RINA TAMBO JENSEN
Case Study—This is a case about how Mozilla, the open source browser company, set out to reconnect with ‘collaborating in the open’ to regain its competitive advantage. This case describes how a multi-disciplinary research team used ethnographic, market, and data analysis to articulate and clarify the problem, and build a strategy towards revitalizing Openness at Mozilla. It will aim to prove that the subsequent change achieved could only have been accomplished by a mixed method research approach. And importantly show, how the team used data to prove the distribution of findings, coupled with ethnography to shine light on the why and how of those findings. The case study will do this by discussing the key insights and how these fueled recommendation and subsequent change in the organisation.
The project presented many problems: from convincing stakeholders of the need to fully explore the problem, to connecting widely different research methods and gleaning insights that built strongly on all strands...
Those who work in research know that we live in a world that is strongly influenced by what Tricia Wang has called the quantification bias. More so than other forms of information, numbers have incredible formative power. In our culture, numbers are seen as trustworthy representations of reality that are strongly associated with objectivity and untainted by human bias and shortcomings. Recently, data science, big data, algorithms, and machine learning have fueled a new wave of the quantification bias. One of the central fascinations of this wave has been the promise that humans now have the power of prediction at their fingertips. In this paper, I reflect on what it means to make predictions and explore the differences in how predictions are accomplished via quantitative modeling and ethnographic observation. While this is not the first time that ethnographic work has been put in conversation and in contrast with quantified practices, most theorists have framed the role of ethnography as providing context...
When our broadband connectivity at home stops working, it’s a crisis. When it does work it is magic, an invisible miracle most of us don’t understand. Our evidence for whether it is working great or barely working at all, is scant, murky and elusive. The telecommunications industry language used to measure and characterize connectivity is obtuse. Data transfer rates, data storage rates, and wireless frequency rates, all sound similar and make no sense to most people. So, what’s wrong with that? Who cares how many megabits per second download speed I’m getting? As long as I can stream The Crown, what difference does it make? That’s what I thought when I began doing research about people’s relationships with connectivity, and then I met people who changed my point of view. Connectivity is fundamental to how we experience the world and to our sense of well-being. We need ways to connect with our connectivity.
Susan Faulkner is a Senior Researcher at Intel Corporation...
FRANCISCO JAVIER PULIDO RAMIREZ
Social media played a fundamental role on Mexico's earthquake, it bring us new solutions but created some other problematics that were unexpected. Millions of users shared their experiences faster than any other traditional media but the use and abuse of their evidence impacted the way we faced the crisis. Earthquakes are extreme case scenarios where social medias couldn't forecast the different consequences of their design decisions that impacts people's lifes. As producers of contents, all our evidence is storage on the digital sphere, always available, unchangeable, static, waiting to be rescue for interpretation. Most of the evidence that generate chaos after the earthquake happened because they were digitally alive, being shared over and over without control, for hours and days and when it finally reach you it was no longer useful. But on a scenario where temporality is crucial and minutes can define life or death, should we kill our evidences in pro for a better...
by TYE RATTENBURY (Salesforce) & DAWN NAFUS (Intel)
As EPIC2018 program co-chairs, we developed the conference theme Evidence to explore how evidence is created, used, and abused. We’ll consider the core types of evidence ethnographers make and use through participant observation, cultural analysis, filmmaking, interviewing, digital and mobile techniques, and other essential methods, as well as new approaches in interdisciplinary and cross-functional teams.1
We’ve also made a special invitation to data scientists to join us in Honolulu to advance the intersection of computational and ethnographic approaches. Why?
One of us is a data scientist (Tye) and the other an ethnographer (Dawn), both working in industry. We regularly see data science and ethnography conceptualized as polar ends of a research spectrum—one as a crunching of colossal data sets, the other as a slow simmer of experiential immersion. Unfortunately, we also see occasional professional stereotyping. A naïve view of “crunching” can make it seem...