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Artisanal Data

This project explores ways we think about data and ways in which society creates data. It examines and reacts to the Big Data movement and looks for new approachs to data production and consumption.

Artisanal data is put forth as a counterpoint to Big Data that reveals the humans and the communities behind the datasets. Unlike Big Data, artisanal data is non-uniform, inefficient and process oriented. It varies greatly in type; it may be qualtitative, indexical, object-based, embodied, or experiential, among others. It is seen with an eye on its source and its particularity to the time, space, and culture. This perspective gives us a new road to understanding data and opens the door for novel understandings of information. This work is informed by other researhers, including Paul Dourish, Mimi Onuoha, Catherine D'Ignazio, and Natalie Jeremijenko, among others.

An artisanal data approach will be used for a number of upcoming projects including Rainbow in the Cloud. I will also teach a course on this topic in future semesters; the tentative syllabus can be found here.

This work builds from my generals exam. Thanks to my generals committee V. Michael Bove, Ethan Zuckerman, and Natalie Jeremijenko.