Blog Category: Context Management

Context platform ROI, measured. IDC’s five categories of hidden spend most organizations are already paying without knowing it.

A data context inventory is the audit step most AI projects skip. Map your context across six dimensions before agents go live.

The five context problems breaking AI agents in production, and how a context platform fixes each without duplicating RAG pipelines.

Data prep made data usable for analysts. Context preparation makes it usable for agents. Why both matter, and why most enterprises have only done one.

Technical metadata says what data is. Business context says what it means. Learn why that gap breaks AI agents.

AI-ready data isn’t enough. Agents need AI-ready context: the definitions, runbooks, and institutional knowledge that give structured data meaning.

A context platform is not a data catalog with AI features. Learn the architectural distinction and why it matters for putting AI agents into production.

Ontology vs. semantic layer explained. Learn what each does, where they overlap, and why both depend on context management.

Context graphs and knowledge graphs share the same shape. The real difference is scope and grounding, and it matters more than the vocabulary.

A metadata knowledge graph connects your data assets, pipelines, and meaning. Here’s what it is and why DataHub calls it a context graph.

A phased, practitioner’s guide to implementing an enterprise context layer on the metadata infrastructure you already have.