DataHub is a context management platform that unifies metadata, lineage, quality, governance, and business knowledge into a trusted context graph for AI agents, data teams, and enterprise applications. With DataHub Cloud and its open source foundation, organizations can keep context accurate, current, and actionable so agents and humans can find, understand, govern, and use data with confidence.

In this episode we explore how DataHub evolved from a LinkedIn data catalog into a vendor-neutral context platform for AI agents. The core idea is that AI needs more than raw data access — it needs complete, trusted, shared context to answer questions accurately. We speak with Shirishanka Das, Co-Founder and CTO of DataHub, and discuss why enterprise AI often fails: models can sound confident while using the wrong definitions or stale data. DataHub’s answer is a graph-based context layer that connects technical, operational, semantic, and business context across structured and unstructured sources, then serves that context to AI agents before they query the data layer.

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