CRISP-DM encourages best practices and offers a set structure for obtaining better, faster and more reliable results from data mining. The CRISP-DM methodology was developed by a consortium of companies and organisations with the idea of standardizing some of the data mining processes for multiple and diverse data mining objectives.

CRISP-DM divides the life cycle of a data mining project into six major phases. The sequence of the phases is not strict. Moving back and forth between different phases is usually required. The outcome of each phase determines what phase will follow or which particular task of a phase needs to be performed next.

The arrows in the CRISP-DM diagram above indicate the most important and frequent dependencies between phases. The outer circle in the diagram symbolizes the iterative nature of the data mining process.

The concept of CRISP-DM is that data mining is a process, which continues well after a solution has been deployed. The lessons learned during this process could trigger new and often more focused investigative questions and queries, leading to subsequent data mining processes which will benefit from the experiences of previous ones.

The objective of CRISP-DM is to establish a data mining standard process that is applicable in diverse industries, with the objective of making data mining projects faster, more efficient, more reliable, more manageable and less costly.

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