Enhancing Data Lineage and Governance in Large-Scale Enterprise Analytics Pipelines

Abstract

Modern enterprises rely on complex analytics pipelines that integrate data from operational systems, third-party providers, and internal reporting platforms. As pipelines grow in scale, tracking data provenance becomes increasingly difficult. This study examines lineage records from a corporate analytics environment processing more than 1.6 petabytes of structured and semi-structured data annually. Lineage gaps were traced to manual transformations, undocumented scripts, and legacy connectors. Nearly 17% of reporting datasets lacked complete upstream metadata. A standardized lineage annotation framework and automated capture mechanism were introduced. Following adoption, compliance auditing time was reduced substantially, and debugging efficiency improved across multiple departments. Data governance effectiveness proved closely tied to tooling consistency.