Managing and Composing Teams in Data Science: An Empirical Study

Timo Aho, Terhi Kilamo, Lucy Lwakatare, Tommi Mikkonen, Outi Sievi-Korte, Sezin Yaman

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

54 Lataukset (Pure)


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.
Otsikko2021 IEEE International Conference on Big Data (Big Data)
ISBN (elektroninen)978-1-6654-3902-2
DOI - pysyväislinkit
TilaJulkaistu - 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Big Data - , Yhdysvallat
Kesto: 15 jouluk. 202118 jouluk. 2021


ConferenceIEEE International Conference on Big Data


  • Jufo-taso 1


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