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CRISP‑DM is a cyclic, industry-neutral process model for data mining projects. It defines six phases—business understanding, data understanding, data preparation, modeling, evaluation and deployment—to organize work, roles and deliverables. Teams use it to align stakeholders, reduce risk and iterate from business goals to operational models.
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CRISP-DM is an industry-neutral process model that breaks data-mining projects into six phases and organizes work, outputs, and responsibilities.
CRISP-DM was developed in 1996 to solve a practical problem: how to structure data-mining projects consistently across domains, data sources, and tool boundaries. In 1997 it became part of the EU ESPRIT program and was led by Integral Solutions Ltd., Teradata, Daimler, NCR, and OHRA. The first version appeared in 1999, and the open standard remained a reference for classic data-mining work.
Think of CRISP-DM as a reusable project cycle: define the business question, understand the data, prepare the data, build a model, evaluate the result, and deploy the solution. After each step, the team can loop back when the data, goal, or evaluation reveals new information. This keeps the work iterative while still traceable.
The six phases provide a shared structure for tasks, outputs, and handoffs.
The sequence is not strictly linear; findings can send the team back to earlier phases.
Goals, success criteria, and domain questions are clarified before analysis begins.
Raw data is cleaned, combined, and shaped into an analyzable form.
Models are checked against the intended use and only then moved into operation.
The model can be used across domains and with different toolchains.
CRISP-DM is useful when a team wants to manage data-mining or analytics projects with clear phases, roles, and deliverables, especially with changing stakeholders or uncertain data conditions. It creates a shared language and reduces jumps between business goals and model work. Limits: CRISP-DM does not replace project management or modern operations and automation practices; productive ML systems often need additional governance and MLOps.
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