A Next-Generation Data Provenance Framework Employing Causal Inference and Graph-Theoretic Lineage Tracking for High-Stakes Enterprise Analytics

Abstract

In the era of increasingly complex enterprise analytics pipelines, data provenance—the ability to trace and audit the origins, transformations, and usage of data—has become a foundational requirement for trust, compliance, and performance optimization. This paper introduces a novel data provenance framework that integrates causal inference techniques with graph-theoretic lineage tracking to provide deep, explainable insight into data workflows and transformations. Our approach supports enterprise-scale analytics by embedding a dual-layered system: one that captures semantic and operational lineage through graph structures, and another that leverages causal modeling to identify the true impact of data interventions across pipeline stages. This hybrid system is evaluated through simulated enterprise environments and benchmarked datasets, with results demonstrating superior traceability, interpretability, and robustness in high-stakes data environments