Personal data are produced through our daily interactions with digital technologies like search engines, social media, and online shopping, and is often referred to as our “digital exhaust.” It has been characterized as the key resource or asset for our economies in the 21st century. This paper focuses on the socio-technical imaginaries of digital personal data as a way to understand how desired forms of data governance are co-produced with collective understandings of personal data as a political-economic asset. We examine (...) the different socio-technical imaginaries that underpinned different developments in data regulations in the United States and EU from 2008 to 2016, focusing specifically on the mutual constitution of law, political economy, and technoscience. We do so in order to understand the “prehistories” of contemporary data governance. We analyze the institutional and legal context around the development of data privacy regulation and data commercialization in these two important jurisdictions and reflect on how this institutional and legal context configured their respective approaches to data governance. (shrink)

This paper serves a dual function: (1) it is intended to proffer a stable understanding of our digital engagement on the Internet as a form of labor that is co-opted by digital firms for private profit; (2) it extends the concept of participatory democracy to our digital world, arguing that our collective or common production of value for digital firms (in the form of what I call“knowledge goods”) requires the implementation of participatory democratic governance mechanisms over these digital firms and (...) the experimentation with democratic digital alternatives.Whereas others have argued in favor of democratic control over digital platforms on the basis of the power that digital firms have over user's lives (Engelmann et al.,2020), this paper adds to this discussion by arguing in favor of digital platform democratization based on the value-generating labor contributed by digital users. (shrink)

Social media commentary, satellite imagery and GPS data are a part of ‘alternative data’, that is, data that originate outside of the standard repertoire of market data but are considered useful for predicting stock prices, detecting different risk exposures and discovering new price movement indicators. With the availability of sophisticated machine-learning analytics tools, alternative data are gaining traction within the investment management and algorithmic trading industries. Drawing on interviews with people working in investment management and algorithmic trading firms utilizing alternative (...) data, as well as firms providing and sourcing such data, we emphasize social media-based sentiment analytics as one manifestation of how alternative data are deployed for stock price prediction purposes. This demonstrates both how sentiment analytics are developed and subsequently utilized by investment management firms. We argue that ‘alternative data’ are an open-ended placeholder for every data source potentially relevant for investment management purposes and harnessing these disparate data sources requires certain standardization efforts by different market participants. Besides showing how market participants understand and use alternative data, we demonstrate that alternative data often undergo processes of prospecting and assetization. We further contend that the widespread embracement of alternative data in investment management and trading encourages a financialization process at the data level which raises new governance issues. (shrink)

Throughout the Global North, policymakers invest in large-scale integration of health-data infrastructures to facilitate the reuse of clinical data for administration, research, and innovation. Debates about the ethical implications of data repurposing have focused extensively on issues of patient autonomy and privacy. We suggest that it is time to scrutinize also how the everyday work of healthcare staff is affected by political ambitions of data reuse for an increasing number of purposes, and how different purposes are prioritized. Our analysis builds (...) on ethnographic studies within the Danish healthcare system, which is internationally known for its high degree of digitalization and well-connected data infrastructures. Although data repurposing ought to be relatively seamless in this context, we demonstrate how it involves costs and trade-offs for those who produce and use health data. Even when IT systems and automation strategies are introduced to enhance efficiency and reduce data work, they can end up generating new forms of data work and fragmentation of clinically relevant information. We identify five types of data work related to the production, completion, validation, sorting, and recontextualization of health data. Each of these requires medical expertise and clinical resources. We propose that the implications for these forms of data work should be considered early in the planning stages of initiatives for large-scale data sharing and reuse, such as the European Health Data Space. We believe that political awareness of clinical costs and trade-offs related to such data work can provide better and more informed decisions about data repurposing. (shrink)