TED@UPS
Mallory Freeman: Your company's data could help end world hunger
Mallory Soldner: Zure konpainiaren datuek munduko gosetea amaitzen lagun dezakete.
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Zure konpainiak agian dirua eman du afera humantarioak konpontzen laguntzeko, baina emateko zerbait erabilgarriagoa izan dezakezue: datuak. Mallory Soldner-ek sektore pribatuko konpainiek arazo handietan --errefuxiatuen krisitik munduko gosetera-- aurrerapen handiak egin ditzaketela erakusten digu. Hau datuak eta erabaki hartzaile zientifikoak emanez lor daiteke. Zertan lagun dezake zure konpainiak?
Mallory Freeman - Data activist
UPS's advanced analytics manager Mallory Freeman researches how to do the most good with data. Full bio
UPS's advanced analytics manager Mallory Freeman researches how to do the most good with data. Full bio
Double-click the English transcript below to play the video.
00:12
June 2010.
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2010eko ekaina.
00:15
I landed for the first time
in Rome, Italy.
in Rome, Italy.
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Lehenbizikoz nintzen
Erroman, Italian.
Erroman, Italian.
00:19
I wasn't there to sightsee.
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Ez nintzen turismoa egitera joan.
00:21
I was there to solve world hunger.
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Munduko gosea konpontzera baizik.
00:25
(Laughter)
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(Barreak)
00:27
That's right.
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Hori da.
25 urteko dokotegaia nintzen
00:28
I was a 25-year-old PhD student
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00:30
armed with a prototype tool
developed back at my university,
developed back at my university,
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nire unibertsitatean sortutako
tresna baten prototipo batekin,
tresna baten prototipo batekin,
00:33
and I was going to help
the World Food Programme fix hunger.
the World Food Programme fix hunger.
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eta Munduko Elikagai Programan
laguntzera nindoan.
laguntzera nindoan.
00:37
So I strode into the headquarters building
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Egoitza nagusira sartu nintzen
00:40
and my eyes scanned the row of UN flags,
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eta NBetako bandera ilarari
begiratu bat bota nion,
begiratu bat bota nion,
00:43
and I smiled as I thought to myself,
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irribarre egin eta pentsatu nuen
00:46
"The engineer is here."
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"ingeniaria hemen da."
00:48
(Laughter)
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(Barreak)
00:50
Give me your data.
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Emaizkidazue zuen datuak.
00:52
I'm going to optimize everything.
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Guztia optimizatzera noa.
00:54
(Laughter)
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(Barreak)
00:56
Tell me the food that you've purchased,
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Esaidazue ze janari erosi duzuen,
00:58
tell me where it's going
and when it needs to be there,
and when it needs to be there,
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nora joan behar duen
eta noizko,
eta noizko,
01:01
and I'm going to tell you
the shortest, fastest, cheapest,
the shortest, fastest, cheapest,
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eta esango dizuet, motzena,
azkarrena eta merkeena
azkarrena eta merkeena
01:03
best set of routes to take for the food.
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den bidea, janaria eramateko.
01:05
We're going to save money,
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Dirua aurreztuko dugu,
01:07
we're going to avoid
delays and disruptions,
delays and disruptions,
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atzerapenak eta eragozpenak
ekidingo ditugu,
ekidingo ditugu,
01:09
and bottom line,
we're going to save lives.
we're going to save lives.
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eta azkenik,
bizitzak salbatuko ditugu.
bizitzak salbatuko ditugu.
01:12
You're welcome.
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Ez horregatik.
01:13
(Laughter)
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(Barreak)
12 hilabetetan egina
izango zela uste nuen,
izango zela uste nuen,
01:15
I thought it was going to take 12 months,
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01:17
OK, maybe even 13.
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ados, agian 13 hilabetetan.
01:19
This is not quite how it panned out.
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Baina ez zen horrela izan.
01:23
Just a couple of months into the project,
my French boss, he told me,
my French boss, he told me,
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Proiektuan pare bat hilabete nindoala
nire nagusiak esan zidan
nire nagusiak esan zidan
01:27
"You know, Mallory,
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"Mallory,
01:29
it's a good idea,
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ideia ona da,
01:30
but the data you need
for your algorithms is not there.
for your algorithms is not there.
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baina zure algoritmoetarako
behar dituzun datuak ez daude hemen.
behar dituzun datuak ez daude hemen.
01:34
It's the right idea but at the wrong time,
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Ideia ona da, baina une txarrean,
01:36
and the right idea at the wrong time
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eta ideia ona une txarrean
01:39
is the wrong idea."
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ideia txarra da."
01:40
(Laughter)
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(Barreak)
01:42
Project over.
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Proiektua amaituta.
01:45
I was crushed.
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Apurtuta nengoen.
01:49
When I look back now
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Orain atzera begiratzean,
01:50
on that first summer in Rome
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Erromako lehen uda hartara,
01:52
and I see how much has changed
over the past six years,
over the past six years,
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eta azken sei urteetan
zenbat aldatu den ikusten dut,
zenbat aldatu den ikusten dut,
01:54
it is an absolute transformation.
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erabateko eraldaketa izan da.
01:57
It's a coming of age for bringing data
into the humanitarian world.
into the humanitarian world.
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Mundu berri bat da informazioaren
eta laguntza humanitarioarentzat.
eta laguntza humanitarioarentzat.
02:02
It's exciting. It's inspiring.
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Kitzikagarria da. Inspiratzailea.
Baina oraindik ez gaude hor.
02:04
But we're not there yet.
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02:07
And brace yourself, executives,
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Eta adi exekutiboak,
02:09
because I'm going to be putting companies
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hor jardungo naizelako,
konpainiak
konpainiak
02:11
on the hot seat to step up
and play the role that I know they can.
and play the role that I know they can.
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errudunen aulkian ipintzen,
euren eginbeharra bete dezaten.
euren eginbeharra bete dezaten.
02:17
My experiences back in Rome prove
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Erroman izan nituen esperientziek
erakutsi zidaten
erakutsi zidaten
informazioa baliatuz
bizitzak salba zitezkeela.
bizitzak salba zitezkeela.
02:20
using data you can save lives.
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02:23
OK, not that first attempt,
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Beno ez lehen saiakeran,
02:25
but eventually we got there.
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baina halako batean lortu genuen.
02:28
Let me paint the picture for you.
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Utz iezaidazue irudia deskribatzen.
Pentsa gosaria, bazkaria eta afaria
planifikatu behar direla
planifikatu behar direla
02:30
Imagine that you have to plan
breakfast, lunch and dinner
breakfast, lunch and dinner
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02:33
for 500,000 people,
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500.000 pertsonentzat,
02:34
and you only have
a certain budget to do it,
a certain budget to do it,
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eta horretarako aurrekontu
bat daukazuela,
bat daukazuela,
02:36
say 6.5 million dollars per month.
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demagun 6.5 milioi dolar hilean.
Zer egin beharko zenukete?
Zein da kudeatzeko modu egokiena?
Zein da kudeatzeko modu egokiena?
02:40
Well, what should you do?
What's the best way to handle it?
What's the best way to handle it?
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02:44
Should you buy rice, wheat, chickpea, oil?
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Arroza, garia, garbantzoak,
olioa erosi beharko lirateke?
olioa erosi beharko lirateke?
02:47
How much?
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Zenbat?
02:49
It sounds simple. It's not.
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Sinplea dirudi. Ez da.
02:51
You have 30 possible foods,
and you have to pick five of them.
and you have to pick five of them.
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30 elikagai desberdin dituzue,
bost aukeratu behar dituzue.
bost aukeratu behar dituzue.
02:54
That's already over 140,000
different combinations.
different combinations.
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140.000 konbinaketa baino
gehiago lirateke.
gehiago lirateke.
02:57
Then for each food that you pick,
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Gainera, elikagai bakoitzeko,
02:59
you need to decide how much you'll buy,
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zenbat erosi erabaki behar da,
03:01
where you're going to get it from,
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non erosiko den,
03:03
where you're going to store it,
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non gordeko den,
03:05
how long it's going to take to get there.
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zenbat denboran eramango den hara.
03:07
You need to look at all of the different
transportation routes as well.
transportation routes as well.
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Garraiobide guztiak aztertu
beharko dira.
beharko dira.
900 milioi aukera baino
gehiago lirateke.
gehiago lirateke.
03:11
And that's already
over 900 million options.
over 900 million options.
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Aukera bakoitza segundu batez
kontsideratuko bazenute,
kontsideratuko bazenute,
03:14
If you considered each option
for a single second,
for a single second,
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03:16
that would take you
over 28 years to get through.
over 28 years to get through.
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28 urte beharko zenituzkete.
03:18
900 million options.
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900 milioi aukera.
03:21
So we created a tool
that allowed decisionmakers
that allowed decisionmakers
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Hortaz, erabakiak hartzeko
tresna sortu genuen
tresna sortu genuen
03:23
to weed through all 900 million options
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900 milioi aukerak aztertzeko
03:26
in just a matter of days.
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egun gutxi batzuetan.
03:28
It turned out to be incredibly successful.
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Oso arrakastatsua izan zen.
03:31
In an operation in Iraq,
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Irak-eko operazio batean,
03:32
we saved 17 percent of the costs,
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kostuaren %17 aurreztu genuen,
03:35
and this meant that you had the ability
to feed an additional 80,000 people.
to feed an additional 80,000 people.
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eta honek 80.000 pertsona gehiago
elikatzea suposatzen zuen.
elikatzea suposatzen zuen.
03:39
It's all thanks to the use of data
and modeling complex systems.
and modeling complex systems.
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Guztia datuen erabilerari esker,
eta sistema konplexuak modelatzeari esker.
eta sistema konplexuak modelatzeari esker.
Baina ez genuen bakarrik egin.
03:44
But we didn't do it alone.
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03:46
The unit that I worked with in Rome,
they were unique.
they were unique.
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Erroman lankide izan nuen unitatea
paregabea zen.
paregabea zen.
03:49
They believed in collaboration.
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Lankidetzan sinesten zuten.
03:51
They brought in the academic world.
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Mundu akademikoa ekarri zuten.
Konpainiak ekarri zituzten.
03:53
They brought in companies.
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03:55
And if we really want to make big changes
in big problems like world hunger,
in big problems like world hunger,
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Eta gosetea bezalako arazo handietan
benetan aldaketak egin nahi baditugu,
benetan aldaketak egin nahi baditugu,
03:58
we need everybody to the table.
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guztion lankidetza behar dugu.
04:02
We need the data people
from humanitarian organizations
from humanitarian organizations
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Erakunde humanitarioetako
datuak aztertzen dituzten pertsonen
datuak aztertzen dituzten pertsonen
04:05
leading the way,
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gidaritza behar dugu,
04:06
and orchestrating
just the right types of engagements
just the right types of engagements
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behar diren bezalako loturak ezarriz
04:08
with academics, with governments.
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akademikoekin eta gobernuekin.
04:10
And there's one group that's not being
leveraged in the way that it should be.
leveraged in the way that it should be.
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Eta bada talde bat beharko lukeen bezala
jokatzen ari ez dena.
jokatzen ari ez dena.
04:14
Did you guess it? Companies.
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Asmatu duzue zein? Konpainiak.
04:16
Companies have a major role to play
in fixing the big problems in our world.
in fixing the big problems in our world.
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Konpainiek gure munduko arazoak
konpontzen paper garrantzitsu bat dute.
konpontzen paper garrantzitsu bat dute.
04:20
I've been in the private sector
for two years now.
for two years now.
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Duela bi urtetik hona
sektore pribatuan nago.
sektore pribatuan nago.
04:23
I've seen what companies can do,
and I've seen what companies aren't doing,
and I've seen what companies aren't doing,
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Konpainiek egin dezaketena ikusi dut,
baita egiten ari ez direna ere,
baita egiten ari ez direna ere,
04:26
and I think there's three main ways
that we can fill that gap:
that we can fill that gap:
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eta uste dut hiru modu daudela
hutsune hori betetzeko:
hutsune hori betetzeko:
04:30
by donating data,
by donating decision scientists
by donating decision scientists
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datuak dohaintzan emanez,
erabaki hartzaile zientifikoak emanez
erabaki hartzaile zientifikoak emanez
04:33
and by donating technology
to gather new sources of data.
to gather new sources of data.
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eta teknologia emanez,
data iturri berriak bilatzeko.
data iturri berriak bilatzeko.
04:37
This is data philanthropy,
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Hau datu filantropia da,
04:39
and it's the future of corporate
social responsibility.
social responsibility.
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eta etorkizuneko
korporazioen ardura soziala da.
korporazioen ardura soziala da.
04:43
Bonus, it also makes good business sense.
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Gainera, ikuspuntu enpresarialetik
ere zentzua du.
ere zentzua du.
04:46
Companies today,
they collect mountains of data,
they collect mountains of data,
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Egun, enpresek datu kantitate
handiak jasotzen dituzte,
handiak jasotzen dituzte,
04:50
so the first thing they can do
is start donating that data.
is start donating that data.
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beraz, egin dezaketen lehen gauza
datuak ematea da.
datuak ematea da.
04:52
Some companies are already doing it.
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Konpainia batzuk jada egiten dute.
04:55
Take, for example,
a major telecom company.
a major telecom company.
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Adibidez, Telecom konpainiak.
04:57
They opened up their data
in Senegal and the Ivory Coast
in Senegal and the Ivory Coast
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Senegalen eta Boli Kostan
euren datuak ireki zituzten
euren datuak ireki zituzten
05:00
and researchers discovered
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eta ikerlariek zera aurkitu zuten,
05:02
that if you look at the patterns
in the pings to the cell phone towers,
in the pings to the cell phone towers,
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telefonoen antenetako errepikapenen
patroiak behatuz gero,
patroiak behatuz gero,
jendea nora doan
ikusi daitekeela.
ikusi daitekeela.
05:05
you can see where people are traveling.
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05:07
And that can tell you things like
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Eta hainbat gauza esan ditzakezula,
adibidez
adibidez
05:09
where malaria might spread,
and you can make predictions with it.
and you can make predictions with it.
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malaria nora hedatu daitekeen,
horrekin iragarpenak eginez.
horrekin iragarpenak eginez.
05:13
Or take for example
an innovative satellite company.
an innovative satellite company.
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Edo, demagun satelite
konpainia berritzaile bat.
konpainia berritzaile bat.
05:15
They opened up their data and donated it,
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Euren datuak ireki eta eman zituzten,
eta horrekin zera
kontrolatu daiteke:
kontrolatu daiteke:
05:18
and with that data you could track
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05:19
how droughts are impacting
food production.
food production.
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lehorteek elikagai ekoizpenean
duten eragina.
duten eragina.
05:22
With that you can actually trigger
aid funding before a crisis can happen.
aid funding before a crisis can happen.
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Horrekin krisia gertatu aurretik
laguntza martxan jar daiteke.
laguntza martxan jar daiteke.
05:27
This is a great start.
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Hori hasiera bikaina da.
05:29
There's important insights
just locked away in company data.
just locked away in company data.
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Aurkikuntza handiak daude
konpainien datuetan giltzapetuta.
konpainien datuetan giltzapetuta.
05:34
And yes, you need to be very careful.
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Eta bai, oso kontuz ibili behar gara.
05:36
You need to respect privacy concerns,
for example by anonymizing the data.
for example by anonymizing the data.
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Pribazitatea errespetatu behar da,
adibidez datuak anonimizatuz.
adibidez datuak anonimizatuz.
05:39
But even if the floodgates opened up,
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Baina uhateak irekiko balira ere,
05:42
and even if all companies
donated their data
donated their data
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eta konpainia guztiek
datuak emango balituzte
datuak emango balituzte
05:45
to academics, to NGOs,
to humanitarian organizations,
to humanitarian organizations,
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akademikoek, GKE-k eta
erakunde humanitarioek erabiltzeko,
erakunde humanitarioek erabiltzeko,
05:48
it wouldn't be enough
to harness that full impact of data
to harness that full impact of data
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ez litzateke nahikoa izango
datuen eragin osoa aprobetxatzeko
datuen eragin osoa aprobetxatzeko
helburu humanitarioak betetzeko.
05:51
for humanitarian goals.
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05:54
Why?
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Zergatik?
05:55
To unlock insights in data,
you need decision scientists.
you need decision scientists.
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Datuek ezkutatzen dutena ikusteko,
erabaki hartzaileak behar dituzu.
erabaki hartzaileak behar dituzu.
Erabaki hartzaile zientifikoak
ni bezalako pertsonak dira.
ni bezalako pertsonak dira.
05:59
Decision scientists are people like me.
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06:02
They take the data, they clean it up,
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Datuak hartu, garbitu,
eraldatu eta algoritmo
erabilgarrietan sartzen dituzte
erabilgarrietan sartzen dituzte
06:04
transform it and put it
into a useful algorithm
into a useful algorithm
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06:06
that's the best choice
to address the business need at hand.
to address the business need at hand.
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hori da aukerarik onena
egin beharrekoa egiteko.
egin beharrekoa egiteko.
06:09
In the world of humanitarian aid,
there are very few decision scientists.
there are very few decision scientists.
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Laguntza humanitarioaren munduan,
erabaki hartzaile gutxi daude.
erabaki hartzaile gutxi daude.
06:13
Most of them work for companies.
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Gehienak konpainietan daude.
Beraz hori da konpainiek egin beharreko
bigarren gauza.
bigarren gauza.
06:16
So that's the second thing
that companies need to do.
that companies need to do.
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06:19
In addition to donating their data,
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Datuak emateaz gain,
06:20
they need to donate
their decision scientists.
their decision scientists.
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erabaki hartzaileak eman behar dituzte.
06:23
Now, companies will say, "Ah! Don't take
our decision scientists from us.
our decision scientists from us.
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Konpainiek zera esango dute: "Eh! ez
kendu guri erabaki hartzaileak.
kendu guri erabaki hartzaileak.
06:29
We need every spare second of their time."
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beraien denbora guztia behar dugu."
06:32
But there's a way.
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Baina bada modu bat.
06:35
If a company was going to donate
a block of a decision scientist's time,
a block of a decision scientist's time,
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Konpainia bat erabaki hartzaile baten
denbora kopuru bat ematera badoa,
denbora kopuru bat ematera badoa,
06:38
it would actually make more sense
to spread out that block of time
to spread out that block of time
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zentzu gehiago izango luke kopuru
hori barreiatzeak
hori barreiatzeak
06:41
over a long period,
say for example five years.
say for example five years.
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denbora tarte luze batean,
esaterako 5 urtetan.
esaterako 5 urtetan.
06:44
This might only amount
to a couple of hours per month,
to a couple of hours per month,
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Horrela agian hilabeteko pare bat
ordu soilik lirateke,
ordu soilik lirateke,
06:47
which a company would hardly miss,
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konpainian ez luke gehiegi eragingo,
06:49
but what it enables is really important:
long-term partnerships.
long-term partnerships.
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baina horrela zera lortuko litzateke:
epe luzeko asoziazioak.
epe luzeko asoziazioak.
Epe luzeko asoziazioek
erlazioak sortzea ahalbideratzen dute,
erlazioak sortzea ahalbideratzen dute,
06:54
Long-term partnerships
allow you to build relationships,
allow you to build relationships,
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06:57
to get to know the data,
to really understand it
to really understand it
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datuak ezagutzera heltzeko,
benetan ulertzeko,
benetan ulertzeko,
07:00
and to start to understand
the needs and challenges
the needs and challenges
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eta erakunde humanitarioak dituen
07:02
that the humanitarian
organization is facing.
organization is facing.
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beharrak eta zailtasunak ezagutzeko.
Erroman, Munduko Elikagaien Programan
luze jo zigun,
luze jo zigun,
07:06
In Rome, at the World Food Programme,
this took us five years to do,
this took us five years to do,
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07:09
five years.
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5 urte.
07:11
That first three years, OK,
that was just what we couldn't solve for.
that was just what we couldn't solve for.
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Lehen hiru urteak, beno
hau ezin da murriztu.
hau ezin da murriztu.
07:14
Then there was two years after that
of refining and implementing the tool,
of refining and implementing the tool,
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Horren ostean bi urtez
tresna hobetu eta martxan jarri
tresna hobetu eta martxan jarri
Irak eta beste herrialdeetako
operazioetan bezala.
operazioetan bezala.
07:17
like in the operations in Iraq
and other countries.
and other countries.
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Ez dut uste planifikazio hori
errealista ez denik
errealista ez denik
07:21
I don't think that's
an unrealistic timeline
an unrealistic timeline
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07:23
when it comes to using data
to make operational changes.
to make operational changes.
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operazioetan datuak erabiliz
aldaketak egitean.
aldaketak egitean.
07:26
It's an investment. It requires patience.
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Inbertsio bat da. Pazientzia eskatzen du.
07:29
But the types of results
that can be produced are undeniable.
that can be produced are undeniable.
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Baina sor daitezkeen emaitzak
ukaezinak dira.
ukaezinak dira.
07:33
In our case, it was the ability
to feed tens of thousands more people.
to feed tens of thousands more people.
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Gure kasuan milaka pertsona gehiago
elikatzea izan zen.
elikatzea izan zen.
07:39
So we have donating data,
we have donating decision scientists,
we have donating decision scientists,
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Beraz datuak ematea, erabaki
hartzaileak ematea,
hartzaileak ematea,
eta konpainiek lagundu ahal izateko
3. modu bat ere badaukagu:
3. modu bat ere badaukagu:
07:43
and there's actually a third way
that companies can help:
that companies can help:
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07:46
donating technology
to capture new sources of data.
to capture new sources of data.
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datu iturri berriak lortzeko
teknologia ematea.
teknologia ematea.
07:49
You see, there's a lot of things
we just don't have data on.
we just don't have data on.
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Hainbat gauza ditugu
zeinaren oraindik daturik ez dugun.
zeinaren oraindik daturik ez dugun.
07:52
Right now, Syrian refugees
are flooding into Greece,
are flooding into Greece,
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Oraintxe Siriar errefuxatuak
Greziara iristen ari dira,
Greziara iristen ari dira,
07:57
and the UN refugee agency,
they have their hands full.
they have their hands full.
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eta NBetako errefuxatuen agentziak
esku bete lan du.
esku bete lan du.
08:01
The current system for tracking people
is paper and pencil,
is paper and pencil,
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Egun jendea jarraitzeko modua
arkatz eta paperezkoa da,
arkatz eta paperezkoa da,
08:04
and what that means is
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horrek zera esan nahi du,
08:05
that when a mother and her five children
walk into the camp,
walk into the camp,
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ama bat bere 5 haurrekin
kanpamentura sartzean,
kanpamentura sartzean,
08:08
headquarters is essentially
blind to this moment.
blind to this moment.
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egoitza nagusiak ez duela jakingo.
08:10
That's all going to change
in the next few weeks,
in the next few weeks,
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Guzti hori aste gutxi barru
aldatuko da,
aldatuko da,
08:13
thanks to private sector collaboration.
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sektore pribatuaren
kolaborazioari esker.
kolaborazioari esker.
08:15
There's going to be a new system based
on donated package tracking technology
on donated package tracking technology
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Paketeen jarraipeneko teknologian
oinarritutako sistema berria egongo da
oinarritutako sistema berria egongo da
08:19
from the logistics company
that I work for.
that I work for.
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nik lan egiten dudan konpainiak
emandakoa.
emandakoa.
Sistema berri honekin,
datuak jarraitu ahalko dira,
datuak jarraitu ahalko dira,
08:22
With this new system,
there will be a data trail,
there will be a data trail,
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08:24
so you know exactly the moment
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zehazki jakiteko
08:25
when that mother and her children
walk into the camp.
walk into the camp.
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ama eta bere haurrak kanpamendura
noiz iritsi diren.
noiz iritsi diren.
08:28
And even more, you know
if she's going to have supplies
if she's going to have supplies
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Are gehiago, hornigaiak izango
dituen jakingo dugu
dituen jakingo dugu
hilabete honetan eta hurrengoan.
08:31
this month and the next.
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08:32
Information visibility drives efficiency.
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Informazioak eraginkortasuna dakar.
08:35
For companies, using technology
to gather important data,
to gather important data,
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Konpainientzat, teknologia erabiltzea
datu garrantzitsuak lortzeko
datu garrantzitsuak lortzeko
08:38
it's like bread and butter.
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ogia eta gurina bezala dira.
08:40
They've been doing it for years,
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Urteak daramatzate hortan,
08:41
and it's led to major
operational efficiency improvements.
operational efficiency improvements.
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eta operazio hobekuntza eraginkorrak
ekarri ditu.
ekarri ditu.
08:45
Just try to imagine
your favorite beverage company
your favorite beverage company
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Imajinatu zure edari konpania gustukoena
08:48
trying to plan their inventory
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euren inbentarioa osatu nahian
08:49
and not knowing how many bottles
were on the shelves.
were on the shelves.
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eta apaletan zenbat boteila dauden
jakin ezinean.
jakin ezinean.
08:52
It's absurd.
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Absurdua da.
08:53
Data drives better decisions.
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Datuek erabaki hobeak dakartzate.
08:57
Now, if you're representing a company,
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Konpainia baten ordezkari bazara,
09:00
and you're pragmatic
and not just idealistic,
and not just idealistic,
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eta idealistaz gain pragmatikoa bazara,
09:03
you might be saying to yourself,
"OK, this is all great, Mallory,
"OK, this is all great, Mallory,
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zeure buruari ariko zara
"Ok, hau ederki dago, Mallory,
"Ok, hau ederki dago, Mallory,
09:06
but why should I want to be involved?"
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baina zertarako sartuko naiz horretan?"
09:09
Well for one thing, beyond the good PR,
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Gauza batengatik,
publizitate onaz gain,
publizitate onaz gain,
09:11
humanitarian aid
is a 24-billion-dollar sector,
is a 24-billion-dollar sector,
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laguntza humanitarioak,
24 bilioi dolar mugitzen ditu,
24 bilioi dolar mugitzen ditu,
09:14
and there's over five billion people,
maybe your next customers,
maybe your next customers,
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eta 5 bilioi pertsona baino gehiago,
agian etorkizuneko bezeroak,
agian etorkizuneko bezeroak,
garapen bideko herrialdeetan
bizi dira.
bizi dira.
09:17
that live in the developing world.
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09:19
Further, companies that are engaging
in data philanthropy,
in data philanthropy,
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Datuen filantropian sartzen
ari diren konpainiak
ari diren konpainiak
09:22
they're finding new insights
locked away in their data.
locked away in their data.
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euren datuetan ezkutatutako
gauza berriak aurkitzen ari dira.
gauza berriak aurkitzen ari dira.
09:25
Take, for example, a credit card company
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Demagun kreditu txartelen konpainiak
09:27
that's opened up a center
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zentro bat ireki duela
09:29
that functions as a hub for academics,
for NGOs and governments,
for NGOs and governments,
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akademiko, GKE eta gobernuentzat
egoitza bezala lan egiten duena,
egoitza bezala lan egiten duena,
09:32
all working together.
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guztiak batera lan eginaz.
09:35
They're looking at information
in credit card swipes
in credit card swipes
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Kreditu txartelen irakurketetako
datuak bilatzen dituzte
datuak bilatzen dituzte
eta datu horiek erabiltzen dituzte
jakiteko Indiako etxeetan nola
jakiteko Indiako etxeetan nola
09:37
and using that to find insights
about how households in India
about how households in India
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bizi, lan egin, irabazi
eta gastatzen den.
eta gastatzen den.
09:40
live, work, earn and spend.
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09:43
For the humanitarian world,
this provides information
this provides information
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Mundu humanitarioarentzat,
honek datuak ematen ditu
honek datuak ematen ditu
09:46
about how you might
bring people out of poverty.
bring people out of poverty.
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jendea txirotasunetik nola atera
asmatzeko.
asmatzeko.
09:48
But for companies, it's providing
insights about your customers
insights about your customers
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Baina konpainientzat, honek
ezagutza berria dakar bezero
ezagutza berria dakar bezero
09:52
and potential customers in India.
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eta Indiako bezero posibleen
inguruan.
inguruan.
09:54
It's a win all around.
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Denek irabazten dute.
09:57
Now, for me, what I find
exciting about data philanthropy --
exciting about data philanthropy --
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Nire kasuan, datuen filantropiatik
liluragarriena iruditzen zaidana
liluragarriena iruditzen zaidana
-- datuak, erabaki hartzaile zientifikoak
eta teknologia ematea --
eta teknologia ematea --
10:01
donating data, donating decision
scientists and donating technology --
scientists and donating technology --
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ni bezalako profesional gazteentzat
duen esanahia da,
duen esanahia da,
10:06
it's what it means
for young professionals like me
for young professionals like me
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10:08
who are choosing to work at companies.
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konpainietan lan egiten
dugunontzat.
dugunontzat.
10:10
Studies show that
the next generation of the workforce
the next generation of the workforce
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Ikerketek diote belaunaldi berrietako
langileriak
langileriak
euren lanak inpaktu handiagoa
izateagatik arduratzen direla.
izateagatik arduratzen direla.
10:13
care about having their work
make a bigger impact.
make a bigger impact.
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10:16
We want to make a difference,
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Gauzak desberdin egin nahi ditugu,
10:19
and so through data philanthropy,
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eta datuen filantropia bidez,
10:21
companies can actually help engage
and retain their decision scientists.
and retain their decision scientists.
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konpainiek erabaki hartzaile zientifiko
batzuk manten ditzakete.
batzuk manten ditzakete.
10:25
And that's a big deal for a profession
that's in high demand.
that's in high demand.
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Eta hau garrantzitsua da eskaera altuko
profesio batean.
profesio batean.
10:29
Data philanthropy
makes good business sense,
makes good business sense,
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Datuen filantropiak zentzua du
negozioetan,
negozioetan,
10:34
and it also can help
revolutionize the humanitarian world.
revolutionize the humanitarian world.
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eta mundu humanitarioa iraultzen
lagundu dezake.
lagundu dezake.
Planifikazioa eta logistika
koordinatzen baditugu
koordinatzen baditugu
10:39
If we coordinated
the planning and logistics
the planning and logistics
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10:41
across all of the major facets
of a humanitarian operation,
of a humanitarian operation,
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prozesu humanitario nagusienen
ezaugarri guztietan,
ezaugarri guztietan,
10:45
we could feed, clothe and shelter
hundreds of thousands more people,
hundreds of thousands more people,
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ehundaka milaka pertsona elikatu,
jantzi eta babestu genitzake,
jantzi eta babestu genitzake,
10:49
and companies need to step up
and play the role that I know they can
and play the role that I know they can
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eta konpainiek aurrerapausua eman eta
joka dezaketen papera jokatu behar dute
joka dezaketen papera jokatu behar dute
10:53
in bringing about this revolution.
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iraultza hau burutzeko.
10:56
You've probably heard of the saying
"food for thought."
"food for thought."
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Ziurrenik entzuna duzue
"jana pentsamenduen truk".
"jana pentsamenduen truk".
10:59
Well, this is literally thought for food.
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Hau literaki pentsamenduen truk
jana litzateke.
jana litzateke.
11:03
It finally is the right idea
at the right time.
at the right time.
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Azkenean ideia zuzena da, une egokian.
11:07
(Laughter)
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(Barreak)
11:08
Très magnifique.
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Très magnifique
11:10
Thank you.
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Mila esker.
11:11
(Applause)
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(txaloak)
ABOUT THE SPEAKER
Mallory Freeman - Data activistUPS's advanced analytics manager Mallory Freeman researches how to do the most good with data.
Why you should listen
Dr. Mallory Freeman is the Lead Data Scientist in the UPS Advanced Technology Group, working on research and development projects for UPS’s smart logistics network. She serves on the advisory board of Neighborhood Nexus, supporting data-driven insights for the greater Atlanta region.
Freeman earned her Ph.D. in industrial engineering from the Georgia Institute of Technology in 2014. Her thesis explored how to measure and improve humanitarian operations in practical ways -- with a special focus on the use of algorithms. While she was in graduate school, she helped lead supply chain optimization projects for the UN World Food Programme.
Freeman earned her Master's in operations research from MIT and her Bachelor's in industrial and systems engineering from Virginia Tech. In her spare time, she enjoys cooking, travelling and volunteering her data skills.
Mallory Freeman | Speaker | TED.com