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Managing Director - Data & AI Governance @ Northridge Analytics
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I help CDOs stop firefighting data problems and fix the decisions blocking their AI roadmap | MD, Northridge Analytics - Data & AI Governance Consultancy | Ex-Gartner
Every CDO I talk to is pushing AI use cases, and almost none of them really trust the data underneath. I've seen a lot of AI projects fail because they never did the foundation, and the foundational work isn't really technical. It's the operating model. Who owns what, how the definitions actually get agreed, how you set up ownership so it holds. The gap I keep seeing is that nearly everything out there is tools and technique. Python, SQL, dashboards, that kind of thing. Very little on the operating model side, and that's the bit almost every organisation is missing. So people ask me which tool to buy first, or what the best business glossary is, and I say the only tool you really need is a room with the right people in it, a pen and a whiteboard. Don't start with the tool. Start with the people and the definitions. Because if you can't agree on the terms, it's super hard to take the next step. You say something in a meeting and five people think of five different things. And it's worse with AI, since AI doesn't ask which definition is the right one. It just takes one and executes. So fix one metric, one owner. I think that's the part that matters most, and the rest is very hard without it, the roles and the responsibilities and the duties and all that kind of thing. That's what I do at Northridge Analytics. Twelve years now, Gartner, Capgemini and KPMG before that, president of DAMA Sweden, writing a book on this with Wendy Batchelder for Packt. If you want an honest read on whether your foundations can carry what's being promised, I do a 30-minute call on exactly that. I'm not selling tooling. A handful each month, so DM me if you want one.
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59% Founder / C-level · 44% Engineering / Data
From 229 people who reacted to or commented on my posts
Public LinkedIn data · Sep 17, 2026
Every leadership team wants AI until someone mentions data governance. The room goes completely quiet the second you bring up definitions, data quality, and ownership. Then everyone gets excited again when the conversation shifts to AI initiatives that actually deliver business value. Those conversations are the exact same thing. When teams skip the foundation to chase early momentum, everything looks promising at the start. Around month 14, the work stalls because nobody owns the underlying metrics and the numbers do not match across departments. To be honest, data governance is simply making sure your critical data has real owners so your teams can build with trust. I've written down the complete 30-day data governance plan here if you want the full breakdown: https://lnkd.in/dRkqe64E (Repost if this sounds familiar)
Everyone wants to reach for the shiny stuff. Nobody wants to build the foundation first. I keep seeing leadership teams launch a cool AI pilot, point it at unowned data, and wonder why it stalls. To be honest, it almost always fails for the same reason. You cannot reach the top when the bottom was never finished. You also do not have to boil the ocean. Striving for perfection across the entire organisation before you build anything just paralyzes the team. The practical approach I use with clients: → Pick a specific use case → Find key stakeholders struggling with a concrete problem → Get good enough data in place with clear definitions, trust, and control → Fix that exact issue before expanding The boring stuff takes time. But in the long term, that is what compounds. I have written down our complete 30-day data governance plan here if you want a practical place to start: https://lnkd.in/dRkqe64E (Repost if this looks familiar)
Most leadership teams explain data governance to the business completely backwards. They usually start by listing compliance policies, guardrails, and audit requirements. To be honest, the business does not care about controls until they understand who actually owns the outcome. I've drawn this out on whiteboards with executive teams when simplifying the structure: → Operating Model: Named owners, review cadence, and escalation paths → Evidence: Audit trails and source attribution to track what happened → Controls: Definition locks and approved sources to set boundaries → Decision Rights: Who is responsible for what gets decided The business always feels the outer ring first. They see the daily operating rhythm, the review forums, and the people running the process. Each ring depends directly on the ring inside it. Without clear decision rights at the center, everything above it remains open exposure. Building momentum requires establishing ownership before adding layers of process. I've written down the complete 30-day data governance plan here if you want the full breakdown: https://lnkd.in/dRkqe64E (Repost if this sounds familiar)
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Based on 229 people who reacted to or commented on my posts. Percentages use the profiles that could be classified.
Based on 229 people who reacted to or commented on my posts. Percentages use the profiles that could be classified.
Based on 229 people who reacted to or commented on my posts. Percentages use the profiles that could be classified.
Based on 229 people who reacted to or commented on my posts. Percentages use the profiles that could be classified.
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John Wernfeldt is a B2B LinkedIn creator based in Sweden with 58,556 followers, posting about AI, Software, and SaaS. On Naano, one sponsored LinkedIn post by John Wernfeldt costs €2,500, a fixed price per post.
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