INTRODUCTION

“To do something interdisciplinary it’s not enough to choose a ‘subject’ (a theme) and gather around it two or three sciences. Interdisciplinary consists in creating a new object that belongs to no one” (Barthes in Clifford 1986)

The relationship between numbers and human and cultural narratives has historically oscillated between trying to forge relationships with each other to mudslinging criticism at each other (Hammersly 1989). The relationship has never been harmonised but instead has been based on a game of justifying importance over each other. Which process is the most relevant? Which is the most ‘true’? Which is the most reliable approach? These questions form some of the common pillars that frame this uncomfortable relationship.

With the ever-increasing visibility of Big Data we are seeing this debate develop further. Gone is the tennis match between ‘quant’ and ‘qual’ and welcome the tennis match between the big serving Big Data verses the human focused approaches to data collection, especially ethnography. How this match will end is unpredictable since little is known about the new big server, however, if we look back at history we might get a clue that neither will run out winners. By using the example between ‘arm-chair’ anthropologists and the birth of ethnography at the turn of the 20^th Century, we can learn that criticisms towards Big Data carries many of the same characteristics directed at arm-chair anthropologists, by the then new anthropologists (especially Malinowski) (1922) that promoted a new field work, ethnography.

This paper will focus on two areas. The first one will be based around a cultural analysis of the fast emerging debate between Big Data and ethnography. It will argue that instead of recreating similar debates around the need for a human centred approach as well has statistical representation, we need to re-evaluate the ‘ethnographic offer’. The second will make the claim that as ethnographers, we need to take a reflective approach to this debate and instead of trying to enter into a similar debate around relevance of data collection, we should explore how the discipline of ethnography needs to develop and fundamentally re-shape its offer within the non-academic space.

This paper will make a bold attempt at beginning of a new discussion on ‘what ethnography is’ in relation to Big Data and make the point that ethnography needs to create a new paradigm that shifts from shaping its identity by claiming to understand the ‘whys’ around cultural behaviour to one where it and Big Data are actually focusing on the same epistemological field, one that is situated within human behaviour and crucially cultural interpretation. Both should therefore be seen as interpretive epistemological approaches to analysis for human behaviour and cultural dispositions. Therefore, I want to try and explore ways in which we can move away from positioning ethnography as something that Big Data needs to one where both are actually ethnographic. In other words, both explore the ‘big’ by understanding culture holistically. They should therefore both sit in a similar space which I call ‘Big Ethnographic Data’.

Before developing this argument, I want to state that this paper should be read as a document to generate discussion that will at least allow us to step back and begin to develop the core offers and merits of an ethnographic approach and theoretical thinking. It is not set up to be fixed. And as the author, I would hope that it would be challenged and developed through discussion and debate. I am not at this stage offering a new model but instead set out the infrastructure to start building one as a community.

DEFINING BIG DATA – A CULTURAL ANALYSIS

Defining Big Data seems to be a complex task since it appears to have the ability to quantify so many areas of our lives, if not all areas of our lives through digitalisation and ‘datafication’. The technology revolution has provided platforms that have enabled data to be collected on mass and at a more rapid rate. Trawling through many definitions I have come across two broad areas of interest. The first is its ability to collect data on large issues relating to, for example, health, illness, economics, planning and energy. Here Big Data works as a means of understanding human centred macro trends. A good example of this is Google Flu Trends¹ that can track in almost near to real time influenza epidemics so to help direct medical resources to the most important places. Big Data is also being used as a ‘preventative’ approach to predicating and forecasting within financial and retail spaces. Leading US banks such as Wells Fargo and Bank of America, consumer goods companies such as Coca-Cola and 3M, and retailers including Wal-Mart are all using Big Data analytics to improve the running of their business models and to anticipate changes in demand before they actually occur (Financial Times, 2013).

The second area, and the one I want to focus on for this paper, is how Big Data makes cultural profiles of who we are. The who-we-are part relates to every aspect of our lives: from what political dispositions we might have, to what we buy as consumers, to the media we attach ourselves to and to who we like and who we worship. Journalist, Steven Poole, has recently written in the New Statesman that through “Big Data analysis, the “cloud” comes to know an awful lot about us. Simply analysing a person’s Facebook “likes” can identify a person’s sexual orientation or history of drug use” (2013). Amazon can now understand more about its readers through e-books than ever before in the publishing world. They can understand the types of books that people enjoy, the length of time it takes to read and when and where in the book people ‘drop off’. Commercially, the outcomes for Amazon are massive, because they are able to understand the taste-zeitgeist of their readers and potentially manufacture books to fit into their readers’ tastes. Or at least dictate to authors how their books should swing. This is revolutionary within the world of publishing that has traditionally relied on reviews and sales to cage consumer taste. Alex Alter (2012), writing in the Wall Street Journal explains that:

“In the past, publishers and authors had no way of knowing what happens when a reader sits down with a book. Does the reader quit after three pages, or finish it in a single sitting? Do most readers skip over the introduction, or read it closely, underlining passages and scrawling notes in the margins? Now, e-books are providing a glimpse into the story behind the sales figures, revealing not only how many people buy particular books, but how intensely they read them.”

What we are seeing is that through social media and technology, Big Data is beginning to move in to a position where it can generate mass social profiles on groups and individuals that offer more than just “what we do” to “what we like” and interestingly, “how we think”. Here we are seeing “datafication” exploring and trying to understand the cultural spaces where human behavior is shaped, similar to what the French anthropologist, Pierre Bourdieu, called the habitus (1977) which focuses on how values, dispositions and tastes are formulated and shaped within social groups in an everyday context. Crucially, the habitus is acquired within the ‘field’ where the relationship between individual subjectivity and social structures is molded. I will explore the similarities in more detail between what Big Data is attempting to focus on and what ethnography does focus on later in this paper. But what is important about Big Data sitting in the space of culture is that the cultural data becomes an attractive area of investigation and a prized asset and commodity. For example, The Financial Times recently wrote an article called Building a Big Data Strategy (June 4^th, 2013) where the journalists claimed that the “data-driven economy is upon us. First-generation internet companies such as Google and Amazon, have demonstrated “data alchemy” – turning data into gold – and now others realise that great opportunity can be seized by using Big Data and the big ideas that come along with it”.

It appears that Big Data is not just about collecting mass amounts of data relevant to human life it also attempts to provide a key outcome of such data – ideas.

QUESTIONING BIG DATA – A GENERAL VIEW

Along with all the positive waves associated with Big Data comes a tsunami of concerns about how Big Data is structured and what it can or cannot offer and for whom and for what purpose. Protecting our privacy against the ability of every part of our lives to be monitored has led to journalists, academics and industry professionals to call for an ethical code that will protect our individual and social privacy so that all powerful bodies do not control what we do through a Foucauldian (1977) gaze where surveillance makes our bodies become “docile”. This takes place under the structured and psychological system of self-regulation of the body and self thus not knowing when we are being watched and monitored. Like prisoners, we act out in a way that makes our behavior suitable to the needs of the institutions of power, or to use Althusser’s (1970) term – the State Apparatus.

Jane Frost, Chief Executive of the Market Research Society in the UK explains in Marketing Magazine (May 30^th 2013) that issues around data privacy are now crucial so that large organizations understand limitations in what they can or cannot have access to. She makes the important point that:

Businesses need to provide clear explanations of why they want personal data, what it will be used for and, critically, what steps they are taking to ensure that they are acquiring and using data in a responsible way…. At the moment, there is no way for consumers to own their data and tie it in with their right to be forgotten. People are requesting data privacy more and more but there is a time when data will suddenly be out there and people will be surprised by how it is being used. Customers see organizations as responsible for the whole supply chain meaning that trust and transparency must be present at every point of interaction.