TEDxCambridge
Tricia Wang: The human insights missing from big data
翠西娅·王: 大数据掩盖下的人性
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为何有了大数据的支持，如此多的企业还是做出了错误的决策？通过诺基亚、Netflix和古希腊先知的故事，翠西娅·王深入浅出地解释了大数据，指出了它的隐患，并建议我们应该更多地关注“厚数据”——珍贵的、不可量化的思想和情感，来源于有血有肉的人——才能做出正确的商业决策，并在新领域取得成功。
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.
在古希腊，
00:12
In ancient Greece,
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从奴隶到士兵，从诗人到政治家，
00:15
when anyone from slaves to soldiers,
poets and politicians,
poets and politicians,
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00:19
needed to make a big decision
on life's most important questions,
on life's most important questions,
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都需要对人生中最重要的问题做决定，
00:23
like, "Should I get married?"
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比如，我该结婚吗？
这次出海我该不该去？
00:24
or "Should we embark on this voyage?"
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00:26
or "Should our army
advance into this territory?"
advance into this territory?"
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我们该不该向那片区域进军？
他们纷纷去请教先知。
00:29
they all consulted the oracle.
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00:33
So this is how it worked:
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过程是这样的：
你问她一个问题，然后跪在她面前，
00:34
you would bring her a question
and you would get on your knees,
and you would get on your knees,
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之后她会进入一种恍惚的状态。
00:37
and then she would go into this trance.
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也许持续几天，
00:39
It would take a couple of days,
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最终她会恢复清醒状态，
00:41
and then eventually
she would come out of it,
she would come out of it,
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给出她的预测，回答你的问题。
00:43
giving you her predictions as your answer.
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00:46
From the oracle bones of ancient China
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从古代中国用骨头占卜，
到古希腊，再到玛雅历法，
00:49
to ancient Greece to Mayan calendars,
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00:51
people have craved for prophecy
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人们祈求能得到预言，
从而知道未来会发生什么。
00:54
in order to find out
what's going to happen next.
what's going to happen next.
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00:58
And that's because we all want
to make the right decision.
to make the right decision.
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因为我们都想做出正确的决定。
01:01
We don't want to miss something.
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我们不想忽略什么。
未来是可怕的，
01:03
The future is scary,
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因此若我们
在做决定时多多少少
在做决定时多多少少
01:05
so it's much nicer
knowing that we can make a decision
knowing that we can make a decision
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能预知结果，会更好。
01:08
with some assurance of the outcome.
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01:11
Well, we have a new oracle,
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如今我们有了新的先知，
01:12
and it's name is big data,
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它的名字叫大数据，
01:14
or we call it "Watson"
or "deep learning" or "neural net."
or "deep learning" or "neural net."
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或者叫它“沃森”或者
“深度学习”或者“神经网络”。
“深度学习”或者“神经网络”。
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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以下就是我们问这位先知的问题。
01:23
like, "What's the most efficient way
to ship these phones
to ship these phones
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“要把这些手机从中国运到瑞典，
01:27
from China to Sweden?"
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怎么做最高效？”
01:29
Or, "What are the odds
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或者“我的孩子出生时
01:30
of my child being born
with a genetic disorder?"
with a genetic disorder?"
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患遗传病的几率是多少？”
01:34
Or, "What are the sales volume
we can predict for this product?"
we can predict for this product?"
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或者“这件产品的预计销量是多少？”
01:40
I have a dog. Her name is Elle,
and she hates the rain.
and she hates the rain.
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我养了一只狗，名叫艾尔，
她讨厌下雨。
她讨厌下雨。
01:44
And I have tried everything
to untrain her.
to untrain her.
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我想了很多办法来帮她。
01:47
But because I have failed at this,
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但是因为我失败了，
01:50
I also have to consult
an oracle, called Dark Sky,
an oracle, called Dark Sky,
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因此每次准备遛狗时，
我都会求助一位先知，叫Dark Sky，
01:53
every time before we go on a walk,
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来获得未来10分钟精准的天气预报。
01:55
for very accurate weather predictions
in the next 10 minutes.
in the next 10 minutes.
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02:01
She's so sweet.
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小狗真可爱。
因此，“先知”大数据是
一项价值1220亿美元的产业。
一项价值1220亿美元的产业。
02:03
So because of all of this,
our oracle is a $122 billion industry.
our oracle is a $122 billion industry.
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02:10
Now, despite the size of this industry,
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但尽管产业规模大，
02:13
the returns are surprisingly low.
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投资回报却出奇地低。
02:16
Investing in big data is easy,
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投资大数据很简单，
02:18
but using it is hard.
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但利用它却很难。
02:21
Over 73 percent of big data projects
aren't even profitable,
aren't even profitable,
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超过73%的大数据项目都不赚钱，
02:26
and I have executives
coming up to me saying,
coming up to me saying,
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有经理来找我说，
02:28
"We're experiencing the same thing.
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“我们的情况也是如此。
我们投资了一些大数据系统，
02:30
We invested in some big data system,
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但雇员们并未因此做出更好的决策。
02:32
and our employees aren't making
better decisions.
better decisions.
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02:35
And they're certainly not coming up
with more breakthrough ideas."
with more breakthrough ideas."
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更别说提出突破性的想法了。”
02:38
So this is all really interesting to me,
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我觉得这个现象很有意思，
02:42
because I'm a technology ethnographer.
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因为我是一名技术人类学家。
02:44
I study and I advise companies
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我研究人们使用技术的模式，
02:47
on the patterns
of how people use technology,
of how people use technology,
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并据此为企业提供建议，
02:49
and one of my interest areas is data.
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数据是我感兴趣的领域之一。
02:52
So why is having more data
not helping us make better decisions,
not helping us make better decisions,
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为什么更多的数据不能
帮我们更好的决策呢？
帮我们更好的决策呢？
02:57
especially for companies
who have all these resources
who have all these resources
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尤其是那些资源丰富，
能投资大数据系统的公司。
03:00
to invest in these big data systems?
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为什么对他们而言，
事情并未变得简单？
事情并未变得简单？
03:02
Why isn't it getting any easier for them?
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03:05
So, I've witnessed the struggle firsthand.
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我亲眼见过这种困境。
03:09
In 2009, I started
a research position with Nokia.
a research position with Nokia.
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2009年，我跟诺基亚
开始进行一项研究。
开始进行一项研究。
03:13
And at the time,
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在当时，
诺基亚是全球最大的
手机生产商之一，
手机生产商之一，
03:14
Nokia was one of the largest
cell phone companies in the world,
cell phone companies in the world,
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03:17
dominating emerging markets
like China, Mexico and India --
like China, Mexico and India --
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在中国、墨西哥和印度等
新兴市场占有巨大份额，
新兴市场占有巨大份额，
03:20
all places where I had done
a lot of research
a lot of research
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我在上述国家进行了大量的研究，
看低收入人群是如何使用技术的。
03:23
on how low-income people use technology.
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03:26
And I spent a lot of extra time in China
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我在中国花了大量时间
03:28
getting to know the informal economy.
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去了解当地的街头经济。
03:31
So I did things like working
as a street vendor
as a street vendor
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我当过街边小贩，
卖饺子给建筑工人。
03:33
selling dumplings to construction workers.
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03:36
Or I did fieldwork,
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我还泡过网吧，
03:37
spending nights and days
in internet cafés,
in internet cafés,
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在那里连续待上几天，
03:40
hanging out with Chinese youth,
so I could understand
so I could understand
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跟中国年轻人
混在一起，来了解
混在一起，来了解
他们如何玩游戏和使用手机，
03:42
how they were using
games and mobile phones
games and mobile phones
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03:45
and using it between moving
from the rural areas to the cities.
from the rural areas to the cities.
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如何在从农村来到城市时使用。
03:50
Through all of this qualitative evidence
that I was gathering,
that I was gathering,
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通过搜集到的这些
高质量的例证，
高质量的例证，
03:54
I was starting to see so clearly
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我开始清晰地看到
03:57
that a big change was about to happen
among low-income Chinese people.
among low-income Chinese people.
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在中国低收入人群中
将发生巨大的变革。
将发生巨大的变革。
04:03
Even though they were surrounded
by advertisements for luxury products
by advertisements for luxury products
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尽管奢华产品的广告随处可见，
比如高级马桶——谁不想要？
04:07
like fancy toilets --
who wouldn't want one? --
who wouldn't want one? --
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04:10
and apartments and cars,
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还有房子和车子，
04:13
through my conversations with them,
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聊天过程中，
04:15
I found out that the ads
the actually enticed them the most
the actually enticed them the most
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我发现最吸引他们的广告，
04:19
were the ones for iPhones,
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是iPhone的广告，
04:21
promising them this entry
into this high-tech life.
into this high-tech life.
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因为感觉可以将他们
带入高科技生活。
带入高科技生活。
04:25
And even when I was living with them
in urban slums like this one,
in urban slums like this one,
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跟他们一起住在
这样的城中村里，
这样的城中村里，
04:28
I saw people investing
over half of their monthly income
over half of their monthly income
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我看到有人花掉超过
半个月的收入
半个月的收入
04:31
into buying a phone,
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去买一部手机，
04:33
and increasingly, they were "shanzhai,"
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“山寨”越来越多，
就是苹果和其他品牌的
廉价仿冒品。
廉价仿冒品。
04:35
which are affordable knock-offs
of iPhones and other brands.
of iPhones and other brands.
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04:40
They're very usable.
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它们也能用。
04:42
Does the job.
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基本功能都有。
04:44
And after years of living
with migrants and working with them
with migrants and working with them
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多年来，我跟这些外地人
一起工作和生活，
一起工作和生活，
04:50
and just really doing everything
that they were doing,
that they were doing,
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跟他们做着同样的事情，
04:54
I started piecing
all these data points together --
all these data points together --
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我开始把很多数据联系起来，
04:57
from the things that seem random,
like me selling dumplings,
like me selling dumplings,
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从随机事件，比如卖饺子，
到比较直观的东西，
05:00
to the things that were more obvious,
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比如看他们会花多少钱买手机。
05:02
like tracking how much they were spending
on their cell phone bills.
on their cell phone bills.
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我更全面地了解了
05:05
And I was able to create
this much more holistic picture
this much more holistic picture
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发生的事。
05:08
of what was happening.
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此时我开始意识到，
05:09
And that's when I started to realize
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即使是中国最穷的人，
也会想拥有一部智能手机，
也会想拥有一部智能手机，
05:11
that even the poorest in China
would want a smartphone,
would want a smartphone,
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05:14
and that they would do almost anything
to get their hands on one.
to get their hands on one.
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而为此他们几乎愿意付出一切。
别忘了，
05:21
You have to keep in mind,
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05:23
iPhones had just come out, it was 2009,
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那是2009年，
iPhone才刚刚出现，
iPhone才刚刚出现，
差不多是8年前，
05:26
so this was, like, eight years ago,
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05:28
and Androids had just started
looking like iPhones.
looking like iPhones.
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而安卓手机刚开始
长得像iPhone。
长得像iPhone。
很多聪明而务实的人断言，
05:30
And a lot of very smart
and realistic people said,
and realistic people said,
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05:33
"Those smartphones -- that's just a fad.
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“这些智能手机，只会昙花一现。
05:36
Who wants to carry around
these heavy things
these heavy things
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谁会愿意拿着这么重的手机，
05:39
where batteries drain quickly
and they break every time you drop them?"
and they break every time you drop them?"
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电量掉得那么快，一摔就坏。”
但我有数据，
05:44
But I had a lot of data,
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我对自己的见解很自信，
05:46
and I was very confident
about my insights,
about my insights,
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05:48
so I was very excited
to share them with Nokia.
to share them with Nokia.
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于是我非常兴奋地告诉诺基亚。
05:53
But Nokia was not convinced,
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但是诺基亚不为所动，
05:55
because it wasn't big data.
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因为我给的不是大数据。
05:59
They said, "We have
millions of data points,
millions of data points,
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他们说，“我们有几百万的数据，
06:01
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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没有数据显示会
有人愿意买智能手机，
有人愿意买智能手机，
而你的数据量只有几百，
还如此分散，毫无说服力，
还如此分散，毫无说服力，
06:05
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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06:10
for us to even take seriously."
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根本不值一提。”
06:12
And I said, "Nokia, you're right.
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我说，“诺基亚，你是对的。
你当然看不到这些，
06:14
Of course you wouldn't see this,
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因为你在调查时就假定
06:16
because you're sending out surveys
assuming that people don't know
assuming that people don't know
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人们不了解智能手机，
06:19
what a smartphone is,
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因此当然得不到数据来了解
06:20
so of course you're not going
to get any data back
to get any data back
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2年之内想买智能手机的人。
06:23
about people wanting to buy
a smartphone in two years.
a smartphone in two years.
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因为你们的调查和方法，
06:25
Your surveys, your methods
have been designed
have been designed
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目的都是优化现有的商业模式，
06:27
to optimize an existing business model,
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而我看到的，是前所未有的
06:29
and I'm looking
at these emergent human dynamics
at these emergent human dynamics
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人类新动向。
06:32
that haven't happened yet.
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06:33
We're looking outside of market dynamics
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我们看的是市场动态之外的东西，
06:36
so that we can get ahead of it."
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因此可以领先一步。”
06:39
Well, you know what happened to Nokia?
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都知道诺基亚的结局吧？
06:41
Their business fell off a cliff.
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他们的生意一落千丈。
06:44
This -- this is the cost
of missing something.
of missing something.
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这就是忽略某些事情的代价。
06:49
It was unfathomable.
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就是那么难以想象。
06:52
But Nokia's not alone.
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而诺基亚并非个案。
06:54
I see organizations
throwing out data all the time
throwing out data all the time
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我看到许多组织总是
对数据视而不见，
对数据视而不见，
因为这些数据并非
来自某种数据模型，
来自某种数据模型，
06:56
because it didn't come from a quant model
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或跟模型不符。
06:59
or it doesn't fit in one.
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07:02
But it's not big data's fault.
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大数据本身并没有错。
07:04
It's the way we use big data;
it's our responsibility.
it's our responsibility.
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是我们使用不当，错在我们。
07:09
Big data's reputation for success
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大数据的声名鹊起
07:11
comes from quantifying
very specific environments,
very specific environments,
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是因为它能量化特定环境，
07:15
like electricity power grids
or delivery logistics or genetic code,
or delivery logistics or genetic code,
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比如电网、物流或者基因编码，
07:20
when we're quantifying in systems
that are more or less contained.
that are more or less contained.
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帮我们量化一定程度上
可控的体系。
可控的体系。
07:24
But not all systems
are as neatly contained.
are as neatly contained.
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然而并非所有的体系
都有很好的可控性。
都有很好的可控性。
07:27
When you're quantifying
and systems are more dynamic,
and systems are more dynamic,
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对一个动态的体系进行量化，
07:31
especially systems
that involve human beings,
that involve human beings,
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尤其是牵涉到人时，
各种因素复杂多变，
07:34
forces are complex and unpredictable,
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07:37
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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有些因素并没有很好的模型。
07:41
Once you predict something
about human behavior,
about human behavior,
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对人的行为进行预测时，
07:44
new factors emerge,
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会出现新的因素，
07:45
because conditions
are constantly changing.
are constantly changing.
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因为条件是在不断变化的。
因此这是个永远的循环。
07:48
That's why it's a never-ending cycle.
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你以为已经懂了，
07:50
You think you know something,
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结果新的未知情况又出现了。
07:51
and then something unknown
enters the picture.
enters the picture.
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07:53
And that's why just relying
on big data alone
on big data alone
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因此，仅仅依靠大数据，
反而会使我们更容易
忽略一些事实，
忽略一些事实，
07:57
increases the chance
that we'll miss something,
that we'll miss something,
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08:00
while giving us this illusion
that we already know everything.
that we already know everything.
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却给了我们已经掌握一切的错觉。
08:04
And what makes it really hard
to see this paradox
to see this paradox
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要看清这样一个矛盾，
08:08
and even wrap our brains around it
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哪怕仅仅去认真思考它，
08:10
is that we have this thing
that I call the quantification bias,
that I call the quantification bias,
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也是困难重重，
原因在于我们偏爱量化，
原因在于我们偏爱量化，
08:14
which is the unconscious belief
of valuing the measurable
of valuing the measurable
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比起不能量化的，
总是不自觉地相信
总是不自觉地相信
能够量化的。
08:18
over the immeasurable.
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08:21
And we often experience this at our work.
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这在工作中很常见。
08:24
Maybe we work alongside
colleagues who are like this,
colleagues who are like this,
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也许我们的同事是这样，
甚至整个公司都是这样，
08:27
or even our whole entire
company may be like this,
company may be like this,
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08:29
where people become
so fixated on that number,
so fixated on that number,
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大家都盯着数字，
而忽略了其他东西，
08:32
that they can't see anything
outside of it,
outside of it,
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即便你将证据摆在他们面前。
08:34
even when you present them evidence
right in front of their face.
right in front of their face.
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08:39
And this is a very appealing message,
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这一点很有意思，
08:42
because there's nothing
wrong with quantifying;
wrong with quantifying;
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因为量化本身并没有什么错，
甚至会让人愉悦。
08:44
it's actually very satisfying.
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08:46
I get a great sense of comfort
from looking at an Excel spreadsheet,
from looking at an Excel spreadsheet,
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看Excel表格时我就感觉挺好的，
08:50
even very simple ones.
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哪怕表格很简单。
08:52
(Laughter)
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（笑声）
那感觉就是，
08:53
It's just kind of like,
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“好！这个公式能用。
都没问题，一切尽在掌握！”
都没问题，一切尽在掌握！”
08:54
"Yes! The formula worked. It's all OK.
Everything is under control."
Everything is under control."
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08:58
But the problem is
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但问题在于，
09:01
that quantifying is addictive.
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量化会让人上瘾。
09:03
And when we forget that
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一旦忘记这点，
09:05
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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又没有什么纠错的机制，
就很容易舍弃
09:08
it's very easy to just throw out data
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09:10
because it can't be expressed
as a numerical value.
as a numerical value.
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无法变成数值的信息。
09:13
It's very easy just to slip
into silver-bullet thinking,
into silver-bullet thinking,
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人们很容易执迷于一招鲜，
好像总有简单的解决方法。
09:16
as if some simple solution existed.
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对任何组织来说这都很要命，
09:19
Because this is a great moment of danger
for any organization,
for any organization,
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09:23
because oftentimes,
the future we need to predict --
the future we need to predict --
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因为通常我们需要预测的未来，
09:26
it isn't in that haystack,
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不是这干草垛，
09:28
but it's that tornado
that's bearing down on us
that's bearing down on us
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而是谷仓外向我们袭来的
09:31
outside of the barn.
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龙卷风。
09:34
There is no greater risk
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最危险的莫过于
忽略未知事物。
09:37
than being blind to the unknown.
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09:39
It can cause you to make
the wrong decisions.
the wrong decisions.
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这会让你做出错误的决定，
忽略重要的事情。
09:41
It can cause you to miss something big.
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09:43
But we don't have to go down this path.
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但我们并非别无选择。
09:47
It turns out that the oracle
of ancient Greece
of ancient Greece
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其实古希腊的先知们
已经掌握了解决问题的关键。
09:50
holds the secret key
that shows us the path forward.
that shows us the path forward.
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09:55
Now, recent geological research has shown
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最近的地质研究表明，
09:58
that the Temple of Apollo,
where the most famous oracle sat,
where the most famous oracle sat,
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最著名的先知
所在的阿波罗神庙
所在的阿波罗神庙
正建在两个地震断层之间。
10:01
was actually built
over two earthquake faults.
over two earthquake faults.
200
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10:04
And these faults would release
these petrochemical fumes
these petrochemical fumes
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断层不断从地下释放出
石油化学气体。
10:07
from underneath the Earth's crust,
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先知们恰好坐在这些断层上，
10:09
and the oracle literally sat
right above these faults,
right above these faults,
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10:13
inhaling enormous amounts
of ethylene gas, these fissures.
of ethylene gas, these fissures.
204
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吸入了从断层中
逸出的大量乙烯，
逸出的大量乙烯，
10:17
(Laughter)
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（笑声）
是真的。
10:18
It's true.
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（笑声）
10:19
(Laughter)
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没骗你们，因此她才会
产生幻觉，开始呢喃，
产生幻觉，开始呢喃，
10:20
It's all true, and that's what made her
babble and hallucinate
babble and hallucinate
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10:23
and go into this trance-like state.
209
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变得神情恍惚,
她正“飘”着呢！
10:25
She was high as a kite!
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10:27
(Laughter)
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（笑声）
10:31
So how did anyone --
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所以怎么可能——
10:34
How did anyone get
any useful advice out of her
any useful advice out of her
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这种情况下，怎么可能
从先知那里得到有用的建议？
10:37
in this state?
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10:39
Well, you see those people
surrounding the oracle?
surrounding the oracle?
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看到先知身旁的人了吗？
10:41
You see those people holding her up,
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他们扶着她，
因为她已经有点晕了。
10:43
because she's, like, a little woozy?
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你看左手边那位老兄，
10:45
And you see that guy
on your left-hand side
on your left-hand side
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2308
10:47
holding the orange notebook?
219
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手里拿着橙色的本子。
他们是神庙向导，
10:50
Well, those were the temple guides,
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10:51
and they worked hand in hand
with the oracle.
with the oracle.
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跟先知一起合作的。
10:56
When inquisitors would come
and get on their knees,
and get on their knees,
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当求问者跪在先知面前时，
10:58
that's when the temple guides
would get to work,
would get to work,
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神庙向导就要开始介入了，
求问者提问后，
11:00
because after they asked her questions,
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向导开始观察他们的精神状态，
11:02
they would observe their emotional state,
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并且问进一步的问题，
11:04
and then they would ask them
follow-up questions,
follow-up questions,
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2324
比如，“你为什么想问这个？你是谁？
11:07
like, "Why do you want to know
this prophecy? Who are you?
this prophecy? Who are you?
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你要用这个答案来做什么？”
11:10
What are you going to do
with this information?"
with this information?"
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神庙向导利用这些与人更相关的
11:12
And then the temple guides would take
this more ethnographic,
this more ethnographic,
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3182
11:15
this more qualitative information,
230
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更有实质意义的信息，
11:17
and interpret the oracle's babblings.
231
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来对先知的呢喃进行解释。
11:21
So the oracle didn't stand alone,
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所以先知并不是孤立的，
大数据也不应如此。
11:23
and neither should our big data systems.
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别误会，
11:26
Now to be clear,
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我不是说大数据吸了乙烯，
11:27
I'm not saying that big data systems
are huffing ethylene gas,
are huffing ethylene gas,
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3459
11:31
or that they're even giving
invalid predictions.
invalid predictions.
236
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2353
或者大数据的预测没有用。
完全不是。
11:33
The total opposite.
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我想说的是，
11:34
But what I am saying
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11:36
is that in the same way
that the oracle needed her temple guides,
that the oracle needed her temple guides,
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正如先知需要神庙向导们一样，
11:40
our big data systems need them, too.
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大数据系统也需要协助。
11:43
They need people like ethnographers
and user researchers
and user researchers
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需要人类学家和用户研究人员，
11:47
who can gather what I call thick data.
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搜集所谓的“厚数据”。
11:50
This is precious data from humans,
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这是来源于人类的宝贵信息，
比如故事、情感和交流
等不能被量化的东西。
等不能被量化的东西。
11:53
like stories, emotions and interactions
that cannot be quantified.
that cannot be quantified.
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11:57
It's the kind of data
that I collected for Nokia
that I collected for Nokia
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像我曾为诺基亚搜集的，
它们来自很小的样本量，
11:59
that comes in in the form
of a very small sample size,
of a very small sample size,
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12:02
but delivers incredible depth of meaning.
247
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却能传达意义重大的信息。
12:05
And what makes it so thick and meaty
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而“厚数据”内涵丰富是因为
12:10
is the experience of understanding
the human narrative.
the human narrative.
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其中包含了理解人类生活的过程。
12:14
And that's what helps to see
what's missing in our models.
what's missing in our models.
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这能帮助我们看清
模型中缺失的东西。
模型中缺失的东西。
12:18
Thick data grounds our business questions
in human questions,
in human questions,
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“厚数据”将商业问题
落实到人类生活，
落实到人类生活，
12:22
and that's why integrating
big and thick data
big and thick data
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因此将大数据和厚数据相结合
能得到更全面的认识。
12:26
forms a more complete picture.
253
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12:28
Big data is able to offer
insights at scale
insights at scale
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大数据能在数量级上提供视角，
12:31
and leverage the best
of machine intelligence,
of machine intelligence,
255
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最大限度利用机器智能，
12:34
whereas thick data can help us
rescue the context loss
rescue the context loss
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而厚数据能补充
在利用大数据时
在利用大数据时
12:37
that comes from making big data usable,
257
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2098
缺失的情境信息，
充分利用人类智慧。
12:40
and leverage the best
of human intelligence.
of human intelligence.
258
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2181
12:42
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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两者结合起来时就很有意思了，
因为这样你不只是在使用
12:45
because then you're no longer
just working with data
just working with data
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搜集到的数据。
12:48
you've already collected.
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你还能利用尚未搜集到的数据。
12:49
You get to also work with data
that hasn't been collected.
that hasn't been collected.
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你可能会问：
12:52
You get to ask questions about why:
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为什么会这样？
12:54
Why is this happening?
264
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12:55
Now, when Netflix did this,
265
763778
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Netflix这么做之后，
他们找到了全新的方式
来进行商业转型。
来进行商业转型。
12:57
they unlocked a whole new way
to transform their business.
to transform their business.
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3035
13:01
Netflix is known for their really great
recommendation algorithm,
recommendation algorithm,
267
769406
3956
Netflix以出色的
推荐算法而闻名，
推荐算法而闻名，
13:05
and they had this $1 million prize
for anyone who could improve it.
for anyone who could improve it.
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4797
他们设立了100万美元的奖金，
寻找可以改进它的人。
寻找可以改进它的人。
13:10
And there were winners.
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1314
有人获奖了。
13:12
But Netflix discovered
the improvements were only incremental.
the improvements were only incremental.
270
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4323
但Netflix发现改进太慢。
13:17
So to really find out what was going on,
271
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为了彻底弄清原因，
他们雇了一位人类学家：
格兰特·麦克拉肯，
格兰特·麦克拉肯，
13:19
they hired an ethnographer,
Grant McCracken,
Grant McCracken,
272
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3741
13:23
to gather thick data insights.
273
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1546
来搜集分析厚数据。
13:24
And what he discovered was something
that they hadn't seen initially
that they hadn't seen initially
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3924
他发现了在一开始的数据分析中
没发现的东西。
13:28
in the quantitative data.
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1355
13:31
He discovered that people loved
to binge-watch.
to binge-watch.
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2728
他发现人们喜欢连续看片。
13:33
In fact, people didn't even
feel guilty about it.
feel guilty about it.
277
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2353
事实上人们才不会内疚。
大家乐在其中。
13:36
They enjoyed it.
278
804201
1255
13:37
(Laughter)
279
805480
1026
（笑声）
于是Netflix觉得，
“噢，这是个新见解。”
“噢，这是个新见解。”
13:38
So Netflix was like,
"Oh. This is a new insight."
"Oh. This is a new insight."
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2356
于是他们找来数据科学团队，
13:40
So they went to their data science team,
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808910
1938
将基于厚数据的观点
13:42
and they were able to scale
this big data insight
this big data insight
282
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2318
13:45
in with their quantitative data.
283
813214
2587
跟量化数据进行对比。
13:47
And once they verified it
and validated it,
and validated it,
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3170
这一观点得到验证后，
13:51
Netflix decided to do something
very simple but impactful.
very simple but impactful.
285
819019
4761
Netflix决定采取
简单却有效的措施。
简单却有效的措施。
13:56
They said, instead of offering
the same show from different genres
the same show from different genres
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824834
6492
他们不再把同一节目
做成不同体裁，
做成不同体裁，
14:03
or more of the different shows
from similar users,
from similar users,
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3888
也不再给同一类用户
推荐不同节目，
推荐不同节目，
14:07
we'll just offer more of the same show.
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2554
而是提供同一节目，
14:09
We'll make it easier
for you to binge-watch.
for you to binge-watch.
289
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2105
便于连续观看。
14:11
And they didn't stop there.
290
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1486
不仅如此，
他们还想尽一切办法
14:13
They did all these things
291
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1474
14:14
to redesign their entire
viewer experience,
viewer experience,
292
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2959
重新规划用户体验，
引导用户连续观看。
14:17
to really encourage binge-watching.
293
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1758
14:20
It's why people and friends disappear
for whole weekends at a time,
for whole weekends at a time,
294
848230
3241
于是大家在周末集体消失，
14:23
catching up on shows
like "Master of None."
like "Master of None."
295
851495
2343
都在追《无为大师》这样的剧。
14:25
By integrating big data and thick data,
they not only improved their business,
they not only improved their business,
296
853862
4173
通过结合大数据和厚数据，
他们不仅发展了业务，
他们不仅发展了业务，
14:30
but they transformed how we consume media.
297
858059
2812
还转变了人们消费媒体的方式。
14:32
And now their stocks are projected
to double in the next few years.
to double in the next few years.
298
860895
4552
他们的股价预计会在
未来几年内翻番。
未来几年内翻番。
14:38
But this isn't just about
watching more videos
watching more videos
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866280
3830
但这不只是关于看更多的视频，
14:42
or selling more smartphones.
300
870134
1620
或者卖更多的智能手机。
14:44
For some, integrating thick data
insights into the algorithm
insights into the algorithm
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4050
对某些人而言，将厚数据的观点
整合到算法中，
整合到算法中，
14:48
could mean life or death,
302
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2263
关乎生死，
14:50
especially for the marginalized.
303
878504
2146
尤其是被边缘化的人群。
14:53
All around the country,
police departments are using big data
police departments are using big data
304
881738
3434
全国各地的警察部门都在将大数据
用于预防性警务，
14:57
for predictive policing,
305
885196
1963
规划牢房数量，
提供量刑建议，
提供量刑建议，
14:59
to set bond amounts
and sentencing recommendations
and sentencing recommendations
306
887183
3084
这种的方法更是强化了已有偏见。
15:02
in ways that reinforce existing biases.
307
890291
3147
15:06
NSA's Skynet machine learning algorithm
308
894296
2423
国安局的天网机器学习算法
15:08
has possibly aided in the deaths
of thousands of civilians in Pakistan
of thousands of civilians in Pakistan
309
896743
5444
可能间接导致了几千
巴基斯坦平民丧生，
巴基斯坦平民丧生，
因为误读了他们的
蜂窝移动设备的元数据。
蜂窝移动设备的元数据。
15:14
from misreading cellular device metadata.
310
902211
2721
15:19
As all of our lives become more automated,
311
907131
3403
随着我们的生活变得更加自动化，
15:22
from automobiles to health insurance
or to employment,
or to employment,
312
910558
3080
从汽车到健康保险到就业，
15:25
it is likely that all of us
313
913662
2350
所有人都可能
15:28
will be impacted
by the quantification bias.
by the quantification bias.
314
916036
2989
会受量化偏见的负面影响。
15:32
Now, the good news
is that we've come a long way
is that we've come a long way
315
920972
2621
不过好消息是，我们已经
有了很大进步，
有了很大进步，
15:35
from huffing ethylene gas
to make predictions.
to make predictions.
316
923617
2450
不再吸入乙烯气体，
而是真正做出预测。
而是真正做出预测。
15:38
We have better tools,
so let's just use them better.
so let's just use them better.
317
926091
3070
我们有了更好的工具，
那就让我们用好它。
那就让我们用好它。
15:41
Let's integrate the big data
with the thick data.
with the thick data.
318
929185
2323
让我们将大数据和
厚数据结合起来，
厚数据结合起来，
为先知配上神庙向导，
15:43
Let's bring our temple guides
with the oracles,
with the oracles,
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931532
2261
无论是在公司、非营利性机构，
15:45
and whether this work happens
in companies or nonprofits
in companies or nonprofits
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933817
3376
还是在政府或者软件公司，
15:49
or government or even in the software,
321
937217
2469
都很重要，
15:51
all of it matters,
322
939710
1792
15:53
because that means
we're collectively committed
we're collectively committed
323
941526
3023
因为这意味着我们共同承诺
提供更好的数据，
15:56
to making better data,
324
944573
2191
15:58
better algorithms, better outputs
325
946788
1836
更好的算法，更好的结果，
并做出更好的决定。
16:00
and better decisions.
326
948648
1643
16:02
This is how we'll avoid
missing that something.
missing that something.
327
950315
3558
这样我们才不会忽略重要信息。
16:07
(Applause)
328
955222
3948
（掌声）
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