AI’s Impact on Data Silos and Knowledge Hubs
Summary
AI spans a spectrum of techniques beyond large language models—keyword search, vector similarity, classical ML, agents, and knowledge graphs—and each plays a role in knowledge sharing. These techniques can create or strengthen centralized knowledge hubs and make enterprise information more uniformly accessible, but they are not a silver bullet for eliminating data silos. Knowledge graphs, especially when combined with language models, can extract schemas and ontologies from diverse repositories (relational, NoSQL, Parquet) and link them into a metadata layer that enables cross-repository queries. Vector approaches provide powerful search and aggregation capabilities, while orchestration and conversational AI help operationalize insights across business units.
