ABOUT THE SPEAKER
Mallory Freeman - Data activist
UPS'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.

More profile about the speaker
Mallory Freeman | Speaker | TED.com
TED@UPS

Mallory Freeman: Your company's data could help end world hunger

Mallory Soldner: Zure konpainiaren datuek munduko gosetea amaitzen lagun dezakete.

Filmed:
1,090,373 views

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?
- Data activist
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.
0
880
1760
2010eko ekaina.
00:15
I landed for the first time
in Rome, Italy.
1
3760
2880
Lehenbizikoz nintzen
Erroman, Italian.
00:19
I wasn't there to sightsee.
2
7800
1896
Ez nintzen turismoa egitera joan.
00:21
I was there to solve world hunger.
3
9720
3120
Munduko gosea konpontzera baizik.
00:25
(Laughter)
4
13160
2096
(Barreak)
00:27
That's right.
5
15280
1216
Hori da.
25 urteko dokotegaia nintzen
00:28
I was a 25-year-old PhD student
6
16520
2096
00:30
armed with a prototype tool
developed back at my university,
7
18640
3096
nire unibertsitatean sortutako
tresna baten prototipo batekin,
00:33
and I was going to help
the World Food Programme fix hunger.
8
21760
3080
eta Munduko Elikagai Programan
laguntzera nindoan.
00:37
So I strode into the headquarters building
9
25840
2736
Egoitza nagusira sartu nintzen
00:40
and my eyes scanned the row of UN flags,
10
28600
2816
eta NBetako bandera ilarari
begiratu bat bota nion,
00:43
and I smiled as I thought to myself,
11
31440
1960
irribarre egin eta pentsatu nuen
00:46
"The engineer is here."
12
34840
1616
"ingeniaria hemen da."
00:48
(Laughter)
13
36480
2216
(Barreak)
00:50
Give me your data.
14
38720
1776
Emaizkidazue zuen datuak.
00:52
I'm going to optimize everything.
15
40520
2176
Guztia optimizatzera noa.
00:54
(Laughter)
16
42720
1736
(Barreak)
00:56
Tell me the food that you've purchased,
17
44480
1896
Esaidazue ze janari erosi duzuen,
00:58
tell me where it's going
and when it needs to be there,
18
46400
2616
nora joan behar duen
eta noizko,
01:01
and I'm going to tell you
the shortest, fastest, cheapest,
19
49040
2736
eta esango dizuet, motzena,
azkarrena eta merkeena
01:03
best set of routes to take for the food.
20
51800
1936
den bidea, janaria eramateko.
01:05
We're going to save money,
21
53760
1496
Dirua aurreztuko dugu,
01:07
we're going to avoid
delays and disruptions,
22
55280
2096
atzerapenak eta eragozpenak
ekidingo ditugu,
01:09
and bottom line,
we're going to save lives.
23
57400
2736
eta azkenik,
bizitzak salbatuko ditugu.
01:12
You're welcome.
24
60160
1216
Ez horregatik.
01:13
(Laughter)
25
61400
1696
(Barreak)
12 hilabetetan egina
izango zela uste nuen,
01:15
I thought it was going to take 12 months,
26
63120
1976
01:17
OK, maybe even 13.
27
65120
1560
ados, agian 13 hilabetetan.
01:19
This is not quite how it panned out.
28
67800
2280
Baina ez zen horrela izan.
01:23
Just a couple of months into the project,
my French boss, he told me,
29
71600
3776
Proiektuan pare bat hilabete nindoala
nire nagusiak esan zidan
01:27
"You know, Mallory,
30
75400
1816
"Mallory,
01:29
it's a good idea,
31
77240
1656
ideia ona da,
01:30
but the data you need
for your algorithms is not there.
32
78920
3336
baina zure algoritmoetarako
behar dituzun datuak ez daude hemen.
01:34
It's the right idea but at the wrong time,
33
82280
2536
Ideia ona da, baina une txarrean,
01:36
and the right idea at the wrong time
34
84840
2296
eta ideia ona une txarrean
01:39
is the wrong idea."
35
87160
1376
ideia txarra da."
01:40
(Laughter)
36
88560
1320
(Barreak)
01:42
Project over.
37
90960
1280
Proiektua amaituta.
01:45
I was crushed.
38
93120
1200
Apurtuta nengoen.
01:49
When I look back now
39
97000
1456
Orain atzera begiratzean,
01:50
on that first summer in Rome
40
98480
1656
Erromako lehen uda hartara,
01:52
and I see how much has changed
over the past six years,
41
100160
2656
eta azken sei urteetan
zenbat aldatu den ikusten dut,
01:54
it is an absolute transformation.
42
102840
2240
erabateko eraldaketa izan da.
01:57
It's a coming of age for bringing data
into the humanitarian world.
43
105640
3400
Mundu berri bat da informazioaren
eta laguntza humanitarioarentzat.
02:02
It's exciting. It's inspiring.
44
110160
2656
Kitzikagarria da. Inspiratzailea.
Baina oraindik ez gaude hor.
02:04
But we're not there yet.
45
112840
1200
02:07
And brace yourself, executives,
46
115320
2296
Eta adi exekutiboak,
02:09
because I'm going to be putting companies
47
117640
1976
hor jardungo naizelako,
konpainiak
02:11
on the hot seat to step up
and play the role that I know they can.
48
119640
3120
errudunen aulkian ipintzen,
euren eginbeharra bete dezaten.
02:17
My experiences back in Rome prove
49
125520
2816
Erroman izan nituen esperientziek
erakutsi zidaten
informazioa baliatuz
bizitzak salba zitezkeela.
02:20
using data you can save lives.
50
128360
2080
02:23
OK, not that first attempt,
51
131440
2456
Beno ez lehen saiakeran,
02:25
but eventually we got there.
52
133920
2576
baina halako batean lortu genuen.
02:28
Let me paint the picture for you.
53
136520
1736
Utz iezaidazue irudia deskribatzen.
Pentsa gosaria, bazkaria eta afaria
planifikatu behar direla
02:30
Imagine that you have to plan
breakfast, lunch and dinner
54
138280
2736
02:33
for 500,000 people,
55
141040
1616
500.000 pertsonentzat,
02:34
and you only have
a certain budget to do it,
56
142680
2136
eta horretarako aurrekontu
bat daukazuela,
02:36
say 6.5 million dollars per month.
57
144840
2240
demagun 6.5 milioi dolar hilean.
Zer egin beharko zenukete?
Zein da kudeatzeko modu egokiena?
02:40
Well, what should you do?
What's the best way to handle it?
58
148920
2762
02:44
Should you buy rice, wheat, chickpea, oil?
59
152280
2760
Arroza, garia, garbantzoak,
olioa erosi beharko lirateke?
02:47
How much?
60
155760
1216
Zenbat?
02:49
It sounds simple. It's not.
61
157000
2136
Sinplea dirudi. Ez da.
02:51
You have 30 possible foods,
and you have to pick five of them.
62
159160
3216
30 elikagai desberdin dituzue,
bost aukeratu behar dituzue.
02:54
That's already over 140,000
different combinations.
63
162400
3416
140.000 konbinaketa baino
gehiago lirateke.
02:57
Then for each food that you pick,
64
165840
1696
Gainera, elikagai bakoitzeko,
02:59
you need to decide how much you'll buy,
65
167560
1976
zenbat erosi erabaki behar da,
03:01
where you're going to get it from,
66
169560
1696
non erosiko den,
03:03
where you're going to store it,
67
171280
1480
non gordeko den,
03:05
how long it's going to take to get there.
68
173760
1976
zenbat denboran eramango den hara.
03:07
You need to look at all of the different
transportation routes as well.
69
175760
3336
Garraiobide guztiak aztertu
beharko dira.
900 milioi aukera baino
gehiago lirateke.
03:11
And that's already
over 900 million options.
70
179120
2080
Aukera bakoitza segundu batez
kontsideratuko bazenute,
03:14
If you considered each option
for a single second,
71
182120
2376
03:16
that would take you
over 28 years to get through.
72
184520
2336
28 urte beharko zenituzkete.
03:18
900 million options.
73
186880
1520
900 milioi aukera.
03:21
So we created a tool
that allowed decisionmakers
74
189160
2456
Hortaz, erabakiak hartzeko
tresna sortu genuen
03:23
to weed through all 900 million options
75
191640
2616
900 milioi aukerak aztertzeko
03:26
in just a matter of days.
76
194280
1360
egun gutxi batzuetan.
03:28
It turned out to be incredibly successful.
77
196560
2240
Oso arrakastatsua izan zen.
03:31
In an operation in Iraq,
78
199400
1256
Irak-eko operazio batean,
03:32
we saved 17 percent of the costs,
79
200680
2536
kostuaren %17 aurreztu genuen,
03:35
and this meant that you had the ability
to feed an additional 80,000 people.
80
203240
4136
eta honek 80.000 pertsona gehiago
elikatzea suposatzen zuen.
03:39
It's all thanks to the use of data
and modeling complex systems.
81
207400
4400
Guztia datuen erabilerari esker,
eta sistema konplexuak modelatzeari esker.
Baina ez genuen bakarrik egin.
03:44
But we didn't do it alone.
82
212800
1280
03:46
The unit that I worked with in Rome,
they were unique.
83
214840
2736
Erroman lankide izan nuen unitatea
paregabea zen.
03:49
They believed in collaboration.
84
217600
1736
Lankidetzan sinesten zuten.
03:51
They brought in the academic world.
85
219360
1696
Mundu akademikoa ekarri zuten.
Konpainiak ekarri zituzten.
03:53
They brought in companies.
86
221080
1280
03:55
And if we really want to make big changes
in big problems like world hunger,
87
223200
3616
Eta gosetea bezalako arazo handietan
benetan aldaketak egin nahi baditugu,
03:58
we need everybody to the table.
88
226840
2560
guztion lankidetza behar dugu.
04:02
We need the data people
from humanitarian organizations
89
230040
2936
Erakunde humanitarioetako
datuak aztertzen dituzten pertsonen
04:05
leading the way,
90
233000
1256
gidaritza behar dugu,
04:06
and orchestrating
just the right types of engagements
91
234280
2576
behar diren bezalako loturak ezarriz
04:08
with academics, with governments.
92
236880
1696
akademikoekin eta gobernuekin.
04:10
And there's one group that's not being
leveraged in the way that it should be.
93
238600
3696
Eta bada talde bat beharko lukeen bezala
jokatzen ari ez dena.
04:14
Did you guess it? Companies.
94
242320
2096
Asmatu duzue zein? Konpainiak.
04:16
Companies have a major role to play
in fixing the big problems in our world.
95
244440
3600
Konpainiek gure munduko arazoak
konpontzen paper garrantzitsu bat dute.
04:20
I've been in the private sector
for two years now.
96
248880
2416
Duela bi urtetik hona
sektore pribatuan nago.
04:23
I've seen what companies can do,
and I've seen what companies aren't doing,
97
251320
3576
Konpainiek egin dezaketena ikusi dut,
baita egiten ari ez direna ere,
04:26
and I think there's three main ways
that we can fill that gap:
98
254920
3376
eta uste dut hiru modu daudela
hutsune hori betetzeko:
04:30
by donating data,
by donating decision scientists
99
258320
3096
datuak dohaintzan emanez,
erabaki hartzaile zientifikoak emanez
04:33
and by donating technology
to gather new sources of data.
100
261440
3480
eta teknologia emanez,
data iturri berriak bilatzeko.
04:37
This is data philanthropy,
101
265920
1576
Hau datu filantropia da,
04:39
and it's the future of corporate
social responsibility.
102
267520
2840
eta etorkizuneko
korporazioen ardura soziala da.
04:43
Bonus, it also makes good business sense.
103
271160
2600
Gainera, ikuspuntu enpresarialetik
ere zentzua du.
04:46
Companies today,
they collect mountains of data,
104
274920
3216
Egun, enpresek datu kantitate
handiak jasotzen dituzte,
04:50
so the first thing they can do
is start donating that data.
105
278160
2762
beraz, egin dezaketen lehen gauza
datuak ematea da.
04:52
Some companies are already doing it.
106
280946
2190
Konpainia batzuk jada egiten dute.
04:55
Take, for example,
a major telecom company.
107
283160
2416
Adibidez, Telecom konpainiak.
04:57
They opened up their data
in Senegal and the Ivory Coast
108
285600
2776
Senegalen eta Boli Kostan
euren datuak ireki zituzten
05:00
and researchers discovered
109
288400
1976
eta ikerlariek zera aurkitu zuten,
05:02
that if you look at the patterns
in the pings to the cell phone towers,
110
290400
3334
telefonoen antenetako errepikapenen
patroiak behatuz gero,
jendea nora doan
ikusi daitekeela.
05:05
you can see where people are traveling.
111
293758
1938
05:07
And that can tell you things like
112
295720
2176
Eta hainbat gauza esan ditzakezula,
adibidez
05:09
where malaria might spread,
and you can make predictions with it.
113
297920
3096
malaria nora hedatu daitekeen,
horrekin iragarpenak eginez.
05:13
Or take for example
an innovative satellite company.
114
301040
2896
Edo, demagun satelite
konpainia berritzaile bat.
05:15
They opened up their data and donated it,
115
303960
2016
Euren datuak ireki eta eman zituzten,
eta horrekin zera
kontrolatu daiteke:
05:18
and with that data you could track
116
306000
1656
05:19
how droughts are impacting
food production.
117
307680
2040
lehorteek elikagai ekoizpenean
duten eragina.
05:22
With that you can actually trigger
aid funding before a crisis can happen.
118
310920
3680
Horrekin krisia gertatu aurretik
laguntza martxan jar daiteke.
05:27
This is a great start.
119
315560
1280
Hori hasiera bikaina da.
05:29
There's important insights
just locked away in company data.
120
317840
2880
Aurkikuntza handiak daude
konpainien datuetan giltzapetuta.
05:34
And yes, you need to be very careful.
121
322480
1816
Eta bai, oso kontuz ibili behar gara.
05:36
You need to respect privacy concerns,
for example by anonymizing the data.
122
324320
3576
Pribazitatea errespetatu behar da,
adibidez datuak anonimizatuz.
05:39
But even if the floodgates opened up,
123
327920
2776
Baina uhateak irekiko balira ere,
05:42
and even if all companies
donated their data
124
330720
2536
eta konpainia guztiek
datuak emango balituzte
05:45
to academics, to NGOs,
to humanitarian organizations,
125
333280
3256
akademikoek, GKE-k eta
erakunde humanitarioek erabiltzeko,
05:48
it wouldn't be enough
to harness that full impact of data
126
336560
2976
ez litzateke nahikoa izango
datuen eragin osoa aprobetxatzeko
helburu humanitarioak betetzeko.
05:51
for humanitarian goals.
127
339560
1520
05:54
Why?
128
342320
1456
Zergatik?
05:55
To unlock insights in data,
you need decision scientists.
129
343800
3240
Datuek ezkutatzen dutena ikusteko,
erabaki hartzaileak behar dituzu.
Erabaki hartzaile zientifikoak
ni bezalako pertsonak dira.
05:59
Decision scientists are people like me.
130
347760
2576
06:02
They take the data, they clean it up,
131
350360
1816
Datuak hartu, garbitu,
eraldatu eta algoritmo
erabilgarrietan sartzen dituzte
06:04
transform it and put it
into a useful algorithm
132
352200
2256
06:06
that's the best choice
to address the business need at hand.
133
354480
2840
hori da aukerarik onena
egin beharrekoa egiteko.
06:09
In the world of humanitarian aid,
there are very few decision scientists.
134
357800
3696
Laguntza humanitarioaren munduan,
erabaki hartzaile gutxi daude.
06:13
Most of them work for companies.
135
361520
1640
Gehienak konpainietan daude.
Beraz hori da konpainiek egin beharreko
bigarren gauza.
06:16
So that's the second thing
that companies need to do.
136
364480
2496
06:19
In addition to donating their data,
137
367000
1696
Datuak emateaz gain,
06:20
they need to donate
their decision scientists.
138
368720
2160
erabaki hartzaileak eman behar dituzte.
06:23
Now, companies will say, "Ah! Don't take
our decision scientists from us.
139
371520
5736
Konpainiek zera esango dute: "Eh! ez
kendu guri erabaki hartzaileak.
06:29
We need every spare second of their time."
140
377280
2040
beraien denbora guztia behar dugu."
06:32
But there's a way.
141
380360
1200
Baina bada modu bat.
06:35
If a company was going to donate
a block of a decision scientist's time,
142
383200
3416
Konpainia bat erabaki hartzaile baten
denbora kopuru bat ematera badoa,
06:38
it would actually make more sense
to spread out that block of time
143
386640
3136
zentzu gehiago izango luke kopuru
hori barreiatzeak
06:41
over a long period,
say for example five years.
144
389800
2200
denbora tarte luze batean,
esaterako 5 urtetan.
06:44
This might only amount
to a couple of hours per month,
145
392600
3056
Horrela agian hilabeteko pare bat
ordu soilik lirateke,
06:47
which a company would hardly miss,
146
395680
2056
konpainian ez luke gehiegi eragingo,
06:49
but what it enables is really important:
long-term partnerships.
147
397760
3480
baina horrela zera lortuko litzateke:
epe luzeko asoziazioak.
Epe luzeko asoziazioek
erlazioak sortzea ahalbideratzen dute,
06:54
Long-term partnerships
allow you to build relationships,
148
402920
2816
06:57
to get to know the data,
to really understand it
149
405760
2656
datuak ezagutzera heltzeko,
benetan ulertzeko,
07:00
and to start to understand
the needs and challenges
150
408440
2416
eta erakunde humanitarioak dituen
07:02
that the humanitarian
organization is facing.
151
410880
2160
beharrak eta zailtasunak ezagutzeko.
Erroman, Munduko Elikagaien Programan
luze jo zigun,
07:06
In Rome, at the World Food Programme,
this took us five years to do,
152
414345
3191
07:09
five years.
153
417560
1456
5 urte.
07:11
That first three years, OK,
that was just what we couldn't solve for.
154
419040
3336
Lehen hiru urteak, beno
hau ezin da murriztu.
07:14
Then there was two years after that
of refining and implementing the tool,
155
422400
3496
Horren ostean bi urtez
tresna hobetu eta martxan jarri
Irak eta beste herrialdeetako
operazioetan bezala.
07:17
like in the operations in Iraq
and other countries.
156
425920
2800
Ez dut uste planifikazio hori
errealista ez denik
07:21
I don't think that's
an unrealistic timeline
157
429520
2096
07:23
when it comes to using data
to make operational changes.
158
431640
2736
operazioetan datuak erabiliz
aldaketak egitean.
07:26
It's an investment. It requires patience.
159
434400
2400
Inbertsio bat da. Pazientzia eskatzen du.
07:29
But the types of results
that can be produced are undeniable.
160
437760
3496
Baina sor daitezkeen emaitzak
ukaezinak dira.
07:33
In our case, it was the ability
to feed tens of thousands more people.
161
441280
3560
Gure kasuan milaka pertsona gehiago
elikatzea izan zen.
07:39
So we have donating data,
we have donating decision scientists,
162
447440
4336
Beraz datuak ematea, erabaki
hartzaileak ematea,
eta konpainiek lagundu ahal izateko
3. modu bat ere badaukagu:
07:43
and there's actually a third way
that companies can help:
163
451800
2696
07:46
donating technology
to capture new sources of data.
164
454520
2976
datu iturri berriak lortzeko
teknologia ematea.
07:49
You see, there's a lot of things
we just don't have data on.
165
457520
2840
Hainbat gauza ditugu
zeinaren oraindik daturik ez dugun.
07:52
Right now, Syrian refugees
are flooding into Greece,
166
460960
2720
Oraintxe Siriar errefuxatuak
Greziara iristen ari dira,
07:57
and the UN refugee agency,
they have their hands full.
167
465120
2560
eta NBetako errefuxatuen agentziak
esku bete lan du.
08:01
The current system for tracking people
is paper and pencil,
168
469000
3056
Egun jendea jarraitzeko modua
arkatz eta paperezkoa da,
08:04
and what that means is
169
472080
1256
horrek zera esan nahi du,
08:05
that when a mother and her five children
walk into the camp,
170
473360
2856
ama bat bere 5 haurrekin
kanpamentura sartzean,
08:08
headquarters is essentially
blind to this moment.
171
476240
2656
egoitza nagusiak ez duela jakingo.
08:10
That's all going to change
in the next few weeks,
172
478920
2336
Guzti hori aste gutxi barru
aldatuko da,
08:13
thanks to private sector collaboration.
173
481280
1880
sektore pribatuaren
kolaborazioari esker.
08:15
There's going to be a new system based
on donated package tracking technology
174
483840
3656
Paketeen jarraipeneko teknologian
oinarritutako sistema berria egongo da
08:19
from the logistics company
that I work for.
175
487520
2040
nik lan egiten dudan konpainiak
emandakoa.
Sistema berri honekin,
datuak jarraitu ahalko dira,
08:22
With this new system,
there will be a data trail,
176
490120
2336
08:24
so you know exactly the moment
177
492480
1456
zehazki jakiteko
08:25
when that mother and her children
walk into the camp.
178
493960
2496
ama eta bere haurrak kanpamendura
noiz iritsi diren.
08:28
And even more, you know
if she's going to have supplies
179
496480
2616
Are gehiago, hornigaiak izango
dituen jakingo dugu
hilabete honetan eta hurrengoan.
08:31
this month and the next.
180
499120
1256
08:32
Information visibility drives efficiency.
181
500400
3016
Informazioak eraginkortasuna dakar.
08:35
For companies, using technology
to gather important data,
182
503440
3256
Konpainientzat, teknologia erabiltzea
datu garrantzitsuak lortzeko
08:38
it's like bread and butter.
183
506720
1456
ogia eta gurina bezala dira.
08:40
They've been doing it for years,
184
508200
1576
Urteak daramatzate hortan,
08:41
and it's led to major
operational efficiency improvements.
185
509800
3256
eta operazio hobekuntza eraginkorrak
ekarri ditu.
08:45
Just try to imagine
your favorite beverage company
186
513080
2360
Imajinatu zure edari konpania gustukoena
08:48
trying to plan their inventory
187
516280
1576
euren inbentarioa osatu nahian
08:49
and not knowing how many bottles
were on the shelves.
188
517880
2496
eta apaletan zenbat boteila dauden
jakin ezinean.
08:52
It's absurd.
189
520400
1216
Absurdua da.
08:53
Data drives better decisions.
190
521640
1560
Datuek erabaki hobeak dakartzate.
08:57
Now, if you're representing a company,
191
525800
2536
Konpainia baten ordezkari bazara,
09:00
and you're pragmatic
and not just idealistic,
192
528360
3136
eta idealistaz gain pragmatikoa bazara,
09:03
you might be saying to yourself,
"OK, this is all great, Mallory,
193
531520
3056
zeure buruari ariko zara
"Ok, hau ederki dago, Mallory,
09:06
but why should I want to be involved?"
194
534600
1840
baina zertarako sartuko naiz horretan?"
09:09
Well for one thing, beyond the good PR,
195
537000
2816
Gauza batengatik,
publizitate onaz gain,
09:11
humanitarian aid
is a 24-billion-dollar sector,
196
539840
2776
laguntza humanitarioak,
24 bilioi dolar mugitzen ditu,
09:14
and there's over five billion people,
maybe your next customers,
197
542640
3056
eta 5 bilioi pertsona baino gehiago,
agian etorkizuneko bezeroak,
garapen bideko herrialdeetan
bizi dira.
09:17
that live in the developing world.
198
545720
1816
09:19
Further, companies that are engaging
in data philanthropy,
199
547560
3096
Datuen filantropian sartzen
ari diren konpainiak
09:22
they're finding new insights
locked away in their data.
200
550680
2976
euren datuetan ezkutatutako
gauza berriak aurkitzen ari dira.
09:25
Take, for example, a credit card company
201
553680
2256
Demagun kreditu txartelen konpainiak
09:27
that's opened up a center
202
555960
1336
zentro bat ireki duela
09:29
that functions as a hub for academics,
for NGOs and governments,
203
557320
3376
akademiko, GKE eta gobernuentzat
egoitza bezala lan egiten duena,
09:32
all working together.
204
560720
1240
guztiak batera lan eginaz.
09:35
They're looking at information
in credit card swipes
205
563040
2736
Kreditu txartelen irakurketetako
datuak bilatzen dituzte
eta datu horiek erabiltzen dituzte
jakiteko Indiako etxeetan nola
09:37
and using that to find insights
about how households in India
206
565800
2976
bizi, lan egin, irabazi
eta gastatzen den.
09:40
live, work, earn and spend.
207
568800
1720
09:43
For the humanitarian world,
this provides information
208
571680
2576
Mundu humanitarioarentzat,
honek datuak ematen ditu
09:46
about how you might
bring people out of poverty.
209
574280
2656
jendea txirotasunetik nola atera
asmatzeko.
09:48
But for companies, it's providing
insights about your customers
210
576960
3016
Baina konpainientzat, honek
ezagutza berria dakar bezero
09:52
and potential customers in India.
211
580000
2040
eta Indiako bezero posibleen
inguruan.
09:54
It's a win all around.
212
582760
1800
Denek irabazten dute.
09:57
Now, for me, what I find
exciting about data philanthropy --
213
585960
3776
Nire kasuan, datuen filantropiatik
liluragarriena iruditzen zaidana
-- datuak, erabaki hartzaile zientifikoak
eta teknologia ematea --
10:01
donating data, donating decision
scientists and donating technology --
214
589760
4336
ni bezalako profesional gazteentzat
duen esanahia da,
10:06
it's what it means
for young professionals like me
215
594120
2376
10:08
who are choosing to work at companies.
216
596520
1840
konpainietan lan egiten
dugunontzat.
10:10
Studies show that
the next generation of the workforce
217
598800
2656
Ikerketek diote belaunaldi berrietako
langileriak
euren lanak inpaktu handiagoa
izateagatik arduratzen direla.
10:13
care about having their work
make a bigger impact.
218
601480
2560
10:16
We want to make a difference,
219
604920
2456
Gauzak desberdin egin nahi ditugu,
10:19
and so through data philanthropy,
220
607400
2416
eta datuen filantropia bidez,
10:21
companies can actually help engage
and retain their decision scientists.
221
609840
3936
konpainiek erabaki hartzaile zientifiko
batzuk manten ditzakete.
10:25
And that's a big deal for a profession
that's in high demand.
222
613800
2880
Eta hau garrantzitsua da eskaera altuko
profesio batean.
10:29
Data philanthropy
makes good business sense,
223
617840
3120
Datuen filantropiak zentzua du
negozioetan,
10:34
and it also can help
revolutionize the humanitarian world.
224
622200
3280
eta mundu humanitarioa iraultzen
lagundu dezake.
Planifikazioa eta logistika
koordinatzen baditugu
10:39
If we coordinated
the planning and logistics
225
627600
2096
10:41
across all of the major facets
of a humanitarian operation,
226
629720
3376
prozesu humanitario nagusienen
ezaugarri guztietan,
10:45
we could feed, clothe and shelter
hundreds of thousands more people,
227
633120
3600
ehundaka milaka pertsona elikatu,
jantzi eta babestu genitzake,
10:49
and companies need to step up
and play the role that I know they can
228
637440
4256
eta konpainiek aurrerapausua eman eta
joka dezaketen papera jokatu behar dute
10:53
in bringing about this revolution.
229
641720
1880
iraultza hau burutzeko.
10:56
You've probably heard of the saying
"food for thought."
230
644720
2936
Ziurrenik entzuna duzue
"jana pentsamenduen truk".
10:59
Well, this is literally thought for food.
231
647680
2240
Hau literaki pentsamenduen truk
jana litzateke.
11:03
It finally is the right idea
at the right time.
232
651560
4136
Azkenean ideia zuzena da, une egokian.
11:07
(Laughter)
233
655720
1216
(Barreak)
11:08
Très magnifique.
234
656960
1576
Très magnifique
11:10
Thank you.
235
658560
1216
Mila esker.
11:11
(Applause)
236
659800
2851
(txaloak)
Translated by Jone Aliri
Reviewed by Ainize Sarrionandia

▲Back to top

ABOUT THE SPEAKER
Mallory Freeman - Data activist
UPS'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.

More profile about the speaker
Mallory Freeman | Speaker | TED.com