Big Data and the Quantified Self

Big data is in a moment that is pre-Coprenican, (original) world-is-flat, and ‘sail west from Europe for the Orient.’ That is to say that big data is in need of not just descriptive tools, but maps, cartographic representations that help to define, concretize, and conceptualize what exactly big data is and how to think about it.

A conceptual mapping of big data is necessary which would most obviously draw upon basic mapping principles (metaphorically and literally) and epistemological models. For example, one dimension of a big data map is numeracy. In the conceptualization of big data, so far the context has been individuated information, information about individual units, like people (e.g.; personal data), and the tensions between the subject of the data and the user of the data, namely institutions. There is now an emergent category (e.g.; group data), the sense of data arising from and belonging to a group.

The quantified self is a vanguard paradigm for understanding personal data and urban data a similar vanguard paradigm for understanding group data. The quantified self is inherently a big data problem, as manually-tracked ‘small data’ is now being replaced by automatically-collected ‘big data,’ and cloud-based methods are required for data processing, analysis, and storage.

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