Data Action: Using Data for Public Good

by Sarah Williams

How to use data as a tool for empowerment rather than oppression.

Big data can be used for good--from tracking disease to exposing human rights violations--and for bad--implementing surveillance and control. Data inevitably represents the ideologies of those who control its use; data analytics and algorithms too often exclude women, the poor, and ethnic groups. In Data Action , Sarah Williams provides a guide for working with data in more ethical and responsible ways. Too often data has been used--and manipulated--to make policy decisions without much stakeholder input. Williams outlines a method that emphasizes collaboration among data scientists, policy experts, data designers, and the public. This approach creates trust and co-ownership in the data by opening the process to those who know the issues best.

Genres: Technology, Science, Nonfiction, Urbanism, Cities

312 pages, Hardcover

Published December 8, 2020

About the author

NOTE: There is more than one author with this name on Goodreads.

See here for the author of "The Old Astronomer", also known as "The Old Astronomer to His Pupil", that begins "Reach me down my Tycho Brahe..." and contains the line "I have loved the stars too fondly to be fearful of the night."

See also: Romance author Sarah Williams

Reviews

[Katherine Key · Rating 5 out of 5]

Great discussion on the opportunities and challenges of working with data in the public sphere. There are a lot of concrete examples that bring the ideas to life. A must read for any urban planner.

[Sara Mesquita · Rating 5 out of 5]

It was a pleasure to read Data Action, full of food for tought and engaging data visualizations. This book has definitely prepared me for better discussions and projects involving data collection and exploration. Divided into four main ideas (Build it, Hack it, Share it and Data as a public good), this book equips the reader with a civic awareness but also elucidates the power that we as individuals have to change the collective mind. Through several projects developed by the author over the years, Sarah Williams shows how it is possible to generate societal change by using openly available data, collaborating with experts and communicating the results to the public. My favorite project: Digital Matatus System.

[Alice · Rating 4 out of 5]

A really good book on how to use data in a more ethical, thoughtful, and impactful manner. The ideas expressed really dovetail with my own work on incorporating more DEI into data viz. The "case study" example projects the author has led are really powerful (and super cool -- I want to be involved in projects like that!), the "theory" side of the book isn't quite as strong as Data Feminism and others. Anyone looking to truly do good with data needs to read this book.

(The book could use another round of proofreading though -- there were at least a handful of typos.)

[Xiaofan Liang · Rating 5 out of 5]

The book is essentially a collection of work done by Sarah Williams and her lab Civic Data Design Lab. It is a pretty good read if you are interested in critical data. Similar to the books "Data Feminism" and "All Data are Local", the book uses case studies to illustrate data action principles. The book is normative, less so on revealing any scientific facts, but more so on how people should use data for the public good.

[Ashley Clubb · Rating 5 out of 5]

My airplane book + gift to myself after Year 1 of grad school. Williams explains the ways big data can be used for good, because all too often big data is manipulated and misused in ways that prevents stakeholder involvement, co-production, or protections of data. Big data doesn't have to be the villain, it can be used for good! Maybe its the way Williams writes or the beautiful graphics included that display data, but it makes me want to become a data analyst....

[Thomas B · Rating 4 out of 5]

Good entry-level text on data and how it is/can be used for the public good. In many ways, it feels like a contemporary cousin to Tufte’s Data Visualization.