Preflight Your Data: Catch Hidden Issues Before Handoff

Handing over your dataset to another person is a key step in sharing your results. Often data looks fine but falls apart due to common issues like missing IDs, random date formats, broken links, and mixed vocabulary. Consistency and human review still matter and especially for foundational metadata. In this talk, I’ll show a practical preflight built in Python: define a small set of rules, run them locally, and generate a report that real people can use to fix metadata quickly. We’ll build a Markdown report with a summary table and failing-row examples. You’ll see how to structure findings and produce an attachable preflight report you can use for data handoffs, pipeline outputs, or repository submissions.