Comparing Data Science Project Management Methodologies via a Controlled Experiment

Jeffrey Saltz, Ivan Shamshurin, Kevin Crowston

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Abstract

Data Science is an emerging field with a significant research focus on improving the techniques available to analyze data. However, there has been much less focus on how people should work together on a data science project. In this paper, we report on the results of an experiment comparing four different methodologies to manage and coordinate a data science project. We first introduce a model to compare different project management methodologies and then report on the results of our experiment. The results from our experiment demonstrate that there are significant differences based on the methodology used, with an Agile Kanban methodology being the most effective and surprisingly, an Agile Scrum methodology being the least effective.
Original languageEnglish (US)
Title of host publicationProceedings of the 50th Hawaii International Conference on System Sciences
DOIs
StatePublished - Jan 4 2017
Event50th Hawaii International Conference on System Sciences 2017 - Waikoloa, United States
Duration: Jan 3 2017Jan 7 2017
Conference number: 50

Conference

Conference50th Hawaii International Conference on System Sciences 2017
Abbreviated titleHICSS
CountryUnited States
CityWaikoloa
Period1/3/171/7/17

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