Factors that influence the selection of a data science process management methodology: An exploratory study

Jeffrey Saltz, Nicholas Hotz

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

Abstract

This paper explores the factors that impact the adoption of a process methodology for managing and coordinating data science projects. Specifically, by conducting semi-structured interviews from data scientists and managers across 14 organizations, eight factors were identified that influence the adoption of a data science project management methodology. Two were technical factors (Exploratory Data Analysis, Data Collection and Cleaning). Three were organizational factors (Receptiveness to Methodology, Team Size, Knowledge and Experience), and three were environmental factors (Business Requirements Clarity, Documentation Requirements, Release Cadence Expectations). The research presented in this paper extends recognized factors for IT process adoption by bringing together influential factors that apply to data science. Teams can use the developed process adoption model to make a more informed decision when selecting their data science project management process methodology.

Original languageEnglish (US)
Title of host publicationProceedings of the 54th Annual Hawaii International Conference on System Sciences, HICSS 2021
EditorsTung X. Bui
PublisherIEEE Computer Society
Pages949-959
Number of pages11
ISBN (Electronic)9780998133140
StatePublished - 2021
Event54th Annual Hawaii International Conference on System Sciences, HICSS 2021 - Virtual, Online
Duration: Jan 4 2021Jan 8 2021

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume2020-January
ISSN (Print)1530-1605

Conference

Conference54th Annual Hawaii International Conference on System Sciences, HICSS 2021
CityVirtual, Online
Period1/4/211/8/21

ASJC Scopus subject areas

  • Engineering(all)

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