Understanding the Role of Knowledge Intelligence in the CRISP-DM Framework: A Guide for Data Science Projects

In today’s rapidly advancing field of data science, where new technologies and methods continuously emerge, it’s essential to have a structured approach to navigate the complexities of data mining and analysis. The CRISP-DM framework–short for Cross-Industry Standard Process for Data Mining–provides a robust methodology that helps data science teams stay organized and efficient from the start of a project to its deployment. When complemented with Knowledge Intelligence (KI), CRISP-DM becomes even more powerful, embedding expert knowledge and business context into every phase of the process. This blog offers a comprehensive guide to each phase of the CRISP-DM framework, enriched with insights on integrating KI to drive actionable insights in your data science projects. What is the CRISP-DM Framework and its relationship with Knowledge Intelligence? CRISP-DM is a widely adopted, six-phase methodology for executing data science projects.