Understanding Data Culture And Society

- How is data shaping our identities? - What was the ′data revolution′, and how did it happen? - How will AI change our societies? We live in the age of datafication: every aspect of our lives has been captured and transformed into data, from our sleeping patterns and step counts to our buying habits and political views. In this exciting new textbook, you will discover the intricate ways in which data and society are interwoven. Explaining key concepts such as ′big data′ and putting theory into practice throughout, this book will make you a better expert in data and society, offering an interdisciplinary overview of a rapidly evolving field. This textbook tackles the implications of big data for democracy, identity and the global economy, showing how we cannot view our lives as separate from the technologies we have come to rely on. With learning objectives, case studies, further reading and extra resources provided in each chapter, this book is the ideal companion for students in the digital humanities and social sciences looking to deepen their understanding of data, culture and society. Topics covered include: - capitalism in the age of data - democracy and politics - identity and subjectivity - machine learning and AI - journalism and disinformation

The ability to gather data that can be crunched by machines is valuable for studying society. The new methods needed to work it require new skills and new ways of thinking about best research practices. This book reflects on the role and usefulness of big data, challenging overly optimistic expectations about what it can reveal, introducing practices and methods for its analysis and visualization, and raising important political and ethical questions regarding its collection, handling, and presentation.

The AI revolution can seem powerful and unstoppable, extracting data from every aspect of our lives and subjecting us to unprecedented surveillance and control. But at ground level, even the most advanced ‘smart’ technologies are not as all-powerful as either the tech companies or their critics would have us believe. From gig worker activism to wellness tracking with sex toys and TikTokers' manipulation of the algorithm, this book shows how ordinary people are negotiating the datafication of society. The book establishes a new theoretical framework for understanding everyday experiences of data and automation, and offers guidance on the ethical responsibilities we share as we learn to live together with data-driven machines. Everyday Data Cultures is essential reading for students and researchers in digital media and communication, as well as for anyone interested in the role of data and AI in society.

A book at the intersection of data science and media studies, presenting concepts and methods for computational analysis of cultural data. How can we see a billion images? What analytical methods can we bring to bear on the astonishing scale of digital culture--the billions of photographs shared on social media every day, the hundreds of millions of songs created by twenty million musicians on Soundcloud, the content of four billion Pinterest boards? In Cultural Analytics, Lev Manovich presents concepts and methods for computational analysis of cultural data. Drawing on more than a decade of research and projects from his own lab, Manovich offers a gentle, nontechnical introduction to the core ideas of data analytics and discusses the ways that our society uses data and algorithms.

As more and more aspects of everyday life are turned into machine-readable data, researchers are provided with rich resources for researching society. The novel methods and innovative tools to work with this data not only require new knowledge and skills, but also raise issues concerning the practices of investigation and publication. This book critically reflects on the role of data in academia and society and challenges overly optimistic expectations considering data practices as means for understanding social reality. It introduces its readers to the practices and methods for data analysis and visualization and raises questions not only about the politics of data tools, but also about the ethics in collecting, sifting through data, and presenting data research.

Data and Society: A Critical Introduction investigates the growing importance of data as a technological, social, economic and scientific resource. It explains how data practices have come to underpin all aspects of human life and explores what this means for those directly involved in handling data. The book fosters informed debate over the role of data in contemporary society explains the significance of data as evidence beyond the "Big Data" hype spans the technical, sociological, philosophical and ethical dimensions of data provides guidance on how to use data responsibly includes data stories that provide concrete cases and discussion questions. Grounded in examples spanning genetics, sport and digital innovation, this book fosters insight into the deep interrelations between technical, social and ethical aspects of data work.

How to analyze data settings rather than data sets, acknowledging the meaning-making power of the local. In our data-driven society, it is too easy to assume the transparency of data. Instead, Yanni Loukissas argues in All Data Are Local, we should approach data sets with an awareness that data are created by humans and their dutiful machines, at a time, in a place, with the instruments at hand, for audiences that are conditioned to receive them. The term data set implies something discrete, complete, and portable, but it is none of those things. Examining a series of data sources important for understanding the state of public life in the United States—Harvard's Arnold Arboretum, the Digital Public Library of America, UCLA's Television News Archive, and the real estate marketplace Zillow—Loukissas shows us how to analyze data settings rather than data sets. Loukissas sets out six principles: all data are local; data have complex attachments to place; data are collected from heterogeneous sources; data and algorithms are inextricably entangled; interfaces recontextualize data; and data are indexes to local knowledge. He then provides a set of practical guidelines to follow. To make his argument, Loukissas employs a combination of qualitative research on data cultures and exploratory data visualizations. Rebutting the “myth of digital universalism,” Loukissas reminds us of the meaning-making power of the local.

'Following the data' is a now-familiar phrase in Covid-19 policy communications. Well-being data are pivotal in decisions that affect our life chances, livelihoods and quality of life. They are increasingly valuable to companies with their eyes on profit, organisations looking to make a social impact, and governments focussed on societal problems. This book follows well-being data back centuries, showing they have long been used to track the health and wealth of society. It questions assumptions that have underpinned over 200 years of social science, statistical and policy work. Understanding Well-being Data is a readable, introductory book with real-life examples. Understanding the contexts of data and decision-making are critical for policy, practice and research that aims to do good, or at least avoid harm. Through its comprehensive survey and critical lens, this book provides tools to promote better understanding of the power and potential of well-being data for society, and the limits of their application.

Technology does not exist in a vacuum. In the same way that a plant needs water and nourishment to grow, technology needs people and process to thrive and succeed. Culture (i.e., people and process) is integral and critical to the success of any new technology deployment or implementation. Big data is not just a technology phenomenon. It has a cultural dimension. It's vitally important to remember that most people have not considered the immense difference between a world seen through the lens of a traditional relational database system and a world seen through the lens of a Hadoop Distributed File System.This paper broadly describes the cultural challenges that accompany efforts to create and sustain big data initiatives in an evolving world whose data management processes are rooted firmly in traditional data warehouse architectures.

Make any team or business data driven with this practical guide to overcoming common challenges and creating a data culture. Businesses are increasingly focusing on their data and analytics strategy, but a data-driven culture grounded in evidence-based decision making can be difficult to achieve. Be Data Driven outlines a step-by-step roadmap to building a data-driven organization or team, beginning with deciding on outcomes and a strategy before moving onto investing in technology and upskilling where necessary. This practical guide explains what it means to be a data-driven organization and explores which technologies are advancing data and analytics. Crucially, it also examines the most common challenges to becoming data driven, from a foundational skills gap to issues with leadership and strategy and the impact of organizational culture. With case studies of businesses who have successfully used data, Be Data Driven shows managers, leaders and data professionals how to address hurdles, encourage a data culture and become truly data driven.