How to use data to make a hit TV show | Sebastian Wernicke

132,856 views ・ 2016-01-27

TED


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00:12
Roy Price is a man that most of you have probably never heard about,
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even though he may have been responsible
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for 22 somewhat mediocre minutes of your life on April 19, 2013.
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He may have also been responsible for 22 very entertaining minutes,
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but not very many of you.
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And all of that goes back to a decision
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that Roy had to make about three years ago.
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So you see, Roy Price is a senior executive with Amazon Studios.
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That's the TV production company of Amazon.
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He's 47 years old, slim, spiky hair,
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describes himself on Twitter as "movies, TV, technology, tacos."
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And Roy Price has a very responsible job, because it's his responsibility
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to pick the shows, the original content that Amazon is going to make.
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And of course that's a highly competitive space.
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I mean, there are so many TV shows already out there,
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that Roy can't just choose any show.
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He has to find shows that are really, really great.
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So in other words, he has to find shows
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that are on the very right end of this curve here.
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So this curve here is the rating distribution
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of about 2,500 TV shows on the website IMDB,
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and the rating goes from one to 10,
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and the height here shows you how many shows get that rating.
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So if your show gets a rating of nine points or higher, that's a winner.
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Then you have a top two percent show.
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That's shows like "Breaking Bad," "Game of Thrones," "The Wire,"
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so all of these shows that are addictive,
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whereafter you've watched a season, your brain is basically like,
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"Where can I get more of these episodes?"
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That kind of show.
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On the left side, just for clarity, here on that end,
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you have a show called "Toddlers and Tiaras" --
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(Laughter)
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-- which should tell you enough
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about what's going on on that end of the curve.
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Now, Roy Price is not worried about getting on the left end of the curve,
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because I think you would have to have some serious brainpower
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to undercut "Toddlers and Tiaras."
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So what he's worried about is this middle bulge here,
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the bulge of average TV,
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you know, those shows that aren't really good or really bad,
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they don't really get you excited.
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So he needs to make sure that he's really on the right end of this.
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So the pressure is on,
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and of course it's also the first time
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that Amazon is even doing something like this,
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so Roy Price does not want to take any chances.
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He wants to engineer success.
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He needs a guaranteed success,
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and so what he does is, he holds a competition.
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So he takes a bunch of ideas for TV shows,
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and from those ideas, through an evaluation,
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they select eight candidates for TV shows,
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and then he just makes the first episode of each one of these shows
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and puts them online for free for everyone to watch.
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And so when Amazon is giving out free stuff,
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you're going to take it, right?
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So millions of viewers are watching those episodes.
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What they don't realize is that, while they're watching their shows,
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actually, they are being watched.
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They are being watched by Roy Price and his team,
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who record everything.
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They record when somebody presses play, when somebody presses pause,
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what parts they skip, what parts they watch again.
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So they collect millions of data points,
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because they want to have those data points
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to then decide which show they should make.
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And sure enough, so they collect all the data,
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they do all the data crunching, and an answer emerges,
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and the answer is,
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"Amazon should do a sitcom about four Republican US Senators."
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They did that show.
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So does anyone know the name of the show?
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(Audience: "Alpha House.")
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Yes, "Alpha House,"
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but it seems like not too many of you here remember that show, actually,
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because it didn't turn out that great.
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It's actually just an average show,
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actually -- literally, in fact, because the average of this curve here is at 7.4,
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and "Alpha House" lands at 7.5,
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so a slightly above average show,
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but certainly not what Roy Price and his team were aiming for.
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Meanwhile, however, at about the same time,
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at another company,
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another executive did manage to land a top show using data analysis,
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and his name is Ted,
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Ted Sarandos, who is the Chief Content Officer of Netflix,
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and just like Roy, he's on a constant mission
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to find that great TV show,
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and he uses data as well to do that,
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except he does it a little bit differently.
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So instead of holding a competition, what he did -- and his team of course --
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was they looked at all the data they already had about Netflix viewers,
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you know, the ratings they give their shows,
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the viewing histories, what shows people like, and so on.
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And then they use that data to discover
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all of these little bits and pieces about the audience:
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what kinds of shows they like,
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what kind of producers, what kind of actors.
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And once they had all of these pieces together,
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they took a leap of faith,
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and they decided to license
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not a sitcom about four Senators
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but a drama series about a single Senator.
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You guys know the show?
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(Laughter)
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Yes, "House of Cards," and Netflix of course, nailed it with that show,
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at least for the first two seasons.
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(Laughter) (Applause)
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"House of Cards" gets a 9.1 rating on this curve,
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so it's exactly where they wanted it to be.
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Now, the question of course is, what happened here?
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So you have two very competitive, data-savvy companies.
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They connect all of these millions of data points,
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and then it works beautifully for one of them,
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and it doesn't work for the other one.
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So why?
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Because logic kind of tells you that this should be working all the time.
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I mean, if you're collecting millions of data points
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on a decision you're going to make,
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then you should be able to make a pretty good decision.
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You have 200 years of statistics to rely on.
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You're amplifying it with very powerful computers.
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The least you could expect is good TV, right?
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And if data analysis does not work that way,
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then it actually gets a little scary,
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because we live in a time where we're turning to data more and more
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to make very serious decisions that go far beyond TV.
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Does anyone here know the company Multi-Health Systems?
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No one. OK, that's good actually.
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OK, so Multi-Health Systems is a software company,
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and I hope that nobody here in this room
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ever comes into contact with that software,
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because if you do, it means you're in prison.
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(Laughter)
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If someone here in the US is in prison, and they apply for parole,
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then it's very likely that data analysis software from that company
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will be used in determining whether to grant that parole.
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So it's the same principle as Amazon and Netflix,
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but now instead of deciding whether a TV show is going to be good or bad,
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you're deciding whether a person is going to be good or bad.
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And mediocre TV, 22 minutes, that can be pretty bad,
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but more years in prison, I guess, even worse.
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And unfortunately, there is actually some evidence that this data analysis,
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despite having lots of data, does not always produce optimum results.
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And that's not because a company like Multi-Health Systems
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doesn't know what to do with data.
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Even the most data-savvy companies get it wrong.
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Yes, even Google gets it wrong sometimes.
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In 2009, Google announced that they were able, with data analysis,
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to predict outbreaks of influenza, the nasty kind of flu,
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by doing data analysis on their Google searches.
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And it worked beautifully, and it made a big splash in the news,
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including the pinnacle of scientific success:
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a publication in the journal "Nature."
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It worked beautifully for year after year after year,
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until one year it failed.
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And nobody could even tell exactly why.
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It just didn't work that year,
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and of course that again made big news,
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including now a retraction
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of a publication from the journal "Nature."
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So even the most data-savvy companies, Amazon and Google,
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they sometimes get it wrong.
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And despite all those failures,
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data is moving rapidly into real-life decision-making --
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into the workplace,
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law enforcement,
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medicine.
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So we should better make sure that data is helping.
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Now, personally I've seen a lot of this struggle with data myself,
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because I work in computational genetics,
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which is also a field where lots of very smart people
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are using unimaginable amounts of data to make pretty serious decisions
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like deciding on a cancer therapy or developing a drug.
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And over the years, I've noticed a sort of pattern
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or kind of rule, if you will, about the difference
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between successful decision-making with data
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and unsuccessful decision-making,
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and I find this a pattern worth sharing, and it goes something like this.
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So whenever you're solving a complex problem,
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you're doing essentially two things.
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The first one is, you take that problem apart into its bits and pieces
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so that you can deeply analyze those bits and pieces,
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and then of course you do the second part.
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You put all of these bits and pieces back together again
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to come to your conclusion.
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And sometimes you have to do it over again,
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but it's always those two things:
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taking apart and putting back together again.
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And now the crucial thing is
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that data and data analysis
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is only good for the first part.
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Data and data analysis, no matter how powerful,
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can only help you taking a problem apart and understanding its pieces.
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It's not suited to put those pieces back together again
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and then to come to a conclusion.
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There's another tool that can do that, and we all have it,
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and that tool is the brain.
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If there's one thing a brain is good at,
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it's taking bits and pieces back together again,
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even when you have incomplete information,
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and coming to a good conclusion,
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especially if it's the brain of an expert.
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And that's why I believe that Netflix was so successful,
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because they used data and brains where they belong in the process.
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They use data to first understand lots of pieces about their audience
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that they otherwise wouldn't have been able to understand at that depth,
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but then the decision to take all these bits and pieces
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and put them back together again and make a show like "House of Cards,"
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that was nowhere in the data.
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Ted Sarandos and his team made that decision to license that show,
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which also meant, by the way, that they were taking
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a pretty big personal risk with that decision.
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And Amazon, on the other hand, they did it the wrong way around.
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They used data all the way to drive their decision-making,
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first when they held their competition of TV ideas,
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then when they selected "Alpha House" to make as a show.
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Which of course was a very safe decision for them,
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because they could always point at the data, saying,
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"This is what the data tells us."
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But it didn't lead to the exceptional results that they were hoping for.
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So data is of course a massively useful tool to make better decisions,
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but I believe that things go wrong
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when data is starting to drive those decisions.
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No matter how powerful, data is just a tool,
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and to keep that in mind, I find this device here quite useful.
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Many of you will ...
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(Laughter)
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Before there was data,
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this was the decision-making device to use.
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(Laughter)
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Many of you will know this.
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This toy here is called the Magic 8 Ball,
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and it's really amazing,
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because if you have a decision to make, a yes or no question,
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all you have to do is you shake the ball, and then you get an answer --
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"Most Likely" -- right here in this window in real time.
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I'll have it out later for tech demos.
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(Laughter)
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Now, the thing is, of course -- so I've made some decisions in my life
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where, in hindsight, I should have just listened to the ball.
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But, you know, of course, if you have the data available,
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you want to replace this with something much more sophisticated,
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like data analysis to come to a better decision.
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But that does not change the basic setup.
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So the ball may get smarter and smarter and smarter,
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but I believe it's still on us to make the decisions
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if we want to achieve something extraordinary,
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on the right end of the curve.
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And I find that a very encouraging message, in fact,
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that even in the face of huge amounts of data,
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it still pays off to make decisions,
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to be an expert in what you're doing
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and take risks.
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Because in the end, it's not data,
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it's risks that will land you on the right end of the curve.
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Thank you.
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(Applause)
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