Cross Industry Standard Process for Data Mining (CRISP-DM) is one of the most popular and widely used processes for data mining and analytics projects. CRISP-DM provides the required framework, which clearly outlines the necessary steps and workflows for executing a data mining and analytics project, from business requirements to the final deployment stages and everything in between.
More popularly known by the acronym itself, CRISP-DM is a tried, tested, and robust industry standard process model followed for data mining and analytics projects. CRISP-DM clearly depicts the necessary steps, processes, and workflows for executing any project, right from formalizing business requirements to testing and deploying a solution to transform data into insights. Data science, data mining, and ML are all about trying to run multiple iterative processes to extract insights and information from data. Hence, we can say that analyzing data is truly both an art as well as a science, because it is not always about running algorithms without reason; a lot of the major effort involves understanding the business, the actual value of the efforts being invested, and proper methods for articulating end results and insights.
Data science and data mining projects are iterative in nature to extract meaningful insights and information from data. Data science is as much art as science and thus a lot of time is spent understanding the business value and the data at hand before applying the actual algorithms (these again go through multiple iterations) and finally evaluations and deployment.
Similar to software engineering projects, which have different life cycle models, CRISP-DM helps us track a data mining and analytics project from start to end. This model is divided into six major steps that cover from aspects of business and data understanding to evaluation and finally deployment, all of which are iterative in nature. See the following diagram:
Let's now have a deeper look into each of the six stages to better understand the CRISP-DM model.