TED@UPS
Mallory Freeman: Your company's data could help end world hunger
馬洛里．索德納: 你們公司的數據可以幫助世界解決饑餓問題
Filmed:
Readability: 4.2
1,090,373 views
你們的公司可能曾經為了人道主義問題做出過金錢上的捐贈，但其實有一種更有價值的捐贈：你們的數據。馬洛里．索德納（Mallory･Soldner）向我們說明企業如何能夠透過捐獻數據和決策科學家，來真正地為世界性問題作出貢獻 -- 從難民危機到饑餓問題 -- 。你們的公司能貢獻什麽呢？
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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2010年六月，
00:15
I landed for the first time
in Rome, Italy.
in Rome, Italy.
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我第一次前往意大利．羅馬。
00:19
I wasn't there to sightsee.
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我不是去觀光的，
00:21
I was there to solve world hunger.
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我是去解決世界飢餓問題的。
00:25
(Laughter)
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（笑聲）
00:27
That's right.
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沒錯。
00:28
I was a 25-year-old PhD student
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我當時是一位25歲的博士生，
00:30
armed with a prototype tool
developed back at my university,
developed back at my university,
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我帶著在大學期間開發的原型工具，
00:33
and I was going to help
the World Food Programme fix hunger.
the World Food Programme fix hunger.
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準備幫助世界糧食計劃署
解決飢餓問題。
解決飢餓問題。
00:37
So I strode into the headquarters building
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我大步走進他們的總部大樓，
00:40
and my eyes scanned the row of UN flags,
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映入眼簾的是一整排的聯合國國旗，
00:43
and I smiled as I thought to myself,
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我開心地對著自己說，
00:46
"The engineer is here."
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「工程師來了！」
00:48
(Laughter)
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（笑聲）
00:50
Give me your data.
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"拿出你們的數據，
00:52
I'm going to optimize everything.
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我要優化所有資料。"
00:54
(Laughter)
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（笑聲）
00:56
Tell me the food that you've purchased,
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"告訴我你們已經購買的食物，
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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告訴我要送到哪裡、
什麼時候需要，
什麼時候需要，
01:01
and I'm going to tell you
the shortest, fastest, cheapest,
the shortest, fastest, cheapest,
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我就會告訴你們最短、最快、
01:03
best set of routes to take for the food.
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最便宜的食物運送路徑。
01:05
We're going to save money,
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我們會節省很多錢，
01:07
we're going to avoid
delays and disruptions,
delays and disruptions,
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我們可以避免延遲和中斷，
01:09
and bottom line,
we're going to save lives.
we're going to save lives.
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最後，我們還可以拯救世人。
01:12
You're welcome.
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（謝謝）不用客氣！"
01:13
(Laughter)
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（笑聲）
01:15
I thought it was going to take 12 months,
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我在想這大概需要
12個月的時間來實現，
12個月的時間來實現，
01:17
OK, maybe even 13.
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好吧，可能要13個月。
01:19
This is not quite how it panned out.
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但事情並沒有想像中的簡單。
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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當我加入這個專案幾個月之後，
我的法國老闆，他告訴我：
我的法國老闆，他告訴我：
01:27
"You know, Mallory,
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「馬洛里，妳知道嗎？
01:29
it's a good idea,
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妳的點子是不錯啦！
01:30
but the data you need
for your algorithms is not there.
for your algorithms is not there.
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但要符合你演算法的數據並不存在。
01:34
It's the right idea but at the wrong time,
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點子是對的，但時機不對，
01:36
and the right idea at the wrong time
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而對的點子在錯誤的時機出現...
01:39
is the wrong idea."
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就是一個錯誤的點子！」
01:40
(Laughter)
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（笑聲）
01:42
Project over.
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專案結束！
01:45
I was crushed.
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我超傷心的。
01:49
When I look back now
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現在當我回頭去看
01:50
on that first summer in Rome
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從羅馬的第一個夏天到現在，
01:52
and I see how much has changed
over the past six years,
over the past six years,
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我看到在這六年來，
01:54
it is an absolute transformation.
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真的是完全轉變了。
01:57
It's a coming of age for bringing data
into the humanitarian world.
into the humanitarian world.
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把數據帶入人道世界的時代來臨了。
02:02
It's exciting. It's inspiring.
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這真是令人興奮、鼓舞人心的。
02:04
But we're not there yet.
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但是我們還沒有達到。
02:07
And brace yourself, executives,
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現場的各位主管，請仔細聽好了，
02:09
because I'm going to be putting companies
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我準備要把你們的公司推上火線，
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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因為我知道你們辦得到。
02:17
My experiences back in Rome prove
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我在羅馬的經驗告訴我，
02:20
using data you can save lives.
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運用數據，你可以拯救生命。
02:23
OK, not that first attempt,
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的確，不是一試就能成功，
02:25
but eventually we got there.
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但最終我們還是能辦到。
02:28
Let me paint the picture for you.
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讓我來解釋一下。
02:30
Imagine that you have to plan
breakfast, lunch and dinner
breakfast, lunch and dinner
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想像一下，
你準備要為50萬人準備早、中、晚餐
02:33
for 500,000 people,
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02:34
and you only have
a certain budget to do it,
a certain budget to do it,
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但你的預算有限，
02:36
say 6.5 million dollars per month.
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比如說，每個月650萬美元。
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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你要怎麼做？
最好的方式是甚麼？
最好的方式是甚麼？
02:44
Should you buy rice, wheat, chickpea, oil?
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你需要買米、小麥、鷹嘴豆和油嗎？
02:47
How much?
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要買多少？
02:49
It sounds simple. It's not.
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聽起來很簡單，但做起來很難。
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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你有30種可能的食物，
你必須從中挑選五種。
你必須從中挑選五種。
02:54
That's already over 140,000
different combinations.
different combinations.
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那樣就會有超過14萬種
不同的食物組合。
不同的食物組合。
02:57
Then for each food that you pick,
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你挑選的每樣食物，
02:59
you need to decide how much you'll buy,
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你要決定準備買多少、
03:01
where you're going to get it from,
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去哪買、
03:03
where you're going to store it,
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買來後要存放在哪、
03:05
how long it's going to take to get there.
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運送到目的地要多久的時間。
03:07
You need to look at all of the different
transportation routes as well.
transportation routes as well.
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你還需要查看
所有不同的運輸路線。
所有不同的運輸路線。
而這樣已經超過九億種選擇了。
03:11
And that's already
over 900 million options.
over 900 million options.
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03:14
If you considered each option
for a single second,
for a single second,
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如果你每個選項都需要思考一秒，
03:16
that would take you
over 28 years to get through.
over 28 years to get through.
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那你要花超過28年的時間
才能把它們全過一遍。
才能把它們全過一遍。
03:18
900 million options.
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九億種選擇啊！
03:21
So we created a tool
that allowed decisionmakers
that allowed decisionmakers
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所以我們創建了一個
03:23
to weed through all 900 million options
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只要花幾天的時間，就可以讓決策者
03:26
in just a matter of days.
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解決九億種選擇的工具。
03:28
It turned out to be incredibly successful.
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果然非常成功。
03:31
In an operation in Iraq,
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在伊拉克的一次任務中，
03:32
we saved 17 percent of the costs,
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我們節省了17％的成本，
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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也就是說，你還有能力
能餵飽另外的八萬人。
能餵飽另外的八萬人。
03:39
It's all thanks to the use of data
and modeling complex systems.
and modeling complex systems.
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這一切都要感謝數據和
複雜的建模系統。
複雜的建模系統。
03:44
But we didn't do it alone.
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但這並不是我們獨自完成的。
03:46
The unit that I worked with in Rome,
they were unique.
they were unique.
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我們在羅馬合作的單位，
他們真的很棒。
他們真的很棒。
03:49
They believed in collaboration.
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他們相信合作的力量。
03:51
They brought in the academic world.
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他們把學術界帶入這個領域，
03:53
They brought in companies.
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把企業帶入這個領域。
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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如果我們希望能在像世界飢餓
這種大問題上做出改變，
這種大問題上做出改變，
03:58
we need everybody to the table.
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我們需要每一個社會成員的加入。
04:02
We need the data people
from humanitarian organizations
from humanitarian organizations
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我們需要來自人道組織的數據人員
04:05
leading the way,
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引領道路，
04:06
and orchestrating
just the right types of engagements
just the right types of engagements
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並組織學術界及政府部門
04:08
with academics, with governments.
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好好地參與合作。
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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還有一種群體沒有被充分利用。
04:14
Did you guess it? Companies.
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猜猜是誰？公司企業。
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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公司在解決世界的大問題方面
扮演了重要的角色。
扮演了重要的角色。
04:20
I've been in the private sector
for two years now.
for two years now.
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我在私人公司已經工作了兩年。
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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我見識到了企業的能力，
以及他們沒有充分做到的部分，
以及他們沒有充分做到的部分，
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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我認為有三個主要方式，
可以填補這個空缺：
可以填補這個空缺：
04:30
by donating data,
by donating decision scientists
by donating decision scientists
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藉由捐贈數據、決策科學家及科技
04:33
and by donating technology
to gather new sources of data.
to gather new sources of data.
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來收集新數據的技術。
04:37
This is data philanthropy,
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這是數據慈善事業，
04:39
and it's the future of corporate
social responsibility.
social responsibility.
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是企業的未來社會責任。
04:43
Bonus, it also makes good business sense.
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好處就是，對公司的形象有幫助。
04:46
Companies today,
they collect mountains of data,
they collect mountains of data,
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如今的公司，收集了一大堆數據，
04:50
so the first thing they can do
is start donating that data.
is start donating that data.
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所以他們可以做的第一件事
就是捐贈數據。
就是捐贈數據。
04:52
Some companies are already doing it.
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有些公司已經在做了。
04:55
Take, for example,
a major telecom company.
a major telecom company.
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舉例，以某一家大型的電信公司為例。
04:57
They opened up their data
in Senegal and the Ivory Coast
in Senegal and the Ivory Coast
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他們開放了位於塞內加爾和
象牙海岸的數據，
象牙海岸的數據，
05:00
and researchers discovered
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研究人員發現，
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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如果你觀察手機傳送到
基地台的數據圖形，
基地台的數據圖形，
05:05
you can see where people are traveling.
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你可以觀察到人們到哪裡活動，
05:07
And that can tell you things like
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像這樣的數據能告訴你，
05:09
where malaria might spread,
and you can make predictions with it.
and you can make predictions with it.
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瘧疾可能傳播的地方，
你可以用它做預測。
你可以用它做預測。
05:13
Or take for example
an innovative satellite company.
an innovative satellite company.
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或者拿另一個創新的衛星公司為例，
05:15
They opened up their data and donated it,
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他們開放並捐獻了數據，
05:18
and with that data you could track
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使用那些數據，你就能夠追蹤
05:19
how droughts are impacting
food production.
food production.
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乾旱是如何影響糧食產量的。
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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有了這些數據，你甚至可以
在危機發生之前就啟動援助資金。
在危機發生之前就啟動援助資金。
05:27
This is a great start.
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這是一個好的開始。
05:29
There's important insights
just locked away in company data.
just locked away in company data.
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在公司的數據中，
禁錮著許多重要的信息。
禁錮著許多重要的信息。
05:34
And yes, you need to be very careful.
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是的，你需要非常小心。
05:36
You need to respect privacy concerns,
for example by anonymizing the data.
for example by anonymizing the data.
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您需要尊重隱私問題，
例如可以用匿名化數據解決。
例如可以用匿名化數據解決。
05:39
But even if the floodgates opened up,
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但即使所有的管道資料都開放了，
05:42
and even if all companies
donated their data
donated their data
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即使所有的公司
都捐贈出他們的數據
都捐贈出他們的數據
05:45
to academics, to NGOs,
to humanitarian organizations,
to humanitarian organizations,
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給學術界、非政府組織、人道組織，
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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光有這些資料，仍無法達到
05:51
for humanitarian goals.
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人道主義的目標。
05:54
Why?
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為什麼？
05:55
To unlock insights in data,
you need decision scientists.
you need decision scientists.
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要解開數據中的信息，
你仍需要決策科學家。
你仍需要決策科學家。
05:59
Decision scientists are people like me.
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像我這樣的決策科學家。
06:02
They take the data, they clean it up,
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他們拿到數據，會稍作整理，
06:04
transform it and put it
into a useful algorithm
into a useful algorithm
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把資料轉換後，帶入有用的演算法裡。
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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這才是解決問題的最佳選擇。
06:09
In the world of humanitarian aid,
there are very few decision scientists.
there are very few decision scientists.
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但在人道援助的領域裡，
決策科學家很罕見。
決策科學家很罕見。
06:13
Most of them work for companies.
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他們大多數都為私人企業工作。
06:16
So that's the second thing
that companies need to do.
that companies need to do.
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所以，公司要做第二件事，
06:19
In addition to donating their data,
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公司除了捐贈他們的數據以外，
06:20
they need to donate
their decision scientists.
their decision scientists.
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他們還需要捐贈他們的決策科學家。
06:23
Now, companies will say, "Ah! Don't take
our decision scientists from us.
our decision scientists from us.
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但公司會說，
「啊！別帶走我們的決策科學家，
「啊！別帶走我們的決策科學家，
06:29
We need every spare second of their time."
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我們分分秒秒都很需要他們。」
06:32
But there's a way.
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但是有一個辦法，
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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如果說一家公司決定貢獻出
它的決策科學家的部分時間，
它的決策科學家的部分時間，
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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那我們就把這些時間分散到長期使用，
這樣才行得通，
這樣才行得通，
06:41
over a long period,
say for example five years.
say for example five years.
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比如說，五年的時間。
06:44
This might only amount
to a couple of hours per month,
to a couple of hours per month,
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這樣分配之後，每個月
可能就只需要幾個小時，
可能就只需要幾個小時，
06:47
which a company would hardly miss,
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對於一家公司來說不足掛齒，
06:49
but what it enables is really important:
long-term partnerships.
long-term partnerships.
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但產生的效果是很重大的：
長期的夥伴關係。
長期的夥伴關係。
06:54
Long-term partnerships
allow you to build relationships,
allow you to build relationships,
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長期的夥伴關係能促進建立友誼，
06:57
to get to know the data,
to really understand it
to really understand it
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對資料更理解，
07:00
and to start to understand
the needs and challenges
the needs and challenges
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而且可以更深入地了解到
07:02
that the humanitarian
organization is facing.
organization is facing.
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人道組織的需求及
目前所面臨到的問題。
目前所面臨到的問題。
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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在羅馬，我們在世界糧食計劃署，
花費了五年時間，五年。
07:09
five years.
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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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前三年，沒錯，我們在
討論解決不了的問題。
討論解決不了的問題。
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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然後我們又花了兩年時間
去更新，完善 我們的工具。
去更新，完善 我們的工具。
07:17
like in the operations in Iraq
and other countries.
and other countries.
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就像我們在伊拉克
和其他國家的行動一樣。
和其他國家的行動一樣。
07:21
I don't think that's
an unrealistic timeline
an unrealistic timeline
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當涉及到使用數據進行操作修改的時候，
07:23
when it comes to using data
to make operational changes.
to make operational changes.
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我不認為這樣的時間安排會有甚麼不妥。
07:26
It's an investment. It requires patience.
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這是一項投資，我們要有耐心。
07:29
But the types of results
that can be produced are undeniable.
that can be produced are undeniable.
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但產生的效果是不可否認的。
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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以我們的個案而言，
它是可以養活好幾萬人的。
它是可以養活好幾萬人的。
07:39
So we have donating data,
we have donating decision scientists,
we have donating decision scientists,
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所以我們需要捐獻數據，
我們需要捐獻決策科學家，
我們需要捐獻決策科學家，
07:43
and there's actually a third way
that companies can help:
that companies can help:
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實際上公司還有
第三種方法可以提供協助：
第三種方法可以提供協助：
07:46
donating technology
to capture new sources of data.
to capture new sources of data.
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透過捐贈技術來取得數據的新來源。
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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你看，還有很多地方，我們都沒有數據。
07:52
Right now, Syrian refugees
are flooding into Greece,
are flooding into Greece,
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目前，敘利亞難民正湧入希臘，
07:57
and the UN refugee agency,
they have their hands full.
they have their hands full.
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而聯合國的難民機構，
他們也忙得不可開交。
他們也忙得不可開交。
08:01
The current system for tracking people
is paper and pencil,
is paper and pencil,
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目前的難民跟進系統
是用紙和筆來作業，
是用紙和筆來作業，
08:04
and what that means is
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意思就是，
08:05
that when a mother and her five children
walk into the camp,
walk into the camp,
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當一個母親帶著她的五個孩子
走進難名營時，
走進難名營時，
08:08
headquarters is essentially
blind to this moment.
blind to this moment.
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總部基本上根本看不到。
08:10
That's all going to change
in the next few weeks,
in the next few weeks,
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在未來幾周中，
這一切都將會改變，
這一切都將會改變，
08:13
thanks to private sector collaboration.
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這要感謝私人機構的合作。
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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我合作的物流公司，
即將捐贈一套全新的追蹤科技系統。
08:19
from the logistics company
that I work for.
that I work for.
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08:22
With this new system,
there will be a data trail,
there will be a data trail,
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有了這個新系統，數據就能被追踪，
08:24
so you know exactly the moment
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所以當一位母親
帶著她的孩子走進難民營時，
帶著她的孩子走進難民營時，
08:25
when that mother and her children
walk into the camp.
walk into the camp.
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你就會知道這件事。
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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甚至，你還可以知道
這個月及下個月
她是否能得到支援。
她是否能得到支援。
08:31
this month and the next.
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08:32
Information visibility drives efficiency.
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數據的能見度驅動了效率。
08:35
For companies, using technology
to gather important data,
to gather important data,
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對公司而言，利用技術收集重要數據，
08:38
it's like bread and butter.
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就像做奶油麵包一樣（簡單）。
08:40
They've been doing it for years,
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他們多年來都在從事這件事，
08:41
and it's led to major
operational efficiency improvements.
operational efficiency improvements.
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並帶來了卓越的效率提升。
08:45
Just try to imagine
your favorite beverage company
your favorite beverage company
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試想一下，你最喜歡的飲料公司，
08:48
trying to plan their inventory
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將要計劃下一批生產
08:49
and not knowing how many bottles
were on the shelves.
were on the shelves.
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卻對正在貨架上的
飲料數量毫不知情，
飲料數量毫不知情，
08:52
It's absurd.
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這是很荒謬的。
08:53
Data drives better decisions.
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數據驅使我們做出更好的決策。
08:57
Now, if you're representing a company,
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現在，如果您代表一個公司，
09:00
and you're pragmatic
and not just idealistic,
and not just idealistic,
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你很務實，不是那種只會空想的人，
09:03
you might be saying to yourself,
"OK, this is all great, Mallory,
"OK, this is all great, Mallory,
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你可能會說，
「沒錯，是很偉大，馬洛里
「沒錯，是很偉大，馬洛里
09:06
but why should I want to be involved?"
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但為什麼我要參與？」
09:09
Well for one thing, beyond the good PR,
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其實，就一件事，提升公司形象，
09:11
humanitarian aid
is a 24-billion-dollar sector,
is a 24-billion-dollar sector,
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人道援助是一個240億美元的公營事業，
09:14
and there's over five billion people,
maybe your next customers,
maybe your next customers,
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有超過50億人口住在發展中國家，
09:17
that live in the developing world.
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很有可能你的下一個客戶就是他們。
09:19
Further, companies that are engaging
in data philanthropy,
in data philanthropy,
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此外，從事數據慈善事業的那些公司，
09:22
they're finding new insights
locked away in their data.
locked away in their data.
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他們正在挖掘
禁錮在數據當中的新信息。
禁錮在數據當中的新信息。
09:25
Take, for example, a credit card company
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例如，以某家信用卡公司為例，
09:27
that's opened up a center
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他們建立了一個數據中心樞紐，
09:29
that functions as a hub for academics,
for NGOs and governments,
for NGOs and governments,
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將學術界、非政府組織和政府
09:32
all working together.
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組織起來一起工作。
09:35
They're looking at information
in credit card swipes
in credit card swipes
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他們透過刷卡紀錄，
09:37
and using that to find insights
about how households in India
about how households in India
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觀察到一般的印度家庭
09:40
live, work, earn and spend.
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他們如何生活、工作、賺錢和消費。
09:43
For the humanitarian world,
this provides information
this provides information
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對人道組織而言，這裡面隱含著
09:46
about how you might
bring people out of poverty.
bring people out of poverty.
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如何使人們擺脫貧困的資訊。
09:48
But for companies, it's providing
insights about your customers
insights about your customers
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但對公司來說，
這就是向他們提供了
這就是向他們提供了
09:52
and potential customers in India.
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在印度的用戶和潛在用戶信息。
09:54
It's a win all around.
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這是一個三贏的局面。
09:57
Now, for me, what I find
exciting about data philanthropy --
exciting about data philanthropy --
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而對我而言，我發現
數據慈善事業是令人振奮的 --
數據慈善事業是令人振奮的 --
10:01
donating data, donating decision
scientists and donating technology --
scientists and donating technology --
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數據捐贈、決策科學家捐贈及科技捐贈--
10:06
it's what it means
for young professionals like me
for young professionals like me
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對我這樣年輕的專家而言，
10:08
who are choosing to work at companies.
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這就是我們選擇待在公司的原因。
10:10
Studies show that
the next generation of the workforce
the next generation of the workforce
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研究表明，下一世代的勞動人口關心的是
10:13
care about having their work
make a bigger impact.
make a bigger impact.
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他們的工作能不能為世界帶來影響。
10:16
We want to make a difference,
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我們想要改變，
10:19
and so through data philanthropy,
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所以透過數據慈善事業，
10:21
companies can actually help engage
and retain their decision scientists.
and retain their decision scientists.
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公司更容易留得住
他們的決策科學家
他們的決策科學家
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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特別是對於這種高需求
的職業來說尤其重要。
的職業來說尤其重要。
10:29
Data philanthropy
makes good business sense,
makes good business sense,
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數據慈善事業
能創造良好的商業形象，
能創造良好的商業形象，
10:34
and it also can help
revolutionize the humanitarian world.
revolutionize the humanitarian world.
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它同時也能夠為人道主義事業
做出巨大變革。
做出巨大變革。
10:39
If we coordinated
the planning and logistics
the planning and logistics
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如果我們可以協調規劃
10:41
across all of the major facets
of a humanitarian operation,
of a humanitarian operation,
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並支援所有人道主義各方面的後勤，
10:45
we could feed, clothe and shelter
hundreds of thousands more people,
hundreds of thousands more people,
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我們就可以為成千上萬的人提供
食物、衣服和住所，
食物、衣服和住所，
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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為了這場改革，
公司需要站出來扮演其中的角色，
公司需要站出來扮演其中的角色，
10:53
in bringing about this revolution.
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因為我知道你們辦的到。
10:56
You've probably heard of the saying
"food for thought."
"food for thought."
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各位也許聽過這個短語「值得思考的食物」。
（英文意思是：值得深思的問題）
（英文意思是：值得深思的問題）
10:59
Well, this is literally thought for food.
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而字面意思就是「想想食物」（要如何分配）
11:03
It finally is the right idea
at the right time.
at the right time.
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我終於在對的時間找到對的方法了！
11:07
(Laughter)
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（笑聲）
11:08
Très magnifique.
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（法語）太棒了！
11:10
Thank you.
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謝謝。
11:11
(Applause)
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（掌聲）
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