TEDxCambridge
Sebastian Wernicke: How to use data to make a hit TV show
Hvordan man bruger data til at lave en populær TV-serie: How to use data to make a hit TV show
Filmed:
Readability: 4
1,628,704 views
Fører indsamlingen af mere data til bedre beslutningstagen? Kokurrencedygtige, data-kyndige firmaer såsom Amazon, Google og Netflix har lært at analysering af data ikke alene er garanti for at producere optimale resultater. I dette foredrag forklarer data forsker Sebastian Wernicke hvad der går galt når vi basere beslutninger på data alene - og foreslår en smartere måde at bruge det på.
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,
0
820
4276
Roy Price er en mand som de fleste af jer
nok aldrig har hørt om,
nok aldrig har hørt om,
00:17
even though he may have been responsible
1
5120
2496
selvom han måske er ansvarlig
00:19
for 22 somewhat mediocre
minutes of your life on April 19, 2013.
minutes of your life on April 19, 2013.
2
7640
6896
for 22 mere eller mindre middelmådige
minutter af jeres liv den 19. april 2013.
minutter af jeres liv den 19. april 2013.
00:26
He may have also been responsible
for 22 very entertaining minutes,
for 22 very entertaining minutes,
3
14560
3176
Han kan også have været ansvarlig for 22
meget underholdende minutter,
meget underholdende minutter,
00:29
but not very many of you.
4
17760
2256
men ikke for særligt mange af jer.
00:32
And all of that goes back to a decision
5
20040
1896
Og alt det stammer fra en beslutning
00:33
that Roy had to make
about three years ago.
about three years ago.
6
21960
2000
som Roy var nød til at tage
omkring tre år siden.
omkring tre år siden.
00:35
So you see, Roy Price
is a senior executive with Amazon Studios.
is a senior executive with Amazon Studios.
7
23984
4832
Ser i, Roy Price
er en overordnet leder hos Amazon Studios.
er en overordnet leder hos Amazon Studios.
00:40
That's the TV production
company of Amazon.
company of Amazon.
8
28840
3016
Det er Amazons TV produktionsselskab.
00:43
He's 47 years old, slim, spiky hair,
9
31880
3256
Han er 47 år gammel, slank, strithår,
00:47
describes himself on Twitter
as "movies, TV, technology, tacos."
as "movies, TV, technology, tacos."
10
35160
4816
beskriver sig selv på Twitter
som "film, TV, teknologi, taco."
som "film, TV, teknologi, taco."
00:52
And Roy Price has a very responsible job,
because it's his responsibility
because it's his responsibility
11
40000
5176
Og Roy Price har et meget ansvarsfuldt job,
fordi det er hans ansvar
fordi det er hans ansvar
00:57
to pick the shows, the original content
that Amazon is going to make.
that Amazon is going to make.
12
45200
4056
at vælge de serier, det originale indhold
som Amazon skal producere.
som Amazon skal producere.
01:01
And of course that's
a highly competitive space.
a highly competitive space.
13
49280
2336
Og det er selvfølgelig
et meget konkurrerende miljø.
et meget konkurrerende miljø.
01:03
I mean, there are so many
TV shows already out there,
TV shows already out there,
14
51640
2736
Det er jo allerede
så mange TV-serier,
så mange TV-serier,
01:06
that Roy can't just choose any show.
15
54400
2176
så Roy kan ikke bare vælge en hvilket som helst serie.
01:08
He has to find shows
that are really, really great.
that are really, really great.
16
56600
4096
Han bliver nød til at finde serier
som er rigtig, rigtig gode.
som er rigtig, rigtig gode.
01:12
So in other words, he has to find shows
17
60720
2816
Med andre ord, skal han finde serier
01:15
that are on the very right end
of this curve here.
of this curve here.
18
63560
2376
som er helt til højre på denne kurve her.
01:17
So this curve here
is the rating distribution
is the rating distribution
19
65960
2656
Denne kurve her er
fordeling af bedømmelser
fordeling af bedømmelser
01:20
of about 2,500 TV shows
on the website IMDB,
on the website IMDB,
20
68640
4376
af omkring 2.500 TV serier
på hjemmesiden IMDB,
på hjemmesiden IMDB,
01:25
and the rating goes from one to 10,
21
73040
2896
og bedømmelserne går fra et til 10,
01:27
and the height here shows you
how many shows get that rating.
how many shows get that rating.
22
75960
2976
og højden her viser hvor mange serier
der får den bedømmelse.
der får den bedømmelse.
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.
23
78960
4696
Så hvis din serie får en bedømmelse på
ni point eller højere er det en vinder.
ni point eller højere er det en vinder.
01:35
Then you have a top two percent show.
24
83680
1816
Så har du en top to procent serie.
01:37
That's shows like "Breaking Bad,"
"Game of Thrones," "The Wire,"
"Game of Thrones," "The Wire,"
25
85520
3896
Det er serier som "Breaking Bad,"
"Game of Thrones," "The Wire,"
"Game of Thrones," "The Wire,"
01:41
so all of these shows that are addictive,
26
89440
2296
Altså alle de her serier
som er vanedannende,
som er vanedannende,
01:43
whereafter you've watched a season,
your brain is basically like,
your brain is basically like,
27
91760
3056
hvor når du har set en sæson,
siger din hjerne bare,
siger din hjerne bare,
01:46
"Where can I get more of these episodes?"
28
94840
2176
"Hvor kan jeg få flere af de her afsnit?"
01:49
That kind of show.
29
97040
1200
Den slags serier.
01:50
On the left side, just for clarity,
here on that end,
here on that end,
30
98920
2496
På venstre side, for at gøre det klart,
i den her side,
i den her side,
01:53
you have a show called
"Toddlers and Tiaras" --
"Toddlers and Tiaras" --
31
101440
3176
er der serier som hedder
"Toddlers and Tiaras" -
"Toddlers and Tiaras" -
01:56
(Laughter)
32
104640
2656
(Latter)
01:59
-- which should tell you enough
33
107320
1536
- hvilket burde sige det hele
02:00
about what's going on
on that end of the curve.
on that end of the curve.
34
108880
2191
omkring hvad der sker i den ende
af kurven.
af kurven.
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,
35
111095
4161
Roy Price er ikke bekymret for at ende
på den venstre side af kurven,
på den venstre side af kurven,
02:07
because I think you would have to have
some serious brainpower
some serious brainpower
36
115280
2936
fordi jeg tror at du virkelig
skal have noget tankevirksomhed
skal have noget tankevirksomhed
02:10
to undercut "Toddlers and Tiaras."
37
118240
1696
for at komme under
"Toddlers and Tiaras."
"Toddlers and Tiaras."
02:11
So what he's worried about
is this middle bulge here,
is this middle bulge here,
38
119960
3936
Så det han er bekymret omkring
er bulen her i midten,
er bulen her i midten,
02:15
the bulge of average TV,
39
123920
1816
bulen med gennemsnitlig TV,
02:17
you know, those shows
that aren't really good or really bad,
that aren't really good or really bad,
40
125760
2856
I ved, den slags serier som ikke er
rigtig gode eller rigtig dårlige
rigtig gode eller rigtig dårlige
02:20
they don't really get you excited.
41
128639
1656
- de begejstrer ikke rigtig.
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.
42
130320
4856
Så han bliver nød til at sikre sig,
at han er på den rigtige side af denne.
at han er på den rigtige side af denne.
02:27
So the pressure is on,
43
135200
1576
Så der er pres på,
02:28
and of course it's also the first time
44
136800
2176
og det er selvfølgelig også første gang,
02:31
that Amazon is even
doing something like this,
doing something like this,
45
139000
2176
at Amazon overhovedet laver sådan noget,
02:33
so Roy Price does not want
to take any chances.
to take any chances.
46
141200
3336
så Roy Price vil ikke tage nogle chancer.
02:36
He wants to engineer success.
47
144560
2456
Han vil skabe succes.
02:39
He needs a guaranteed success,
48
147040
1776
Han har brug for en garanteret succes,
02:40
and so what he does is,
he holds a competition.
he holds a competition.
49
148840
2576
og derfor afholder han en konkurrence.
02:43
So he takes a bunch of ideas for TV shows,
50
151440
3136
Han tager en bunke idéer til TV-serier
02:46
and from those ideas,
through an evaluation,
through an evaluation,
51
154600
2296
og gennem en evaluering af de idéer
02:48
they select eight candidates for TV shows,
52
156920
4096
bliver otte kandidater til TV-serier valgt
02:53
and then he just makes the first episode
of each one of these shows
of each one of these shows
53
161040
3216
og så laver han kun den
første episode til hver serie
første episode til hver serie
02:56
and puts them online for free
for everyone to watch.
for everyone to watch.
54
164280
3136
og lægger dem online
så alle kan se dem gratis.
så alle kan se dem gratis.
02:59
And so when Amazon
is giving out free stuff,
is giving out free stuff,
55
167440
2256
Og når Amazon giver dig noget gratis
03:01
you're going to take it, right?
56
169720
1536
så tager du imod det, ikke?
03:03
So millions of viewers
are watching those episodes.
are watching those episodes.
57
171280
5136
Så millioner af seere ser disse episoder.
03:08
What they don't realize is that,
while they're watching their shows,
while they're watching their shows,
58
176440
3216
Hvad de ikke indser er
at mens de ser deres serie
at mens de ser deres serie
03:11
actually, they are being watched.
59
179680
2296
bliver de faktisk selv iagttaget.
03:14
They are being watched
by Roy Price and his team,
by Roy Price and his team,
60
182000
2336
De bliver iagttaget af Roy Price
og hans hold
og hans hold
03:16
who record everything.
61
184360
1376
som optager alting,
03:17
They record when somebody presses play,
when somebody presses pause,
when somebody presses pause,
62
185760
3376
De optager når nogen trykker play.
når nogen trykker pause,
når nogen trykker pause,
03:21
what parts they skip,
what parts they watch again.
what parts they watch again.
63
189160
2536
hvilke dele de springer over,
hvilke dele de ser igen.
hvilke dele de ser igen.
03:23
So they collect millions of data points,
64
191720
2256
Så de samler millioner af data point,
03:26
because they want
to have those data points
to have those data points
65
194000
2096
fordi de vil have de data point
03:28
to then decide
which show they should make.
which show they should make.
66
196120
2696
til når de skal beslutte hvilken serie de vil lave.
03:30
And sure enough,
so they collect all the data,
so they collect all the data,
67
198840
2176
Som sagt så gjort,
de samlede al data,
de samlede al data,
03:33
they do all the data crunching,
and an answer emerges,
and an answer emerges,
68
201040
2576
de analyserede denne
og et svar dukkede op,
og et svar dukkede op,
03:35
and the answer is,
69
203640
1216
og det svar er,
03:36
"Amazon should do a sitcom
about four Republican US Senators."
about four Republican US Senators."
70
204880
5536
"Amazon bør lave en komedieserie
om fire republikanske senatorer."
om fire republikanske senatorer."
03:42
They did that show.
71
210440
1216
De lavede den serie.
03:43
So does anyone know the name of the show?
72
211680
2160
Er der nogen der kender navnet på den serie?
03:46
(Audience: "Alpha House.")
73
214720
1296
(Publikum: "Alpha House.")
03:48
Yes, "Alpha House,"
74
216040
1456
Ja, "Alpha House,"
03:49
but it seems like not too many of you here
remember that show, actually,
remember that show, actually,
75
217520
4096
men det virker som om der ikke er så
mange af jer som faktisk husker den serie,
mange af jer som faktisk husker den serie,
03:53
because it didn't turn out that great.
76
221640
1856
fordi den endte med ikke
at blive så god.
at blive så god.
03:55
It's actually just an average show,
77
223520
1856
Det er faktisk kun
en gennemsnitlig serie
en gennemsnitlig serie
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,
78
225400
4576
endda bogstavelig talt,
fordi gennemsnittet på denne kurve er 7,4
fordi gennemsnittet på denne kurve er 7,4
04:02
and "Alpha House" lands at 7.5,
79
230000
2416
og "Alpha House" er på 7,5,
04:04
so a slightly above average show,
80
232440
2016
så en lige over gennemsnittet serie,
04:06
but certainly not what Roy Price
and his team were aiming for.
and his team were aiming for.
81
234480
2920
men helt sikkert ikke hvad Roy Price
og hans hold gik efter.
og hans hold gik efter.
04:10
Meanwhile, however,
at about the same time,
at about the same time,
82
238320
2856
I mellemtiden,
04:13
at another company,
83
241200
1576
i et andet firma,
04:14
another executive did manage
to land a top show using data analysis,
to land a top show using data analysis,
84
242800
4216
lykkedes det en anden leder at skaffe en
top serie ved hjælp af data analysering,
top serie ved hjælp af data analysering,
04:19
and his name is Ted,
85
247040
1576
og hans navn er Ted,
04:20
Ted Sarandos, who is
the Chief Content Officer of Netflix,
the Chief Content Officer of Netflix,
86
248640
3416
Ted Sarandos, som er
Chief Content Officer hos Netflix.
Chief Content Officer hos Netflix.
04:24
and just like Roy,
he's on a constant mission
he's on a constant mission
87
252080
2136
og ligesom Roy, er han på
en konstant mission
en konstant mission
04:26
to find that great TV show,
88
254240
1496
for at finde en god TV-serie,
04:27
and he uses data as well to do that,
89
255760
2016
og til det bruger han også data,
04:29
except he does it
a little bit differently.
a little bit differently.
90
257800
2015
bortset fra,
at han gør det lidt anderledes.
at han gør det lidt anderledes.
04:31
So instead of holding a competition,
what he did -- and his team of course --
what he did -- and his team of course --
91
259839
3737
I stedet for at afholde en konkurrence,
han - og hans hold selvfølgelig -
han - og hans hold selvfølgelig -
04:35
was they looked at all the data
they already had about Netflix viewers,
they already had about Netflix viewers,
92
263600
3536
så på al den data
de allerede havde om Netflix seere.
de allerede havde om Netflix seere.
04:39
you know, the ratings
they give their shows,
they give their shows,
93
267160
2096
I ved, de bedømmelser
de giver deres serier,
de giver deres serier,
04:41
the viewing histories,
what shows people like, and so on.
what shows people like, and so on.
94
269280
2696
afspilningshistorik,
hvilke serier folk kan lide, osv.
hvilke serier folk kan lide, osv.
04:44
And then they use that data to discover
95
272000
1896
Og så bruger de den data til at opdage
04:45
all of these little bits and pieces
about the audience:
about the audience:
96
273920
2616
alle disse små dele
af informationer om seererne:
af informationer om seererne:
04:48
what kinds of shows they like,
97
276560
1456
hvilken slags serier,
04:50
what kind of producers,
what kind of actors.
what kind of actors.
98
278040
2096
producere, skuespillere de kan lide.
04:52
And once they had
all of these pieces together,
all of these pieces together,
99
280160
2576
Og når de havde samlet alle disse dele,
04:54
they took a leap of faith,
100
282760
1656
tog de en chance,
04:56
and they decided to license
101
284440
2096
og de besluttede at lave
04:58
not a sitcom about four Senators
102
286560
2456
- ikke en komedieserie om fire senatorer -
05:01
but a drama series about a single Senator.
103
289040
2880
men en drama serie om en enkelt senator.
05:04
You guys know the show?
104
292760
1656
Kender i denne serie?
05:06
(Laughter)
105
294440
1296
(Latter)
05:07
Yes, "House of Cards," and Netflix
of course, nailed it with that show,
of course, nailed it with that show,
106
295760
3736
Ja, "House of Cards," og Netflix
selvfølgelig ramte rigtig med den serie,
selvfølgelig ramte rigtig med den serie,
05:11
at least for the first two seasons.
107
299520
2136
i hvert fald de første to sæsoner,
05:13
(Laughter) (Applause)
108
301680
3976
(Latter) (Klapsalver)
05:17
"House of Cards" gets
a 9.1 rating on this curve,
a 9.1 rating on this curve,
109
305680
3176
"House of Cards" får 9,1 på denne kurve,
05:20
so it's exactly
where they wanted it to be.
where they wanted it to be.
110
308880
3176
så det er præcis der hvor
de gerne ville have den skulle være.
de gerne ville have den skulle være.
05:24
Now, the question of course is,
what happened here?
what happened here?
111
312080
2416
Så er spørgsmålet selvfølgelig
hvad skete der her?
hvad skete der her?
05:26
So you have two very competitive,
data-savvy companies.
data-savvy companies.
112
314520
2656
Du har to konkurrencedygtige,
data-kløgtige firmaer.
data-kløgtige firmaer.
05:29
They connect all of these
millions of data points,
millions of data points,
113
317200
2856
De samler alle de her
millioner af data point,
millioner af data point,
05:32
and then it works
beautifully for one of them,
beautifully for one of them,
114
320080
2376
Og det fungerer fantastisk for en af dem
05:34
and it doesn't work for the other one.
115
322480
1856
og det virker ikke for den anden.
05:36
So why?
116
324360
1216
Hvorfor?
05:37
Because logic kind of tells you
that this should be working all the time.
that this should be working all the time.
117
325600
3456
Fordi logisk set burde det
egentlig fungerer hver gang.
egentlig fungerer hver gang.
05:41
I mean, if you're collecting
millions of data points
millions of data points
118
329080
2456
Hvis du samler millioner af data point
05:43
on a decision you're going to make,
119
331560
1736
til en beslutning du skal tage,
05:45
then you should be able
to make a pretty good decision.
to make a pretty good decision.
120
333320
2616
så burde du kunne tage
en ret god beslutning.
en ret god beslutning.
05:47
You have 200 years
of statistics to rely on.
of statistics to rely on.
121
335960
2216
Du har 200 års statistik
at falde tilbage på.
at falde tilbage på.
05:50
You're amplifying it
with very powerful computers.
with very powerful computers.
122
338200
3016
Det forstærker du
med kraftfulde computere.
med kraftfulde computere.
05:53
The least you could expect
is good TV, right?
is good TV, right?
123
341240
3280
Det mindste du kan forvente
er vel god TV, ikke?
er vel god TV, ikke?
05:57
And if data analysis
does not work that way,
does not work that way,
124
345880
2720
Og hvis data analyse
ikke virker på den måde,
ikke virker på den måde,
06:01
then it actually gets a little scary,
125
349520
2056
så er det faktisk lidt skræmmende,
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
126
351600
3816
fordi vi lever i en tid
hvor vi bruger data mere og mere
hvor vi bruger data mere og mere
06:07
to make very serious decisions
that go far beyond TV.
that go far beyond TV.
127
355440
4480
til at tage seriøse valg langt udover TV.
06:12
Does anyone here know the company
Multi-Health Systems?
Multi-Health Systems?
128
360760
3240
Er der nogen her der kender firmaet
Multi-Health Systems?
Multi-Health Systems?
06:17
No one. OK, that's good actually.
129
365080
1656
Ingen. Okay, det er faktisk godt.
06:18
OK, so Multi-Health Systems
is a software company,
is a software company,
130
366760
3216
Okay, så Multi-Health Systems
er et software firma,
er et software firma,
06:22
and I hope that nobody here in this room
131
370000
2816
og jeg håber ikke at nogen i dette lokale
06:24
ever comes into contact
with that software,
with that software,
132
372840
3176
nogensinde kommer i kontakt
med den software,
med den software,
06:28
because if you do,
it means you're in prison.
it means you're in prison.
133
376040
2096
fordi så betyder det at I er i fængsel.
06:30
(Laughter)
134
378160
1176
(Latter)
06:31
If someone here in the US is in prison,
and they apply for parole,
and they apply for parole,
135
379360
3536
Hvis nogen her i USA er i fængsel og
ansøger om prøveløsladelse,
ansøger om prøveløsladelse,
06:34
then it's very likely that
data analysis software from that company
data analysis software from that company
136
382920
4296
så er det meget sandsynligt,
at data analyse software fra det firma
at data analyse software fra det firma
06:39
will be used in determining
whether to grant that parole.
whether to grant that parole.
137
387240
3616
vil blive brugt til at bestemme om
der skal gives prøveløsladelse.
der skal gives prøveløsladelse.
06:42
So it's the same principle
as Amazon and Netflix,
as Amazon and Netflix,
138
390880
2576
Så det er det samme princip
som Amazon og Netflix,
som Amazon og Netflix,
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,
139
393480
4616
men i stedet for at bestemme om
en TV-serie bliver god eller dårlig,
en TV-serie bliver god eller dårlig,
06:50
you're deciding whether a person
is going to be good or bad.
is going to be good or bad.
140
398120
2896
bestemmer man om en fange
bliver god eller dårlig.
bliver god eller dårlig.
06:53
And mediocre TV, 22 minutes,
that can be pretty bad,
that can be pretty bad,
141
401040
5496
Og middelmådig TV, 22 minutter,
det kan være ret dårligt,
det kan være ret dårligt,
06:58
but more years in prison,
I guess, even worse.
I guess, even worse.
142
406560
2640
men flere år i fængsel,
går jeg udfra, er endnu værre.
går jeg udfra, er endnu værre.
07:02
And unfortunately, there is actually
some evidence that this data analysis,
some evidence that this data analysis,
143
410360
4136
Og uheldigvis er der faktisk bevis for.
at denne data analyse,
at denne data analyse,
07:06
despite having lots of data,
does not always produce optimum results.
does not always produce optimum results.
144
414520
4216
på trods af masser af date,
ikke altid producerer optimale resultater.
ikke altid producerer optimale resultater.
07:10
And that's not because a company
like Multi-Health Systems
like Multi-Health Systems
145
418760
2722
Og det er ikke fordi et firma
som Multi-Health Systems
som Multi-Health Systems
07:13
doesn't know what to do with data.
146
421506
1627
ikke bruger den data korrekt.
07:15
Even the most data-savvy
companies get it wrong.
companies get it wrong.
147
423158
2298
Selv de mest data-kyndige
firmaer fejler.
firmaer fejler.
07:17
Yes, even Google gets it wrong sometimes.
148
425480
2400
Ja, selv Google fejler noglegange.
07:20
In 2009, Google announced
that they were able, with data analysis,
that they were able, with data analysis,
149
428680
4496
I 2009 annoncerede Google,
at de var i stand til, med data analyse,
at de var i stand til, med data analyse,
07:25
to predict outbreaks of influenza,
the nasty kind of flu,
the nasty kind of flu,
150
433200
4136
at forudse udbrud ad influenza,
den slemme slags,
den slemme slags,
07:29
by doing data analysis
on their Google searches.
on their Google searches.
151
437360
3776
ved at lave data analyse
på deres Google søgninger.
på deres Google søgninger.
07:33
And it worked beautifully,
and it made a big splash in the news,
and it made a big splash in the news,
152
441160
3856
Og det virkede fantastisk, og det
blev en stor historie i nyhederne,
blev en stor historie i nyhederne,
07:37
including the pinnacle
of scientific success:
of scientific success:
153
445040
2136
inklusiv højdepunktet
i videnskabelig succes:
i videnskabelig succes:
07:39
a publication in the journal "Nature."
154
447200
2456
en udgivelse i tidskriftet "Nature."
07:41
It worked beautifully
for year after year after year,
for year after year after year,
155
449680
3616
Det virkede fantastisk
år efter år efter år,
år efter år efter år,
07:45
until one year it failed.
156
453320
1656
indtil det et år fejlede.
07:47
And nobody could even tell exactly why.
157
455000
2256
Og ingen kunne forklare præcis hvorfor.
07:49
It just didn't work that year,
158
457280
1696
Det virkede bare ikke det år,
07:51
and of course that again made big news,
159
459000
1936
og det blev selvfølgelig også en nyhed,
07:52
including now a retraction
160
460960
1616
med efterfølgende tilbagetrækning
07:54
of a publication
from the journal "Nature."
from the journal "Nature."
161
462600
2840
af udgivelsen i tidskriftet "Nature."
07:58
So even the most data-savvy companies,
Amazon and Google,
Amazon and Google,
162
466480
3336
Så selv de mest data-kyndige firmaer,
Amazon og Google,
Amazon og Google,
08:01
they sometimes get it wrong.
163
469840
2136
fejler sommetider.
08:04
And despite all those failures,
164
472000
2936
Og på trods af disse fiaskoer,
08:06
data is moving rapidly
into real-life decision-making --
into real-life decision-making --
165
474960
3856
rykker data hastigt ind det virkelig livs
beslutningstagen -
beslutningstagen -
08:10
into the workplace,
166
478840
1816
ind på arbejdspladsen,
08:12
law enforcement,
167
480680
1816
retshåndhævelsen,
08:14
medicine.
168
482520
1200
lægevidenskab.
08:16
So we should better make sure
that data is helping.
that data is helping.
169
484400
3336
Så vi må hellere være sikker på.
at data hjælper.
at data hjælper.
08:19
Now, personally I've seen
a lot of this struggle with data myself,
a lot of this struggle with data myself,
170
487760
3136
Personligt har jeg set megen af denne
kamp med data selv,
kamp med data selv,
08:22
because I work in computational genetics,
171
490920
1976
da jeg arbejder med databehandletgenetik,
08:24
which is also a field
where lots of very smart people
where lots of very smart people
172
492920
2496
hvilket også er et felt
hvor mange meget smart folk
hvor mange meget smart folk
08:27
are using unimaginable amounts of data
to make pretty serious decisions
to make pretty serious decisions
173
495440
3656
bruger utrolige mængder data til
at træffe nogle ret seriøse beslutninger,
at træffe nogle ret seriøse beslutninger,
08:31
like deciding on a cancer therapy
or developing a drug.
or developing a drug.
174
499120
3560
såsom valg vedrørende kræft terapi
eller udvikle et medikament.
eller udvikle et medikament.
08:35
And over the years,
I've noticed a sort of pattern
I've noticed a sort of pattern
175
503520
2376
Og i årenes løb
har jeg lagt mærke til et mønster
har jeg lagt mærke til et mønster
08:37
or kind of rule, if you will,
about the difference
about the difference
176
505920
2456
eller en slags regel, om du vil,
om forskellen
om forskellen
08:40
between successful
decision-making with data
decision-making with data
177
508400
2696
mellem succesfuld
beslutningstagen med data
beslutningstagen med data
08:43
and unsuccessful decision-making,
178
511120
1616
og fejlende beslutningstagen,
08:44
and I find this a pattern worth sharing,
and it goes something like this.
and it goes something like this.
179
512760
3880
og jeg fandt dette mønster værd at dele,
og det er sådan her.
og det er sådan her.
08:50
So whenever you're
solving a complex problem,
solving a complex problem,
180
518520
2135
Hver gang du løser
et kompleks problem,
et kompleks problem,
08:52
you're doing essentially two things.
181
520679
1737
gør du i virkeligheden to ting.
08:54
The first one is, you take that problem
apart into its bits and pieces
apart into its bits and pieces
182
522440
3296
Den første er at du deler problemet
op i mindre stykker
op i mindre stykker
08:57
so that you can deeply analyze
those bits and pieces,
those bits and pieces,
183
525760
2496
så du kan analysere disse stykker i dybden
09:00
and then of course
you do the second part.
you do the second part.
184
528280
2016
og så laver du selvfølgelig
den anden del.
den anden del.
09:02
You put all of these bits and pieces
back together again
back together again
185
530320
2656
Du samler alle disse stykker igen
for at komme
for at komme
09:05
to come to your conclusion.
186
533000
1336
frem til din konklusion.
09:06
And sometimes you
have to do it over again,
have to do it over again,
187
534360
2336
Og noglegange skal du begynde forfra,
09:08
but it's always those two things:
188
536720
1656
men det er altid disse to ting:
09:10
taking apart and putting
back together again.
back together again.
189
538400
2320
skille det ad
og samle det igen.
og samle det igen.
09:14
And now the crucial thing is
190
542280
1616
Og den vigtige del er,
09:15
that data and data analysis
191
543920
2896
at data og data analyse
09:18
is only good for the first part.
192
546840
2496
er kun brugbart til den første del.
09:21
Data and data analysis,
no matter how powerful,
no matter how powerful,
193
549360
2216
Data og data analyse,
uanset hvor stærk,
uanset hvor stærk,
09:23
can only help you taking a problem apart
and understanding its pieces.
and understanding its pieces.
194
551600
4456
kan kun hjælpe med at skille problemet ad
og forstå stykkerne.
og forstå stykkerne.
09:28
It's not suited to put those pieces
back together again
back together again
195
556080
3496
Det er ikke lavet til at samle
stykkerne igen
stykkerne igen
09:31
and then to come to a conclusion.
196
559600
1896
og komme frem til en konklusion.
09:33
There's another tool that can do that,
and we all have it,
and we all have it,
197
561520
2736
Der er et andet værktøj der kan gøre det
og alle har det,
og alle har det,
09:36
and that tool is the brain.
198
564280
1296
og det værktøj er hjernen.
09:37
If there's one thing a brain is good at,
199
565600
1936
Hvis det er en ting hjernen er god til,
09:39
it's taking bits and pieces
back together again,
back together again,
200
567560
2256
så er det at samle stykker sammen igen,
09:41
even when you have incomplete information,
201
569840
2016
selv hvis du ikke har al informationen,
09:43
and coming to a good conclusion,
202
571880
1576
og nå en god konklusion,
09:45
especially if it's the brain of an expert.
203
573480
2936
især hvis det er en eksperts hjerne.
09:48
And that's why I believe
that Netflix was so successful,
that Netflix was so successful,
204
576440
2656
Og det er derfor jeg tror,
at Netflix var så succesfulde
at Netflix var så succesfulde
09:51
because they used data and brains
where they belong in the process.
where they belong in the process.
205
579120
3576
fordi de brugte data og hjerne
der hvor de passede i processen.
der hvor de passede i processen.
09:54
They use data to first understand
lots of pieces about their audience
lots of pieces about their audience
206
582720
3536
De brugte først data til at forstå
en masse stykker af deres seere
en masse stykker af deres seere
09:58
that they otherwise wouldn't have
been able to understand at that depth,
been able to understand at that depth,
207
586280
3416
som de ellers ikke kunne have
forstået på sådan et niveau,
forstået på sådan et niveau,
10:01
but then the decision
to take all these bits and pieces
to take all these bits and pieces
208
589720
2616
men beslutningen om at tage
alle disse stykker
alle disse stykker
10:04
and put them back together again
and make a show like "House of Cards,"
and make a show like "House of Cards,"
209
592360
3336
og samle dem igen
og lave en serie som "House of Cards,"
og lave en serie som "House of Cards,"
10:07
that was nowhere in the data.
210
595720
1416
var ikke i nærheden af data.
10:09
Ted Sarandos and his team
made that decision to license that show,
made that decision to license that show,
211
597160
3976
Ted Sandaros og hans hold
tog beslutningen om at lave den serie,
tog beslutningen om at lave den serie,
10:13
which also meant, by the way,
that they were taking
that they were taking
212
601160
2381
hvilket i øvrigt også betød,
at de løb
at de løb
10:15
a pretty big personal risk
with that decision.
with that decision.
213
603565
2851
en ret stor personlig risiko
med den beslutning.
med den beslutning.
10:18
And Amazon, on the other hand,
they did it the wrong way around.
they did it the wrong way around.
214
606440
3016
Og Amazon, på den anden side,
de gjorde det på den forkerte led.
de gjorde det på den forkerte led.
10:21
They used data all the way
to drive their decision-making,
to drive their decision-making,
215
609480
2736
De brugte data hele vejen
i deres beslutningstagen,
i deres beslutningstagen,
10:24
first when they held
their competition of TV ideas,
their competition of TV ideas,
216
612240
2416
først da de afholdte konkurrencen
med TV idéer
med TV idéer
10:26
then when they selected "Alpha House"
to make as a show.
to make as a show.
217
614680
3696
senere da de valgte at lave
"Alpha House" til en serie.
"Alpha House" til en serie.
10:30
Which of course was
a very safe decision for them,
a very safe decision for them,
218
618400
2496
Hvilket selvfølgelig var
en sikker beslutning for dem
en sikker beslutning for dem
10:32
because they could always
point at the data, saying,
point at the data, saying,
219
620920
2456
fordi de altid kunne
pege på dataen og sige:
pege på dataen og sige:
10:35
"This is what the data tells us."
220
623400
1696
"Dette er hvad dataen fortalte os."
10:37
But it didn't lead to the exceptional
results that they were hoping for.
results that they were hoping for.
221
625120
4240
Men det første ikke til det exceptionelle
resultat som de håbede på.
resultat som de håbede på.
10:42
So data is of course a massively
useful tool to make better decisions,
useful tool to make better decisions,
222
630120
4976
Data er selvfølgelig et enormt brugbart
værktøj til at tage bedre valg
værktøj til at tage bedre valg
10:47
but I believe that things go wrong
223
635120
2376
men jeg tror, at det går galt
10:49
when data is starting
to drive those decisions.
to drive those decisions.
224
637520
2576
når data begynder at styre de valg.
10:52
No matter how powerful,
data is just a tool,
data is just a tool,
225
640120
3776
Uanset hvor stærk data er,
er det kun et værktøj
er det kun et værktøj
10:55
and to keep that in mind,
I find this device here quite useful.
I find this device here quite useful.
226
643920
3336
og for at huske på dette,
finder jeg denne her meget brugbar.
finder jeg denne her meget brugbar.
10:59
Many of you will ...
227
647280
1216
Mange af jer vil ...
11:00
(Laughter)
228
648520
1216
(Latter)
11:01
Before there was data,
229
649760
1216
Før der var data,
11:03
this was the decision-making
device to use.
device to use.
230
651000
2856
var dette beslutningstager-enheden
man brugte.
man brugte.
11:05
(Laughter)
231
653880
1256
(Latter)
11:07
Many of you will know this.
232
655160
1336
Mange af kender denne.
11:08
This toy here is called the Magic 8 Ball,
233
656520
1953
Dette legetøj her hedder en magisk 8-ball
11:10
and it's really amazing,
234
658497
1199
og den er helt utrolig,
11:11
because if you have a decision to make,
a yes or no question,
a yes or no question,
235
659720
2896
fordi hvis du har et valg du skal tage.
et ja/nej spørgsmål,
et ja/nej spørgsmål,
11:14
all you have to do is you shake the ball,
and then you get an answer --
and then you get an answer --
236
662640
3736
det eneste du skal gøre er
at ryste kuglen og så får du et svar -
at ryste kuglen og så får du et svar -
11:18
"Most Likely" -- right here
in this window in real time.
in this window in real time.
237
666400
2816
"Højst sandsynlig" - lige her
i dette vindue i realtid.
i dette vindue i realtid.
11:21
I'll have it out later for tech demos.
238
669240
2096
Jeg vil demonstrere derude senere.
11:23
(Laughter)
239
671360
1216
(Latter)
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
240
672600
3576
Sagen er, at jeg har taget
nogle valg i mit liv
nogle valg i mit liv
11:28
where, in hindsight,
I should have just listened to the ball.
I should have just listened to the ball.
241
676200
2896
hvor jeg skulle have lyttet
til kuglen, set i bakspejlet.
til kuglen, set i bakspejlet.
11:31
But, you know, of course,
if you have the data available,
if you have the data available,
242
679120
3336
Men, I ved, hvis man har data til rådighed
11:34
you want to replace this with something
much more sophisticated,
much more sophisticated,
243
682480
3056
vil man erstatte den
med noget langt mere sofistikeret,
med noget langt mere sofistikeret,
11:37
like data analysis
to come to a better decision.
to come to a better decision.
244
685560
3616
såsom data analyse
for at komme frem til en bedre beslutning.
for at komme frem til en bedre beslutning.
11:41
But that does not change the basic setup.
245
689200
2616
Men det ændrer ikke den basale opsætning.
11:43
So the ball may get smarter
and smarter and smarter,
and smarter and smarter,
246
691840
3176
Kuglen bliver måske klogere
og klogere og klogere,
og klogere og klogere,
11:47
but I believe it's still on us
to make the decisions
to make the decisions
247
695040
2816
men jeg mener stadig,
at vi skal tage beslutningerne
at vi skal tage beslutningerne
11:49
if we want to achieve
something extraordinary,
something extraordinary,
248
697880
3016
hvis vi vil opnå noget ekstraordinært,
11:52
on the right end of the curve.
249
700920
1936
i den højre ende af kurven.
11:54
And I find that a very encouraging
message, in fact,
message, in fact,
250
702880
4496
Og det mener jeg faktisk
er en meget opmuntrende besked,
er en meget opmuntrende besked,
11:59
that even in the face
of huge amounts of data,
of huge amounts of data,
251
707400
3976
at selv overfor masser af data
12:03
it still pays off to make decisions,
252
711400
4096
betaler det sig stadig
at tage beslutninger,
at tage beslutninger,
12:07
to be an expert in what you're doing
253
715520
2656
at være en ekspert til det du laver
12:10
and take risks.
254
718200
2096
og take chancer.
12:12
Because in the end, it's not data,
255
720320
2776
For når alt kommer til alt,
er det ikke data
er det ikke data
12:15
it's risks that will land you
on the right end of the curve.
on the right end of the curve.
256
723120
3960
det er chancer der vil føre dig
til den højre ende af kurven.
til den højre ende af kurven.
12:19
Thank you.
257
727840
1216
Tak.
12:21
(Applause)
258
729080
3680
(Klapsalver)
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