Big Data and Cloud Culture - To the Cloud: Big Data in a Turbulent World - page 193

digital-humanities movement has sparked rigorous debate, with propo-
nents making reference to the “backward” humanities and opponents
using words like “diabolical” to describe Franco Moretti, one of its
leading practitioners (Sunyer 2013).
There is nothing new in the principles behind big-data analytics. For
many years social scientists have been working on large data sets to ind
relationships among seemingly unrelated variables. But the difference now
is the concerted effort to make it the singularly most important tool in
research and, for some, the magical alternative to the methods that have
guided research in science as well as the humanities for centuries. Big data
is not just a method; it is a myth, a sublime story about conjuring wisdom
not from the lawed intelligence of humans, with all of our well-known
limitations, but from the pure data stored in the cloud.
Proclaiming “the end of theory,” Chris Anderson got the ball rolling in
a 2008 Wired magazine article in which he stated, “the data deluge makes
the scientiic method obsolete” (Anderson 2008). For Anderson, big data
marks nothing short of a revolution in what it means to know. This view
is mythic because it envisions big data as a revolutionary development
that does not just make science better, but ends science as we know it
and replaces it with a new way of knowing. Like many myths, Anderson's
tale imagines a new world where what was universally accepted yesterday
is rejected and discarded today in favor of a simple alternative that solves
the world's problems. Out with the scientiic method, in with big-data
correlations. Following an example of how Google is revolutionizing
advertising, Anderson proclaimed, “The big target here isn't advertising,
though. It's science.” Or more precisely, it is the core of science embodied
in an approach to knowledge. “The scientiic method is built around test-
able hypotheses. These models, for the most part, are systems visualized
in the minds of scientists. The models are then tested, and experiments
conirm or falsify theoretical models of how the world works. This is the
way science has worked for hundreds of years.” It no longer has to work
this way, but scientists have to give up their cherished notions. “Scientists
are trained to recognize that correlation is not causation, that no con-
clusions should be drawn simply on the basis of a correlation between X
and Y (it could just be a coincidence). Instead, you must understand the
underlying mechanisms that connect the two. Once you have a model, you
can connect the data sets with conidence. Data without a model is just