TED@UPS
Mallory Freeman: Your company's data could help end world hunger
马洛里･索德纳: 你们的数据可以帮助终结世界饥饿问题
Filmed:
Readability: 4.2
1,090,373 views
你们的公司可能曾经为人道主义事业做出过捐款，但其实有一种更具价值的捐赠：你们的数据。马洛里･索德纳向我们展现了企业如何能够通过贡献数据和决策科学家，从而真正为世界性问题——从难民危机到饥饿问题——的解决作出贡献。你们的公司又能做点儿什么呢？
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6月，
00:15
I landed for the first time
in Rome, Italy.
in Rome, Italy.
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我第一次来到意大利罗马。
00:19
I wasn't there to sightsee.
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我不是去那儿观光旅游的。
00:21
I was there to solve world hunger.
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我是去解决世界饥饿问题的。
00:25
(Laughter)
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（笑声）
00:27
That's right.
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就是这样的。
00:28
I was a 25-year-old PhD student
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我是一名25岁的博士生，
00:30
armed with a prototype tool
developed back at my university,
developed back at my university,
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带着在大学研发的设备原型，
00:33
and I was going to help
the World Food Programme fix hunger.
the World Food Programme fix hunger.
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去帮助世界粮食计划署
解决饥饿问题。
解决饥饿问题。
00:37
So I strode into the headquarters building
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所以我大步跨入总部大楼，
00:40
and my eyes scanned the row of UN flags,
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用眼睛扫视了那一排联合国旗帜，
00:43
and I smiled as I thought to myself,
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我边笑边暗自想：
00:46
"The engineer is here."
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“工程师来啦。”
00:48
(Laughter)
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（笑声）
00:50
Give me your data.
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把你们的数据给我，
00:52
I'm going to optimize everything.
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我要优化这一切。
00:54
(Laughter)
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（笑声）
00:56
Tell me the food that you've purchased,
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告诉我你们购买过的食物，
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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告诉我食物需要
何时送达何地
何时送达何地
01:01
and I'm going to tell you
the shortest, fastest, cheapest,
the shortest, fastest, cheapest,
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我就能告诉你
最短，最快，最便宜
最短，最快，最便宜
01:03
best set of routes to take for the food.
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的最佳食物运输道路。
01:05
We're going to save money,
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我们就能节约资金，
01:07
we're going to avoid
delays and disruptions,
delays and disruptions,
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我们就能避免延误和干扰，
01:09
and bottom line,
we're going to save lives.
we're going to save lives.
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最重要的是，
我们能挽救生命。
我们能挽救生命。
01:12
You're welcome.
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不用谢。
01:13
(Laughter)
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（笑声）
01:15
I thought it was going to take 12 months,
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我想这大概会需要
用12个月的时间来实现，
用12个月的时间来实现，
01:17
OK, maybe even 13.
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好吧，或者是13个月。
01:19
This is not quite how it panned out.
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但这并不怎么成功。
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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当我加入这个项目几个月之后，
我的法国老板就告诉我：
我的法国老板就告诉我：
01:27
"You know, Mallory,
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“你知道的，马洛里，
01:29
it's a good idea,
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这是一个好想法，
01:30
but the data you need
for your algorithms is not there.
for your algorithms is not there.
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但是你的算法
需要的数据并不在这儿。
需要的数据并不在这儿。
01:34
It's the right idea but at the wrong time,
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这想法的思路正确，
但是出现在了错误的时间点上
但是出现在了错误的时间点上
01:36
and the right idea at the wrong time
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错误时间点上的所谓正确想法，
01:39
is the wrong idea."
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就是错误的想法。”
01:40
(Laughter)
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（笑声）
01:42
Project over.
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于是项目中止。
01:45
I was crushed.
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我非常沮丧。
01:49
When I look back now
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现在当我回顾
01:50
on that first summer in Rome
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我在罗马度过的第一个夏天，
01:52
and I see how much has changed
over the past six years,
over the past six years,
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我看到了六年来发生的巨大变化，
01:54
it is an absolute transformation.
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真是绝对的大改变。
01:57
It's a coming of age for bringing data
into the humanitarian world.
into the humanitarian world.
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这是一个让我们将数据
带到人道主义世界的时代。
带到人道主义世界的时代。
02:02
It's exciting. It's inspiring.
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这真是令人兴奋，鼓舞人心。
02:04
But we're not there yet.
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但是我们还没完全做到。
02:07
And brace yourself, executives,
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请振作精神，高管们，
02:09
because I'm going to be putting companies
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因为我正要把
企业放到焦点位置，
企业放到焦点位置，
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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提高它们的作用并
尽其所能。
尽其所能。
02:17
My experiences back in Rome prove
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我在罗马的经历证明了
02:20
using data you can save lives.
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数据可以拯救生命。
02:23
OK, not that first attempt,
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好吧，并不是第一次的尝试，
02:25
but eventually we got there.
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但是我们最终做到了。
02:28
Let me paint the picture for you.
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让我来展开这幅图景。
02:30
Imagine that you have to plan
breakfast, lunch and dinner
breakfast, lunch and dinner
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假设你需要为50万人准备三餐，
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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你的预算是固定的，
02:36
say 6.5 million dollars per month.
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比方说每月650万美元。
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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好的，那你应该怎么做呢？
处理这件事的最好方法是什么呢？
处理这件事的最好方法是什么呢？
02:44
Should you buy rice, wheat, chickpea, oil?
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你是该买大米、小麦、
鹰嘴豆还是油呢？
鹰嘴豆还是油呢？
02:47
How much?
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又要买多少呢？
02:49
It sounds simple. It's not.
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这听起来很简单，
实则不然。
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种食物可供选择，
你需要从中选出五种。
02:54
That's already over 140,000
different combinations.
different combinations.
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那样就会有超过14万种
不同的食物组合。
不同的食物组合。
02:57
Then for each food that you pick,
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针对你所挑选的每一种食物，
02:59
you need to decide how much you'll buy,
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你还需要考虑购买量的问题，
03:01
where you're going to get it from,
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考虑购买地的问题，
03:03
where you're going to store it,
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考虑储存地的问题，
03:05
how long it's going to take to get there.
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考虑运输时间的问题。
03:07
You need to look at all of the different
transportation routes as well.
transportation routes as well.
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你还需要查看
所有不同的运输线路。
所有不同的运输线路。
这样下来就会有
超过9亿种不同的选择。
超过9亿种不同的选择。
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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如果考虑一种选择需要1秒钟，
03:16
that would take you
over 28 years to get through.
over 28 years to get through.
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那你就需要28年时间
才能把它们全过一遍。
才能把它们全过一遍。
03:18
900 million options.
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9亿种选择。
03:21
So we created a tool
that allowed decisionmakers
that allowed decisionmakers
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所以我们设计了一种工具，
使得决策者能够在短短几天之内，
03:23
to weed through all 900 million options
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就成功过滤完这9亿种选择。
03:26
in just a matter of days.
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03:28
It turned out to be incredibly successful.
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事实证明，工具十分成功。
03:31
In an operation in Iraq,
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在伊拉克的一次行动中，
03:32
we saved 17 percent of the costs,
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我们节约了原成本中17%的开销，
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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这就意味着你有额外
的能力再供养8万人
的能力再供养8万人
03:39
It's all thanks to the use of data
and modeling complex systems.
and modeling complex systems.
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这一切都要归功于数据
以及对复杂系统建模的能力。
以及对复杂系统建模的能力。
03:44
But we didn't do it alone.
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但这并不是我们独自完成的。
03:46
The unit that I worked with in Rome,
they were unique.
they were unique.
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我在罗马工作的单位十分独特。
03:49
They believed in collaboration.
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他们相信合作的力量。
03:51
They brought in the academic world.
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他们引入学术界的帮助，
03:53
They brought in companies.
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引入企业界的帮助。
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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如果我们希望能在像全球饥饿问题
等重大问题上创造奇迹，
等重大问题上创造奇迹，
03:58
we need everybody to the table.
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我们需要每一个社会成员的加入。
04:02
We need the data people
from humanitarian organizations
from humanitarian organizations
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我们需要来自人道组织的
数据人员指引道路，
数据人员指引道路，
04:05
leading the way,
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04:06
and orchestrating
just the right types of engagements
just the right types of engagements
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将观点一致的学者们和政府
04:08
with academics, with governments.
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紧密连接在一起。
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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还有一个群体没有被充分利用。
04:14
Did you guess it? Companies.
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猜到了吗？是企业。
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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企业将在解决我们的世界
重大问题上发挥重要作用。
重大问题上发挥重要作用。
04:20
I've been in the private sector
for two years now.
for two years now.
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我已经在私人部门干了两年了。
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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我见识到了企业的能力，
以及他们没有充分去做的部分，
以及他们没有充分去做的部分，
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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我认为主要有三种方式
去填补那些空缺：
去填补那些空缺：
04:30
by donating data,
by donating decision scientists
by donating decision scientists
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通过贡献数据，
通过贡献决策科学家，
04:33
and by donating technology
to gather new sources of data.
to gather new sources of data.
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和通过贡献收集新数据的技术。
04:37
This is data philanthropy,
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这是一种数据慈善事业，
04:39
and it's the future of corporate
social responsibility.
social responsibility.
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是未来的企业社会责任。
04:43
Bonus, it also makes good business sense.
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当然它也有很好的商业意义。
04:46
Companies today,
they collect mountains of data,
they collect mountains of data,
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当今的企业，
收集大量的数据，
收集大量的数据，
04:50
so the first thing they can do
is start donating that data.
is start donating that data.
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所以他们所能做的
第一件事就是贡献这些数据
第一件事就是贡献这些数据
04:52
Some companies are already doing it.
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一部分企业已经开始提供数据。
04:55
Take, for example,
a major telecom company.
a major telecom company.
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就以一家主流电信公司为例，
04:57
They opened up their data
in Senegal and the Ivory Coast
in Senegal and the Ivory Coast
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他们开放了位于
塞内加尔和科特迪瓦的数据
塞内加尔和科特迪瓦的数据
05:00
and researchers discovered
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研究人员由此发现，
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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通过观察信号塔接收到
的手机信号模式图
的手机信号模式图
05:05
you can see where people are traveling.
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你就能了解人们正前往何处。
05:07
And that can tell you things like
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通过这些数据你还能了解到
05:09
where malaria might spread,
and you can make predictions with it.
and you can make predictions with it.
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疟疾可能传播的地方，
你可以由此作出预测。
你可以由此作出预测。
05:13
Or take for example
an innovative satellite company.
an innovative satellite company.
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或者再举一个
创新性卫星公司的例子。
创新性卫星公司的例子。
05:15
They opened up their data and donated it,
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他们公开提供了他们的数据，
05:18
and with that data you could track
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通过那些数据，你就能够追踪
05:19
how droughts are impacting
food production.
food production.
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干旱是如何影响粮食产量的。
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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有了这些数据，你甚至可以
在危机发生之前就启动援助资金。
在危机发生之前就启动援助资金。
05:27
This is a great start.
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这是一个好的开始。
05:29
There's important insights
just locked away in company data.
just locked away in company data.
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在企业们的数据中，
封存着许多重要的信息。
封存着许多重要的信息。
05:34
And yes, you need to be very careful.
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是的，你需要格外的小心。
05:36
You need to respect privacy concerns,
for example by anonymizing the data.
for example by anonymizing the data.
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你需要尊重隐私问题，
比如可以将数据匿名化。
比如可以将数据匿名化。
05:39
But even if the floodgates opened up,
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但即使放开了约束，
05:42
and even if all companies
donated their data
donated their data
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即使所有的公司
都将他们的数据捐献给
都将他们的数据捐献给
05:45
to academics, to NGOs,
to humanitarian organizations,
to humanitarian organizations,
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学术界，非政府组织
和人道主义组织，
和人道主义组织，
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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这依然不足以充分使用数据，
05:51
for humanitarian goals.
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实现人道主义目标。
05:54
Why?
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为什么？
05:55
To unlock insights in data,
you need decision scientists.
you need decision scientists.
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为了解锁数据中的重要信息，
你仍需要决策科学家。
你仍需要决策科学家。
05:59
Decision scientists are people like me.
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像我一样的决策科学家。
06:02
They take the data, they clean it up,
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他们得到数据，整理它，
06:04
transform it and put it
into a useful algorithm
into a useful algorithm
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改造它，再把数据
用于有用的算法中，
用于有用的算法中，
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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这是企业解决手头的
业务需求的最好选择。
业务需求的最好选择。
06:09
In the world of humanitarian aid,
there are very few decision scientists.
there are very few decision scientists.
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在人道主义救援领域，
决策科学家十分短缺。
决策科学家十分短缺。
06:13
Most of them work for companies.
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他们中的大部分都为企业工作。
06:16
So that's the second thing
that companies need to do.
that companies need to do.
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所以公司需要做的第二件事，
06:19
In addition to donating their data,
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除了贡献他们的数据以外，
06:20
they need to donate
their decision scientists.
their decision scientists.
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他们还需要贡献决策科学家。
06:23
Now, companies will say, "Ah! Don't take
our decision scientists from us.
our decision scientists from us.
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然后企业就会说，“啊！
别带走我们的决策科学家。
别带走我们的决策科学家。
06:29
We need every spare second of their time."
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我们每时每刻都需要他们。”
06:32
But there's a way.
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当然有解决方法。
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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如果说一家公司愿意贡献出
它的决策科学家的部分时间，
它的决策科学家的部分时间，
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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那我们应该把这部分贡献时间
分散到很长的周期里去使用，
分散到很长的周期里去使用，
06:41
over a long period,
say for example five years.
say for example five years.
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比如说五年，这样更加有意义。
06:44
This might only amount
to a couple of hours per month,
to a couple of hours per month,
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这样分配之后，每个月
可能就只需要几个小时，
可能就只需要几个小时，
06:47
which a company would hardly miss,
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对于一家公司来说微不足道，
06:49
but what it enables is really important:
long-term partnerships.
long-term partnerships.
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但这促成的结果却意义非凡：
一种长期的合作关系。
一种长期的合作关系。
06:54
Long-term partnerships
allow you to build relationships,
allow you to build relationships,
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长期的合作关系
能够促成友谊，
能够促成友谊，
06:57
to get to know the data,
to really understand it
to really understand it
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提供渠道去接触数据，
真正理解它们，
真正理解它们，
07:00
and to start to understand
the needs and challenges
the needs and challenges
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从而体会人道主义组织
07:02
that the humanitarian
organization is facing.
organization is facing.
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正面对的需求与挑战。
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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在罗马，我们在世界粮食计划署
花费了整整五年，
花费了整整五年，
07:09
five years.
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五年时间。
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，好吧，我们用于
讨论解决不了的问题。
讨论解决不了的问题。
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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然后我们又花了两年时间
去更新，完善我们的工具
去更新，完善我们的工具
07:17
like in the operations in Iraq
and other countries.
and other countries.
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就像在伊拉克和
其他一些国家的行动一样。
其他一些国家的行动一样。
07:21
I don't think that's
an unrealistic timeline
an unrealistic timeline
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当讨论到使用数据，
做出可操作改变时，
做出可操作改变时，
07:23
when it comes to using data
to make operational changes.
to make operational changes.
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我认为我们提出的
时间线是十分现实的。
时间线是十分现实的。
07:26
It's an investment. It requires patience.
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这是一种投资。
我们需要有耐心。
我们需要有耐心。
07:29
But the types of results
that can be produced are undeniable.
that can be produced are undeniable.
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至少最终取得的
效益是不可忽视的。
效益是不可忽视的。
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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对我们而言，这种效益
就是供养成千上万的人口。
就是供养成千上万的人口。
07:39
So we have donating data,
we have donating decision scientists,
we have donating decision scientists,
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所以企业贡献了数据，
企业还贡献了决策科学家，
企业还贡献了决策科学家，
07:43
and there's actually a third way
that companies can help:
that companies can help:
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其实企业还有第三种帮忙的方式：
07:46
donating technology
to capture new sources of data.
to capture new sources of data.
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通过贡献收集新数据的技术。
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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就像你能看到的，
我们在很多地方还缺失数据。
我们在很多地方还缺失数据。
07:52
Right now, Syrian refugees
are flooding into Greece,
are flooding into Greece,
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此时此刻，叙利亚的难民
还在持续涌入希腊，
还在持续涌入希腊，
07:57
and the UN refugee agency,
they have their hands full.
they have their hands full.
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联合国难民委员会
忙的不可开交。
忙的不可开交。
08:01
The current system for tracking people
is paper and pencil,
is paper and pencil,
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现行的体系是通过
笔和纸追踪人员的
笔和纸追踪人员的
08:04
and what that means is
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这就是说，
08:05
that when a mother and her five children
walk into the camp,
walk into the camp,
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当一位母亲领着她的五个孩子
走进难民营的时候，
走进难民营的时候，
08:08
headquarters is essentially
blind to this moment.
blind to this moment.
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总部基本上就
无视这件事的发生。
无视这件事的发生。
08:10
That's all going to change
in the next few weeks,
in the next few weeks,
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在未来几周中，
这一切都将会改变，
这一切都将会改变，
08:13
thanks to private sector collaboration.
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感谢私企的合作。
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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我正在工作的物流公司，
给我们提供了一种全新的
给我们提供了一种全新的
08:19
from the logistics company
that I work for.
that I work for.
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基于包裹跟踪的数据技术。
08:22
With this new system,
there will be a data trail,
there will be a data trail,
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这样一个系统，
将为我们提供数据追踪，
将为我们提供数据追踪，
08:24
so you know exactly the moment
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这样当妈妈和她的孩子们
走进难民营的那一刻
走进难民营的那一刻
08:25
when that mother and her children
walk into the camp.
walk into the camp.
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你就会知道这件事。
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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不仅如此，你还会得知
下个月和下下个月，
下个月和下下个月，
08:31
this month and the next.
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她是否会有足够的物需供给。
08:32
Information visibility drives efficiency.
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信息的可视性驱动了效率。
08:35
For companies, using technology
to gather important data,
to gather important data,
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对于企业来说，
使用技术去收集重要数据，
使用技术去收集重要数据，
08:38
it's like bread and butter.
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是它们的主要经济来源。
08:40
They've been doing it for years,
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他们多年来都在从事这件事，
08:41
and it's led to major
operational efficiency improvements.
operational efficiency improvements.
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并带来了卓越的效率提升。
08:45
Just try to imagine
your favorite beverage company
your favorite beverage company
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想象一下
你最喜欢的饮料公司
你最喜欢的饮料公司
08:48
trying to plan their inventory
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将要计划下一批产品清单，
08:49
and not knowing how many bottles
were on the shelves.
were on the shelves.
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却对正在货架上的
饮料数量毫不知情。
饮料数量毫不知情。
08:52
It's absurd.
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这听起来该多荒唐。
08:53
Data drives better decisions.
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数据驱使我们做出更好的决策。
08:57
Now, if you're representing a company,
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现在，假设你正代表着一家公司。
09:00
and you're pragmatic
and not just idealistic,
and not just idealistic,
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你是一个实用主义
而并非理想主义的人
而并非理想主义的人
09:03
you might be saying to yourself,
"OK, this is all great, Mallory,
"OK, this is all great, Mallory,
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你也许会对自己说：
“好吧，这听起来不错，马洛里，
“好吧，这听起来不错，马洛里，
09:06
but why should I want to be involved?"
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但是我为什么
会想要加入其中呢？”
会想要加入其中呢？”
09:09
Well for one thing, beyond the good PR,
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首先，除了有好的公共关系外，
09:11
humanitarian aid
is a 24-billion-dollar sector,
is a 24-billion-dollar sector,
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人道救援组织是一个
价值240亿的行业，
价值240亿的行业，
09:14
and there's over five billion people,
maybe your next customers,
maybe your next customers,
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所以来自发展中国家超过50亿的人口，
09:17
that live in the developing world.
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他们都可能成为你的下一批用户。
09:19
Further, companies that are engaging
in data philanthropy,
in data philanthropy,
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另一方面，从事数据慈善业
的那些公司，
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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他们正在挖掘
封存在数据当中的新信息。
封存在数据当中的新信息。
09:25
Take, for example, a credit card company
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举个例子，一家信用卡公司
09:27
that's opened up a center
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开放了一个集中场所，
09:29
that functions as a hub for academics,
for NGOs and governments,
for NGOs and governments,
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作为一个中心
使学者，非政府组织和政府
能够一起工作。
能够一起工作。
09:32
all working together.
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09:35
They're looking at information
in credit card swipes
in credit card swipes
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他们查看信用卡中刷出的信息，
09:37
and using that to find insights
about how households in India
about how households in India
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运用这些信息从而得出
在印度的家庭
在印度的家庭
是如何生活，工作，
获得收入和开销的。
获得收入和开销的。
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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对于人道主义世界来说，
这就为我们提供了
这就为我们提供了
09:46
about how you might
bring people out of poverty.
bring people out of poverty.
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帮助人们摆脱贫困问题的方案。
09:48
But for companies, it's providing
insights about your customers
insights about your customers
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但对于公司来说，
这就是向他们提供了
这就是向他们提供了
在印度的用户和潜在用户信息。
09:52
and potential customers in India.
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09:54
It's a win all around.
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这是双赢的局面。
09:57
Now, for me, what I find
exciting about data philanthropy --
exciting about data philanthropy --
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现在，对于数据慈善业——
10:01
donating data, donating decision
scientists and donating technology --
scientists and donating technology --
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贡献数据，贡献决策科学家，
以及贡献技术——
以及贡献技术——
10:06
it's what it means
for young professionals like me
for young professionals like me
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我激动不已，
对于像我一样选择在公司工作
的年轻学者而言意义非凡。
的年轻学者而言意义非凡。
10:08
who are choosing to work at companies.
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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，新一代的劳动者
10:13
care about having their work
make a bigger impact.
make a bigger impact.
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更加关注他们的工作是否
能对社会产生更大的影响力。
能对社会产生更大的影响力。
10:16
We want to make a difference,
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我们都想为世界做出不同，
10:19
and so through data philanthropy,
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所以通过数据慈善业，
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
他们的决策科学家
他们的决策科学家
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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特别是对于这种高需求
的职业来说尤其重要。
的职业来说尤其重要。
10:29
Data philanthropy
makes good business sense,
makes good business sense,
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数据慈善业有很好的商业价值，
10:34
and it also can help
revolutionize the humanitarian world.
revolutionize the humanitarian world.
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它同时也能够为人道主义事业
做出巨大变革。
做出巨大变革。
10:39
If we coordinated
the planning and logistics
the planning and logistics
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如果我们能够把这些
策划和物流进度
策划和物流进度
10:41
across all of the major facets
of a humanitarian operation,
of a humanitarian operation,
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运用到人道主义进程
的各种领域之中，
的各种领域之中，
10:45
we could feed, clothe and shelter
hundreds of thousands more people,
hundreds of thousands more people,
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我们就能够给更多的人
提供食物，衣物和住所，
提供食物，衣物和住所，
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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公司需要去加强和充分发挥
他们在这场革新当中
能够发挥的那些作用。
能够发挥的那些作用。
10:53
in bringing about this revolution.
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10:56
You've probably heard of the saying
"food for thought."
"food for thought."
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你也许听过这个短语“值得思考的食物”
（英文习语，意思是：值得深思的问题）
（英文习语，意思是：值得深思的问题）
10:59
Well, this is literally thought for food.
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字面意思就是思考食物。
11:03
It finally is the right idea
at the right time.
at the right time.
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我们终于在正确的时间
想出了正确的主意。
想出了正确的主意。
11:07
(Laughter)
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（笑声）
11:08
Très magnifique.
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多么美妙！
11:10
Thank you.
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谢谢！
11:11
(Applause)
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（掌声）
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