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Data in SocietyChallenging Statistics in an Age of Globalisation$
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Data in Society: Challenging Statistics in an Age of Globalisation

Jeff Evans, Sally Ruane, and Humphrey Southall

Print publication date: 2019

Print ISBN-13: 9781447348214

Published to Policy Press Scholarship Online: May 2020

DOI: 10.1332/policypress/9781447348214.001.0001

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PRINTED FROM POLICY PRESS SCHOLARSHIP ONLINE (www.policypress.universitypressscholarship.com). (c) Copyright Policy Press, 2022. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in PPSO for personal use.date: 06 July 2022

Data analytics

Data analytics

Chapter:
(p.35) 3 Data analytics
Source:
Data in Society
Author(s):

Ifan Shepherd

Gary Hearne

Publisher:
Policy Press
DOI:10.1332/policypress/9781447348214.003.0004

Data analytics have emerged in recent years as a family of overlapping, competing and hybridising products and practices. They have been championed by technology companies, academics, business users and governments alike, and in a short period of time have earned business developers and adopters billions of pounds in revenue and unprecedented levels of market domination. Data analytics have also provided distinct benefits in terms of an increasing democratisation of digital tools, but at the same time are giving rise to increasing levels of societal and governmental concern. This chapter has four aims: to help intelligent outsiders and old school data analysts make sense of the many competing methodologies and technologies that inhabit the data analytics ecosystem; to assist readers understand which of the many techniques and methodologies represent genuine additions to the state of the art rather than simply old wine in new bottles; to provide a brief overview of the software tools currently available for data analytics; and to identify societal issues and concerns that attend this family of technical and social practices, and the extent to which they are being adequately addressed by developers, users and society at large.

Keywords:   Data analytics, machine learning, digital data, analytics software, data ethics

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