Database Reference
In-Depth Information
Wiggins:
I would say it's an exciting time to be working in data science, both
in academia and at The New York Times. Data science is really being birthed
as an academic field right now. You can find the intellectual roots of it in a
proposal by the computational statistician Bill Cleveland in 2001. Clearly, you
can also find roots for data scientists as such in job descriptions, the most
celebrated examples being DJ Patil's at LinkedIn and Jeff Hammerbacher's at
Facebook. However, in some ways, the intellectual roots go back to writings
by the heretical statistician John Tukey in 1962.
There's been something brewing in academia for half a century, a disconnect
between statistics as an ever more and more mathematical field, and the prac-
tical fact that the world is producing more and more data all the time, and
computational power is exponentiating over time. More and more fields are
interested in trying to learn from data.
My research over the last decade or more at Columbia has been in what we
would now call “data science”—what I used to call “machine learning applied to
biology” but now might call “data science in the natural sciences.” There the goal
was to collaborate with people who have domain expertise—not even neces-
sarily quantitative or mathematical domain expertise—that's been built over
decades of engagement with real questions from problems in the workings of
biology that are complex but certainly not random. The community grappling
with these questions found itself increasingly overwhelmed with data.
So there's an intellectual challenge there that is not exactly the intellectual
challenge of machine learning. It's more the intellectual challenge of trying
to use machine learning to answer questions from a real-world domain. And
that's been exciting to work through in biology for a long time.
It's also exciting to be at The New York Times because The New York Times
is one of the larger and more economically stable publishers, while defending
democracy and historically setting a very high bar for journalistic integrity. They
do that through decades and centuries of very strong vocal self-introspection.
They're not afraid to question the principles, choices, or even the leadership
within the organization, which I think creates a very healthy intellectual culture.
At the same time, though, although it's economically strong as a publisher, the
business model of publishing for the last two centuries or so has completely
evaporated just over the last 10 years; over 70 percent of print advertis-
ing revenue simply evaporated, most precipitously starting around 2004.
2
So
although this building is full of very smart people, it's undergoing a clear sea
change in terms of how it will define the future of sustainable journalism.
