TED@IBM
Susan Etlinger: What do we do with all this big data?
苏珊•埃特林格: 如何应对大数据？| 苏珊•埃特林格|TED@IBM
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一组数据让你感觉更舒服了？感觉更成功了？那很有可能是你解读错了。在这个发人深省的演讲中，苏珊•埃特林格解释了为何我们在面对越来越多的数据时，应锻炼批判性思维能力。否则，我们很难从统计数据的层面上更进一步，真正地理解数据。
Susan Etlinger - Data analyst
Susan Etlinger promotes the smart, well-considered and ethical use of data. Full bio
Susan Etlinger promotes the smart, well-considered and ethical use of data. Full bio
Double-click the English transcript below to play the video.
00:13
Technology has brought us so much:
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科技极大程度上改变了世界：
00:16
the moon landing, the Internet,
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登月计划，互联网，基因组测序。
00:18
the ability to sequence the human genome.
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00:21
But it also taps into a lot of our deepest fears,
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但随之而来的是我们内心深处的忧虑，
00:24
and about 30 years ago,
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大约30年前，
00:26
the culture critic Neil Postman wrote a book
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文学评论家尼尔•波兹曼出了一本书，
00:29
called "Amusing Ourselves to Death,"
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名为《娱乐至死》，
00:31
which lays this out really brilliantly.
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将这个问题展现得淋漓尽致。
00:34
And here's what he said,
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他这样写道，
00:35
comparing the dystopian visions
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将乔治•奥威尔和阿道司•赫胥黎
00:38
of George Orwell and Aldous Huxley.
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两人的反乌托邦观点做比较，
00:41
He said, Orwell feared we would become
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奥威尔害怕我们的文化成为「受制文化」。
00:44
a captive culture.
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00:47
Huxley feared we would become a trivial culture.
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赫胥黎担心的是我们的文化成为「琐碎文化」
00:50
Orwell feared the truth would be
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奥威尔害怕的是真理被隐瞒，
00:52
concealed from us,
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00:54
and Huxley feared we would be drowned
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赫胥黎担心的是我们被淹没在
00:57
in a sea of irrelevance.
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无聊烦琐的世事中。
00:59
In a nutshell, it's a choice between
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简言之，这是「老大哥」看你
01:01
Big Brother watching you
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01:04
and you watching Big Brother.
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还是你看「老大哥」的选择。
（译者注：「老大哥」典出奥威尔名著《1984》）
（译者注：「老大哥」典出奥威尔名著《1984》）
01:06
(Laughter)
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（笑声）
01:08
But it doesn't have to be this way.
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但事实不尽然，
01:10
We are not passive consumers
of data and technology.
of data and technology.
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我们不是只能被动地接受数据和科技。
01:13
We shape the role it plays in our lives
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我们能改变科技在我们生活中扮演的角色，
01:16
and the way we make meaning from it,
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也能改变享受数据带来的恩惠的方式，
01:18
but to do that,
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但要实现这一目的，
01:20
we have to pay as much attention to how we think
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思考方式固然重要， 我们也要对如何解读数据
01:23
as how we code.
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投以同样高的关注度。
01:25
We have to ask questions, and hard questions,
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我们需要问问题，要问深刻的问题，
01:28
to move past counting things
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不再单纯地统计数据，
01:30
to understanding them.
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而是要进一步理解数据。
01:33
We're constantly bombarded with stories
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我们身边充斥着那些
01:35
about how much data there is in the world,
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讲述世界上有海量数据的故事，
01:38
but when it comes to big data
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但当我们面临大数据，
01:39
and the challenges of interpreting it,
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面临理解大数据所的挑战，
01:42
size isn't everything.
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数据量的大小不代表一切。
01:44
There's also the speed at which it moves,
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还有数据传播的速度，
01:47
and the many varieties of data types,
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数据的类型，
01:49
and here are just a few examples:
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举几个例子：
01:51
images,
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图像，
01:53
text,
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文字，
01:57
video,
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视频，
01:59
audio.
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音频。
02:01
And what unites this disparate types of data
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不同类型的数据能有机地结合在一起，
02:04
is that they're created by people
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因为正是人类创造了这些数据，
02:06
and they require context.
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而且要在一定背景前提下理解特定数据。
02:09
Now, there's a group of data scientists
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目前，一个来自伊利诺大学
芝加哥分校的数据科学家团队，
芝加哥分校的数据科学家团队，
02:12
out of the University of Illinois-Chicago,
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02:14
and they're called the Health Media Collaboratory,
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自称「健康媒体合作实验室」，
02:16
and they've been working with
the Centers for Disease Control
the Centers for Disease Control
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正与疾控中心合作，
02:19
to better understand
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试图进一步了解
02:21
how people talk about quitting smoking,
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人们谈论戒烟的方式，
02:23
how they talk about electronic cigarettes,
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谈论电子烟的方式，
02:26
and what they can do collectively
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以及他们如何协作
02:28
to help them quit.
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来帮助人们戒烟。
02:30
The interesting thing is, if you want to understand
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有趣的是，如果你想了解
02:32
how people talk about smoking,
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人们谈论吸烟的方式，
02:34
first you have to understand
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首先需要了解
02:36
what they mean when they say "smoking."
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「烟」在他们口中的含义。
02:39
And on Twitter, there are four main categories:
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在Twitter上，「烟」的含义通常有四类：
02:43
number one, smoking cigarettes;
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第一，吸烟；
02:46
number two, smoking marijuana;
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第二，抽大麻；
02:48
number three, smoking ribs;
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第三，烟熏肋排；
02:51
and number four, smoking hot women.
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第四，闻香识女。
02:55
(Laughter)
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（笑声）
02:58
So then you have to think about, well,
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然后你就会想，
03:00
how do people talk about electronic cigarettes?
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人们是如何谈论电子烟的呢？
03:02
And there are so many different ways
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人们谈论电子烟的方式非常多，
03:04
that people do this, and you can see from the slide
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从屏幕上你们可以看到谈论的方式是如此繁多。
03:07
it's a complex kind of a query.
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03:09
And what it reminds us is that
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这就让我们想到，
03:13
language is created by people,
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语言是人类创造的，
03:15
and people are messy and we're complex
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人类的语言是复杂混乱的，
03:17
and we use metaphors and slang and jargon
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我们用各种语言，无时无刻不在讲着比喻，
说着俚语和术语，
说着俚语和术语，
03:20
and we do this 24/7 in many, many languages,
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03:23
and then as soon as we figure it out, we change it up.
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好不容易弄清了，立马就又变掉了。
03:27
So did these ads that the CDC put on,
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那么，疾控中心投放的广告，
03:32
these television ads that featured a woman
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以及电视上那种看起来让人非常不安的
03:34
with a hole in her throat and that were very graphic
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形象地画了一个喉咙烧出来洞的女性的广告，
03:36
and very disturbing,
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03:38
did they actually have an impact
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这些广告会影响人们戒烟吗？
03:40
on whether people quit?
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03:43
And the Health Media Collaboratory
respected the limits of their data,
respected the limits of their data,
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健康媒体合作实验室承认其数据的有限性，
03:46
but they were able to conclude
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但他们还是做了这样的结论，
03:48
that those advertisements —
and you may have seen them —
and you may have seen them —
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那些广告——或许你们都见到过——
03:51
that they had the effect of jolting people
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确实会震颤人的内心，
03:54
into a thought process
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让他们有所思考，
03:56
that may have an impact on future behavior.
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这样或许会影响他们未来的行为。
03:59
And what I admire and
appreciate about this project,
appreciate about this project,
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这个项目让我尊重和欣赏的地方，
04:03
aside from the fact, including the fact
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不仅在于该项目基于人们的真实需求，
04:05
that it's based on real human need,
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04:09
is that it's a fantastic example of courage
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还在于它充分诠释了面对「无聊烦琐的世事」
04:12
in the face of a sea of irrelevance.
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展现出来的勇气。
04:16
And so it's not just big data that causes
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因此，并不只是大数据在挑战我们对事物的理解，
04:19
challenges of interpretation, because let's face it,
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让我们直面这一事实吧，
04:22
we human beings have a very rich history
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不管处理多少数据，哪怕再少的数据，
04:25
of taking any amount of data, no matter how small,
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人们也能把它搞得一团糟，
04:27
and screwing it up.
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「见多不怪」了。
04:29
So many years ago, you may remember
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你或许会记得，几年前，
04:33
that former President Ronald Reagan
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前总统罗纳德•里根
04:35
was very criticized for making a statement
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在声称「事实是愚蠢的」后
04:37
that facts are stupid things.
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被严厉指责。
04:40
And it was a slip of the tongue, let's be fair.
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平心而论，这是一个口误。
04:43
He actually meant to quote John Adams' defense
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他原本是想引用约翰•亚当斯
04:45
of British soldiers in the Boston Massacre trials
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在波士顿惨案审判为英军士兵的辩言
04:48
that facts are stubborn things.
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「事实是顽固不化的。」
04:51
But I actually think there's
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但事实上，我认为
04:54
a bit of accidental wisdom in what he said,
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里根总统那句话蕴含着些许智慧，
04:57
because facts are stubborn things,
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事实固然顽固不化，
05:00
but sometimes they're stupid, too.
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有时确实是愚蠢的。
05:03
I want to tell you a personal story
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这对我意义深远，
05:05
about why this matters a lot to me.
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我讲一个私人故事来告诉你们为什么。
05:08
I need to take a breath.
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我要深吸一口气。
05:11
My son Isaac, when he was two,
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我的儿子艾萨克，在他两岁的时候，
05:13
was diagnosed with autism,
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被诊断出患有自闭症，
05:16
and he was this happy, hilarious,
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在我们眼里，他是个幸福、欢快、
05:18
loving, affectionate little guy,
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充满爱意、惹人喜欢的小孩，
05:20
but the metrics on his developmental evaluations,
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但该发展水平评估
05:23
which looked at things like
the number of words —
the number of words —
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关注的指标是诸如言多言寡——
05:25
at that point, none —
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当时，是零——
05:29
communicative gestures and minimal eye contact,
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互动性姿势和最少目光接触，
05:33
put his developmental level
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根据这套评估标准的结果，
05:35
at that of a nine-month-old baby.
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他的发展水平相当于9月大的婴儿。
05:39
And the diagnosis was factually correct,
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按照这套标准，结果无可厚非，
05:42
but it didn't tell the whole story.
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但这不是全部。
05:45
And about a year and a half later,
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一年半之后，
05:46
when he was almost four,
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在他快要四岁的时候，
05:48
I found him in front of the computer one day
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有一天我发现他坐在电脑前，
05:51
running a Google image search on women,
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在Google图片搜索中搜索「women」
05:56
spelled "w-i-m-e-n."
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拼成了「wimen」
06:00
And I did what any obsessed parent would do,
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接下来我做了任何有心的父母都会做的事，
06:02
which is immediately started
hitting the "back" button
hitting the "back" button
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我立马就按了后退按钮，
06:04
to see what else he'd been searching for.
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看看他还搜索了什么。
06:08
And they were, in order: men,
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查到了，按顺序来：男人，
06:10
school, bus and computer.
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学校，汽车和电脑。
06:17
And I was stunned,
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我目瞪口呆，
06:19
because we didn't know that he could spell,
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因为我们还不知道他会拼单词，
06:21
much less read, and so I asked him,
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更别说读写了，因此我问他，
06:23
"Isaac, how did you do this?"
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「艾萨克，你是如何做到的？」
06:25
And he looked at me very seriously and said,
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他很严肃地看着我说，
06:28
"Typed in the box."
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「在搜索框里输入。」
06:31
He was teaching himself to communicate,
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他一直在自我学习如何去沟通，
06:35
but we were looking in the wrong place,
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但我们将注意力投在了别处，
06:38
and this is what happens when assessments
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很显然，那些发展水平评估
06:40
and analytics overvalue one metric —
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过分注重了一个指标——
06:43
in this case, verbal communication —
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言语沟通——
06:45
and undervalue others, such
as creative problem-solving.
as creative problem-solving.
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而忽视了其他指标，如问题解决能力。
06:51
Communication was hard for Isaac,
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沟通对于艾萨克而言很难，
06:53
and so he found a workaround
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所以他找到了一个变通方法，
06:55
to find out what he needed to know.
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自己去探索想要知道的信息。
06:58
And when you think about it, it makes a lot of sense,
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你考虑一下，这确实很有道理，
07:00
because forming a question
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因为提出一个问题
07:02
is a really complex process,
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是复杂的过程，
07:05
but he could get himself a lot of the way there
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但他能通过在搜索框中输入单词来达到同样目的。
07:07
by putting a word in a search box.
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07:11
And so this little moment
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因此，这一个小插曲
07:14
had a really profound impact on me
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深深影响了我和我的家庭，
07:17
and our family
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07:18
because it helped us change our frame of reference
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因为它让我们对发生在他身上的一切
有了全新的认识，
有了全新的认识，
07:21
for what was going on with him,
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07:24
and worry a little bit less and appreciate
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也不那么担心他了，
而且更加欣赏他的「人小鬼大」。
07:27
his resourcefulness more.
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07:29
Facts are stupid things.
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事实是愚蠢的，
07:32
And they're vulnerable to misuse,
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极容易被误用，
07:34
willful or otherwise.
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有意或无意地。
07:36
I have a friend, Emily Willingham, who's a scientist,
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我有一个叫Emily Willingham的朋友，是科学家，
07:39
and she wrote a piece for Forbes not long ago
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不久前他为福布斯杂志写过一篇文章，
07:42
entitled "The 10 Weirdest Things
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名为《十个最奇怪的跟自闭症相关的事情》
07:44
Ever Linked to Autism."
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07:45
It's quite a list.
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此文深得我心。
07:48
The Internet, blamed for everything, right?
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「互联网」，一切罪恶的源头，对吧？
07:52
And of course mothers, because.
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当然，「母亲」也是其中一条。
07:56
And actually, wait, there's more,
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事实上，没这么简单，
07:57
there's a whole bunch in
the "mother" category here.
the "mother" category here.
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「母亲」还进一步细分为多条。
08:01
And you can see it's a pretty
rich and interesting list.
rich and interesting list.
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你们可以看到这个清单真的内涵丰富又有趣。
08:05
I'm a big fan of
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我很「欣赏」那些在在高速路旁怀孕的人。
08:08
being pregnant near freeways, personally.
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08:11
The final one is interesting,
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最后一条很有趣，
08:13
because the term "refrigerator mother"
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因为「冰箱母亲」在最初被认为是
08:16
was actually the original hypothesis
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孩童自闭症的原因，
08:19
for the cause of autism,
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08:20
and that meant somebody
who was cold and unloving.
who was cold and unloving.
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这个词表示那些冰冷的、没有爱心的人。
08:23
And at this point, you might be thinking,
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话已至此，你们也许会问，
08:24
"Okay, Susan, we get it,
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「好吧，苏珊，我们明白了，
08:26
you can take data, you can
make it mean anything."
make it mean anything."
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你能理解数据，你可以决定数据的意义。」
08:28
And this is true, it's absolutely true,
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这是对的，这绝对是没问题的，
08:32
but the challenge is that
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但挑战在于，
08:38
we have this opportunity
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你们自己也有机会明白数据的意义，
08:40
to try to make meaning out of it ourselves,
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08:43
because frankly, data doesn't
create meaning. We do.
create meaning. We do.
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因为，坦白地讲，数据自己不会创造意义，
是我们创造数据的意义。
是我们创造数据的意义。
08:48
So as businesspeople, as consumers,
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因此，作为商人，作为消费者，
08:51
as patients, as citizens,
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作为病人，作为公民，
08:54
we have a responsibility, I think,
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我认为我们都有责任
花更多时间来锻炼批判性思维能力。
08:56
to spend more time
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08:58
focusing on our critical thinking skills.
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09:01
Why?
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为什么？
09:02
Because at this point in our history, as we've heard
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因为历史发展到今天，
09:06
many times over,
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我们总是听到这样的说法，
09:07
we can process exabytes of data
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我们能以闪电般速度
09:09
at lightning speed,
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处理海量数据，
09:11
and we have the potential to make bad decisions
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这就意味着我们能以更快地速度做出错误的决策，
09:15
far more quickly, efficiently,
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09:17
and with far greater impact than we did in the past.
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带给我们史无前例的巨大影响。
09:22
Great, right?
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没错吧？
09:23
And so what we need to do instead
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因此，我们需要做的就是
09:26
is spend a little bit more time
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多花一点时间在
09:29
on things like the humanities
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人文学，
09:31
and sociology, and the social sciences,
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社会学，社会科学，
09:35
rhetoric, philosophy, ethics,
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修辞学，哲学，伦理学，
09:37
because they give us context that is so important
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因为这些知识非常有助于帮助我们理解大数据，
09:40
for big data, and because
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09:42
they help us become better critical thinkers.
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而且也能锻炼我们的批判性思维。
09:45
Because after all, if I can spot
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毕竟，如果我能在一个论断中发现问题，
09:49
a problem in an argument, it doesn't much matter
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这个问题是以文字还是数字的形式呈现并不那么重要。
09:52
whether it's expressed in words or in numbers.
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09:54
And this means
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而且，这些知识会
09:57
teaching ourselves to find
those confirmation biases
those confirmation biases
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让我们有能力辨识出事实与偏见，
10:02
and false correlations
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错误的关联信息，
10:03
and being able to spot a naked emotional appeal
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有能力在30码开外就看透赤裸裸的情感诉求，
10:05
from 30 yards,
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10:07
because something that happens after something
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因为，乙事件发生在甲事件之后，
10:10
doesn't mean it happened
because of it, necessarily,
because of it, necessarily,
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并不意味着是甲导致乙的发生，
10:13
and if you'll let me geek out on you for a second,
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允许我耍一下酷，
10:15
the Romans called this
"post hoc ergo propter hoc,"
"post hoc ergo propter hoc,"
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罗马人称之为
「post hoc ergo propter hoc」
「post hoc ergo propter hoc」
10:19
after which therefore because of which.
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即「后此谬误」。
10:22
And it means questioning
disciplines like demographics.
disciplines like demographics.
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这意味着我们要对人口统计学
这样的学科打个问号。
这样的学科打个问号。
10:26
Why? Because they're based on assumptions
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为什么？因为这样的学科基于的假设是
10:29
about who we all are based on our gender
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性别、年龄和住址等数据
10:31
and our age and where we live
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决定我们的身份，
10:32
as opposed to data on what
we actually think and do.
we actually think and do.
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而不是基于我们的思想和行为。
10:36
And since we have this data,
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我们获取了这些数据，
10:38
we need to treat it with appropriate privacy controls
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我们需要做好隐私控制，
10:41
and consumer opt-in,
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并保证民众的选择权，
10:44
and beyond that, we need to be clear
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除此之外，我们需要弄清楚所做的假设，
10:47
about our hypotheses,
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10:49
the methodologies that we use,
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采用的研究方法，
10:52
and our confidence in the result.
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以及对结果的信任。
10:55
As my high school algebra teacher used to say,
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就像高中代数老师曾对我说的，
10:57
show your math,
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给我看看你的解题步骤，
10:59
because if I don't know what steps you took,
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因为如果我不知道你的步骤，
11:02
I don't know what steps you didn't take,
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我就不知道你落下了哪些步骤，
11:04
and if I don't know what questions you asked,
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如果我不知道你问了些什么，
11:07
I don't know what questions you didn't ask.
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我就不知道哪些问题你没有问。
11:10
And it means asking ourselves, really,
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我们应该问自己这个最难回答的问题，
11:11
the hardest question of all:
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这真是值得的：
11:13
Did the data really show us this,
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数据真的显示出了这个结果，
11:16
or does the result make us feel
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还是这样的结果让我们感觉更成功、更舒服？
11:19
more successful and more comfortable?
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11:23
So the Health Media Collaboratory,
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因此，健康媒体合作实验室
11:25
at the end of their project, they were able
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在该项目结束时发现，
11:27
to find that 87 percent of tweets
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谈论那些很形象、令人不安的广告的推特中，
11:30
about those very graphic and disturbing
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11:32
anti-smoking ads expressed fear,
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有87%的表达出了恐惧，
11:36
but did they conclude
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但他们做出这些广告让人戒烟的结论了吗？
11:38
that they actually made people stop smoking?
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11:41
No. It's science, not magic.
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没有。这是科学，但不是魔法。
11:44
So if we are to unlock
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因此，如果我们想要激发
11:47
the power of data,
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数据中潜在的能量，
11:50
we don't have to go blindly into
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我们没必要盲目地
11:54
Orwell's vision of a totalitarian future,
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游走于奥威尔所谓的极端未来，
11:57
or Huxley's vision of a trivial one,
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或赫胥黎所谓的琐碎的未来，
12:00
or some horrible cocktail of both.
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或两种思想的杂糅。
12:03
What we have to do
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我们需要做的就是，
12:05
is treat critical thinking with respect
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积极进行批判性思维，
12:08
and be inspired by examples
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并学习健康媒体合作实验室的做法，
12:10
like the Health Media Collaboratory,
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12:13
and as they say in the superhero movies,
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就像超级英雄电影里说的那样，
12:15
let's use our powers for good.
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力量用在行善上。
12:17
Thank you.
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谢谢。
12:19
(Applause)
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（掌声）
ABOUT THE SPEAKER
Susan Etlinger - Data analystSusan Etlinger promotes the smart, well-considered and ethical use of data.
Why you should listen
Susan Etlinger is an industry analyst with Altimeter Group, where she focuses on data and analytics. She conducts independent research and has authored two intriguing reports: “The Social Media ROI Cookbook” and “A Framework for Social Analytics.” She also advises global clients on how to work measurement into their organizational structure and how to extract insights from the social web which can lead to tangible actions. In addition, she works with technology innovators to help them refine their roadmaps and strategies.
Etlinger is on the board of The Big Boulder Initiative, an industry organization dedicated to promoting the successful and ethical use of social data. She is regularly interviewed and asked to speak on data strategy and best practices, and has been quoted in media outlets like The Wall Street Journal, The New York Times, and the BBC.
Susan Etlinger | Speaker | TED.com