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In ancient Greece,
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when anyone from slaves to soldiers,
poets and politicians,
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needed to make a big decision
on life's most important questions,
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like, "Should I get married?"
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or "Should we embark on this voyage?"
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or "Should our army
advance into this territory?"
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they all consulted the oracle.
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So this is how it worked:
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you would bring her a question
and you would get on your knees,
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and then she would go into this trance.
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It would take a couple of days,
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and then eventually
she would come out of it,
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giving you her predictions as your answer.
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From the oracle bones of ancient China
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to ancient Greece to Mayan calendars,
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people have craved for prophecy
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in order to find out
what's going to happen next.
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And that's because we all want
to make the right decision.
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We don't want to miss something.
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The future is scary,
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so it's much nicer
knowing that we can make a decision
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with some assurance of the outcome.
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Well, we have a new oracle,
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and it's name is big data,
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or we call it "Watson"
or "deep learning" or "neural net."
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And these are the kinds of questions
we ask of our oracle now,
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like, "What's the most efficient way
to ship these phones
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from China to Sweden?"
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Or, "What are the odds
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of my child being born
with a genetic disorder?"
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Or, "What are the sales volume
we can predict for this product?"
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I have a dog. Her name is Elle,
and she hates the rain.
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And I have tried everything
to untrain her.
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But because I have failed at this,
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I also have to consult
an oracle, called Dark Sky,
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every time before we go on a walk,
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for very accurate weather predictions
in the next 10 minutes.
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She's so sweet.
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So because of all of this,
our oracle is a $122 billion industry.
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Now, despite the size of this industry,
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the returns are surprisingly low.
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Investing in big data is easy,
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but using it is hard.
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Over 73 percent of big data projects
aren't even profitable,
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and I have executives
coming up to me saying,
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"We're experiencing the same thing.
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We invested in some big data system,
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and our employees aren't making
better decisions.
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And they're certainly not coming up
with more breakthrough ideas."
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So this is all really interesting to me,
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because I'm a technology ethnographer.
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I study and I advise companies
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on the patterns
of how people use technology,
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and one of my interest areas is data.
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So why is having more data
not helping us make better decisions,
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especially for companies
who have all these resources
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to invest in these big data systems?
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Why isn't it getting any easier for them?
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So, I've witnessed the struggle firsthand.
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In 2009, I started
a research position with Nokia.
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And at the time,
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Nokia was one of the largest
cell phone companies in the world,
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dominating emerging markets
like China, Mexico and India --
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all places where I had done
a lot of research
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on how low-income people use technology.
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And I spent a lot of extra time in China
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getting to know the informal economy.
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So I did things like working
as a street vendor
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selling dumplings to construction workers.
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Or I did fieldwork,
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spending nights and days
in internet cafés,
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hanging out with Chinese youth,
so I could understand
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how they were using
games and mobile phones
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and using it between moving
from the rural areas to the cities.
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Through all of this qualitative evidence
that I was gathering,
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I was starting to see so clearly
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that a big change was about to happen
among low-income Chinese people.
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Even though they were surrounded
by advertisements for luxury products
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like fancy toilets --
who wouldn't want one? --
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and apartments and cars,
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through my conversations with them,
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I found out that the ads
the actually enticed them the most
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were the ones for iPhones,
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promising them this entry
into this high-tech life.
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And even when I was living with them
in urban slums like this one,
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I saw people investing
over half of their monthly income
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into buying a phone,
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and increasingly, they were "shanzhai,"
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which are affordable knock-offs
of iPhones and other brands.
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They're very usable.
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Does the job.
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And after years of living
with migrants and working with them
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and just really doing everything
that they were doing,
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I started piecing
all these data points together --
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from the things that seem random,
like me selling dumplings,
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to the things that were more obvious,
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like tracking how much they were spending
on their cell phone bills.
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And I was able to create
this much more holistic picture
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of what was happening.
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And that's when I started to realize
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that even the poorest in China
would want a smartphone,
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and that they would do almost anything
to get their hands on one.
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You have to keep in mind,
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iPhones had just come out, it was 2009,
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so this was, like, eight years ago,
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and Androids had just started
looking like iPhones.
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And a lot of very smart
and realistic people said,
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"Those smartphones -- that's just a fad.
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Who wants to carry around
these heavy things
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where batteries drain quickly
and they break every time you drop them?"
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But I had a lot of data,
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and I was very confident
about my insights,
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so I was very excited
to share them with Nokia.
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But Nokia was not convinced,
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because it wasn't big data.
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They said, "We have
millions of data points,
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and we don't see any indicators
of anyone wanting to buy a smartphone,
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and your data set of 100,
as diverse as it is, is too weak
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for us to even take seriously."
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And I said, "Nokia, you're right.
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Of course you wouldn't see this,
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because you're sending out surveys
assuming that people don't know
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what a smartphone is,
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so of course you're not going
to get any data back
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about people wanting to buy
a smartphone in two years.
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Your surveys, your methods
have been designed
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to optimize an existing business model,
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and I'm looking
at these emergent human dynamics
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that haven't happened yet.
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We're looking outside of market dynamics
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so that we can get ahead of it."
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Well, you know what happened to Nokia?
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Their business fell off a cliff.
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This -- this is the cost
of missing something.
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It was unfathomable.
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But Nokia's not alone.
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I see organizations
throwing out data all the time
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because it didn't come from a quant model
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or it doesn't fit in one.
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But it's not big data's fault.
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It's the way we use big data;
it's our responsibility.
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Big data's reputation for success
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comes from quantifying
very specific environments,
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like electricity power grids
or delivery logistics or genetic code,
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when we're quantifying in systems
that are more or less contained.
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But not all systems
are as neatly contained.
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When you're quantifying
and systems are more dynamic,
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especially systems
that involve human beings,
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forces are complex and unpredictable,
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and these are things
that we don't know how to model so well.
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Once you predict something
about human behavior,
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new factors emerge,
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because conditions
are constantly changing.
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That's why it's a never-ending cycle.
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You think you know something,
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and then something unknown
enters the picture.
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And that's why just relying
on big data alone
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increases the chance
that we'll miss something,
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while giving us this illusion
that we already know everything.
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And what makes it really hard
to see this paradox
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and even wrap our brains around it
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is that we have this thing
that I call the quantification bias,
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which is the unconscious belief
of valuing the measurable
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over the immeasurable.
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And we often experience this at our work.
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Maybe we work alongside
colleagues who are like this,
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or even our whole entire
company may be like this,
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where people become
so fixated on that number,
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that they can't see anything
outside of it,
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even when you present them evidence
right in front of their face.
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And this is a very appealing message,
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because there's nothing
wrong with quantifying;
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it's actually very satisfying.
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I get a great sense of comfort
from looking at an Excel spreadsheet,
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even very simple ones.
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(Laughter)
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It's just kind of like,
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"Yes! The formula worked. It's all OK.
Everything is under control."
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But the problem is
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that quantifying is addictive.
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And when we forget that
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and when we don't have something
to kind of keep that in check,
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it's very easy to just throw out data
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because it can't be expressed
as a numerical value.
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It's very easy just to slip
into silver-bullet thinking,
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as if some simple solution existed.
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Because this is a great moment of danger
for any organization,
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because oftentimes,
the future we need to predict --
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it isn't in that haystack,
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but it's that tornado
that's bearing down on us
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outside of the barn.
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There is no greater risk
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than being blind to the unknown.
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It can cause you to make
the wrong decisions.
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It can cause you to miss something big.
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But we don't have to go down this path.
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It turns out that the oracle
of ancient Greece
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holds the secret key
that shows us the path forward.
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Now, recent geological research has shown
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that the Temple of Apollo,
where the most famous oracle sat,
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was actually built
over two earthquake faults.
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And these faults would release
these petrochemical fumes
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from underneath the Earth's crust,
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and the oracle literally sat
right above these faults,
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inhaling enormous amounts
of ethylene gas, these fissures.
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(Laughter)
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It's true.
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(Laughter)
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It's all true, and that's what made her
babble and hallucinate
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and go into this trance-like state.
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She was high as a kite!
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(Laughter)
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So how did anyone --
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How did anyone get
any useful advice out of her
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in this state?
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Well, you see those people
surrounding the oracle?
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You see those people holding her up,
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because she's, like, a little woozy?
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And you see that guy
on your left-hand side
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holding the orange notebook?
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Well, those were the temple guides,
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and they worked hand in hand
with the oracle.
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When inquisitors would come
and get on their knees,
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that's when the temple guides
would get to work,
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because after they asked her questions,
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they would observe their emotional state,
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and then they would ask them
follow-up questions,
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like, "Why do you want to know
this prophecy? Who are you?
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What are you going to do
with this information?"
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And then the temple guides would take
this more ethnographic,
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this more qualitative information,
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and interpret the oracle's babblings.
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So the oracle didn't stand alone,
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and neither should our big data systems.
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Now to be clear,
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I'm not saying that big data systems
are huffing ethylene gas,
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or that they're even giving
invalid predictions.
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The total opposite.
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But what I am saying
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is that in the same way
that the oracle needed her temple guides,
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our big data systems need them, too.
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They need people like ethnographers
and user researchers
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who can gather what I call thick data.
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This is precious data from humans,
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like stories, emotions and interactions
that cannot be quantified.
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It's the kind of data
that I collected for Nokia
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that comes in in the form
of a very small sample size,
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but delivers incredible depth of meaning.
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And what makes it so thick and meaty
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is the experience of understanding
the human narrative.
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And that's what helps to see
what's missing in our models.
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Thick data grounds our business questions
in human questions,
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and that's why integrating
big and thick data
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forms a more complete picture.
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Big data is able to offer
insights at scale
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and leverage the best
of machine intelligence,
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whereas thick data can help us
rescue the context loss
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that comes from making big data usable,
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and leverage the best
of human intelligence.
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And when you actually integrate the two,
that's when things get really fun,
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because then you're no longer
just working with data
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you've already collected.
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You get to also work with data
that hasn't been collected.
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You get to ask questions about why:
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Why is this happening?
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Now, when Netflix did this,
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they unlocked a whole new way
to transform their business.
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Netflix is known for their really great
recommendation algorithm,
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and they had this $1 million prize
for anyone who could improve it.
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And there were winners.
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But Netflix discovered
the improvements were only incremental.
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So to really find out what was going on,
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they hired an ethnographer,
Grant McCracken,
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to gather thick data insights.
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And what he discovered was something
that they hadn't seen initially
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in the quantitative data.
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He discovered that people loved
to binge-watch.
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In fact, people didn't even
feel guilty about it.
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They enjoyed it.
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(Laughter)
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So Netflix was like,
"Oh. This is a new insight."
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So they went to their data science team,
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and they were able to scale
this big data insight
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in with their quantitative data.
285
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And once they verified it
and validated it,
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Netflix decided to do something
very simple but impactful.
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They said, instead of offering
the same show from different genres
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or more of the different shows
from similar users,
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we'll just offer more of the same show.
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We'll make it easier
for you to binge-watch.
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And they didn't stop there.
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They did all these things
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to redesign their entire
viewer experience,
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to really encourage binge-watching.
295
00:14:20,050 --> 00:14:23,291
It's why people and friends disappear
for whole weekends at a time,
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00:14:23,315 --> 00:14:25,658
catching up on shows
like "Master of None."
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By integrating big data and thick data,
they not only improved their business,
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but they transformed how we consume media.
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And now their stocks are projected
to double in the next few years.
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But this isn't just about
watching more videos
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or selling more smartphones.
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For some, integrating thick data
insights into the algorithm
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could mean life or death,
304
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especially for the marginalized.
305
00:14:53,558 --> 00:14:56,992
All around the country,
police departments are using big data
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for predictive policing,
307
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to set bond amounts
and sentencing recommendations
308
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in ways that reinforce existing biases.
309
00:15:06,116 --> 00:15:08,539
NSA's Skynet machine learning algorithm
310
00:15:08,563 --> 00:15:14,007
has possibly aided in the deaths
of thousands of civilians in Pakistan
311
00:15:14,031 --> 00:15:16,752
from misreading cellular device metadata.
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00:15:18,951 --> 00:15:22,354
As all of our lives become more automated,
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from automobiles to health insurance
or to employment,
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it is likely that all of us
315
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will be impacted
by the quantification bias.
316
00:15:32,792 --> 00:15:35,413
Now, the good news
is that we've come a long way
317
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from huffing ethylene gas
to make predictions.
318
00:15:37,911 --> 00:15:40,981
We have better tools,
so let's just use them better.
319
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Let's integrate the big data
with the thick data.
320
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Let's bring our temple guides
with the oracles,
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00:15:45,637 --> 00:15:49,013
and whether this work happens
in companies or nonprofits
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or government or even in the software,
323
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all of it matters,
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00:15:53,346 --> 00:15:56,369
because that means
we're collectively committed
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to making better data,
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00:15:58,608 --> 00:16:00,444
better algorithms, better outputs
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00:16:00,468 --> 00:16:02,111
and better decisions.
328
00:16:02,135 --> 00:16:05,693
This is how we'll avoid
missing that something.
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(Applause)