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Crowdsourcing Data Science: A Qualitative Analysis of Organizations’ Usage of Kaggle Competitions

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Title:Crowdsourcing Data Science: A Qualitative Analysis of Organizations’ Usage of Kaggle Competitions
Authors:Tauchert, Christoph
Buxmann, Peter
Lambinus, Jannis
Keywords:Collaboration for Data Science
crowdsourcing
data science
organization
success
Date Issued:07 Jan 2020
Abstract:In light of the ongoing digitization, companies accumulate data, which they want to transform into value. However, data scientists are rare and organizations are struggling to acquire talents. At the same time, individuals who are interested in machine learning are participating in competitions on data science internet platforms. To investigate if companies can tackle their data science challenges by hosting data science competitions on internet platforms, we conducted ten interviews with data scientists. While there are various perceived benefits, such as discussing with participants and learning new, state of the art approaches, these competitions can only cover a fraction of tasks that typically occur during data science projects. We identified 12 factors within three categories that influence an organization’s perceived success when hosting a data science competition.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63768
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.029
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Collaboration for Data Science


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