Factors that Influence the Selection of a Data Science Process Management Methodology: An Exploratory Study

dc.contributor.author Saltz, Jeffrey
dc.contributor.author Hotz, Nicholas
dc.date.accessioned 2020-12-24T19:10:17Z
dc.date.available 2020-12-24T19:10:17Z
dc.date.issued 2021-01-05
dc.description.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 are applicable within a data science context. Teams can use the developed process adoption model to make a more informed decision when selecting their data science project management process methodology.
dc.format.extent 11 pages
dc.identifier.doi 10.24251/HICSS.2021.116
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70728
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Big Data and Analytics: Pathways to Maturity
dc.subject big data
dc.subject data science
dc.subject project management
dc.title Factors that Influence the Selection of a Data Science Process Management Methodology: An Exploratory Study
prism.startingpage 949
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