Clicks and particulates: Value,alienation,and attunement as unifying themes in big data studies

Abstract

Critiques of data colonialism and surveillance capitalism focus on data collected from online behavior. We propose that analytical concepts from these critiques—namely, regimes of value and patterns of alienation and attunement—could be applied more widely to better understand the threats that datafication poses to equity and democracy in the social and environmental realms. Regimes of value, which include the institutions and technologies that make data meaningful and render them selectively available for appropriation, are relevant both to for-profit companies’ data practices and to states’ participation in the datafication of the environment; examining regimes of value raises questions about how data are exploited and how they are neglected. Patterns of alienation associated with datafication include the potential for alienation from the environment; however, at least in some value regimes, alienation may be accompanied by possibilities for attunement to natural and social phenomena that might otherwise have escaped notice.

Big data pose imminent threats to democracy, equity, and selfhood, according to critiques by Shoshanna Zuboff in The Age of Surveillance Capitalism and Nick Couldry and Ulises Mejias in The Costs of Connection (Zuboff, 2019; Couldry and Mejias, 2019). These authors note that people's lives are being used as raw material and appropriated for private profit, with the effect of undermining democratic governance and deepening inequity. Couldry and Mejias further warn that datafication puts us in danger of ceasing to understand ourselves as social actors and losing the capacity for collective reflection on social life.

While these critiques are far-reaching, their object is specific. They refer to data generated through individuals’ interactions with apps, websites, and smart devices and leveraged for profit. The intimacy of the data and relatively unchecked power of corporations that wield them figure as core reasons that we should be alarmed by surveillance capitalism, in Zuboff's terms, or data colonialism, in Couldry and Mejias's. But how are we to understand other sorts of data, arising in contexts where the pursuit of profit may not be a primary aim? Public institutions have long been producers and stewards of large data sets (Edwards, 2010), and governments are increasingly establishing sensor networks whose data can inform their operations in real time (Kitchin, 2014). Grassroots groups and individuals also deploy sensors for a variety of purposes, from regaining control over their health (Nafus, 2016) to advocating for political change (Wylie et al., 2014). While some of these data may be behavioral, as in the “social quantification” sector described by Zuboff and Couldry and Mejias, others reflect the state of the more-than-human world ¹ : they measure things like air pollution, soil temperature, and the movement of city busses.

The complexities of civil society and public institutions’ engagement with data are not interrogated within the surveillance capitalism and data colonialism frameworks, which are focused on private sector extraction. Yet the central analytical tools from these theories, when framed more broadly, are in fact applicable to a much larger range of big data, including what we might loosely think of as environmental data (see Gabrys, 2016a). Looking at sites of data collection and use outside for-profit corporations suggests not only how these analytical tools can be extended, but also how framing them more broadly can contribute to our understanding of the dynamics of social quantification.

In this commentary, we argue that big data of all sorts are embedded in regimes of value that shape their social and political consequences. These regimes of value include the concepts giving data meaning, the mechanisms through which they are analyzed, and the ways they become available for appropriation by some actors and not others. Surveillance capitalism and data colonialism refer to one kind of regime of value. While it is dominant, others co-exist. We argue also that patterns of alienation characterize big data, and that these occur simultaneously with possibilities for attunement, alienation's flip side. How alienation and attunement intertwine in our experience of big data depends in no small part on how and for whom those data come to have value and meaning. While these themes are already evident in the literatures on social quantification data, environmental data, and other data representing human and more-than-human experience, we suggest that thinking more broadly about regimes of value and patterns of alienation and attunement as explicit objects of analysis could help build a systematic understanding of variation and commonality across a plurality of datafied realms.

Regimes of value

At the heart of Surveillance Capitalism is a story about value. Zuboff shows how corporations like Google generate profits from data generated by online behavior. Value is created not primarily through using data to optimize products; rather, as Zuboff and others (e.g., Birch, 2022) contend, companies create value by using data to manipulate users into taking actions lucrative for the company, for example through targeted advertising to induce consumer spending. ² As Couldry and Mejias point out, the realization of value is dependent on companies’ being able to claim property rights to the data that come from individuals’ online behavior; “data colonialism” could not exist in its current form if it were necessary to buy those data from each of us, or if we could refuse to allow our behavior to be datafied. In addition, companies need an algorithmic apparatus to analyze data and turn behavior into something predictive and manipulable. When Zuboff (2021) laments the growing disparity “between what I can know and what can be known about me,” she means not only that companies have data about us that we ourselves can’t access, but also that few of us would have the processing power or analytical skills to make meaning of the data if it were under our control.

These issues of value, appropriation, and interpretation are no less relevant to data collected from sources other than online behavior. Social scientists have a history of considering how environmental data come to have value for policy-makers, especially how data become actionable (Gabrys, 2016a; Jasanoff, 2017). Recent work underscores the ways that data about the more-than-human may come to have monetary value with particular implications for justice and democracy. First, the logics of data colonialism in online behavioral data may be reproduced in data that would typically be considered environmental. Industrial farm equipment, for example, produces data about things like soil quality, rainfall, and fertilizer consumption. Those data have become a commodity in their own right, and an asset for firms like John Deere, who have signed legal agreements of data transfer with fertilizer companies (Bronson and Sengers, 2022). Although farm field or machinery data are arguably not intimate or personal in the way that data reflecting purchasing or browsing behaviors are, the patterns of appropriation echo online behavioral tracking in concerning ways: farmers are not credited for their data collection efforts or rewarded with a portion of profits generated from the data; nor do agriculture companies make available their reworked data to contribute to environmental knowledge that could help in addressing socio-environmental problems like climate change.

Second, in instances where environmental data is collected by public institutions and infrastructures, governments risk enriching for-profit corporations that have the means to develop interpretive tools, without necessarily recouping economic value for the public. Weather apps, for example, don’t usually deploy their own satellites; instead, they apply proprietary algorithms to government-generated weather data, creating weather forecasts that generate revenue from ads. “Smart City” initiatives, in which municipal governments leverage real-time data to improve the operation and governance of city services, may involve contracting with technology companies to provide dashboards that interpret data coming from a wide array of sources (Kitchin, 2014). In addition, to pay for Smart City infrastructures, municipalities may well look for ways to monetize the data they collect, including by selling them to businesses who can extract further profit from those data (Samuel and Gupta, 2022).

There is a third possibility, as well: that data which are not as easy to convert into profit for private corporations may not receive scientific attention, even if they are important for non-economic reasons. A 2017 California law requiring continuous air monitoring at the fencelines of all oil refineries in the state has resulted in the generation of large amounts of new data that could, for example, inform assessments of chemical exposures in marginalized communities (Ottinger and Zurer, 2011). Yet no infrastructure has emerged to make sense of these data, and they remain largely unanalyzed. It seems no accident that the people who would benefit most from such an analysis are marginalized communities, and the deep-pocketed players, the oil companies, have little to gain and much to lose from a closer examination of the data.

Zuboff and Couldry and Mejias's critiques of capitalist appropriation in the social quantification sector have been powerful in showing how the work of making data profitable is deepening inequality. We argue that data collected outside of online behavior, on air pollution or soil acidity, for instance, also affect lives and livelihoods and are ripe for analysis under these frameworks. Examining these other realms points to greater diversity in the mechanisms through which data come to have value, and the kinds of value that they embody. Further, the heavy involvement of the state in data collection, interpretation, and monetization calls for attention to and oversight of data colonialist practices in the environmental realm (c.f. Vera et al., 2019), as well as the recognition that the disuse of data, or its use to produce ignorance, may be just as important a factor in creating inequality and undermining democratic governance as corporations’ efforts to know, predict, and manipulate (Ghosh and Faxon, 2023; Ottinger, 2022; Shelton, 2020). Understanding the variety and nuance in regimes of value seems necessary if we are to gain a full picture of big data's implications for justice and democracy.

Patterns of alienation and attunement

Recreating regimes of value to be more equitable would not on its own neutralize the potentially corrosive effects of big data. Couldry and Mejias (2019) argue that the process of datafication itself poses a threat to autonomy and sociality. Rendering human experience as data, they argue, limits human thought and imagination and encourages us to see ourselves as objects, rather than subjects of knowledge (see also Crawford et al., 2014). At a societal level, ubiquitous quantification undermines contextual social knowledge and our capacity to reason collectively. Through the creation of data as a resource, in other words, we become alienated from ourselves and our social worlds.

Datafication could also alienate people from the more-than-human world. Altrudi (2021) makes the case that the iNaturalist app, meant to “connect people to nature” by generating data about local flora and fauna, entrenches a conceptual divide between humans and nature, as well as the idea that nature is available to be commodified and consumed. Datafication of the more-than-human is also reproducing alienation between farmers and their land, a process that started with the earliest agricultural technologies (see Kloppenberg, 1988). Bronson (2022) suggests that farmers rely on automated data-driven systems that advise them on when to plant and fertilize as a means to validate the experiential expertise that had previously guided these judgments. Farmer alienation is accelerated by institutional arrangements that position big data and algorithms as superior to other forms of knowledge. Take the example of the Agroclimate Impact Reporter (AIR), a big data-based platform developed and moderated by the Canadian government (see Agriculture and Agri-Food Canada, 2020). Using this platform to validate assessments of drought risk gives farmers eligi­bility for insurance programs and subsidies, as well as tax deferrals; reporting mechanisms that don’t involve AIR are no longer as readily accepted.

While datafication can alienate people from the more-than-human world, the process can simultaneously attune people to problems like pollution and climate change, and render them amenable to political action (Gabrys, 2016b; Warde et al., 2018). We cannot track carbon dioxide levels in the atmosphere using our senses alone; even our experiences of weather do not give us a reliable picture of the climate. In some cases, datafication is indispensable to formulating and solving real problems. Further, what is true for the environment may also be true for aspects of our personal lives. People who systematically track their mood, sleep, eating, and other behaviors find that the data they generate can attune them to patterns they had not noticed, and suggest ways to improve their wellness (Nafus and Sherman, 2014), while potentially directing their attention away from other aspects of their experience.

To say that datafication can be useful and even necessary does not negate Couldry and Mejias's critique. The questions for analysts and activists, then, are how particular practices of data collection and use may be weighted toward attunement or toward alienation; to what phenomena they attune or alienate us; and who is helped or harmed by these tendencies. The specific configuration of the regimes of value in which data practices are embedded can be expected to shape these patterns of alienation and attunement. To the extent that data are hoarded by companies for their profit-making potential, as under surveillance capitalism, they are unlikely to contribute commensurately to individual or collective attunement. But where people collect and control data that can attune them to patterns in their own lives, as in the quantified self movement, any accompanying alienation may seem reasonable.

Conclusion

Critiques of surveillance capitalism and data colonialism have offered important insights into the problems of social quantification. We argue here for expanding the scope of these analytical tools. The regimes of value and patterns of alienation that these critiques have highlighted are generalizable to data about the more-than-human world, and to data practices in which public sector and other not-for-profit institutions are primary actors in data collection and use. Applying these concepts to a plurality of kinds of data and data relations suggests lines of inquiry that have not yet been thoroughly explored. Key among these are how regimes of value may shape the neglect of certain data, alongside the exploitation of other data, and how the tension between alienation and attunement plays out in a variety of contexts. We propose that, by taking regimes of value and patterns of alienation and attunement as objects of analysis potentially relevant in all datafied realms, future big data scholarship could speak more effectively to common issues of democracy, equity, and personhood arising around behavioral and environmental data.