Databases Reference
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useful to contribute to making data science into a more legitimate field
that has the power to have a positive impact on society.
So, what is eyebrow-raising about Big Data and data science? Let's
count the ways:
1. There's a lack of definitions around the most basic terminology.
What is “Big Data” anyway? What does “data science” mean? What
is the relationship between Big Data and data science? Is data sci‐
ence the science of Big Data? Is data science only the stuff going
on in companies like Google and Facebook and tech companies?
Why do many people refer to Big Data as crossing disciplines (as‐
tronomy, finance, tech, etc.) and to data science as only taking
place in tech? Just how
big
is big? Or is it just a relative term? These
terms are so ambiguous, they're well-nigh meaningless.
2. There's a distinct lack of respect for the researchers in academia
and industry labs who have been working on this kind of stuff for
years, and whose work is based on decades (in some cases, cen‐
turies) of work by statisticians, computer scientists, mathemati‐
cians, engineers, and scientists of all types. From the way the
media describes it, machine learning algorithms were just inven‐
ted last week and data was never “big” until Google came along.
This is simply not the case. Many of the methods and techniques
we're using—and the challenges we're facing now—are part of the
evolution of everything that's come before. This doesn't mean that
there's not new and exciting stuff going on, but we think it's im‐
portant to show some basic respect for everything that came
before.
3. The hype is crazy—people throw around tired phrases straight
out of the height of the pre-financial crisis era like “Masters of the
Universe” to describe data scientists, and that doesn't bode well.
In general, hype masks reality and increases the noise-to-signal
ratio. The longer the hype goes on, the more many of us will get
turned off by it, and the harder it will be to see what's good un‐
derneath it all, if anything.
4. Statisticians already feel that they are studying and working on
the “Science of Data.” That's their bread and butter. Maybe you,
dear reader, are not a statisitican and don't care, but imagine that
for the statistician, this feels a little bit like how identity theft might
feel for you. Although we will make the case that data science is
not
just a rebranding of statistics or machine learning but rather
