An Interpretive Approach to Data Science
The Interpretive Data Science (IDeaS) group brings together management and organization scholars who study data, algorithms, and artificial intelligence in organizations. The group combines qualitative and computational methods, and holds a small, intensive conference most years. It began as a collaboration between the University of Alberta and the University of British Columbia, and now spans institutions in North America and Europe.
Research Focus
Our work draws together three topics that are normally studied in separate scholarly communities:
- New data-analytic techniques in the social sciencesThe reflexive, theoretically informed use of techniques such as topic modeling, natural language processing, and other forms of machine learning.
- The everyday work of data analysts in organizationsHow analysts construct knowledge practices and epistemic infrastructures — as an ethnographic topic in its own right, and as a source of reflexivity about our own methods.
- The societal transformations attending the rise of dataChanging forms of privacy, governance, and accountability as data and analytics reshape organizations and public life.
Conferences
IDeaS meets for a small, intensive workshop most years. Programs and materials for each conference are collected here.
Publications
Work developed through the conferences has led to an edited volume and two journal special issues.
- Edited volumeGlaser, V. L., Moser, C., Anderson, D. A., & Jennings, P. D. (Eds.) (2025). Algorithmic Organizing. Research in the Sociology of Organizations, Vol. 95. Emerald Publishing.
- Special issueOrganization Studies — Algorithmic Organizing: Exploring the Pervasive Impact of Algorithms on Organizations and Society.
- Special issueInformation & Organization — Algorithmic Assemblages: Fields, Ecosystems, and Platforms. An Interpretive Approach.