TEDxCambridge
Tricia Wang: The human insights missing from big data
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Why do so many companies make bad decisions, even with access to unprecedented amounts of data? With stories from Nokia to Netflix to the oracles of ancient Greece, Tricia Wang demystifies big data and identifies its pitfalls, suggesting that we focus instead on "thick data" -- precious, unquantifiable insights from actual people -- to make the right business decisions and thrive in the unknown.
Tricia Wang - Technology ethnographer
With astronaut eyes and ethnographer curiosity, Tricia Wang helps corporations grow by discovering the unknown about their customers. Full bio
With astronaut eyes and ethnographer curiosity, Tricia Wang helps corporations grow by discovering the unknown about their customers. Full bio
Double-click the English transcript below to play the video.
00:12
In ancient Greece,
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when anyone from slaves to soldiers,
poets and politicians,
poets and politicians,
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needed to make a big decision
on life's most important questions,
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?"
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,
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,
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.
what's going to happen next.
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And that's because we all want
to make the right decision.
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
knowing that we can make a decision
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with some assurance of the outcome.
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01:11
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."
or "deep learning" or "neural net."
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01:19
And these are the kinds of questions
we ask of our oracle now,
we ask of our oracle now,
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like, "What's the most efficient way
to ship these phones
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?"
with a genetic disorder?"
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Or, "What are the sales volume
we can predict for this product?"
we can predict for this product?"
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I have a dog. Her name is Elle,
and she hates the rain.
and she hates the rain.
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And I have tried everything
to untrain her.
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,
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.
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.
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,
aren't even profitable,
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and I have executives
coming up to me saying,
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.
better decisions.
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And they're certainly not coming up
with more breakthrough ideas."
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,
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,
not helping us make better decisions,
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especially for companies
who have all these resources
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.
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,
cell phone companies in the world,
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dominating emerging markets
like China, Mexico and India --
like China, Mexico and India --
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all places where I had done
a lot of research
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
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,
in internet cafés,
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hanging out with Chinese youth,
so I could understand
so I could understand
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how they were using
games and mobile phones
games and mobile phones
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and using it between moving
from the rural areas to the cities.
from the rural areas to the cities.
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Through all of this qualitative evidence
that I was gathering,
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.
among low-income Chinese people.
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Even though they were surrounded
by advertisements for luxury products
by advertisements for luxury products
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like fancy toilets --
who wouldn't want one? --
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
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.
into this high-tech life.
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And even when I was living with them
in urban slums like this one,
in urban slums like this one,
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I saw people investing
over half of their monthly income
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.
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
with migrants and working with them
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and just really doing everything
that they were doing,
that they were doing,
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I started piecing
all these data points together --
all these data points together --
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from the things that seem random,
like me selling dumplings,
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.
on their cell phone bills.
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And I was able to create
this much more holistic picture
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,
would want a smartphone,
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and that they would do almost anything
to get their hands on one.
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.
looking like iPhones.
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And a lot of very smart
and realistic people said,
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
these heavy things
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where batteries drain quickly
and they break every time you drop them?"
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,
about my insights,
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so I was very excited
to share them with Nokia.
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,
millions of data points,
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and we don't see any indicators
of anyone wanting to buy a smartphone,
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
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
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
to get any data back
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about people wanting to buy
a smartphone in two years.
a smartphone in two years.
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Your surveys, your methods
have been designed
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
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.
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
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.
it's our responsibility.
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Big data's reputation for success
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comes from quantifying
very specific environments,
very specific environments,
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like electricity power grids
or delivery logistics or genetic code,
or delivery logistics or genetic code,
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when we're quantifying in systems
that are more or less contained.
that are more or less contained.
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But not all systems
are as neatly contained.
are as neatly contained.
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When you're quantifying
and systems are more dynamic,
and systems are more dynamic,
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especially systems
that involve human beings,
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.
that we don't know how to model so well.
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Once you predict something
about human behavior,
about human behavior,
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new factors emerge,
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because conditions
are constantly changing.
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.
enters the picture.
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And that's why just relying
on big data alone
on big data alone
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increases the chance
that we'll miss something,
that we'll miss something,
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while giving us this illusion
that we already know everything.
that we already know everything.
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And what makes it really hard
to see this paradox
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,
that I call the quantification bias,
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which is the unconscious belief
of valuing the measurable
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,
colleagues who are like this,
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or even our whole entire
company may be like this,
company may be like this,
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where people become
so fixated on that number,
so fixated on that number,
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that they can't see anything
outside of it,
outside of it,
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even when you present them evidence
right in front of their face.
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;
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,
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."
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,
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.
as a numerical value.
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It's very easy just to slip
into silver-bullet thinking,
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,
for any organization,
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because oftentimes,
the future we need to predict --
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
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.
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
of ancient Greece
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holds the secret key
that shows us the path forward.
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,
where the most famous oracle sat,
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was actually built
over two earthquake faults.
over two earthquake faults.
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And these faults would release
these petrochemical fumes
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,
right above these faults,
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inhaling enormous amounts
of ethylene gas, these fissures.
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
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
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?
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
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.
with the oracle.
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When inquisitors would come
and get on their knees,
and get on their knees,
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that's when the temple guides
would get to work,
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,
follow-up questions,
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like, "Why do you want to know
this prophecy? Who are you?
this prophecy? Who are you?
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What are you going to do
with this information?"
with this information?"
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And then the temple guides would take
this more ethnographic,
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,
are huffing ethylene gas,
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or that they're even giving
invalid predictions.
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,
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
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.
that cannot be quantified.
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It's the kind of data
that I collected for Nokia
that I collected for Nokia
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that comes in in the form
of a very small sample size,
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.
the human narrative.
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And that's what helps to see
what's missing in our models.
what's missing in our models.
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Thick data grounds our business questions
in human questions,
in human questions,
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and that's why integrating
big and thick data
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
insights at scale
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and leverage the best
of machine intelligence,
of machine intelligence,
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whereas thick data can help us
rescue the context loss
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.
of human intelligence.
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And when you actually integrate the two,
that's when things get really fun,
that's when things get really fun,
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because then you're no longer
just working with data
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.
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.
to transform their business.
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Netflix is known for their really great
recommendation algorithm,
recommendation algorithm,
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and they had this $1 million prize
for anyone who could improve it.
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.
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,
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
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.
to binge-watch.
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In fact, people didn't even
feel guilty about it.
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."
"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
this big data insight
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in with their quantitative data.
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And once they verified it
and validated it,
and validated it,
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Netflix decided to do something
very simple but impactful.
very simple but impactful.
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They said, instead of offering
the same show from different genres
the same show from different genres
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or more of the different shows
from similar users,
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.
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,
viewer experience,
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to really encourage binge-watching.
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It's why people and friends disappear
for whole weekends at a time,
for whole weekends at a time,
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catching up on shows
like "Master of None."
like "Master of None."
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By integrating big data and thick data,
they not only improved their business,
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.
to double in the next few years.
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But this isn't just about
watching more videos
watching more videos
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or selling more smartphones.
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For some, integrating thick data
insights into the algorithm
insights into the algorithm
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could mean life or death,
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especially for the marginalized.
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All around the country,
police departments are using big data
police departments are using big data
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for predictive policing,
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to set bond amounts
and sentencing recommendations
and sentencing recommendations
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in ways that reinforce existing biases.
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NSA's Skynet machine learning algorithm
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has possibly aided in the deaths
of thousands of civilians in Pakistan
of thousands of civilians in Pakistan
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from misreading cellular device metadata.
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As all of our lives become more automated,
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from automobiles to health insurance
or to employment,
or to employment,
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it is likely that all of us
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will be impacted
by the quantification bias.
by the quantification bias.
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Now, the good news
is that we've come a long way
is that we've come a long way
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from huffing ethylene gas
to make predictions.
to make predictions.
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We have better tools,
so let's just use them better.
so let's just use them better.
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Let's integrate the big data
with the thick data.
with the thick data.
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Let's bring our temple guides
with the oracles,
with the oracles,
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and whether this work happens
in companies or nonprofits
in companies or nonprofits
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or government or even in the software,
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all of it matters,
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because that means
we're collectively committed
we're collectively committed
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to making better data,
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better algorithms, better outputs
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and better decisions.
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This is how we'll avoid
missing that something.
missing that something.
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(Applause)
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ABOUT THE SPEAKER
Tricia Wang - Technology ethnographerWith astronaut eyes and ethnographer curiosity, Tricia Wang helps corporations grow by discovering the unknown about their customers.
Why you should listen
For Tricia Wang, human behavior generates some of the most perplexing questions of our times. She has taught global organizations how to identify new customers and markets hidden behind their data, amplified IDEO's design thinking practice as an expert-in-residence, researched the social evolution of the Chinese internet, and written about the "elastic self," an emergent form of interaction in a virtual world. Wang is the co-founder of Sudden Compass, a consulting firm that helps companies unlock new growth opportunities by putting customer obsession into practice.
Wang's work has been featured in The Atlantic, Al Jazeera, and The Guardian. Fast Company spotlighted her work in China: "What Twitter Can Learn From Weibo: Field Notes From Global Tech Ethnographer Tricia Wang." In her latest op-ed on Slate, she discusses how attempts to stop terrorists on social media can harm our privacy and anonymity. Her Medium post, "Why Big Data Needs Thick Data," is a frequently cited industry piece on the importance of an integrated data approach. One of her favorite essays documents her day in the life of working as a street vendor in China.
Known for her lively presentations that are grounded in her research and observations about human behavior and data, Wang has spoken at organizations such as Proctor & Gamble, Nike, Wrigley, 21st Century Fox and Tumblr. Her most recent talk at Enterprise UX delved into why corporate innovation usually doesn’t work and what to do about it. She delivered the opening keynote at The Conference to a crowd of marketers and creatives, delving into the wild history of linear perspective and its influence on how we think and form organizations.
Wang holds affiliate positions at Data & Society, Harvard University's Berkman Klein Center for Internet Studies and New York University's Interactive Telecommunication Program. She oversees Ethnography Matters, a site that publishes articles about applied ethnography and technology. She co-started a Slack community for anyone who uses ethnographic methods in industry.
Wang began her career as a documentary filmmaker at NASA, an HIV/AIDS activist, and an educator specializing in culturally responsive pedagogy. She is also proud to have co-founded the first national hip-hop education initiative, which turned into the Hip Hop Education Center at New York University, and to have built after-school technology and arts programs for low-income youth at New York City public schools and the Queens Museum of Arts. Her life philosophy is that you have to go to the edge to discover what’s really happening. She's the proud companion of her internet famous dog, #ellethedog.
Tricia Wang | Speaker | TED.com