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

Saltz, Jeffrey
Hotz, Nicholas
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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.
Big Data and Analytics: Pathways to Maturity, big data, data science, project management
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