Data Provenance and Governance in Industrial Big Data Systems

Abstract

The exponential growth of industrial big data has introduced significant challenges in ensuring data quality, trust, and accountability. Data provenance and governance are critical components that facilitate transparency, traceability, and compliance across complex data workflows. This paper presents an extensive analysis of data provenance techniques and governance frameworks in industrial environments, focusing on manufacturing, energy, transportation, and logistics sectors. By integrating provenance tracking mechanisms with governance policies, industries can enhance decision-making, streamline operations, and comply with regulatory standards. The study introduces a hybrid provenance model combining event-based and lineage-based tracking with blockchain for immutable logging. We explore the role of machine learning in enhancing data curation and anomaly detection. Through a case study in a smart manufacturing setup, we evaluate the efficacy of our model. The results indicate improved traceability, reduced error propagation, and enhanced policy compliance. This paper aims to provide a comprehensive framework for industries to build robust and scalable big data infrastructures with embedded provenance and governance capabilities.