Predicting data science sociotechnical execution challenges by categorizing data science projects

Jeffrey Saltz, Ivan Shamshurin, Colin Connors

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


The challenge in executing a data science project is more than just identifying the best algorithm and tool set to use. Additional sociotechnical challenges include items such as how to define the project goals and how to ensure the project is effectively managed. This paper reports on a set of case studies where researchers were embedded within data science teams and where the researcher observations and analysis was focused on the attributes that can help describe data science projects and the challenges faced by the teams executing these projects, as opposed to the algorithms and technologies that were used to perform the analytics. Based on our case studies, we identified 14 characteristics that can help describe a data science project. We then used these characteristics to create a model that defines two key dimensions of the project. Finally, by clustering the projects within these two dimensions, we identified four types of data science projects, and based on the type of project, we identified some of the sociotechnical challenges that project teams should expect to encounter when executing data science projects.

Original languageEnglish (US)
Pages (from-to)2720-2728
Number of pages9
JournalJournal of the Association for Information Science and Technology
Issue number12
StatePublished - Dec 2017

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences


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