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Data, data science, and measurement in the UK non-profit sector – Collaboration and competition between instrument constituencies

Civil Society
Public Policy
Qualitative
Technology
Big Data
Policy-Making
Ville Aula
The London School of Economics & Political Science
Ville Aula
The London School of Economics & Political Science

Abstract

Data science is one of the newest tools in the long history of quantitative measurement in public policy. The paper analyses initiatives to promote the use of data and data science in the UK non-profit sector, situating them in the wider landscape of quantitative measurement in public policy. Non-profit organisations are central to provision of health and social services in the UK, making them key stakeholders in both policymaking and service delivery. Evidence on the use of data science in the non-profit sector is therefore valuable for the understanding of data science in public policy more broadly. The paper uses instrument constituencies to conceptualise the promotion of data and data science. Instrument constituencies are here understood as promoters of specific measurement techniques, not solely as promoters of specific policy tools. The paper is based on 37 interviews with non-profit data professionals. The findings suggest that data has become a keyword that unites the promotion of various measurement techniques that also compete against each other. Five different instrument constituencies of measurement were identified: 1) general promotion of data and measurement 2) promotion of data science 3) promotion of performance measurement 4) promotion of impact measurement and RCTs 5) promotion of public statistics and open data sources. The paper suggests that these are competing sets of measurement techniques that lead to different understandings of non-profit work. Amidst this competition, data science was found to have only limited use in the non-profit sector despite the hype around it. The findings are valuable for the study of measurement, evidence, and science in public policy because they underscore the competition between different measurement techniques. They underscore that the techniques associated with data science should be understood as part of the general conundrum of measurement in public policy, but potentially in competition and conflict with other measurement techniques.