For years, ethnographically inspired research has been conducted to help us understand how technologies are adopted (or not) and adapted in everyday life. While some of this research has societal and technology-governance implications, most of it is more specifically scoped, in service of technology design writ small and large, focused on interfaces and interactions, applications, services, and/or infrastructures.
Design-focused areas of investigation and study include design ethnography and rapid ethnography . Design ethnographies aim to provide the foundation for new and refined user experiences by building an understanding of existent, emergent, or potential user practices and developing empathy with current and future users. Focused on the why as well as the what, design ethnographies focus on people’s values and motivations as well as their behaviors, on contexts of use as well as product/service feature use, on future appropriation and adaptation as well as immediate interaction, and on ethics as well as exigence.
Typically positioned as being at the other end of the research-method spectrum from design ethnography, the dominant behavioral-research paradigm in industry is analysis of digital data logs and activity traces. Such analyses focus on user behavior, and are used to drive design decisions about product-feature introduction and/or refinement, and to assess product viability.
Both research approaches have advantages and disadvantages, but great advantage may come from intertwining the two . Ethnographic work is often challenged for not scaling (although there are strong arguments why that is not a reasonable assumption), and while behavioral log analysis offers excellent “10,000 foot” views onto user behavior, it does not yield much insight into context of use and product or service value to people over time. It also does not engage with the social and ethical consequences of a product’s existence.
One advantage of intertwining an ethnographic perspective with data mining and log analysis is gaining a better understanding of people’s activities across different devices and services where cross-device and service logs are not available. More and more digital services are accessed across multiple devices, within various locales of activity, and through multiple forms of I/O with multiple interaction models. Think of experiences that move from the voice-controlled, audio-interaction service avatar in your home to your desktop and a keyboard interaction, or to your mobile and a tap-and-swipe interaction. Or think about how you move from one context to another, such as looking up the directions to a place on your phone in your living room and then moving to your car, where it’s great if the directions are loaded and ready to be audibly delivered. Taking an approach that allows us to see typical patterns of movement across locales of activity and to understand why and when those are likely to occur will help us to design truly useful human-centered predictive models.
Aside from the direct effects of grounding and deepening our understanding of actual and potential users and technology uses, such an intertwining might also lead to some indirect benefits. These potential indirect benefits include the expansion of success and failure metrics, and the addition of new terms and measures of product success and failure to complement current industry standards such as satisfaction, daily actives, engagement, churn, and abandonment. I note that it is all too easy to lose sight of the humanity that lies within our data trails, as we focus on abstract data, product features, business metrics, and the rather limited ideals of product success and failure. If we look at data trails with a more ethnographic perspective, we may shift to seeing data as a reflection of what people care about, keeping in the forefront of our minds that behavior reflects people’s values and people’s lives. We may deepen our empathy and, in so doing, decrease product incrementalism. Notably, such a perspective shift is in keeping with and reflects design thinking, a viewpoint that has captured the minds of executives worldwide.
However, such a perspective shift also may have organizational implications. For an organization to embrace developing a true ethnography + data mining and log analysis perspective, we will need to encourage dialogue horizontally across the organization, across teams, and across existing product silos. This relates to Conway’s Law, which states: “Organizations which design systems ... are constrained to produce designs which are copies of the communication structures of these organizations.” Coined in 1967, according to the Wikipedia page:
The law is based on the reasoning that in order for a software module to function, multiple authors must communicate frequently with each other. Therefore, the software interface structure of a system will reflect the social boundaries of the organization(s) that produced it, across which communication is more difficult .
Embracing an ethnographic approach could lead to the productive reorganization of work teams and work practices, and the breakdown of silos that exist because of legacy engineering or business divisions. So, as we extend engineering thinking with business-value thinking and design thinking, let’s also extend and deepen our ethnographic thinking. I am sure it will be worth it.
1. While some critics lobby that many studies conducted under the auspices of design or rapid ethnography are not, in the strictest sense of the word, truly ethnographies, it is clear that ethnographic methods and an ethnographic sensibility continue to be successfully brought to bear on technology design decisions.
2. Invoking the concept of “ethnomining” [4,5], I’ve argued elsewhere  that the analysis of data with an ethnographic lens should be not only a go-to secondary source for more effectively grounding our ethnographic studies as we go into the field or immerse ourselves, but also a potential primary source for understanding the role of technologies and technical infrastructures in everyday lives.
4. Burrell, J. The ethnographer’s complete guide to big data: Answers (part 2 of 3). Ethnography Matters. June 11, 2012; http://ethnographymatters.net/blog/2012/06/11/the-ethnographers-complete-guide-to-big-data-part-ii-answers/
5. Nova, N. Ethnomining. Ethnography Matters. 2013; http://ethnographymatters.net/editions/ethnomining/
6. Churchill, E. The ethnographic lens: Perspectives and opportunities for new data dialects. EPIC: Advancing the Value of Ethnography in Industry. 2017; https://www.epicpeople.org/ethnographic-lens/
Originally from the U.K., Elizabeth Churchill has been leading corporate research at top U.S. companies for the past 18 years. Her research interests include social media, distributed collaboration, mediated communication, and ubiquitous and embedded computing applications. firstname.lastname@example.org
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