I read an interesting interview this weekend with Benjamin Bratton in the third Situated Technologies pamphlet. It (sort of) refuted a view I’ve taken for granted, that we’ll have an inevitable “layer” of self-reporting material that comes from a world covered in computers and sensors: an invisible infrastructure that we just come to tune out like we have so many others. There is often little of purpose in its output (especially as such sensors would likely be general purpose), but that data is somehow of value for its existence, a cloud of data that is often characterized as a data smog.
Bratton argues that we’ve become overwhelmed already by the idea of this and its early examples, and that there is an inherent value in reputation-based data collection practices in academic settings that can counter that smog. He states that academic reputation serves as a powerful filter for the integrity of research-oriented data collection, vs. the regulation-based data collection and reporting that occurs in industry. The differences in motivation are obvious and fundamental to the act of collection. However, I think these distinctions serve as good indicators for how we might approach future data-gathering exercises, especially for open and citizen scientist activities.
These photos of space suit movement tests from the San Diego Air and Space museum are a beautiful counter to the “catch all” net of passive data collection we see from industry and open data networks, and serve as a worthwhile reminder to designers doing anything for networks and groups. Purposeful data collection and the artifacts that can emerge highlight what should be fundamental to any type of data collection: purpose. And that purpose should be fundamental in how we design our tools for observation.