Plausibly Correct, Evidently Wrong: Why Enterprise Meaning Needs a Living Ontology

Enterprise meaning has to become a living framework - auditable, and updated because it must be, because it’s a runtime component of machine and analyst use and decision-making, if it’s going to be governed and actionable by machines or people.

So today I’m reading different thought leaders and reconciling their proposed solutions against what I know about entropy, organizations, data, and every prior attempt to solve this.

I agree with this: "While data catalogs focus on documenting datasets, schemas, lineage, and ownership, pragmatic ontology defines domain objects and relationships that are directly usable by both humans and AI agents at runtime." — Emmanuel Klinger

Throwing gold-layer metadata into a repository, graph, or vector won't cut it.

The models are already showing us the gap.

They guess for you: plausibly correct, evidently wrong, and it’s what your analysts have been telling you for years, it's what is discovered after using dashboards for a few months.

You just weren’t listening until a machine said it: by producing reasonably deduced but wrong outputs.

Outputs you are now authorizing it to act on, unsupervised.