Crowdsourcing data science: A qualitative analysis of organizations' usage of kaggle competitions

14Citations
Citations of this article
49Readers
Mendeley users who have this article in their library.

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.

Cite

CITATION STYLE

APA

Tauchert, C., Buxmann, P., & Lambinus, J. (2020). Crowdsourcing data science: A qualitative analysis of organizations’ usage of kaggle competitions. In Proceedings of the Annual Hawaii International Conference on System Sciences (Vol. 2020-January, pp. 229–238). IEEE Computer Society. https://doi.org/10.24251/hicss.2020.029

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free