Data lineage answers a fundamental question in data governance: where did this data come from, and where has it gone? For data engineers, lineage is primarily a quality and debugging tool. For compliance and governance teams, it has become a critical accountability mechanism.
From a privacy perspective, data lineage enables organizations to trace how personal data flows from collection to downstream use, including which systems hold copies, which analytics processes have touched it, and whether it has been shared with third parties. This is essential for Data Subject Access Requests (where all copies must be located), for deletion requests (where all copies must be removed), and for consent enforcement.
For AI governance, data lineage connects training data to model outputs, enabling organizations to assess whether personal or restricted data was incorporated into a model's weights. As regulatory requirements around AI documentation grow, lineage from raw data through training to model deployment is likely to become an increasingly important audit evidence requirement.