Exploring project management methodologies used within data science teams

Jeffrey Saltz, Nicholas Hotz, David Wild, Kyle Stirling

Research output: Chapter in Book/Entry/PoemConference contribution

31 Scopus citations

Abstract

There are many reasons data science teams should use a well-defined process to manage and coordinate their efforts, such as improved collaboration, efficiency and stakeholder communication. This paper explores the current methodology data science teams use to manage and coordinate their efforts. Unfortunately, based on our survey results, most data science teams currently use an ad hoc project management approach. In fact, 82% of the data scientists surveyed did not follow an explicit process. However, it is encouraging to note that 85% of the respondents thought that adopting an improved process methodology would improve the teams' outcomes. Based on these results, we described six possible process methodologies teams could use. To conclude, we outlined plans to describe best practices for data science team processes and to develop a process evaluation framework.

Original languageEnglish (US)
Title of host publicationAmericas Conference on Information Systems 2018
Subtitle of host publicationDigital Disruption, AMCIS 2018
PublisherAssociation for Information Systems
ISBN (Print)9780996683166
StatePublished - 2018
Event24th Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018 - New Orleans, United States
Duration: Aug 16 2018Aug 18 2018

Publication series

NameAmericas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018

Other

Other24th Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018
Country/TerritoryUnited States
CityNew Orleans
Period8/16/188/18/18

Keywords

  • Big data
  • Data science
  • Process methodology
  • Project management

ASJC Scopus subject areas

  • Information Systems

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