Ten Data Science Mistakes that Cost Millions and How to Resolve Each Issue скачать в хорошем качестве

Ten Data Science Mistakes that Cost Millions and How to Resolve Each Issue

This text outlines ten common data science mistakes that quietly destroy real business value and explains how to fix them. First, teams often optimize accuracy instead of financial outcomes, when models should be tuned to expected profit using explicit cost matrices. Second, data leakage occurs when models train on information unavailable in production, creating impressive tests that collapse in reality. Third, organizations over-engineer complex models when simple rules would deliver better ROI with lower cost and risk. Fourth, many models decay after deployment because drift is ignored, even as markets, customers, and data pipelines change. Fifth, relying on historical averages in non-stationary environments leads to fragile forecasts that fail during regime shifts. Sixth, confusing correlation with causation causes decision systems to break once actions change behavior, requiring causal methods like A/B testing or uplift modeling. Seventh, models without a clear decision owner produce insights that no one acts on, rendering them useless. Eighth, overfitting to “perfect” historical labels embeds bias and fails when policies or conditions change. Ninth, treating model outputs as absolute truth instead of uncertain inputs leads to poor decisions without confidence thresholds or human oversight. Finally, measuring model performance instead of economic impact obscures the truth that data science only creates value when it directly improves real-world decisions and financial outcomes.