CRISP-DM for Data Science: Strengths, Weaknesses and Potential Next Steps

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper explores the strengths and weaknesses of CRISP-DM when used for data science projects. The paper then explores what key actions data science teams using CRISP-DM should consider that addresses CRISP-DM's weaknesses. In brief, CRISP-DM, which is the most popular framework teams use to execute data science projects, provides an easy to understand description of the data science project workflow (i.e., the data science life cycle). However, CRISP-DM's project phases miss some key aspects of the data science project life cycle. In addition, CRISP-DM's task-focused approach fails to address how a team should prioritize tasks, and in general, collaborate and communicate. Hence, this paper also describes how CRISP-DM could be combined with a team coordination framework, such as Scrum or Data Driven Scrum, which is a newer collaboration framework developed to address the unique data science coordination challenges.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
EditorsYixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2337-2344
Number of pages8
ISBN (Electronic)9781665439022
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States
Duration: Dec 15 2021Dec 18 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference2021 IEEE International Conference on Big Data, Big Data 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/15/2112/18/21

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Information Systems

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