ETHNOGRAPHY QUESTIONING BIG DATA

I am interested in how the commercial world of ethnography has responded to Big Data’s popularity. This is partly because I am an anthropologist and ethnographer that works in the business world, but also because I think the current discourses around what Big Data is offers ethnography the conceptual space to deconstruct its own offer, both internally within the ethnographic discipline and to the outside world.

I want to explore some important points that have been made from within the ethnographic field so as a means of marking where the current arguments are situated. Later on in this paper I will take these to understand how we might re-think the ethnographic offer in relation to Big Data.

Core to ethnography is its ability to understand everyday life and how humans, as social beings, make sense of their worlds and social structures. Using theories on ritual, symbolism, metaphor and weaving these with theoretical discourses around politics, religion, identity, gender, power, art and consumption (to name a few) provide the ethnographic field with a holistic foundation to aid in the epistemological exploration of knowledge and understanding. If done right, we can take these analytical theories and apply them to the world of business, politics, design, branding, education and health so to create opportunities of clients.

The Big Data party has led to ethnographers staunchly defending their approach by showing what ethnography can offer and what Big Data cannot offer and how importance must be given to ethnography and Big Data working in ‘partnership’. This is a point made in a recent blog by Tricia Wang where she states that ethnographers “must engage with Big Data. Otherwise our work can be all too easily shoved into another department, minimized as a small line item on a budget, and relegated to the small data corner” (2013).

One of the central distinctions that have been identified between the two is the fact that ethnography provides deep understanding in to the ‘hows’ and the ‘whys’ of human behavior. Sam Ladner explains that Big Data fails to provide adequate insight into why users use a product because this data lacks holistic understanding. She goes on to state that Big Data “provides a culturally illiterate portrait” (2012:33). Fundamentally, the point that Ladner is making is that Big Data needs ethnography to uncover the ‘hows’ and ‘whys’ of human behaviour.

This argument has also been central to ethnographic blog discussion points. For example, Tricia Wang distinguishes between what Big Data offers and what ethnography needs to offer. She proposes the concept of ‘Thick Data’, which she describes as the:

best method for mapping unknown territory. When organizations want to know what they do not already know, they need Thick Data because it gives something that Big Data explicitly does not—inspiration. The act of collecting and analyzing stories produces insights (2013).

The problem however with these arguments is that other areas of qualitative research are also now making similar claims as ethnography is to understanding the why as a means of producing creative and inspiring insights so to distinguish themselves from Big Data. Returning to Jane Frost, Chief Executive of the Market Research Society in the UK who makes the point that “Big data may tell you how many customers you have won or lost but not necessarily why. This (the why) is the intelligence that can really make a difference” (Market Research Magazine, May 30^th 2013). The quote assumes that qualitative approaches are positioned within an elite of creating game change insights while Big Data is less capable of doing this. The irony is that many of the distinctions we make as ethnographers in relation to Big Data are also the same arguments we have used in the past to distinguish ourselves from more traditional qualitative research methods, namely the ‘dreaded’ use of focus groups. In other words, focus groups are based on asking questions while ethnography understands the thick description – the why.

I am not refuting that ethnography is excellent at generating deep insights. However, I question the over use of this term – the why. We are in serious danger of over using it as a default term as means of distinguishing ourselves within a highly competitive market. It is therefore at risk of becoming meaningless or even worst, attempting to assume that what the why represents is ‘fact’ or ‘truth’. Understanding the why is extremely complex, both in relation to methodological approach but also professional ability. It is highly psychoanalytical which itself is embedded heavily within culture, while also being shaped by culture.

We need to start by rethinking how we frame the why by stepping back and asking ourselves two interlinked questions. 1) What would the insight world look like if Big Data also claimed to position itself within the why space? And 2) What would stories look like and feel like if generated through Big Data? The previous examples of Tesco understanding why new fathers need discounted beer, or the future of amazon being able to write literature to fit in to the psychological and emotional needs of the reader hints at the point that Big Data will, in the future, be able to understand the why and tell stories…. literately. If this is the case, then the cultural space where ethnographers sit will also change. Therefore it is crucial that ethnography challenges itself rather than using the default button and attempt to explain what it offers and what Big Data cannot offer. By doing this will enable ethnography to build its identity and offers.

MOVING TOWARDS ‘BIG ETHNOGRAPHIC DATA’

I think there are two steps we need to start the process of ethnographic change. The first is based on taking a brief look back in time where we see that this tension between data and anthropology has been played out for a while. The second is to understand Big Data anthropologically. By doing this, we can then begin to reframe what the ethnographic offer could look like.

The social anthropologist Adam Kuper describes the mood of British Anthropology in the first few decades of the 20^th century as one that “would have to stress the overriding concern with the accumulation of data” (Kuper, 5:1983). Anthropological data previously relied on missionaries providing travel logs to ‘arm chair’ anthropologists that sat in comfortable libraries or universities to make sense of the data. Such was this new data on new and ‘exotic’ worlds it symbolized the ‘Big Data’ of its time and helped shape grand and universal models of culture and evolution. Enter Bronislaw Malinowski and his desire to develop anthropology through a new professional form of data collection, ethnography. Malinowski rejected the ‘arm chair’ methodology of creating meaning through second hand data and advocated that models of culture and evolution needed to be built on data collected first hand so to be tested in detail and scientifically.

In the early 20^th Century Malinowski, as the only professionally trained anthropologist to carry out such field research, drew up three kinds of data that had to be collected.

1. “Statistical documentation through concrete evidence”
2. Observing and recording social actions in an ethnographic diary
3. Collecting ethnographic statements (Kuper, 15:1983)

If we look at these three data points, they all dovetail together as a means of not creating ‘small data’ but creating big and deep data of the society being studied. Importantly, Malinowski was not attempting to create just ethnographic stories but his main aim was to develop robust scientific models of societies by collecting many different levels of data so to feed in to his functionalist model of society and culture.

So, if we interpret Malinowski’s approach to ethnography we can see that much of his focus was on developing accounts of human life based on in-depth and broad data collected over a long period of time. This was not a rejection of Western science but rather an attempt to create even better and more robust data on human and social life both big and deep.

There are two really important talks given by anthropologists who have started to position Big Data as more than a methodology. Genevieve Bell at Intel has suggested that we see Big Data as a person, meaning that it needs things like relationships and to not look bad. Furthermore, it needs to be situated within a location or country.

The second talk is by Mary Gray at Microsoft Research entitled ‘Anthropology as Big Data’ (October 2011). An anthropologist and senior researcher at Microsoft Research Gray makes the important point that we need to not see anthropology and Big Data as not being in opposition to each other, but should be seen as both focusing on data from interpretative stand points. Big Data being more based on ‘snap shot’ approaches, while anthropology being more akin to ‘time-lapsed photography’ where over time data is understood, like what Malinowski proposed. Both forms of data, Gray explains, produce significant data that tell us something about life. This coming together needs to form ‘collaborative epistemologies’.

I feel that both these anthropological interpretations are similar to Malinowski because they begin a process of creating a new paradigm shift in what ethnography can offer because they both have begun to reframe what Big Data is from an anthropological perspective.

We need to start seeing Big Data as more than coders, algorithms and something that ethnography is not. We need to see Big Data as fundamentally another process of offering cultural interpretation. Key outputs from Big Data are framed around culture while also informing and shaping culture. As are the stories and observations we choose to document as ethnographers. Like ethnography, Big Data is more than just a methodology but a format to understand human behaviour and shaping our responses to it–from how we live to how we behave to how we consume cultural codes.