Revisiting the assumptions of the data revolution as an accelerator of the Sustainable Development Goals
Revisiting the assumptions of the data revolution as an accelerator of the Sustainable Development Goals
When the Sustainable Development Goals were negotiated from 2012-2015, global policymakers assumed that advances in data technology and capability, what was dubbed the ‘data revolution’, would accelerate development outcomes by improving policy efficiency and accountability. The 2014 report to the United Nations Secretary General, “A World That Counts” framed the data-for-development agenda and proposed four pathways to impact: measuring for accountability, generating disaggregated and real-time data supplies, improving policymaking, and implementing efficiency. The subsequent experience suggests that while many recommendations have been implemented globally to advance the production of data and statistics, the impact on SDG outcomes has been inconsistent. Progress towards SDG targets has stalled despite advances in statistical systems capability, data production and data analytics. The coherence of the SDG policy agenda has undoubtedly improved some aspects of data collection and supply, with SDG frameworks standardizing greater indicator reporting. However, other events including the response to COVID-19 have played catalytic roles in statistical system innovation. Overall, increased financing for statistical systems has not materialized, though planning and tracking for national systems may have longer-term impacts.
This article reviews how assumptions about the data revolution have evolved and where new assumptions are necessary to advance the data to impact value chain. These include focusing on measuring what matters most for polycentric institutional decision making processes, leveraging the SDGs for global data standardization and
strategic financial mobilization, closing data gaps while enhancing policymaker analytic capabilities, and fostering collective intelligence to drive data innovation, trust, and sustainable development outcomes.
SDGs, United Nations, collective intelligence, data revolution and innovation, development finance, statistics policy
Fischer, Alex
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Cameron, Grant
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Tilus, Castelline
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Espey, Jessica
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Badiee, Shaida
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14 July 2025
Fischer, Alex
6621403c-8898-45b6-b4ad-285f90f5ec0b
Cameron, Grant
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Tilus, Castelline
9da20fcf-e5b2-4736-b13c-2ea11283c8a4
Espey, Jessica
cb16d2a6-2e51-43df-a274-e85776ab605a
Badiee, Shaida
8f54bfbd-4a62-4485-89a8-887a0eecfacd
Fischer, Alex, Cameron, Grant, Tilus, Castelline, Espey, Jessica and Badiee, Shaida
(2025)
Revisiting the assumptions of the data revolution as an accelerator of the Sustainable Development Goals.
Data & Policy, 7, [e49].
(doi:10.1017/dap.2025.10015).
Abstract
When the Sustainable Development Goals were negotiated from 2012-2015, global policymakers assumed that advances in data technology and capability, what was dubbed the ‘data revolution’, would accelerate development outcomes by improving policy efficiency and accountability. The 2014 report to the United Nations Secretary General, “A World That Counts” framed the data-for-development agenda and proposed four pathways to impact: measuring for accountability, generating disaggregated and real-time data supplies, improving policymaking, and implementing efficiency. The subsequent experience suggests that while many recommendations have been implemented globally to advance the production of data and statistics, the impact on SDG outcomes has been inconsistent. Progress towards SDG targets has stalled despite advances in statistical systems capability, data production and data analytics. The coherence of the SDG policy agenda has undoubtedly improved some aspects of data collection and supply, with SDG frameworks standardizing greater indicator reporting. However, other events including the response to COVID-19 have played catalytic roles in statistical system innovation. Overall, increased financing for statistical systems has not materialized, though planning and tracking for national systems may have longer-term impacts.
This article reviews how assumptions about the data revolution have evolved and where new assumptions are necessary to advance the data to impact value chain. These include focusing on measuring what matters most for polycentric institutional decision making processes, leveraging the SDGs for global data standardization and
strategic financial mobilization, closing data gaps while enhancing policymaker analytic capabilities, and fostering collective intelligence to drive data innovation, trust, and sustainable development outcomes.
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revisiting-the-assumptions-of-the-data-revolution-as-an-accelerator-of-the-sustainable-development-goals
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Accepted/In Press date: 13 May 2025
e-pub ahead of print date: 14 July 2025
Published date: 14 July 2025
Keywords:
SDGs, United Nations, collective intelligence, data revolution and innovation, development finance, statistics policy
Identifiers
Local EPrints ID: 503271
URI: http://eprints.soton.ac.uk/id/eprint/503271
ISSN: 2632-3249
PURE UUID: 55f8e7d1-bfe1-4e1c-8972-49e277077550
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Date deposited: 28 Jul 2025 16:32
Last modified: 18 Sep 2025 02:14
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Contributors
Author:
Alex Fischer
Author:
Grant Cameron
Author:
Castelline Tilus
Author:
Jessica Espey
Author:
Shaida Badiee
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