The nominalist trap in Big Data analysis

Nominalism, formerly the novelty of a few, wrote Jorge Luis Borges, today embraces all people; its victory is so vast and fundamental that its name is useless. Nobody declares himself nominalist because there is nobody who is anything else. He didn't go on to write This is why even successful Big Data projects often fail to have an impact (except in some volumes kept in the Library of Babel), but his understandable omission doesn't make the diagnosis any less true.

Nominalism, to oversimplify the concept enough for the case at hand, is simply the assumption that just because there are many things in our world which we call chairs, that doesn't imply that the concept itself of a chair is real in a concrete sense, that there is an Ultimate, Really-Real Chair, perhaps standing in front of an Ultimate Table. We have things we call chairs, and we have the word "chair", and those are enough to furnish our houses and our minds, even if some carpenters still toss around at night, haunted by half-glimpses of an ideal one.

It has become a commonplace, quite successful way of thinking, so it's natural for it to be the basis of what's perhaps the "standard" approach to Big Data analysis. Names, numbers, and symbols are loaded into computers (account identifiers, action counters, times, dates, coordinates, prices, numbers, labels of all kinds), and then they are obsessively processed in an almost cabalistic way, organizing and re-organizing them in order to find and clarify whatever mathematical structure, and perhaps explanatory or even predictive power, they might have — and all of this data manipulation, by and large, takes place as if nothing were real but the relationships between the symbols, the data schemas and statistical correlations. Let's not blame the computers for it: they do work in Platonic caves filled with bits, with further bits being the only way in which they can receive news from the outside world.

This works quite well; well enough, in fact, to make Big Data a huge industry with widespread economic and, increasingly, political impact, but it can also fail in very drastic yet dangerously understated ways. Because, you see, from the point of view of algorithms, there *are* such things as Platonic ideals — us. Account 3788 is a reference to a real person (or a real dog, or a real corporation, or a real piece of land, or a real virus) and although we cannot right now put all of the relevant information about that person in a file, and associate it with the account number, that information, the fact of its being a person represented by a data vector, rather than a data vector, makes all the difference between the merely mathematically sophisticated analyst and the effective one. Properly performed, data analysis is the application of inferential mathematics to abstract data, together with the constant awareness and suspicion of the reality the data describes, and what this gap, all the Unrecorded bits, might mean for the problem at hand.

Massive multi-user games have failed because their strategic analysis confused the player-in-the-computer (who sought, say, silver) with the player-in-the-real-world (who sought fun, and cared for silver only insofar as that was fun). Technically flawless recommendation engines sometimes have no effect on user behavior, because even the best items were just boring to begin with. Once, I spent an hour trying to understand a sudden drop in the usage of a certain application in some countries but not in others, until I realized that it was Ramadan, and those countries were busy celebrating it.

Software programmers have to be nominalists — it's the pleasure and the privilege of coders to work, generally and as much as possible, in symbolic universes of self-contained elegance — and mathematicians are basically dedicated to the game of finding out how much truth can be gotten just from the symbols themselves. Being a bit of both, data analysts are very prone to lose themselves in the game of numbers, algorithms, and code. The trick is to be able to do so while also remembering that it's a lie — we might aim at having in our models as much of the complexity of the world as possible, but there's always (so far?) much more left outside, and it's part of the work of the analyst, perhaps her primary epistemological duty, to be alert to this, to understand how the Unrecorded might be the most important part of what she's trying to understand, and to be always open and eager to expand the model to embrace yet another aspect of the world.

The consequences of not doing this can be more than technical or economic. Contemporary civilization is impossible without the use of abstract data to understand and organize people, but the most terrible forms of contemporary barbarism, at the most demencial scales, would be impossible without the deliberate forgetfulness of the reality behind the data.