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Learning by Doing versus Learning by Viewing: An Empirical Study of Data Analyst Productivity on a Collaborative Platform, Proceedings of the ACM on Human-Computer Interaction

Abstract

Effective data analytics affects business success by enhancing managerial decision-making. Companies, however, often struggle to maintain growth in the productivity of their data analysts. In this paper, we empirically quantify how data analyst productivity benefits from collaborative platforms that facilitate writing their own queries as well as viewing other colleagues' queries. Productivity is measured using the time from creating a new query to its first execution. Using a sample of 2,027 data analysts that wrote 79,797 queries over 4 years, we find that 1) learning by doing is significantly and positively associated with productivity and the effect is stronger when prior experience uses multiple data bases beyond the focal database queried; 2) learning by viewing depends on the type of analyst who created the viewed query: only “all-star” data analysts who have both high productivity and high viewership are significantly and positively associated with productivity.

Type

Article

Author(s)

Itai Gurvich, Debora Seys, Jan A. Van Mieghem

Date Published

2018

Citations

Gurvich, Itai, Debora Seys, and Jan A. Van Mieghem. 2018. Learning by Doing versus Learning by Viewing: An Empirical Study of Data Analyst Productivity on a Collaborative Platform. Proceedings of the ACM on Human-Computer Interaction. 2: article 193.

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