even though he may have been responsible

Roy Price這個人， 各位可能都未曾聽過，

for 22 somewhat mediocre minutes of your life on April 19, 2013.

即使他曾負責過 你生命中平凡無奇的22分鐘，

He may have also been responsible for 22 very entertaining minutes,

在2013年4月19日這一天。

but not very many of you.

他也許也曾負責帶給 各位非常歡樂的22分鐘，

And all of that goes back to a decision

但你們其中也許很多人並沒有。

that Roy had to make about three years ago.

而這一切全部要回到

So you see, Roy Price is a senior executive with Amazon Studios.

Roy在三年前的一個決定。

That's the TV production company of Amazon.

所以，你明白，Roy Price是 Amazon廣播公司的一位資深決策者。

He's 47 years old, slim, spiky hair,

這是Amazon旗下的一家 電視節目製作公司。

describes himself on Twitter as "movies, TV, technology, tacos."

他47歲，身材不錯，尖頭髮，

And Roy Price has a very responsible job, because it's his responsibility

在Twitter上形容自己是 “電影、電視、科技、墨西哥捲餅 。”

to pick the shows, the original content that Amazon is going to make.

Roy Price有一個 責任非常重大的工作，

And of course that's a highly competitive space.

因為他要負責幫Amazon挑選 即將製作的原創內容節目。

I mean, there are so many TV shows already out there,

當然，這是高度競爭的領域。

that Roy can't just choose any show.

我的意思是， 外面已經有那麼多的電視節目，

He has to find shows that are really, really great.

Roy不能隨便亂挑一個節目。

So in other words, he has to find shows

他必須找出真正、 真正很讚的節目。

that are on the very right end of this curve here.

換句話說，

So this curve here is the rating distribution

他必須從這條曲線上的右邊挑選節目。

of about 2,500 TV shows on the website IMDB,

這條曲線是 IMDB網路電影資料庫裡

and the rating goes from one to 10,

2500個電視節目的 客戶評分分布圖，

and the height here shows you how many shows get that rating.

評分從 1到10，

So if your show gets a rating of nine points or higher, that's a winner.

最高的地方代表 有多少節目達到這個評分。

Then you have a top two percent show.

所以如果你的節目達到 9分或更高， 你就是贏家。

That's shows like "Breaking Bad," "Game of Thrones," "The Wire,"

你就是那百分之二的頂尖節目。

so all of these shows that are addictive,

例如像是" 絕命毒師 、 權力遊戲、火線重案組 "

whereafter you've watched a season, your brain is basically like,

全部都是會讓你上癮的節目，

"Where can I get more of these episodes?"

看完一季之後，你的大腦基本上像是 ...

That kind of show.

" 我要去哪裡找到更多這部片的影集？ "

On the left side, just for clarity, here on that end,

等等這類的節目。

you have a show called "Toddlers and Tiaras" --

左邊末端，很明顯地，

(Laughter)

你們有個叫" 小小姐與后冠 "的節目

-- which should tell you enough

（笑聲）

about what's going on on that end of the curve.

一個足夠讓你明白

Now, Roy Price is not worried about getting on the left end of the curve,

為什麼它會在曲線末端的節目。

because I think you would have to have some serious brainpower

現在，Roy Price不擔心 在曲線左邊末端的節目。

to undercut "Toddlers and Tiaras."

因為我認為你們都會想 有一些嚴肅的判斷力

So what he's worried about is this middle bulge here,

來降低" 小小姐與后冠 "的評分 。

the bulge of average TV,

所以，他擔心的是中間多數的這些節目，

you know, those shows that aren't really good or really bad,

多到爆的這些一般性電視節目，

they don't really get you excited.

你知道，這些節目 既不是很好也不是很壞，

So he needs to make sure that he's really on the right end of this.

它們不會真正地讓你興奮。

So the pressure is on,

所以他要確保他真的 是在右邊的末端這裡，

and of course it's also the first time

所以，壓力就來了，

that Amazon is even doing something like this,

所以當然，這也是第一次 Amazon

so Roy Price does not want to take any chances.

也想要做類似這樣的事情，

He wants to engineer success.

Roy Price不想冒風險，

He needs a guaranteed success,

他想要建造成功，

and so what he does is, he holds a competition.

他要一個保證的成功，

So he takes a bunch of ideas for TV shows,

所以他就舉辦一個比賽。

and from those ideas, through an evaluation,

他為電視節目帶來了很多想法，

they select eight candidates for TV shows,

並且透過一個評估，形塑這些想法，

and then he just makes the first episode of each one of these shows

他們為電視節目挑選了八個候選名單，

and puts them online for free for everyone to watch.

然後他製作每一個節目的第一集，

And so when Amazon is giving out free stuff,

然後把他們放到網路上， 讓每個人免費觀看。

you're going to take it, right?

所以當Amazon要給你免費的東西時，

So millions of viewers are watching those episodes.

你就會拿，對吧？

What they don't realize is that, while they're watching their shows,

所以上百萬人在看這些影集，

actually, they are being watched.

而這些人不明白的是， 當他們在觀看節目的時候，

They are being watched by Roy Price and his team,

實際上他們也正被觀查中。

who record everything.

他們被Roy Price及他的團隊觀查，

They record when somebody presses play, when somebody presses pause,

他們紀錄了每一件事。

what parts they skip, what parts they watch again.

他們紀錄了，那些人按了撥放， 那些人按了暫停，

So they collect millions of data points,

那些部分他們跳過， 那些部分他們又重看一遍。

because they want to have those data points

所以他們收集了上百萬的數據資料，

to then decide which show they should make.

因為他們想要用這些數據資料來決定

And sure enough, so they collect all the data,

要做甚麼樣的節目。

they do all the data crunching, and an answer emerges,

確定好後，他們收集所有的數據，

and the answer is,

他們做完所有數據處理後， 得到一個答案，

"Amazon should do a sitcom about four Republican US Senators."

而答案就是，

They did that show.

" Amazon需要製作一個有關 美國共和黨參議員的喜劇 "。

So does anyone know the name of the show?

他們做了，

(Audience: "Alpha House.")

有人知道這個節目嗎？

Yes, "Alpha House,"

（觀眾：" 艾爾發屋 "）

but it seems like not too many of you here remember that show, actually,

是的，" 艾爾發屋 "

because it didn't turn out that great.

但實際上，你們大部人 應該不記得有這部片子，

It's actually just an average show,

因為這部片並不那麼賣座。

actually -- literally, in fact, because the average of this curve here is at 7.4,

它實際上僅是一般的節目，

and "Alpha House" lands at 7.5,

實際上，一般的節目差不多 坐落在曲線上的 7.4分，

so a slightly above average show,

而" 艾爾發房屋 "落在7.5分，

but certainly not what Roy Price and his team were aiming for.

所以比一般的節目高一點點，

Meanwhile, however, at about the same time,

但絕對不是Roy Price與 他的團隊所要達到的目標。

at another company,

這時，然而，同一時間，

another executive did manage to land a top show using data analysis,

另一家公司的另一個決策者，

and his name is Ted,

用同樣的數據分析做了一個頂尖的節目，

Ted Sarandos, who is the Chief Content Officer of Netflix,

他的名字是 Ted，

and just like Roy, he's on a constant mission

Ted Sarandos是Netflix的 首席節目內容決策者，

to find that great TV show,

就跟 Roy一樣，他也要不停的找

and he uses data as well to do that,

最棒的節目，

except he does it a little bit differently.

而他也使用數據來這樣做，

So instead of holding a competition, what he did -- and his team of course --

但他的做法，有點不太一樣。

was they looked at all the data they already had about Netflix viewers,

不是舉辦比賽，當然，他和他的團隊

you know, the ratings they give their shows,

也有觀察Netflix已經有的觀眾數據，

the viewing histories, what shows people like, and so on.

觀眾對節目的評分、觀看紀錄、

And then they use that data to discover

那些節目是人們喜歡的等等，

all of these little bits and pieces about the audience:

他們也使用數據去發掘

what kinds of shows they like,

觀眾所有的小細節：

what kind of producers, what kind of actors.

他們喜歡甚麼類型的節目、

And once they had all of these pieces together,

甚麼類型的製作人、甚麼類型的演員，

they took a leap of faith,

一旦他們收集全部的細節後，

and they decided to license

他們很有信心地

not a sitcom about four Senators

決定要製作一部，

but a drama series about a single Senator.

不是四個參議員的喜劇，

You guys know the show?

而是一系列有關一位 單身參議員的戲劇。

(Laughter)

各位知道那個節目嗎？

Yes, "House of Cards," and Netflix of course, nailed it with that show,

（笑聲）

at least for the first two seasons.

是的，" 纸牌屋 "，Netflix ，當然，

(Laughter) (Applause)

至少頭二季，用這節目盯住那個分數。

"House of Cards" gets a 9.1 rating on this curve,

（笑聲）（掌聲）

so it's exactly where they wanted it to be.

" 纸牌屋 "在這曲線上拿到 9.1分，

Now, the question of course is, what happened here?

這當然是他們想要的。

So you have two very competitive, data-savvy companies.

現在，當然問題就是 這到底是怎麼一回事？

They connect all of these millions of data points,

你有兩個非常有競爭力、 精通數據資料的公司。

and then it works beautifully for one of them,

他們連結了所有的數據資料，

and it doesn't work for the other one.

然後，其中一個做的很漂亮，

So why?

而另一個卻沒有，

Because logic kind of tells you that this should be working all the time.

為什麼？

I mean, if you're collecting millions of data points

因為邏輯上告訴你， 這應該每次都有效啊，

on a decision you're going to make,

我的意思是， 如果你收集了所有的數據資料

then you should be able to make a pretty good decision.

來決定一個決策，

You have 200 years of statistics to rely on.

那你應該可以得到一個 相當不錯的決策。

You're amplifying it with very powerful computers.

你有 200年的統計數據做後盾，

The least you could expect is good TV, right?

你用很強大的電腦去增強它，

And if data analysis does not work that way,

至少你可以期待到一個 好的電視節目，對吧？

then it actually gets a little scary,

但如果數據分析 並沒有想像中的有效，

because we live in a time where we're turning to data more and more

那，這真的有點恐怖，

to make very serious decisions that go far beyond TV.

因為我們正轉向一個 數據越來越多的時代，

Does anyone here know the company Multi-Health Systems?

來做出遠比電視節目 還要嚴肅的決策。

No one. OK, that's good actually.

你們當中有人知道" MHS "這家公司嗎？

OK, so Multi-Health Systems is a software company,

沒人？好，這樣很好，

and I hope that nobody here in this room

好的，MHS是一家軟體公司，

ever comes into contact with that software,

而我希望在座的各位，

because if you do, it means you're in prison.

沒有人與這個軟體有牽連，

(Laughter)

因為如果你有，代表你在監獄中

If someone here in the US is in prison, and they apply for parole,

（笑聲）

then it's very likely that data analysis software from that company

在美國這裡如果有人被判入監， 然後要申請假釋，

will be used in determining whether to grant that parole.

很有可能那家公司的數據分析軟體

So it's the same principle as Amazon and Netflix,

會被用來判定是否能獲得假釋。

but now instead of deciding whether a TV show is going to be good or bad,

所以，它也是採用 Amazon 和 Netflix 公司相同的原則，

you're deciding whether a person is going to be good or bad.

但不同的是， 他們是用來決定電視節目將來的好壞，

And mediocre TV, 22 minutes, that can be pretty bad,

你是用來決定一個人將來的好壞，

but more years in prison, I guess, even worse.

表現普通22分鐘的電視節目，很糟糕，

And unfortunately, there is actually some evidence that this data analysis,

但，我猜，要做更多年的牢，更糟糕。

despite having lots of data, does not always produce optimum results.

但不幸的是，實際上已經有證據顯示， 該數據分析除了擁有龐大的數據外，

And that's not because a company like Multi-Health Systems

它並不總是跑出適當的結果。

doesn't know what to do with data.

但並不只有像是MHS這樣的軟體公司

Even the most data-savvy companies get it wrong.

不明白數據怎麼了，

Yes, even Google gets it wrong sometimes.

甚至最頂尖的數據公司也會出錯，

In 2009, Google announced that they were able, with data analysis,

是的，甚至Google有時也會出錯。

to predict outbreaks of influenza, the nasty kind of flu,

2009年，Google宣布他們可以用數據分析，

by doing data analysis on their Google searches.

來預測流行性感冒，討人厭的流感，

And it worked beautifully, and it made a big splash in the news,

經由他們的Google搜尋引擎來做數據分析。

including the pinnacle of scientific success:

而且它準確無比，當時造成一股新聞的轟動，

a publication in the journal "Nature."

包含一個科學界成功的高峰：

It worked beautifully for year after year after year,

在 "自然期刊"上發表文章。

until one year it failed.

之後的每一年，它都預測地很漂亮，

And nobody could even tell exactly why.

直到有一年它失敗了。

It just didn't work that year,

沒有人能正確地說明到底甚麼原因。

and of course that again made big news,

那一年它就是不準了，

including now a retraction

當然，又造成了一次大新聞，

of a publication from the journal "Nature."

包含現在

So even the most data-savvy companies, Amazon and Google,

被" 自然期刊 "撤銷發表的文章

they sometimes get it wrong.

所以，即使是最頂尖的數據分析公司， Amazon和Google，

And despite all those failures,

他們有時也會出錯。

data is moving rapidly into real-life decision-making --

但儘管有這些失敗，

into the workplace,

數據正快速地進入我們 實際生活上的決策、

law enforcement,

進入工作職場、

medicine.

法律執行、

So we should better make sure that data is helping.

醫藥界。

Now, personally I've seen a lot of this struggle with data myself,

所以，我們應該確保數據是有幫助的。

because I work in computational genetics,

我個人已經經歷過很多 自己在數據上的掙扎，

which is also a field where lots of very smart people

因為我在計算遺傳學界工作，

are using unimaginable amounts of data to make pretty serious decisions

這個領域有很多非常聰明的人

like deciding on a cancer therapy or developing a drug.

使用多到難以想像的數據 來制定相當嚴肅的決策，

And over the years, I've noticed a sort of pattern

像是癌症治療決策或藥物開發。

or kind of rule, if you will, about the difference

經過這幾年，我已經注意到一種模式

between successful decision-making with data

或者規則，如果你要這麼說也行，

and unsuccessful decision-making,

就是有關於用數據做出

and I find this a pattern worth sharing, and it goes something like this.

成功決策和不成功決策，

So whenever you're solving a complex problem,

我發現這個模式值得分享， 它是這樣的......

you're doing essentially two things.

當你要解決一個複雜問題時，

The first one is, you take that problem apart into its bits and pieces

本質上你會做兩件事，

so that you can deeply analyze those bits and pieces,

第一件事是，你會把問題拆分得很仔細，

and then of course you do the second part.

所以你可以深度地分析這些細節，

You put all of these bits and pieces back together again

當然你的第二件事就是，

to come to your conclusion.

你會再把這些細節拿回來整合一起，

And sometimes you have to do it over again,

來得出你要的結論。

but it's always those two things:

有時候你必須一做再做，

taking apart and putting back together again.

就這兩件事：

And now the crucial thing is

拆分、再合併一起。

that data and data analysis

但，關鍵是

is only good for the first part.

數據與數據分析

Data and data analysis, no matter how powerful,

只適用於第一步驟，

can only help you taking a problem apart and understanding its pieces.

無論數據與數據分析多麼地強大，

It's not suited to put those pieces back together again

它只能幫助你拆分問題及了解細節，

and then to come to a conclusion.

它不適用於把細節 拿回來放在一起再整合，

There's another tool that can do that, and we all have it,

來得出一個結論。

and that tool is the brain.

有一個工具可以這麼做， 而我們都擁有它，

If there's one thing a brain is good at,

那工具就是大腦。

it's taking bits and pieces back together again,

如果要說大腦有一項能力很強，

even when you have incomplete information,

那就是，它很會把事情 拆分細節後再整合一起，

and coming to a good conclusion,

即使當你有的只是不完整的資訊，

especially if it's the brain of an expert.

也能得到一個好的決策，

And that's why I believe that Netflix was so successful,

特別是專家的大腦。

because they used data and brains where they belong in the process.

而這也是為什麼我相信 Netflix會這麼成功的原因，

They use data to first understand lots of pieces about their audience

因為他們在過程中使用數據與大腦。

that they otherwise wouldn't have been able to understand at that depth,

他們利用數據， 首先了解很多觀眾的細節，

but then the decision to take all these bits and pieces

否則沒有這些數據， 他們沒有能力可以了解這麼深，

and put them back together again and make a show like "House of Cards,"

但做出拆分、整合

that was nowhere in the data.

及製作" 紙牌屋 "的

Ted Sarandos and his team made that decision to license that show,

這兩個決策，是數據中無法幫你決定的。

which also meant, by the way, that they were taking

Ted Sarandos和他的團隊做出 許可該節目的這個決策，

a pretty big personal risk with that decision.

總之，意思就是，

And Amazon, on the other hand, they did it the wrong way around.

他們在做出決策當下， 也正在承擔很大的個人風險。

They used data all the way to drive their decision-making,

而另一方面，Amazon他們把它搞砸了。

first when they held their competition of TV ideas,

他們全程依賴數據來制定決策，

then when they selected "Alpha House" to make as a show.

首先，他們舉辦節目想法的競賽，

Which of course was a very safe decision for them,

然後當他們選擇" 艾爾發屋 "來作為節目，

because they could always point at the data, saying,

當然啦，對他們而言， 這是一個非常安全的決策，

"This is what the data tells us."

因為他們總是可以指著數據說，

But it didn't lead to the exceptional results that they were hoping for.

＂這是數據告訴我們的＂

So data is of course a massively useful tool to make better decisions,

但這並沒有帶領他們到 他們所希望的傑出結果。

but I believe that things go wrong