data sharing Archives — LIRNEasia


LIRNEasia’s Senior Research Manager, Gayani Hurulle, was invited to conduct a session on Leveraging Digitalization for Inclusive Growth at a regional workshop on Best Practices for Accelerated Pro-Poor and Inclusive Growth Initiatives, held from 24 to 26 June 2025 in Bangkok, Thailand. The event was jointly organized by the Asian Development Bank (ADB), the BIMSTEC Secretariat, and the United Nations Economic and Social Commission for Asia and the Pacific (ESCAP). The workshop brought together government officials and experts from BIMSTEC Member States: Bangladesh, Bhutan, Myanmar, Nepal, Sri Lanka, and Thailand, along with representatives from ASEAN countries, think tanks, and development organizations. The goal was to share knowledge and experiences on strategies that have successfully addressed poverty and supported inclusive economic growth. In her session, Gayani shared insights from LIRNEasia’s nationally representative surveys conducted in 2017/2018 (After Access) and 2021, to highlight gaps in access and usage, and insights from two case studies on social protection and labour.
In the ever-evolving landscape of data-driven progress, the promise of harnessing private sector data to achieve Sustainable Development Goals (SDGs) is crucial. However, it has become evident that the road to effective public-private data partnerships in the Global South is laden with challenges. LIRNEasia together with CEPEI recently held a roundtable discussion at the 18th International Governance Forum (IGF) in Kyoto, Japan, on October 9, 2023 with the participation of a diverse panel of stakeholders from Africa, Asia, Latin America, the Caribbean, and the Middle East, who discussed many areas, including the private sector’s role in the data revolution, policy and practical challenges, and methods to overcome them. The session was moderated by LIRNEasia CEO, Helani Galpaya. The panelists included: 1.
A research paper exploring an alternative approach to address the concern of privacy in sharing big data datasets by generating privacy-preserving artificial call detail records (CDRs) in accordance with the desired macro features of the dataset.