Temporal Consistency in Synthetic Databases: The Silent Failure That Breaks Time-Aware ML Models

Your synthetic data has. That does not mean it understands time. The strangest model failure I have seen looked like a feature bug, a data bug, and a model bug at the same time. We were testing a churn model for a subscription business. The model used a simple feature set: days since signup, days since last login, number of purchases in the last 30 days, and average order value over the customer’s lifetime. In staging, the model looked solid. Offline metrics were stable. The feature pipeline ran cleanly. Then we ran it against a larger internal environment and churn scores went sideways.