Data Provenance & Lineage

Data provenance answers the question "where did this data come from?" and data lineage tracks how it's been transformed along the way. Together, they give you a complete history of every piece of data flowing through your AI systems. This matters more than it might sound. When a model produces a questionable output, provenance lets you trace it back to the training data that contributed to that behaviour. When a data source turns out to be unreliable or legally problematic, lineage tells you which models and pipelines it has touched. Without these records, debugging AI systems becomes guesswork, and compliance becomes nearly impossible. In practice, maintaining provenance and lineage requires tooling and discipline. Every transformation, filter, merge, or enrichment step needs to be logged. Metadata needs to travel with the data. This creates overhead, and many teams skip it under time pressure - then regret it later when something goes wrong. The EU AI Act and similar regulations are increasingly requiring organisations to demonstrate traceability in their AI systems. Even without regulatory pressure, provenance is simply good engineering practice. You wouldn't deploy software without version control; you shouldn't deploy AI without knowing what went into it.