two weeks, I posted about the common challenges in data governance projects, particularly when business stakeholders aren’t engaged early enough, and shared a rare success story of a #CDO who avoided this pitfall through proactive engagement from day one. This week, we’ll turn to remedies: strategies for early stakeholder engagement. To lead with the conclusion, for the most part, there is no secret methodology. It’s as simple as identifying the #business unit or functional leaders and asking them a few questions. In fact, especially in the beginning, the simpler, the better. No slides, no demos – focus on the business context. One recommendation is to separate the understanding of the use cases from the data-related analysis. For example, if it has been determined that a company cannot create effective customer segmentation because data from different sources cannot be linked together, the first step is to thoroughly understand the use case. Articulate this understanding clearly—don’t settle for a mental check. Ensure the use case is explicitly defined and described. Describe at a high level what is being done and how it drives value. Also ask the business stakeholders about the impact. In the case of the segmentation, how much could customer experience increase, how much could churn be reduced, how much could cross-sell be increased? This step is extraordinarily critical now. It is very tempting to dive into practical solutions for the data integration problem, but if you cannot articulate a defensible value rationale at this point, you will not be able to build your business case later on either. The business case for the data capabilities will have an upper bound that is equal to the value of the entire use case, as only a part of that value can later be attributed to the data solutions. My advice is to make this a hard go/no-go tollgate: if you do not have a strong value rationale that is supported by the business, do not proceed. This first hurdle is the most substantial one. Once you have a clear value statement, then business buy-in will be guaranteed. Moreover, because the expected outcome is clearly quantified, throughout the program, solution requirements can be much more transparently reviewed. To effectively engage stakeholders early in the project, consider the techniques shown on the attached image to: 🗣️ Initiate Open Dialogues 🧑🤝🧑 Create Collaborative Workshops 🌟 Bring in External Success Stories For more details, see the full article ➡️ https://lnkd.in/exkiQYXE One Data #datastrategy #datagovernance

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?