Cross-industry standard process for data mining

For IoT problems, the most used data management (DM) methodology is cross-industry standard process for data mining (CRISP-DM) proposed by Chapman et al. It's a process model that states the tasks that need to be carried out for successfully completing DM. It's a vendor-independent methodology divided into these six different phases:

  1. Business understanding
  2. Data understanding
  3. Data preparation
  4. Modelling
  5. Evaluation
  6. Deployment

Following diagram shows the different stages:

 Different stages in CRISP-DM

As we can see, it's a continuous process model with data science and AI playing important roles in steps 2–5.

The details about CRISP-DM and all its phases can be read in the following:
Marbán, Óscar, Gonzalo Mariscal, and Javier Segovia. A data mining & knowledge discovery process model. Data Mining and Knowledge Discovery in Real Life Applications. InTech, 2009.