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MENTAL MODEL · M6566

Cross-Industry Standard Process for Data Mining

Cross-Industry Standard Process for Data Mining
TechnicalHigh supportData Science
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Updated 2026-08-09

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INTRODUCTION

English translation pending.

CORE DEFINITION

CRISP-DM, the Cross-Industry Standard Process for Data Mining, was launched in 1996 by companies including IBM and SPSS. Its six stages are Business Understanding, defining the business goals and requirements; Data Understanding, collecting, exploring and evaluating the data; Data Preparation, cleaning, transforming and building the analytical dataset; Modeling, selecting and applying modeling techniques; Evaluation, checking whether the business goal is actually met; and Deployment, going live and continuously monitoring. The stages need not run strictly in sequence and often loop back, for instance when modeling reveals a data problem. The core is business understanding first and evaluation last, which prevents modeling for its own sake.

SCAFFOLDING EFFECT

psychology

Reduce cognitive load

- Start from the business goal: always define the real target before touching any data. - Expect iteration: plan honestly for loopbacks from modeling back into data preparation. - Judge by outcome: evaluate against the business goal and not against model accuracy.

anchor

Anchor fast decisions

Built on the view that a data science project is a business-driven iterative loop rather than a purely technical exercise. The six stages need not run strictly linearly and frequently return, and the discipline of business understanding first and evaluation last prevents building models that are technically precise but commercially useless.

MINIMUM ACTION

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Source support: Explicit

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