Aporetic and Productive Undecidedness of (Data in) Neoliberalism

Aporias of Neoliberal Data

How can aporias of neoliberal data trouble the ideology of neoliberalism from ontological, epistemological, and ethical positionings? Data, as an ontological practice, pose questions of knowledge, subjectivity, relationality, politics, and power, to name a few. Data are not one thing, or even many things: There is an inherited multiplicity of data that, if not overlooked, challenges the established order of things, and expected outcomes and ready-to-be-delivered computations and solutions. Data, if not simplified, fill the production practice not to colonize them but to free them. Practices of allowing a view of the connectedness to different political structures, discursive variations, multitudes of scholarly adventures and contesting the linguistic assumptions are engraved in the notion of aporia that this article addresses. We question how data can function, are produced, and multiply in different contexts under the neoliberal ideology. Indeed, the notions of neoliberal data associated with methodological practices are not static, but continue to change as a result of neoliberal forces, privatization discourses, market-driven decision-making that governs human subjects’ ontological positioning toward the world, epistemology that we are allowed to inherit, and the ethical relations that we form. These ideas are also relevant to the institutional practices of higher education, scholarship, and curriculum.

Aporia, Greek for difficulty, puzzle, or impassable, plays a significant role in Derrida’s conceptualization of ethics, decision-making, and limits of truth. Also viewed as “a momentary paralysis in the face of the impasse, it [aporia] is the ‘testing out of the undecidable; only in this testing can a decision come about’” (Derrida, 2005, p. 154). Aporia “duplicates itself interminably, fissures itself, and contradicts itself without remaining the same” (Derrida, 1993, p. 16). Furthermore, as Wang (2005) argues, “[i]t is in the very event of exceeding borderlines—an impossible passage—that aporia is experienced” (p. 46). In the process of exceeding borderlines and impossible passages, the aporias of suspension, undecidability, and urgency both enable and disable researchers’ (and readers’) responsibility (see Edgoose, 2001). Applying aporia to data, data become nondata, data without direction, and data without limits. Data without data. Data face their own limit questions and (un)remember their own memories. According to Derrida (1993), aporetic data (especially in neoliberal times) have a plural logic. First, the impermeable nonpassage, a door that does not open; second, the absence of limit or too porous a limit, that is too easily permeable as a border; and third, the impossible. Neoliberal data generate contradiction without a pass, step, criteria, and replacement that aligns with neoliberal “principles.” There are no more data with clear direction and path, and data in neoliberalism thus function as an arrivant; de-identified, de-contextualized, and remaining in waiting (of the arrival). Derrida (1993) introduced the function of arrivant; arrival of the de-identified and arrival becoming. Under the governing ideology, the arrivant (data) does not have an identity yet and the place of arrival is de-identified. The arrivant does no longer or not yet know his country, context, or personal history. In addition, the arrivant does not violate or intrude as violation and intrusion presuppose a self-identity. But it does perform under a particular neoliberal blanket.

Data in neoliberalism are aporetic and undecided. Individualized, deregulated, free, private data, values of free competition and self-regulation are misleadingly used to produce “big” data industry and data paradox. Big data, the onto-epistemological cornerstone of neoliberalism, are seemingly not individualized, deregulated, free, or private. Lambert, Wright, Currie, and Pascoe (2015) explained how the efficiency-driven neoliberal desire subsumes the education field imposing quantification, measurement, and competition (of data). According to these authors, neoliberal pursuance of efficiency incurred obsessive reliance on data, which they call “dataphilia” or “data-fetish,” and resulted in the marginalization of human subjects in that frame.

Furthermore, in an age where the Internet of Things (IoT) and its embedded surveillance tools and digitized data collection apparatuses breed hybridized, easily accessible, and desirable free contributions from interdisciplinary scholars, human subjects have gained access to an unprecedented and overwhelming amount of aporetic data and knowledge. Under the agenda of neoliberal ideology of data governance, the human subject is perceived as an individual decision maker in the “open” data market and is, ultimately, expected to analyze and then evaluate data from various directions and conflicting sources. Neoliberal data “must” be “rational” and “logical”: There is thus limited space for critical reflection or marginal individuality in this neoliberal climate and data-machinery, where the industry of ideology is strongest of all (Horkheimer & Adorno, 1944). Neoliberal-methodology-machinery has a unique and highly specialized task, to produce a particular kind of knowledge. It is preferably reaching and covering all consumers, and constitutes a knowledge enterprise. However, within this neoliberal-methodology-machinery running on privatization, deregulation, and fiscal austerity, only a certain type of “data” qualifies as meaningful, valuable, and desirable knowledge. This paradox of neoliberal data, as deregulated and accessible by all, yet highly governed by external funding, data banks, and publication guidelines and peer reviews, is at the center of our argument. As such, data in neoliberalism are problematic, paradoxical, continuously changing, and thus ultimately aporetic.

In this article, we focus on this paradox of neoliberal data at multiple levels. Through the content of the article, we aim to highlight how data in neoliberalism are in some ways historical, relational, and collective yet situational, atomic, and individual. In particular, we trace early signs of neoliberal data back to the ashes of past nation states and political events changing the economic structures in Europe. However, we also highlight the possibility that neoliberal data have been and still are in the search of the immanent other, friend, concept, or practice. Therefore, aporetic data escape singular contexts or explanations and exemplify plural logic. In addition, by dividing our writing into multiple inquiries and at least partially emerging and contradictory sections, we hope to leave our argument about data in the Western version of neoliberalism questioning itself, undecided, and aporetic.

Data Capital?

The neoliberal economists who sprouted in the early 1980s in the United States contained their roots from a much earlier time (Harvey, 2007). The Allied Victory in World War II in 1945 initiated a concerted effort from European and Western states to build nation states based on a principle of the management of economic scarcity (Peters, 2009). To prevent the rise of yet another neo-Nazi regime, leaders from the West concluded that governments needed to instill an economic structure that would manage competition, labor force, and the “free market” (Peters, 2009). Germany’s economic collapse ushered in authoritarian forces, which precipitated the rise of Hitler to destructive and deleterious effects. Keynesian economic theory guided nation-states to construct economies that stabilize Europe and Western countries (Peters, 2009), and the notion that economic systems could solidify durable inter- and intrarelations. Classical economic theory of the government that governs least governs best undergirds Keynesian economic theory. Not all economists agreed with this approach: Some neoliberal economists argued that governments that managed economies and scarcity in a nation-state practice the rationalities of authoritarianism. Coalescing economic power in the hands of the state represented a repackaging of fascist impulses. The conceptual and statistical models of neoliberalism germinated from the ashes of a destroyed and humiliated Germany. In this sense, the neoliberalism seeks to distribute state power especially over the control of the economy, while Keynesian economics seek to manage power especially in terms of the economy (Dean, 1999).

In some ways, data have become the capital of neoliberal economists and stabilizing force of the past and presence of nation states. Much like the concept of Human Capital (Harvey, 2007), Data Capital moves on its own to continually do and undo the uses of data in qualitative inquiry. Concepts such as privatization, deregulation, free market, risk, and security play a significant role in the area of qualitative inquiry and neoliberal (qualitative) inquiry produces what we call Data Capital. What does it mean to liberalize the economy of data in qualitative inquiry? How does Data Capital work in various global and scholarly contexts?

Data Truth-Tellings?

Foucault (2001) analyzes the notion of parrhesia as an incorporation of truth and explores how it needs to be lived and experienced, and essentially linked to the way one lives one’s life and understands one’s own body, and thus how human subject identifies, collects, colonizes, treats, and performs data. Similarly, this is where Havel’s (1985) concern with the system of governance, and with the significance of living within the truth, comes close to Foucault’s (2001) genealogical analysis of parrhesia in the history of philosophy. Foucault focuses on the developments of this free, fearless speech, where one can say anything and everything, without manipulation or generalization. This practice can also lead to data without manipulation and generalization. Foucault was concerned with the notion of parrhesia as an act of truth-telling, where the parrhesiastes need to possess, and must have, the courage to make a shift into the public discourse. Can something dangerous also be expected from data? Foucault also reminds us that “the fact that a speaker says something dangerous—different from what the majority believes—is a strong indication that he is a parrhesiastes” (p. 15, emphasis in original). Risk-taking and the truth are ultimately connected, and through using parrhesia, Foucault considers the act of telling the truth as a duty, or, similarly to Havel, as a responsibility. Parrhesia also involves risks to the person speaking as the person practicing parrhesia must have something important or valuable to lose. Likewise, data become a responsibility and a risk-taking activity. Human subjects are formed in relation to data.

Furthermore, there is no power that forces human subjects to act particular ways, but parrhesia functions as an “urge” that compels one to step out of the private realm and to enter the public domain. If a human subject—an academic in this case—under a neoliberal ideology wants to live a “free” life, then he needs to treat data in a way that the idea of parrhesia operates. Thus, human subjects should question how neoliberal data function as a form of truth-telling. What does it mean to live within the truth and with data while taking risks and shifting public discourses?

Neoliberal Privatization of Data, Inc.?

The privatization (of data) is the principle that rather than rely on taxes for welfare services from the state apparatus, governments need to give those services over to private entities (Dean, 1999; Harvey, 2007). Furthermore, rather than build a state based on social or communitarian ethics, neoliberalism promotes private ownership and privacy (Harvey, 2007). Regarding data, privatization illustrates uses of data as private entities, which allow the researcher, user, and reader to individualize data production, data analysis, and applications of research. Following this logic, there should be no core or regulatory agency that administers and monitors data construction or data fabrication. Data as privatization permits data to be localized and exchanged in the free market of the research setting based on its value in the market place. Scarcity, desirability, and potential symbolic worth determine its value. In qualitative inquiry, the accumulation of data and the preference for data to excavate even more data perpetuate necessity for increasing data capital in research studies. The focus of data in research studies franchises it and minimizes other aspects of the research process.

In such a privatized world of data Inc., as Foucault (2001) claims, the human subject needs to choose “frankness instead of persuasion, truth instead of falsehood or silence, the risk of death instead of life and security, criticism instead of flattery, and moral duty instead of self-interest and moral apathy” (p. 20). The danger lies in facing the tyranny, facing the hegemonic structures, or facing the real fear of repercussion. Both Foucault and Havel emphasized that an act of parrhesia is unplanned and cannot be rehearsed, and both are not necessarily concerned with the problem of whether there is a truth, or what constitutes the truth, but with the act of necessary risk taking embedded in truth-telling as it is communicated in the context of one’s life. Herein, can data as friendship become unplanned and unrehearsed, and not just be part of neoliberal networking to gather more data? Can neoliberal data be living within the truth and perform parrhesia?

Data Friendships?

Through Havel’s work, we can perceive academic friendships in the center of power relations or truth makings. It is possible that no academic believes in the academic system that they understand that there is an “inside” and “outside” discourse of academia under the neoliberal ideology. Academics do understand that there are neoliberal academic friendships that are necessary to maintain and those that they want to maintain, and those that they desire may often overlap. In Havel’s thinking, all academics, then, live in a lie. They live within this lie as they perform their subject positionings, and deal with data, within the neoliberal ideology and not outside of it. So the academic’s private life multiplies into plurality, diversified through their interests, ideas and friendships, as they self-govern within the acceptable level of freedom and independence. Academic’s personal domain is outside of the public sphere that demands that they adopt certain discursive positions. These positions include the self-discipline to follow orders, to be ready to conform with other academics, and to uphold the same ideals as protected and secured by institutional review board (IRB) regulations, whereas data depositories enable seemingly democratic yet highly controlled access to knowledge and information. Data Capital permits data to morph, to fashion themselves—data are in charge of their own destiny. Where data are generated and how they retrain and retool themselves depend on the situation. Data remain in a state of flux, in a continual anxious predicament and unsettlement in relation to the other. Data Capital is caught in a double-bind; it acquires freedom, mobility, and flexibility, which create relational anxiety. Data Capital accentuates the individual and retail over the communal and the wholesale. In that condition, data can masquerade, evade, and mask themselves to fit both relational and atomic research situations. Data Capital builds the more specific forms and types of data, which become used in privileged and pristine research situations. Data Capital assumes that certain types of data garner greater value than others in the free market of research. There is something uneasy about these friendships with Data Capital.

What also follows is the need for god-like analytic practice requiring a diffuse orchestration of (data) care and precision. However, this demigod intellectual performance of human subjects does little to respect the limitations of academics’ cognitive/visual system and desires. So instead of being boosted from our plenitude of data, many academics are being systematically disempowered by it. Operating under the neoliberal data blanket, academics become hollow 9-to-5-ers turning coffee into conjecture. And as the complexity of our work builds, the sense-making of our world is being offloaded to machines imputed with data analysis software packages. These data analysis packages run algorithmic computation on a rapid and repetitive basis to spit out needed outputs and/or code trees. They are sleek and user-friendly. They make regulatory data management “sexy” again. Data analysis packages are the hidden data not unlike pornography invigorating our needed publication output, while buffering us from the eyesore that our unruly data could become. As such, friendships with data pornography can become troubling and problematic quite fast.

([Methodologies of] Data) Automatism?

Deregulation is the principle that rather than regulating the free market, state legislators need to loosen the rules for what businesses can do in the market place (Harvey, 2007). Regarding data, the epistemological and ontological rules that constitute data and permit them to circulate in the market place of ideas is opened up. Data become an unethical, free-floating potentiality in the free-play of data exchanges within diverse systems.

Havel’s (1985) critique of the system emphasizes the notion of “automatism.” Automatisms are the predictable, expected answers to everyday questions that academics are asked. Also, answers about and for data. Academics form a part of the University and governing structures—they all work and learn, they all behave, they all live their life with these automatisms determining their positions in the public sphere. They all use and become data. Academics live with these automatisms: they cannot remove themselves from the public, they demonstrate public approval with the system, they maintain the power relations that hold it together, and at the same time, they whisper in private, as they do not trust the governing system. Do they trust data? Do they trust themselves? In most cases, academics must publicly behave as if they do trust and believe in the system and then observe this façade. So academics need to be seen in public acting as if they actually care about and agree with the governing system, with the data, not just as if they tolerate it. By doing so, academics accept the social contract. Havel (1985) claims that then the academics “confirm the system, fulfil the system, make the system, are the system” (p. 31).

The social contract becomes the ritual, the automatism, which is the bridging element between the human subject and the public structures of the system. This contract also bridges subjects and data. During the human subject construction—if there is no centralized or stable entity called data—it’s like a bubble that floats and merges and then re-appropriates itself with other bubbles that continue to float in the market place of ideas. Deregulation is vital to ensuring that data can move about based on market forces in various ways and along different and even competing entities.

If human subjects—academics—were capable of departing from neoliberal practices or of operating in the liminal spaces between neoliberalism and democratic and ethical responsibility, they might be interested in different data questions. They might not ask what data are or how they profit, but how they function beyond production and financial profit, how they resist, deconstruct, counter, transgress, transform, multiply, what they enable and disable, and how they meet the other, the unknown, the strange, and yet becoming. Maybe data’s different extensions function as discursive apparatuses that can regulate diverse effects of power (Foucault, 1980). Data may not be separated from truths, or truth-telling, but can they be deliberated and released from the grip of normative science? After all, as deregulated data could take any emerging and immanent forms, data in neoliberalism could be seen as a productive illusion or partially imaginative practice that can create movement in researchers, participants, data’s surroundings, and diverse political contexts. The ritualistic automatism, within which the academic participates, maintains the strength of the ideology through the academics’ everyday support. Havel (1985) claims that the ideology does not serve the power, but that the power serves the ideology as “theory itself, ritual itself, ideology itself, makes decisions that affect people—academics, and not the other way around” (p. 33).

The ideology within the system is the binding substance that lets human subjects—academics—exist in the public discourse without disturbance. This ideological substance needs to remain untouchable, undisturbed, and unchallenged, as the system depends on its ideology to be stable, to be publicly visible to continue to operate and fulfill its function of supporting the governing system. The purpose is to protect the substance, the ideology, the data’s social contract. Because of these mechanics, power operates in ritualistic and anonymous ways. Human subjects “are then almost dissolved in the ritual,” claims Havel (1985), so there is “automatic operation of a power structure thus dehumanized and made anonymous” (p. 33). The human subjects in their anonymous existence within the automatic power structure follow the ritual of everyday friendships that they need to and want to maintain, and thus live within a lie: and within data’s automatized social contract.

Havel’s (1985) concern is that “automatism is far more powerful than the will of any individual [academic]; and should someone possess a more independent will, he or she must conceal it behind a ritually anonymous mask to have an opportunity to enter” (p. 34) academic friendships constituted through discursive practices and within networks of power. Methodologies and forms of new materialism, posthumanism, and post-qualitative research produce potentially more deregulated data and knowledge, and allow for different forms of academic friendships. For example, in our recent work with undocumented students, data became a shifting and moving experiment with a variety of emerging sounds (Koro-Ljungberg, Hendricks, McTier, & Bojórquez, 2016). Sounds were recorded, found, compiled, and passed around and between authors, including one undocumented student, who then produced their own sounds in relation to those they had heard in the students’ interviews and earlier sound encounters. Each iteration ended with a collective conversation that also began the next iteration. Some moments from these conversations gave a pause, created questions, and disrupted the flow of inquiry and exploration. Sometimes the authors produced sounds that they felt related very much to the experiences of the undocumented students (i.e., fabricated conversations between friends at a bar, dads at the park, or political rallies), whereas other times, sounds were chosen that had more or less of a symbolic connection with students’ experiences. There were also sounds that began to connect with undocumented students’ stories and experiences in unexpected ways or that we expected to work in one way, but took another.

Similarly, in letting go of presumptions that carve linearity and logical neatness into our data through causal reasoning, human subjects—academics—may more freely attend to illogical drivers of random data, encounters, and enactments to perform different academics and data friendships. However, as noted before, academics and data friendships are dangerous—they may become an ideology in itself, and an ideology of a safety net, a support mechanism that enables academics to live a life within the system and the neoliberal ideology, that “transcends the physical aspects of power, something that dominates it to a considerable degree and, therefore, tends to assure its continuity as well” (Havel, 1985, p. 34). The foundations of the governing system’s stability rely on the ideology as successful. This means that the academic friendships are not unionized but remain within the neoliberal data framework, and that they operate on the principle that human subjects will accept the social contract of the public, neoliberal panorama.

Parrhessiastic Academic Friendships as Data?

The narrative of Havelian–Foucauldian neoliberal academic friendships between human subjects, and with data, relate to the formation of academic subjectivities. Why do we, the neoliberal academics, need to display the need to have networking neoliberal academic friendships and not only the parrhessiastic academic friendships, and therefore publicly relate to, and in a way support the governing system? Why do we need to be loyal to the governing system in such a visible way that all other academics and deans can observe it? What does it do with the data?

Academics—human subjects—we have always done all that was expected of us, we have obeyed and been loyal, “good,” “not difficult” academics, and no one could question our ability to work cooperatively, participate or be within the expected promotion milestones, and collect data. So the Havelian concern is why do we—academics—feel that we have to form our networking academic friendships and thus live within a lie? If we—human subjects—do not pursue networking academic friendships, we could demonstrate a resistance to the hegemonic discourse by not acting, and therefore not conforming with the demands of the governing system.

If such a resistance between human subjects—academics—would occur, if we are to remain free from certain friendships, perhaps it would allow us to engage in particular relations with a theory and data production. If those shackles would be loose, we would be freed from value claims, and permit qualitative researchers to ponder social practices as quotidian and seemingly unguided as children carrying stones (Rautio, 2013) or how hanging laundry is being conceived, entangled, and materialized (Pink, Mackley, & Moroşanu, 2015).

Human subjects living their public and private lives and academic neoliberal networking friendships form what Havel (1985) calls the “panorama of everyday life” (p. 34). The concept of a panorama paints a landscape within which academic friendships are just one small component without which the landscape would be incomplete. So the predicament that academics face is not whether someone notices or doesn’t notice their behaviors, but that by not publicly portraying, displaying, and performing their academic networking friendships, they would become an anomaly of the governing system itself. The governing system needs this panorama to be solid and compact for all human subjects—academics—as it indicates to all how other human subjects behave and perform, and therefore how they should behave and perform themselves. And their data should behave and perform. If human subjects would not exhibit their public approval with the governing system and ideology, they would be “excluded, fall into the isolation, alienate themselves from society, break the rules of the game, and risk the loss of their peace and tranquillity and security” (p. 34), no matter how fake and artificial these options may be.

Data Rebel?

(Every)thing in academia focuses on the survival of the governing system, so any disruption in academia disturbs the entire system. Apart from the interwoven subjectivities of victim and supporter, that Havel (1985) suggests, there is the disrupting element of a rebel in each academic. The Academic thus can be the cause of the crack, the disturbing rebel who unsettles the system. What data might unsettle this governing system?

The Academic—rebel—would not have committed just an individual offense. By opting out and deciding not to participate in academic networking friendships, the Academic would have disturbed the entire balance of power structures of academia. The Academic would expose the system, destroy the beautifully painted surface of the rhetoric of everyday public academia, and therefore let other academics see and feel the foundations of its power and ideology. Academics could then see and feel the data. The Academic would have allowed other academics to have a peek behind the curtain of the governing system and its ideology, and data that structure it. The Academic would shatter the power structures of the system, would illuminate what the system is about, and show other academics that she or he does not care about potential repercussions, that she or he is not afraid. The Academic’s position would not be a threat because of the status or power that she or he may have, but because of the light that she or he would have cast on the gray surroundings, and the potential cracks, in the everyday academic panorama. The neoliberal (im)possibilities of data would be revealed. The Academic would expose and let other academics see that it is possible, albeit not without consequences, to challenge the system and neoliberal data. As Havel (1985) notes, public resistance, or any deviance from the plan, is not tolerated. Havel (1985) argues that

everyone who steps out of line denies . . . [system] in principle and threatens . . . [system] in its entirety . . . it is utterly unimportant how large a space this alternative occupies: its power does not consist in its physical attributes but in the light it casts on those pillars of the system and on its unstable foundations. (p. 40)

A rebel might refuse data all together or impersonalize them to challenge the system or deviate from the social data contract. No longer relating personally to data has real consequences, both politically and practically. Foucault (2003) identifies the administrative or bureaucratic grotesque as a system of neoliberal governmentality, “in which the person to whom power is given is at the same time ridiculed or made abject or shown in an unfavorable light, through a number of rites and ceremonies” (p. 13). Despite the fact that these rituals often position those who wield power negatively or even horrifically, this does not limit their power. On the contrary, it strengthens and perversely legitimates their hold on power. According to Foucault (2003), the bureaucratic or administrative grotesque grants “a striking form of expression to the unavoidability, the inevitability of power, which can function in its full rigor and at the extreme point of its rationality even when in the hands of someone who is effectively discredited” (p. 13).

Applying this logic of the bureaucratic grotesque to the neoliberal-methodology-machinery, we look at a broken engine that we know is failing us intellectually, spiritually, and more often than not also somatically. Data transform themselves into self-regulated, self-directed, entrepreneurship and research capital. As a result, researchers’ perceptions of what counts as data or knowledge might become too confusing and overly challenging. Academics are being force-fed with preapproved desires that deregulation, data autonomy, and efficiency amount to excellence. This type of surreptitious social control leads to passivity and restlessness (Horkheimer & Adorno, 1944). We crave academic data friendship whereas others utilize the productive paradox and perhaps unforeseen liberties granted by neoliberalist ideals.

In the Aftermath of Aporetic Data and/or What Might Qualitative Researcher Do Now?

Walking over broken (data) rubble bears implications for helping to shift dusty paradigms within qualitative inquiry, such that “messy vitality” takes the place of “obvious unity” (Castaldo & Mocchi, 2016; Venturi, 1977). We do this through casting doubt upon any singular context of data, as all data are aporetic in that it exemplifies multiplicity and the continuous becoming of complexity and contradiction. Indeed, positioning human subjects in relation to data, data multiply and become. As such, they embed themselves within intersecting cultures to provide a lasting example of shifting demographics within diverse cultural spaces, as “hybrid” elements are now rapidly replacing any notion of “pure” ones. Moreover, data friendships divert neoliberalism, confuse the ideology, and multiply the multiplicity. They produce aporia and undecidedness of the system. And data friendships that are open—and spoken up against powers to be—are those that allow qualitative inquiry to remain nonstatic and ethical, yet very productive. From this, we see how truth-telling in/through qualitative data help to disrupt public discourses as a “both-and” paradigm becomes privileged over an “either-or” and “richness of meaning” finds itself more suited to liberatory data that than any universal “clarity of meaning” (Mayer, 2012; Montgomery, 2011; Venturi, 1977). Wait- “data”? Power?

Breathing the aftermath of our aporetic data, qualitative researchers might look at this productively broken and hesitant (data) machine as a grotesque piece of speculative fiction. Data in qualitative research are no longer what they used to be. New imaginings, becomings, and reconceptualizations of data have changed inquiry and researchers’ relationality and their thinking-doing. Many qualitative researchers might have become more hesitant and unsure about data. Data have become a question rather than an answer. In addition, data cannot be taken for granted but they need to be invented and created again and again in different spacetimes. These contemporary notions of data also raise different questions about data. Qualitative scholars might no longer ask definitional, singular, and categorical questions about what is data but how, why, when data do. And, perhaps most importantly, what might data become? (Koro-Ljungberg, Löytönen, & Tesar, 2017). Wait- how are data?

At this juncture, scholars’ own perception of what counts as knowledge, and data, might become too confusing. Quite frequently, scholars are being force-fed preapproved desires that efficiency is excellence and bigger is better. This type of surreptitious social control often leads to passivity and restlessness (Horkheimer & Adorno, 1944). Even though docile minds go along with this trend, with the growing of data comes the growing of confusion. Qualitative researchers know they are producing and creating but they do not necessarily know not what. Data may overtake data in productive ways and language fails. Yet, scholarship and scholars remain—frontstage center, maybe the disempowered and vacillating deus ex machina. They crave data friendship. Wait- where are data?

For neoliberal data to emerge among parrhesia, academics may suggest a less human-centered and post-anthropocentric approach. Against the horizon of hope (Braidotti, 2005; Braidotti, 2013), Rautio and Vladimirova (2017) imagine data friendship as “becoming other” where one will go beyond simple analytic acts of thematic extraction paid unto data. Befriending (data) snow, they then live through and with data in all its sensuality, which brings audible, visual, tactile, gustatory, and olfactory experiences. More than this though, they open the possibility of snow anthropomorphically acting out the relationality of friendship in the ways that it continually changes, evolves, or gives way entirely to nothingness. When data are befriended, researchers practice a level of unwavering and very necessary attachment. Latour (2004) surmises that throughout life, one’s natural curiosity and sensitivity shapes this ability to become attached to data. Thus, the data relationship forms just as naturally and quickly as the feeling of curiosity and being affected. The relevant “unit of analysis” then becomes not separate and distinct entities under examination, but the post-attachment interdependency between the snow and human (Pickering, 2005). Under this argument then, a skilled academic’s most significant skill, when producing data, is the skills needed to get attached and to befriend others. Wait- why are data?

Academics thus contrast the technological inventions and methodologies that are employed to simplify if not to speed up the process and the outputs. Under the neoliberal ideology, some qualitative scholars are also compelled to tame and reject the very purpose of their inquiry. Some of the methodologies that scholars have worked through are still not accepted in many places in the world and neoliberal thinking has become hostile to local traditions. Paradoxically, neoliberalism and its schemes of funding and focus disregard deregulated and less controllable “post” and “new” turns in methodologies, as they need seductive and docile data, to convince local and indigenous tribes that there is ONE useful, effective, independent, important, and correct pathway to govern the human subject—to govern any kind of academic data friendships. Wait- when are data?

Yet, with such an abundance of data collected, the challenge lies not only in translating this overproduced information into knowledge but also in communicating the value and importance of Data Capital, friendship, social, economical, methodological, past and presence to the participants, readers, and the public. The increasingly unmanageable amounts of data, spurned by our information overload economy, might trigger paralysis in researchers. This is of particular concern in our neoliberal climate, as many scholars are systematically encouraged to capitalize on these rigorous and powerful data collection strategies so prized in the literature. Without attending to highly controlled and collective data, scholars warn us that we risk producing “incomplete answers to research questions and potentially inappropriate inferences based on findings” (Ercikan & Roth, 2006, p. 14). Wait- “data”? Now?

A dim glow edges from the amphitheater side curtain. Enter data friendship stage left. Begin again. What this data friendship does to neoliberal design is it breaks open a provocative space for new and unable-to-regulate movement in researchers, data, and surroundings. Knowledge/truth no longer resides in the domain of conquer and destroy. In digging up ever-novel ontologically significant research companions, perhaps qualitative researchers can unearth different strands of inquiry and unforeseen discursive practices aligned from altogether divergent networks of power. Such acts of truth-telling and searches for the immanent other, friend, concept, or practice can allow neoliberal data to ground themselves (through us) in diverse realities of being/acting/reacting/embodying/responding/interacting in the world. It is only in this way that we can teach our tiny organic data engines to fly. Wait- fly? Why?