Umberto Eco, during his residency at the Louvre Museum, embarked on an investigation of the human phenomenon of cataloging and collecting. In his subsequent book The Infinity of Lists: An Illustrated Essay, featuring lavish reproductions of artworks from the Louvre and other world-famous collections, Eco examined our predilection for list-making, an impulse that has recurred through the ages from music to literature to art.
In a Der Spiegel interview, Eco explained how “the list is the origin of culture. It’s part of the history of art and literature. What does culture want? To make infinity comprehensible. It also wants to create order — not always, but often. And how, as a human being, does one face infinity? How does one attempt to grasp the incomprehensible? Through lists, through catalogs, through collections in museums and through encyclopedias and dictionaries. There is an allure to enumerating how many women Don Giovanni slept with: It was 2,063, at least according to Mozart’s librettist, Lorenzo da Ponte. We also have completely practical lists — the shopping list, the will, the menu — that are also cultural achievements in their own right.”
To echo Eco’s words, I believe that making infinity comprehensible is a great way to describe the goal of big data analytics. It also wants to create order out of seeming chaos, helping us grasp the incomprehensible by applying varying degrees of structure. Its lists, catalogs, and collections of data points are often painted in the art of data visualization, the works of which we often stare at with a fascination somewhat similar to when we view the Louvre’s collection of paintings.
“A person contemplating a painting,” Eco explained, “feels a need to open the frame and see what things look like to the left and to the right of the painting. This sort of painting is truly like a list, a cutout of infinity. The list is the mark of a highly advanced, cultivated society because a list allows us to question the essential definitions. The essential definition is primitive compared with the list.”
Perhaps the greatest contemporary example of the human predilection for list-making, as well as the poster child for big data analytics, is Google with its mission statement from the outset being “to organize the world’s information and make it universally accessible and useful.”
Eco is leery of this particular form list-making. “Google makes a list, but the minute I look at my Google-generated list, it has already changed. These lists can be dangerous — not for old people like me, who have acquired their knowledge in another way, but for young people, for whom Google is a tragedy. Schools ought to teach the high art of how to be discriminating. The same approach should be used in school when dealing with the Internet.”
Eco described this with the example of a teacher giving students the task of searching 25 different web pages and, by comparing them, trying to figure out which one has good information. “If 10 pages describe the same thing,” he noted, “it can be a sign that the information printed there is correct. But it can also be a sign that some sites merely copied the others’ mistakes.”
Signal and noise are both amplified within the infinity of information surrounding us in the era of big data. Which is why, in our attempts to make infinity comprehensible with big data analytics, in our pursuits to paint ourselves a cutout of infinity, we should never forget that just because we may draw a conclusion that’s comprehensible, it’s not necessarily correct.
Nonetheless, to once again echo Eco, it is liberating to try to see the world from a different perspective, allowing our established definitions to be destroyed, and the range of our understanding to be tremendously expanded. And that is what the very best of big data analytics can help us do.