Databases Reference
In-Depth Information
It's a compelling tale, with an easy and attractive bifurcation of old
and new forms of knowledge. Yet good data scientists have been far
more reflective about the dangers of throwing away existing domain
knowledge and its experts entirely.
Origin stories add legitimacy to hierarchies of expertise. Data mining
has long had a popular, albeit somewhat apocryphal, origin story: the
surprising discovery, using an
association algorithm
,
that
men who
Traditional marketing people, with their quant folk psychologies and
intuitions about business, were heretofore to be vanquished before
what the press probably still called an “electronic brain.” The story
follows a classic template. Probability and statistics from their
origins
in the European Enlightenment have long challenged traditional
forms of expertise: the pricing of insurance and annuities using data
rather than reflection of character of the applicant entailed the di‐
minution and disappearance of older experts. In the
topic
that intro‐
duced the much beloved (or dreaded) epsilons and deltas into real
analysis, the great mathematician Augustin-Louis Cauchy blamed
statisticians for the French Revolution: “Let us cultivate the mathe‐
matical sciences with ardor, without wanting to extend them beyond
their domain; and let us not imagine that one can attack history with
formulas, nor give sanction to morality through theories of algebra
or the integral calculus.”
These narratives fit nicely into the celebration of disruption so central
to Silicon Valley libertarianism, Schumpeterian capitalism, and cer‐
tain variants of tech journalism. However powerful in extirpating
rent-seeking forms of political analysis and other disciplines, the di‐
chotomy mistakes utterly the real skills and knowledge that appear
often to give the data sciences the traction they have. The preceding
chapters—dedicated to the means for cultivating the diverse capaci‐
ties of the data scientist—make mincemeat of any facile dichotomy
of the data expert and the traditional expert.
Doing data science
has
put a tempering of hubris, especially algorithmic hubris, at the center
of its technical training.
Obama's data team
explained
that much of their success came from
taking the dangers of hubris rather seriously, indeed, in building a
technical system premised on avoiding the dangers of overestimation,
from the choice and turning of algorithms to the redundancy of the
backend and network systems: “I think the Republicans f**ked up in
the hubris department,” Harper Reed explained to Atlantic writer
Alexis Madrigal. “I know we had the best technology team I've ever
worked with, but we didn't know if it would work. I was incredibly
