[Vision2020] What Data Can’t Do

[Art Deco]

  [image: The New York Times] <http://www.nytimes.com/>

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February 18, 2013
What Data Can’t Do By DAVID
BROOKS<http://topics.nytimes.com/top/opinion/editorialsandoped/oped/columnists/davidbrooks/index.html>

Not long ago, I was at a dinner with the chief executive of a large bank.
He had just had to decide whether to pull out of Italy, given the weak
economy and the prospect of a future euro crisis.

The C.E.O. had his economists project out a series of downside scenarios
and calculate what they would mean for his company. But, in the end, he
made his decision on the basis of values.

His bank had been in Italy for decades. He didn’t want Italians to think of
the company as a fair-weather friend. He didn’t want people inside the
company thinking they would cut and run when times got hard. He decided to
stay in Italy and ride out any potential crisis, even with the short-term
costs.

He wasn’t oblivious to data in making this decision, but ultimately, he was
guided by a different way of thinking. And, of course, he was right to be.
Commerce depends on trust. Trust is reciprocity coated by emotion. People
and companies that behave well in tough times earn affection and
self-respect that is extremely valuable, even if it is hard to capture in
data.

I tell this story because it hints at the strengths and limitations of data
analysis. The big novelty of this historic moment is that our lives are now
mediated through data-collecting computers. In this world, data can be used
to make sense of mind-bogglingly complex situations. Data can help
compensate for our overconfidence in our own intuitions and can help reduce
the extent to which our desires distort our perceptions.

But there are many things big data does poorly. Let’s note a few in
rapid-fire fashion:

*Data struggles with the social.* Your brain is pretty bad at math (quick,
what’s the square root of 437), but it’s excellent at social cognition.
People are really good at mirroring each other’s emotional states, at
detecting uncooperative behavior and at assigning value to things through
emotion.

Computer-driven data analysis, on the other hand, excels at measuring the
quantity of social interactions but not the quality. Network scientists can
map your interactions with the six co-workers you see during 76 percent of
your days, but they can’t capture your devotion to the childhood friends
you see twice a year, let alone Dante’s love for Beatrice, whom he met
twice.

Therefore, when making decisions about social relationships, it’s foolish
to swap the amazing machine in your skull for the crude machine on your
desk.

*Data struggles with context.* Human decisions are not discrete events.
They are embedded in sequences and contexts. The human brain has evolved to
account for this reality. People are really good at telling stories that
weave together multiple causes and multiple contexts. Data analysis is
pretty bad at narrative and emergent thinking, and it cannot match the
explanatory suppleness of even a mediocre novel.

*Data creates bigger haystacks.* This is a point Nassim Taleb, the author
of “Antifragile,” has made. As we acquire more data, we have the ability to
find many, many more statistically significant correlations. Most of these
correlations are spurious and deceive us when we’re trying to understand a
situation. Falsity grows exponentially the more data we collect. The
haystack gets bigger, but the needle we are looking for is still buried
deep inside.

One of the features of the era of big data is the number of “significant”
findings that don’t replicate the expansion, as Nate Silver would say, of
noise to signal.

*Big data has trouble with big problems.* If you are trying to figure out
which e-mail produces the most campaign contributions, you can do a
randomized control experiment. But let’s say you are trying to stimulate an
economy in a recession. You don’t have an alternate society to use as a
control group. For example, we’ve had huge debates over the best economic
stimulus, with mountains of data, and as far as I know not a single major
player in this debate has been persuaded by data to switch sides.

*Data favors memes over masterpieces.* Data analysis can detect when large
numbers of people take an instant liking to some cultural product. But many
important (and profitable) products are hated initially because they are
unfamiliar.

*Data obscures values.* I recently saw an academic book with the excellent
title, “ ‘Raw Data’ Is an Oxymoron.” One of the points was that data is
never raw; it’s always structured according to somebody’s predispositions
and values. The end result looks disinterested, but, in reality, there are
value choices all the way through, from construction to interpretation.

This is not to argue that big data isn’t a great tool. It’s just that, like
any tool, it’s good at some things and not at others. As the Yale professor
Edward Tufte has said, “The world is much more interesting than any one
discipline.”
  --
Art Deco (Wayne A. Fox)
art.deco.studios at gmail.com
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