Published Paper Details:

In the age of data-driven enterprises, metadata lineage frameworks have emerged as a critical enabler for effective data governance, regulatory compliance, and trust-building within analytical ecosystems. This review synthesized key academic and industry insights around metadata lineage concepts, current tools, architectural designs, and experimental benchmarks. Findings indicate that while several commercial and open-source platforms offer lineage functionality, challenges persist in automation, semantic depth, real-time coverage, and integration across hybrid environments. By introducing a five-layer theoretical model and evaluating lineage tools across performance, compliance, and usability, this review provides a structured understanding for organizations striving to improve governance maturity. The paper also outlines future opportunities in AI-enhanced lineage detection, cross-platform interoperability, and sustainability-aware data governance.