Why Your Marketing Data Lakes Are Failing Due to Schema Drift

Episode notes

In this episode, Lucas and Luna tackle a problem that quietly destroys marketing data initiatives: schema drift. As data sources like social media APIs, CRM exports, and event tracking tools evolve without warning, your carefully built data lake can start accepting fields that don't match your expectations. Lucas explains how a single Instagram API change in early 2026 caused one DTC brand's entire attribution model to misattribute 20% of conversions for two weeks before anyone noticed. Luna shares a story from a SaaS company where a forgotten vendor update turned their lead scoring algorithm into a black box. They walk through the practical signs of schema drift — missing fields, corrupted joins, and mysterious nulls — and offer a lightweight fix: schema registry middleware that validates and alerts before data enters the lake. No jargon, no scare tactics — just a real ops problem with a concrete solution you can implement this quarter.