The next enterprise architecture asset: Ontologies for AI

A data ontology starts with a simple but powerful shift: organizing data by meaning, not just structure. In practice, it provides a shared semantic framework that defines what data represents, how key entities relate and how that meaning is consistently understood across systems, teams and acquisitions. By integrating data across silos and domains, an ontology ensures information is interpretable by both humans and machines, enabling a uniform understanding regardless of source or context. More formally, a data ontology is the explicit specification of concepts, attributes and relationships within a domain, encoded in a machine‑readable form. This allows systems to reason over data rather than merely store it, transforming disparate tables and records into a cohesive knowledge layer. For CIOs, this is not academic, it’s foundational. A well‑designed data ontology underpins modern data science, enterprise knowledge management and AI, turning raw data into an asset that can be trusted, s