Mind the context gap
Blog post from PostHog
Product development teams are increasingly finding that AI initiatives stall because fragmented, unreliable, and poorly governed data prevents agents from understanding full business context, according to industry statistics cited from BetterCloud, dbt Labs, Monte Carlo, Astronomer, Matillion, Fivetran, and Databricks. The piece argues that organizations commonly rely on numerous SaaS applications and fragile pipelines to consolidate data, while data teams spend substantial time maintaining those systems and still may not trust the information supporting AI outputs. It proposes defining data readiness as having all relevant data accessible in one place for both people and agents, rather than viewing readiness solely as clean tables, pipeline uptime, or governance policies. As a solution, it promotes the concept of a “context warehouse,” which combines data ingestion, modeling, storage, and querying to reduce pipeline dependencies, and presents PostHog’s platform as an example that unifies product data with external business sources, analytics tools, semantic definitions, and AI-agent access.
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