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Comparing Data Science Project Management Methodologies via a Controlled Experiment

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Title: Comparing Data Science Project Management Methodologies via a Controlled Experiment
Authors: Saltz, Jeffrey
shamshurin, Ivan
Crowston, Kevin
Keywords: Big Data
Data Science
Project Management
Issue Date: 04 Jan 2017
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.
Pages/Duration: 10 pages
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.120
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Big Data and Analytics: Concepts, Methods, Techniques and Applications Minitrack

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