A4 Article in conference proceedings
Managing and Composing Teams in Data Science : An Empirical Study (2021)

Aho, T., Kilamo, T., Lwakatare, L., Mikkonen, T., Sievi-Korte, O., & Yaman, S. (2021). Managing and Composing Teams in Data Science : An Empirical Study. In Y. Chen, H. Ludwig, Y. Tu, U. Fayyad, X. Zhu, X. Hu, S. Byna, X. Liu, J. Zhang, S. Pan, V. Papalexakis, J. Wang, A. Cuzzocrea, & C. Ordonez (Eds.), IEEE Big Data 2021 : Proceedings of the 2021 IEEE International Conference on Big Data (pp. 2291-2300). IEEE. https://doi.org/10.1109/bigdata52589.2021.9671737

JYU authors or editors

Publication details

All authors or editors: Aho, Timo; Kilamo, Terhi; Lwakatare, Lucy; Mikkonen, Tommi; Sievi-Korte, Outi; Yaman, Sezin

Parent publication: IEEE Big Data 2021 : Proceedings of the 2021 IEEE International Conference on Big Data

Parent publication editors: Chen, Yixin; Ludwig, Heiko; Tu, Yicheng; Fayyad, Usama; Zhu, Xingquan; Hu, Xiaohua; Byna, Suren; Liu, Xiong;
Zhang, Jianping; Pan, Shirui; Papalexakis, Vagelis; Wang, Jianwu; Cuzzocrea, Alfredo; Ordonez, Carlos

Place and date of conference: Virtual, 15.-18.12.2021

ISBN: 978-1-6654-4599-3

eISBN: 978-1-6654-3902-2

Publication year: 2021

Pages range: 2291-2300

Publisher: IEEE

Place of Publication: Piscataway, NJ

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/bigdata52589.2021.9671737

Persistent website address: http://dx.doi.org/10.1109/bigdata52589.2021.9671737

Publication open access: Not open

Publication channel open access:


Data science projects have become commonplace over the last decade. During this time, the practices of running such projects, together with the tools used to run them, have evolved considerably. Furthermore, there are various studies on data science workflows and data science project teams. However, studies looking into both workflows and teams are still scarce and comprehensive works to build a holistic view do not exist. This study bases on a prior case study on roles and processes in data science. The goal here is to create a deeper understanding of data science projects and development processes. We conducted a survey targeted at experts working in the field of data science (n=50) to understand data science projects’ team structure, roles in the teams, utilized project management practices and the challenges in data science work. Results show little difference between big data projects and other data science. The found differences, however, give pointers for future research on how agile data science projects are, and how important is the role of supporting project management personnel. The current study is work in progress and attempts to spark discussion and new research directions.

Keywords: data science; projects; project management; project work; teams; teamwork; big data

Free keywords: data science; agile practices; teamwork; project management

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2021

JUFO rating: 1

Last updated on 2022-19-08 at 19:29