A big promise of the Internet of Things is that by analyzing millions of new sources of data from embedded, networked devices our experience of the world becomes better and more efficient. The environment automatically predicts our behavior and adjusts to it, anticipating problems and intercepting them before they occur. The notion is seductive and almost magical: an automatic espresso machine that starts a fresh latte as you’re thinking it’s a good time for coffee; office lights that dim when it’s sunny and electricity is expensive; a taco truck that arrives just as the crowd in the park is getting peckish. Exciting in theory, this promise is rather unspecific in the details. Exactly how will our experience of the world, our ability to use all the collected data, become more efficient and more pleasurable?

We don’t have good examples for designing user experiences of predictive analytics, so this talk will lay out the challenges and some approaches to addressing them.