Towards Collaborative Data Analysis with Diverse Crowds – A Design Science Approach

Abstract

The last years have witnessed an increasing shortage of data experts capable of analyzing the omnipresent data and producing meaningful insights. Furthermore, some data scientists mention data preprocessing to take up to 80% of the whole project time. This paper proposes a method for collaborative data analysis that involves a crowd without data analysis expertise. Orchestrated by an expert, the team of novices conducts data analysis through iterative refinement of results up to its successful completion. To evaluate the proposed method, we implemented a tool that supports collaborative data analysis for teams with mixed level of expertise. Our evaluation demonstrates that with proper guidance data analysis tasks, especially preprocessing, can be distributed and successfully accomplished by non-experts. Using the design science approach, iterative development also revealed some important features for the collaboration tool, such as support for dynamic development, code deliberation, and project journal. As such we pave the way for building tools that can leverage the crowd to address the shortage of data analysts.

Leading teams in the digital age: Four perspectives on technology and what they mean for leading teams

“…Research has found that peer production group members use other's history of project activity to form impressions of other's expertise (Marlow, Dabbish, & Herbsleb, 2013). An expert leader in a crowd worker team can also be helpful in leading non-experts to successfully accomplish data analysis tasks (Feldman, Anastasiu, & Bernstein, 2018). Finally, collective intelligence systems can help organizations to create optimal teams from crowds to fit specific organizational needs (Malone et al, 2010).…”