CRISPDM

CRISP-DM, or Cross-Industry Standard Process for Data Mining, is a vendor-neutral process model that describes a standard life cycle for data mining and analytics projects. It provides a structured yet flexible approach to planning, executing, and evaluating data mining work, emphasizing understanding the business objective and the data, iterative refinement, and delivering actionable results.

The model was developed in the late 1990s by a consortium of industry and academic partners, including SPSS (now IBM), DaimlerChrysler, and NCR. It was published around 2000 as a practical, repeatable framework intended to span industries and tool choices, enabling organizations to manage data mining projects consistently.

CRISP-DM is organized into six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Each phase has specific goals and deliverables. The framework supports non-linear progression, with teams revisiting earlier phases as new insights emerge, and it emphasizes thorough documentation of decisions, methods, and results.

In practice, CRISP-DM guides the production of outputs such as business understanding briefs, data dictionaries, data preparation scripts, model documentation, evaluation reports, and deployment plans. It is widely used as a high-level, technology-agnostic blueprint for data mining and analytics work, adaptable to various tools and contexts.

Limitations include its age relative to modern ML pipelines and big data environments, with limited prescriptive guidance on data engineering, governance, and scalable deployment. Despite this, CRISP-DM remains influential and is still taught and applied as a foundational framework, often integrated into broader data science and MLOps practices.