Data & AI Governance

Data Lineage & Dependency Mapping

Trace source, movement, transformation and dependency so governance follows the real data flow.

A practical governance and architecture model for understanding where data came from, what happened to it, where it moved and what now depends on it.

Lineage model

SourceOrigin / system
of record

IngestAPI / EDI / file
event / pipeline

TransformMap / enrich
mask / calculate

StoreRaw / curated
operational

ServeAPI / semantic
data product

ConsumeReport / process
third party / AI

Retain / DeleteArchive / dispose
propagate deletion

Lineage is more than metadata

For important datasets, lineage supports governance, change impact, incident response, data quality, migration, system retirement, M&A, divestment and AI provenance.

Capture the control context

Authoritative source and accountable owner.
Classification and interface type.
Material transformations and semantic changes.
Downstream systems, reports, third parties and AI consumers.
Quality controls, access model, retention and evidence.

Use proportionate depth

Business lineage for ownership and executive governance.
System lineage for architecture, change, integration and operations.
Technical lineage for engineering, regulated data, quality and model provenance.

AI extends the lineage boundary

Modern lineage may continue through embedding, indexing, vector stores, RAG workflows, agents, models and user outputs. Provenance, refresh, permitted use and deletion propagation become part of the same control story.

Core principle

You cannot govern data well if you cannot explain where it came from, what happened to it, and where it goes next.

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