Paper I: “Personal data in an uncertain world”

How to reference this paper:

Pink, S., D. Lupton, M. Berg, P. Dourish, A. Dyer, V. Fors, E. Gómez Cruz, H. Horst, P. Lacasa, John P., S. Sumartojo, and E. Witkowski (2016) DATA ETHNOGRAPHIES (1): personal data in an uncertain world. Available online at

DATA ETHNOGRAPHIES (1): personal data in an uncertain world

Sarah Pink, Deborah Lupton, Martin Berg, Paul Dourish, Adrian Dyer, Vaike Fors, Edgar Gómez Cruz, Heather Horst, Pilar Lacasa, John Postill, Shanti Sumartojo, Emma Witkowski

Big Data has rapidly become a core thing for the social sciences. It is a process, a form of digital materiality, and a field of research and activity. It has also become something of a promise for society, government and companies, offering the idea of endless knowledge possibilities (even if these might be able to be played out). In fact, Big Data stands for a layer of not necessarily determinate or visible knowledge that emanates from and permeates everyday life, organisations, government and activism. It raises concerns, anxieties, opportunities, and has generated a new academic journal Big Data and Society as well as new institutional contexts for studying Big Data (such as the Data & Society Institute in New York).

This first jointly authored workshop paper reports on the deliberations of the first Data Ethnographies Lab workshop. It is a position paper, since its primary aim is to establish a position for Data Ethnographies as we continue our workshop series throughout 2016. This paper refers to some of the key ideas outlined in the presentations given by our first two speakers in the series (Sarah Pink and Deborah Lupton). However, as our workshop was largely discussion-based it also represents ideas shared and discussed and terms coined by the group members and authors.

Digital Ethnographies

Figure 1: The authors assembled at RMIT for Data Ethnographies (1). Photo by Edgar Gómez Cruz.

As academics we are just as subject to the data world as anyone. As Edgar Gómez Cruz reminded us, we live and work in an academic context where metrics have come to play a major role in the ways that our careers and everyday working lives are structured, from citation indices which stand in for academic reputation and Key Performance Indicators that determine the amount of time allocated to research as well as our promotion and job security. There are a number of anthropological and sociological commentaries on the metrification of academia, which place the academic subject in almost auto(data)ethnographic focus at the centre of the debate (including a chapter of Lupton’s book Digital Sociology on the digitised academic and a blog post on the quantified academic self).

Yet at the same time, we as academics occupy an ambiguous position in this world, where it appears that big data might also assist us in our task of understanding and calling for a ‘better’ world. Big data analytics, it has been argued, could herald a new epistemology for understanding the world in their ability to be used not just to answer questions or to analyse a state of affairs as it is, but also to pose questions about the world; as Rob Kitchin puts it ‘Big Data analytics enables an entirely new epistemological approach for making sense of the world; rather than testing a theory by analysing relevant data, new data analytics seek to gain insights “born from the data”’ (2014: 2). We are now in a phase of the study and interrogation of Big Data study which has moved beyond arguing against Chris Anderson’s (2008) suggestion that big data will replace all need for ’empirical’ studies to one where scholars are trying to understand the complexities of what data more broadly is and can be and how people sense and make sense of data (including data science and data harvesting professionals). It is also a context where this ‘data sensing’ is a concern and practice for companies, educational institutions, government agencies, security and policing agencies.

However, the emergence of Big Data as a field of data analytics and of knowledge about the world also indicates a glaring gap which researchers have not yet developed ways to account for fully: for all the opportunities that Big Data offers us to view the world differently to the way it was statistically rendered in the past, its meanings are always relational to the contingencies and the specificities through which it is produced. That is, it depends on how people, organisations, government, activists and others engage with the world and through this shape the everyday occurrences, logics, anxieties, comforts, hopes and aspirations through which Small Data is made. If we do not understand Small Data we stand to gain little of the value that the current surge of interest and investment (time and funding) in Big Data ought to merit. While this gap has been acknowledged (Kitchin 2014), and some arguments have been made for bringing together, for instance ethnography and big data as complementary approaches (eg Curran 2013) there is a distinct lack of research aimed towards resolving it.

In February 2016 the authors of this paper met to begin to probe such questions in the first Data Ethnographies workshop to be held by the Digital Ethnography Research Centre (DERC at RMIT, Melbourne). DERC researchers (Sarah Pink, Heather Horst and Larissa Hjorth who are leading the workshop series) had begun to realize that rather than Big Data being something that ‘ethnographers don’t do’ it in fact implied a field that needed our critical attention. This is particularly so in a context where since the emergence of ‘digital methods’ (as a system of thought that limits and shapes a narrative of what is possible with particular digital methods) often closes down the analytical possibilities that are present in our encounters with digital data, media and technologies. Anthropological ethnography as a research practice conversely opens up the field of enquiry, to account not only for what research questions directly ask, but for the issues they raise, the leads that emerge from our encounters with people, things and processes. Significantly, an ethnographic approach invites us to account for the experience of living in a world of data, the ways that people encounter, engage with and feel about data in everyday life. This includes investigation into the different way that data is produced by people, the reasons for this, and what habits and routines are reinforced or altered through their engagements with data, along with identifying the implications of this for how we understand how Big Data is constituted. One example of what such an ethnographic approach to data can do, can be found in Pink, Fors and Bergs (in press) work on visual and sensory methodologies for researching the experience of physical activity by the use of body monitoring devices. Here they argue that the body monitoring technology itself (and the data produced in its use) can play a key role in the process through which we seek to research and understand people’s experience of it.

There are therefore, effectively (at least) two sides to data ethnographies. That is what they can tell us about how people are learning to live in a world of data, and how they can provide new insights on what Big Data actually is and what we can know and learn about society from it. That is not to say there have been no moves to offer alternative understandings of Big Data. In particular anthropologists have offered significant accounts (e.g. Boellstorff et al 2015). However, as yet we know very little about how digital data are incorporated into everyday life practices, concepts and experiences. One of the first steps that our Data Ethnographies initiative will take in critically responding to the Big Data analytics flurry is to delve into our existing ethnographies of digital technologies and media in everyday life – that is the sites of everyday life where people participate in the production, navigation, and use of data.

How can we characterize data in a contemporary world?

We started our workshop with Sarah Pink’s introduction to the idea of Data Ethnographies relating to questions of what it might feel like to live in a world of big data, including people’s concerns, comfort, and the value that small and big data has in our lives. These ways of thinking about data are intended to acknowledge, but go beyond some of the existing (useful) attempts to define Big Data with a definition that accounts for the experiential, sensory and affective dimensions of living with data. This however is a challenging task since Big Data as a category is difficult to define. For example, in a series of articles Rob Kitchin identified Big Data as ‘huge in volume’; ‘high in velocity, being created in or near real-time’; ‘diverse in variety; ‘exhaustive in scope’; ‘fine-grained in resolution  and uniquely indexical  in identification’; ‘relational in nature’; and ‘flexible, holding the traits of extensionality … and scaleability’ (Kitchin 2014). His most recent analysis calls for an interrogation of the ontology of Big Data, suggesting that the most important of the previously identified characteristics are velocity and exhaustivity and emphasising that it is important that we acknowledge the seemingly obvious but previously ignored point that there are ‘multiple forms of Big Data’ (Kitchin 2016). We would add, in the spirit of this definition that all types of Big Data are drawn from small data that is continually or at least ongoingly produced but perhaps in ways that are not consistent across even the same technologies or in the trajectories of individuals (and that ethnography is particularly well equipped for researching such sites of production), and that might be contingent on the ways in which living in a world of data is experienced. That is, a closer emphasis is needed on the contingencies and circumstances under which data is constituted and experienced, and that these considerations also need to be accounted for in how it is defined.

The experience of data

Our first workshop focused in on the experience of data, and how this is configured in relation to the qualities and affordances of data as it is situated in everyday life. Deborah Lupton’s presentation focused in part on mapping out the place that data is playing in our lives, and in doing so offered us a series of alternative ways of considering what it means to live in a world of data.  We do not describe Lupton’s full presentation here but highlight in particular how by thinking about data through concepts that emphasise what it becomes when it is part of a lived reality, our perspective on what data means and can be, beyond Big Data analytics shifts. Particular salient to this are for example Lupton’s notions of lively data and data visceralisation, which offer a nuanced way of thinking about how data is impacting on our worlds.

Figure 2: Lively Data and Data Visceralisation explained graphically (Deborah Lupton)

As these concepts show, data is something that we feel the presence of in multiple ways, and not only does it shape the world we are in but that we also shape the way it is present in our worlds. It also forms part of the way that we experience everyday temporalities. These points are particularly relevant to personal data. However they also raise significant questions relating to how data comes into being, and is worked on and with across a range of contexts. This raises and includes the need for us to understand how people are making sense of and using big data in their everyday lives – including the everyday working lives of data scientists and analysts and companies and institutions that are seeking to use big data, and ethnographies of database design, big data storage and care.

Thinking about Data Ethnographies through Personal Data

Our workshop focused primarily on questions related to personal data, in relation to research into self-tracking by Pink and Lupton (this will expand to other research themes in later workshops). Self-tracking, practices, platforms and content constitute a site where the data that is harvested for big data analytics is produced and also where people’s everyday encounters with data play out, through data visualisations, alerts, sharing and more. Sarah Pink focused on her work on everyday self-trackers for whom their self-tracking data becomes embedded in a variety of mundane everyday routines, and whose very routines of use of self-tracking data and technologies tend to insulate them from feelings of anxiety about third party users of their data. Deborah Lupton discussed examples from several projects from her Living Digital Data program that showed how people conceptualise and come to terms with the liveliness of their personal data — or, in other words, how they gain purchase on their personal digital data assemblages and incorporate these assemblages into their lives. As she emphasised, living data has several meanings: data as part of and about people’s lives and the data themselves as having their own social lives.

We will not here dwell on the findings of the projects that were discussed, since they will form the basis for our Data Ethnographies book project in greater depth and length. Below we outline the discussion themes that emerged from our considerations of these two presentations, since our objective was to use the presentations to spark discussion and conceptualise what data ethnographies could mean, rather than to discuss the details of the findings of any particular data ethnography itself.

Self-tracking ethnographies are interesting for our task of understanding what Big Data stands for. Self-tracking data represents only a small segment of what has become a broad field of Big Data. However it offers us a route into studying the discursive relationship between big data as and small data: since Big Data is part of our environments on the one hand, while on the other the experience of living in this data-saturated world informs how we feel about data, and our data practices, anxieties and comforts. A core question we discussed in relation to self-tracking emerging from Pink’s paper, and that also inspired us to think about the same issues for other areas of small data, was what Paul Dourish coined as the comforts of data. Here the concern is on the one hand with how people use data to make themselves feel comfortable in the world, for instance, through everyday routines or ‘data practices’ and through the relationships they make between their bodies and their data. On the other it relates to the question of how people ensure that they feel comfortable about the ways in which their data is being shared or used in the world (i.e. by companies with rights over the data and by third parties). Lupton’s concept of data visceralisation raised similar questions about how we feel with and in response to data. The question this raises refers to how deeper and better understandings of the human activities, practices, routines and habits that develop around these comforts of data have implications for the meanings that Big Data could have beyond those attributed to it by conventional Big Data analytics.

Relational to the comforts of data are data anxieties. Self-tracking is increasingly practiced as a part of workplaces, schools, health care contexts, and more. It is becoming connected to health and life insurance risk calculations and customised premiums. This raises the question of how people are navigating self-tracking in these environments. Existing research has shown that people might resist the algorithms, viewing data as ‘dead’ or ‘stuck’ (Nafus), or as Pink discussed in her paper they might see their own data as incomplete, inaccurate and dispersed across platforms and devices in such a way that anxieties about third party use are quelled, or put to one side (Pink). Indeed an American market research survey found  that half of fitness tracker owners had given up using them; a third of owners had done so within six months of acquiring their device (see This itself merits further investigation.

The affective dimensions of data were emphasized, along with the notion of being overwhelmed by data. This could refer the feeling of being overwhelmed by the quantity of data available, for people working with data – either as academics or in organisations. The possession of data, but not knowing what to do with it or how to understand its meaning in everyday life also manifests feelings of being overwhelmed. Our concern relating to this is that when faced with such questions organisations tend to call on Big Data analysis to determine the meaning of data. This, as we have pointed out above, is only ever half of the story.

We also discussed the tangibility of data, and how it affords different ways for people to talk about, feel and conceptualise themselves and their activities in their environment when personal data actually can be printed out, visualised and touched, or when it is represented and made social. These re-enactments of corporeal activities provide new routes to uncover and articulate previously hidden and unspoken dimensions of people’s’ lives and embodied learning practices. Ethnographic approaches also offer us ways to explore the experiential qualities of data as it is materialised. That is, shifting the making the visualisation of data as information from its privileged position as ‘knowledge’, towards emphasising the greater relevance of how people come to know through their experiences of data when it is made tangible.

Towards a conclusion?

These ways of understanding how data is part of everyday worlds that can be investigated ethnographically also raise the question of how ethnography and big data analytics might be usefully brought together. Our discussions covered how data harvested from human self-tracking technologies, or social media is produced continually (even though there can be some stops and starts in the ways that individual users produce). Self-tracking data is visualized as cut-through representations of human activity, and there is made static. Deborah Lupton has referred to this as  data ‘made solid’ or ‘frozen’ (in ways that dialogue with the metaphors of liquidity that have been associated with big data) but because these visualisations are ongoingly produced, they stand for moments in a process, not end results. There can similarly never be a final result with big data, given their constant generation and therefore dynamic nature – or their liveliness, as Lupton has referred to it. Such Big Data, made visible and available, offers a remarkably interesting resource in and view of the continually changing world, which we as humans are also part of. This point echoes the processual and emergent approaches of contemporary ethnographic practice (Pink, Ardevol and Lanzeni 2016). Indeed this means that methodologically the possibility of bridging Big Data analysis and ethnographic research may be closer than initially appears obvious. Anthropological approaches to ethnography recognise that our research likewise takes place within the flow and movement on an ongoingly changing world, rendering anything that we might know ethnographically immediately to the past, while the ethnographer, her or himself stands for ever on the cusp of an ongoingly emergent immediate future. Future topics for discussion in Data Ethnographies could include how we could bring together understandings of ethnographic and big data analytic practice to work through a shared understanding of the temporalities of knowing then exciting opportunities for collaboration could play out.

To sum up: ethnography is uniquely positioned to answer to the questions of what Big Data means, but in particularly to respond to questions concerning the comforts of data and data anxieties — as well as resistances, reinventions and even ennui towards digital data.  Ethnography helps us to access the visceral qualities and affordances of data, and is undertaken precisely in those sites where data becomes lively. These affective domains of data concern in part how people derive comfort from data and how they navigate data in such ways that they feel comfortable with it (but might also feel uncomfortable, irritated, frustrated). But we also refer to the ways that corporations feel about data – the need to feel comfortable and reassured that an organizational approach to using other people’s data is responsible, ethical and legal – which will be a core focus on our post from Data Ethnographies (2).


Anderson, C. (2008). The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. [Blog] Wired. Available at: [Accessed 28 Mar. 2016].

Boellstorff, T., G. Bell, B. Maurer, M. Gregg and N. Seaver. (2015) Data, Now Bigger and Better! Prickly Paradigm Press.

Curran, J. (2013), Big Data or ‘Big Ethnographic Data’? Positioning Big Data within the ethnographic space. Ethnographic Praxis in Industry Conference Proceedings, 2013: 62–73. doi:10.1111/j.1559-8918.2013.00006.x

Pink, S., Fors, V. & Berg, M. (In Press). Sensory, Digital and Visual Methodologies for Researching the Experience of Physical Activity. I: M. Silk, H. Thorpe, & D. Andrews (red.), Routledge Handbook of Physical Cultural Studies. London & New York: Routledge