Most organizations have accumulated fragmented source schemas, legacy data models, inconsistent terminology, and disconnected definitions of important business entities. These inconsistencies become especially consequential when generative and agentic AI systems use enterprise data to answer questions, make recommendations, or take action.
This presentation explains how semantic ontologies can transform fragmented data structures into connected, machine-readable business context. It follows the architectural journey from source schemas and legacy models to canonical entities, attributes, relationships, business definitions, and knowledge graph representations. It also clarifies the differences among schemas, taxonomies, business glossaries, semantic models, knowledge graphs, and vector retrieval. It shows how these capabilities complement one another rather than compete.
Using a practical, vendor-neutral example, the session will demonstrate how ontology mapping, validation, versioning, stewardship, provenance, and policy-aware context can improve AI readiness. Attendees will learn how to establish shared business meaning that can be consistently understood and applied by data practitioners, applications, and AI agents.