TEDxCambridge
Sebastian Wernicke: How to use data to make a hit TV show
Sebastijan Vernike (Sebastian Wernicke): Kako da koristite podatke da biste napravili uspešnu TV seriju
Filmed:
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Da li prikupljanje što većeg broja podataka vodi boljem odlučivanju? Konkurentne kompanije, vične s podacima, poput Amazona, Gugla i Netfliksa su naučile da sama obrada podataka uvek ne daje optimalne rezultate. U ovom govoru, naučnik za podatke, Sebastijan Vernike, nam obrazlaže gde grešimo kada donosio odluke zasnovane isključivo na podacima - i predlaže razumniji način kako da ih koristimo.
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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Roj Prajs je čovek za koga većina vas
verovatno nije nikad čula,
verovatno nije nikad čula,
00:17
even though he may have been responsible
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iako je možda odgovoran
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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za 22 prilično osrednja minuta
vašeg života, 19. aprila 2013.
vašeg života, 19. aprila 2013.
00:26
He may have also been responsible
for 22 very entertaining minutes,
for 22 very entertaining minutes,
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Možda je takođe odgovoran
za 22 veoma zabavna minuta,
za 22 veoma zabavna minuta,
00:29
but not very many of you.
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ali za mali broj vas.
00:32
And all of that goes back to a decision
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A sve se svodi na odluku
koju je Roj morao da donese
pre oko tri godine.
pre oko tri godine.
00:33
that Roy had to make
about three years ago.
about three years ago.
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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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Dakle, vidite, Roj Prajs
je viši producent u Amazon Studios.
je viši producent u Amazon Studios.
00:40
That's the TV production
company of Amazon.
company of Amazon.
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To je Amazonova TV produkcijska firma.
00:43
He's 47 years old, slim, spiky hair,
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On ima 47 godina, vitak je,
ima jež frizuru,
ima jež frizuru,
00:47
describes himself on Twitter
as "movies, TV, technology, tacos."
as "movies, TV, technology, tacos."
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sebe opisuje na Tviteru kao nekog ko voli
"filmove, TV, tehnologiju i takose".
"filmove, TV, tehnologiju i takose".
00:52
And Roy Price has a very responsible job,
because it's his responsibility
because it's his responsibility
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A Roj Prajs ima veoma odgovoran posao
jer je njegova odgovornost
jer je njegova odgovornost
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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da izabere serije, originalne sadržine,
koje će Amazon da snima.
koje će Amazon da snima.
01:01
And of course that's
a highly competitive space.
a highly competitive space.
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I naravno, to je izuzetno
konkurentan prostor.
konkurentan prostor.
01:03
I mean, there are so many
TV shows already out there,
TV shows already out there,
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Mislim, već postoji toliko TV serija
01:06
that Roy can't just choose any show.
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te Roj ne može prosto
da izabere bilo koju.
da izabere bilo koju.
01:08
He has to find shows
that are really, really great.
that are really, really great.
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Mora da pronađe serije
koje su zaista, zaista sjajne.
koje su zaista, zaista sjajne.
01:12
So in other words, he has to find shows
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Drugim rečima, mora da pronađe serije
01:15
that are on the very right end
of this curve here.
of this curve here.
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koje su na samom desnom kraju
ove krive ovde.
ove krive ovde.
01:17
So this curve here
is the rating distribution
is the rating distribution
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Dakle, ova kriva ovde
predstavlja raspodelu ocena
predstavlja raspodelu ocena
01:20
of about 2,500 TV shows
on the website IMDB,
on the website IMDB,
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oko 2,500 TV serija sa vebsajta IMDB,
01:25
and the rating goes from one to 10,
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a ocene se kreću od jedan do deset,
01:27
and the height here shows you
how many shows get that rating.
how many shows get that rating.
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a visina krive vam pokazuje
koliko je serija imalo tu ocenu.
koliko je serija imalo tu ocenu.
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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Pa, ako vaša serija dobije ocenu
od devet poena ili više, to je pobednik.
od devet poena ili više, to je pobednik.
01:35
Then you have a top two percent show.
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Onda imate seriju iz prvih dva posto.
01:37
That's shows like "Breaking Bad,"
"Game of Thrones," "The Wire,"
"Game of Thrones," "The Wire,"
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To su serije, poput: "Čiste hemije",
"Igre prestola", "Žice",
"Igre prestola", "Žice",
01:41
so all of these shows that are addictive,
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dakle, sve te serije
koje vam uđu pod kožu,
koje vam uđu pod kožu,
01:43
whereafter you've watched a season,
your brain is basically like,
your brain is basically like,
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gde nakon što ste odgledali prvu sezonu,
vaš mozak je u fazonu:
vaš mozak je u fazonu:
01:46
"Where can I get more of these episodes?"
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"Gde da nađem još ovih epizoda?"
01:49
That kind of show.
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Taj tip serije.
01:50
On the left side, just for clarity,
here on that end,
here on that end,
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Na levoj strani, čisto da bude jasno,
ovde na kraju,
ovde na kraju,
01:53
you have a show called
"Toddlers and Tiaras" --
"Toddlers and Tiaras" --
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imate seriju koja se zove
"Devojčice i dijademe" -
"Devojčice i dijademe" -
01:56
(Laughter)
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(Smeh)
01:59
-- which should tell you enough
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- to vam dovoljno govori
02:00
about what's going on
on that end of the curve.
on that end of the curve.
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o tome šta se dešava na tom kraju krive.
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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E sad, Roja Prajsa ne brine
da će završiti na levoj strani krive
da će završiti na levoj strani krive
02:07
because I think you would have to have
some serious brainpower
some serious brainpower
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jer mislim da morate da posedujete
prilično ozbiljne umne moći
prilično ozbiljne umne moći
da podrijete "Devojčice i dijademe".
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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Dakle, njega brine
ovo središnje ispupčenje ovde,
ovo središnje ispupčenje ovde,
02:15
the bulge of average TV,
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tu su prosečne TV serije,
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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znate, one serije
koje nisu naročito dobre, ni loše,
koje nisu naročito dobre, ni loše,
zbog njih se ne uzbuđujete previše.
02:20
they don't really get you excited.
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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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Pa mora da se postara
da zaista bude na desnom kraju ovoga.
da zaista bude na desnom kraju ovoga.
02:27
So the pressure is on,
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Dakle, prisutan je pritisak
02:28
and of course it's also the first time
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i, naravno, takođe je prvi put
02:31
that Amazon is even
doing something like this,
doing something like this,
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da Amazon uopšte radi nešto slično,
02:33
so Roy Price does not want
to take any chances.
to take any chances.
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pa Roj Prajs ne želi da rizikuje.
02:36
He wants to engineer success.
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Želi da isplanira uspeh.
02:39
He needs a guaranteed success,
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Potreban mu je zagarantovan uspeh,
02:40
and so what he does is,
he holds a competition.
he holds a competition.
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pa on organizuje takmičenje.
02:43
So he takes a bunch of ideas for TV shows,
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Uzima gomilu ideja za TV serije
02:46
and from those ideas,
through an evaluation,
through an evaluation,
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i od tih ideja, kroz procenu,
02:48
they select eight candidates for TV shows,
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biraju osam kandidata za TV serije,
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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a potom samo snimaju prvu epizodu
svake od ovih serija
svake od ovih serija
02:56
and puts them online for free
for everyone to watch.
for everyone to watch.
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i postavljaju ih besplatno na internet
gde ih svako može gledati.
gde ih svako može gledati.
02:59
And so when Amazon
is giving out free stuff,
is giving out free stuff,
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A kada Amazon poklanja nešto,
03:01
you're going to take it, right?
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uzećete to, zar ne?
03:03
So millions of viewers
are watching those episodes.
are watching those episodes.
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Dakle, milioni gledalaca
gledaju ove epizode.
gledaju ove epizode.
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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Međutim, ne shvataju
da dok gledaju svoje serije,
da dok gledaju svoje serije,
03:11
actually, they are being watched.
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zapravo njih gledaju.
03:14
They are being watched
by Roy Price and his team,
by Roy Price and his team,
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Posmatraju ih Roj Prajs i njegova ekipa,
03:16
who record everything.
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koji sve snimaju.
03:17
They record when somebody presses play,
when somebody presses pause,
when somebody presses pause,
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Snimaju kad neko pritisne start,
kad neko pritisne pauzu,
kad neko pritisne pauzu,
03:21
what parts they skip,
what parts they watch again.
what parts they watch again.
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koje delove preskaču,
koje delove iznova gledaju.
koje delove iznova gledaju.
03:23
So they collect millions of data points,
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Tako su sakupili milione jedinica podataka
03:26
because they want
to have those data points
to have those data points
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jer su im potrebne te jedinice podataka
03:28
to then decide
which show they should make.
which show they should make.
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kako bi potom odlučili
koju će seriju snimati.
koju će seriju snimati.
03:30
And sure enough,
so they collect all the data,
so they collect all the data,
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I svakako, sakupili su sve podatke,
03:33
they do all the data crunching,
and an answer emerges,
and an answer emerges,
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usitnili su sve podatke
i pojavio se odgovor,
i pojavio se odgovor,
03:35
and the answer is,
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a odgovor je:
03:36
"Amazon should do a sitcom
about four Republican US Senators."
about four Republican US Senators."
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"Amazon bi trebalo da snimi sitkom
o četiri američka republikanska senatora."
o četiri američka republikanska senatora."
03:42
They did that show.
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Snimili su tu seriju.
03:43
So does anyone know the name of the show?
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Da li iko zna naziv te serije?
03:46
(Audience: "Alpha House.")
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(Publika: "Alpha House.")
03:48
Yes, "Alpha House,"
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Da, "Alpha House",
03:49
but it seems like not too many of you here
remember that show, actually,
remember that show, actually,
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ali čini se da se nekolicina vas ovde
zapravo seća te serije
zapravo seća te serije
03:53
because it didn't turn out that great.
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jer nije ispala naročito dobro.
03:55
It's actually just an average show,
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Zapravo se radi o prosečnoj seriji,
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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zapravo - bukvalno, uistinu, jer
prosečna vrednost na ovoj krivoj je 7,4,
prosečna vrednost na ovoj krivoj je 7,4,
04:02
and "Alpha House" lands at 7.5,
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a "Alpha House" se nalazi na 7,5,
04:04
so a slightly above average show,
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dakle, samo malo iznad prosečne serije,
04:06
but certainly not what Roy Price
and his team were aiming for.
and his team were aiming for.
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ali svakako to nije ono što su
Roj i njegova ekipa želeli.
Roj i njegova ekipa želeli.
04:10
Meanwhile, however,
at about the same time,
at about the same time,
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U međuvremenu, međutim,
otprilike istovremeno,
otprilike istovremeno,
04:13
at another company,
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u drugoj firmi,
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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drugi producent je uspeo da isporuči
vrhunsku seriju, obradom podataka,
vrhunsku seriju, obradom podataka,
04:19
and his name is Ted,
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a on se zove Ted,
04:20
Ted Sarandos, who is
the Chief Content Officer of Netflix,
the Chief Content Officer of Netflix,
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Ted Sarantos, on je
glavni referent sadržaja na Netfliksu,
glavni referent sadržaja na Netfliksu,
04:24
and just like Roy,
he's on a constant mission
he's on a constant mission
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i baš kao i Roj, on je stalno na misiji
04:26
to find that great TV show,
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pronalaženja sjajne TV serije,
04:27
and he uses data as well to do that,
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i on koristi podatke da bi to postigao,
04:29
except he does it
a little bit differently.
a little bit differently.
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samo što to on radi malčice drugačije.
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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Pa su umesto organizovanja takmičenja,
on - i naravno njegova ekipa -
on - i naravno njegova ekipa -
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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pogledali sve podatke
koje su već imali o gledaocima Netfliksa,
koje su već imali o gledaocima Netfliksa,
04:39
you know, the ratings
they give their shows,
they give their shows,
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znate, ocene koje daju serijama,
04:41
the viewing histories,
what shows people like, and so on.
what shows people like, and so on.
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istoriju gledanja,
koje serije ljudi vole i tako dalje.
koje serije ljudi vole i tako dalje.
A onda su koristili te podatke da otkriju
04:44
And then they use that data to discover
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04:45
all of these little bits and pieces
about the audience:
about the audience:
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sve te sitnice i pojedinosti o publici:
04:48
what kinds of shows they like,
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koje serije vole,
04:50
what kind of producers,
what kind of actors.
what kind of actors.
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koje producente, koje glumce.
04:52
And once they had
all of these pieces together,
all of these pieces together,
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I kada su sklopili sve te komadiće,
04:54
they took a leap of faith,
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otisnuli su se u nepoznato
04:56
and they decided to license
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i odlučili da odobre,
04:58
not a sitcom about four Senators
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ne sitkom o četvorici senatora,
05:01
but a drama series about a single Senator.
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već dramu o jednom senatoru.
05:04
You guys know the show?
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Znate li, ljudi, tu seriju?
05:06
(Laughter)
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(Smeh)
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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Da, "Kuća od karata" i Netfliks
je naravno trijumfovao tom serijom,
je naravno trijumfovao tom serijom,
05:11
at least for the first two seasons.
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bar tokom prve dve sezone.
05:13
(Laughter) (Applause)
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(Smeh) (Aplauz)
05:17
"House of Cards" gets
a 9.1 rating on this curve,
a 9.1 rating on this curve,
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"Kuća od karata" ima ocenu
od 9,1 na ovoj krivoj,
od 9,1 na ovoj krivoj,
05:20
so it's exactly
where they wanted it to be.
where they wanted it to be.
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dakle, tu su i želeli da stignu.
05:24
Now, the question of course is,
what happened here?
what happened here?
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E sad, pitanje je naravno,
šta se ovde desilo?
šta se ovde desilo?
05:26
So you have two very competitive,
data-savvy companies.
data-savvy companies.
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Imate dve veoma konkurentne firme,
spretne s podacima.
spretne s podacima.
05:29
They connect all of these
millions of data points,
millions of data points,
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One spajaju sve ove
milione jedinica podataka,
milione jedinica podataka,
05:32
and then it works
beautifully for one of them,
beautifully for one of them,
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a onda se za jednu sve završi lepo,
05:34
and it doesn't work for the other one.
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a za drugu ne.
05:36
So why?
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Zašto?
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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Jer, logika vam govori
da bi ovo trebalo stalno da funkcioniše.
da bi ovo trebalo stalno da funkcioniše.
05:41
I mean, if you're collecting
millions of data points
millions of data points
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Mislim, ako sakupljate milione
jedinica podataka
jedinica podataka
05:43
on a decision you're going to make,
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za odluku koju donosite,
onda bi trebalo da ste u stanju
da donesete valjanu odluku.
da donesete valjanu odluku.
05:45
then you should be able
to make a pretty good decision.
to make a pretty good decision.
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05:47
You have 200 years
of statistics to rely on.
of statistics to rely on.
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Imate da se oslonite
na 200-godišnju statistiku.
na 200-godišnju statistiku.
05:50
You're amplifying it
with very powerful computers.
with very powerful computers.
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Nju ste pojačali
izuzetno moćnim kompjuterima.
izuzetno moćnim kompjuterima.
05:53
The least you could expect
is good TV, right?
is good TV, right?
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Najmanje što biste očekivali
je dobra televizija, zar ne?
je dobra televizija, zar ne?
05:57
And if data analysis
does not work that way,
does not work that way,
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A, ako obrada podataka
ne funkcioniše tako,
ne funkcioniše tako,
06:01
then it actually gets a little scary,
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onda zapravo postaje malčice zastrašujuće
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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jer živimo u vremenu
u kom se sve više okrećemo podacima
u kom se sve više okrećemo podacima
06:07
to make very serious decisions
that go far beyond TV.
that go far beyond TV.
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da bismo doneli veoma ozbiljne odluke
koje sežu mimo televizije.
koje sežu mimo televizije.
06:12
Does anyone here know the company
Multi-Health Systems?
Multi-Health Systems?
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Da li iko ovde zna za firmu
Multi-Health Systems?
Multi-Health Systems?
06:17
No one. OK, that's good actually.
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Niko. U redu, to je zapravo dobro.
06:18
OK, so Multi-Health Systems
is a software company,
is a software company,
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U redu, Multi-Health Systems
je softverska firma
je softverska firma
06:22
and I hope that nobody here in this room
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i nadam se da niko iz ove prostorije
06:24
ever comes into contact
with that software,
with that software,
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nikada neće doći u dodir s tim softverom
jer ako dođete,
to će značiti da ste u zatvoru.
to će značiti da ste u zatvoru.
06:28
because if you do,
it means you're in prison.
it means you're in prison.
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06:30
(Laughter)
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(Smeh)
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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Ako je neko, ovde u SAD-u, u zatvoru
i prijavi se za uslovnu,
i prijavi se za uslovnu,
06:34
then it's very likely that
data analysis software from that company
data analysis software from that company
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onda će verovatno
softver te firme za obradu podataka
softver te firme za obradu podataka
06:39
will be used in determining
whether to grant that parole.
whether to grant that parole.
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da koriste kako bi utvrdili
da li da vam odobre uslovnu.
da li da vam odobre uslovnu.
06:42
So it's the same principle
as Amazon and Netflix,
as Amazon and Netflix,
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Dakle, isti je princip
kao kod Amazona i Nefliksa,
kao kod Amazona i Nefliksa,
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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ali sad umesto odlučivanja o tome
da li će serija da bude dobra ili loša,
da li će serija da bude dobra ili loša,
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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odlučuje se da li će osoba
da bude dobra ili loša.
da bude dobra ili loša.
06:53
And mediocre TV, 22 minutes,
that can be pretty bad,
that can be pretty bad,
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A prosečna televizijska 22 minuta
mogu da budu vrlo loša,
mogu da budu vrlo loša,
06:58
but more years in prison,
I guess, even worse.
I guess, even worse.
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ali dodatne godine u zatvoru
su, valjda, još gore.
su, valjda, još gore.
07:02
And unfortunately, there is actually
some evidence that this data analysis,
some evidence that this data analysis,
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I nažalost, zapravo imamo neke dokaze
da ova obrada podataka,
da ova obrada podataka,
07:06
despite having lots of data,
does not always produce optimum results.
does not always produce optimum results.
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uprkos velikom broju podataka,
ne daje uvek optimalne rezultate.
ne daje uvek optimalne rezultate.
07:10
And that's not because a company
like Multi-Health Systems
like Multi-Health Systems
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A to nije zato što firma,
poput Multi-Health Systems
poput Multi-Health Systems
07:13
doesn't know what to do with data.
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ne zna šta da radi s podacima.
07:15
Even the most data-savvy
companies get it wrong.
companies get it wrong.
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Čak i firme
koje su stručnjaci za podatke, greše.
koje su stručnjaci za podatke, greše.
07:17
Yes, even Google gets it wrong sometimes.
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Da, čak i Gugl ponekad pogreši.
07:20
In 2009, Google announced
that they were able, with data analysis,
that they were able, with data analysis,
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Godine 2009, Gugl je najavio
da su u stanju, koristeći obradu podataka,
da su u stanju, koristeći obradu podataka,
07:25
to predict outbreaks of influenza,
the nasty kind of flu,
the nasty kind of flu,
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da predvide epidemiju influence,
gadnog oblika gripa,
gadnog oblika gripa,
07:29
by doing data analysis
on their Google searches.
on their Google searches.
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koristeći obradu podataka
s Guglovih pretraga.
s Guglovih pretraga.
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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I savršeno je funkcionisalo
i dospeli su u glavne vesti,
i dospeli su u glavne vesti,
07:37
including the pinnacle
of scientific success:
of scientific success:
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uključujući i vrhunac naučnog uspeha:
07:39
a publication in the journal "Nature."
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objavu u magazinu "Nejčer".
07:41
It worked beautifully
for year after year after year,
for year after year after year,
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Savršeno je funkcionisalo
godinu za godinom,
godinu za godinom,
07:45
until one year it failed.
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sve dok jedne godine nije zatajilo.
07:47
And nobody could even tell exactly why.
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A niko nije čak mogao da tačno kaže zašto.
07:49
It just didn't work that year,
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Prosto nije funkcionisalo te godine
i, naravno, to je ponovo bila glavna vest,
07:51
and of course that again made big news,
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07:52
including now a retraction
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uključujući sada i povlačenje
07:54
of a publication
from the journal "Nature."
from the journal "Nature."
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članka iz časopisa "Nejčer".
07:58
So even the most data-savvy companies,
Amazon and Google,
Amazon and Google,
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Dakle, čak i stručnjaci za podatke,
Amazon i Gugl,
Amazon i Gugl,
08:01
they sometimes get it wrong.
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ponekad pogreše.
08:04
And despite all those failures,
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I uprkos svim tim neuspesima,
08:06
data is moving rapidly
into real-life decision-making --
into real-life decision-making --
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podaci sve brže postaju deo
odlučivanja u stvarnom životu -
odlučivanja u stvarnom životu -
08:10
into the workplace,
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na poslu,
08:12
law enforcement,
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u policiji,
08:14
medicine.
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medicini.
08:16
So we should better make sure
that data is helping.
that data is helping.
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Dakle, trebalo bi da se postaramo
da nam podaci budu korisni.
da nam podaci budu korisni.
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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Sad, lično sam video
mnogo ove borbe s podacima
mnogo ove borbe s podacima
08:22
because I work in computational genetics,
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jer radim na polju računarske genetike,
08:24
which is also a field
where lots of very smart people
where lots of very smart people
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što je takođe oblast
gde mnogo veoma pametnih ljudi
gde mnogo veoma pametnih ljudi
08:27
are using unimaginable amounts of data
to make pretty serious decisions
to make pretty serious decisions
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koristi nezamislivu količinu podataka
da bi doneli veoma ozbiljne odluke,
da bi doneli veoma ozbiljne odluke,
08:31
like deciding on a cancer therapy
or developing a drug.
or developing a drug.
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poput odlučivanja o terapiji za rak
ili o razvoju leka.
ili o razvoju leka.
08:35
And over the years,
I've noticed a sort of pattern
I've noticed a sort of pattern
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I vremenom sam
primetio nešto nalik obrascu
primetio nešto nalik obrascu
08:37
or kind of rule, if you will,
about the difference
about the difference
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ili nekakvom pravilu,
ako hoćete, o razlici
ako hoćete, o razlici
08:40
between successful
decision-making with data
decision-making with data
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između uspešnog odlučivanja
pomoću podataka
pomoću podataka
08:43
and unsuccessful decision-making,
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i neuspešnog odlučivanja
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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i smatram da ovaj obrazac vredi deliti,
a radi se o sledećem.
a radi se o sledećem.
08:50
So whenever you're
solving a complex problem,
solving a complex problem,
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Dakle, kad god rešavate složen problem,
08:52
you're doing essentially two things.
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u suštini radite dve stvari.
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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Prvo razlažete dati problem
na sastavne komadiće i delove
na sastavne komadiće i delove
08:57
so that you can deeply analyze
those bits and pieces,
those bits and pieces,
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kako biste podrobno analizirali
te komadiće i delove
te komadiće i delove
09:00
and then of course
you do the second part.
you do the second part.
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i potom, naravno, prelazite na drugi deo.
09:02
You put all of these bits and pieces
back together again
back together again
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Sastavljate ponovo sve ove
komadiće i delove
komadiće i delove
09:05
to come to your conclusion.
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da biste došli do zaključka.
09:06
And sometimes you
have to do it over again,
have to do it over again,
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A ponekad to morate da uradite više puta,
09:08
but it's always those two things:
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ali uvek se radi o ove dve stvari:
09:10
taking apart and putting
back together again.
back together again.
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rastavljanju i ponovnom sastavljanju.
09:14
And now the crucial thing is
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E sad, ključno je
09:15
that data and data analysis
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da su podaci i obrada podataka
09:18
is only good for the first part.
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jedino korisni u prvom delu.
09:21
Data and data analysis,
no matter how powerful,
no matter how powerful,
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Podaci i obrada podataka,
ma koliko moćni bili,
ma koliko moćni bili,
09:23
can only help you taking a problem apart
and understanding its pieces.
and understanding its pieces.
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jedino vam mogu pomoći da razložite
problem i da razumete njegove delove.
problem i da razumete njegove delove.
09:28
It's not suited to put those pieces
back together again
back together again
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Neodgovarajući su za ponovno
sastavljanje tih delova
sastavljanje tih delova
09:31
and then to come to a conclusion.
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i za dolaženje do zaključka.
Postoji drugo oruđe
koje može to da uradi i svi ga imamo,
koje može to da uradi i svi ga imamo,
09:33
There's another tool that can do that,
and we all have it,
and we all have it,
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09:36
and that tool is the brain.
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a to oruđe je mozak.
09:37
If there's one thing a brain is good at,
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Ako je mozak u nečemu dobar,
09:39
it's taking bits and pieces
back together again,
back together again,
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to je ponovno sastavljanje
komadića i delova,
komadića i delova,
09:41
even when you have incomplete information,
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čak i kad imate nepotpunu informaciju,
09:43
and coming to a good conclusion,
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i dolaženje do dobrog zaljučka,
09:45
especially if it's the brain of an expert.
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naročito ako se radi o mozgu stručnjaka.
09:48
And that's why I believe
that Netflix was so successful,
that Netflix was so successful,
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I zato verujem da je Netfliks bio uspešan
09:51
because they used data and brains
where they belong in the process.
where they belong in the process.
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jer su koristili podatke i mozak
tamo gde im je i mesto u procesu.
tamo gde im je i mesto u procesu.
Koristili su podatke kako bi prvobitno
shvatili gomile stvari o svojoj publici
shvatili gomile stvari o svojoj publici
09:54
They use data to first understand
lots of pieces about their audience
lots of pieces about their audience
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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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koje u suprotnom ne bi bili u stanju
da razumeju tako podrobno,
da razumeju tako podrobno,
10:01
but then the decision
to take all these bits and pieces
to take all these bits and pieces
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ali potom je odluka
da uzmu sve te komadiće i delove
da uzmu sve te komadiće i delove
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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i da ih ponovo sastave
i naprave seriju, poput "Kuće od karata",
i naprave seriju, poput "Kuće od karata",
10:07
that was nowhere in the data.
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toga nije bilo u podacima.
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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Ted Sarandos i njegova ekipa
su odlučili da odobre tu seriju,
su odlučili da odobre tu seriju,
10:13
which also meant, by the way,
that they were taking
that they were taking
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što je takođe značilo, usput,
da su preuzimali
da su preuzimali
10:15
a pretty big personal risk
with that decision.
with that decision.
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prilično veliki lični rizik tom odlukom.
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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A Amazon, s druge strane,
oni su pogrešili u postupku.
oni su pogrešili u postupku.
10:21
They used data all the way
to drive their decision-making,
to drive their decision-making,
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Vodili su se podacima
sve vreme donošenja odluke,
sve vreme donošenja odluke,
10:24
first when they held
their competition of TV ideas,
their competition of TV ideas,
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prvo kada su organizovali takmičenje
u TV idejama,
u TV idejama,
10:26
then when they selected "Alpha House"
to make as a show.
to make as a show.
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potom kada su odabrali
da snimaju seriju "Alpha House".
da snimaju seriju "Alpha House".
10:30
Which of course was
a very safe decision for them,
a very safe decision for them,
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Što je naravno bila
veoma bezbedna odluka za njih
veoma bezbedna odluka za njih
10:32
because they could always
point at the data, saying,
point at the data, saying,
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jer su uvek mogli da
pokažu na podatke i kažu:
pokažu na podatke i kažu:
10:35
"This is what the data tells us."
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"Tako su nam podaci rekli."
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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Međutim to nije dovelo do izvanrednih
rezultata kojima su se nadali.
rezultata kojima su se nadali.
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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Dakle, podaci su svakako izuzetno
korisno oruđe da bolje odlučujete,
korisno oruđe da bolje odlučujete,
10:47
but I believe that things go wrong
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ali verujem da stvari kreću po zlu
10:49
when data is starting
to drive those decisions.
to drive those decisions.
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kada su podaci vodeći u odlučivanju.
10:52
No matter how powerful,
data is just a tool,
data is just a tool,
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Ma koliko moćni, podaci su samo oruđe,
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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a da biste imali to na umu, smatram
da je ovo sredstvo ovde korisno.
da je ovo sredstvo ovde korisno.
10:59
Many of you will ...
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Mnogi od vas će...
11:00
(Laughter)
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(Smeh)
11:01
Before there was data,
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Pre podataka,
11:03
this was the decision-making
device to use.
device to use.
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ovo je bio uređaj koji ste
koristili u odlučivanju.
koristili u odlučivanju.
11:05
(Laughter)
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(Smeh)
11:07
Many of you will know this.
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Mnogima je ovo poznato.
11:08
This toy here is called the Magic 8 Ball,
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Ova igračka se zove magična osmica
11:10
and it's really amazing,
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i zaista je izvanredna
11:11
because if you have a decision to make,
a yes or no question,
a yes or no question,
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jer ako treba da odlučite o nečemu,
o pitanju sa da ili ne,
o pitanju sa da ili ne,
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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sve što je potrebno je da protresete kuglu
i potom dobijate odgovor -
i potom dobijate odgovor -
11:18
"Most Likely" -- right here
in this window in real time.
in this window in real time.
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"najverovatnije" - baš tu
u ovom prorezu, u realnom vremenu.
u ovom prorezu, u realnom vremenu.
11:21
I'll have it out later for tech demos.
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Kasnije ću je podvrći tehničkim probama.
11:23
(Laughter)
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(Smeh)
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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Sad, radi se, naravno -
doneo sam neke odluke u svom životu
doneo sam neke odluke u svom životu
za koje se kasnije ispostavilo
da je trebalo da poslušam kuglu.
da je trebalo da poslušam kuglu.
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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11:31
But, you know, of course,
if you have the data available,
if you have the data available,
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Međutim, znate, naravno,
ako su vam podaci dostupni,
ako su vam podaci dostupni,
11:34
you want to replace this with something
much more sophisticated,
much more sophisticated,
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želećete da zamenite ovo nečim
daleko prefinjenijim,
daleko prefinjenijim,
11:37
like data analysis
to come to a better decision.
to come to a better decision.
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poput obrade podataka,
da biste doneli bolju odluku.
da biste doneli bolju odluku.
11:41
But that does not change the basic setup.
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Ali to ne menja osnovnu postavku.
11:43
So the ball may get smarter
and smarter and smarter,
and smarter and smarter,
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Dakle, kugla može da postaje
sve pametnija i pametnija,
sve pametnija i pametnija,
11:47
but I believe it's still on us
to make the decisions
to make the decisions
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ali verujem da je odlučivanje
i dalje na nama,
i dalje na nama,
11:49
if we want to achieve
something extraordinary,
something extraordinary,
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ako želimo da postignemo nešto izuzetno
11:52
on the right end of the curve.
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na desnom kraju ove krive.
11:54
And I find that a very encouraging
message, in fact,
message, in fact,
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I za mene je to zapravo
veoma ohrabrujuća poruka,
veoma ohrabrujuća poruka,
11:59
that even in the face
of huge amounts of data,
of huge amounts of data,
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da čak i kad ste suočeni
s ogromnom količinom podataka,
s ogromnom količinom podataka,
12:03
it still pays off to make decisions,
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i dalje se isplati odlučivati,
12:07
to be an expert in what you're doing
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biti stručnjak u onome što radite
12:10
and take risks.
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i preuzimati rizike.
12:12
Because in the end, it's not data,
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Jer, naposletku, neće vas podaci
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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na desni kraj ove krive.
12:19
Thank you.
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12:21
(Applause)
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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