Digesting Lisa Gitelman’s admonition about data and rawness, most of her argument went down fine: after all, disciplinary cuisines aside (171), what she most stridently calls for is the disclosure of epistemological and methodological concerns, or recipes, and for us to discard the notion of any particular palate—whether one makes purportedly subjective or objective claims for a living—being truer than another. However, the jumbo shrimp analogy (168) stuck like a bone in my throat, in spite of how cheerfully Gitelmen provides and then discards it. I found it ill-conceived, and here’s why: “raw” is an absolute term, and while it may be an inaccurate and blinkered way to refer to data, it doesn’t really bear comparison to “jumbo,” which is a relative term for shrimp. These shrimp found here are more hefty than the already-large shrimp found near, which are in turn more generously-proportioned than the pedestrian, moderately-sized shrimp found over there—thus, “jumbo” in the first case.
Mark Twain famously attributed to Benjamin Disraeli the quip fond to many a wag: “there are three types of lies: lies, damned lies, and statistics.” To cite either as the author, while often done, seems superfluous—it is such a truism that it doesn’t matter on whose authority we have it. And Gitelman and Manovich agree; both argue that the essential problem with data analysis, no matter how clever, is that sampling facts is a ticklish business. Carelessness and perfidiousness in the case of data collection and analysis end the same, and so whether by accident or design, prejudiced samples produce less generalizable results. The operative principle, then, is nuance, and not only acknowledging that data is always “cooked” but learning how to select it, season it, how to render most effectively the particular characteristics of interest. Thus, I would argue that in his call for “wide data” and in hers for frank disclosure of disciplinary predilections, Manovich and Gitelman in fact do apply the wisdom of fishmongers and think about scales of data as useful metrics. The jumbo shrimp, after all, is only oxymoronic in one iteration—elsewhere the langoustine and the tiger prawn produce none of the consternation apparently plaguing the American shopper.
I think it bears pointing out that the issues Gitelman has with jumbo shrimp and raw data seem to come down to the notion that somehow we’re being tricked into thinking the thing at hand it something other than it is: so, the shrimp is not a shrimp, it’s a jumbo shrimp, and data isn’t contingent observation about the material world, it is unbracketed, unadulterated truth. And while conflating industrial aquaculture and the empirical or positivist bent may work on many levels—after all, industrial aquaculture and many of its devastating consequences originate in notions that the world is entirely knowable and always improvable—there are some close to the surface in which that shorthand just doesn’t.