Data lineage documents the flow of data through an organization's systems, tracking its origin, transformations, and final use. It provides a meta-view of data flow, enabling teams to trace dependencies and anomalies, strengthen data governance for better data quality, control, and regulatory compliance. Data lineage is particularly important in dynamic environments where knowledge graphs have emerged as an effective solution for storing and analyzing data lineage, capturing how information flows and transforms across systems. Unlike data provenance, which focuses on the origins of data, data lineage describes the full data life cycle, encompassing where data comes from, where it moves next, how it is used, and relevant dependencies. Data lineage serves multiple purposes, including impact analysis and change management, migration planning, data quality and trust, root cause analysis, data governance, regulatory compliance, and analytics for machine learning, by providing a clear view of how data moves and transforms throughout systems.