Reconciling data to improve quantity and quality.
Monitoring progress towards national plans and the SDGs requires more data than ever before. The integration of non-official or non-national level datasets offers a potential solution to these data demands. SDSN TReNDS explored the ways in which data reconciliation can be applied as a method to address data gaps across disparate sectors and different actors.
With funding from the Hewlett Foundation and support from SDSN, TReNDS and in-country partners worked together to explore the governance and technical requirements for effective data sharing between different public and private data producers.
Data Reconciliation In Action
Colombia
Can a technological reconciliation platform support public-private data sharing? After analysis showed gaps in Colombia's SDG data, SDSN and Centro de Pensamiento Estratégico Internacional (Cepei) began testing new methods of data collection. Their source: the private sector. Learn more in this process brief.
Bangladesh
University of Oxford scholar Alexander Fischer investigated the utility of data reconciliation for SDG 6 data in Bangladesh. Reviewing data sources relating to access to quality and affordable water supply, Fischer created a set of recommendations for how to assess data gaps and a framework for when data reconciliation is the most effective and efficient tool.