I've spent five years working with predictive models in marketing, and I keep seeing the same pattern: companies rush to deploy machine learning without checking if their training data actually reflects the real world. Yesterday a colleague showed me a churn model that was 94% accurate but completely ignored seasonal spikes in cancellations, and that gap costs us real money every quarter. So here's my question: should data science teams be required to prove their datasets are unbiased before their models ever go into production, or is that just slowing down innovation for no good reason?