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

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dc.contributor.authorSaltz, Jeffrey
dc.contributor.authorHotz, Nicholas
dc.date.accessioned2020-12-24T19:10:17Z
dc.date.available2020-12-24T19:10:17Z
dc.date.issued2021-01-05
dc.description.abstractThis 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.extent11 pages
dc.identifier.doi10.24251/HICSS.2021.116
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/70728
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBig Data and Analytics: Pathways to Maturity
dc.subjectbig data
dc.subjectdata science
dc.subjectproject management
dc.titleFactors that Influence the Selection of a Data Science Process Management Methodology: An Exploratory Study
prism.startingpage949

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