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Big Data: accumulation and enclosure

Jodi Dean

On May 1, a working group instructed by US President Barak

Obama to undertake a 90 day study of big data and privacy issued

its report. The group was headed by the President’s Counselor,

John Podesta, and included senior administration officials such

as the Secretaries of Commerce and Energy. The report’s cover

letter opens dramatically: “We are living in the midst of a

social, economic, and technological revolution.” The White House

blog linking to the report similarly pronounces:

While big data presents new challenges, it also

presents immense opportunities to improve lives; the

United States is perhaps better suited to lead this

conversation than any other nation on earth. Our

innovative spirit, technological know-how, and deep

commitment to values of privacy, fairness, non-

discrimination, and self-determination will help us

harness the benefits of the big data revolution and

encourage the free flow of information while working

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with our international partners to protect personal

privacy.i

When I first read the term ‘revolution,’ I rolled my eyes.

This is ideology on stilts, and not least because the US is

significantly behind the EU with respect to data protection

regulation.ii Replace big data in the above quote with Web 2.0,

the internet, and personal computing and you have the basic form

for the techno-optimistic soundbites of the last forty years.

As I carried out this little thought experiment, though, I

realized that I was missing the point. These sentences from the

White House are not just hype. They are true. We have been

experiencing ongoing technological revolutions (be sure on Marx

slide):

The bourgeoisie cannot exist without constantly

revolutionizing the instruments of production, and

thereby the relations of production, and with them the

whole relations of society. Conservation of the old

modes of production in unaltered forms, was, on the

contrary, the first condition of existence for all

earlier industrial classes. Constant revolutionizing of

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production, uninterrupted disturbance of all social

conditions, everlasting uncertainty and agitation,

distinguish the bourgeois epoch from all earlier

ones.iii [change slide, #11]

Capital as a class can do nothing but seek new avenues for

profit, new opportunities for accumulation. It engages in

creative destruction, looking for new markets, develops new

commodities, struggles with labor over wages and hours, and tries

to find new ways to extract value. So the White House

announcement of a revolution needs to be taken at its word,

recognized as a statement of class struggle, and understood as

made in the interest of capital as a class.

This leads, then, to the questions I will focus on today:

1. How does big data benefit capital as a class? What is

its role in class war?

2. How should we understand the emphasis on individual

privacy that comes up in so many discussions of big data?

The second question connects to ideology. Since big data is

always accompanied by crazy, unfathomable numbers, always

associated with algorithms and complexity and technologies

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interacting in ways beyond our understanding and control, it’s

odd that the policy question of big data would be concentrated

into the singular figure of the individual and her privacy. [at

slide #16] What does the individual have to do with so much

bigness? How can the individual and her wants or choices count at

all in a setting measured in exabytes and zettabytes and where

multiple data sources flow together into pools and lakes that can

be algorithmically filtered to reveal patterns answering

questions she never imagined someone could ask? Why is privacy

the name for the issue big data poses?

The Podesta report on big data and privacy recognizes this

mismatch when it says that “the most common privacy risks today

still involve ‘small data’—the targeted compromise of, for

instance, personal banking information for purposes of financial

fraud.”iv Moreover, a second report on big data from the

President’s Council of Advisors on Science and Technology (PCAST)

issued at the same time as the Podesta report makes it clear that

individuals no longer have a reasonable expectation of privacy

when it comes to personal data. The PCAST report asserts that,

given the large benefits of “near-ubiquitous data collection” as

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well as how data collection fuels “an increasingly important set

of economic activities,” thinking we can limit data collection is

the wrong approach. Technology’s and economy’s own imperatives

determine our options. For example, we can’t even know what data

is stored on an individual much less verify that any data has

been destroyed. So this report recommends that regulatory focus

be placed not on data collection or analysis but on use: does the

use of data harm an individual or class of individual?v

Given these open acknowledgements of privacy’s supercession,

the White House’s emphasis on personal privacy seems like

displacement. Obama called for the study of big data and privacy

in a speech on January 17, 2014. That speech was primarily

concerned with the National Security Agency and the controversy

that erupted in the wake of the outpouring of information leaked

by Edward Snowden regarding NSA surveillance (slide #25). The

leaks included evidence of the PRISM program (whereby the NSA

directly accesses data held by Google, Apple, Facebook, Skype,

Microsoft and others), evidence that the NSA collected internet

metadata in bulk from 2001 to 2011, evidence of the NSA’s storing

of metadata on millions of web users, evidence that it regularly

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monitors the mobile phones and internet traffic of foreign

leaders, diplomats, and corporations, evidence that is collects

data from Angry Birds and has infiltrated massive multiplayer

online games, and more. In his January 17th speech, Obama

addressed concerns with the bulk collection of phone metadata,

trying both to reassure “ordinary Americans” that the government

was not examining their phone records and to justify what the NSA

was doing in the interest of keeping Americans safe and “not

repeating the tragedy of 9/11.” Incidentally, using 9/11 to

justify massive surveillance was included on an NSA list of

talking points, obtained by Al Jazeera through a Freedom of

Information Act request.

In the January 17th speech Obama issued a new presidential

directive on overseas surveillance that said that the US does not

collect information to suppress dissent nor does collect

information to try to provide a competitive advantage for US

companies. NSA documents leaked by Snowden had suggested

otherwise. Some of the documents treat political and legal

opponents to the US drone program as national security threats.

Further, the NSA spied on negotiators in advance of the 2009

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climate summit, which ended in disarray after the US tried to ram

through an agreement most other countries rejected. vi The NSA

targeted financial transactions in the Visa and SWIFT networks.

It regularly sweeps up data from foreign corporations such as the

giant Brazilian oil conglomerate, Petrobras. Drawing from

documents leaked by Snowden, a Brazilian television report

“emphasized that the company controlled vast quantities of data

on Brazil’s offshore oil fields.”vii The NSA treats securing

access to fossil fuels and securing an economic advantage over

Japan and Brazil as primary strategic objectives.viii [slide #37]

Given NSA activities, it isn’t surprising that the Podesta group

putting together the report on big data and privacy included the

Secretaries of Commerce and Energy. Given the description of the report

and its goal of protecting personal privacy, however, it is surprising that

there was no one in the working group from, say, the Civil Rights

section of the Justice Department, or the American Civil

Liberties Union, or the Center for Constitutional Rights. This

disconnect is thus one indication of displacement.

The Snowden leaks are the background against which Obama

announced that he had instructed Podesta, along with PCAST (the

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President’s Council of Advisors on Science and Technology), to

undertake two separate reviews of big data and privacy. Rather

than really focusing on privacy, he is trying to build trust. He

wants to reassure “ordinary Americans” that they don’t need to

worry about the NSA. And he is trying to do this by shifting

focus onto something Americans understand, namely, their own

personal activity and self-interest. Personal privacy takes

pressure off the NSA, displacing it onto the private sector where

data can be linked to consumer benefit and economic growth. The

report put together by Podesta’s group, “Big Data: Seizing

Opportunities, Preserving Values,” is explicit here: “This report

largely leaves issues raised by the use of big data in signals

intelligence to be addressed through the policy guidance that the

President announced in January.”ix It looks instead at big data

as an economic resource, albeit one that brings with it potential

risks. With the shift in focus, big data becomes something quasi-

natural, generated through everyday use of everyday items in

everyday tasks. It is also inevitable – an outgrowth of

technological development. Understood as natural, growing, and

inevitable, big data is, potentially, a resource for immense

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social good. Because it can reveal patterns and “deliver incisive

results,” big data can drive new economic opportunities. The

report insists: “No matter how serious and consequential the

questions posed by big data, this Administration remains

committed to supporting the digital economy and the free flow of

data that drives innovation” (pg. 9). The report takes as its aim

encouraging “the use of data to advance social good, particularly

where markets and existing institutions do not otherwise support

such progress,” while at the same time protecting core values

(which will have to adapt).

The language of privacy in the Obama administration’s

discussion of big data displaces concern from surveillance to

social good and from government in its national security role to

government in its economic role. Even as energy and commerce

underlie both, concern is condensed in the figure of the

individual whose privacy is to be protected. How this individual

is imagined should tell us something about the ideological field

of which big data is a part. As I will show, the individual is

rarely a worker, figured in face via the constitutive absence of

workers. The preoccupation with privacy thus not only echoes the

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refrain of US liberal individualism but does so symptomatically,

indexing the proletarianized individual trapped in communicative

capitalism and unable to do anything but perpetually give herself

up to the algorithms of big data analytics. She produces for

capital whether or not she receives a wage. More fundamentally,

that concern with big data is condensed into the individual form

gives us insight into communicative capitalism’s enclosure and

expropriation of the common via capture and analytics. Access to

the common, to the flows and relations of sociality, is what big

data promises. And it can only deliver on that promise by

pulverizing it into and tracing it through the ever more granular

movements of the individual. To paraphrase Karl Polanyi, the

individual fiction, “therefore, supplies a vital organizing

principle” (76), that is, the fiction of what is valued and

protected even as it is generated as mechanism for embedding into

the social substance capitalism’s means of expropriation.

1. Communicative capitalism [slide #48/ change to 49, monopoly

banker]

Communicative capitalism designates that form of late

capitalism in which values heralded as central to democracy take

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material form in networked communications technologies. Ideals of

access, inclusion, discussion and participation come to be

realized in and through expansions, intensifications and

interconnections of global telecommunications. In communicative

capitalism, capitalist productivity derives from its

expropriation and exploitation of communicative processes. This

does not mean that information technologies have replaced

manufacturing; in fact, they drive a wide variety of mining,

chemical, and biotechnological industries. Nor does it mean that

networked computing has enhanced productivity outside the

production of networked computing itself. Rather, it means that

capitalism has subsumed communication such that communication

does not provide a critical outside. Communication serves

capital, whether in affective forms of care for producers and

consumers, the mobilization of sharing and expression as

instruments for “human relations” in the workplace, or

contributions to ubiquitous media circuits.

Marx’s analysis of value in Capital helps explain how

communication can be a vehicle for capitalist subsumption. In his

well-known discussion of the commodity, Marx considers how it is

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that different sorts of goods can be exchanged with one another.

His answer is human labor; understood as quanta of time, labor

renders different goods commensurable with one another. But how

is this possible? Why would an hour of mining labor be

commensurate with an hour of farming labor? The answer to this

question involves the fundamentally social character of labor.

What is common to different kinds of human labor is that they are

all labor in the abstract, components of the larger homogeneous

mass of human labor. Labor, and hence value, is, inextricable

from the relations of production and reproduction constitutive of

society. Products of labor are “crystals of this social

substance, common to them all,” that is to say, values.

Communicative capitalism seizes, privatizes, and attempts to

monetize the social substance without waiting for its

crystallization in products of labor. It doesn’t depend on the

commodity-thing. It directly exploits the social relation at the

heart of value. Social relations don’t have to take the fantastic

form of the commodity to generate value for capitalism. Via

networked, personalized communication and information

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technologies, capitalism has found a more straightforward way to

appropriate value.

One of the clearest expressions of communicative

capitalism’s direct exploitation of the social substance is

Metcalfe’s Law: “The value of a communications network is

proportional to the square of the number of its users.” The basic

idea is that the more people using a network, the more valuable

it is. The truth in Metcalfe’s Law is its association of value

with the communicative network itself. Value is a property of the

relations, the links, between and within pages. Google’s PageRank

algorithm, for example, is one of most successful information

retrieval algorithms because it takes linking into account,

mining and extracting common knowledge PageRank puts to use the

fact that networked communications are the form of capitalism’s

subsumption of the social substance to its terms and dynamics.

Matteo Pasquinelli thus argues that “Google is a parasitic

apparatus of capture of the value produced by common

intelligence.” He treats the prestige that PageRank attends to

(and reflexively enhances) in terms of the “network value” of any

given link. “Network value” describes a link’s social relations:

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How many other links is it related to? Are those links related to

other links? How many? Google captures this value, the link’s

social substance, its place within a general system of social

relations. [slide #56, yoda, change to next].

And this leads us back to big data. Communicative capitalism

subsumes everything we do. It turns not just our mediated

interactions but all our interactions into raw material for

capital.x Financial transactions, interactions that are caught on

video or are photographed, gps location data, RFID tags, as well

as , soon, the data generated by the multiple small ubiquitous

sensors in what is called the internet of things encloses every

aspect of our life into the data form. A few years ago we might

have understood this as a communicative commons. Now, with the

absorption of a wide array of forms of unstructured data into

massive data pools, it’s clear that we are dealing with something

even more all encompassing. Big data is the capitalists’ name for

this material that Marx understood as the social substance.

2. Data dispossession

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Two metaphors stick out in big data rhetoric: data as oil

and data as gold, that is to say, fuel and money, energy and

commerce, something that powers and something that circulates.

Both metaphors link data with the opportunity for immense profits

and the rush for these profits that ensues.xi Both treat data as

a natural resource. And both highlight the fact that this natural

resource has to be worked on, whether refined and processed out

of a deluge substance or identified and mined from within a

dense, useless, mass. The oil metaphor first appeared in 2006.xii

Gold is later, around 2012, in the context of reports

acknowledging the challenges executives were facing in drawing

actionable information from their massive stores of unstructured

data.xiii

The oil and gold metaphors are telling in that they identify

big data as the natural resource on which communicative

capitalism relies. This resource, produced by all in common, is

being seized, enclosed, and privatized in a new round of

primitive accumulation. Everyone probably recalls Capital’s famous

Part VIII where Marx discusses the “historic process of divorcing

the producer from the means of production.” This process

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involved the forcible enclosure of the commons. Landlords,

assisted by the law, expropriated for themselves what had

belonged to the people in common. Property based on the labor of

the owner is thereby replaced with property based on capitalist

ownership. In Marx’s words, “the pigmy property of the many” is

concentrated into the huge property of the few.”

David Harvey rightly points out that far from existing

outside of capitalist processes as some sort of origin, the

practices associated with primitive accumulation coexist with

capitalism. He thus emphasizes accumulation by dispossession,

associating various schemes of privatization, financialization,

and commodification with a new enclosure of the commons.xiv

Dispossession, rather than happening all at once, is an ongoing

process.

No one will deny the ongoingness of data dispossession.

Sometimes it is blatant: the announcement that our call will be

monitored for quality assurance, the injunctions to approve again

Apple’s privacy changes, the necessity of renewing passwords and

credit card information. Sometimes the ongoingness is more

subtle, naturalized in maps, GPS signals, video surveillance, and

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the RFID tags on and in items we purchase. And sometimes the

ongoingness is completely beyond our grasp, as when datasets are

combined and mined so as to give states and corporations

actionable data for producing products, patterns, and policies

based on knowing things about our interrelations one to another

that we do not know ourselves. Here the currents of lives as they

are lived are frozen into infinitely separable, countable, and

combinatory data-points.

There is a, however, strangeness to data dispossession that

differentiates it from the dispossession that accompanies debt,

privatization, foreclosure. It’s not as if we no longer have our

location when our location data is sold to advertisers in a real-

time auction. We still have our names and email addresses when we

provide them in exchange for access to a website. [slide #72]

It’s not even that we somehow lose control over our names,

addresses, and other identifying information – such control has

always been a myth that treats markers that pinpoint us for state

and capital as crucial to a similarly fantastic vision of deep,

unique, and authentic individual self. We are always already

deeply imbricated with others such that their thoughts and

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feelings, desires and drives are inseparable from our own. Where

we search for uniqueness, we always find the Other. More

prosaically, rumors and gossip circulated long before the

internet. The dispossession of big data, then, is not about

control of our individual identities.

Rather we are dispossessed of a certain kind of temporality

and a certain kind of being together. These are expropriated from

us and put to alien use. Two kinds of temporality are

expropriated: the momentary and the futural. We lose the momentary

because everything leaves a storable trace. Moving through space

with a mobile phone creates data. Touching a screen, looking at a

screen, creates data. Rather than a time of instants, we have a

time of permanents. In this time, mistakes, errors, and lies

coexist with corrections. Learning, falsifiability, become

attributes of systems, of algorithms, rather than dimensions of

meaning. We lose a dimension of futurity in that a primary mode of

data analysis is predictive: the search for patterns is in order

to predict, and intervene in, the future, even if that future is

only microseconds ahead, as happens in high frequency trading, or

days and weeks ahead, as in the case with the NSA climate summit

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spying. In each case, data analytics try to eliminate surprise,

the very possibility that something could happen inseparable from

futurity. I should add that the shortening of the future into

ever smaller bits of time may also be part of data dispossession.

I say this because of the failure of big data to result in

significant long-term predictions. Although Google has been

credited with being able to predict flu trends, it over-estimated

US cases in 100 out of 108 weeks. Relying on CDC data on actual

cases yielded better results.xv An MIT scientist claimed to be

able to use Twitter to predict social unrest, but his evidence

was retroactive. And, the National Consumer Law Center looked at

Silicon Valley backed start-ups using big data to generate new

loan products for people with poor credit history. According to

an article in the Wall Street Journal it concluded that “big data

doesn’t make a big difference.”xvi

Data dispossession likewise changes our modes of being

together in that now our being together is for the private

benefit of another. In the words of a 2014 World Economic Forum

report on big data, “Our collective discussions, comments, likes,

dislikes, and networks of social connections are now all data,

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and their scale is massive.”xvii Sociality – and not just person

to person but persons to animals, environment, and things – is

enclosed, analyzed for past patterns and held for future ones in

the interest of squeezing out some competitive advantage.

Communication, culture, and care are seized and tagged. We can do

nothing that is not already for capital. To invoke words from

Karl Polanyi, data dispossession separates “the people from power

over” our own communicative life. And the way that this matters

is not individual but collective, our common power, exercised in

multiple, fluid, indirect, and uncertain ways, over the relations

we create in common.xviii

Big data pushes for data driven analytics, prediction, and

decision-making for everything but its own adoption. That is to

say, even as the big data mantra is that with the proper

analytics big data can provide real, actionable knowledge, the

move to big data is itself exempt, unbacked by either big data or

analytics that prove its benefits. Its promises remain

provisional, speculative, the matter of multiple small

experiments and an enormous push by governments and the

information and telecommunications sector. A skeptical Wall Street

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Journal report as recent as 2012 notes the failure of an early

Pentagon big data test. Battlefield commanders were provided

“exquisite situational awareness,” that is, information about

everyone’s whereabouts. “What they found was that just giving bad

generals more information didn't make them good generals; they

were still bad generals, just better informed.”xix Nevertheless,

the installation of big data as an economic force isn’t itself

subject to big data, much like the emergence of an economy rooted

in commodity production wasn’t itself the product of markets.

3. Capital accumulation

Understood as the basic resource of the knowledge economy,

big data has the characteristic of being self-renewing. It’s

inexhaustible, co-extensive with the reproduction of social life.

It reaches through and beyond work, even beyond the reproduction

of workers, into the social substance itself. The Podesta report

asserts that big data can “help create entirely new forms of

value.xx”

It’s worth considering this claim closely. Nothing in the

report would lead one to conclude that its authors have in mind

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something like the collective benefit that accrues from common

modes of being or the strength that emerges out of pulling

together to address the global challenges of climate change and

economic inequality. Rather, they seem instead to have in mind

capitalist value or value that leads to capital accumulation.

That it is taking a “new form” could mean that it is value that

exceeds the wage relation and even the property relation, which

is what I’ve been arguing in terms of communicative capitalism’s

direct expropriation of the social substance in the form of big

data. But this probably isn’t what they have in mind either. They

probably are just thinking of new opportunities for capital

accumulation. A contribution to the World Economic Forum Global

Information Technology Report 2014 on the rewards and risks of

big data invokes value in this sense when it describes the

potential for gains of 14.4 trillion dollars in added value in

the commercial sector over the next ten years:

This opportunity exists in the form of new value

created by technology innovation, market share gains,

and increasing competitive advantage. It translates

into an opportunity to increase global corporate

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profits by approximately 21 percent, driven by

improvements in asset utilization (reducing costs and

improving capital efficiency), employee productivity

(improved labor efficiency), supply chain logistics

(eliminating waste and improving process efficiency),

customer experience (adding more customers), and

innovation (reducing time to market).xxi

Value here is a matter of global corporate profit, of capital

accumulation by the capitalist class. It accumulates from cutting

the labor force (“reducing costs” and “supply chain logistics” in

their terms), squeezing the remaining workers (“improved labor

efficiency”), trying to get people to spend more money, and

becoming more competitive. This last benefit is necessarily short

term. Even if big data gives a competitive advantage to early

adopters, as it becomes standard, that competitive edge will

diminish; this is the case in the adoption of any technology.

Consider, though, a couple of the big data experiments that

have generated corporate value. Bank of America put tracking

sensors on ninety workers and discovered that the most productive

workers frequently engaged with their colleagues. The bank

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started mandating group breaks and saw a ten percent productivity

increase.xxii UPS installed sensors as well as GPS in its trucks

in an effort to increase efficiency and control costs. Data on

more than 200 elements is collected, including truck speed,

number of times the truck is put in reverse, driver seat belt

use, the length of time a truck is idling.xxiii It can now decrease

fuel consumption while increasing the number of deliveries per

truck.xxiv A Forbes article on workforce analytics indicates the

importance of big data for “controlling labor costs,” which for

the healthcare, education, and service industries is upward of 50

percent of operating budget. Many such enterprises “track the

time employees arrive, what they do at work, when they leave for

breaks, the times they call in sick, schedule details, personal

information and much more,” writes the author, Bill Barlow.

Workforce analytics lets a company use this information “to

optimize its labor force by scheduling the right mix of full-

time, part-time and temporary labor on a variety of schedules.”xxv

Another way to make the same point: big data increases worker

precarity as it enables companies to do more with less.

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Approached in terms of class struggle, big data looks like

further escalation of capital’s war against labor. If earlier

ways of automation displaced industrial workers, big data

portends the displacement of post-industrial or knowledge

workers. It should come as no surprise, then, that education and

health care, two of the last remaining sites of intensive, face-

to-face relatively high-paying labor are often singled out in

media, industry, and governmental discussions of big data. An

excellent study, “The Future of Employment,” by Carl Benedikt

Frey and Michael A. Osborne, explains why this is the case.xxvi

Historically, those tasks could be computerized which followed

clear, rule-based routines. Too many variables meant too hard to

program. More recently, though, there have been technological

breakthroughs wherein non-routine tasks could be turned into

well-defined problems. And this becomes possible because of data.

As Frey and Osborne write:

Data is required to specify the many contingencies a

technology must manage in order to form an adequate

substitute for human labor. With data, objective and

quantifiable measures of success of an algorithm can be

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produced, which aid the continual improvement of its

performance relative to humans … As a result

computerization is no longer confined to routine tasks

that can be written as rule-based software queries, but

is spreading to every non-routine task where big data

becomes available.”xxvii

Examples include the development of voice recognition capacities

that enable call centers to replace people with algorithms,

Google’s driverless cars, as well as developments in robotics.

They include as well the kind of knowledge work previously seen

as invulnerable. Massive amounts of data enable the automation of

an array of decision-making tasks: medical diagnoses and

treatment, fraud detection, legal services, ad design, purchase,

and placement, stock-trading. The struggle over education,

particularly with MOOCs and efforts to “personalize” student

learning by conducting ever more of it on screens, is still in

its early stages. Frey and Osborne find that “47 percent of total

US employment” is at high risk of being automated within the next

two decades.xxviii I’ll add that they put most jobs in healthcare

and education at relatively low risk for automation insofar as

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they require assisting and caring for others, persuasion, social

perceptiveness, and manual dexterity. Perhaps not surprisingly,

CEOs do not also to appear to be at risk.

The value in and of big data is for capital, not for the

people from whom it is expropriated. One of the contributions to

the World Economic Forum 2014 report is explicit on this point.

The authors, Peter Haynes and M-H Carolyn Nguyen, note that “the

greater the role that data play in the global economy, the less

the majority of individuals will be worth.” In fact, “this could

mean that a data-driven economy may become a contracting

economy.”xxix Although Haynes and Nguyen propose various schema

for remunerating people for their data, when they say that “the

concept of fair value exchange no longer exists,” they imply that

the train has already left the station (ship has sailed, horse is

out of the barn?). Many of us already give away massive amounts

of data, and “corporations are making significant profits as a

result because their cost of materials is essentially zero.”xxx

They quote Jaron Lanier, “The dominant principle of the new

economy, the information economy, has lately been to conceal the

value of information…. We’ve decided not to pay most people for

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performing the new roles that are valuable in relation to the

latest technologies. Ordinary people ‘share,’ while elite network

presences generate unprecedented fortunes.”xxxi

4. The individual

I should try, if I can, to get back to the Obama

administration’s reports on big data and privacy. They don’t have

anything to say about the impact of big data on workers. The

Podesta report presents individuals primarily as consumers

(secondarily as citizens) whose access to free goods on the

internet is subsidized by the marketing and advertising

industries (pg. 50). These consumers are primarily at risk of

discriminatory uses of their data, and the report recommends that

expertise be developed to be able to determine if this occurs.

The PCAST report, however, is different. The second chapter

of the report sketches “examples and scenarios” of the benefits

of big data. It begins with an unstructured list of some big data

experiments. These include differential pricing (the practices

that construct prices according to consumer rather than item or

service), license plate readers that alert police if a vehicle

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associated with an outstanding warrant passes, and inferences

regarding a person’s emotional state based on subtle changes in

facial coloration.

The report then groups its examples and scenarios around

health, education, and the “special status of the home,”

emphasizing the benefits of big data while also acknowledging

potential risks to privacy. The healthcare section highlights

“personalization.” It associates “personalization” with

researchers’ access to millions of health records as well as the

hypothetical use of a mobile device connected to “a personal

assistant in the cloud” capable of detecting Alzheimer symptoms.

Through the device, data about the patient’s medications,

symptoms, and habits is produced and uploaded into the cloud.

Personalization should thus not be confused with personal. There

is nothing personal here. Instead of interacting with a person,

who would need to be paid, a patient uses a mobile device and a

cloud assistant like Siri or OK Google. The report mentions Siri

and OK Google directly, perhaps because Google’s Executive

Chairman, Eric Schmidt, was a member of the big data and privacy

working group, along with a senior advisor to the CEO of

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Microsoft and the founder and managing director of Zetta Venture

Partners, a venture capital fund focused on analytics.

The education section is interested in “innovative pedagogy,

but not teaching. Teachers and professors are completely missing.

What appears instead are MOOCs and the opportunity to compile

longitudinal data on millions of students. The pedagogical

innovation seems to be turning education into an opportunity for

data production.

This will include not just broad aggregate information

like grades, but fine-grained profiles of how

individual students respond to multiple new kinds of

teaching techniques, how much help they need to master

concepts at various levels of abstraction, what their

attention span is in various contexts, and so forth. A

MOOC platform can record how long a student watches a

particular video; how often a segment is repeated, sped

up, or skipped; how well a student does on a quiz; how

many times he or she misses a particular problem; and

how the student balances watching context to reading a

text . . . With this information, it will be possible

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not only to greatly improve education, but also to

discover what skills, taught to which individuals at

which points in childhood, lead to better adult

performance in certain tasks, or to adult personal and

economic success.xxxii

In the absence of teachers, this looks like a transfer of wealth

from education to technology. It also looks like an

intensification of surveillance. Every moment of a child’s

education becomes part of a data stream feeding an assessment of

adult performance. Schools and districts become data farms,

providing an unending supply of harvestable data.

The section on the home draws out the data to be produced by

the internet of things: air quality and environmental sensors,

motion detectors, commodities signaling their presence,

“omnipresent audio and video collection.” The home appears as a

site of multiple appliances intensifying the dependence and

vulnerability of the in-data-ed person. On the one hand, the

appliances seem to enhance their occupants’ quality of life:

Netflix, Amazon, Microsoft Kinect, and Google Glass all make an

appearance as the occupants are imagined playing games and

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consuming media. The report asks: “If the camera in your

television knows what brand of beer you are drinking while

watching a football game, and knows whether you opened the bottle

before or after the beer as, who (if anyone) is allowed to sell

this information to the beer company, or to its competitors?”(pg

16). On the other hand, the occupants are positioned as

vulnerable to intrusive controls: environmental monitors that

detect heroin smoke and introduce possibilities of police

notification and insurance cancellation, landlords that make the

presence of multiple sensors requirements for renters. The home

is a data factory. To live in it is to produce data, whether one

wants to or not.

In his 2007 book, iSpy, Mark Andrejevic uses “digital

enclosure” as a term for “the creation of an interactive realm

wherein every action and transaction generates information about

itself.”xxxiii He describes the processes whereby people submit to

monitoring in order to enjoy the benefits of using a mobile

phone, making a purchase on the internet, or digitally recording

a television show. And he draws the powerful connection between

early twentieth century workplace management techniques using

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monitoring to increase worker efficiency and early twenty-first

century forms of domestic monitoring to increase consumption.

Andrejevic writes, ‘Convincing members of the public to submit to

this type of monitoring, or rendering surveillance so passive and

invisible that they may not recognize it, remains the challenge

of the twenty-first century.”xxxiv The PCAST report adds a third

option: making data production and monitoring a condition of

being so that choice and consent no longer make sense as options.

Conclusion

I’ve argued that the erasure of the worker in the Obama

administration’s recent reports on privacy and big data is

symptomatic of a focus on capital accumulation from the

perspective of capital as well as on a view of data as a common

resource that, under communicative capitalism, we can’t not

produce. This leads me to a final element in the PCAST report:

Taylor Rodriguez. Taylor Rodriguez figures in the future scenario

that closes the discussion of events and scenarios. She is the

one worker who appears in the report and so gives us insight into

how the privacy and big data working group imagines workers. In

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the scenario, she doesn’t appear in a specific workplace. She’s

going on a business trip. She seems to be quite successful:

She packed a bag the night before and put it outside

the front door of her home for pickup. No worries that

it will be stolen: The camera on the streetlight was

watching it; and, in any case, almost every item in it

has a tiny RFID tag . . . Nor is there any need to give

explicit instructions to the delivery company, because

the cloud knows Taylor’s itinerary and plans . . . At

the airport, Taylor walks directly to the gate – no

need to go through any security . . . There are no

boarding passes and no organized lines. Why bother,

when Taylor’s identity (as for everyone else who enters

the airport) has been tracked and is known absolutely?

When her known information emanations (phone, RFID tags

in clothes, facial recognition, gait, emotional state)

are known to the cloud, vetted and essentially

unforgeable?xxxv

The report acknowledges that this might seem creepy to us. But it

concludes nonetheless that in this world of “the cloud and its

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robotic servants … major improvements in the convenience and

security of everyday life” have been possible.

Competition in communicative capitalism is intensifying.

Corporations look for advantages, using big data and workplace

analytics to squeeze out new efficiencies so they can get more

with fewer employees. Advertisers try to get closer and closer to

the customers, using data from social media, credit cards, search

patterns and click streams to dream up ways to get attention,

make sales. In the finance sector, big data drives high frequency

trading and other questionable banking practices. New big data

efforts in health and education create even more opportunities

for data production, reaching behind the backs of paid labor as

they render ever more practices into generative opportunities for

the data resource necessary for trillions of dollars of new

capital accumulation. Taylor Rodriguez looks like one of the

winners in this economy. Served by hordes of personal digital

assistants, everything flows smoothly, seamlessly. The real

workers making her life possible –picking up the bag, driving the

car, staffing the airport, flying the plane, not to mention the

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mass of those unemployed because of developments in technology --

don’t have to appear at all.

i http://www.whitehouse.gov/blog/2014/05/01/findings-big-data-and-privacy-working-group-reviewii http://www3.weforum.org/docs/WEF_GlobalInformationTechnology_Report_2014.pdf pg.76iii Marx, The Communist Manifestoriv P. 21 Podesta reportv http://www.whitehouse.gov/sites/default/files/microsites/ostp/PCAST/pcast_big_data_and_privacy_-_may_2014.pdf xii-xiiivi http://www.theguardian.com/environment/2014/jan/30/snowden-nsa-spying-copenhagen-climate-talksvii http://www.nytimes.com/2013/09/09/world/americas/nsa-spied-on-brazilian-oil-company-report-says.html?_r=0viii NYT Nov 2 2013 No Morsel Too Miniscule for All-Encompassing NSA, Scott Shane http://www.nytimes.com/2013/11/03/world/no-morsel-too-minuscule-for-all-consuming-nsa.htmlix Podesta report pg 9x WEF report 35, data as resource, new input like labor and capitalxi http://tech.fortune.cnn.com/2013/09/04/big-data-employment-boom/xii Clive Humby 2006, said “Data is the New Oil” adopted in 2011 by World Economic ForumData is the new oil, Michael Palmer, http://ana.blogs.com/maestros/2006/11/data_is_the_new.htmlxiii Kronos report, “data+analysis+action=gold” http://www.kronos.com/showAbstract.aspx?id=12884902528&rr=1&sp=y&LangType=1033&ecid=ABEA-56QT5Sxiv Harvey, “The New Imperialism,” Socialist Register 75xv http://www.theguardian.com/technology/2014/mar/27/google-flu-trends-predicting-fluxvi http://online.wsj.com/news/articles/SB10001424052702304732804579425631517880424xvii http://www3.weforum.org/docs/WEF_GlobalInformationTechnology_Report_2014.pdf pg3xviii Polanyi 234xix “Big data’s big problem: little talent,” The Wall Street Journal http://online.wsj.com/news/articles/SB10001424052702304723304577365700368073674xx Podesta 37xxi Pg 38

http://www3.weforum.org/docs/WEF_GlobalInformationTechnology_Report_2014.pdfxxii Rachel Emma Silverman, “Tracking Sensors Invade the Workplace,” http://online.wsj.com/news/articles/SB10001424127887324034804578344303429080678xxiii http://www.automotive-fleet.com/channel/green-fleet/article/story/2010/07/green-fleet-telematics-sensor-equipped-trucks-help-ups-control-costs/page/1.aspxxxiv https://www.google.com/search?q=sensors+in+ups+big+data&rlz=1C1GGGE_enUS484US484&oq=sensors+in+ups+big+data&aqs=chrome..69i57.5770j0j4&sourceid=chrome&es_sm=122&ie=UTF-8xxv ‘Work Smarter: how big data can boost labor performance” http://www.forbes.com/sites/ciocentral/2012/07/05/work-smarter-how-big-data-can-boost-labor-performance/xxvi http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf

xxvii Frey and Osborne 15-16xxviii Frey and Osborne 38xxix “Rebalancing Socio-Economic Symmetry in a Data-drive economy,” Peter Haynes andM-H. Carolyn Nguyen, WEF report http://www3.weforum.org/docs/WEF_GlobalInformationTechnology_Report_2014.pdf pg. 70xxx WEF 69xxxi 69xxxii PCAST 14xxxiii Mark Andrejevic, iSpy (University of Kansas 2007) 2.xxxiv 91xxxv PCAST 17