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你们的数据可以帮助终结世界饥饿问题 – 译学馆
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你们的数据可以帮助终结世界饥饿问题

Your company's data could help end world hunger | Mallory Soldner

2010年6月
June 2010.
我第一次来到意大利罗马
I landed for the first time in Rome, Italy.
我不是去那儿观光旅游的
I wasn’t there to sightsee.
我是去解决世界饥饿问题的
I was there to solve world hunger.
(笑声)
(Laughter)
就是这样的
That’s right.
我是一名25岁的博士生
I was a 25-year-old PhD student
带着在大学研发的设备原型
armed with a prototype tool developed back at my university,
去帮助世界粮食计划署 解决饥饿问题
and I was going to help the World Food Programme fix hunger.
所以当我大步跨入总部大楼
So I strode into the headquarters building
用眼睛扫视了那一排联合国旗帜
and my eyes scanned the row of UN flags,
我边笑边暗自想
and I smiled as I thought to myself,
工程师来啦
“The engineer is here.”
(笑声)
(Laughter)
把你们的数据给我
Give me your data.
我要优化这一切
I’m going to optimize everything.
(笑声)
(Laughter)
告诉我你们购买过的食物
Tell me the food that you’ve purchased,
告诉我食物需要 何时送达何地
tell me where it’s going and when it needs to be there,
我就能告诉你 最短 最快 最便宜
and I’m going to tell you the shortest, fastest, cheapest,
的最佳食物运输道路
best set of routes to take for the food.
我们就能节约资金
We’re going to save money,
我们就能避免延误和干扰
we’re going to avoid delays and disruptions,
最重要的是我们能挽救生命
and bottom line, we’re going to save lives.
不用谢
You’re welcome.
(笑声)
(Laughter)
我想这大概会需要 用12个月的时间来实现
I thought it was going to take 12 months,
好吧 或者是13个月
OK, maybe even 13.
但这并不怎么成功
This is not quite how it panned out.
当我加入这个项目几个月之后我的法国老板就告诉我
Just a couple of months into the project, my French boss, he told me,
你知道的 马洛里
“You know, Mallory,
这是一个好想法
it’s a good idea,
但是你的算法 需要的数据并不在这儿
but the data you need for your algorithms is not there.
这想法的思路正确但是出现在了错误的时间点上
It’s the right idea but at the wrong time,
错误时间点上的所谓正确想法
and the right idea at the wrong time
就是错误的想法
is the wrong idea.”
(笑声)
(Laughter)
于是项目中止
Project over.
我非常沮丧
I was crushed.
现在当我回顾
When I look back now
我在罗马度过的第一个夏天
on that first summer in Rome
我看到了六年来发生的巨大变化
and I see how much has changed over the past six years,
真是绝对的大改变
it is an absolute transformation.
这是一个让我们将数据 带到人道主义世界的时代
It’s a coming of age for bringing data into the humanitarian world.
这真是令人兴奋 鼓舞人心
It’s exciting. It’s inspiring.
但是我们还没完全做到
But we’re not there yet.
请振作精神 高管们
And brace yourself, executives,
因为我正要把 企业放到焦点位置
because I’m going to be putting companies
提高它们的作用并 尽其所能
on the hot seat to step up and play the role that I know they can.
我在罗马的经历证明了
My experiences back in Rome prove
数据可以拯救生命
using data you can save lives.
好吧 并不是第一次的尝试
OK, not that first attempt,
但是我们最终做到了
but eventually we got there.
让我来展开这幅图景
Let me paint the picture for you.
想象一下你要准备早餐 午餐 晚餐
Imagine that you have to plan breakfast, lunch and dinner
为5000000人的
for 500,000 people,
你的预算是固定的
and you only have a certain budget to do it,
比方说每月650万美元
say 6.5 million dollars per month.
好的 那你应该怎么做呢处理这件事的最好方法是什么呢
Well, what should you do? What’s the best way to handle it?
你是该买大米 小麦鹰嘴豆还是油呢
Should you buy rice, wheat, chickpea, oil?
又要买多少呢
How much?
听起来很简单 做起来却很难
It sounds simple. It’s not.
你有30种可选择的食物但是你只能选择其中的5种
You have 30 possible foods, and you have to pick five of them.
那样就会有超过14万种 不同的食物组合
That’s already over 140,000 different combinations.
针对你所挑选的每一种食物
Then for each food that you pick,
你还需要考虑购买量的问题
you need to decide how much you’ll buy,
考虑购买地的问题
where you’re going to get it from,
考虑储存地的问题
where you’re going to store it,
考虑运输时间的问题
how long it’s going to take to get there.
如果你看了所有可以的运输路线
You need to look at all of the different transportation routes as well.
它可能有超过9亿种选择
And that’s already over 900 million options.
如果考虑一种选择需要1秒钟
If you considered each option for a single second,
那你就需要28年时间 才能把它们全过一遍
that would take you over 28 years to get through.
9亿种选择
900 million options.
所以我们开发了一种工具它帮决策者
So we created a tool that allowed decisionmakers
处理分析9亿种路线选择
to weed through all 900 million options
只需要一天的时间
in just a matter of days.
事实证明 工具十分成功
It turned out to be incredibly successful.
在伊拉克的一次行动中
In an operation in Iraq,
我们节约了原成本中17%的开销
we saved 17 percent of the costs,
这就意味着你有额外 的能力再供养8万人
and this meant that you had the ability to feed an additional 80,000 people.
这一切都要归功于数据 以及对复杂系统建模的能力
It’s all thanks to the use of data and modeling complex systems.
但这并不是我们独自完成的
But we didn’t do it alone.
我在罗马工作的单位十分独特
The unit that I worked with in Rome, they were unique.
他们相信合作的力量
They believed in collaboration.
他们引入学术界的帮助
They brought in the academic world.
引入企业界的帮助
They brought in companies.
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
And if we really want to make big changes in big problems like world hunger,
我们需要每一个社会成员的加入
we need everybody to the table.
我们需要来自人道组织的处理数据方面的人才
We need the data people from humanitarian organizations
作为引路人
leading the way,
将这些数据处理分析的人紧密地和
and orchestrating just the right types of engagements
专业学者 政府联系在一起
with academics, with governments.
还有一个群体没有被充分利用
And there’s one group that’s not being leveraged in the way that it should be.
猜到了吗 是企业
Did you guess it? Companies.
企业将在解决我们的世界 重大问题上发挥重要作用
Companies have a major role to play in fixing the big problems in our world.
我已经在私人部门干了两年了
I’ve been in the private sector for two years now.
我见识到了企业的能力以及他们没有充分去做的部分
I’ve seen what companies can do, and I’ve seen what companies aren’t doing,
我认为主要有三种方式 去填补那些空缺:
and I think there’s three main ways that we can fill that gap:
通过贡献数据通过贡献决策科学家
by donating data, by donating decision scientists
和通过贡献收集新数据的技术
and by donating technology to gather new sources of data.
这是一种数据慈善事业
This is data philanthropy,
是未来的企业社会责任
and it’s the future of corporate social responsibility.
当然它也有很好的商业意义
Bonus, it also makes good business sense.
当今的企业收集大量的数据
Companies today, they collect mountains of data,
所以他们所能做的 第一件事就是贡献这些数据
so the first thing they can do is start donating that data.
一部分企业已经开始提供数据
Some companies are already doing it.
就以一家主流电信公司为例
Take, for example, a major telecom company.
他们开放了位于 塞内加尔和科特迪瓦的数据
They opened up their data in Senegal and the Ivory Coast
研究人员由此发现
and researchers discovered
通过观察信号塔接收到 的手机信号模式图
that if you look at the patterns in the pings to the cell phone towers,
你就能了解人们正前往何处
you can see where people are traveling.
通过这些数据你还能了解到
And that can tell you things like
疟疾可能传播的地方你可以由此作出预测
where malaria might spread, and you can make predictions with it.
或者再举一个 创新性卫星公司的例子
Or take for example an innovative satellite company.
他们公开提供了他们的数据
They opened up their data and donated it,
通过那些数据 你就能够追踪
and with that data you could track
干旱是如何影响粮食产量的
how droughts are impacting food production.
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
With that you can actually trigger aid funding before a crisis can happen.
这是一个好的开始
This is a great start.
在企业们的数据中封存着许多重要的信息
There’s important insights just locked away in company data.
是的 你需要格外的小心
And yes, you need to be very careful.
你需要尊重隐私问题比如可以将数据匿名化
You need to respect privacy concerns, for example by anonymizing the data.
但即使放开了约束
But even if the floodgates opened up,
即使所有的公司 都将他们的数据捐献给
and even if all companies donated their data
学术界 非政府组织 和人道主义组织
to academics, to NGOs, to humanitarian organizations,
这依然不足以充分使用数据
it wouldn’t be enough to harness that full impact of data
实现人道主义目标
for humanitarian goals.
为什么
Why?
为了解锁数据中的重要信息你仍需要决策科学家
To unlock insights in data, you need decision scientists.
像我一样的决策科学家
Decision scientists are people like me.
他们得到数据 整理它
They take the data, they clean it up,
改造它 再把数据 用于有用的算法中
transform it and put it into a useful algorithm
这是企业解决手头的 业务需求的最好选择
that’s the best choice to address the business need at hand.
在人道主义救援领域决策科学家十分短缺
In the world of humanitarian aid, there are very few decision scientists.
他们中的大部分都为企业工作
Most of them work for companies.
所以公司需要做的第二件事
So that’s the second thing that companies need to do.
除了贡献他们的数据以外
In addition to donating their data,
他们还需要贡献决策科学家
they need to donate their decision scientists.
然后企业就会说别带走我们的决策科学家
Now, companies will say, “Ah! Don’t take our decision scientists from us.
我们每时每刻都需要他们
We need every spare second of their time.”
当然有解决方法
But there’s a way.
如果说一家公司愿意贡献出 它的决策科学家的部分时间
If a company was going to donate a block of a decision scientist’s time,
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
it would actually make more sense to spread out that block of time
比如说五年 这样更加有意义
over a long period, say for example five years.
这样分配之后 每个月 可能就只需要几个小时
This might only amount to a couple of hours per month,
对于一家公司来说微不足道
which a company would hardly miss,
但这促成的结果却意义非凡一种长期的合作关系
but what it enables is really important: long-term partnerships.
长期的合作关系 能够促成友谊
Long-term partnerships allow you to build relationships,
提供渠道去接触数据真正理解它们
to get to know the data, to really understand it
从而体会人道主义组织
and to start to understand the needs and challenges
正面对的需求与挑战
that the humanitarian organization is facing.
在罗马 我们在世界粮食计划署 花费了整整五年
In Rome, at the World Food Programme, this took us five years to do,
五年时间
five years.
前三年 好吧 我们用于 讨论解决不了的问题
That first three years, OK, that was just what we couldn’t solve for.
然后我们又花了两年时间 去更新 完善我们的工具
Then there was two years after that of refining and implementing the tool,
就像在伊拉克和 其他一些国家的行动一样
like in the operations in Iraq and other countries.
当讨论到使用数据做出可操作改变时
I don’t think that’s an unrealistic timeline
我认为我们提出的 时间线是十分现实的
when it comes to using data to make operational changes.
这是一种投资我们需要有耐心
It’s an investment. It requires patience.
至少最终取得的 效益是不可忽视的
But the types of results that can be produced are undeniable.
对我们而言 这种效益 就是供养成千上万的人口
In our case, it was the ability to feed tens of thousands more people.
所以企业贡献了数据企业还贡献了决策科学家
So we have donating data, we have donating decision scientists,
其实企业还有第三种帮忙的方式
and there’s actually a third way that companies can help:
通过贡献收集新数据的技术
donating technology to capture new sources of data.
就像你能看到的我们在很多地方还缺失数据
You see, there’s a lot of things we just don’t have data on.
此时此刻 叙利亚的难民 还在持续涌入希腊
Right now, Syrian refugees are flooding into Greece,
联合国难民委员会 忙的不可开交
and the UN refugee agency, they have their hands full.
现行的体系是通过 笔和纸追踪人员的
The current system for tracking people is paper and pencil,
这就是说
and what that means is
当一位母亲领着她的五个孩子 走进难民营的时候
that when a mother and her five children walk into the camp,
总部基本上就 无视这件事的发生
headquarters is essentially blind to this moment.
在未来几周中这一切都将会改变
That’s all going to change in the next few weeks,
感谢私企的合作
thanks to private sector collaboration.
我正在工作的物流公司给我们提供了一种全新的
There’s going to be a new system based on donated package tracking technology
基于包裹跟踪的数据技术
from the logistics company that I work for.
这样一个系统将为我们提供数据追踪
With this new system, there will be a data trail,
这样当妈妈和她的孩子们 走进难民营的那一刻
so you know exactly the moment
你就会知道这件事
when that mother and her children walk into the camp.
不仅如此 你还会得知 下个月和下下个月
And even more, you know if she’s going to have supplies
她是否会有足够的物需供给
this month and the next.
信息的可视性驱动了效率
Information visibility drives efficiency.
对于企业来说使用技术去收集重要数据
For companies, using technology to gather important data,
是它们的主要经济来源
it’s like bread and butter.
他们多年来都在从事这件事
They’ve been doing it for years,
并带来了卓越的效率提升
and it’s led to major operational efficiency improvements.
想象一下 你最喜欢的饮料公司
Just try to imagine your favorite beverage company
将要计划下一批产品清单
trying to plan their inventory
却对正在货架上的 饮料数量毫不知情
and not knowing how many bottles were on the shelves.
这听起来该多荒唐
It’s absurd.
数据驱使我们做出更好的决策
Data drives better decisions.
现在 假设你正代表着一家公司
Now, if you’re representing a company,
你是一个实用主义 而并非理想主义的人
and you’re pragmatic and not just idealistic,
你也许会对自己说好吧 这听起来不错 Mallory
you might be saying to yourself, “OK, this is all great, Mallory,
但是我为什么 会想要加入其中呢
but why should I want to be involved?”
首先 除了有好的公共关系外
Well for one thing, beyond the good PR,
人道救援组织是一个 价值240亿的行业
humanitarian aid is a 24-billion-dollar sector,
所以来自发展中国家超过50亿的人口
and there’s over five billion people, maybe your next customers,
他们都可能成为你的下一批用户
that live in the developing world.
另一方面 从事数据慈善业 的那些公司
Further, companies that are engaging in data philanthropy,
他们正在挖掘 封存在数据当中的新信息
they’re finding new insights locked away in their data.
举个例子 一家信用卡公司
Take, for example, a credit card company
开放了一个集中场所
that’s opened up a center
可以促进学者 NGO和政府集中学术研究交流
that functions as a hub for academics, for NGOs and governments,
使全部人在一起工作研发
all working together.
他们查看信用卡中刷出的信息
They’re looking at information in credit card swipes
运用这些信息从而得出 在印度的家庭
and using that to find insights about how households in India
生活 工作 收入和开销
live, work, earn and spend.
对于人道主义世界来说这就为我们提供了信息
For the humanitarian world, this provides information
帮助人们摆脱贫困问题的方案
about how you might bring people out of poverty.
它可以让公司更好的洞悉客户群
But for companies, it’s providing insights about your customers
以及在印度的潜在客户
and potential customers in India.
这是双赢的局面
It’s a win all around.
现在 对于数据慈善业
Now, for me, what I find exciting about data philanthropy —
贡献数据 贡献决策科学家以及贡献技术
donating data, donating decision scientists and donating technology —
那意味着对于像我这样的年轻的技术人员
it’s what it means for young professionals like me
选择在公司工作的人群
who are choosing to work at companies.
研究表明 新一代的劳动者
Studies show that the next generation of the workforce
更加关注他们的工作是否 能对社会产生更大的影响力
care about having their work make a bigger impact.
我们都想为世界做出不同
We want to make a difference,
所以通过数据慈善业
and so through data philanthropy,
公司更容易留得住 他们的决策科学家
companies can actually help engage and retain their decision scientists.
特别是对于这种高需求 的职业来说尤其重要
And that’s a big deal for a profession that’s in high demand.
数据慈善业有很好的商业价值
Data philanthropy makes good business sense,
它同时也能够为人道主义事业 做出巨大变革
and it also can help revolutionize the humanitarian world.
如果我们能够把这些 策划和物流进度
If we coordinated the planning and logistics
运用到人道主义进程 的各种领域之中
across all of the major facets of a humanitarian operation,
我们就能够给更多的人 提供食物 衣物和住所
we could feed, clothe and shelter hundreds of thousands more people,
公司需要在这件事情迈出步伐起到至关重要的角色
and companies need to step up and play the role that I know they can
来引领变革
in bringing about this revolution.
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)
You’ve probably heard of the saying “food for thought.”
字面意思就是思考食物
Well, this is literally thought for food.
我们终于在正确的时间 想出了正确的主意
It finally is the right idea at the right time.
(笑声)
(Laughter)
多么美妙
Très magnifique.
谢谢
Thank you.
(掌声)
(Applause)

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视频概述

视频通过介绍数据可以解决生活中许多问题 以及对慈善事业喜欢重要的作用 并鼓励企业共享数据和人才实现人道主义共同进步的社会

听录译者

收集自网络

翻译译者

ss

审核员

与光同尘

视频来源

https://www.youtube.com/watch?v=Sa27SUR0Mlo

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