Three models of big data governance: A comparative analysis of privacy policies of contact tracing apps in China, Singapore, and Germany

• The COVID-19 outbreak has fueled global rollouts of contact tracing apps (CTAs). • This study examines how China, Singapore, and Germany balance efficiency and privacy. • We propose a dialectical “legal-technical” framework of big data governance. • We apply this framework to examine the privacy policies of CTAs in each country. • We articulate three big data governance models. The COVID-19 outbreak has spawned a global boom in big data governance worldwide regarding the rollout of contact tracing apps (CTAs). Governments face the dilemma of balancing enhancing governance efficiency to curb the pandemic and protecting citizens’ data privacy. This comparative study examines how China, Singapore, and Germany, three exemplary countries in launching CTAs, address this dilemma in their big data governance process. We first propose a dialectical “legal-technical” framework to articulate the interplay of data protection laws and surveillance technologies in shaping big data governance. We then apply this analytical framework to examine the privacy policies of CTAs in each country. We articulate three big data governance models representing different trade-offs in governance efficiency and data privacy: a centralized and intrusive model in China, a decentralized and unintrusive model in Germany, and a semi-centralized and semi-intrusive model in Singapore. Our findings contribute to a nuanced discussion of adopting information and communication technology in big data governance in post-pandemic societies.