Ecomediatic Data: An Introduction to Critical Big Data Studies
Acoustic Space #12 - Peer-reviewed Journal for Transdisciplinary Research on Art, Science, Technology and Society, "Techno-Ecologies 2", Media Art Histories, (Der.) Rasa Smite, Armin Medosch and Raitis Smits, Riga, 2014, p. 262-273
Big data and dataism can be considered as the emerging and renewed totalitarian ideology of neo-imperial capitalism. Within the contemporary economic and political production lines, human and nonhuman data is the capital of hegemonic power. On the other hand, administrates of corporate culture, such as Douglas Merill, Google’s former chief information officer and the founder of ZestFinance – a startup that leverages big data to provide credit scoring information note that they just don’t know how to use it yet. The problem seems to be located not only on innovating ways of mining exhaustive loads of data but also on making data ‘fertile’ for the sake of control through empirical and structural analysis.
The paper questions whether it would be possible to consider data and data analysis from a minor post-feminist perspective. It aims to present a critical perspective for big data. Considering data and data analysis as tools of precarious sustainability, ecomediatic data poses such questions: What is the nature of ‘infertile’ data that are excluded and expired within the dominant reasoning processing? If data and data analysis are becoming the ideological and hegemonic power of neocolonialist political economy, what kind of experimental audio-visual artworks offer new perspectives for pitching ecomediatic data?
Keywords: big data, noise, ecology, infertile data, credit, friction, control
Today the phenomenon of big data is considered to be “a revolution that will transform how we live, work and think” (Mayer-Schönberger and Cukier 2013). Especially after the wide use of credit cards and computing systems in state, education, health and finance sectors, different amounts and sorts of data are collected with an increasing trend. What was called 15 years ago as ‘data warehouses’ converted to ‘big data’ of today: the elusive pursuit of correlation, and linear algebra to solve ‘just in time’ processes by algorithmic applications. What makes big data novel in our present day is related with the collaboration of some government and market actors that use and share their massive datasets. In this way, big data is becoming the capital for the sustainability and authority of a network of dominant political-economic power.
Especially in popular media, business, computer science and computer industry, the term “big data” is often used with rather optimistic and vague descriptions. As Manovich points out, for instance in June 2008 one of such accounts was published in Wired magazine as follows: “Our ability to capture, warehouse, and understand massive amounts of data is changing science, medicine, business, and technology. As our collection of facts and figures grows, so will the opportunity to find answers to fundamental questions.” Despite this optimistic view, there are also critical problems raised with big data. Therefore in this paper, developing a critical approach to “big data,” I will try to reflect on the problems emerged, reiterated and increased with big data.
One of the problems with big data is linked with curating, which means selection, preservation, maintenance, collecting, processing and archiving of data artifacts, “datafacts”. In their 2006 text, On Misanthropy, Alexander Galloway and Eugene Thacker note, “[t]he act of curating not only refers to the selection, exhibition, and storage of artifacts, but it also means doing so with care…” But within the curation of datafacts, what specifically is considered as data and how it is deployed is not always definite. Challenges with political economic hegemony, privacy and data circuit transparency are still cultivating. How large-scale data collection and analysis by the private and public sectors affecting privacy and democratic participation needs careful examination and care.
Today those who can access massive transactional databases of web searches, applications, sensor data and mobile phone records are only the accredited actors of media, finance, government, pharmaceutical and other industry giants, which provide the necessary infrastructure to manufacture big data. Although Small Players in a Big Data World has recently been announced in Forbes magazine as a need, “The term big data implies big organizations.” The integration and collaboration of large data sets challenge to overcome this problem by correlating and running multiple programs among smaller and bigger organizations but Manovich (2011) points out a critical issue: “even researchers working inside the largest social media companies can’t simply access all the data collected by different services in a company.” So the control of big data does not have a distributed network structure but a decentralized network structure, in which some nodes are connected to several hubs of market giants and governmental agencies. It is not fully meshed.
Appraisal and promotion of big data also comprises of some extinction claims about certain disciplines and fields. Some argue, like Savage and Burrows (2007), that big data is a subset of a new “knowing capitalism”, in which companies will be able to conduct the empirical sociology much better than sociologists themselves. Such an approval of death (of a discipline along with its scholars) coincides with what Braidotti (2013, 122) calls as “contemporary necro-politics”, the politics of death on a global regional scale. Indeed, except the sociologists hired by these corporations, we don’t know “What Facebook Knows” and with whom share their analysis and data sets. Since large data sets and analysis tools are only available to dominant actors, such as big corporations, institutions and governments, the control of curating datafacts calls for the participatory performance of democratic mechanisms.
Experimenting with Big Data
In contrast to affirmative and romantic accounts of the 1990s and the early 2000s, which celebrated the role of Internet for participatory democracy and open access, it becomes obvious that today there is a growing control, which is executed with big data. The scandal around PRISM program of The United States has recently proved how government agencies gathered information by disregarding the privacy of its citizens and increased its control. Under the guise of security and anti-terrorism, the National Security Agency (NSA) collected, via the net giants, the personal data of millions of citizens worldwide, a massive, menacing and indiscriminate collection. According to Johnson et al (2013) these companies include Microsoft in 2007, Yahoo! in 2008, Google in 2009, Facebook in 2009, Paltalk in 2009, YouTube in 2010, AOL in 2011, Skype in 2011 and Apple in 2012.
The control over data is established via regulatory mechanisms and judicial instruments as well. Emerging legislative measures and Internet laws are passed (i.e. Law No. 5651 on Regulating Broadcasting in the Internet and Fighting Against Crimes Committed through Internet Broadcasting in Turkey) in many countries so that big data based mass surveillance, access restrictions and censorship via Internet Service Providers can well be executed on a legal basis.
Furthermore, it should also be noted that as hegemonic dominance, in Gramscian context, ultimately relies on consented coercion, it is not only individuals but also institutions and governments give their consent and will to become the manufacturers and labor of big data. In order to survive within subordinating power of the financial elite, which gradually coerce to conform to their rules, some governments sell the data of their citizens – indirectly via another company – and present their countries as an open laboratory for several industries, such as Andorra, presented as a “Nation as Living Lab.” According to Travis Rich, an MIT Media Lab researcher and the director of the technology of the Smart Country program in Andorra, “The goal is to develop a platform that is sourced with data from the telecom, energy provider, installed sensor infrastructures, the bus company, tourist buying habits, store directories, store inventories, restaurants, etc., and to make it available to researchers, entrepreneurs, and companies as a tool for understanding and experimenting with new technologies and ideas.”
So this experiment can be considered as part of a large effort in investigating ways to anonymize the data that flows through the API (Application Programming Interface) before it ever reaches an ISP (Internet Service Provider), Andorra Telecom server in this case, in a way that would make it useless to anyone trying to track and collect personal information from it, including the governments. Once again, we see that Democracy and Development are remediated. Still, who can access that data and for what purpose will it be used and processed remains a big question due to the lack of transparency and self-governed watchdog infrastructures.
Datafacts / Data-failures
Despite the claims of objective truth, in big data, the selection, preservation, maintenance, collecting, processing and archiving of datafacts are subject to interpretative basis. Focusing on the increasing surveillance of daily online lives by major corporations who mine this data to sell to others for commercial profit, Jamie Allen and David Gautier (2014) developed an artistic project about big data use, Critical Infrastructure, and note, “Avoiding too much interpretation is the key.” However, messiness and randomness is traded off with precision and objectivity in big data. In return for lowering the standards and criteria, one can sometimes access more data but have also more error.
Failure is not a problem in scientific knowledge production. Instead, as emphasized in Laboratory Life: The Social Construction of Scientific Facts by Latour and Woolgar (1986) a typical experiment produces only inconclusive data that is attributed to failure of the apparatus or experimental method. The point in having failure is to learn how to make the subjective decision of what data to keep and what data to throw out. For untrained observers, the entire process resembles not an unbiased search for truth and accuracy but a mechanism for ignoring some data. The failure corresponds to the failure of dealing with content.
Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic is one of the most well known failures of big data analysis. Google predicted 11% of the US had flu instead of the actual figure, 4.5-4.8%. Although in this case the error percentage is indeed high and usually “3 per cent margin of error” is treated as a normal threshold, it is within this 3 per cent that the excluded subcategories would lead to major transformation. Besides the predictions based on Twitter messages during Hurricane Sandy can also be a case. Given the way the data was presented, there was a huge data-gap from communities unrepresented in the Twitter sphere.
With the aim of extracting the patterns and predicting the propensities of the large sum, some data can be ignored, excluded and disregarded. Propagative power of the small can sometimes be underrated. Big data analysis alone would lead to exaggerated or distorted results as “pure facts” to be used in decision-making, evaluation and for definition of user’s rights, access, benefits and restrictions. This data is used to locate citizen’s position in a society. However, many people are not aware of the multiplicity of agents and algorithms around personal data collection, storing of their data for future use, possible uses of their data – and about the dimensions of profitable personal data economy (Boyd and Crawford 2012).
Big Data vs. Small Data / Imitation
Even the integrated data that is accessed through APIs cannot be processed ‘properly’ and is qualified as ‘useless’, still, with big data, it is often we hear, ‘size matters’. The reproduction of this familiar, gendered and hierarchical discourse is embedded especially in popular accounts. Mayer-Schönberger and Cukier (2013, 6) argue, “Big data refers to things one can do at a larger scale that cannot be done at a smaller one, to extract new insights, or create new forms of value, in ways that change markets, organizations, the relationship between citizens and governments, and more.” Indeed, although the amount of stored information grows four times faster than the world economy, some argue, “Big Data is notable not because of its size, but because of its relationality to other data.” (Boyd and Crawford 2011)
The account of size is also augmented with what Wang (2013) calls “thick data,” another problematic analogy drawing from the anthropological views of Geertz on “thick description,” that is, “to make sense of the seemingly infinite array of data in the world” (Stoller, 2013). Wang (2013) applauses what big data needs: “Thick Data is the best method for mapping unknown territory. When organizations want to know what they do not already know, they need Thick Data because it gives something that Big Data explicitly does not—inspiration.”
Apart from this rather mystical approach to big data, in their recent paper, When Size Matters: The Complementarity of Big and Small Data in Future Social Science, Blok and Pederson (2013) draw a distinction between Big (computational, quantitative) and Small (embodied, qualitative) Data. Following what Latour et al (2012) proposed as an “intermediary level” in The Whole is Always Than It’s Parts: A Digital Test of Gabriel Tarde’s Monads, they suggest using these bifurcated research methodologies complementarily. But in Tarde’s view, “smallest entities are always richer in difference and complexity” (Latour 2001, 82). If one reads this without having comprehensive reading of Tarde, it becomes possible to draw a conclusion in favor of small data. However, for Tarde, it is not big or small data but the imitative and contagious recurrence is at stake.
So from critical big data studies’ perspective, it should be noted that rather than focusing on big and/or small data, it is more important to notice that the very recurrence of this micro/macro distinction itself is an imitation of desires and beliefs in the empirical, which is also the extension of the imperial ideology. This is the problematic issue of big data: while talking about revolutionary change, the power and authority of dominant modern thinking is embedded merely with some newer versions of the already expired. While web data mining and crowd sourced tracking systems are becoming the ingredients of surveillance-based research, Valleron, an epidemiologist running a monitoring network says in Nature state, “The new systems depend too much on old existing ones to be able to live without them.” These “beams of imitation” (des faisceaux digitations) (Tarde 1895, 207) indicate the exact similarities within the mass of beliefs and desires. For this reason, big data is also a fabricated social, which is only made visible.
In other words, current status of big data is strongly linked to representative samples and their competence for producing objective truth. Introduced and popularized as the hot methodological research norm of our times, “data mining becomes a shallow version of neo-empiricism” (Braidotti 2013, 3) which serves for exploitative and profit-minded pursuit of capital. Analysis of large data sets is subjected to prediction and prevention measures. And this “dataist” approach operates as a plug-in for an updated totalitarian ideology of neo-imperial hegemony.
Tarde gave importance to both archeology and statistics by insisting on extracting the imitational and repetitive aspects. For Tarde, object of knowledge is not static but truly dynamic. He provides a kind of methodology for statistics and this can perhaps be used in critical big data studies: “1st determining the imitative power of each invention, in a given time and country; 2nd showing the favorable or adverse effects resulting from the imitation of each of them.” (Tarde 1890, 170) Therefore, for Tarde, after determining similarities among different body units, the propagation and imitation of a single one, no matter how big or small, as difference or invention, is at stake. Then it’s propagation impact that passes from one to another is under focus. Uncovering the archeology of the trends of imitation, it is not the size but how the dynamic contagion flows and the power of transforming the other are used should be considered.
Fertile Data / Infertile Data
Knowing through a mass of data is acknowledged to have not whole but only partial information about others. For this reason, information leaks lead to an inevitable collaboration among the owners of data-capital, accompanied by mutual debt relations. That is, since each owner only possesses some information, by way of collaborative lending, owners of data-capital aim to make profit by correlating, predicting and controlling a wider spectrum.
But in terms of media ecology, not all data is fertile for the sake of market growth or control. Douglas Merill, Google’s former chief information officer and the founder of ZestFinance—a startup that leverages big data to provide credit scoring information, notes, “We feel like all data is credit data, we just don’t know how to use it yet.” That is, although the tracked and collected data is infertile, it is considered as an investment. Therefore fecundity, the potential reproductive capacity, of data is taken for granted not having a useless value but as an investment capital.
In Merill’s account, credit data refers to the potentiality of pure data as raw material. This has a potential change our views about contemporary political economy. On one hand, in The Making of The Indebted Man Lazzarato (2012: 7) argues, “The debtor-creditor relationship intensifies mechanisms of exploitation and domination at every level of society.” Credit is one of the most effective instruments of exploitation man has managed to create, since certain people, by producing credit, are able to appropriate labor and wealth of others (Ardant 1976, 320, cited in Lazzarato 2012, 20-21). In a similar fashion, Griziotti (2014) emphasizes that “a mercenary political class, subject to a financial élite that forces it to privatize welfare, no longer has any margin for exercising their antiquated social democratic mediations; a new strategy of control over life and society, based on technological subjection and the generation of debt, has replaced it.”
From a contrary perspective, one can also argue that the interaction of the owner and the lender of data mark somehow also an inverse economy. Today the credit-givers and credit-receivers are reversed. If data is capital, it is also government and market actors, which seek credit-data, the capital. Without data provided by the users, the algorithms of a web site cannot function, hence cannot exist. In this way, epistemological approval of the transcendental power of control authorities is not imitated, as Tarde argues (cited in Latour 2012, 14) “To be part of a whole is no longer to ‘enter into’ a higher entity or to ‘obey’ a dispatcher (no matter if this dispatcher is a corporate body, a sui generis society, or an emergent structure), but for any given monad it is to lend part of itself to other monads without either of them losing their multiple identities.” However since the control over data is invisible and the emphasis is usually given to the authority and control, both users and materials as agencies are not recognized with the prospective power they possess.
Friction / Noise
Despite the growing demand of accessing and processing large volumes of data in nanoseconds, tracking, collecting, analyzing and correlating data can be multi-staged, slow and expanded in time. Within this process, there is also a large amount of messiness, uncertainty and noise amongst the high-frequency flowing information that investors need to use in order to make sound, long-term predictions and decisions. Different interdependent financial species are constantly processing market noise in an attempt to reduce it as much as possible to relevant information.
Yet noise is not always a problem. Sometimes the subsequent decisions and market orders generate more noise for the other participants. In this case, bigger actors manage to thrive by exploiting information gradients that slower market actors are unable to access. In this way, it becomes more efficient to make short-term predictions rather than taking risks for long-term investments. With this regard, Schleifer and Summers (1990, 23) suggest that sometimes noise traders with different models can also cancel each other out in this process: “many trading strategies based on pseudo-signals, noise, and popular models are correlated, leading to aggregate demand shifts. The reason for this is that judgment biases afflicting investors in processing information tend to be the same. Subjects in psychological experiments tend to make the same mistake; they do not make random mistakes. Many of these persistent mistakes are relevant for financial markets.”
Therefore, distorting the flows of data-capital leads to the control of judgment biases, decision-making mechanisms and choices of production. In their study of high frequency trading, Wilkins and Dragos (2013) note, “As long as there is enough disparity and enough heterogeneity in the market, high-frequency traders can profit from the underlying friction and produce more noise. It is precisely this persistent inefficiency of markets that informs heterodox economics.”
Profiting from friction and producing more noise are important aspects to examine how contemporary media operates along with hegemonic actors of neo-liberal control. Friction generates errors and noise. Although the smooth flow of transnational capital contradicts with friction, today big data oriented control agencies create various forms of friction and noise in society with regards to conflicts, pollution, surveillance, privacy and transparency. For instance Edwards et al (2011) reveal, “metadata may be a source of friction between scientific collaborators, impeding data sharing.” Edwards (2010) also introduces different categories of friction such as “metadata friction, the difﬁculty of recovering contextual knowledge about old record,” “computational friction, which prevented serious attempts to simulate weather or climate mathematically,” and “data friction: the great difﬁculty, cost, and slow speed of gathering large numbers of records in one place in a form suitable for massive calculation.”
These physical dynamics of Earth also sheds a light to a more materialistic approach for critical big data studies. Rather than using ecology as a mere analogy for political economic environment of media (Dutton et al 2010), one can return to the fact that “the resources and materials gathered from geological depths enable our media technologies to function” (Parikka 2013). Therefore, within critical big data studies, friction and noise call for a more materialistic approach, such as the energy consumption, turbulence and heat production caused by the operation of big data and data centers.
If one especially considers infertile data, it is inevitable to argue that the resources of and materials of Earth are consumed for their capacity to be renewed, recycled and recomposed in the future because the digital trash of our everyday life can be the gem of investment capital. So, instead of expecting how the bigger actors of big data convert infertile data into an economic profit-making instrument, and control society, experimentation of other actors becomes more important today.
Although large sets of data are in use for commercial, scientific and intelligence purposes, data is also unfolded through some emerging conceptual and artistic practices, which can frame it as part of critical interventions while drawing attention to its invisible processes. These works create a sort of friction and noise as well. Artworks of soundscapes and sounds that are captured, transformed and recycled bring big data back into issues of ecology and politics. Media critics, artists and hacktivists are reversing the operations of big data organizations by producing ecomediatic data.
Ecomediatic data is not green data. It is generated by the reiteration of big data operations for a critical purpose. It uses friction and noise to subvert the authority and control of hegemonic actors of big data. For instance, one of these critical interventions setbacks the security debate and raises critical queries about stalking and data-theft. While security concerns are redundantly served and imposed in order to increase the measures of control, ecomediatic data aims to reveal the dim side of big data operations, which are hard to grasp.
An online project of Alessandro Ludovico and Paolo Cirio, Face to Facebook involved stealing 1 million Facebook profiles, filtering them with face-recognition software, and then posting them on a custom-made dating website. In this work, they create noise for data processing and they also attempt to decrease the pace of big data operations by creating a friction and manipulating the flow of information. Face to Facebook is the third work in a series that began with Google Will Eat Itself and Amazon Noir. All of these works use custom programmed software in order to exploit three of the biggest online corporations, Google, Amazon and Facebook, exploiting conceptual hacks that generate unexpected holes in their marketing system. In this way, the artists present ecomediatic data in which hacking big online corporations has proven to challenge the funding principles of the digital economy and of digital communication at large.
One of the artists of the project, Cirio, emphasized that working with modern devices like advertising strategies, political spin, entertainment, economic and legal language, which structure information to build specific societal organizations, belief systems and social trends, this work, as an active agent, able to influence its subject by employing creative reconfigurations of the power of information. Ecomediatic data fabricated by this artistic and hacktivist project is an extension of researching methodologies for manipulating information. So, by means of inventing new ways to inform critically, invent utopias and provoke subversive and paratactical acts through visual and emotional sensational impact, this work creates, orchestrates and illustrates the processes by which the structuring flows of information generate as “the social realities.”
There are also minor-artistic interventions like Bites of Ali Miharbi. Bites is basically a software, with a custom audio mixer to adjust the duration and volume of the audio-visual record of the artist biting a selection of natural and artificial food products. Focusing on the ephemeral and transient characteristics of noise as raw material or credit data, in this work, each viewer creates an audio-visual composition, which is taken over by the next viewer. The piece humorously explores remix culture, standard software and hardware interfaces that are promoted as ‘democratizing creativity’ despite the new hierarchies they form and the ever more complex relationship between immaterial production and consumption after the Internet. Highlighting the manipulative data seeking instruments and big data errors of marketing agencies, Miharbi (2010, 30) note, “With the overload of information, categories have failed where users often times find themselves watching an unexpected video or listening to something stumbled upon.” Therefore, ecomediatic data produced in this work is strongly related with the manipulative noise created by the hegemonic actors of big data operations.
In Katja Vetter’s works, such as in Slice // Jockey for Pure Data, the infertility of data is considered as value. Vetter asserts that the operation will continue, recording will not stop, even when you are silent, the buffer is filled mainly with noise. Relating with the constant invisible operation of big data tracking, Vetter introduces alternative instruments by extending the limits with DIY for an alternative purpose.
Today access and control of massive transactional databases are only open to the accredited actors of media, finance, government, pharmaceutical and other industry giants, which provide the necessary infrastructure to manufacture big data. The lack of transparency and restriction of access is limiting the impacts of participatory democracy. For this reason, rather than focusing on the recursive loops of small data and big data distinction, this paper suggests exploring how the dynamic imitative and inventive flows are used within the relationality of big data. Besides the operative performance of friction, noise, errors and failures should be questioned by working with the related works of artists and hacktivists that increase critical awareness about the operations of big data. In this way, instead of drowning in the generated noise and becoming depleted by the frictions manufactured by the controlling agencies, new tools and methodologies to study big data with a critical insight can be explored and experimented further.
Ebru Yetiskin is an Istanbul based sociologist and curator. She teaches in Istanbul Technical University as a full-time lecturer and writes on contemporary forms of neocolonialism and the interaction of science, art and technology. She has an MA degree on Science, Technology and Society from Istanbul Technical University and Université Louis Pasteur. She completed the PhD program in sociology in Mimar Sinan Fine Arts University in Turkey. She conducted the preliminary phase of her doctorate research in Centre Sociologie de L’Innovation in Ecole Nationale Supérieure des Mines de Paris. As a member of International Association of Art Critics, she is actively involved in giving performance-lectures, workshops and talks in both national and international circles and cultural events, such as Transmediale Festival, Contemporary Istanbul, Tanz-im August-Berlin, Amber Art and Technology Festival. She recently curated an exhibition entitled, Cacophony, with emerging artists from Turkey in Açıkekran New Media Art Gallery in Istanbul.
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