Big data and data science in global governance: anticipating future needs and applications in the UN and beyond - Lanxin Li; Jiarou Wang; Xi Wang; Peng Peng; Jiaying Shen; Haining Zhu; Ziyang Zhang (Frontiers in Political Science)
This paper explores the transformative potential of big data and data science in global governance, with particular emphasis on their application in international organizations addressing sustainable development challenges. Through comprehensive analysis of theoretical frameworks, current applications, and future directions, we examine how big data technologies enhance decision-making processes and operational efficiency in global governance frameworks, particularly within United Nations agencies and affiliated international organizations. The research identifies the “4Vs” of big data (Volume, Velocity, Variety, and Veracity) as fundamental characteristics reshaping governance approaches while highlighting innovative applications like UN Global Pulse, SDG tracking systems, and AI-driven predictive analytics in crisis prevention. We assess technical, ethical, and organizational challenges, including data quality inconsistencies, interoperability issues, privacy concerns, algorithmic bias, and resource constraints that impede the full integration of big data into governance systems. The paper proposes forward-looking strategies for infrastructure development, skills enhancement, and policy frameworks that can maximize big data's benefits while addressing ethical considerations and regulatory requirements. Our findings suggest that big data, when properly governed through international cooperation and ethical frameworks, can significantly enhance crisis response capabilities, improve resource allocation, and accelerate progress toward sustainable development goals. This research contributes to the evolving understanding of big data's role in addressing transnational challenges through improved monitoring systems, predictive capabilities, and evidence-based policy interventions.