Data Lineage

Data lineage traces data from its origins through transformations to its destinations. It helps a reviewer follow the route behind an output. IBM

For an AI-assisted project, that route can explain which input file was used, which reviewed changes were applied, and which deliverable contains the result. It also reveals where the record stops.

A lightweight illustrative chain

This is a fictional teaching example:

feedback.csv → reviewed-codes.csv → memo.md

The first file holds supplied comments. The second records reviewed labels. The memo uses checked counts and selected observations. A useful register names the versions and the transformations between them, including who reviewed the assignments when that is documented.

If the input is missing, the reviewer cannot inspect the starting evidence. If the input exists but the labeling decision is undocumented, the missing piece is interpretation. Those gaps need different repairs.

What traceability establishes

A hash identifies file bytes. A citation points to an evidence location. Lineage records movement and change across the chain. None alone proves correctness, permission, complete coverage, or legal compliance.

The AI project-handoff guide uses a source register as a lightweight traceability example, rather than a full lineage system. Snapshot Handoff inventories the supplied files; Project Handoff Auditor reviews the prepared packet against them. Those jobs still require evidence for decision status.

Grounding connects an answer to supporting material. Lineage adds the path through the materials and transformations that produced the deliverable.