Data Lineage

Technical explanation

Lineage connects sources, pipelines, transformations, schemas, datasets, reports, models, and destinations. It may be captured from code, query logs, orchestration metadata, or manual documentation and represented at system, table, column, or record level.

Business relevance

Lineage supports impact analysis, root-cause investigation, auditability, migration, privacy requests, and confidence in reports and AI systems.

Implementation example

Before changing a CRM field, a team uses column-level lineage to identify affected warehouse models, dashboards, automation, and machine-learning features.

Limitations and common misconceptions

Lineage can be incomplete or stale, especially across manual exports and opaque vendor systems. It shows movement and transformation but does not by itself prove quality, lawful use, or semantic correctness.

Topics

Sources