As modern computers became increasingly more popular and larger amounts of digital data were available, different methodologies were proposed to extract information from data. CRISP-DM methodology quickly spread and is currently one of the most popular approaches used for data analysis. However, it has some shortcomings, such as being too general or business-centered. Different authors have proposed variations more suitable to specific fields in order to overcome those limitations. The present paper reviews CRISP-DM, some variations and similar methodologies, and proposes a Methodology for Industrial Data Analysis (MIDA)—a methodology conceived and improved over time, based on previous experience in industrial engineering processes. MIDA consists of eight steps and partially overlaps with CRISP-DM. It has been successfully applied in several previous projects.
Mendes et al. (Sat,) studied this question.