Big Data for Development (BD4D) Research Network is a Southern-led partnership with the objective of developing policy relevant research on big data for development that is conceptualized and implemented by Southern organizations. The network acts to develop capacity amongst researchers from the Global South, focusing on activities linked to the Sustainable Development Goals (SDGs) and to those that address gender related issues. The work encompasses both big data analyses as well as capacity development, while innovating on data partnerships and collaborations with private sector companies and policy makers. The network is supported by a grant from the International Development Research Centre (IDRC), Canada.
Centro de Pensamiento Estratégico Internacional (CEPEI) is eager to open up and promote a dialogue across the Latin American region, and beyond, to discuss practical approaches, guidelines and strategies on how to bring big data into real politics.
LIRNEasia conducts big data analyses in emerging Asia, and engages deeply with policy makers, as well as private sector that currently possess the majority (if not the only source) of big data of relevance for developmental policy.
The Centre for Internet and Society (CIS) is studying BD4D innovations, policy, practices, and discourse with a focus on the research methods involved; and offering research and pedagogic support towards other hubs of the Network.
The partner organizations of the Big Data for Development Network (BD4D) met in Dubai, within the framework of the UN World Data Forum, to present the progress of the initiative and discuss the potential of South-South cooperation and peer learning to generate better data in the Global South.
This briefing document prepared by Cepei, explains what is Big Data for development, what is the progress and challenges of Big Data for development in Latin America, and how is Colombia in relation to the use and production of Big Data.
Prior work has shown that mobile network big data can be used as a high-frequency alternative data source to derive proxy measures that have strong predictive capacity to estimate census and poverty data in developing countries... Our results confirm the applicability of this methodology in a Sri Lankan, post-conflict setting, and highlight potential areas that need to be addressed in order to improve the accuracy of our prediction models.