TED@IBM
Susan Etlinger: What do we do with all this big data?
蘇珊‧艾特林格: 我們應該拿這些大數據怎麼辦？
Filmed:
Readability: 4.7
1,344,301 views
你會因為某些數據，而覺得更自在、更成功嗎？那麼你的詮釋很可能有誤。在這個動人的演講，蘇珊‧艾特林格解釋為什麼擁有了更多資料，我們更要加強批判性思考能力。因為要超越單純的計算，達到真正的了解，是非常不容易的事。
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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科技帶給我們很多美好的事物：
00:16
the moon landing, the Internet,
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登陸月球、網路、
00:18
the ability to sequence the human genome.
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人類基因組定序。
00:21
But it also taps into a lot of our deepest fears,
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但也挖掘出我們內心深處的許多恐懼。
00:24
and about 30 years ago,
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大約 30 年前，
00:26
the culture critic Neil Postman wrote a book
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文化評論家尼爾．波茲曼寫了一本書，
00:29
called "Amusing Ourselves to Death,"
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叫做《娛樂至死》，
書中把這個現象說得很妙。
00:31
which lays this out really brilliantly.
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00:34
And here's what he said,
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他是這樣說的：
比較歐威爾和赫胥黎的兩種反烏托邦，
00:35
comparing the dystopian visions
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00:38
of George Orwell and Aldous Huxley.
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他說，歐威爾擔心我們會成為
00:41
He said, Orwell feared we would become
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00:44
a captive culture.
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圈養的文化。
00:47
Huxley feared we would become a trivial culture.
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赫胥黎則擔心我們會成為庸俗的文化。
00:50
Orwell feared the truth would be
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歐威爾擔心真相會被隱瞞，
00:52
concealed from us,
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00:54
and Huxley feared we would be drowned
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赫胥黎則擔心我們會被瑣碎的汪洋吞沒。
00:57
in a sea of irrelevance.
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簡單點說，
00:59
In a nutshell, it's a choice between
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我們可以選擇「老大哥監視你」
01:01
Big Brother watching you
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或是「你監視老大哥」
01:04
and you watching Big Brother.
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（觀眾笑聲）
01:06
(Laughter)
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01:08
But it doesn't have to be this way.
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其實不必這樣，
01:10
We are not passive consumers
of data and technology.
of data and technology.
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我們不是被動地消費資料和科技，
01:13
We shape the role it plays in our lives
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我們可以決定科技在生活中扮演的角色，
01:16
and the way we make meaning from it,
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和它對我們的意義。
01:18
but to do that,
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但是要這麼做，
01:20
we have to pay as much attention to how we think
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我們必須重視思考的方式，
不只重視編碼的方式。
01:23
as how we code.
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01:25
We have to ask questions, and hard questions,
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我們必須問問題，難解的問題，
超越單純的算術，
01:28
to move past counting things
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01:30
to understanding them.
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試圖去了解。
01:33
We're constantly bombarded with stories
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我們不斷聽到世界上有多少資料，
01:35
about how much data there is in the world,
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01:38
but when it comes to big data
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但是談到大數據，
01:39
and the challenges of interpreting it,
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以及詮釋這些數據資料的挑戰，
01:42
size isn't everything.
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光看數量是不夠的，
01:44
There's also the speed at which it moves,
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還必須關注資料成長的速度，
01:47
and the many varieties of data types,
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以及眾多不同的資料類型。
01:49
and here are just a few examples:
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我略舉幾個例子：
01:51
images,
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圖像、
01:53
text,
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文字、
[請稍候，直到你有用處的時候，謝謝。]
[請稍候，直到你有用處的時候，謝謝。]
01:57
video,
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影片、
01:59
audio.
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聲音。
02:01
And what unites this disparate types of data
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這些不同資料類型的共通處在於
02:04
is that they're created by people
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它們都是人建立的，
02:06
and they require context.
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也都不能斷章取義來詮釋。
02:09
Now, there's a group of data scientists
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舉例，有一個資料科學家小組，
02:12
out of the University of Illinois-Chicago,
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成員來自伊利諾大學芝加哥分校，
02:14
and they're called the Health Media Collaboratory,
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這小組叫做「衛生媒體合作實驗室」。
02:16
and they've been working with
the Centers for Disease Control
the Centers for Disease Control
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他們和美國疾病管制中心合作，
02:19
to better understand
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想要更了解
02:21
how people talk about quitting smoking,
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人們怎樣談論戒菸、
02:23
how they talk about electronic cigarettes,
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怎樣談論電子香煙，
02:26
and what they can do collectively
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以及怎樣一起幫助吸菸者戒菸。
02:28
to help them quit.
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02:30
The interesting thing is, if you want to understand
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有趣的是，
若要了解人們如何談論抽菸 smoking，
02:32
how people talk about smoking,
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02:34
first you have to understand
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就要先了解人們說 smoking 是什麼意思。
02:36
what they mean when they say "smoking."
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02:39
And on Twitter, there are four main categories:
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在推特上大致分成四類：
02:43
number one, smoking cigarettes;
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第一類，抽菸；
02:46
number two, smoking marijuana;
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第二類，抽大麻；
02:48
number three, smoking ribs;
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第三類，煙熏肋排；
02:51
and number four, smoking hot women.
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第四類，嗆辣正妹；
02:55
(Laughter)
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（觀眾笑聲）
02:58
So then you have to think about, well,
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接著要思考，
03:00
how do people talk about electronic cigarettes?
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人們怎麼談論電子香菸？
03:02
And there are so many different ways
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講法五花八門，
03:04
that people do this, and you can see from the slide
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就像這張投影片所列的，
03:07
it's a complex kind of a query.
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這種檢索非常複雜。
03:09
And what it reminds us is that
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這提醒我們，
03:13
language is created by people,
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語言是人創造的，
03:15
and people are messy and we're complex
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而人是複雜、亂無章法的，
03:17
and we use metaphors and slang and jargon
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我們會用隱喻、俚語、行話，
03:20
and we do this 24/7 in many, many languages,
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無時無刻的製造，各式各樣的語言，
03:23
and then as soon as we figure it out, we change it up.
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好不容易破解語言，就立刻又改變了。
03:27
So did these ads that the CDC put on,
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那麼，疾管中心拍的這些戒菸文宣，
03:32
these television ads that featured a woman
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電視廣告裡，一名女子喉嚨破了大洞，
03:34
with a hole in her throat and that were very graphic
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畫面驚悚嚇人，
03:36
and very disturbing,
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03:38
did they actually have an impact
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這些廣告真的有效嗎？
03:40
on whether people quit?
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真的讓人戒菸了嗎？
03:43
And the Health Media Collaboratory
respected the limits of their data,
respected the limits of their data,
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衛生媒體合作實驗室尊重其數據的限制，
03:46
but they were able to conclude
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但仍能做出結論，
03:48
that those advertisements —
and you may have seen them —
and you may have seen them —
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認為這些廣告—也許你們看過，
03:51
that they had the effect of jolting people
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成功地刺激人們開始反省，
03:54
into a thought process
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可能影響未來的行為。
03:56
that may have an impact on future behavior.
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03:59
And what I admire and
appreciate about this project,
appreciate about this project,
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這個計畫讓我最欽佩、欣賞的地方是，
04:03
aside from the fact, including the fact
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除了它是在解決人的實際需要以外，
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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同時它提供了絕佳的典範，
展現了人類面對瑣碎汪洋的勇氣。
04:12
in the face of a sea of irrelevance.
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04:16
And so it's not just big data that causes
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所以，詮釋的挑戰不只因為資料龐大，
04:19
challenges of interpretation, because let's face it,
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因為，老實說，歷史上有很多的例子顯示，
04:22
we human beings have a very rich history
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04:25
of taking any amount of data, no matter how small,
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無論資料再少，我們向來很能把它搞砸。
04:27
and screwing it up.
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04:29
So many years ago, you may remember
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大家可能記得，很多年前，
04:33
that former President Ronald Reagan
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前總統雷根曾被痛罵，
04:35
was very criticized for making a statement
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因為他說，事實是愚笨的東西。
04:37
that facts are stupid things.
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04:40
And it was a slip of the tongue, let's be fair.
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憑良心說，他只是一時口誤，
他其實是想引用約翰．亞當斯在
04:43
He actually meant to quote John Adams' defense
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04:45
of British soldiers in the Boston Massacre trials
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為因波士頓慘案受審的英軍辯護時說的：
04:48
that facts are stubborn things.
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事實是固執難拗、不容改變的。
04:51
But I actually think there's
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但我其實認為，
04:54
a bit of accidental wisdom in what he said,
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這口誤可能湊巧講出幾分智慧，
04:57
because facts are stubborn things,
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因為事實確實很固執，
05:00
but sometimes they're stupid, too.
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但是有時也真的很愚笨。
05:03
I want to tell you a personal story
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我要講一個自己的故事，
05:05
about why this matters a lot to me.
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解釋為什麼這對我這麼重要。
05:08
I need to take a breath.
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我要先吸一口氣。
05:11
My son Isaac, when he was two,
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我兒子艾薩克兩歲的時候，
05:13
was diagnosed with autism,
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被診斷為自閉兒。
05:16
and he was this happy, hilarious,
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但他是個快樂、搞笑、
05:18
loving, affectionate little guy,
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有愛心、喜歡親密的孩子，
05:20
but the metrics on his developmental evaluations,
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但是他的發展評估測驗數據
05:23
which looked at things like
the number of words —
the number of words —
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檢視的是：
他當時會說幾個字？零個。
05:25
at that point, none —
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05:29
communicative gestures and minimal eye contact,
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只靠手勢溝通，
眼神接觸也極少，
讓他的發展程度
05:33
put his developmental level
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05:35
at that of a nine-month-old baby.
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被評為九個月大的嬰兒。
按照數據，診斷並沒有錯，
05:39
And the diagnosis was factually correct,
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05:42
but it didn't tell the whole story.
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卻跟實際狀況有落差。
05:45
And about a year and a half later,
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大概過了一年半，兒子快滿四歲，
05:46
when he was almost four,
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05:48
I found him in front of the computer one day
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有一天，我看到他坐在電腦前面，
05:51
running a Google image search on women,
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在用 Google 搜尋女性的照片，
05:56
spelled "w-i-m-e-n."
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他把女性 (women) 拼成 "w-i-m-e-n"。
06:00
And I did what any obsessed parent would do,
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我的反應跟任何偏執妄想的父母一樣，
06:02
which is immediately started
hitting the "back" button
hitting the "back" button
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立刻開始按瀏覽器的「返回」按鈕，
06:04
to see what else he'd been searching for.
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看看他還搜尋過什麼。
06:08
And they were, in order: men,
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結果發現他依序搜尋過：男性 (men)、
06:10
school, bus and computer.
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學校 (school)、公車 (bus)、
和電腦（錯拼成 cpyutr）。
06:17
And I was stunned,
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我很吃驚，
06:19
because we didn't know that he could spell,
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因為我們根本不知道他會拼字，
06:21
much less read, and so I asked him,
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更別說閱讀。
所以我問他：
「艾薩克，你怎麼辦到的？」
「艾薩克，你怎麼辦到的？」
06:23
"Isaac, how did you do this?"
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06:25
And he looked at me very seriously and said,
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他認真的看著我，說：
「在搜尋欄裡打字啊！」
06:28
"Typed in the box."
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06:31
He was teaching himself to communicate,
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他在教自己溝通，
只是我們都找錯方向了。
06:35
but we were looking in the wrong place,
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會發生這種情況，
06:38
and this is what happens when assessments
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是因為評量和分析太重視單一面向，
06:40
and analytics overvalue one metric —
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06:43
in this case, verbal communication —
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就像他的自閉症評量，
單看口語表達，
單看口語表達，
06:45
and undervalue others, such
as creative problem-solving.
as creative problem-solving.
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而忽視其他要素，
例如，創造性地解決問題。
溝通對艾薩克來說很困難，
06:51
Communication was hard for Isaac,
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06:53
and so he found a workaround
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所以他找到了替代方法，
06:55
to find out what he needed to know.
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來找解答。
06:58
And when you think about it, it makes a lot of sense,
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想想很有道理，
07:00
because forming a question
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因為問問題是很複雜的過程，
07:02
is a really complex process,
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07:05
but he could get himself a lot of the way there
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但他只要在搜尋欄輸入一個字，
07:07
by putting a word in a search box.
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就成功了一大半。
於是這個小小的時刻
07:11
And so this little moment
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07:14
had a really profound impact on me
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對我影響深遠，
07:17
and our family
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對我們全家都是。
07:18
because it helped us change our frame of reference
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因為，這改變了我們的判斷標準，
07:21
for what was going on with him,
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用全新的眼光看待兒子的狀況，
07:24
and worry a little bit less and appreciate
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比較不那麼擔憂，
轉而欣賞他解決問題的能力。
07:27
his resourcefulness more.
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07:29
Facts are stupid things.
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事實，真的是愚笨的。
07:32
And they're vulnerable to misuse,
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事實也很容易被誤用，
07:34
willful or otherwise.
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不論是有心或無意。
07:36
I have a friend, Emily Willingham, who's a scientist,
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我的朋友艾蜜莉．威靈漢是個科學家，
07:39
and she wrote a piece for Forbes not long ago
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她不久前為《富比士》寫了一篇文章，
07:42
entitled "The 10 Weirdest Things
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叫做〈 自閉症怪異印象十大排行榜〉，
07:44
Ever Linked to Autism."
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07:45
It's quite a list.
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內容挺可怕的：
07:48
The Internet, blamed for everything, right?
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「網路」，萬惡淵藪，對吧？
07:52
And of course mothers, because.
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當然「媽媽」也上榜，
不言自明。
等等，還有，
07:56
And actually, wait, there's more,
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07:57
there's a whole bunch in
the "mother" category here.
the "mother" category here.
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這裡有一大類，都跟「媽媽」有關係，
08:01
And you can see it's a pretty
rich and interesting list.
rich and interesting list.
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你可以看到，原因很多、很有意思。
08:05
I'm a big fan of
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我最喜歡的是
08:08
being pregnant near freeways, personally.
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「在高速公路附近受孕」。
08:11
The final one is interesting,
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最後一項很有趣，
08:13
because the term "refrigerator mother"
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因為「冰箱母親」這個封號
08:16
was actually the original hypothesis
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是自閉症原因最早的假說，
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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用來描述冷漠沒有愛心的母親。
08:23
And at this point, you might be thinking,
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現在，你可能會想：
08:24
"Okay, Susan, we get it,
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「好了，蘇珊，我們懂了，
08:26
you can take data, you can
make it mean anything."
make it mean anything."
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你可以對資料做任何詮釋。」
08:28
And this is true, it's absolutely true,
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這也沒錯，
絕對正確。
08:32
but the challenge is that
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但是挑戰在於，
08:38
we have this opportunity
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我們自己有這個機會，
08:40
to try to make meaning out of it ourselves,
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可以賦予資料意義，
08:43
because frankly, data doesn't
create meaning. We do.
create meaning. We do.
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因為老實說，資料不會自己產生意義。
我們才可以。
08:48
So as businesspeople, as consumers,
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所以，身為商人、消費者、
08:51
as patients, as citizens,
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病人、公民等等，
08:54
we have a responsibility, I think,
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我想我們有責任
08:56
to spend more time
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多花點時間
08:58
focusing on our critical thinking skills.
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提升我們的批判性思考能力。
09:01
Why?
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為什麼？
09:02
Because at this point in our history, as we've heard
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我們聽過很多次，
因為在歷史的這一刻，
因為在歷史的這一刻，
09:06
many times over,
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09:07
we can process exabytes of data
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已經能用光速
處理數十億 GB 的資料量，
處理數十億 GB 的資料量，
09:09
at lightning speed,
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可能更快速、更有效地
做出錯誤的決定，
做出錯誤的決定，
09:11
and we have the potential to make bad decisions
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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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影響之大可能更甚以往。
09:22
Great, right?
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這下好了，對吧？
09:23
And so what we need to do instead
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所以，我們反而必須
09:26
is spend a little bit more time
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多花時間
09:29
on things like the humanities
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發展人文、
09:31
and sociology, and the social sciences,
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社會學和社會科學，
09:35
rhetoric, philosophy, ethics,
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修辭、哲學、倫理，
09:37
because they give us context that is so important
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因為這些知識
構成我們的背景涵養，
構成我們的背景涵養，
09:40
for big data, and because
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對大數據非常重要，
也因為這能幫助我們更會思辨，
09:42
they help us become better critical thinkers.
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09:45
Because after all, if I can spot
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因為畢竟，
如果我能看出命題裡的問題，
09:49
a problem in an argument, it doesn't much matter
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那麼無論是
用文字或數據表達都可以。
用文字或數據表達都可以。
09:52
whether it's expressed in words or in numbers.
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09:54
And this means
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這表示，
09:57
teaching ourselves to find
those confirmation biases
those confirmation biases
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要教育我們自己
去發覺各種確認的偏見
和謬誤的關聯，
10:02
and false correlations
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10:03
and being able to spot a naked emotional appeal
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並且能對赤裸裸的情感訴求保持警覺。
10:05
from 30 yards,
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10:07
because something that happens after something
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因為甲事之後發生了乙事，
10:10
doesn't mean it happened
because of it, necessarily,
because of it, necessarily,
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並不代表
甲事必定是乙事的肇因。
甲事必定是乙事的肇因。
10:13
and if you'll let me geek out on you for a second,
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如果大家容我書呆一下，
10:15
the Romans called this
"post hoc ergo propter hoc,"
"post hoc ergo propter hoc,"
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羅馬人稱這現象為「後此謬誤」
"post hoc ergo propter hoc"，
"post hoc ergo propter hoc"，
10:19
after which therefore because of which.
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後此，故因此。
10:22
And it means questioning
disciplines like demographics.
disciplines like demographics.
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這表示要質疑像人口統計這樣的方法。
10:26
Why? Because they're based on assumptions
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為什麼？
因為這些都假設
我們一定是某種人，
我們一定是某種人，
10:29
about who we all are based on our gender
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只憑我們的性別、年齡、居住地，
10:31
and our age and where we live
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10:32
as opposed to data on what
we actually think and do.
we actually think and do.
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而忽視我們實際的思考和行為資料。
現在有了這些資料，
10:36
And since we have this data,
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我們必須做好隱私權控管，
10:38
we need to treat it with appropriate privacy controls
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10:41
and consumer opt-in,
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以及讓消費者自願參與。
10:44
and beyond that, we need to be clear
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再來，
我們必須很清楚我們的假設、
10:47
about our hypotheses,
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使用的方法，
10:49
the methodologies that we use,
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10:52
and our confidence in the result.
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以及我們對結果的信心。
10:55
As my high school algebra teacher used to say,
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就像我高中代數老師常說的：
10:57
show your math,
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「算給我看。
10:59
because if I don't know what steps you took,
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因為如果我不知道
你做了哪些步驟，
你做了哪些步驟，
11:02
I don't know what steps you didn't take,
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就不知道哪些步驟你沒有做。
11:04
and if I don't know what questions you asked,
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如果我不知道你問了哪些問題，
11:07
I don't know what questions you didn't ask.
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就不知道哪些問題你沒有問。」
11:10
And it means asking ourselves, really,
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這表示我們要問自己
11:11
the hardest question of all:
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最難的一個問題：
11:13
Did the data really show us this,
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「數據資料真的有這樣說嗎？
還是這種結果讓我們覺得
11:16
or does the result make us feel
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11:19
more successful and more comfortable?
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比較成功和自在？」
11:23
So the Health Media Collaboratory,
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衛生媒體合作實驗室在計畫結束時，
11:25
at the end of their project, they were able
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發現 87% 的推文
11:27
to find that 87 percent of tweets
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11:30
about those very graphic and disturbing
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回應那些令人不安的戒菸廣告時，
11:32
anti-smoking ads expressed fear,
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表達了恐懼。
11:36
but did they conclude
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但是，
他們有說那些廣告讓人成功戒菸嗎？
11:38
that they actually made people stop smoking?
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沒有。這是科學，不是魔術。
11:41
No. It's science, not magic.
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11:44
So if we are to unlock
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所以，
如果想要釋放數據的力量，
11:47
the power of data,
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11:50
we don't have to go blindly into
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我們不必盲目地踏進
11:54
Orwell's vision of a totalitarian future,
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歐威爾預見的極權主義未來，
11:57
or Huxley's vision of a trivial one,
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或是赫胥黎的瑣碎世界，
12:00
or some horrible cocktail of both.
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或是兩者的可怕綜合體。
12:03
What we have to do
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我們必須做的是，
12:05
is treat critical thinking with respect
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重視批判性思考，
12:08
and be inspired by examples
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並且向衛生媒體合作室
這樣的典範學習。
這樣的典範學習。
12:10
like the Health Media Collaboratory,
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就像超級英雄電影常講的：
12:13
and as they say in the superhero movies,
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12:15
let's use our powers for good.
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「讓我們把我們的力量用在正途。」
12:17
Thank you.
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謝謝。
（觀眾掌聲）
12:19
(Applause)
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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