Data 1.0

Posted on | August 18, 2015 | 2 Comments

For media studies* data is a key term because of the role it plays in orchestrating contemporary power relations through the collecting capacities of knowledge generating machines. In an information economy, data can provide both the record and the source of individual energy, self-enlightenment and collective opposition. Here are some qualities we ascribe to data.

Data are collected insights. They begin with an individual fact – a datum, the Latin singular – and attract further instances to lay the foundation for an argument. Historically, the word has conveyed different meanings, but it has always referred to the tension between truth and persuasion.

The destiny of data is to facilitate narrative. An isolated activity that produces no evidence does not become data. It is rogue, discountable, exceptional. It is a tree falling in the woods. Without a script, data are unemployed actors.

Data gain significance through association. They come together to say something. But to do so, first they must be assembled. This work of crafting association is necessarily rhetorical, since it is never possible to capture all information adequately.

Media technologies capture data. They provide the recording vehicles for activity. Technologies are thus tools for communicating the stories data tell. In media studies, these stories tend to take two forms.

1. Information about individuals, self-assembled: Data capture that is self-nominated, in which people have some say in crafting the narrative.

In the Quantified Self subculture, people choose to adopt tracking technologies such as wearable fitness monitors to record physical activity, heart rate and sleeping patterns. In productivity tools, software developers build platforms that can record device activity so that users have an archive of on-screen practices. In each case, data visualisations and statistical measures are the outputs that operate as points of reflection. This is their rhetorical power: data prompt a process of enlightenment as individuals seek self-knowledge. Data allows us to perfect aspects of a hidden lifeworld not always available to the conscious mind or witnessing eye. Compelling data prompt reform and improvement.

2. Information about individuals, assembled by others: Data capture that is aggregated en-masse for particular purposes, from enhanced civic services to commercial profit, with or without individual consent.

When National Security Agency analyst Edward Snowden acted as a whistleblower to reveal the extent of unacknowledged data surveillance in the United States, individuals responded by claiming new rights to privacy to oppose such widespread monitoring of intimate life. Payment transactions, traffic routes, energy consumption and phone conversations are some of the most well known data sets amassed by external bodies and institutions. This emerging context for popular governance is vexed given that citizens are not always told about or actually understand the ways their data are collected. The most common justifications for the capture of non-identifying behavior is the convenience of predictive services (e.g. Google Maps) or matters of civic patriotism, safety and care (against terrorism, say, or in response to disasters like Hurricane Sandy). Collecting data is the means to secure favorable social ends.

In both instances, data produce actionable knowledge; the difference lies in our awareness of the process. When data are self-assembled, we experience a feeling of control. The notion of freedom we put in practice by choosing to record activities is one that privileges will as the best kind of agency. Conversely, when data are collected without assent, we become subjects in Foucault’s sense. We are agents only insofar as our activities are recorded in the terms of others, for the purview and measure of an external authority. Helen Nissenbaum calls this ‘information asymmetry’: the ethical dilemma that arises when individuals have little chance to influence the terms upon which their information is gathered and used.

In media studies, the data economy is typically understood as operating within these axes, where the morality of tracking behaviour is plotted according to the benefits brought to oneself in relation to or opposed to others. The underlying framework is the idea of pouvoir-savoir, the articulation of power/knowledge that is the grounding principle of Michel Foucault’s early work. In an information economy, it would seem, knowledge is power. Data allow us to craft our own stories and understand ourselves better, just as they enable authorities to abstract the significance of highly personal narratives to impose order and extract profit. But is this the best way to understand data as we continue to advance a marketplace and a polity ruled by large data sets? Is power really secured through knowledge, or do we need another account to fit the times?

Our research develops different metaphors and frameworks to challenge the idea of sovereignty that has dominated ideas of property and personhood – two aspects of identity that US privacy law often conflates. When sovereignty commands the visual and conceptual field, to know something is to own it. By extension, to see data is to reify and ostensibly possess the knowledge on display. This is the scopophilic fantasy that data visualizations often fulfil, and it is one which typically obscures the tools and labor of assembly.

To explain the often unspectacular experiences of data exchange in everyday life, we are attracted to more organic concepts. For example, the notion of data sweat draws attention to a natural phenomenon that happens to all of us that is an emission of meaningful information depending on the context. What media studies can sometimes miss, partly because of the focus on text and format, is the difference that place makes in perception. More recent theorists are beginning to identify the significance of dwelling, habit and transition in understanding our engagement with media environments. For those of us who live life in transit, moving between many places, this is a necessary development. It recognizes that we are always leaving traces of ourselves in the different settings and contexts we encounter.


The current fascination with data metrics and analytics can be read optimistically as an interest in technology’s role in helping us tell new kinds of stories. This is the last gasp of what has been called ‘participatory media.’ When communication technologies and people are equally mobile, we are no longer observing discrete bodies interacting with static media entities so much elaborating a hybrid relationship of collaboration. The media studies to come will need to explain our engagement with data and their capturing devices as an accommodation, a co-habitation, a shared breath, mutual dwelling.

*Given some recent writing, I am contributing to a new media studies keywords collection. My term is ‘data’. I’m writing it with my colleague Dawn Nafus, who has her own book coming out on the topic. By way of motivation, I thought I’d share some ideas in process. This is the first draft, yet to benefit from Dawn’s input, and it shows – for some reason everything I write lately is using a script metaphor. I suppose that is the humanities training coming through. But it might also be a sign that I am always searching for words…


2 Responses to “Data 1.0”

  1. Kris
    August 19th, 2015 @ 9:01 am

    This is really great Mel. It makes me realize that data is big category in my work without ever really being thematized as such. I tend to use the word commodity, and then qualify it in the direction of data. So why not use data, I’m now wondering? Probably because I don’t know that literature well enough yet.

    In any case, about the types of data recognized by media studies: type 1 very often produces type 2 right? I think so anyway, which is why I talk about networked life as being caught between two types of collective forms: publics and populations. Although, by your description of type 1, I’m not sure “public” is quite the right concept. But I think of type 2 as producing populations as its collective form. Database is also right, but I wanted something that inflects more toward subjectivity and ordinary life.

    About Foucault: in his work on neoliberalism, his idea about the centrality of populations is a theory of the subject, but a subject caught between type 1 and type 2 of data production: where what feel like self-enlightening activities (e.g., search engines) also produce data capture for others (populations, in my language). Although he was far more interested type 2. Interestingly, new media technologies that automatically log data force a collision between his biopolitical populations (type 2) and his care of the self (type 1).

    BTW: do you know Stanley Cavell’s _The World Viewed_? He wants to reconceptualize medium there (meant mostly in its art world sense, which is not totally unrelated to its media studies sense) as automatism, which is very close to what you’re doing in this work on data.

    In any case, I can’t wait to read more.


  2. melgregg
    August 29th, 2015 @ 8:40 am

    Thanks for the feedback and the reference – glad it is testing some ideas we both like to think with. Perhaps the distinction I am making to test your public/population model is one of awareness. If type 2 were only ever a result of type 1 it would leave out a lot of tracking activity that takes place without our having the technical knowledge to realise it. So yeah, it is only sometimes, not always, that type 1 leads to 2. Also, I don’t use publics because I don’t think people are necessarily engaging in self-nominated data collection for the purposes of building a social bond. A public has some sense of the social embedded in it, for me. When collecting activity stays squarely at the level of insular self-perfectionism, then I don’t think it constitutes a public, because there is no audience or intended circulation desired for the data. Publics commence at the moment of attention, Warner says, so data tracking for one’s own purposes and consumption doesn’t quite fit.

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