Under What Conditions Are Data Valuable for Development?
Data produced by the public sector can have transformational impacts on development outcomes through better targeting of resources, improved service delivery, cost savings in policy implementation, increased accountability, and more. Around the world, the amount of data produced by the public sector is increasing at a rapid pace, yet their transformational impacts have not been realized fully. Why has the full value of these data not been realized yet This paper outlines 12 conditions needed for the production and use of public sector data to generate value for development and presents case studies substantiating these conditions. The conditions are that data need to have adequate spatial and temporal coverage (are complete, frequent, and timely), are of high quality (are accurate, comparable, and granular), are easy to use (are accessible, understandable, and interoperable), and are safe to use (are impartial, confidential, and appropriate).
Main Authors: | , , , , |
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Format: | Working Paper biblioteca |
Language: | English |
Published: |
World Bank, Washington, DC
2021-10
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Subjects: | DEVELOPMENT DATA, STATISTICS, PUBLIC SERVICE DELIVERY, |
Online Access: | http://documents.worldbank.org/curated/undefined/966371634648040365/Under-What-Conditions-Are-Data-Valuable-for-Development http://hdl.handle.net/10986/36429 |
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Summary: | Data produced by the public sector
can have transformational impacts on development outcomes
through better targeting of resources, improved service
delivery, cost savings in policy implementation, increased
accountability, and more. Around the world, the amount of
data produced by the public sector is increasing at a rapid
pace, yet their transformational impacts have not been
realized fully. Why has the full value of these data not
been realized yet This paper outlines 12 conditions needed
for the production and use of public sector data to generate
value for development and presents case studies
substantiating these conditions. The conditions are that
data need to have adequate spatial and temporal coverage
(are complete, frequent, and timely), are of high quality
(are accurate, comparable, and granular), are easy to use
(are accessible, understandable, and interoperable), and are
safe to use (are impartial, confidential, and appropriate). |
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