The financial applications of data technology have enabled the rise of Chinese fintech industries. As part of people's everyday lives, fintech apps have helped companies collect vast amounts of user data for business profit and social good. This paper takes an open-systems approach to study the constructs of this emerging idea of data governance, particularly its operational logic, involved stakeholders, and socio-cultural consequences in the context of fintech industries in China. It asserts that data governance at the company level has (...) been realized through three types of tasks: standardization, configuration, and monetization of big data. These tasks are oriented by the imperative of business innovation which is considered pivotal for the company to survive the competition. An ensemble of internal and external actors joined the governing process, but internal actors hold the stake. The goal of innovation drives the companies to speedily collect data from a wider variety of sources, thus having to establish more sophisticated systems to manage and utilize those data. These complicated systems, however, have urged the government to strengthen regulatory controls alongside the general support of business innovation. This study unveils the performative aspects of big-data-based innovation at fintech firms. It also helps to understand the pathology of the innovation-governance paradox shared in the data economy in the global context. (shrink)

Amidst the climate of crisis surrounding the rise in opioid-related overdose in the USA, early in 2019, Google and Deloitte launched ‘Opioid360’. Here came a platform combining browser histories, credit, insurance, social media, and traditional survey data to sell the service of risk calculation in population health. Opioid360's approach to automating risk calculation not only promised to identify persons ‘at risk’ of opioid dependence, but also paved the way for broader applications anticipating common chronic diseases and coordinating logistical operations involved (...) in pandemic response. Beginning with this experimental platform, this paper develops an analysis of the Big Data mode of risk calculation - an epistemological and political shift that involves technology companies, investors, insurers, governments, and public health institutions. The analysis focuses on the re-emergence of ‘social determinants of health’ in the rhetoric accompanying novel analytic platforms that estimate, calculate, and compute individual health risks. While the treatment of SDOH has always been a site of political contestation within the discipline of public health, powerful interests are crystallising around the concept and instrumentalising it in platforms that sell algorithmic prediction. Silicon Valley's breed of asset-oriented technoscience appears not only to be amplifying the behaviouralist elements of public health. Among the stakes of the Big Data mode is the paradoxical retreat from changing social conditions that contribute to the prevalence of health and illness in populations; and instead, the promotion of an apparatus for pricing and exchanging individual risk or excluding from services those who bear risk most acutely. (shrink)