Untangling the vocabulary of data and information modeling

The Terminology Jungle

If you've spent any time in the data modeling space, you've likely encountered a bewildering array of terms: data model, conceptual model, logical model, physical model, concept model, semantic model, information model, fact-oriented model. These terms are sometimes used interchangeably, sometimes mean completely different things depending on who's speaking, and often cause more confusion than clarity.

This article aims to untangle these terms, trace their historical origins, and explain why the distinctions matter—especially as organizations grapple with integrating systems across departments and making sense of decades of accumulated data.

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The semantic grounding problem that glossaries, ontologies, and vector databases can't solve on their own

You've probably noticed the pattern by now. You feed your data to an AI, ask it a question, and get an answer that's technically plausible but semantically wrong. The AI confuses "customer" in your sales system with "client" in your support tickets. It hallucinates relationships between fields that share similar names but mean entirely different things. Ask the same question twice with slightly different phrasing, and you get contradictory responses.

Welcome to semantic drift—the silent killer of AI reliability.

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Why Data Modeling Must Start with Human Understanding

In a recent conversation with a data professional exploring database development tools, Marco Wobben offered a perspective that cuts straight through the noise surrounding modern data projects. With over 25 years in information modeling, he's seen the same pattern repeat: technology advances rapidly, yet the biggest failures trace back to something very human.

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