Administrative data are increasingly vital for monitoring sustainable development, providing timely, cost-effective, and regularly updated information. Yet, their full potential remains underutilized in many countries. Maximizing the value requires strong institutional coordination, well-defined methodologies, and robust systems that safeguard data quality, harmonization, privacy, and security, which are the essential foundations for reliable policy decisions and evidence-based development monitoring.
This event convenes National Statistical Offices (NSOs) and key development partners to exchange practical lessons learned on harnessing administrative data for SDG monitoring and beyond. The discussion will focus on concrete country experiences using administrative data across key policy domains, including environment and climate, crime, justice and governance. It will cover methodological approaches, data integration processes, and institutional arrangements.
Through country experiences, the session will explore shared challenges, enabling conditions, and promising practices for leveraging administrative data to complement traditional statistical sources and enhance the quality and completeness of SDG reporting. Through interactive dialogue, participants will exchange perspectives and discuss how the experiences shared can be applied in their national contexts.
More details here.
About
The Collaborative on Administrative Data (CAD), led by the United Nations Statistics Division (UNSD), the Global Partnership and UN WOMEN, was established in 2020 to strengthen the capacity of national statistical systems to leverage the use of administrative data for statistical purposes, to fill gaps in the data available to policy and decision makers to monitor progress and implement the 2030 Agenda. The Collaborative brings together stakeholders from countries as well as from regional and international agencies and is a space for learning and exchange of experiences to strengthen the capacity of countries to produce and use administrative data sources for statistical purposes.
