Integration Isn’t Optional: Why AI-Ready Data Needs URIs and Ontologies

For years, the Semantic Web has carried a reputation: too complex.
But here’s the twist - the Semantic Web is not the problem.

The real complexity is distributed data integration.

It’s like a lump under a rug: you can push it around, hide it for a while, but it never goes away. Leave it too long and it will cost more, hurt more, and be far harder to fix.

We are now edging into the age of AI. Organisations want intelligent agents that can act, reason, and collaborate - with each other and with humans.
For that to work, those agents need two things:

🔹 Clear, consistent semantics

🔹 A connected network of data

That makes integration no longer a background chore - it’s the central strategic task.

Graphs help by putting connectivity front and centre. But graphs alone aren’t enough. Without URIs for stable identity and shared ontologies for shared meaning, you’re just storing up the problem for a painful future in some virtualisation layer.

Like bugs in code, integration issues are cheapest and safest to fix early. Delay, and the cost compounds.

This is why we built DPROD - a specification for publishing data products with integration baked in:
URIs as identifiers.
Mappings to common ontologies.
Semantics ready from day one.

Face the lump now. Your future AI agents will thank you.