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Big Data Is Not a Monolith
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.
Contributors: Ryan 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
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.
Contributors: Ryan 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
284 pages, Hardcover
Published October 21, 2016
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About the author
Cassidy R. Sugimoto
6 booksCassidy R. Sugimoto is Associate Professor in the School of Informatics and Computing at Indiana University Bloomington and the coeditor of Beyond Bibliometrics (MIT Press).
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Displaying 1 of 1 review
January 24, 2017
Definitely not light reading, as this book addresses difficult concepts, terminology and issues.
The editors’ compilation forms a more measured view of big data, considering unforeseen and potentially negative consequences of big data and the ramifications, away from the hype, “data harm” for example.
This book represents a refreshing antidote to the “devil may care” approach to the use and application of big data and analytics. It looks at the major issues often neglected by the pro big data enthusiasts, such as those related to epistemological, social, political, economic and especially privacy issues.
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!
Review by Deryn Graham FBCS
Originally posted: http://www.bcs.org/content/conWebDoc/...
The editors’ compilation forms a more measured view of big data, considering unforeseen and potentially negative consequences of big data and the ramifications, away from the hype, “data harm” for example.
This book represents a refreshing antidote to the “devil may care” approach to the use and application of big data and analytics. It looks at the major issues often neglected by the pro big data enthusiasts, such as those related to epistemological, social, political, economic and especially privacy issues.
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!
Review by Deryn Graham FBCS
Originally posted: http://www.bcs.org/content/conWebDoc/...
Displaying 1 of 1 review