As both a researcher and participant in her local Portland QS Meetup group, Dawn Nafus has been engaging in a process of understanding how people learn about their lives through personal data. As part of this work Dawn and her colleagues at Intel Research have been working on creating Intel Data Sense. In this short talk, given at the 2014 Quantified Self Europe Conference, Dawn describes what she’s learned through the process of studying the QS community and building this new sense making platform.
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many “big data” platform for data analytics and visualization are experimenting with user-created “recipes” (back to your cooking analogy); Domo, GoodData, Datameer, Host Analytics, Cloudphysics all want to be a platform for others to create different insights, different views on data; a bit like IFTTT recipes that create meaningful signals and data out of a pool of data and events.
Playing and exploring is one trend. But transfer of the findings out of playing and exploring is difficult: We can provide a snapshot (“infographic”) of one finding; but how we arrived there, what we learned along the way, why we used a certain taxonomy, why we decided on a specific threshold, etc. is very difficult. In effect, I have to walk my colleagues through my journey and hope they follow along. Statisticians become story tellers. A good book is a book that you can read over and over again, and always find a new facet, and always being entertained. “Good Data” has to be like a good book.