Big Data,urban governance,and the ontological politics of hyperindividualism

Introduction

Data politics dominated newspaper headlines in New York City at the end of 2015. Controversy erupted when a former Police Commissioner charged that the city’s method of collecting crime data underreported actual events. He cited as an example the NYPD’s practice of recording a “shooting” only if a bullet wounds a victim. According to the New York Times account:

a shooting … is recorded only if someone is hit …. If a bullet tears a person’s clothing but does not wound the victim, the episode is not included in the Police Department’s official tally of shootings … Gunfire at a car in which the occupants are wounded by shattered glass but not by a bullet is not recorded as a shooting. (Goodman, 2015)

Meanwhile, some 100 miles to the south, in the economically devastated city of Camden, New Jersey, police officials reported a large-scale expansion of that city’s “ShotSpotter” automated gunfire detection system (Adomaitis, 2015). ShotSpotter is described by its corporate provider as “an acoustic surveillance technology that incorporates audio sensors to detect, locate and alert police agencies of gunfire incidents in real time …. The alerts include … the precise time and location (latitude and longitude) represented on a map and other situational intelligence” (ShotSpotter Fact Sheet, 2016). The expanded ShotSpotter system in Camden was part of a larger strategy of augmented video surveillance and data collection designed to reassert the appearance of police control in a city that routinely tops national rankings in the incidence of violent crimes (NeighborhoodScout, 2016).

What counts as a “gunshot” in Camden, in many cases, would not register as a “shooting” in New York City. Whereas New York construes a “shooting” in the narrowest possible terms requiring the presence of a shooter, a bullet, and a victim’s blood, Camden’s citywide acoustic surveillance system automatically records every “digital alert” of an “actual gun discharge” as a “gunshot crime in progress” pinpointed in time and space (ShotSpotter Fact Sheet, 2016). These differences between New York City and Camden cannot be separated from their political context. The outcome of mayoral elections in New York City, as well as the city’s attractiveness for residents, tourists, and investors, depends on the public perception of safety and security, exerting downward pressure, in turn, on the practice of collecting and documenting crime statistics. The NYPD’s CompStat program tracks weekly crime data by precinct as a tool for managing organizational personnel and resources but it is equally a tool for managing public opinion (Eterno and Silverman, 2010). In a similar manner but conveying a different message, Camden’s expanded ShotSpotter detection system deploying sensors and monitors in every neighborhood also influences political opinion by establishing a visible police presence throughout the city.

A related controversy over categories, exclusions, and measurement erupted over data on New York City’s homeless population at a time when visible homelessness, like crime, had become a political liability for the city’s mayor. The annual homelessness count reported by the U.S. Department of Housing and Urban Development (HUD) in late 2015 found 75,323 homeless individuals in New York City but that number was quickly challenged by advocates for the homeless and HUD acknowledged uncertainty in the “reliability and consistency” of the data (Stewart, 2015a; U.S. Department of Housing and Urban Development, 2015). The ambiguities in the data were manifold. Individuals and families who became homeless through eviction, fire, landlord harassment or other reasons, and were living doubled-up with friends or relatives were not considered homeless by HUD’s definition and were excluded from the count and HUD’s report listed as zero the number of chronically homeless families in New York City not in homeless shelters. Although the city’s Human Resources Administration (HRA) funds 45 emergency and transitional shelters for women and their children forced to flee their homes due to domestic violence, HUD also reported as zero the number of homeless domestic violence (DV) victims in shelters because the DV shelters operated by HRA were considered separate from the homeless shelters operated by the Department of Homeless Services (New York City Department of Homeless Services, 2016). Simultaneously, the Mayor’s Office announced an “unprecedented expansion” in the number of shelter beds for homeless victims of domestic violence to accommodate “a 50 percent increase over the current 8,800 individuals served yearly” (New York City Office of the Mayor, 2015; Stewart, 2015b). Further confounding HUD’s data, HUD’s count of 1706 homeless youth almost certainly underestimated a significant subgroup of the homeless who, advocates said, might exceed 10,000 (Gibson, 2011) but “avoid public places where they could be counted for fear of referral to Child Protective Services and … avoid shelters out of safety concerns” (Navarro, 2015; Stewart, 2015a, 2016).

The selective practices of categorization and measurement illustrated in these examples might easily be dismissed as the intrusion of political agendas in the otherwise objective and politically neutral construction of data as, in the words of the NYPD Commissioner, “factual” and “the truth.” If this were the case, a solution might lie in the rationalization and depoliticization of methods of data collection, categorization, and analysis, bringing actual practices into closer alignment with normative claims. The ubiquity of Big Data as a technique of governance, biopolitics, and bureaucratic control, however, has expanded the scope of the problem and amplified the challenge of delineating solutions. My argument in this paper is that the challenge of (and to) Big Data is not confined only to the politicization of its practices but rather is situated in its foundational ontological premises, involving the evisceration of context through an ontology of hyperindividualism. An ontology of atomistic individualism underlies the construction of calculative data in general (Hacking, 1990, 1991, 2006) but the arrival of Big Data, involving the algorithmic production, manipulation, and application of very large datasets, has exacerbated and expanded the scope of the problem by obscuring from critical scrutiny its foundational hyperindividualist ontology.

This paper aims at a partial corrective by examining Big Data’s underlying calculative ontology. By ontology I mean “a set of contentions about the fundamental character of human being and the world” (Bennett, 2001: 160) or simply “a theory of objects and their ties” (Theory and History of Ontology, 2016). Specifying Big Data’s “ontological imaginary” (Bennett, 2001: 161) answers the question starkly posed by Wagner-Pacifici et al. (2015: 5) who ask, with respect to Big Data: “Just what is our basic ‘ontological unit?’” or, even more plainly, “What is a thing?” (see also Beauregard, 2015, 2016). Big Data’s “onto-story” (Bennett, 2001: 161) can be briefly summarized in the premise that the world is knowable via calculation and measurement and can be represented as the aggregation of discrete, independent, empirically observable units. These units are the “data points” representing, to list only a few examples, gunshots, homeless people, sociodemographic characteristics, credit card swipes, Internet searches, or geo-tagged locational coordinates captured from smartphones (Goldstein, 2016; Kitchin, 2013, 2014; Wagner-Pacifici et al., 2015; Weber, 1946). This calculative ontology both relies on and reproduces a form of atomistic individualism in which the ontological unit of analysis is the discrete data point, the meaning and identity of which inheres in itself, preceding, separate, and independent from its context or its relation to any other data point.

By the hyperindividualism of Big Data, I refer to the practice of disaggregation and reaggregation that proceeds through a multistep process of interconnected and interdependent constructions of the world. Big Data’s ontological imaginary involves (1) the division and disaggregation of data fields (“variables”) into ever-smaller units measured at ever finer-grained levels of resolution, (2) the practice of counting each individual observation as an autonomous unit—a thing-in-itself—extracted from and independent of its context, and (3) the reaggregation and recontextualization of the resultant data “bits” through the automated algorithmic search for statistical patterns and correlations hidden within the dataset. While an ontology of atomistic individualism underlies calculative practices in general, the diffusion of Big Data both relies on and produces a form of hyperindividualism of an unprecedented scope and scale. The hyperindividualization of Big Data results, first, from the hyperdisaggregation of data fields in what Kitchin (2014: 2) describes as the production of “massive, dynamic flows of diverse, fine-grained, relational data” recording and counting, for example, Internet transactions, selected words within social media posts, demographic “variables,” real-time spatiotemporal registers, and so on, where the identity or meaning of each data point is self-evidently and inherently given as a thing-in-itself divorced from its context. That hyperindividualization permits, second, the reaggregation and intercorrelation of data observations to construct new observations and “facts,” the meaning of which is based on, imposed by, and imputed from the discursive categorical labels in the data table rather than from the meaning residing in the lived experience of the original units of observation.

Consideration of Big Data’s ontology of hyperindividualism moves beyond epistemological debates over definitions, categorizations, data collection methods, and data accuracy. The interrogation of such matters derives from an internal critique of Big Data’s ontological framework while adopting and remaining within its ontological assumptions and focusing on problems of operationalization and implementation, that is, on problems of method (Lake, 2014). Motivating such internal critique is the belief that better (i.e. more accurate, consistent, objective, or comprehensive) methods of data collection, aggregation, and analysis will produce better knowledge. Beyond merely addressing internal operational mechanics, however, internecine conflicts over the “how” of Big Data have constitutive effects. By performing and naturalizing Big Data’s ontological assumptions, debates over what gets counted, through what methods, via what algorithms (Kwan, 2016), and despite what omissions and (mis)categorizations reproduce its foundational premises while deflecting attention away from a critical assessment of those underlying principles (Zaloom, 2003). The practice of Big Data governed by an ontology of hyperindividualism is also constitutive of that ontology, naturalizing and diffusing it through practices of governance and, from there, throughout myriad dimensions of everyday life. The challenge for governance is that problems inherent in the ontology underlying a practice cannot be resolved by altering the practice but must be addressed at the level of foundational ontological assumptions. Changing those ontological assumptions, however, destabilizes the entire edifice of practice built up on the prior underlying foundation that allowed the politicization of data construction to proceed in the first place. As Garfinkel observed, there are often “‘good’ organizational reasons for ‘bad’ clinical records” (Garfinkel, 1967: 186). Resistance to change on the part of interests invested in those current practices (e.g. the police or the mayor) all but guarantees the preservation of the status quo.

My purpose in this paper, accordingly, is to consider the implications for governance of Big Data’s ontology of hyperindividualism. Rather than taking Big Data’s ontological assumptions as the starting point of the analysis, however, my concern is to sketch a brief genealogical account of their emergence. A genealogical narrative understands practices (and their consequences) as situated in the confluence of the circumstances from which they emerged (Foucault, 1984; Hacking, 1991; Nietzsche, 1913). “History matters,” Trevor Barnes (2013: 298) reminds us, but, unlike history’s search for origins or causes, a genealogical approach problematizes the given-ness of Big Data’s ontological premises by unraveling and exposing their contingent emergence. Focusing on emergence rather than origins helps, as Jane Bennett (2001: 11) observes, to “counter the teleological tendency of one’s thoughts.” For Colin Koopman:

Genealogical problematization … provokes a question by rendering the inevitable contingent ….A genealogy also shows us how that which we took to be inevitable was contingently composed. A genealogy does not just show us that our practices in the present are contingent rather than necessary, for it also shows how our practices in the present contingently became what they are. The history of that which was once presumed inevitable not only makes us forget the inevitability, it also provides us with the materials we would need to transformatively work on that which we had taken to be a necessity. (Koopman, 2011: 545)

Big Data’s ontology of hyperindividualism

Clarifying Big Data’s ontological imaginary and answering the deceptively simple question “What is a thing?” reflects the centrality of what Latour (2005) calls Dingpolitik or the politics of the thing. As explained by Ignacio Farias:

Urban politics is … not about subjects, subjectivities or discourses, but about things, complex entangled objects, socio-material interminglings. This is what Latour (2005a) calls a Dingpolitik: the understanding that urban politics can no longer be understood as conflict between human or, better, class interests, but involves conflicts over different ‘cosmograms’, that is, ways of articulating the elements of the world and their mutual connections. (Farias, 2011: 371)

In contrast to Big Data’s individuated ontology of the data point is a relational ontology in which meaning devolves from the whole to its constituent parts and the identity of the individual data point emerges from its relationship to and membership in the whole. Kitchin notes the highly relational character of Big Data’s data in which “common fields … enable the conjoining of different data sets” (2014: 2) through the ability to overlay; juxtapose; or correlate data layers, fields, or categories at will. But this relationality is correlative rather than constitutive, a relationality of juxtaposition rather than of ontological cocreation and codependence. These “relational databases” construct an accidental relationality in which the meaning of the data point is first abstracted and removed from its constitutive contextuality and then recontextualized through relational juxtaposition with other data points that have been similarly distanced from their contextually constructed ontological identity. In Big Data’s correlative, juxtapositional and accidental relationality, each field, layer, or variable in the dataset can be associated with any other, it can be contextualized or decontextualized, or it can be correlated with one data layer today and a different one tomorrow—all without altering the inherent, immutable meaning, value, or identity of each data point comprising the dataset.

In a coconstitutive ontology, in contrast, the meaning or identity of the individual data point does not preexist its context. If in the individuated ontology of Big Data the whole is the aggregation of its individual parts, in a relational ontology the individual parts derive their identity from and through their membership in the whole. Because the meaning of the individual data point is established by its context, it cannot be removed from its context without losing, altering, or obscuring its meaning. When the ontological meaning of the “thing”—the individual data point—resides in its relationality within a contextual network or assemblage of things, “everything is already within the individual” observational unit or data point (Law, 2004: 22). Indeed, “the notion of assemblage involves no outside, no exteriority” (Farias, 2011: 369) and “there is no distinction between individual and environment. There are no natural, pregiven boundaries …. Everything is connected and contained within everything else” (Law, 2004: 22).

Viewed within a relational ontology, therefore, a dot on the gunshot map of Camden no longer represents merely the localized discharge of a firearm marked as an individuated “thing.” The dot instantiates the recording of a certain acoustical signal at a designated electronic frequency but also so much more. Contained in that dot is a set of political and economic structures and processes producing a population differentiated by indicators of poverty and inequality; the operation of urban, suburban, and regional land-use practices of inclusion and exclusion that situate that dot here rather than there within a regional landscape; a portfolio of legal, illegal, and extra-legal provisions and practices governing the availability, distribution, and cost of firearms; the design and implementation of law enforcement and surveillance practices and the training of personnel in their use; the technological capacity to design, construct, and operate gunshot detection devices in a chaotic urban environment; a political decision-making process allocating scarce financial resources in a cash-strapped city to acquire, install, and operate the detection system; and more. All this, as John Law notes, “is already within the individual” gunshot pinpointed at a specific place and time but all of these layers of meaning fall away and disappear when the identity of the acoustic signal is reduced to a “digital alert … of a gunshot crime in progress.”

As the pragmatist philosopher Richard Rorty concludes:

it does not pay to be essentialist about tables, stars, electrons, human beings, academic disciplines, social institutions, or anything else ….[T]here is nothing to be known about (objects) except an initially large, and forever expanding, web of relations to other objects. Everything that can serve as the term of a relation can be dissolved into another set of relations, and so on for ever. There are, so to speak, relations all the way down, all the way up, and all the way out in every direction: you never reach something which is not just one more nexus of relations ….There is nothing to be known about anything save its relations to other things. (Rorty, 1999: 53–54)

Meanwhile, a different but equally narrowly construed criterion is constitutive of a “shooting” in New York City. Here a gunshot that misses its target is not a shooting, nor is a gunshot that wounds its target by shattering the glass of a car window. The NYPD’s narrowly construed definition excludes from the category all but one of the multitude of contextual relationalities comprising the multitude of different “things” called gunshots. Gunshots whose identities correspond to those excluded categories are experienced in the world but do not exist in the dataset constituting the world of Big Data. A contextual, relational ontology of homelessness fares no better in New York City. Homeless youth are not counted as homeless and thus populate the ontological category of “not homeless,” not because they are in fact not homeless but because they have learned to evade HUD’s count of the city’s homeless population. Doubled-up families are defined and categorized as “not homeless” despite having been rendered homeless by fire, eviction, domestic violence, or landlord harassment. The category “not homeless” contains both homeless and not-homeless individuals who, nonetheless, are ontologically constructed as identical within the decontextualized, hyperindividuated dataset of homelessness. When the dataset of homelessness is then algorithmically correlated with similarly constructed datasets to reveal unexpected statistical patterns and associations, the apparent clarity enabled by Big Data’s emergent relationality of juxtaposition conceals the incoherence of the data entered into the analysis.

These practices of data collection, categorization, and correlation correspond to and reproduce an individuated ontology in which identity inheres in the discrete, autonomous observational unit irrespective of its constitutive context. Big Data’s characteristics of high volume, high velocity, fine-grained resolution, and comprehensive scope (Kitchin, 2013), coupled with its increasing pervasiveness throughout more and more spheres of everyday life, have elevated the individuated ontology to what may justifiably be considered an ontology of hyperindividualism. Enabled by technological developments in data acquisition, storage, database management, and analysis, hyperindividualism proceeds through the categorization of data fields at ever more finely grained levels of resolution and ever more comprehensive levels of coverage. To cite only two examples, DNA barcoding that allows the definitive categorization of unique biological species (www.barcodeoflife.org) extends individuation across all living things while obscuring ecological interdependencies and coconstitutive ontologies. The Open Research and Contributor ID system that “provides a persistent digital identifier that distinguishes you from every other researcher” (www.orcid.org) institutionalizes the individuation of knowledge while obscuring the dense network of influences and interdependencies within which any process of knowledge production is situated (Wyly, 2014a).

The genealogy of hyperindividualism

How, then, did an ontology of hyperindividuation become possible and from where did it emerge? For Trevor Barnes: “what is forgotten in the celebration of big data is history” (Barnes, 2013: 297) and Barnes situates Big Data’s assumptions and practices in the quantitative revolution in geography and the social sciences in the mid-20th century. Those developments, of course, were themselves inscribed within a process of individuation of much longer duration. The making of individuation has deep roots extending from and through the Enlightenment ideal of scientific objectivity (Hacking, 1990, 1999; Harding, 2015; Latour, 1993); political theory from Rousseau and Montesquieu to Bentham, Locke, and Mill; development of a psychology of the self (Rose, 1989, 1998); and more. In Genealogy of Morals, Nietzsche describes the process of “first making man to a certain extent … uniform, like among his like, regular, and consequently calculable.” And, Nietzsche continues:

the actual work of man on himself during the longest period of the human race, his whole prehistoric work, finds its meaning, its great justification … in this fact: man, with the help of the morality of customs and of social strait-waistcoats, we made genuinely calculable

That sovereign individual, Nietzsche emphatically insists, was not born but made. As Claire Rasmussen convincingly demonstrates in her genealogy of the autonomous subject, “the ‘individual’ as a form of subjectivity is the product of a particular social imaginary that institutionalizes the individual through secondary institutions such as the economy, a system of rights, and so on” (Rasmussen, 2011: 11). Those institutions, however, do not do their work in the abstract. For social historian Patrick Joyce, the construction of liberal individualism proceeded through myriad mundane practices of governance that spurred “the growth of privacy and the individuation of the subject” through which “people became available to be identified as individual” (Joyce, 2003: 22). The invention of letter writing in the early 19th century, for example, stimulated the development of a postal system which, in turn, required the introduction and proliferation of street addresses and individual house numbers. These assigned a unique location and, therefore, a unique, enumerated identity to each dwelling, each letter writer, and each recipient. City directories soon followed, aggregating information about individual residents in the first comprehensive urban databases (Joyce, 2003: 197–198). What Joyce calls “the hygienisation of the city” accelerated “the individuation of the self” by “creating spaces around and between bodies, protecting them from others’ contact and smells” and the resulting privatization “brought people into a new encounter with themselves” (Joyce, 2003: 73). The introduction of indoor sanitation literally wrapped the individual body in a cloak of privacy, physically separating and thus distinguishing each privatized body from another and from the generalized body public. Joyce similarly describes the cultural history of the bed in the 18th century as contributing to the privatization of sleeping linked to emerging ideas of liberal individualism:

In France the individual bed eventually became integral to notions of the Rights of Man, finding its way into political reason in this form: a decision of the Convention of 1793 in France ordained that state institutions such as hospitals and asylums should provide individual beds as a natural extension of the Rights of Man. (Joyce, 2003: 73)

Writing in 1929 at a moment of global economic crisis, John Dewey observed that “the problem of constructing a new individuality consonant with the objective conditions under which we live is the deepest problem of our times” (Dewey, 1929: 56). In a series of essays titled “Individualism, Old and New,” Dewey described the “perversion of the whole idea of individualism” (1929: 49) wrought by the reformulation of liberalism from the (old) political individualism to the (new) economic individuation of industrial society. The Lockean, political liberalism of the early industrial revolution, Dewey wrote, liberated the individual from the strictures of religious and monarchical rule: “liquefied the static property concepts of feudalism,’ and ‘gave a secular and worldly turn to the career of the individual” (1929: 78). The subsequent expansion of the market economy, however, “subordinate(d) political to economic activity” (1935: 18) and replaced political individualism with economic individuation “to such an extent that individuality is suppressed” (1929: 66). If the old liberalism liberated the political individual from subservience to despotic rule, the transformation from political to economic liberalism reduced the individual to so many units of labor power subservient to the rule of the market. Commodification and marketization produced “a conception of individuality as something ready-made, already possessed” (1935: 46) and thus available to be bought and sold in the labor market. Under the new circumstances of mass production and consumption: “liberty becomes a well-nigh obsolete term; we start, go, and stop at the signal of a vast industrial machine” (1929: 46). In the process, those aspects of being human that escape the commodification of labor power—the fully human context of labor—simply disappear from view (Arendt, 1958).

Dewey attributed the loss of individuality to the forces of “quantification, mechanization and standardization,” which he called “the marks of the Americanization that is conquering the world” (1929: 52). In an eerily prophetic statement, Dewey observed that

The marks and signs of this ‘impersonalization’ of the human soul are quantification of life, with its attendant disregard of quality; its mechanization and the almost universal habit of esteeming technique as an end, not as a means, so that organic and intellectual life is also ‘rationalized’; and, finally, standardization. Differences and distinctions are ignored and overridden; agreement, similarity, is the ideal ….Homogeneity of thought and emotion has become an ideal. (1929: 52)

The radical expansion, intensification, and mystification of this process through the practices of Big Data reproduce an ontology of hyperindividualism. For the 75,323 individuals comprising HUD’s dataset of homeless individuals in New York City in 2015 (and the tens of thousands similarly categorized in other jurisdictions), the essentialized identity of homelessness derives from their inclusion in the dataset rather than from the contextual dynamics that produce the condition and experience of homelessness. In the circular logic of Big Data, the homeless are those who are counted as homeless. The ontological identity of homelessness that inheres to those individualized and decontextualized bodies when the dataset of which they are constituents is correlated and recontextualized with other datasets, producing new relationalities, may have little if anything in common with the original context-dependent meaning of homelessness experience by the individuals comprising the dataset.

Hyperindividualism and urban governance

Big Data’s ontology of hyperindividualism exerts a debilitating influence on the conceptualization and practice of urban governance. Understanding the world as an aggregation of individuated data points reduces governance to the management of atomistic behavior, undermines the contribution of urban complexity as a resource for governance, erodes the potential for urban democracy, and eviscerates the possibility of collective resistance.

First, as Patrick Joyce observes: “before populations can be governed they must be known or identified” (2003: 13) and measurement, quantification, statistical compilation, and mapping have long been used and understood as techniques of governance (e.g. Hacking, 1990, 1999, 2006; Mitchell, 2002; Rose, 1991). An optimistic version of this claim is that when quantification and measurement make problems visible, governments are provoked to produce a solution or risk a crisis of governmental legitimacy (Habermas, 1975). While the visibility of a problem may prompt government action, however, the form of its representation critically influences the form and substance of the governmental response. By constituting urban problems as the aggregation of individual empirical observations, Big Data’s ontology of hyperindividualism reduces governance to the management of atomistic behavior or characteristics. A problem represented as a pattern of dots on a map—a concentration of subprime loans, poor test scores, low-income households, homeless individuals, or gunshots, for example—prompts a response aimed at altering the number or distribution of dots. A governance strategy that focuses on visible symptoms deflects attention from underlying causes, at worst blaming the victim and at best addressing acute needs of individuals at risk without preventing the continuing (re)emergence of problems at their source.

Second, Big Data’s relationship to urban complexity presents a paradoxical challenge to urban governance. While technological developments in data production, collection, and management have yielded an exponential increase in the number and variety of data categories available for analysis, the decontextualization, homogenization, and standardization of data within categories reduce complexity that might otherwise serve as a resource for governance. The hyperindividualism of Big Data echoes the ascendancy of objectification in GIS more than two decades ago, in which “access to massive databases causes the analyst to transform those to whom the data refer from subjectively differentiated individuals to an objectified ‘other’” (Lake, 1993: 408; see also Curry, 1993). The loss of the subjective viewpoint flattens complexity that could enrich governance by bringing multiple perspectives to bear on a problem.

Third, Big Data undermines democracy in the practice of urban governance. Nikolas Rose describes at length the “constitutive interrelationship between quantification and democratic government” (1991: 675) in which democratic participation requires a public conversant with numbers and able to comprehend the world in the statistical form through which it is presented for public deliberation. The ascendancy of Big Data, however, renders obsolete the public’s political numeracy as the chart and the map are replaced by the black box of data management software and the complexity of the visible world is replaced by the hidden complexity of the algorithm (Wyly, 2014b). The ontology of hyperindividualism, furthermore, reduces active political agents to the status of generators of data, whether through volunteering of data (Elwood, 2008; Elwood et al., 2012) or as passive targets of data-scraping technology.

Finally, Big Data’s hyperindividuated ontology eviscerates the possibility of collective resistance. The disaggregating, objectifying, and decontextualizing practices informed by these ontological presuppositions undermine collective action by inculcating a worldview comprised of atomistic individuals. These individualistic foundational premises correspond to and reproduce hegemonic commitments to a prevailing ideology of individual responsibility and personal culpability. What Dewey observed in 1923 applies with equal or greater force today:

When the self is regarded as something complete within itself, then it is readily argued that only internal moralistic changes are of importance in general reform …. The result is to throw the burden for social improvement upon free-will in its most impossible form. Moreover, social and economic passivity are encouraged. Individuals are led to concentrate in moral introspection upon their own vices and virtues, and to neglect the character of the environment ….And while saints are engaged in introspection, burly sinners run the world. (Dewey, 1923: 113)

Conclusion

With Big Data a ubiquitous presence in modern life, critical reflection urges caution in adopting its tenets and practices to inform policy-making and urban governance. Most discussion of Big Data and governance proceeds through internal critique; finding inconsistencies, ambiguities, and lacunae in the processes of data collection, aggregation, and analysis comprising Big Data’s operating manual. I have argued in this paper that Big Data’s challenge to urban governance cannot sufficiently be addressed at the level of internal critique and that its ontological presuppositions provide an unreliable foundation for the practice of urban governance in a democratic society.