Roy Price is a man that most of you have probably never heard about, even though he may have been responsible for 22 somewhat mediocre minutes of your life on April 19, 2013. He may have also been responsible for 22 very entertaining minutes, but not very many of you. And all of that goes back to a decision that Roy had to make about three years ago.
Roy Price這個人, 各位可能都未曾聽過, 即使他曾負責過 你生命中平凡無奇的22分鐘, 在2013年4月19日這一天。 他也許也曾負責帶給 各位非常歡樂的22分鐘, 但你們其中也許很多人並沒有。 而這一切全部要回到 Roy在三年前的一個決定。
So you see, Roy Price is a senior executive with Amazon Studios. That's the TV production company of Amazon. He's 47 years old, slim, spiky hair, describes himself on Twitter as "movies, TV, technology, tacos." And Roy Price has a very responsible job, because it's his responsibility to pick the shows, the original content that Amazon is going to make. And of course that's a highly competitive space. I mean, there are so many TV shows already out there, that Roy can't just choose any show. He has to find shows that are really, really great. So in other words, he has to find shows that are on the very right end of this curve here.
所以,你明白,Roy Price是 Amazon廣播公司的一位資深決策者。 這是Amazon旗下的一家 電視節目製作公司。 他47歲,身材不錯,尖頭髮, 在Twitter上形容自己是 “電影、電視、科技、墨西哥捲餅 。” Roy Price有一個 責任非常重大的工作, 因為他要負責幫Amazon挑選 即將製作的原創內容節目。 當然,這是高度競爭的領域。 我的意思是, 外面已經有那麼多的電視節目, Roy不能隨便亂挑一個節目。 他必須找出真正、 真正很讚的節目。 換句話說, 他必須從這條曲線上的右邊挑選節目。
So this curve here is the rating distribution of about 2,500 TV shows on the website IMDB, and the rating goes from one to 10, and the height here shows you how many shows get that rating. So if your show gets a rating of nine points or higher, that's a winner. Then you have a top two percent show. That's shows like "Breaking Bad," "Game of Thrones," "The Wire," so all of these shows that are addictive, whereafter you've watched a season, your brain is basically like, "Where can I get more of these episodes?" That kind of show. On the left side, just for clarity, here on that end, you have a show called "Toddlers and Tiaras" --
這條曲線是 IMDB網路電影資料庫裡 2500個電視節目的 客戶評分分布圖, 評分從 1到10, 最高的地方代表 有多少節目達到這個評分。 所以如果你的節目達到 9分或更高, 你就是贏家。 你就是那百分之二的頂尖節目。 例如像是" 絕命毒師 、 權力遊戲、火線重案組 " 全部都是會讓你上癮的節目, 看完一季之後,你的大腦基本上像是 ... " 我要去哪裡找到更多這部片的影集? " 等等這類的節目。 左邊末端,很明顯地, 你們有個叫" 小小姐與后冠 "的節目
(Laughter)
(笑聲)
-- which should tell you enough about what's going on on that end of the curve.
一個足夠讓你明白 為什麼它會在曲線末端的節目。
Now, Roy Price is not worried about getting on the left end of the curve, because I think you would have to have some serious brainpower to undercut "Toddlers and Tiaras." So what he's worried about is this middle bulge here, the bulge of average TV, you know, those shows that aren't really good or really bad, they don't really get you excited. So he needs to make sure that he's really on the right end of this.
現在,Roy Price不擔心 在曲線左邊末端的節目。 因為我認為你們都會想 有一些嚴肅的判斷力 來降低" 小小姐與后冠 "的評分 。 所以,他擔心的是中間多數的這些節目, 多到爆的這些一般性電視節目, 你知道,這些節目 既不是很好也不是很壞, 它們不會真正地讓你興奮。 所以他要確保他真的 是在右邊的末端這裡,
So the pressure is on, and of course it's also the first time that Amazon is even doing something like this, so Roy Price does not want to take any chances. He wants to engineer success. He needs a guaranteed success, and so what he does is, he holds a competition.
所以,壓力就來了, 所以當然,這也是第一次 Amazon 也想要做類似這樣的事情, Roy Price不想冒風險, 他想要建造成功, 他要一個保證的成功, 所以他就舉辦一個比賽。
So he takes a bunch of ideas for TV shows, and from those ideas, through an evaluation, they select eight candidates for TV shows, and then he just makes the first episode of each one of these shows and puts them online for free for everyone to watch. And so when Amazon is giving out free stuff, you're going to take it, right? So millions of viewers are watching those episodes.
他為電視節目帶來了很多想法, 並且透過一個評估,形塑這些想法, 他們為電視節目挑選了八個候選名單, 然後他製作每一個節目的第一集, 然後把他們放到網路上, 讓每個人免費觀看。 所以當Amazon要給你免費的東西時, 你就會拿,對吧? 所以上百萬人在看這些影集,
What they don't realize is that, while they're watching their shows, actually, they are being watched. They are being watched by Roy Price and his team, who record everything. They record when somebody presses play, when somebody presses pause, what parts they skip, what parts they watch again. So they collect millions of data points, because they want to have those data points to then decide which show they should make. And sure enough, so they collect all the data, they do all the data crunching, and an answer emerges, and the answer is, "Amazon should do a sitcom about four Republican US Senators." They did that show.
而這些人不明白的是, 當他們在觀看節目的時候, 實際上他們也正被觀查中。 他們被Roy Price及他的團隊觀查, 他們紀錄了每一件事。 他們紀錄了,那些人按了撥放, 那些人按了暫停, 那些部分他們跳過, 那些部分他們又重看一遍。 所以他們收集了上百萬的數據資料, 因為他們想要用這些數據資料來決定 要做甚麼樣的節目。 確定好後,他們收集所有的數據, 他們做完所有數據處理後, 得到一個答案, 而答案就是, " Amazon需要製作一個有關 美國共和黨參議員的喜劇 "。 他們做了,
So does anyone know the name of the show? (Audience: "Alpha House.") Yes, "Alpha House," but it seems like not too many of you here remember that show, actually, because it didn't turn out that great. It's actually just an average show, actually -- literally, in fact, because the average of this curve here is at 7.4, and "Alpha House" lands at 7.5, so a slightly above average show, but certainly not what Roy Price and his team were aiming for. Meanwhile, however, at about the same time, at another company, another executive did manage to land a top show using data analysis, and his name is Ted, Ted Sarandos, who is the Chief Content Officer of Netflix, and just like Roy, he's on a constant mission to find that great TV show, and he uses data as well to do that, except he does it a little bit differently. So instead of holding a competition, what he did -- and his team of course -- was they looked at all the data they already had about Netflix viewers, you know, the ratings they give their shows, the viewing histories, what shows people like, and so on. And then they use that data to discover all of these little bits and pieces about the audience: what kinds of shows they like, what kind of producers, what kind of actors. And once they had all of these pieces together, they took a leap of faith, and they decided to license not a sitcom about four Senators but a drama series about a single Senator. You guys know the show?
有人知道這個節目嗎? (觀眾:" 艾爾發屋 ") 是的," 艾爾發屋 " 但實際上,你們大部人 應該不記得有這部片子, 因為這部片並不那麼賣座。 它實際上僅是一般的節目, 實際上,一般的節目差不多 坐落在曲線上的 7.4分, 而" 艾爾發房屋 "落在7.5分, 所以比一般的節目高一點點, 但絕對不是Roy Price與 他的團隊所要達到的目標。 這時,然而,同一時間, 另一家公司的另一個決策者, 用同樣的數據分析做了一個頂尖的節目, 他的名字是 Ted, Ted Sarandos是Netflix的 首席節目內容決策者, 就跟 Roy一樣,他也要不停的找 最棒的節目, 而他也使用數據來這樣做, 但他的做法,有點不太一樣。 不是舉辦比賽,當然,他和他的團隊 也有觀察Netflix已經有的觀眾數據, 觀眾對節目的評分、觀看紀錄、 那些節目是人們喜歡的等等, 他們也使用數據去發掘 觀眾所有的小細節: 他們喜歡甚麼類型的節目、 甚麼類型的製作人、甚麼類型的演員, 一旦他們收集全部的細節後, 他們很有信心地 決定要製作一部, 不是四個參議員的喜劇, 而是一系列有關一位 單身參議員的戲劇。 各位知道那個節目嗎?
(Laughter)
(笑聲)
Yes, "House of Cards," and Netflix of course, nailed it with that show, at least for the first two seasons.
是的," 纸牌屋 ",Netflix ,當然, 至少頭二季,用這節目盯住那個分數。
(Laughter) (Applause)
(笑聲)(掌聲)
"House of Cards" gets a 9.1 rating on this curve, so it's exactly where they wanted it to be.
" 纸牌屋 "在這曲線上拿到 9.1分, 這當然是他們想要的。
Now, the question of course is, what happened here? So you have two very competitive, data-savvy companies. They connect all of these millions of data points, and then it works beautifully for one of them, and it doesn't work for the other one. So why? Because logic kind of tells you that this should be working all the time. I mean, if you're collecting millions of data points on a decision you're going to make, then you should be able to make a pretty good decision. You have 200 years of statistics to rely on. You're amplifying it with very powerful computers. The least you could expect is good TV, right?
現在,當然問題就是 這到底是怎麼一回事? 你有兩個非常有競爭力、 精通數據資料的公司。 他們連結了所有的數據資料, 然後,其中一個做的很漂亮, 而另一個卻沒有, 為什麼? 因為邏輯上告訴你, 這應該每次都有效啊, 我的意思是, 如果你收集了所有的數據資料 來決定一個決策, 那你應該可以得到一個 相當不錯的決策。 你有 200年的統計數據做後盾, 你用很強大的電腦去增強它, 至少你可以期待到一個 好的電視節目,對吧?
And if data analysis does not work that way, then it actually gets a little scary, because we live in a time where we're turning to data more and more to make very serious decisions that go far beyond TV. Does anyone here know the company Multi-Health Systems? No one. OK, that's good actually. OK, so Multi-Health Systems is a software company, and I hope that nobody here in this room ever comes into contact with that software, because if you do, it means you're in prison.
但如果數據分析 並沒有想像中的有效, 那,這真的有點恐怖, 因為我們正轉向一個 數據越來越多的時代, 來做出遠比電視節目 還要嚴肅的決策。 你們當中有人知道" MHS "這家公司嗎? 沒人?好,這樣很好, 好的,MHS是一家軟體公司, 而我希望在座的各位, 沒有人與這個軟體有牽連, 因為如果你有,代表你在監獄中
(Laughter)
(笑聲)
If someone here in the US is in prison, and they apply for parole, then it's very likely that data analysis software from that company will be used in determining whether to grant that parole. So it's the same principle as Amazon and Netflix, but now instead of deciding whether a TV show is going to be good or bad, you're deciding whether a person is going to be good or bad. And mediocre TV, 22 minutes, that can be pretty bad, but more years in prison, I guess, even worse.
在美國這裡如果有人被判入監, 然後要申請假釋, 很有可能那家公司的數據分析軟體 會被用來判定是否能獲得假釋。 所以,它也是採用 Amazon 和 Netflix 公司相同的原則, 但不同的是, 他們是用來決定電視節目將來的好壞, 你是用來決定一個人將來的好壞, 表現普通22分鐘的電視節目,很糟糕, 但,我猜,要做更多年的牢,更糟糕。
And unfortunately, there is actually some evidence that this data analysis, despite having lots of data, does not always produce optimum results. And that's not because a company like Multi-Health Systems doesn't know what to do with data. Even the most data-savvy companies get it wrong. Yes, even Google gets it wrong sometimes.
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, 它並不總是跑出適當的結果。 但並不只有像是MHS這樣的軟體公司 不明白數據怎麼了, 甚至最頂尖的數據公司也會出錯, 是的,甚至Google有時也會出錯。
In 2009, Google announced that they were able, with data analysis, to predict outbreaks of influenza, the nasty kind of flu, by doing data analysis on their Google searches. And it worked beautifully, and it made a big splash in the news, including the pinnacle of scientific success: a publication in the journal "Nature." It worked beautifully for year after year after year, until one year it failed. And nobody could even tell exactly why. It just didn't work that year, and of course that again made big news, including now a retraction of a publication from the journal "Nature." So even the most data-savvy companies, Amazon and Google, they sometimes get it wrong. And despite all those failures, data is moving rapidly into real-life decision-making -- into the workplace, law enforcement, medicine. So we should better make sure that data is helping.
2009年,Google宣布他們可以用數據分析, 來預測流行性感冒,討人厭的流感, 經由他們的Google搜尋引擎來做數據分析。 而且它準確無比,當時造成一股新聞的轟動, 包含一個科學界成功的高峰: 在 "自然期刊"上發表文章。 之後的每一年,它都預測地很漂亮, 直到有一年它失敗了。 沒有人能正確地說明到底甚麼原因。 那一年它就是不準了, 當然,又造成了一次大新聞, 包含現在 被" 自然期刊 "撤銷發表的文章 所以,即使是最頂尖的數據分析公司, Amazon和Google, 他們有時也會出錯。 但儘管有這些失敗, 數據正快速地進入我們 實際生活上的決策、 進入工作職場、 法律執行、 醫藥界。 所以,我們應該確保數據是有幫助的。
Now, personally I've seen a lot of this struggle with data myself, because I work in computational genetics, which is also a field where lots of very smart people are using unimaginable amounts of data to make pretty serious decisions like deciding on a cancer therapy or developing a drug. And over the years, I've noticed a sort of pattern or kind of rule, if you will, about the difference between successful decision-making with data and unsuccessful decision-making, and I find this a pattern worth sharing, and it goes something like this.
我個人已經經歷過很多 自己在數據上的掙扎, 因為我在計算遺傳學界工作, 這個領域有很多非常聰明的人 使用多到難以想像的數據 來制定相當嚴肅的決策, 像是癌症治療決策或藥物開發。 經過這幾年,我已經注意到一種模式 或者規則,如果你要這麼說也行, 就是有關於用數據做出 成功決策和不成功決策, 我發現這個模式值得分享, 它是這樣的......
So whenever you're solving a complex problem, you're doing essentially two things. The first one is, you take that problem apart into its bits and pieces so that you can deeply analyze those bits and pieces, and then of course you do the second part. You put all of these bits and pieces back together again to come to your conclusion. And sometimes you have to do it over again, but it's always those two things: taking apart and putting back together again.
當你要解決一個複雜問題時, 本質上你會做兩件事, 第一件事是,你會把問題拆分得很仔細, 所以你可以深度地分析這些細節, 當然你的第二件事就是, 你會再把這些細節拿回來整合一起, 來得出你要的結論。 有時候你必須一做再做, 就這兩件事: 拆分、再合併一起。
And now the crucial thing is that data and data analysis is only good for the first part. Data and data analysis, no matter how powerful, can only help you taking a problem apart and understanding its pieces. It's not suited to put those pieces back together again and then to come to a conclusion. There's another tool that can do that, and we all have it, and that tool is the brain. If there's one thing a brain is good at, it's taking bits and pieces back together again, even when you have incomplete information, and coming to a good conclusion, especially if it's the brain of an expert.
但,關鍵是 數據與數據分析 只適用於第一步驟, 無論數據與數據分析多麼地強大, 它只能幫助你拆分問題及了解細節, 它不適用於把細節 拿回來放在一起再整合, 來得出一個結論。 有一個工具可以這麼做, 而我們都擁有它, 那工具就是大腦。 如果要說大腦有一項能力很強, 那就是,它很會把事情 拆分細節後再整合一起, 即使當你有的只是不完整的資訊, 也能得到一個好的決策, 特別是專家的大腦。
And that's why I believe that Netflix was so successful, because they used data and brains where they belong in the process. They use data to first understand lots of pieces about their audience that they otherwise wouldn't have been able to understand at that depth, but then the decision to take all these bits and pieces and put them back together again and make a show like "House of Cards," that was nowhere in the data. Ted Sarandos and his team made that decision to license that show, which also meant, by the way, that they were taking a pretty big personal risk with that decision. And Amazon, on the other hand, they did it the wrong way around. They used data all the way to drive their decision-making, first when they held their competition of TV ideas, then when they selected "Alpha House" to make as a show. Which of course was a very safe decision for them, because they could always point at the data, saying, "This is what the data tells us." But it didn't lead to the exceptional results that they were hoping for.
而這也是為什麼我相信 Netflix會這麼成功的原因, 因為他們在過程中使用數據與大腦。 他們利用數據, 首先了解很多觀眾的細節, 否則沒有這些數據, 他們沒有能力可以了解這麼深, 但做出拆分、整合 及製作" 紙牌屋 "的 這兩個決策,是數據中無法幫你決定的。 Ted Sarandos和他的團隊做出 許可該節目的這個決策, 總之,意思就是, 他們在做出決策當下, 也正在承擔很大的個人風險。 而另一方面,Amazon他們把它搞砸了。 他們全程依賴數據來制定決策, 首先,他們舉辦節目想法的競賽, 然後當他們選擇" 艾爾發屋 "來作為節目, 當然啦,對他們而言, 這是一個非常安全的決策, 因為他們總是可以指著數據說, "這是數據告訴我們的" 但這並沒有帶領他們到 他們所希望的傑出結果。
So data is of course a massively useful tool to make better decisions, but I believe that things go wrong when data is starting to drive those decisions. No matter how powerful, data is just a tool, and to keep that in mind, I find this device here quite useful. Many of you will ...
所以,數據當然是做決策時的 一個強大的工具, 但我相信,當數據開始主導這些決策時, 事情也會開始出錯。 不管它有多麼的強大, 數據僅是一個工具, 並把這個記在腦裡, 我發現這個裝置相當有用。 你們很多人將會 ...
(Laughter)
(笑聲)
Before there was data, this was the decision-making device to use.
在有數據之前, 這就是用來做決策的工具
(Laughter)
(笑聲)
Many of you will know this. This toy here is called the Magic 8 Ball, and it's really amazing, because if you have a decision to make, a yes or no question, all you have to do is you shake the ball, and then you get an answer -- "Most Likely" -- right here in this window in real time. I'll have it out later for tech demos.
你們很多人應該知道這個玩意。 這個玩具在這裡稱做"魔術 8號球", 它真的很奇妙, 因為如果你要做一個 "是或不是"的決策時, 你只要搖一搖這顆球, 然後你就可以得到答案了-- "很有可能是"-- 就在這視窗裡及時顯現給你看, 我會帶它去做技術示範。
(Laughter)
(笑聲)
Now, the thing is, of course -- so I've made some decisions in my life where, in hindsight, I should have just listened to the ball. But, you know, of course, if you have the data available, you want to replace this with something much more sophisticated, like data analysis to come to a better decision. But that does not change the basic setup. So the ball may get smarter and smarter and smarter, but I believe it's still on us to make the decisions if we want to achieve something extraordinary, on the right end of the curve. And I find that a very encouraging message, in fact, that even in the face of huge amounts of data, it still pays off to make decisions, to be an expert in what you're doing and take risks. Because in the end, it's not data, it's risks that will land you on the right end of the curve.
事情是,當然啦 -- 我已經在我人生中做出一些決定, 但早知道,我就應該聽這顆球的話。 但,當然,如果你有有效的數據, 你想要用超複雜的方式來取代這顆球, 例如,用數據分析來得到更好的決策。 但這無法改變基本的設定, 所以這球會越來越聰明, 但我相信,如果我們想達成某些 曲線右邊末端的非凡成就, 最後我們自己還是得做出決定, 事實上,我發現 一個非常激勵人心的訊息, 即使面對龐大的數據, 你仍會有很大的收穫, 在你做出決策、 變成一位該領域的專家 並承擔風險時。 因為,最後,不是數據, 是風險會帶你來到曲線的右邊末端。
Thank you.
謝謝各位。
(Applause)
(掌聲)