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

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dc.contributor.authorTauchert, Christoph
dc.contributor.authorBuxmann, Peter
dc.contributor.authorLambinus, Jannis
dc.date.accessioned2020-01-04T07:10:54Z
dc.date.available2020-01-04T07:10:54Z
dc.date.issued2020-01-07
dc.description.abstractIn 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.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.029
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63768
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCollaboration for Data Science
dc.subjectcrowdsourcing
dc.subjectdata science
dc.subjectorganization
dc.subjectsuccess
dc.titleCrowdsourcing Data Science: A Qualitative Analysis of Organizations’ Usage of Kaggle Competitions
dc.typeConference Paper
dc.type.dcmiText

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