melissa gregg inside the data spectacle

8/9/2019 Melissa Gregg Inside the Data Spectacle

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Inside the data spectacle

Melissa Gregg

Forthcoming in Television and New Media , 2015.

Accounting for the spectacle of Big Data 1 entails understanding the aesthetic pleasure and

visual allure of witnessing large data sets at scale. This paper identifies the scopophilic tendency

underwriting key sites and conventions inside the tech industry which pivot on large scale data

set visualization. I use Joh Cald ell s (2008) not io of i dust ial efle i it to explain the

charismatic power and performative effects that attend representations of data as visual

spectacle, namely, the fantasy of command and control through seeing (Halperin 2014).

Drawing on 12 months of personal experience working for a large technology company, and

observations from a number of relevant showcases, conferences and events, this production

studies approach (Mayer et al., 2010) illustrates the forms of commonsense produced in

industry settings. 2 Due to the proprietary nature of high tech, few scholars have access to the

points of ideological and intellectual transfer in which the promises of Big Data are actively

debated and constructed. I offer instructive examples of this process, negotiating the boundary

of intellectual property restrictions and participant observation. 3



The second objective of the paper is to theorize the labor of data. An important area of

attention in the emerging data economy is to assess exactly how users online activity involves

them in profitable transactions, often without their knowledge (Scholz 2013). The analysis that

follows adds nuance to this debate by identifying two instances of below the line labor

(Mayer 2011) in the Big Data era. The first of these is the work of assembling the data spectacle,

specifically the rhetorical work of the tech demo in selling the visions on display. This genre and

its default evangelism are normative features in the broader calendar of events for technology

companies, large and small. Combined, they are a leading instance of what Caldwell calls critical

industrial practice :

t ade ethods a d o e tio s i ol i g i te p eti e s he as the iti al

dimension) that are deployed within specific institutional contexts and relationships (the

i dust ial e i o e t he su h a ti ities a e a ifest du i g te h i al p od uction

tasks o p ofessio al i te a tio s la o a d p a ti e Cald ell 2008, 1).

Professional interactions in the high tech industry involve generating commonsense

assumptions – of te h olog s e efits ; of technological progress as inherently good – a

process that is pivotal to the broader experience of contemporary data o k .4 Pursuing an

analogy between the Hollywood lo atio s that a e Cald ell s fo us , and what is by now the

rival center of mythologized cultural power in the US, Silicon Valley, I use an example from a

recent developer forum in San Francisco as an opportunity to unpack the ideological work of

this type of industry event, one of many routine settings in which Big Data rhetoric launches

and lands. 5 These elite occasions for transferring insider knowledge operate as a flagpole

running exercise for messages that will be sold to consumers later in the product cycle. Yet their



distance from everyday users inevitably affects their ability to make appropriate judgments as

to market desire and need. As such, tech events often pivot on a combination of self-

aggrandizement and hot air recycling referred to i the i dust as eati g ou o dog food .

The se o d aspe t of below the line labor I attribute to Big Data is the work that data does on

our behalf, with or without informed consent. Recent popular distrust of government agencies

and technology companies colluding in the traffic of privileged information reflects the growing

realization that labor in the new economy is as much a matter of non-human agency as it is the

materiality of working bodies. After the algorithm has been implemented, sensors, screens and

recording tools require little human interference, even if the consequences of their scripts and

commands only become known after deployment. The political economy of data exhaust (A

Williams 2013) – or what I will call, using a more organic metaphor, data sweat – requires

deliberate strategies to overcome substantial power asymmetries (Brunton and Nissenbaum

2011). Informed by recent media studies documenting the environmental impact of machines

that produce, harvest and store Big Data (Maxwell and Miller 2012, Gabrys 2011) the second

part of this paper offers concepts that endorse responsible participation in a data economy. My

hope is that these terms may assist in holding the purveyors of our data accountable for their

actions.

In the move to a more material media studies (Gillespie et al. 2014), there has been a

hesitancy to draw together humanistic thinking with notions of the non-human, a blockage that

prevents an holistic account of labor in the digital conjuncture. 6 Bringing these two aspects of

data work together, I aim to demonstrate the combined relevance of humanities and social

science methods in highlighting the ethical dimensions of technology innovation, which include



the social consequences of data work at the level of the worker and her data. Given my position

within the tech industry, my sense of the overall landscape for Big Data is perhaps more

positive than others ; it is e tai l o e opti isti tha efe e e to De o d s Society of the

Spectacle would imply. The objective of this article is to suggest that, if the forms of

representation that commoditize our experience are today primarily visual (Halperin 2004),

then television and new media scholars have a unique and urgent role.

Visual pleasure and the rhetoric of data

The delight and comfort that can occur in the process of conceptualizing Big Data comes, at

least partially, from witnessing the achievement of large data sets represented at scale. The

aesthetic pleasure summoned in these various constructions of data – from word clouds to heat

maps or the color codes of quantification platforms – derives from their resolution of complex

information through visual rhetoric (cf Massumi 2005). Beautiful data is the esult of a

century of modernist thought dedicated to adjusting the ways we see, visualize and manage

i fo atio . As Halpe i ites, i the Weste t aditio , isio ope rates metaphorically as a

term organizing how we know about and represent t he o ld Halpe i , 19). It is:

a metaphor for knowledge, and for the command over a world beyond or outside or

subjective experience. To be seen by another, to see, to be objective, to survey, all these

definitions apply in etymology and philosophy to the Latin root —videre (ibid).

Shari g the sa e oot as the o d e ide e , isio is the o d that alig s t uth a d

knowledge in different historical moments. In the case of Big D ata isualizatio , it is a out



making the inhuman, that which is beyond or outside sensory recognition, relatable to the

hu a ei g… the fo ulatio of a i te a tio et ee diffe e t s ales a d age ts— human,

network, global, non- hu a Halpe i , 18). The tech industry competes to provide this

super-human insight via unique tools of data assembly. This explains why in corporate settings,

the possibility of data visualization is regularly celebrated at the expense of considering the

materiality of that which is processed. A recent company showcase provides a case in point.

At a demo booth illustrating the work of a research center dedicated to Big Data, onlookers

were encouraged to watch, electrified, as synchronized TV screens displayed dynamic images

and patterns panning out from a point of origin. The effect of this performance was doubtlessly

impressive, even if, to a lay viewer, the morphing blobs of color brought to mind little more

tha the la a la ps a d fashio s of s dis o. E gagi g the spe tato s isio , si ulati g the

e pe ie e of t a e si g if ot uite t ippi g th ough data, the de o se rved the purpose of

illustrating the vastness of the information being navigated. Yet when the presenter was asked,

hat is the data set e a e seei g? it e a e lea that the data itself as fi ti e. The e as

no actual sample underwriting the demo, it was just a demo . The source of the data was

irrelevant for a genre that only requires the indication of potential to achieve veracity. Like the

trade rituals of film and video production, the tech demo exists within a wider ecology of

subjunctive thinking that is the default mode of th e de elope fo u : a ea s fo imagining

– and showcasing – industrial possibilities on a liminal/corporate stage (Caldwell 2008, 105).

The affective properties of data visualization summoned by and through the demo bring to

mind previous examples of representing scale —the 1977 Ray and Charles Eames film, Powers of

Ten, being the most familiar. 7 In this sense, it was only fitting that a keynote speaker for the



2013 Association for Computing Machinery s Computer Human Interaction (ACM SIG-CHI)

conference in Paris was local sociologist, Bruno Latour. The e pa si e ie Latou chose to

critique in his address drew from his previous writing on monadology (Latour et al. 2012). This

work is informed by the ideas of Gabriel Tarde, and before him, Gottfried Leibniz, whose

mathematical modeling questioned neat distinctions between individual and collective

phenomena. At a conference dominated by discussions about Big Data, Latour challenged the

congregation of industry and academic researchers, many of whom had relied on the falla of

th e zoo i thei e pi i al elia e o data isualizatio . I Latou s argument, a collective

view provides no more accurate a representation than that of an individual – indeed, it is

precisely the move to an expansive view that threatens accuracy and specificity.

Latour s a ee -long investigations highlight the role played by tools in assembling vision. He

questions the status and veracity of scale as a means of authorizing vision, and points to the

labor left out of the frame, lens or medium through which we view representations of reality.

This app oa h a k o ledges the sele ti e atu e of that hi h is gi e i hat e thi k e

see. The tool of assembly (the camera, say, or the algorithm) has agency in shaping sight

towards certainties of apprehension. This recognition allows a degree of caution in thinking

about Big Data when to do so means becoming unusually enamored with vision. It also suggests

the relevance of aesthetics in explaining the role that visual pleasure plays in securing solace,

excitement and trust (Mulvey 1975).



Figure 1. Bruno Latour ’s closing plenary, ACM-SIG CHI, Paris, 2013.

The authority we attribute to scale is the result of historical accretion. According to Anna

McCarthy (2006), initial definitions of scale rested on the musical sense of capturing a sequence

of notes in order. Think of the gradually ascending tone structure of instruments we understand

to be producing notes higher as opposed to lower in pitch. Like climbing a ladder, the series or

progression implied in the idea of scale is a neat way to conceive relative order. We progress by

degrees through positions that are taken to be naturally equidistant. Of the 17th century

thinkers McCarthy determines as asserting this basic metaphysical hierarchy, Francis Bacon

brought mathematical systematicity to the idea of scale. Central to this is an understanding of



scale as proportion, which allows the significance of something to e o se ed si pl

o pa i g it to othe thi gs, ithout efe e e to e te al sta da ds of judg e t McCarthy

2006, 22). As a mode of reasoning, scale eventually stretched to influence not only practices of

mapping geographical territory but nascent ideas of political representation as well. Bearing

resemblance to a thing – for example, a constituency – confirmed the ability for something or

someone to stand in place of and for others. This was also the period in which scale took on

adjectival form. The consequences of this have proven resilient in the longer history of

episte olog . “ ale p o ides a e ha is of t a slatio , o appi g, hi h o e ts

material things and their representations in a precise, repeatable, and empirically known

relationship which extends to the process of representation in thought McCarthy 2006, 23).

Reason could move from the particular to the universal only as a result of these early

articulations, which bestowed an obvious logic to graduating concepts of measure.

I M Ca th s eadi g, s ale helps sta ilize a e essa il u k di hoto : the elatio ship

et ee ph si al o se atio a d e tal spe ulatio i i du ti e easo i g . F o spatial

representations of hierarchy (epitomized in the ladder) to dominant ideas of proportion (e.g.

the map), a critical leap is necessary to join individual phenomena and broader conditions.

Co st u ti g the idge et ee these t o easu es, s ale egula izes the p o ess of

knowledge production by implying that there is a proportional relation between the datum, the

defi ite a io , a d the ge e al a io McCarthy 2006, 24). The point here is that scale took

on the function of reason through an induction, which constitutes a rhetorical maneuver. To

summon the term scale is to mobi lize a th ead of a tio a d heto i a ti el o e ti g

thought a d thi g, o se atio a d spe ulatio McCarthy 2006, 25). The execution of this



link, and the continuum of empirical validity it suggests, is what we see playing out in tech

demos today. Presenting data at scale invokes an epistemological claim in the mere act of

display. It makes permanent what was once only plausible; a cultural pe fo a e of

meaning that, while lacking a sound empirical referent, bears the hallmarks of the

instrumental and inductive pe spe ti e favored in industry thinking (Caldwell 2008, 18).

Daniel Rosenberg (2013) offers another means by which to think historically about data s

rhetorical work. In previous centuries, he suggests, datu as u de stood a s something

given in an argument, something taken for granted. The obviousness of data, its taken-for-

granted-ness , e a ated f o the Lati o igi of the o d, hi h i the si gula ea s gift ,

o so ethi g that is gi e . In the domain of philosophy, religion, and mathematics, data was

used throughout the seventeenth-century to designate that category of facts and principles

that were beyond debate. It referred to things that were assumed, essential to, and hence

already known before a problem was introduced for discussion. Data contained the parameters

for thinking, the foundation upon which later deductions would take place. Data is not,

therefore, the same thing as fact. Data is something presumed prior to discussion; a framework

creating the possibility for discussion. It therefore already contains judgments and decisions

about what counts as a prior-ity (both priority and a priori share the same Latin root; priorities

a e take f o that hi h o es efo e . A data set , the , is al ead i terpreted by the fact

that it is a set , a o di g to T a is D. Willia s: so e ele e ts a e p i ileged i lusio ,

hile othe s a e de ied ele a e th ough e lusio , . Like M Ca th s et olog of

scale, these details draw attention to the cultural specificity of reasoning. Even within the

context of the English language, from previous usage we see that:



facts are ontological, evidence is epistemological, data is rhetorical. A datum may also

be a fact, just as a fact may be evidence. But, from its first vernacular formulation, the

existence of a datum has been independent of any consideration of corresponding

ontological truth (Rosenberg 2013, 18).

Rhetoric is a strategy of persuasion in the classical tradition. It is the art of convincing others

the veracity and truth of something in spite of selective emphasis and exposure. So while we

might continue to think of data as that which is given, as that which is regarded as bearing

t uth, e a see that the te s shifti g e phasis th oughout h istory removes considerations

of partiality. O l e e tl did it e o e t pi al to thi k of data as the esult of a

i estigatio athe tha its p e ise T Williams 2013, 33).

In the scripts tech workers perform during a de o, data s po e lies i th e assumption that it is

synonymous with fact. In the future-oriented mode of the genre, historicity is removed and the

benefits of the knowledge being assembled and transferred are commonsense. Taking a

production studies approach, the further rhetorical effect at play in this process is the

entrepreneurial imperative of the evangelist. If Caldwell warns of the dangers of industry-

supplied PR in the Hollywood scene, and develops scrupulous methods to contextualize

partisan spin, the digital optimism and venture capital-directed pitching that constitutes the

tech demo requires similar analytical precision. It is not just the urgency and brevity of the

encounter that illustrates the central role of rhetoric in this default industry ritual. In the

developer forum, the selective showcasing of products and prototypes creates its own

revelation, a preferred take on the best that a company currently has to offer. In these settings,

all encounters have the character of a pitch (Gill 2011), right down to the questions of



journalists and industry analysts whose career status rides in tandem with the quality of

insights and scoops provided by a o pa s star media performers. The hierarchy of access

constituting these events means it is never simply a matter of reporting objectively from the

showcase on offer but securing invitations to additional features and segments of

uninterrupted time with talent. Persuasion operates on a multitude of levels: in the data being

presented; in the scripted lines of the worker out front of the demo; in gaining access to what is

a heavily orchestrated display of the present and future of computing. It continues in to the

p ess iefi gs, T itte feeds a d olu i hes that o st u t the pu li s appa e tl

insatiable appetite for new media devices, technologies and apps. In addition to the visual

pleasure and power of data on display, then, the work involved in assembling and authorizing

the spectacle taking place within the convention center, tech campus or downtown hotel is

performed by a host of subsidiary workers acting after the fact, to one side, behind-the-scenes,

and after hours.

Data agents

If demo booths are a crucial site for the assembly and rhetorical illustration of Big Data s

commercial potential, the work that data does on our behalf – through data mining practices

and other forms of network analysis – is an already established area of concern for media

studies (e.g. Andrejevic 2013, Arvidsson 2011). From an industry perspective, the challenge

posed by the data economy is less to do with limiting the scope of algorithmic surveillance as it

is a race to define a profitable vocabulary for transactions that have the potential to bring new



opportunities for connection, exchange and wonder. 8 If the prospect of data forming social

relationships on our behalf brings untold risks, a business point of view sees infinite

possibilities. The proliferation of music recommendation services (Seaver 2013) and online

dating sites (Slater 2013) are just two of these convivial applications, in addition to the so-called

sharing economy. With data as our agent, matching information with or without our direct

involvement, algorithms create new matches, suggestions and relationships that we are unable

to achieve on our own. Data agents allow us to contemplate and revel in the possibilities

afforded by strangers (Bezaitis 2013), whose profiles and tastes might anticipate or assuage our

time-pressed needs. The very secrecy of online algorithmic sorting – the extent to which hook-

up sites and platforms flourish through the partial revelation of identities and locations, for

example – can foster collective social practices that mainstream cultures may not wish to draw

to light, presenting a boon for sexual and other minorities (Race forthcoming).

M use of the te data age t thus refers to occasions in which the sorting, categorizing and

matching capabilities of data algorithms act like a highly competent appendage, a publicist, or

even, to adopt some detective imagery, as our shadow. In the world of Cald ell s Hollywood, of

course, agents have their own role. Agents act behind the scenes – their work happens to the

side and in the background of stages upon which more visibly rewarding and profitable

performances take place. Yet the age t s work is essential in filtering a surfeit of information to

a manageable and actionable set of options, matching available opportunities with potential

investments. In the future already being built, the data we produce will be used to do

something similar, that is, to work through algorithms to make decisions in our best interests,

to sift out attractive or unsuitable options, and favor encounters that accord with previously



identified preferences. This is one way that data will entail new kinds of agency (if not an

actually-existing, incorporated agency, like the talent sco ut… although the e a e e it in

experimenting with this analogy too).

Decades ago, in The Presentation of Self in Everyday Life , Erving Goffman (1973) relied on a

similarly theatrical framework in his theory of region behavior. He divided social performances

into two realms: the front region, which was deemed to be action on show to a public, and the

back region, the site of relaxation and regeneration. Goffman suggested both regions host

carefully cultivated performances that respond to cues elicited and interpreted in their

respective settings. In the data society, a great deal of social work takes place off-stage, by non-

human agents, as a result of processing choices engineered by computers. These programming

decisions are made before any audience or user encounters the stage upon which

communication later takes place. In orchestrating the setting for an encounter, algorithms and

platforms are default editors for social messages. In assembling and choreographing the stage

for digitally mediated performances, they also incorporate the work of key grip and set

designer. An entire production studies lifeworld is employed in this complex infrastructure

through which our data is assembled, rendered visible and profitable. To recognize these layers

thus requires engaging at multiple levels, part of a broader project of understanding the worth

of elo the li e la o Ma e .

Data sweat

Yet the idea of data agents still presumes a degree of distance between the individual and the

information that circulates about an individual. It implies segregation as much as a process: I



give my data to someone or something that can use it, hopefully to my advantage. Any number

of events suggests the naivety of this aspiration, especially where there is a profit to be made. A

more accurate way to think about our relation to data that avoids this gift economy is through

the body. It is true, for example, that data may act like a shadow at times: our identifying data

casts a shadow when we place ourselves in the glare of certain platforms or transactions. When

recorded and processed at scale, data offers a rough outline of who we are and the form and

function of our digital projection for anyone motivated and literate enough to see. But this kind

of analogy suggests we have some say in the interactions we choose to make; that we can

predict, like the turning of the sun, the ways in which our data will be rendered visible and

available. Instead of the visual metaphor of the shadow, then, we might consider an alternative

and more visceral language to think past ocular-centric ideas of information sovereignty.

The idea of data sweat came to me in the course of giving a talk as a visiting speaker at a virus

protection company in Taipei. The topic for discussion was data privacy and security, and as we

were chatting, the air-conditioned building had a varied effect on the workers in attendance.

Sitting in the crowded room, each person had their own way of dealing with the pre-typhoon

heat, from fanning to slouching to wiping damp brows. Locals knew that any attempt to leave

the building to walk the mid-afternoon streets would lead to gross discomfort. This contextual

awareness led them to make all kinds of climate-dependent decisions, from choice of footwear

(no heels) to transport (train or taxi), or just staying late at the office. One of the most

enthusiastic audience members to introduce herself following my talk carried a tissue in hand

to ameliorate her facial sweat, a taken for granted part of her daily ensemble.



Sweat is a characteristically human trait. It is a vital sign that our bodies are working, even if

cultural norms differ as to how much this expression should be public. In some cultures, for

example, sweat can show enlightenment, possession or commitment. It can just as easily

suggest fear, anxiety or arousal. Given this, sweat can appear when we may not want it. A

whole industry of perfumes, deodorants and other innovations now accommodates the need

for disguise and masquerade in the process of maintaining social acceptability. Organic,

corporeal phenomena like sweat (but also microbes and genomes) 9 illustrate the existence of

data that is essential about us. This is data that speaks, albeit voicelessly, on our behalf. Sweat

literalizes porosity: it seeps out at ti es a d i o te ts that e a ish it did t. It a e a

annoyance or an accomplishment depending on the situation. But it is always a measure of our

participation, our vitalism, and our presence in the social. Sweat leaves a trace of how we pass

through the world, and how we are touched by it in return. It is the classic means by which the

od sig als its apa it to affe t a d e affe ted , to use “pi oza s te s. U de stood this

way, the labor we engage in as we exercise and exchange our data – especially in our efforts to

clean up our image, present a hygienic picture, and make ourselves look good – is a kind of

sweat equity for the digital economy. 10 It is a form of work we perform in the attempt to

control what is ultimately out of our capacity. 11

The current experience of Big Data is one in which powerful interests benefit from exploiting

this lack of control. Turning the frame from one of personal sovereignty to data sweat gives us a

better way of recognizing a rights-based contribution to this economy; it describes the

particular form of labor contributing to this common wealth (Hardt and Negri 2009). This is not

labor that can be measured in terms of hours worked on the clock. To paraphrase Gordon



Gekko: data e e sleeps . Data o k is e o d the easu e of lo k ti e , and yet, to the

extent that it generates profits that require compensation, it requires us to think about value

beyond measure. As Adkins (2009) argues:

While the break with the hegemony of clock time may lead to a break with certain kinds

of measure – especially those forms which operate externally to entities – this break

may also involve the emergence of new kinds of measure, specifically ones whose co-

ordinates may emerge from entities themselves.

Data exhaust

To move towards such an alternative way of thinking, I want to conclude by pushing the idea of

data sweat to a plausible endpoint, through the notion of exhaust. This is not to signal

exhaustion, since we have seen how data production and management takes place happily

backstage, with or without our conscious effort. But rather, if data is a trail that we leave in our

wake as a result of our encounters with the world and things, then this trail clearly has some

u desi a le effe ts. Withi the te h i dust , data e haust , o te tia data a es the

value that our presence retains after a unique transaction (A Williams 2013). It is used to

quantify the multiple applications that our digital identity provides beyond the gestures of an

initial performance; to build business models based on the profits predicted from behavior cast

by data. But exhaust is a term with further connotations, especially when thinking ecologically

about the hazards posed by the massive computation of data on an increasingly fragile

environment.



The clearest example of the environmental impact of Big Data is the investment in property and

ele t i it o e ui ed se e fa s that hold the o ld s see i gl i fi ite pa kets of

information. If data is the new oil, then data centers are the ports, wells and tankers. The move

to loud o puti g is othi g if o t a misnomer in this regard. Data that appears to be

pushed to some higher, opaque place requires enormous physical infrastructure on the ground.

To ignore these relationships, and the geopolitics they engender, is to perpetuate long-standing

asymmetries in the experience of computing (Pellow and Park 2002).

The further consequences of the data traffic moving between pipes and satellites across the

globe include the logistical transfer, freight, assembly and dis-assembly of always imminently

redundant hardware (Rossiter 2014). Activists are documenting the human impact of this

transport, manufacturing and scavenging ecology, from the labor camps attached to Foxconn

factories (Andrijasevic and Sacchetto 2013) to the coltan mines of the Congo. 12 As wealthy

countries ship toxic e-waste back to point of origin for disposal, the pleasures enjoyed through

new social networks generate an international chain of service and manual labor. To evoke the

legacy of an earlier moment of dystopic web theory, Big Data today translates to even bigger

data t ash K oke a d Wei stei .

Beyond the sovereign spectacle

An awareness of data exhaust invites us to take responsibility for the colonial legacy

underwriting Silicon Valley mythology (Dourish and Mainwaring 2012) – the material conditions

attached to the abstract philosophy of freedom through computing. If our ideas of data are to



remain wedded to the imaginary of prosthetics (something that is attached to, once it is taken

from us) then ideas of sweat and exhaust may yet prove to have mobilizing potential. They can

bring an assessment of environmental justice to bear upon the empowering mythologies

emanating from Silicon Valley. The view I advocate in this paper, then, is that notions of

personhood and sovereignty that perpetuate the fallacy that we can control our data will not

assist in the cause of advancing an ethical data economy. We need terms that account for

data s age i ta de ith the hu a o se ue es of this e ode of p odu tio . Fil

and television studies provide a register to explain this double movement, in which the

assembly of data and its apa it to a t o ou ehalf ea h i sta tiate a fo of elo the

li e la o .

In his classic account of The Gift (1990/1922), Marcel Mauss explains that nothing of value ever

really comes for free. The forms of obligation that accompany a gift are social and pressing.

They involve calculations of honor, status and reciprocity. To offer a gift is to offer a part of

oneself – the o je t is e e o pletel sepa ated f o the i stigato of the e ha ge. I a

highly mediated economy, in which data is often trade d ithout ou k o ledge, Mauss theory

takes an interesting twist. If we are never fully aware of the context in which our data is given,

the social bond that is formed lacks guidelines and nuance. The terms of obligation demanded

of the giver and receiver remain compromised and unclear.

To date, Big Data has appeared as a gift for tech companies seeking to reinvent themselves

from the triumphant years of desktop computing and lead the charge in to a new market for

software services, security and storage. As this frenzy has taken place, we have lacked a human

vision of rights in what is now regularly referred to as an Internet of Things. Television and



new media studies have always acknowledged connections between the worlds of business,

entertainment and everyday life and governance (Ouellette and Hay 2008, Andrejevic 2004,

Miller 2001). And just as audience studies needed the insights of production studies to square

the account, Big Data demands analyses that are attuned to both on-screen and behind-the-

scenes components of digital life. This paper identifies a vital role for new media theory in

encouraging better descriptions of data work. Applying media studies methods to Silicon Valley

not only expands the reach and purchase of these legacies for a new moment, it creates a new

set of political and ethical questions for the field. Writing from an industry position – from

inside the data spectacle – I hope to encourage greater numbers of voices and actors to engage

directly with those o ki g elo the li e i the data e o o , to speak loudl i suppo t of

different and more inclusive casting choices and participants, and to drive different possibilities

for computing and data processing from within. In the data industries of the future, a range of

skills and literacies are going to be necessary to maintain just and fair opportunities for all. As I

have shown, it is the rhetorical and visual effects of data compiled in the aggregate that

television and new media studies are especially well placed to assess. The aura enacted in the

performance of the data spectacle demands both theoretical precision and appropriate

accountability. It requires new rights to be imagined and secured for the mass of individuals

currently captured in – if not wholly captivated by – Big Data visions.



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Notes

1 I use the capitalized proper noun throughout in recognition of the special issue this article

joins. For more specific discussion and critique of the Big Data conjunction and its present

popularity see the collection of papers assembled from research in the Intel Center for Social

Computing in Maurer (forthcoming).

2 Writing this paper coincided with my first year as Principal Engineer in User Experience at Intel

Labs, USA. As co-director of the Intel Science and Technology Center for Social Computing, my

role is to work with academic partners across multiple universities on five organizing themes:

algorithmic living, creativity and collectivity, materialities of information, subjectivities of

information, and information ecosystems. These topics provide a framework for collaborative

research that guides industry professionals to better understand the social aspects of

computing that may be overlooked in traditional engineering approaches. This paper draws on

observations and conversations at a range of ISTC and tech industry events in the US, Europe

and Taiwan over a 12 month period. Specific conversations are acknowledged where possible.

3 While my key reference for this kind of industrial reflexivity is Caldwell (2004), another

inspiration for this paper is Georgina Born (2004), whose rigorous study of machinations within

the BBC was a source of consolation throughout my first year at a leading technology company.

4 I am indebted to Katie Pine for this term and ongoing observations of how instruments for

auditing, accountability and measure affect the everyday experience of a range of workers,



especially in the fields of healthcare and medical practice. See, for example, Pine and

Mazmanian (2014).

5 Cald ell s otio of p odu tio ultu e e plai s the ehi nd-the-scenes labor underwriting

Holl ood s p i a positio i the fil a d tele isio i dust . It also offe s a useful f a e fo

the unique configuration of cultural authority now emanating from Silicon Valley. Social

anxieties currently attached to tech work in the Bay Area bear an interesting correlation to

previous concerns about television. To name just a few: how each communication technology

(television vs. the internet) creates a new industry for targeted advertising; the overinflated

concentration of industry talent in one geographical area (LA vs. San Francisco); the celebrity

status of key participants (screen stars vs. hackers), and their exceptionalism in the face of

social norms; let alone the universalizing ideological aspirations of the industry as a whole,

hi h, as a fo of soft po e i i te atio al t ade a d diplo a , a ts as a i de of U“

imperialism. Thanks to Jason Wilson for helpful conversations on these points.

6 Referencing the new materialism risks conflating specific traditions of thinking which

encompass the actor-network theories and applications inspired primarily by the work of Bruno

Latour, various strands of materialism understood through Deleuzian vitalism (e.g. Braidotti

2013), German media theory traditions now most closely aligned with writers like Parikka

(2012), and object-oriented ontology (Harman 2002). In the ISTC for Social Computing, the

materiality of information theme has conducted research on auditing and measure that

accompany the quantification of society (see Nafus, forthcoming); it also refers to the material

practices of making, hacking and repurposing that are accompanying the rise of consumer DIY



electronics and maker culture. For another attempt to avoid binaristic thinking in labor theory,

see Qiu et al., 2014.

7 See http://www.powersof10.com/film . Accessed June 15, 2014.

8 The somewhat discordant experience of intimacy produced through this novel combination of

global communications infrastructure, logistics and system sorting is deftly captured in the

Fa e ook sloga , “hip Lo e “loa e .

9 Thanks to Lana Swarz for prompting this idea.

10 Tha ks to ke a de so fo the idea of s eat e uit , a d fo a othe fo s of suppo t

as I wrote this article.

11 Ellie Harmon takes this idea one step further to suggest that companies like Facebook are like

the bacteria that live on our bodies and sweat. Personal communication.

12 See http://www.gongchao.org/en/frontpage for updates on Foxconn in particular. Accessed

June 15, 2014. The Guardian has covered the ethics of coltan mining for several years: see

Taylor (2012) for a moving example. In January 2014, Intel CEO Brian Krzanich announced a new

indu st sta da d fo sou i g o fli t f ee i e als. “ee:

http://www.intel.com/content/www/us/en/corporate-responsibility/conflict-free-

minerals.html a d elated a ti is th ough the E ough p oje t:

http://www2.americanprogress.org/t/1676/campaign.jsp?campaign_KEY=6265. Accessed June

15, 2014.

http://www.powersof10.com/film



http://www.gongchao.org/en/frontpage

http://www.intel.com/content/www/us/en/corporate-responsibility/conflict-free-minerals.html


http://www2.americanprogress.org/t/1676/campaign.jsp?campaign_KEY=6265

http://www2.americanprogress.org/t/1676/campaign.jsp?campaign_KEY=6265



http://www.gongchao.org/en/frontpage


melissa gregg inside the data spectacle

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