Thick Data and Thickness of Description

Original reference of “thick data” is attributed to Gilbert Ryle in ‘le penseur’:

“… for example, chess or wheeling a wheelbarrow with someone in it), these too are amenable to various sorts of ‘thin’ or ‘thick’ descriptions (such that playing chess, for example, will involve moving pieces in certain directions according to rules, which, in turn, will involve grasping the bits of carved wooden or ivory figures, which in turn will involve … and so on). It is clearly, notes Ryle, the ‘thick’ descriptions which will normally be informative as to what someone can be said to be doing. Thus, one instance of ‘thinking’ can be trying to recollect where I left my glasses, which in turn will involve my pondering my recent whereabouts, which in turn will involve my occasionally muttering location names to myself, and so on. In other contexts and for other purposes, my muttering location names sub voce may have nothing whatsoever to do with trying to recall where I left my glasses, but perhaps figure in an effort to memorise a sequence of geographical areas prior to listing them alphabetically for an examination.”

via Jeff Coulter

referred to by Geertz:

If anthropological interpretation is constructing a reading of what
happens, then to divorce it from what happens — from what, in this time
or that place, specific people say, what they do, what is done to them,
from the whole vast business of the world — is to divorce it from its applications and render it vacant. A good interpretation of anything — a
poem, a person, a history, a ritual, an institution, a society-takes us
into the heart of that of which it is the interpretation. When it does not
do that, but leads us instead somewhere else-into an admiration of its
own elegance, of its author’s cleverness, or of the beauties of Euclidean
order — it may have its intrinsic charms; but it is something else than
what the task at hand-figuring out what all that rigamarole with the
sheep is about — calls for.

via Clifford Geertz, p. 18

and referred to by Tricia Wang as “thick data” as a counter to “big data”:

Thick Data analysis primarily relies on human brain power to process a small “N” while big data analysis requires computational power (of course with humans writing the algorithms) to process a large “N”. Big Data reveals insights with a particular range of data points, while Thick Data reveals the social context of and connections between data points. Big Data delivers numbers; thick data delivers stories. Big data relies on machine learning; thick data relies on human learning.

Tricia Wang

Thick Data: ethnographic approaches that uncover the meaning behind Big Data visualization and analysis.

Tricia Wang