Drawing from a study of everyday home practices from a material objects' perspective, this paper examines the potential that a thing ethnography holds for both design and anthropology. In doing so, the paper challenges anthropocentric assumptions about the world, and opens up ways of understanding relationships among people, objects and use practices that would be difficult to elicit through traditional observations and interviews alone.

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In the current Internet of Things (IoT) environment, objects are tagged with sensors without a clear understanding of people’s individual and collective patterns of behaviour. We argue that designers can create more meaningful and effective networked objects through collaborating with ethnographers and Machine Learning (ML) experts. In this paper, we present the approach and preliminary insights of two analysts from those disciplines on the same data set, and speculate on how they complement one another and the design process. Ethnographic data can indicate the questions that are interesting to study with ML algorithms and help interpret the data generated by ML by positioning it into wider socio-cultural situations. Ultimately, this collaboration can inspire designers to create meaningful products, services, and processes of IoT. @en