Data lessons from inside Meta — what a real org does differently

Meta's data team shared a roundup of hard-won lessons from running analytics at massive scale. The TLDR is practical: they treat data like a product, not a byproduct. Tables and pipelines have owners. Models get SLAs. If a dashboard breaks, someone knows. If a metric drifts, the team sees it before the execs do.

The pieces they highlight — schema evolution, query performance, the difference between a good model and a great one — are the kind of things most teams only learn after burning through months of slow queries and stale reports. Meta's playbook is to bake it in early: test schemas before they land, version the models like code, and keep the documentation close to the source.

Why this matters for us: the same discipline that keeps a giant's data honest is how any small team avoids the slow bleed of bad reports and stale dashboards — it's just a matter of doing it before the mess grows.