Big Data Is Not a Monolith

From Information Policy

Big Data Is Not a Monolith

Edited by Cassidy R. Sugimoto, Hamid R. Ekbia and Michael Mattioli

Perspectives on the varied challenges posed by big data for health, science, law, commerce, and politics.





Perspectives on the varied challenges posed by big data for health, science, law, commerce, and politics.

Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identify linguistic correlations in large corpuses of texts. This book examines big data not as an undifferentiated whole but contextually, investigating the varied challenges posed by big data for health, science, law, commerce, and politics. Taken together, the chapters reveal a complex set of problems, practices, and policies.

The advent of big data methodologies has challenged the theory-driven approach to scientific knowledge in favor of a data-driven one. Social media platforms and self-tracking tools change the way we see ourselves and others. The collection of data by corporations and government threatens privacy while promoting transparency. Meanwhile, politicians, policy makers, and ethicists are ill-prepared to deal with big data's ramifications. The contributors look at big data's effect on individuals as it exerts social control through monitoring, mining, and manipulation; big data and society, examining both its empowering and its constraining effects; big data and science, considering issues of data governance, provenance, reuse, and trust; and big data and organizations, discussing data responsibility, “data harm,” and decision making.

ContributorsRyan Abbott, Cristina Alaimo, Kent R. Anderson, Mark Andrejevic, Diane E. Bailey, Mike Bailey, Mark Burdon, Fred H. Cate, Jorge L. Contreras, Simon DeDeo, Hamid R. Ekbia, Allison Goodwell, Jannis Kallinikos, Inna Kouper, M. Lynne Markus, Michael Mattioli, Paul Ohm, Scott Peppet, Beth Plale, Jason Portenoy, Julie Rennecker, Katie Shilton, Dan Sholler, Cassidy R. Sugimoto, Isuru Suriarachchi, Jevin D. West


Out of Print ISBN: 9780262035057 312 pp. | 7 in x 9 in 7 b&w illus., 6 tables


$30.00 X ISBN: 9780262529488 312 pp. | 7 in x 9 in 7 b&w illus., 6 tables


Cassidy R. Sugimoto

Cassidy R. Sugimoto is Professor of Informatics at the School of Informatics, Computing, and Engineering at Indiana University, Bloomington.

Hamid R. Ekbia

Hamid Ekbia is Professor, School of Informatics, Computing, and Engineering, Indiana University, Bloomington. He is the author of Heteromation and Other Stories of Computing and Capitalism (MIT Press) and Artificial Dreams: The Quest for Non-Biological Intelligence (Cambridge University Press).

Michael Mattioli

Michael Mattioli is Associate Professor at the Indiana University Maurer School of Law.


  • Well informed (up to date references), well written, an interesting read. This text is highly recommended, for all readers and practitioners with a serious interest in Big Data Analytics (BDA). Simply excellent!

    British Computer Society


  • Big data pervades and crosses organizations and domains, posing multiple challenges, yet these challenges are dwarfed by the opportunities and issues posed by big data analytics (BDA). For a researcher working on BDA, this volume opens multiple perspectives on an amazingly rich cross-section of those and explores what to do about them.

    Alan Porter

    codirector of the Program in Science, Technology & Innovation Policy (STIP), Georgia Tech

  • This book combines expertise from different areas of scholarship to give valuable insights into what big data is doing, what it can do, and what it should be allowed to do. It is essential reading for those wishing to understand the widespread societal implications of the big data revolution.

    Mike Thelwall

    Professor of Information Science, University of Wolverhampton

  • Big Data Is Not a Monolith is required reading for those who find themselves in the thrall of big data but want to move beyond the hype to understand the social context of the current big data computerization movement. The collected authors ably grapple with how big data as a socio-technical system contributes to knowledge, shapes human behavior and choices, and has become increasingly integral to our social, legal, political, and economic systems.

    Eric T. Meyer

    Professor of Social Informatics, University of Oxford; coauthor of Knowledge Machines: Digital Transformations of the Sciences and Humanities