Source of Truth
The single, authoritative system or table where a piece of information is officially defined — the place every other copy of it must trace back to.
Production AI is a system: context, tools, permissions, traces, evals, and feedback loops around the model.
What it is
A source of truth is the agreed-upon authoritative source for a piece of data. For customer records, it might be the CRM. For invoices, the billing system. For product specs, a specific repo. Every dashboard, AI retrieval, downstream system, and report should resolve back to the source of truth — not to a copy made last quarter that diverged silently.
Why it matters
Without declared sources of truth, the same information lives in three places and the three disagree. Reports contradict each other; AI cites the stale copy; teams argue about whose number is right. Declaring sources of truth — and making everything else read from them — is half of what governed data looks like in practice.
How it works
Each data domain gets an owner who declares the source of truth, the access boundaries, and the freshness contract. Downstream copies (warehouse, lakehouse, search indexes) are derived through documented pipelines. The Data Catalog encodes the relationship; lineage tools enforce it.
Related resources
A navigable map of every system your data lives in — schemas, documents, code, tickets, events, owners, and permissions — so an AI agent can find the right source and respect the right access boundary.
Contracts, validation, lineage, freshness, and ownership for the data your AI reads from — not a one-time cleanup project, an ongoing operating discipline.
A family tree for your data — where every table and column comes from and everything it feeds — so 'if I change this, what breaks?' takes a query instead of a week.
Explicit agreements between a data source and the systems that depend on it — what shape, how fresh, who owns it, what counts as broken — so pipeline failures become attributable instead of mysterious.