Inside Meta's data lessons — and why classification matters

TLDR Data published a look at what Meta's engineering team learned about data classification — how they organize, label, and verify their data so it actually works for ML and analytics. The post draws on lessons from inside the company's data engineering practice.

Data classification is one of those unsexy parts of the stack that breaks everything when done wrong. Meta's experience shows the real cost of messy data taxonomies: models trained on mislabeled data, reports that don't match, and teams spending hours stitching together the same information from different sources. The lesson isn't new — it's that taxonomies don't organize themselves, and the people who work with the data should be helping build it, not just consuming it.

Why this matters for us: the same data-classification problems hit when companies build products for our communities — bad labels, bad training data, bad models — and it's the Brown and Black folks who end up paying for it.