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Stop Mixing CDM, Ontology, and Knowledge Graphs
Or: Why Your Data Architecture Feels More Complicated Than It Should
Let me start with a question that has saved more than one architecture discussion from going off the rails: What problem are we actually trying to solve here?
Let me start with a question that has saved more than one architecture discussion from going off the rails: What problem are we actually trying to solve here?
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Because if you’ve ever been in a workshop where someone casually throws around “Let’s build a knowledge graph,” you probably know the feeling. Heads nod. People get excited. Someone mentions AI. Someone else talks about APIs. And before long, three different concepts get mashed into one fuzzy idea.
Canonical Data Model. Ontology. Knowledge Graph. Same thing, right?
Not even close. And yet, they keep getting mixed up. Over and over again. In whiteboard sessions, in architecture decks, in vendor pitches. The result? Confusion, over-engineering, and systems that try to do everything at once — and end up doing none of it well.
So let’s slow this down and untangle the mess. Not academically. Not in theory. But in a way you can actually use the next time someone…
