The Data Cooperative Model: Combating the Monopolization of Data
By Dana GongPublished January 17, 2022
Data is the new oil. As Larry Page, one of Google’s co-founders, said a decade ago: “our ultimate ambition is to transform the overall Google experience, making it beautifully simple, almost automagical, because we understand what you want and can deliver it instantly.” Understanding these ever-expanding needs of users through data requires the commodification of the human experience, which strips away the decision rights of consumers in the process.
It is important to note that data is only valuable in aggregate, which incentivizes the monopolization of data that is so common today. Data ownership at many levels is hard to assign, as it is highly networked and interdependent. If an email to a friend is sent, and the friend receives it, who owns the email and who owns the metadata? Analysis of the concept extends much further than the simple notion of private property. Additionally, top-down movement in the regulation of data has surpassed many outdated laws, which have had a hard time catching up. “Technology changes exponentially, but social, economic, and legal systems change incrementally.” Take, for example, Verizon’s “zombie cookie,” which violated consumer privacy through an undetectable tracker. The Federal Communications Commission took years to acknowledge public resistance and uncover the intrusion by eventually forcing Verizon to allow users to opt-out. By that time, Verizon had already purchased AOL (an online service provider) to redirect data collection and to supply routes to which the law did not extend. Another example is the introduction of Google Street view which, in 2010, the German Federal Commission for Data Protection found to be camouflaging a covert data sweep, secretly collecting personal data from private unencrypted wifi networks. These networks included names, telephone numbers, credit info, passwords, records of online dating, medical information, photo libraries, etc., which could all be stitched together into the detailed profile of an identifiable person. Although regulations tried to enforce just policies, Google was still not fully held accountable, and such practices have rooted themselves into global normalcy. It is clear that data does not have one form and should not be treated as such.
These examples highlight the need for overarching structural changes in how data flows, rather than undergoing case-by-case regulations. The data cooperative model achieves this goal by forming a new technical and institutional layer between those who have data and those who use it. Starting from the premise that our data is fundamentally collective (instead of private property) allows for the creation of an intermediary collective structure. This structure is built to mediate data flows by negotiating with companies to establish guidelines around how our shared data is governed, used, reused, and transferred. It also gives benefits back to consumers in some way, whether it be in dollars, transparency, or access.
In the European Union, there are many instances of data cooperatives that work toward shared stewardship. Salus Coop, for example, is a non-profit data cooperative that aims to create a citizen-driven model of collaborative governance and health data management, specifically by “[legitimizing] citizens’ rights to control their own health records while facilitating data sharing to accelerate research innovation in healthcare.” The cooperative entails specific conditions for the governance of its member data, including allowing it to only be used for non-commercial and health-related purposes, requiring the free and anonymous sharing of research results, and giving members the ability to change or cancel the usage of their data at any point. Moreover, it establishes principled data rights requiring full transparency and access between researchers and members. Salus Coop provides detailed benefits for its members––“specifically a set of powers, rights and constraints over the use of their personal health data––in such a way as to also benefit the wider community by providing data for health research.”
While data cooperatives appeal to a growing need for data democracy, there are many challenges before such a model can become commonplace. As the model becomes scaled up, it is accountable for larger amounts of data, making it harder to address each individual’s needs. In addition, since these cooperatives do not treat data as a free commodity, the expected return is much lower. Overall, data cooperatives need to establish that they are appropriate and beneficial at larger scales. While they do not have the economic imperative to aggregate data at the cost of the consumer, these data intermediaries need to be trusted. Appropriate norms for privacy regulations and benefits to consumers need to be established. While this model has to improve in development and research regarding uptake, scale, and financing, the main hurdle is acceptance and effort at all levels.