TEDxCambridge
Sebastian Wernicke: How to use data to make a hit TV show
塞巴斯蒂安 • 韦尼克: 如何运用数据做出一个爆红的电视节目
Filmed:
Readability: 4
1,628,704 views
收集更多的数据会导向更好的决策吗？有竞争力、擅长数据分析的公司，如亚马逊、谷歌和Netflix已经发现数据分析本身并不总能产生最佳的效果。在这次讲座中，数据科学家塞巴斯蒂安 • 韦尼克剖析了当我们纯粹使用数据做决策时会出现什么错误——并建议了一个更明智的方式来使用它。
Sebastian Wernicke - Data scientist
After making a splash in the field of bioinformatics, Sebastian Wernicke moved on to the corporate sphere, where he motivates and manages multidimensional projects. Full bio
After making a splash in the field of bioinformatics, Sebastian Wernicke moved on to the corporate sphere, where he motivates and manages multidimensional projects. Full bio
Double-click the English transcript below to play the video.
00:12
Roy Price is a man that most of you
have probably never heard about,
have probably never heard about,
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Roy Price这个人，
在座的绝大多数可能都没听说过，
在座的绝大多数可能都没听说过，
即使他曾经在2013年4月19日这一天
00:17
even though he may have been responsible
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00:19
for 22 somewhat mediocre
minutes of your life on April 19, 2013.
minutes of your life on April 19, 2013.
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占用了你们生命中普通的22分钟。
00:26
He may have also been responsible
for 22 very entertaining minutes,
for 22 very entertaining minutes,
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他也许曾经带给了
各位非常欢乐的22分钟，
各位非常欢乐的22分钟，
00:29
but not very many of you.
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但对你们当中很多人来说
可能并不是这样。
可能并不是这样。
而这一切全部要追溯到
00:32
And all of that goes back to a decision
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Roy在三年前的一个决定。
00:33
that Roy had to make
about three years ago.
about three years ago.
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实际上，Roy Price是
亚马逊广播公司的一位资深决策者。
亚马逊广播公司的一位资深决策者。
00:35
So you see, Roy Price
is a senior executive with Amazon Studios.
is a senior executive with Amazon Studios.
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这是亚马逊旗下的一家
电视节目制作公司。
电视节目制作公司。
00:40
That's the TV production
company of Amazon.
company of Amazon.
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他47岁，身材不错，
短发梳得很有型，
短发梳得很有型，
00:43
He's 47 years old, slim, spiky hair,
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他在Twitter上形容自己是
"电影、电视、科技、墨西哥卷饼（爱好者）"。
"电影、电视、科技、墨西哥卷饼（爱好者）"。
00:47
describes himself on Twitter
as "movies, TV, technology, tacos."
as "movies, TV, technology, tacos."
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Roy Price有一个
责任非常重大的工作，
责任非常重大的工作，
00:52
And Roy Price has a very responsible job,
because it's his responsibility
because it's his responsibility
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因为他要负责帮亚马逊挑选
即将制作的原创节目。
即将制作的原创节目。
00:57
to pick the shows, the original content
that Amazon is going to make.
that Amazon is going to make.
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当然，这个领域的竞争非常激烈。
01:01
And of course that's
a highly competitive space.
a highly competitive space.
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01:03
I mean, there are so many
TV shows already out there,
TV shows already out there,
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我是说，其他公司已经有
那么多的电视节目，
那么多的电视节目，
Roy不能只是随便乱挑一个节目。
01:06
that Roy can't just choose any show.
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01:08
He has to find shows
that are really, really great.
that are really, really great.
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他必须找出真正会走紅的节目。
换句话说，他挑选的节目
01:12
So in other words, he has to find shows
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必须落在这条曲线的右侧。
01:15
that are on the very right end
of this curve here.
of this curve here.
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这条曲线是IMDB
（译注：网络电影资料库）里
（译注：网络电影资料库）里
01:17
So this curve here
is the rating distribution
is the rating distribution
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01:20
of about 2,500 TV shows
on the website IMDB,
on the website IMDB,
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2500个电视节目的
客户评分曲线图，
客户评分曲线图，
评分从1到10，
01:25
and the rating goes from one to 10,
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纵轴表明有多少节目达到这个评分。
01:27
and the height here shows you
how many shows get that rating.
how many shows get that rating.
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01:30
So if your show gets a rating
of nine points or higher, that's a winner.
of nine points or higher, that's a winner.
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所以如果你的节目达到9分或更高，
你就是赢家，
你就是赢家，
01:35
Then you have a top two percent show.
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你就拥有那2%的顶尖节目。
例如像是“绝命毒师”、
“权力的游戏”、“火线重案组”，
“权力的游戏”、“火线重案组”，
01:37
That's shows like "Breaking Bad,"
"Game of Thrones," "The Wire,"
"Game of Thrones," "The Wire,"
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全部都是会让人上瘾的节目，
01:41
so all of these shows that are addictive,
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01:43
whereafter you've watched a season,
your brain is basically like,
your brain is basically like,
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看完一季之后，
你基本马上就会想，
你基本马上就会想，
“我要去哪里找到剩下的剧集？”
01:46
"Where can I get more of these episodes?"
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01:49
That kind of show.
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基本就是这类的节目。
曲线左边，不妨选个最靠边，
比较明显的点，
比较明显的点，
01:50
On the left side, just for clarity,
here on that end,
here on that end,
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01:53
you have a show called
"Toddlers and Tiaras" --
"Toddlers and Tiaras" --
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这儿有个叫“选美小天后"
（译注：儿童选秀类）的节目——
（译注：儿童选秀类）的节目——
01:56
(Laughter)
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（笑声）
——足够让你明白
01:59
-- which should tell you enough
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曲线最左端代表了什么。
02:00
about what's going on
on that end of the curve.
on that end of the curve.
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02:03
Now, Roy Price is not worried about
getting on the left end of the curve,
getting on the left end of the curve,
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现在，Roy Price并不担心
会选个落在曲线最左边的节目，
会选个落在曲线最左边的节目，
02:07
because I think you would have to have
some serious brainpower
some serious brainpower
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因为我认为你们都具备
严肃的判断力
严肃的判断力
来给 "选美小天后" 打个低分 。
02:10
to undercut "Toddlers and Tiaras."
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02:11
So what he's worried about
is this middle bulge here,
is this middle bulge here,
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他担心的是
中间多数的这些节目，
中间多数的这些节目，
02:15
the bulge of average TV,
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多到爆的这些一般的电视节目，
02:17
you know, those shows
that aren't really good or really bad,
that aren't really good or really bad,
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这些节目不算好，但也不是很烂，
它们不会真正地让你感兴趣。
02:20
they don't really get you excited.
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所以他要确保他看好的节目
是落在最右端这里。
是落在最右端这里。
02:22
So he needs to make sure
that he's really on the right end of this.
that he's really on the right end of this.
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02:27
So the pressure is on,
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那么压力就来了，
当然，这也是亚马逊第一次
02:28
and of course it's also the first time
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想要做这类事情，
02:31
that Amazon is even
doing something like this,
doing something like this,
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所以Roy Price不想只是碰运气。
02:33
so Roy Price does not want
to take any chances.
to take any chances.
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02:36
He wants to engineer success.
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他想要打造成功。
他要一个万无一失的成功，
02:39
He needs a guaranteed success,
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于是，他举办了一个竞赛。
02:40
and so what he does is,
he holds a competition.
he holds a competition.
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他带来了很多关于电视节目的想法，
02:43
So he takes a bunch of ideas for TV shows,
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通过一个评估，
02:46
and from those ideas,
through an evaluation,
through an evaluation,
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他们挑了八个候选的电视节目，
02:48
they select eight candidates for TV shows,
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然后他为每一个节目制作了第一集，
02:53
and then he just makes the first episode
of each one of these shows
of each one of these shows
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再把它们放到网上，
让每个人免费观看。
让每个人免费观看。
02:56
and puts them online for free
for everyone to watch.
for everyone to watch.
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当亚马逊要给你免费的东西时，
02:59
And so when Amazon
is giving out free stuff,
is giving out free stuff,
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你就会拿，对吧？
03:01
you're going to take it, right?
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所以几百万人在看这些剧集，
03:03
So millions of viewers
are watching those episodes.
are watching those episodes.
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而这些人不知道的是，
当他们在观看节目的时候，
当他们在观看节目的时候，
03:08
What they don't realize is that,
while they're watching their shows,
while they're watching their shows,
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实际上他们也正被观察着。
03:11
actually, they are being watched.
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他们被Roy Price及他的团队观察，
03:14
They are being watched
by Roy Price and his team,
by Roy Price and his team,
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他们纪录了所有的一切。
03:16
who record everything.
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他们纪录了哪些人按了拨放，
哪些人按了暂停，
哪些人按了暂停，
03:17
They record when somebody presses play,
when somebody presses pause,
when somebody presses pause,
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哪些部分他们跳过了，
哪些部分他们又重看了一遍。
哪些部分他们又重看了一遍。
03:21
what parts they skip,
what parts they watch again.
what parts they watch again.
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他们收集了几百万个数据，
03:23
So they collect millions of data points,
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因为他们想要用这些数据来决定
03:26
because they want
to have those data points
to have those data points
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要做什么样的节目。
03:28
to then decide
which show they should make.
which show they should make.
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03:30
And sure enough,
so they collect all the data,
so they collect all the data,
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理所当然，他们收集了所有的数据，
处理过后得到了一个答案，
03:33
they do all the data crunching,
and an answer emerges,
and an answer emerges,
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而答案就是，
03:35
and the answer is,
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“亚马逊需要制作一个有关
四个美国共和党参议员的喜剧”。
四个美国共和党参议员的喜剧”。
03:36
"Amazon should do a sitcom
about four Republican US Senators."
about four Republican US Senators."
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他们真的做了。
03:42
They did that show.
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有人知道这个节目吗？
03:43
So does anyone know the name of the show?
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（观众：" 阿尔法屋。"）
03:46
(Audience: "Alpha House.")
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是的，就是"阿尔法屋"。
03:48
Yes, "Alpha House,"
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03:49
but it seems like not too many of you here
remember that show, actually,
remember that show, actually,
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但看起来你们大部人都
不记得有这部片子，
不记得有这部片子，
因为这部片子收视率并不太好。
03:53
because it didn't turn out that great.
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它其实只是个一般的节目，
03:55
It's actually just an average show,
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03:57
actually -- literally, in fact, because
the average of this curve here is at 7.4,
the average of this curve here is at 7.4,
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实际上，一般的节目差不多
对应曲线上大概7.4分的位置，
对应曲线上大概7.4分的位置，
而 “阿尔法屋” 落在7.5分，
04:02
and "Alpha House" lands at 7.5,
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所以比一般的节目高一点点，
04:04
so a slightly above average show,
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但绝对不是Roy Price和
他的团队想要达到的目标。
他的团队想要达到的目标。
04:06
but certainly not what Roy Price
and his team were aiming for.
and his team were aiming for.
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但在差不多同一时间，
04:10
Meanwhile, however,
at about the same time,
at about the same time,
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另一家公司的另一个决策者，
04:13
at another company,
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同样用数据分析
却做出了一个顶尖的节目，
却做出了一个顶尖的节目，
04:14
another executive did manage
to land a top show using data analysis,
to land a top show using data analysis,
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他的名字是 Ted，
04:19
and his name is Ted,
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Ted Sarandos是Netflix的
首席内容官，
首席内容官，
04:20
Ted Sarandos, who is
the Chief Content Officer of Netflix,
the Chief Content Officer of Netflix,
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就跟 Roy一样，他也要不停地寻找
04:24
and just like Roy,
he's on a constant mission
he's on a constant mission
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最棒的节目，
04:26
to find that great TV show,
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而他也使用了数据分析，
04:27
and he uses data as well to do that,
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但他的做法有点不太一样。
04:29
except he does it
a little bit differently.
a little bit differently.
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不是举办竞赛，他和他的团队
04:31
So instead of holding a competition,
what he did -- and his team of course --
what he did -- and his team of course --
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04:35
was they looked at all the data
they already had about Netflix viewers,
they already had about Netflix viewers,
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观察了Netflix已有的所有观众数据，
比如观众对节目的评分、
04:39
you know, the ratings
they give their shows,
they give their shows,
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观看纪录、
哪些节目最受欢迎等等。
哪些节目最受欢迎等等。
04:41
the viewing histories,
what shows people like, and so on.
what shows people like, and so on.
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他们用这些数据去挖掘
04:44
And then they use that data to discover
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观众的所有小细节：
04:45
all of these little bits and pieces
about the audience:
about the audience:
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他们喜欢什么类型的节目、
04:48
what kinds of shows they like,
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什么类型的制作人、
什么类型的演员。
什么类型的演员。
04:50
what kind of producers,
what kind of actors.
what kind of actors.
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就在他们收集到全部的细节后，
04:52
And once they had
all of these pieces together,
all of these pieces together,
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他们信心满满地
04:54
they took a leap of faith,
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决定要制作一部，
04:56
and they decided to license
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不是四个参议员的喜剧，
04:58
not a sitcom about four Senators
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而是一系列有关一位
单身参议员的电视剧。
单身参议员的电视剧。
05:01
but a drama series about a single Senator.
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各位知道那个节目吗？
05:04
You guys know the show?
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（笑声）
05:06
(Laughter)
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是的，“纸牌屋”，
当然，Netflix至少在头两季
当然，Netflix至少在头两季
05:07
Yes, "House of Cards," and Netflix
of course, nailed it with that show,
of course, nailed it with that show,
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在这个节目上赚到了极高的收视率。
05:11
at least for the first two seasons.
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05:13
(Laughter) (Applause)
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（笑声）（掌声）
“纸牌屋” 在这个曲线上拿到了 9.1分，
05:17
"House of Cards" gets
a 9.1 rating on this curve,
a 9.1 rating on this curve,
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他们绝对实现了最初的目标。
05:20
so it's exactly
where they wanted it to be.
where they wanted it to be.
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很显然，问题来了，
这到底是怎么回事？
这到底是怎么回事？
05:24
Now, the question of course is,
what happened here?
what happened here?
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有两个非常有竞争力、
精通数据分析的公司。
精通数据分析的公司。
05:26
So you have two very competitive,
data-savvy companies.
data-savvy companies.
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05:29
They connect all of these
millions of data points,
millions of data points,
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他们整合了所有的数据，
然后，其中一个干的很漂亮，
05:32
and then it works
beautifully for one of them,
beautifully for one of them,
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而另一个却没有，
05:34
and it doesn't work for the other one.
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这是为什么呢？
05:36
So why?
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毕竟逻辑分析会告诉你，
这种方法应该每次都有效啊，
这种方法应该每次都有效啊，
05:37
Because logic kind of tells you
that this should be working all the time.
that this should be working all the time.
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我是说，
如果你收集了所有的数据
如果你收集了所有的数据
05:41
I mean, if you're collecting
millions of data points
millions of data points
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来制定一个决策，
05:43
on a decision you're going to make,
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那你应该可以得到一个
相当不错的决策。
相当不错的决策。
05:45
then you should be able
to make a pretty good decision.
to make a pretty good decision.
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你有200年的统计方法做后盾。
05:47
You have 200 years
of statistics to rely on.
of statistics to rely on.
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你用高性能的计算机
去增强它的效果。
去增强它的效果。
05:50
You're amplifying it
with very powerful computers.
with very powerful computers.
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至少你可以期待得到一个
还不错的电视节目，对吧？
还不错的电视节目，对吧？
05:53
The least you could expect
is good TV, right?
is good TV, right?
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但如果数据分析
并没有想像中的有效，
并没有想像中的有效，
05:57
And if data analysis
does not work that way,
does not work that way,
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这就有点吓人了，
06:01
then it actually gets a little scary,
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因为我们生活在一个
越来越依赖数据的时代，
越来越依赖数据的时代，
06:03
because we live in a time
where we're turning to data more and more
where we're turning to data more and more
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06:07
to make very serious decisions
that go far beyond TV.
that go far beyond TV.
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我们要用数据做出远比电视节目
还要严肃重要的决策。
还要严肃重要的决策。
你们当中有人知道 "MHS" 这家公司吗？
06:12
Does anyone here know the company
Multi-Health Systems?
Multi-Health Systems?
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没人？好，这就好。
06:17
No one. OK, that's good actually.
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好的，MHS是一家软件公司，
06:18
OK, so Multi-Health Systems
is a software company,
is a software company,
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而我希望在座的各位
06:22
and I hope that nobody here in this room
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没人与他们的软件有任何关系，
06:24
ever comes into contact
with that software,
with that software,
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因为如果你有，
就表示你犯了罪被判刑了。
就表示你犯了罪被判刑了。
06:28
because if you do,
it means you're in prison.
it means you're in prison.
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（笑声）
06:30
(Laughter)
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如果有人在美国被判入狱，
要申请假释，
要申请假释，
06:31
If someone here in the US is in prison,
and they apply for parole,
and they apply for parole,
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06:34
then it's very likely that
data analysis software from that company
data analysis software from that company
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很有可能那家公司的数据分析软件
就会被用来判定你是否能获得假释。
06:39
will be used in determining
whether to grant that parole.
whether to grant that parole.
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它也是采用跟
亚马逊和Netflix公司相同的原则，
亚马逊和Netflix公司相同的原则，
06:42
So it's the same principle
as Amazon and Netflix,
as Amazon and Netflix,
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但并不是要决定
某个电视节目收视率的好坏，
某个电视节目收视率的好坏，
06:45
but now instead of deciding whether
a TV show is going to be good or bad,
a TV show is going to be good or bad,
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而是用来决定
一个人将来的行为是好是坏。
一个人将来的行为是好是坏。
06:50
you're deciding whether a person
is going to be good or bad.
is going to be good or bad.
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06:53
And mediocre TV, 22 minutes,
that can be pretty bad,
that can be pretty bad,
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一个22分钟的普通电视节目
可以很糟糕，
可以很糟糕，
但我觉得要坐很多年的牢，更糟糕。
06:58
but more years in prison,
I guess, even worse.
I guess, even worse.
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但不幸的是，实际上已经有证据显示，
这项数据分析尽管可以依靠
这项数据分析尽管可以依靠
07:02
And unfortunately, there is actually
some evidence that this data analysis,
some evidence that this data analysis,
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07:06
despite having lots of data,
does not always produce optimum results.
does not always produce optimum results.
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庞大的数据资料，
它并不总能得出最优的结果。
它并不总能得出最优的结果。
但并不只有像MHS这样的软件公司
07:10
And that's not because a company
like Multi-Health Systems
like Multi-Health Systems
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不确定到底怎么分析数据，
07:13
doesn't know what to do with data.
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就连最顶尖的数据公司也会出错。
07:15
Even the most data-savvy
companies get it wrong.
companies get it wrong.
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是的，甚至谷歌有时也会出错。
07:17
Yes, even Google gets it wrong sometimes.
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2009年，谷歌宣布
他们可以用数据分析来
他们可以用数据分析来
07:20
In 2009, Google announced
that they were able, with data analysis,
that they were able, with data analysis,
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预测流行性感冒何时爆发，
就是那种讨人厌的流感，
就是那种讨人厌的流感，
07:25
to predict outbreaks of influenza,
the nasty kind of flu,
the nasty kind of flu,
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他们用自己的搜寻引擎
来做数据分析。
来做数据分析。
07:29
by doing data analysis
on their Google searches.
on their Google searches.
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结果证明它准确无比，
引得各路新闻报道铺天盖地，
引得各路新闻报道铺天盖地，
07:33
And it worked beautifully,
and it made a big splash in the news,
and it made a big splash in the news,
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甚至还达到了一个科学界的顶峰：
07:37
including the pinnacle
of scientific success:
of scientific success:
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在 “自然” 期刊上发表了文章。
07:39
a publication in the journal "Nature."
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之后的每一年，它都预测得准确无误，
07:41
It worked beautifully
for year after year after year,
for year after year after year,
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直到有一年，它失败了。
07:45
until one year it failed.
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没有人知道到底是什么原因。
07:47
And nobody could even tell exactly why.
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那一年它就是不准了，
07:49
It just didn't work that year,
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当然，这又成了一个大新闻，
07:51
and of course that again made big news,
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包括现在
07:52
including now a retraction
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被 "自然” 期刊撤稿。
07:54
of a publication
from the journal "Nature."
from the journal "Nature."
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所以，即使是最顶尖的数据分析公司，
亚马逊和谷歌，
亚马逊和谷歌，
07:58
So even the most data-savvy companies,
Amazon and Google,
Amazon and Google,
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他们有时也会出错。
08:01
they sometimes get it wrong.
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但尽管出现了这些失败，
08:04
And despite all those failures,
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数据仍然在马不停蹄地渗透进我们
实际生活中的决策——
实际生活中的决策——
08:06
data is moving rapidly
into real-life decision-making --
into real-life decision-making --
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进入工作场所、
08:10
into the workplace,
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执法过程、
08:12
law enforcement,
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医药领域。
08:14
medicine.
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08:16
So we should better make sure
that data is helping.
that data is helping.
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所以，我们应该确保数据是
能够帮助我们解决问题的。
能够帮助我们解决问题的。
08:19
Now, personally I've seen
a lot of this struggle with data myself,
a lot of this struggle with data myself,
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我个人也曾经多次
被数据分析搞的焦头烂额，
被数据分析搞的焦头烂额，
因为我在计算遗传学领域工作，
08:22
because I work in computational genetics,
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这个领域有很多非常聪明的人
08:24
which is also a field
where lots of very smart people
where lots of very smart people
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在用多到难以想像的数据
来制定相当严肃的决策，
来制定相当严肃的决策，
08:27
are using unimaginable amounts of data
to make pretty serious decisions
to make pretty serious decisions
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08:31
like deciding on a cancer therapy
or developing a drug.
or developing a drug.
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比如癌症治疗，或者药物开发。
经过这几年，我已经注意到一种模式
08:35
And over the years,
I've noticed a sort of pattern
I've noticed a sort of pattern
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08:37
or kind of rule, if you will,
about the difference
about the difference
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或者规则，你们也可以这么理解，
08:40
between successful
decision-making with data
decision-making with data
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就是有关于用数据做出
成功决策和不成功决策，
08:43
and unsuccessful decision-making,
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我觉得这个模式值得分享，
大概是这样的。
大概是这样的。
08:44
and I find this a pattern worth sharing,
and it goes something like this.
and it goes something like this.
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当你要解决一个复杂问题时，
08:50
So whenever you're
solving a complex problem,
solving a complex problem,
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你通常必然会做两件事。
08:52
you're doing essentially two things.
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首先，你会把问题拆分得非常细，
08:54
The first one is, you take that problem
apart into its bits and pieces
apart into its bits and pieces
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这样你就可以深度地分析这些细节，
08:57
so that you can deeply analyze
those bits and pieces,
those bits and pieces,
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09:00
and then of course
you do the second part.
you do the second part.
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当然你要做的第二件事就是，
再把这些细节重新整合在一起，
09:02
You put all of these bits and pieces
back together again
back together again
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来得出你要的结论。
09:05
to come to your conclusion.
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有时候你必须重复几次，
09:06
And sometimes you
have to do it over again,
have to do it over again,
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但基本都是围绕这两件事：
09:08
but it's always those two things:
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拆分、再整合。
09:10
taking apart and putting
back together again.
back together again.
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那么关键的问题在于，
09:14
And now the crucial thing is
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数据和数据分析
09:15
that data and data analysis
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只适用于第一步，
09:18
is only good for the first part.
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无论数据和数据分析多么强大，
09:21
Data and data analysis,
no matter how powerful,
no matter how powerful,
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它都只能帮助你拆分问题和了解细节，
09:23
can only help you taking a problem apart
and understanding its pieces.
and understanding its pieces.
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它不适用于把细节重新整合在一起
09:28
It's not suited to put those pieces
back together again
back together again
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来得出一个结论。
09:31
and then to come to a conclusion.
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有一个工具可以实现第二步，
我们每个人都有，
我们每个人都有，
09:33
There's another tool that can do that,
and we all have it,
and we all have it,
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那就是大脑。
09:36
and that tool is the brain.
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如果要说大脑很擅长某一件事，
09:37
If there's one thing a brain is good at,
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那就是，它很会把琐碎的细节
重新整合在一起，
重新整合在一起，
09:39
it's taking bits and pieces
back together again,
back together again,
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即使你拥有的信息并不完整，
09:41
even when you have incomplete information,
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也能得到一个好的结论，
09:43
and coming to a good conclusion,
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特别是专家的大脑。
09:45
especially if it's the brain of an expert.
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而这也是我相信
Netflix会这么成功的原因，
Netflix会这么成功的原因，
09:48
And that's why I believe
that Netflix was so successful,
that Netflix was so successful,
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因为他们在分析过程中同时
使用了数据和大脑。
使用了数据和大脑。
09:51
because they used data and brains
where they belong in the process.
where they belong in the process.
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他们利用数据，
首先去了解观众的若干细节，
首先去了解观众的若干细节，
09:54
They use data to first understand
lots of pieces about their audience
lots of pieces about their audience
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没有这些数据，
他们不可能进行这么透彻的分析，
他们不可能进行这么透彻的分析，
09:58
that they otherwise wouldn't have
been able to understand at that depth,
been able to understand at that depth,
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但在之后，要做出重新整合，
10:01
but then the decision
to take all these bits and pieces
to take all these bits and pieces
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制作像"纸牌屋"这样的节目的决策，
10:04
and put them back together again
and make a show like "House of Cards,"
and make a show like "House of Cards,"
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就无法依赖数据了。
10:07
that was nowhere in the data.
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是Ted Sarandos和他的团队（通过思考）
做出了批准该节目的这个决策，
做出了批准该节目的这个决策，
10:09
Ted Sarandos and his team
made that decision to license that show,
made that decision to license that show,
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这也就意味着，
10:13
which also meant, by the way,
that they were taking
that they were taking
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他们在做出决策的当下，
也正在承担很大的个人风险。
也正在承担很大的个人风险。
10:15
a pretty big personal risk
with that decision.
with that decision.
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10:18
And Amazon, on the other hand,
they did it the wrong way around.
they did it the wrong way around.
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而另一方面，亚马逊把事情搞砸了。
他们全程依赖数据来制定决策，
10:21
They used data all the way
to drive their decision-making,
to drive their decision-making,
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首先，举办了关于节目创意的竞赛，
10:24
first when they held
their competition of TV ideas,
their competition of TV ideas,
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然后他们决定选择制作 "阿尔法屋"。
10:26
then when they selected "Alpha House"
to make as a show.
to make as a show.
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当然，对他们而言，
这是一个非常安全的决策，
这是一个非常安全的决策，
10:30
Which of course was
a very safe decision for them,
a very safe decision for them,
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10:32
because they could always
point at the data, saying,
point at the data, saying,
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因为他们总是可以指着数据说，
“这是数据告诉我们的。”
10:35
"This is what the data tells us."
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但数据并没有带给他们
满意的结果。
满意的结果。
10:37
But it didn't lead to the exceptional
results that they were hoping for.
results that they were hoping for.
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当然，数据依然是做决策时的
一个强大的工具，
一个强大的工具，
10:42
So data is of course a massively
useful tool to make better decisions,
useful tool to make better decisions,
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但我相信，当数据开始
主导这些决策时，
主导这些决策时，
10:47
but I believe that things go wrong
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10:49
when data is starting
to drive those decisions.
to drive those decisions.
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并不能保证万无一失。
不管它有多么的强大，
数据都仅仅是一个工具，
数据都仅仅是一个工具，
10:52
No matter how powerful,
data is just a tool,
data is just a tool,
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记住这句话之后，
我发现这个装置相当有用。
我发现这个装置相当有用。
10:55
and to keep that in mind,
I find this device here quite useful.
I find this device here quite useful.
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10:59
Many of you will ...
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你们很多人就会......
11:00
(Laughter)
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（笑声）
在有数据之前，
11:01
Before there was data,
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这就是用来做决策的工具。
11:03
this was the decision-making
device to use.
device to use.
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（笑声）
11:05
(Laughter)
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你们很多人应该知道这个玩意儿。
11:07
Many of you will know this.
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这个玩具称做“魔术8号球”，
11:08
This toy here is called the Magic 8 Ball,
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它真的很奇妙，
11:10
and it's really amazing,
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因为如果你要做一个
“是” 或 “不是” 的决策时，
“是” 或 “不是” 的决策时，
11:11
because if you have a decision to make,
a yes or no question,
a yes or no question,
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你只要摇一摇这颗球，
就可以得到答案了——
就可以得到答案了——
11:14
all you have to do is you shake the ball,
and then you get an answer --
and then you get an answer --
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“很有可能是”——
在这个视窗里，马上就可以看到。
在这个视窗里，马上就可以看到。
11:18
"Most Likely" -- right here
in this window in real time.
in this window in real time.
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我回头会带它去做技术示范。
11:21
I'll have it out later for tech demos.
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（笑声）
11:23
(Laughter)
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事实上，当然——
我已经在我人生中做出了一些决定，
我已经在我人生中做出了一些决定，
11:24
Now, the thing is, of course --
so I've made some decisions in my life
so I've made some decisions in my life
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虽然事后证明，
我当初应该直接用这颗球。
我当初应该直接用这颗球。
11:28
where, in hindsight,
I should have just listened to the ball.
I should have just listened to the ball.
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但，当然，如果你手里有数据，
11:31
But, you know, of course,
if you have the data available,
if you have the data available,
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你就会想用更尖端的方式
来取代这颗球，
来取代这颗球，
11:34
you want to replace this with something
much more sophisticated,
much more sophisticated,
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比方说，用数据分析来得到更好的决策。
11:37
like data analysis
to come to a better decision.
to come to a better decision.
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但这无法改变基本的设定。
11:41
But that does not change the basic setup.
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这球可能会变得越来越智能，
11:43
So the ball may get smarter
and smarter and smarter,
and smarter and smarter,
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但我相信，如果我们想达成某些
像曲线最右端那样
像曲线最右端那样
11:47
but I believe it's still on us
to make the decisions
to make the decisions
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11:49
if we want to achieve
something extraordinary,
something extraordinary,
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出色的成就，最后的决定权
还是应该落在我们身上。
11:52
on the right end of the curve.
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11:54
And I find that a very encouraging
message, in fact,
message, in fact,
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事实上，我还发现了
一件非常鼓舞人心的事，
一件非常鼓舞人心的事，
即使面对庞大的数据，
11:59
that even in the face
of huge amounts of data,
of huge amounts of data,
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12:03
it still pays off to make decisions,
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当你要做出决定，
想要变成一位该领域的专家
并承担风险时，
并承担风险时，
12:07
to be an expert in what you're doing
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12:10
and take risks.
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你仍然会有很大的收获。
因为到最后，不是数据，
12:12
Because in the end, it's not data,
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而是风险，会把你引到曲线的最右端。
12:15
it's risks that will land you
on the right end of the curve.
on the right end of the curve.
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12:19
Thank you.
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谢谢各位。
12:21
(Applause)
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（掌声）
Translated by Chia Shimin
ABOUT THE SPEAKER
Sebastian Wernicke - Data scientistAfter making a splash in the field of bioinformatics, Sebastian Wernicke moved on to the corporate sphere, where he motivates and manages multidimensional projects.
Why you should listen
Dr. Sebastian Wernicke is the Chief Data Scientist of ONE LOGIC, a data science boutique that supports organizations across industries to make sense of their vast data collections to improve operations and gain strategic advantages. Wernicke originally studied bioinformatics and previously led the strategy and growth of Seven Bridges Genomics, a Cambridge-based startup that builds platforms for genetic analysis.
Before his career in statistics began, Wernicke worked stints as both a paramedic and successful short animated filmmaker. He's also the author of the TEDPad app, an irreverent tool for creating an infinite number of "amazing and really bad" and mostly completely meaningless talks. He's the author of the statistically authoritative and yet completely ridiculous "How to Give the Perfect TEDTalk."
Sebastian Wernicke | Speaker | TED.com