The advent of open APIs, ubiquitous sensor-laden computing devices, and new data-sharing platforms all raise a much more fundamental question: how can open source help people get something useful and meaningful out of the data they generate? For example, open source enthusiasts are likely to get excited about getting access to accelerometer data at the most granular level, well before it becomes something we can call “steps.” Data at this level bores most people to tears. Yet, there might be something deep down in the weeds that, if put together in the right ways, could in fact be personally meaningful to someone, or even of some larger public significance. Distinguishing useful from useless data depends on two things: the public’s ability to understand, interpret and use that data, and developers’ ability to meet them halfway. This session will give both hardware and software developers the tools to understand what it is people actually do when they encounter datasets, and how to improve user experience based on that knowledge. It won’t teach you how to dumb down data so that the people in the lowest common denominator can understand it. In fact, there is empirical evidence that suggests “dumbing it down” is not likely to be a helpful approach. Instead, based on extensive anthropological research conducted at Intel Labs and elsewhere, it will show what people without formal mathematical training are surprisingly good at when it comes to understanding numbers. It will also suggest some approaches for building those human perspectives into a design approach.
Dawn Nafus is an anthropologist at Intel, where she conducts social science research to inspire new products and strategies. She holds a PhD from Cambridge University, and was previously a research fellow at University of Essex. She is best known for her work on gender in open source communities. Currently, her research focuses on how non-technical people make sense of complex datasets.
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