Datafication and data fiction: Narrating data and narrating with data

Introduction

Whatever their sources—sensor streams or written records, scientific instruments or ethnographic observations—data do not stand alone. They do their work in relation to multiple other entities. First, they do their work in relation to other data and to other data sets, through many different sorts of relations—providing supporting or countervailing evidence, for example, through massification, by means of aggregation, or in the nature of their singular difference. Second, they do their work with respect to systems of processing—computers, databases, programs, algorithms, formulae, procedures, classifications, and counts. Third and perhaps most importantly, though, they do their work in relation to people. They frame new understandings, reinforce assumptions or experiences, decenter expectations, challenge dominant narratives, reveal phenomena, hide problems, and justify decisions. In this last relation, though, there remains a critical mediator. Data tells stories in the ways in which it is animated, explained, offered, and shared (Gabrys et al., 2016; Sharon and Zandbergen, 2017). Papacharissi (2015) uses the term “digital orality” to describe the ways in which data is embedded in narratives that produce situated knowledge. The ways in which data works often depend crucially on the way in which they can be narrated, and the way that these narratives deal with what Pink et al. (2018) describe as “incomplete, contingent and fractured character of digital data”. As Veel (2018) importantly notes, the ongoing entwining of data and narrative does not only constitute a reframing of data, but is also part of a reformulation of narrative and narrative practice.

The material presented here reflects discussions among a group of ethnographic researchers who came together at a series of workshops at RMIT University in Melbourne to discuss contemporary topics around data from a distinctly ethnographic perspective. ¹ We came together not so much to share ethnographic accounts of data and data work, but rather, to ask what ethnography as a mode of inquiry might teach us about contemporary interests in data-driven scholarship and practice. As ethnographic researchers, we recognize that questions of perspective and voice are central to our methodology and to how ethnographic results are put to use. Our goal here is to draw on that experience in order to interrogate questions of how data speaks. Accordingly, we take up the questions of the relationship between data and narrative with an understanding that the two are deeply mutually entwined. We give no priority to one topic or the other, neither seeing data as inherently beholden to narrative nor narrative as entirely bounded by data. Indeed, we see neither data nor narrative as pre-given. Bearing in mind Strathern’s (2003) observation that ethnography is a method for generating more data than the ethnographer is aware of at the time, we recognize that the issue of how things become data or are taken to be data is itself fraught and complex.

We understand “data” broadly here. As ethnographic researchers, our own data is of disparate types; not just notes, transcripts, and observations, but jottings, artifacts, feelings, and experiences; as Ortner (2006) comments, ethnography is a practice that uses “the self … as the primary instrument of knowing”. We speak here also to data in other forms, including the sensor data and large-scale quantitative foundations of typical data analytics efforts. van Dijck (2014) borrows the term “datafication” from Mayer-Schoenberger and Cukier (2013) to refer to “the transformation of social action into online quantified data, thus allowing for real-time tracking and predictive analysis.” (p. 198). We use the term here in a related manner, focusing not so much on the production of quantified data, but on the processes of symbolic and imaginative work that underlie coming to think of something as “data” in the first place. We use it too, of course, for its resonant play with the term “data fiction” (Nadim, 2016), highlighting not just the inevitable partiality of data but also its purposive role.

We think of the relationship between data and narrative in terms of two “scalar moves” in Big Data settings. The first move is the move from small to large, or from datum to data set, the massification involved in the collation of large collections of information. This move depends on logics of equivalence, and the claim that these data are sufficiently “alike” as to be able to be combined, compared, added, and divided, as exemplars or instances of a singular phenomenon, claims that have been productively examined in a range of domains by Martin and Lynch (2009). The second move is the move from large to small implicit in the drawing of conclusions or categories from data analysis. This move, by contrast, depends on the logic of correspondence; it rests upon the auspices by which we might say that a feature in the data corresponds to a feature in the world—a class of consumer, a type of event, or an item of interest in the domain about which the data “speak”.

In both of these moves, acts of narration are key; both logics are narrative resources of the sort that ethnography often takes as central (Boellstorff, 2013). Our purpose here is to find these narrative acts at work and to examine some of the consequences and limits of narrative within the broader processes of data-driven analysis.

An anchoring case

To contextualize this discussion, we offer one example from prior work, discussed in greater detail elsewhere (see Shklovski et al., 2009, 2015; Troshynski et al., 2008). This case centers on efforts made in the mid-2000s by the California Department of Corrections and Rehabilitation to develop and test a system for the continual monitoring of the location and movements of paroled sex offenders.

Responding to legislative moves elsewhere in the United States, the department was interested in whether such a system was both technologically and organizationally feasible. Technological feasibility concerned questions such as battery life, accuracy, reliability, and the cost of tracking technologies. Organizational feasibility, on the other hand, turned on questions such as the organization’s ability to manage, store, and work with the data that such a system would generate.

When released on parole, parolees are subject to a series of parole conditions, violation of which may cause them to be incarcerated again. These might include participation in group therapy processes and other rehabilitative schemes. Further, a series of spatial limits are typically placed on their movements, requiring that they stay within a range of, say, 25 miles of their place of residence, and excluding them from zones of 2000 feet around public parks, playgrounds, swimming pools, libraries, and schools. Conditions of this sort were laid down even before the deployment of GPS technology as part of parole, but GPS was viewed as a mechanism by which these conditions could be monitored and enforced. Given that the GPS tracking device itself then enters into the parole process, parole conditions require parolees to ensure that the device is not removed and that it is maintained in an operable state (e.g. kept charged.)

We ² studied the process of conducting this evaluation from two perspectives, that of the parolees themselves, and that of the parole officers who managed their cases. In each case, we found narrative central to the ways in which they managed, oriented towards, and understood the data generated by the system in relation to their own experience.

From the perspective of the parolees, we will draw attention briefly here to two particular issues. The first was the opportunity to use the data in order to frame alternative narratives of their own movements and habits. That is, repeat offenders who had previously spent time in prison and outside were familiar with the way in which they might be subject to police “hassles” and often targeted for attention when incidents, scares, or suspicions surfaced about sexual predation in the neighborhoods in which they lived. On these occasions, they might find themselves needing to be able to account for their movements or generate evidence about their presence and absence from particular places (in order, for instance, to signal that they had not been at the location of a particular incident that had taken place. Such evidence was often difficult to produce in usual circumstances, but the GPS monitor that was now attached to their ankle provided to the parolees a technological source of evidence of their own innocence in the face of these hassles. They could now point out to police officers that their location was recorded and available directly to criminal justice authorities, and that this record would show that they had not been present at the scene of an incident. The fact that the data record was not self-generated but rather data of their movements authorized by the state made such “testimony” more reliable than that of friends, workmates, or neighbors, while at the same time saving the parolees the potential embarrassment of having to explain to friends, workmates, or neighbors why such evidence might be needed. In other words, the location data that was being collected offered an alternative narrative of their movements to the potential narrative being explored by police officers, and helped the parolees to counter those narratives.

At the same time, the fact of the data—the presence of the device, the condition of monitoring, and the presence of location data as a basis of individual’s engagements with the authorities—also itself supported particular narratives of identity. That is, for repeat offenders who were subject to the parole program and participated in the trial, the permanent presence of the tracking device strapped to their body reinforced an account of them as “sex offenders”. It was a reminder of prior crimes and reinforced a notion that they had come to adopt of their crimes as a key element of their identity. They referred to it as a reminder of who they were and a constant presence that helped them guard against possible future infractions or the conditions that might lead to recidivism. This aspect of their experience became particularly marked in our data because, during the course of our study, a new law in California passed that mandated lifetime, real-time location monitoring of sex offenders. Where, in the initial stages of our study, we had worked primarily with repeat offenders who, on leaving prison, had opted to participate in this program, the subject population for the later stages of the study, after the new law had passed, was markedly different, including a number of people for whom “sex offender” was by no means part of their self-image. These people, for instance, might never have served time in prison, but who had an old conviction for a minor sex offence (such as public lewdness or nudity, which might in some circumstances arise from public urination) and who now found themselves subject to a law that they felt was really designed for someone else. This group also found that the presence of the device—for themselves as much as for anyone else—re-narrated their identity in ways that they strongly rejected but could not ignore. Perhaps more importantly, it embedded them within a prior, pre-established narrative that seemed to close off possibilities for the ways in which they might be able to present themselves to others and navigate social encounters.

Questions of narrative, and the processes by which data could be placed in context in order to become meaningful, also manifested itself regularly in the work of the parole officers, who managed the data streams being generated by the tracking devices. Again, we focus on just two of these here, and refer the interested reader to more detailed elaborations in prior publications.

As evidenced by the nature of the parole conditions, detailed above, space, spatiality, and movement is an important element in the logic of parole enforcement for sex offenders. Questions of the places that people might be, what else might be going on there, with whom they might interact, and so on, were central elements of how parole officers think about cases and about parolees. Accordingly, the simple information that the GPS units might provide—essentially latitude and longitude—needed to be transformed, for the parole officers, into signals of “good” and “bad” places—the sorts of places where someone might be and the sorts of places that they should avoid. Similarly, patterns of movement mattered; deviations from the norm, systematic patterns of “hanging out” in one sort of place or another, and the broader implications of such patterns were also of great interest. Consequently, the parole officers found themselves needing to narrate the movements of parolees in their case-load in different ways; as people “on the right path,” as people who might be exposed to dangers or problems, as people who were in stable patterns or “on the slide”. The problem for parole officers, who were now encouraged to replace “eyes-on” surveillance with digital, is that it was difficult for them to determine what sorts of places were showing up on the map. They might know some places, but specifics are important (for instance, distinguishing whether the subject is in a hardware store, or in the bar next door). Parole officers found themselves needing to take the logs of someone’s movements and visit those places in order to establish real-world correlates to the data in the system, and in order to figure out what story someone’s presence in a place might tell, or how their presence could be slotted into a series of conventional narratives about the experiences of parole and post-prison life.

Finally, here, the other key act of narration in which parole officers engaged was one of their own actions and their sense of responsibility. Especially in the later part of our studies, after mandatory monitoring had been legislated, parole officers found themselves managing their cases essentially through the lens of data, rather than through the combination of regular meetings and surveillance that had earlier characterized their work. What is more, the granularity of the data, both spatially (the geographical resolution that it offered) and temporally (the frequency of tracking signals) radically reconfigured the work that they had to do. Prior to the technology deployment, a parolee might have a parole condition that required them to stay more than 2000 feet from a school, for example; after the deployment, on the other hand, any occasion on which a parolee was, say, 1993 feet from a school was digitally signaled and needed to be accounted for. Since it would clearly be politically unacceptable for there to be signals on a digital trace that were left unexamined in the context of some later criminal act, all failures to meet parole conditions needed to be investigated and accounted for within the system. Much of this work, needless to say, was relatively insignificant—the result of walking down the wrong side of the road, or taking a bus through an unfamiliar neighborhood. Nonetheless, the very fact of a digital trace produced the necessity of an account. Parole officers talked about this in terms of the responsibilities that they took on, and which they could effectively discharge. They felt a responsibility towards the parolees whom they supervised, but found that the reduced amount of time that they now spent with them made it harder to fulfill those obligations. They felt a responsibility towards the public at large, whom they sought to protect from harm through recidivism or as a part of the broader criminal justice infrastructure, but felt that this was also harder to discharge given the difficulty of distinguishing “relevant” from “irrelevant” signals in the data. The responsibility that they felt that they could still discharge, and which occupied an ever-greater amount of their time, was one towards organizational processes, or even to the data itself. They suggested that, while the other responsibilities may be beyond their reach, they could ensure that organizational accountabilities (such as the requirement to document and account for problematic data signals) be maintained, even if this necessitated a shift in their sense of their own responsibilities and their professional role.

This case provides some anchoring for a broader inquiry into questions of the relationship between narrative and data. We see multiple actors engaged in narrative acts of different sorts: around the data, with the data, before and after data, in line with or in contradiction to data, and more. Further, these acts of narration tell different stories for different purposes in different moments. In some cases, it is only through narration that the data can speak; in others, narration extends the data’s reach. We find, too, people struggling with the problems that what was once narrative is now data, and that the data must be treated or responded to in ways quite different than narrative might once allowed. Finally, we also note the work involved in making data work: as Thornham and Gómez Cruz note elsewhere, “discursive, operational and material constructions of data obscure and mask the enormous effort surrounding data that is necessary to position it as self- legitimating and self-fulfilling” (Thornham and Gómez Cruz, 2016: 8).

This example begins to reveal the way in which sensor-derived data nonetheless needs to be accounted for, both in terms of its production and in terms of its consequences, within social settings. In seeing or framing data as a trace of an event or an action, we inherently invoke narrative elements: actors, motives, expectations, actions, types, histories, proclivities, habits, intents, and on. It is these elements that help us make sense of data as it moves around in the world: as it moves from technical settings into social or organizational ones, for example, or as it moves between different institutions. In these settings, data are narrated differently, and made to operate within different interpretive frames. By the same token, within each of these settings, conventional (if evolving) sets of tropes are invoked in order to make sense of different data streams; we might expect to see different data streams generated by domestic appliances and embedded devices linked by an “Internet of Things” in different homes, but we nonetheless expect some common narratives of domestic life to appear—narratives of rhythm and routine, of intimacy and care, of chores, celebrations, and sleep.

However, the goal of this paper is not to explore a specific ethnographic case of the entwining of data and narrative, but rather to explore more broadly how it is that data practices and narrative practices are entwined, and with what consequences. So we want to move here from this material to a broader conceptualization, albeit one that remains thoroughly grounded in ethnographic methodology.

An ethnographic perspective

There are a number of reasons why, as a group of researchers with a particular interest in ethnographic methods, we might take up this topic—and similarly, a number of reasons to believe that ethnography has some particular insight to offer here.

One reason is that ethnography proceeds not just by telling stories but by tracing them, analyzing the spread of ideas, expressions, attitudes, and ways of thinking through organizations, communities, and cultures. A sensitivity towards the work that stories do and the way that they are used to make sense of goings-on, to orient others towards shared concerns, and to develop a collective repertoire of thought is at the heart of the ethnographic enterprise, and so it is perhaps not surprising that, as ethnographers, we should be particularly attentive to these elements of data practice. Further, ethnographic approaches provide a set of tools for tracking and unpacking the way that data and narrative are bound together.

A second aspect of ethnographic practice that renders it particularly useful here is its attentiveness to the processes by which inert objects are enlivened, and particular things—objects, places, practices, and ideas—are made to “live” within cultural settings. Proceeding from the recognition that the meaning of cultural things is not fixed but ongoingly produced by people in the ways in which they talk about, make appeal to, explain, contest, celebrate, and debate the significance and values of those things, ethnography sets about unpacking those practices by which meanings are produced at particular times and in particular places. The enlivening of data and data sets as they are mobilized in and through narratives falls squarely within the ethnographer’s standard operating procedure. It is not by chance that there is an increasing ethnographic interest in studying algorithms, an essential element on how the narrative about data being “objective” is shaped (Dourish, 2016; Gillespie, 2013; Seaver, 2017).

Finally, here, ethnographers have been particularly sensitive to the ways that their research practices are not just broadly scholarly but particularly literary and textual. The -graphy of ethnography renders the writing process indivisible from the research endeavor, and questions of voice, authority, partiality, ownership, perspective, and polyvocality have been widely debated as they arise in the production and dissemination of ethnography’s scholarly outputs. So the idea that data must be narrated to be made to work within specific communities and in particular moments is not framed here as a criticism or a problem to be resolved; rather, the very inescapability of these concerns demonstrates how important it is that they be examined and taken up as objects of attention.

Elements of data narrative

A turn to narrative here is essentially a semiotic move. Rather than see data as indexical—that is, as existing by dint of an actual relation to events or objects—we start to see them here as symbolic, and so as taking on meaning through processes of interpretation, “translation” (Bolin and Andersson Schwarz, 2015) and “framing” (Markham, 2013). ³ The process of narration is one by which data is found to be meaningful, and indeed, as Genevieve Bell notes, data has responsibilities: “a story it was compelled to say” (Bell, 2015: 19). Taking a step back, then, we can begin to ask, in what ways do data and narrative interact, and with what consequences? What conceptual commitments come along with a narrative practice in data-centered settings? How does data come to signify, and with what patterns and constraints? These commitments and consequences help to make clear why thinking about data and narrative together provides value within the social study of Big Data and digital data practice.

Conclusions

The argument that data has essentially supplanted theory—explicit in popular writing, but still present, if implicit, in much adoption of data-driven technologies—is the idea that launched a thousand data science programs (c.f. McKie and Ryan, 2015). From predictive policing to diagnostic visual analytics, data is imagined to speak for themself. This is a claim, of course, that has been subject to significant critique from the social science community, with examinations of problems of bias (Angwin et al., 2016), of error (Garnett, 2016), of locality (Bartlett et al., 2018), of power (Currie et al., 2016), and of opacity (Burrell, 2016).

We share these concerns with the limits of data but seek to bring into focus the pragmatics of data practice. In particular, we have been concerned with the processes of sense-making in and around data, and the logics of equivalence and correspondence upon which data sense-making depends. Data makes sense only to the extent that we have frames for making sense of it, and the difference between a productive data analysis and a random-number generator is a narrative account of the meaningfulness of their outputs. Moreover, one of the most powerful narratives about data is precisely that it demands no interpretation or narration because of its self-evidentiary character. Bringing to this conversation our experience as ethnographic researchers, and as part of a broader investigation of relationships between data and ethnographic methods, we have been especially concerned here with questions of narration.

The particular significance of the narrative perspective is both how it animates a series of culturally-available tropes—actors, motives, encounters, and on—and also how it lends a temporal arc to data and the objects that the data is read to represent. These speak importantly to the cultural embeddings of data narratives, and perhaps to questions of “decolonizing” data (c.f. Smith, 1999) or at least recognizing the importance that these embeddings play in the creation of meaning and the mobilization of action around data that might otherwise seem to speak for itself. These concerns shape not just the encounter with data or with the ways in which data are put to work; they concern too how data is imagined to flow, with how data is seen to represent, and with the ways in which data processing is understood and enacted (Passi and Jackson, 2016).

Indeed, the narrative power of data in itself is remarkable. As Loukissas and Pollock (2017) argue, the upset of the 2016 US presidential election, and the difference between the results and pre-election statistical analyses and predictions, did not, it seems, weaken people’s confidence in data, despite the fact that the data had let them down. Rather, data’s allure remained intact, as commentators argued that, if the data did not anticipate the results, then we must simply not have had enough of it, or not have had the right data. Here, data assumes a role itself in a broader narrative.

The approach we have taken here is one that is particularly ethnographic not in the sense that it has arisen through an ethnographic investigation but rather in that it is informed by an ethnographic outlook. So, in exploring narration in data practices, our goal is not to casually undermine data efforts but rather to resituate them within social circumstances and cultural settings (Neff et al., 2017). A focus on narrative does not necessarily imply falsity; stories, after all, often conjure fictional worlds in order to tell deeper truths. What is interesting instead is how narrative works, and then how it is put to work within the context of data. Taking a cue from Latour (2007), we are interested in the way in which data itself may not sustain truth claims without a narrative framework to make it effective. At the same time, when narrative enframes data, it does so in ways that are pre-figured and embedded in particular locales, cultural settings, and moments in time. As ethnographic researchers, we see much opportunity in the study of narrative opportunities and limits in analysis of data and its social contexts, and indeed, as suggested by Ford (2014), this may itself constitute a new basis for collaborative engagements between ethnographers and (other) data scientists.