TY - GEN
T1 - Factors that influence the selection of a data science process management methodology
T2 - 54th Annual Hawaii International Conference on System Sciences, HICSS 2021
AU - Saltz, Jeffrey
AU - Hotz, Nicholas
N1 - Publisher Copyright:
© 2021 IEEE Computer Society. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:85108366581
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
SP - 949
EP - 959
BT - Proceedings of the 54th Annual Hawaii International Conference on System Sciences, HICSS 2021
A2 - Bui, Tung X.
PB - IEEE Computer Society
Y2 - 4 January 2021 through 8 January 2021
ER -