AI governance is often treated as something fundamentally new. New risks. New rules. New committees. New frameworks. The moment AI enters a system, organisations feel compelled to redesign governance from scratch. That instinct is understandable but it is also where most confusion begins. A more grounded way to think about
Data products have become one of those ideas that almost everyone agrees with.If you say you’re building data products, people nod.If you say data products are the future, few disagree.If something around data isn’t working, the answer is often simple: we need data products. That’
I’ve been thinking about the Data Product Owner role for a while now, and the more I look at it, the more uneasy I become. Not because the intent is bad. Not because the people in the role are incompetent. But because the role itself feels like something that
Most conversations about data governance start in the wrong place. They start with roles. * Who is the Data Owner? * Where do they sit? * What is their RACI? But data governance doesn’t begin with people on an org chart. It begins with something more basic: data ownership. Ownership is the
We built lineage to prove control. Somewhere along the way, we started treating lineage as control. What We Use Lineage For Today In most organisations, lineage has turned into a thing on its own.There’s a tool, a budget, a roadmap, a slide in every steering deck.Ask what
Every organisation loves the phrase golden source. It sounds pure, controlled, and final, as if once declared, the truth is secured forever. But the reality is less polished. Many systems that wear the golden badge routinely produce data that fails basic validation checks. Yet they remain golden because a policy
In my previous article, The DSA: Where Purpose Meets Permission, I explored how Data Sharing Agreements form the bridge between intent and accountability defining why data moves and under what conditions. This follow-up looks at the next question every mature organisation eventually faces: How can you scale that principle without
Everywhere I look, firms are racing to be regulatory-ready. Policies are rewritten, controls refreshed, and certifications renewed—again and again. Yet regulatory-ready is not the same as opportunity-ready. The first protects reputation; the second builds it. And now, artificial intelligence is amplifying both promise and risk. AI can accelerate insight
What is a Data Sharing Agreement (DSA)?
At first glance, the Bronze–Silver–Gold medallion architecture appears simple and logical, offering a clean framework for data management…
Introduction
In regulated environments like banking, insurance, and healthcare, trust in data is essential — it directly influences your license to…