Big data analytics for making decisions and managing risk

Beth Coulthard, Brian Taylor

Research output: Chapter in Book/Report/Conference proceedingChapter


This chapter explains concepts of big data in relation to decision-making and predicting harm as social work tasks. We define and explain what big data is, and the associated technologies of data linkage and predictive risk modelling. We offer a case example, describing one of the best established and sophisticated predictive risk models currently in use. We locate the big data revolution within longstanding traditions of statistical risk tools and decision-making—the building blocks of big data—to highlight sources of error and how we can understand and reduce them. We examine some of the vocal criticisms of algorithms that have been implemented in social work, weighing each and summarising key points to focus our analysis. We argue that the concepts and issues of transparency and bias are foundational to any approach to decision-making, and that the advent of new technologies is highlighting rather than creating these issues. We conclude by asking whether it is possible to bridge an apparent impasse between technological systems and human resistance to embrace algorithms, signposting avenues that may help to realise the huge potential that big data systems offer to improve the lives of the most vulnerable in society.
Original languageEnglish
Title of host publicationThe SAGE Handbook of Decision Making, Assessment and Risk in Social Work
EditorsBrian Taylor, John D Fluke, J Christopher Graham, Emily Keddell, Campbell Killick, Aron Shlonsky, Andrew Whittaker
Place of PublicationLondon
PublisherSAGE Publications
Number of pages14
ISBN (Electronic)978-1-5297-9019-1
ISBN (Print)978-1-5297-9019-1
Publication statusPublished (in print/issue) - 30 Aug 2023

Publication series

NameSage Handbooks


  • decision making
  • assessment
  • risk
  • big data
  • predictive analytics
  • machine learning
  • applied statistics
  • social work
  • social care


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