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

Agentic aiData ArchitectureEnterprise ArchitectureData CatalogKnowledge Graphs
Written By Cheryl Dopp

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.

Cheryl Dopp

Cheryl Dopp builds the data foundations that make enterprise AI actually work. Nearly three decades across financial institutions, insurers, utilities, distributors, and healthcare — working the guts of the functional areas within them, and everywhere those systems connect. She writes about the unglamorous layer beneath every successful AI initiative — because that's where the real work happens.

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