Data lineage tracks every data asset from origin to consumption, covering sources, transformations, pipelines, and destinations. Column-level lineage is now the minimum viable standard for enterprise use cases, enabling faster root-cause analysis, confident impact analysis before schema changes, and regulatory compliance (BCBS 239, GDPR, SOX). Lineage also underpins AI/ML readiness by providing auditable provenance chains for feature stores and RAG pipelines. A six-step implementation guide covers auditing pipeline coverage, prioritizing column-level granularity, automating capture, connecting lineage to observability, exposing business lineage to non-technical stakeholders, and integrating lineage with governance workflows.

14m read timeFrom decube.io
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Key takeawaysWhat is data lineage?Why is data lineage the foundation of enterprise data infrastructure? {#why-foundation}What are the types of data lineage?What are the enterprise benefits of data lineage?How does data lineage support AI and ML?How does data lineage enable regulatory compliance?‍ Data lineage vs. data catalog: what's the difference?How do you implement data lineage at enterprise scale?FAQs about data lineage

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