Aslam Hayat (Senior Policy Fellow LIRNEasia, Country Researcher for Pakistan), and Pranesh Prakash (Policy Fellow LIRNEasia, Co-Principal Investigator), drew on research carried out under LIRNEasia’s ‘Harnessing Data for Democratic Development in South and Southeast Asia’ project to discuss aspects of data governance in Pakistan and other countries. This was part of a forum hosted by the Sustainable Development Policy Institute (SDPI) in Pakistan, under the theme, “Public-Private Dialogue (PPD) on Data Governance in Pakistan.” The forum brought together key voices from government, academia, civil society, and the private sector in Pakistan, and was held on 23 April 2025. Aslam Hayat highlighted key findings from the research carried out in Pakistan, outlining the data governance framework in the country, identifying policy gaps and good practices. Pranesh Prakash gave an overview of the research carried out by the Harnessing Data for Democratic Development project, and discussed concepts related to data governance, privacy, and open standards. 
Aslam Hayat (Senior Policy Fellow LIRNEasia, Country Researcher for Pakistan), and Pranesh Prakash (Policy Fellow LIRNEasia, Co-Principal Investigator), drew on research carried out under LIRNEasia’s ‘Harnessing Data for Democratic Development in South and Southeast Asia’ project to discuss aspects of data governance in Pakistan and other countries. This was part of a forum hosted by the Sustainable Development Policy Institute (SDPI) in Pakistan, under the theme, “Public-Private Dialogue (PPD) on Data Governance in Pakistan.” The forum brought together key voices from government, academia, civil society, and the private sector in Pakistan, and was held on 23 April 2025. Aslam Hayat highlighted key findings from the research carried out in Pakistan, outlining the data governance framework in the country, identifying policy gaps and good practices. Pranesh Prakash gave an overview of the research carried out by the Harnessing Data for Democratic Development project, and discussed concepts related to data governance, privacy, and open standards. 
Pinpointing where poverty is most severe and tracking its changes over time is crucial for helping communities effectively. However, traditional benchmarks like household surveys and national censuses often fall short—they’re expensive, slow, and infrequent. In countries like Sri Lanka, this means we’re often relying on outdated information, hindering our ability to respond to sudden economic shocks or disasters. On top of that, poverty cannot be determined by income data alone, rather its multidimensional, where factors such as infrastructure, access to services, and economic activity also play a role in determining a community’s well-being. To capture these complexities, our DAP team (Data, Algorithms, and Policy) explored something different: how to rethink the way we measure poverty in Sri Lanka using AI with non-traditional data sources?