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

Jeffrey S. Saltz

doi:10.1109/BigData52589.2021.9671634

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

Jeffrey S. Saltz

School of Information Studies

Research output: Chapter in Book/Entry/Poem › Conference contribution

40 Scopus citations

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 language	English (US)
Title of host publication	Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
Editors	Yixin 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
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2337-2344
Number of pages	8
ISBN (Electronic)	9781665439022
DOIs	https://doi.org/10.1109/BigData52589.2021.9671634
State	Published - 2021
Event	2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States Duration: Dec 15 2021 → Dec 18 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference	2021 IEEE International Conference on Big Data, Big Data 2021
Country/Territory	United States
City	Virtual, Online
Period	12/15/21 → 12/18/21

ASJC Scopus subject areas

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

Access to Document

10.1109/BigData52589.2021.9671634

Cite this

Saltz, J. S. (2021). CRISP-DM for Data Science: Strengths, Weaknesses and Potential Next Steps. In Y. Chen, H. Ludwig, Y. Tu, U. Fayyad, X. Zhu, X. T. Hu, S. Byna, X. Liu, J. Zhang, S. Pan, V. Papalexakis, J. Wang, A. Cuzzocrea, & C. Ordonez (Eds.), Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 (pp. 2337-2344). (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData52589.2021.9671634

CRISP-DM for Data Science: Strengths, Weaknesses and Potential Next Steps. / Saltz, Jeffrey S.
Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. ed. / Yixin 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. Institute of Electrical and Electronics Engineers Inc., 2021. p. 2337-2344 (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021).

Research output: Chapter in Book/Entry/Poem › Conference contribution

Saltz, JS 2021, CRISP-DM for Data Science: Strengths, Weaknesses and Potential Next Steps. in Y Chen, H Ludwig, Y Tu, U Fayyad, X Zhu, XT Hu, S Byna, X Liu, J Zhang, S Pan, V Papalexakis, J Wang, A Cuzzocrea & C Ordonez (eds), Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, Institute of Electrical and Electronics Engineers Inc., pp. 2337-2344, 2021 IEEE International Conference on Big Data, Big Data 2021, Virtual, Online, United States, 12/15/21. https://doi.org/10.1109/BigData52589.2021.9671634

Saltz JS. CRISP-DM for Data Science: Strengths, Weaknesses and Potential Next Steps. In Chen Y, Ludwig H, Tu Y, Fayyad U, Zhu X, Hu XT, Byna S, Liu X, Zhang J, Pan S, Papalexakis V, Wang J, Cuzzocrea A, Ordonez C, editors, Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 2337-2344. (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021). doi: 10.1109/BigData52589.2021.9671634

Saltz, Jeffrey S. / CRISP-DM for Data Science : Strengths, Weaknesses and Potential Next Steps. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. editor / Yixin 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. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 2337-2344 (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021).

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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.",

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

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