Big Data and Cloud Culture - To the Cloud: Big Data in a Turbulent World - page 194

noise. But faced with massive data, this approach to science—hypothesize,
model, test—is becoming obsolete” (ibid.).
At their core, myths help us to cope with life's uncertainties, from the
little banalities, such as what to have for breakfast, to the grand ques-
tions of how to ind meaning and face mortality. They do not just offer
an answer; they provide the answer, typically with convincing clarity,
simplicity, and fervor. Big data is not just one among many instruments
to understand and change the world; it is the essential one, and all others,
including science, the method that has guided the modern world and its
way of knowing, can be swept into the dustbin of history. Some understand
this well. People like Chris Anderson and Ray Kurzweil are today's seers,
who know the way that draws the curtain on an old age and foreshadows
the new. Most myths are about endings, whether the end of history, of
theory, or of science. They call on us to celebrate our good fortune to live
at the end of an era and to begin to experience the new. For Anderson,
today's visionary is Google because it is not just a successful company,
a leading force in informational capitalism, but primarily because it is
using the correlations it inds in mountains of big data to change what
it means to know: “The new availability of huge amounts of data, along
with the statistical tools to crunch these numbers, offers a whole new way
of understanding the world. Correlation supersedes causation, and science
can advance even without coherent models, uniied theories, or really any
mechanistic explanation at all. There's no reason to cling to our old ways.
It's time to ask: What can science learn from Google?” (ibid.)
For some, the new visionary is the data scientist who magically conjures
truth from mountains of seemingly unrelated information. According to
one observer, “big data has created a mythical god called the data scientist:
a lone-wolf, super-smart human with a solid foundation in computer sci-
ence, modeling, statistics, analytics, math, and strong business acumen,
coupled with the ability to communicate indings to both business and
IT leaders in a way that can inluence how an organization approaches a
business challenge” (Walker 2013). One observer sees the data scientist
as the successor to the iconic “Mad Men” of advertising (Steel 2012a).
Myths matter. In this case the emergence of the data scientist as the latest
mythical god is having a signiicant impact on higher education, where
universities are scrambling to produce programs to train aspirants for what
the Harvard Business Review , no stranger to hyperbolic excess, calls “the