From The Theme
POTENTIAL, PERFORMANCE AND PRODUCTIVITY
What if we could better understand best practices for data analytics?
WHAT WE SET OUT TO DO
We set out to illuminate the processes and practices through which individuals from diverse professional backgrounds enact data analytics. Specifically, we conducted an organizational field study to investigate how different groups and departments within an organization understand and apply data analytics. The goal of the project was to leverage these various understandings for insights into best practices for different contexts.
WHAT WE FOUND
Through analysis of interview and observational data, researchers discovered that pressures to provide “actionable insights” can lead to oversimplification and inaccuracies as data analysts struggle to navigate both a widespread discomfort with uncertainty, and incentive structures that discourage communicating uncertainty. This highlights a need for a language to productively communicate uncertainty and ambiguity. Our study also revealed the influence of interpersonal relationships and personal networks on the choices made by analysts when selecting data sources.
PEOPLE BEHIND THE PROJECT
Pamela J. Hinds is Professor and Director of the Center on Work, Technology, and Organization in the Department of Management Science and Engineering, Stanford University. She studies the effect of technology on teams and collaboration. Dr. Hinds has conducted extensive research on the dynamics of geographically distributed work teams, particularly those spanning national boundaries. She explores issues of culture, language, identity, conflict, and the role of site visits in promoting knowledge sharing and collaboration. She has published extensively on the relationship between national culture and work practices, particularly exploring how work practices or technologies created in one location are understood and appropriated at distant sites. Dr. Hinds also has a body of research on human-robot interaction in the work environment and the dynamics of human-robot teams.
Ryan Stice-Lusvardi is a PhD candidate in Management Science and Engineering. She is interested in examining how changing applications and increasing adoption of data analytics are shaping the future of work and organizations. In her current research, she seeks to illuminate the ways in which assumptions and values of data analysts are embedded in analytical practices and how this shapes data insights and decisions.