Use CRISP-DM before asking AI to analyze data

A churn CSV produced a quick AI pattern, but the team may act on a misleading predictor before defining the decision and checking data quality. CRISP-DM: business understanding -> data understanding -> preparation -> modeling -> evaluation -> deployment. The common trap is chart-first analysis: accepting the model's strongest-looking pattern before checking whether it is causal, timely, or actionable. Business understanding Define the decision: which accounts need intervention before renewal this month? This prevents the analysis from drifting into interesting but unusable churn explanations. Data understanding Check fields for timing, missingness, definitions, and leakage. Discount usage was recorded after risk…

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