In management circles and beyond, companies are rushing to integrate, adapt and exploit big data in their organisations. Following example by Amazon, Netflix, Spotify, Zappos, and Walmart, companies are building big-data solutions to profile customers and to enhance marketing effectiveness via recommendation algorithms seeking to predict what customers are likely to be interested in buying each moment. Business schools, too, are quick to restructure their offering around big data and analytics – it seems as if nothing more is needed. Yet little is said about the kind of understanding and reflexivity that is needed when working with such voluminous data. We believe that important lesson can be learned from ethnographic research, which should be taught to managers obsessed with big data.
Big data limitations
Surprisingly few talk about the potential limitations. First, due to the thirst for ever more data, there seems to be no end to how much is enough data. At the same time, collecting, storing, updating, and curating big data is – of course – extremely costly. For the record, many have also claimed that much of this data is hardly useful at all. But since they do not know which data could be interesting or not, managers have decided to keep on collecting it. In many cases, unfortunately, companies do not have resources to properly distil meaningful insight from it.
Second, big data relies on petabytes of what we call “decontextualised” data – in other words, data points extracted from the actual situation from which they were produced in the first place. The number of “clicks” or “views”, for example, are often closely measured and recorded but they do not inform managers about the immediate contexts, moods or situations in which users were clicking and viewing the website. Despite technological progress, a significant part of this context will always remain impossible to measure because of its inherent complexity. Yet it is a crucial factor for the understanding and explaining the studied behaviour at stake: doings and sayings become meaningful only in their immediate sociocultural context.
Third, we argue big data is unable to address embodied, sensory and affective experiences. When seeking to measure an emotion, for example, big data may only hope to measure the physiological reactions of the persons captured via sensors (muscles tension, sweat, heart rate, brain, etc.), but not the acute meaningful emotional states that people live through. When analysing tweets to determine people’s emotions, data analysts agree they could not address emotions themselves but only traces of their narration. This is a crucial caveat as the sensory dimensions are essential toward fostering understanding of the experiences that people actually live through.
Finally, it is safe to say that big data alone is not helpful for developing a “deep” understanding. What big data scientists can find out are correlations between variables (what is or happens), not causation (why and how it happens). Hence, big data is an interesting and useful tool but should not be the only focus of attention. This is why we suggest examining ethnographic thinking and research as a potential antidote for big data obsession.
Benefits of ethnography for managers
While big data analytics are quickly entering the curriculum of most business schools, ethnographic methods often remain reserved to social sciences departments of universities, despite their relevance for understanding consumer behaviour, service experience, branding, and strategy.
First, ethnography is all about gathering in-depth data about lived experiences and situations. Anthropologist Clifford Geertz famously described this kind of data as “thick descriptions”: long-term and deep reflections about the experiences that people live. Expert on Balinese culture and rituals, Geertz crafted his insights on first-hand participant observation with the idea that the ethnographer needs to live through the same experiences as the studied people. Thus, she/he is committed to discovering and sharing a common phenomenological sensibility and understanding – in a way, in his attempt to get inside the “skins of others”. The method has been a staple in anthropology and sociology for over a hundred years, but it is gaining acuity and relevance in understanding today’s fast-paced society and markets.
Second, ethnography insists on reflexivity. This means that the ethnographer seeks to question her/his own preconceptions about the studied phenomena – a sort of unlearning about “what we think we know” is thus required. Also, it means that the ethnographer is mindful about the way she/he participates in shaping the studied realities: the kinds of questions being presented and the power exerted over those studied. In practice, this means being sensitive toward ensuring that people indeed share their unique views, experiences and narratives. Ethnographers are taught to mistrust what they consider “natural”, “normal” behaviour and “objective” evidence. Ethnography can thus help managers foster reflexivity about the “limits” of their own experience and being attentive to difference and multiplicity of understandings and truths.
Third, ethnography seeks a profound understanding of the situational context. Within it, the objective is to uncover the social processes that help explain reasons why people are bound or likely to act the way they do. In 2013, Netflix worked with anthropologist Grant McCracken to understand the emerging online video-streaming phenomenon. McCracken’s ethnographic work revealed the meaning and importance of “binge watching” for contemporary consumers. For him, our “digital lifestyle, where storytelling is often reduced to bite-sized, 140 character conversations or images, leaves us craving the kind of long narrative of storytelling”. McCracken found that 73% of consumers feel good about “binging”, i.e., watching multiple series or movies in one viewing. This kind of analysis was indeed fruitful for Netflix toward better serving their customers.
Fourth, in radical contrast to big data approaches, ethnography is concerned with the building up of “embodied data and knowledge”. In other words, the building of analytical accounts produced by our very own bodies (by way of seeing, sensing, touching, hearing, tasting) – about life. Ethnography is particularly sensitive to the multisensory aspects of people’s experiences. A thrilling or relaxing atmosphere in a concert or service experience can only be felt in our bodies. We argue such aliveness and vivid flow of experiences cannot not be captured by “dead” big data descriptions, cut out of their contexts and summarised in static charts or representations.
Toward teaching a reflexive mind-set
The above points emphasise a crucial fact: that for producing knowledge and insight about human behaviour, we may need more than big data. Ethnography calls for a curious and reflexive mind that is open to explore novel understandings and perspectives, challenging taken for granted assumptions and norms. It also insists on an economic principle: we need to gather new data until a “saturation point” is reached – when gathering new data produces no further insight.
We argue teaching ethnographic thinking to managers is now more acute than ever. The world is changing with stunning speed, a flood of data is being produced by computing systems, and there is only little time to make decisions. Ethnographic mind-set enforces managers to:
- continuously reflect on the “right” questions and perspectives they may adopt,
- exercise participant-observation which can be a “lifelong” asset,
- critically analyse the kinds of seemingly “objective” empirical evidence offered to them (no matter how voluminous)
- take a few healthy steps away from the ocean of data they may easily drown in.
Recommendations for companies
As highlighted above, companies wishing to foster reflexive ethnographic thinking to re-balance focus on big data, it is crucial to establish processes in which managers are regularly exposed to the first-hand experience of their customers – and especially different embodied and sensory experiences using the company’s products in their habitual environment.As in the case of Netflix, this would mean that managers spend time with different kinds of customers (young, old, couples, different social class etc.) when they binge in or prepare for watching episodes of streamed content. In doing so, they would be able to better learn and feel the experience from the point of view of distinct viewers, assess the kinds of routines but also common frustrations and hindrances that the experience entails. What kind of rituals and negotiations the customers engage in when setting up their desired show? What kinds of atmospheres and material set-up they seek to build when making the night perfect? How do they indulge in the act of viewing but also socializing about it? These aspects are crucial in understanding how a product becomes integrated into the lives of people.
Likewise, managers should be regularly exposed to customers not using their companies’ products – although they could. This information is also not registered into the big data systems. Why certain customers do not seek to integrate new routines to their lives – or quit them soon after initial adoption – is a matter of understanding the complexity of their existing and dominant ones. Observing these routines thus helps companies to keep in pace with the gradually changing lifestyles in society more broadly speaking.