CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories

• Published in: IEEE transactions on knowledge and data engineering Vol. 33; no. 8; pp. 3048 - 3061
• Main Authors: Martinez-Plumed, Fernando, Contreras-Ochando, Lidia, Ferri, Cesar, Hernandez-Orallo, Jose, Kull, Meelis, Lachiche, Nicolas, Ramirez-Quintana, Maria Jose, Flach, Peter
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-08-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Business; Computer Science; Data management; Data mining; Data models; Data science; Data science trajectories; data-driven methodologies; Industry standards; Knowledge discovery; knowledge discovery process; Machine Learning; Project planning; Science; Trajectory
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2019.2962680

CRISP-DM(CRoss-Industry Standard Process for Data Mining) has its origins in the second half of the nineties and is thus about two decades old. According to many surveys and user polls it is still the de facto standard for developing data mining and knowledge discovery projects. However, undoubtedly the field has moved on considerably in twenty years, with data science now the leading term being favoured over data mining . In this paper we investigate whether, and in what contexts, CRISP-DM is still fit for purpose for data science projects. We argue that if the project is goal-directed and process-driven the process model view still largely holds. On the other hand, when data science projects become more exploratory the paths that the project can take become more varied, and a more flexible model is called for. We suggest what the outlines of such a trajectory-based model might look like and how it can be used to categorise data science projects (goal-directed, exploratory or data management). We examine seven real-life exemplars where exploratory activities play an important role and compare them against 51 use cases extracted from the NIST Big Data Public Working Group. We anticipate this categorisation can help project planning in terms of time and cost characteristics.