I landed for the first time in Rome, Italy.

2010年六月，

I wasn't there to sightsee.

我第一次前往意大利．羅馬。

I was there to solve world hunger.

我不是去觀光的，

(Laughter)

我是去解決世界飢餓問題的。

That's right.

（笑聲）

I was a 25-year-old PhD student

沒錯。

armed with a prototype tool developed back at my university,

我當時是一位25歲的博士生，

and I was going to help the World Food Programme fix hunger.

我帶著在大學期間開發的原型工具，

So I strode into the headquarters building

準備幫助世界糧食計劃署 解決飢餓問題。

and my eyes scanned the row of UN flags,

我大步走進他們的總部大樓，

and I smiled as I thought to myself,

映入眼簾的是一整排的聯合國國旗，

"The engineer is here."

我開心地對著自己說，

(Laughter)

「工程師來了！」

Give me your data.

（笑聲）

I'm going to optimize everything.

"拿出你們的數據，

(Laughter)

我要優化所有資料。"

Tell me the food that you've purchased,

（笑聲）

tell me where it's going and when it needs to be there,

"告訴我你們已經購買的食物，

and I'm going to tell you the shortest, fastest, cheapest,

告訴我要送到哪裡、 什麼時候需要，

best set of routes to take for the food.

我就會告訴你們最短、最快、

We're going to save money,

最便宜的食物運送路徑。

we're going to avoid delays and disruptions,

我們會節省很多錢，

and bottom line, we're going to save lives.

我們可以避免延遲和中斷，

You're welcome.

最後，我們還可以拯救世人。

(Laughter)

（謝謝）不用客氣！"

I thought it was going to take 12 months,

（笑聲）

OK, maybe even 13.

我在想這大概需要 12個月的時間來實現，

This is not quite how it panned out.

好吧，可能要13個月。

Just a couple of months into the project, my French boss, he told me,

但事情並沒有想像中的簡單。

"You know, Mallory,

當我加入這個專案幾個月之後， 我的法國老闆，他告訴我：

it's a good idea,

「馬洛里，妳知道嗎？

but the data you need for your algorithms is not there.

妳的點子是不錯啦！

It's the right idea but at the wrong time,

但要符合你演算法的數據並不存在。

and the right idea at the wrong time

點子是對的，但時機不對，

is the wrong idea."

而對的點子在錯誤的時機出現...

(Laughter)

就是一個錯誤的點子！」

Project over.

（笑聲）

I was crushed.

專案結束！

When I look back now

我超傷心的。

on that first summer in Rome

現在當我回頭去看

and I see how much has changed over the past six years,

從羅馬的第一個夏天到現在，

it is an absolute transformation.

我看到在這六年來，

It's a coming of age for bringing data into the humanitarian world.

真的是完全轉變了。

It's exciting. It's inspiring.

把數據帶入人道世界的時代來臨了。

But we're not there yet.

這真是令人興奮、鼓舞人心的。

And brace yourself, executives,

但是我們還沒有達到。

because I'm going to be putting companies

現場的各位主管，請仔細聽好了，

on the hot seat to step up and play the role that I know they can.

我準備要把你們的公司推上火線，

My experiences back in Rome prove

因為我知道你們辦得到。

using data you can save lives.

我在羅馬的經驗告訴我，

OK, not that first attempt,

運用數據，你可以拯救生命。

but eventually we got there.

的確，不是一試就能成功，

Let me paint the picture for you.

但最終我們還是能辦到。

Imagine that you have to plan breakfast, lunch and dinner

讓我來解釋一下。

for 500,000 people,

想像一下，

and you only have a certain budget to do it,

你準備要為50萬人準備早、中、晚餐

say 6.5 million dollars per month.

但你的預算有限，

Well, what should you do? What's the best way to handle it?

比如說，每個月650萬美元。

Should you buy rice, wheat, chickpea, oil?

你要怎麼做？ 最好的方式是甚麼？

How much?

你需要買米、小麥、鷹嘴豆和油嗎？

It sounds simple. It's not.

要買多少？

You have 30 possible foods, and you have to pick five of them.

聽起來很簡單，但做起來很難。

That's already over 140,000 different combinations.

你有30種可能的食物， 你必須從中挑選五種。

Then for each food that you pick,

那樣就會有超過14萬種 不同的食物組合。

you need to decide how much you'll buy,

你挑選的每樣食物，

where you're going to get it from,

你要決定準備買多少、

where you're going to store it,

去哪買、

how long it's going to take to get there.

買來後要存放在哪、

You need to look at all of the different transportation routes as well.

運送到目的地要多久的時間。

And that's already over 900 million options.

你還需要查看 所有不同的運輸路線。

If you considered each option for a single second,

而這樣已經超過九億種選擇了。

that would take you over 28 years to get through.

如果你每個選項都需要思考一秒，

900 million options.

那你要花超過28年的時間 才能把它們全過一遍。

So we created a tool that allowed decisionmakers

九億種選擇啊！

to weed through all 900 million options

所以我們創建了一個

in just a matter of days.

只要花幾天的時間，就可以讓決策者

It turned out to be incredibly successful.

解決九億種選擇的工具。

In an operation in Iraq,

果然非常成功。

we saved 17 percent of the costs,

在伊拉克的一次任務中，

and this meant that you had the ability to feed an additional 80,000 people.

我們節省了17％的成本，

It's all thanks to the use of data and modeling complex systems.

也就是說，你還有能力 能餵飽另外的八萬人。

But we didn't do it alone.

這一切都要感謝數據和 複雜的建模系統。

The unit that I worked with in Rome, they were unique.

但這並不是我們獨自完成的。

They believed in collaboration.

我們在羅馬合作的單位， 他們真的很棒。

They brought in the academic world.

他們相信合作的力量。

They brought in companies.

他們把學術界帶入這個領域，

And if we really want to make big changes in big problems like world hunger,

把企業帶入這個領域。

we need everybody to the table.

如果我們希望能在像世界飢餓 這種大問題上做出改變，

We need the data people from humanitarian organizations

我們需要每一個社會成員的加入。

leading the way,

我們需要來自人道組織的數據人員

and orchestrating just the right types of engagements

引領道路，

with academics, with governments.

並組織學術界及政府部門

And there's one group that's not being leveraged in the way that it should be.

好好地參與合作。

Did you guess it? Companies.

還有一種群體沒有被充分利用。

Companies have a major role to play in fixing the big problems in our world.

猜猜是誰？公司企業。

I've been in the private sector for two years now.

公司在解決世界的大問題方面 扮演了重要的角色。

I've seen what companies can do, and I've seen what companies aren't doing,

我在私人公司已經工作了兩年。

and I think there's three main ways that we can fill that gap:

我見識到了企業的能力， 以及他們沒有充分做到的部分，

by donating data, by donating decision scientists

我認為有三個主要方式， 可以填補這個空缺：

and by donating technology to gather new sources of data.

藉由捐贈數據、決策科學家及科技

This is data philanthropy,

來收集新數據的技術。

and it's the future of corporate social responsibility.

這是數據慈善事業，

Bonus, it also makes good business sense.

是企業的未來社會責任。

Companies today, they collect mountains of data,

好處就是，對公司的形象有幫助。

so the first thing they can do is start donating that data.

如今的公司，收集了一大堆數據，

Some companies are already doing it.

所以他們可以做的第一件事 就是捐贈數據。

Take, for example, a major telecom company.

有些公司已經在做了。

They opened up their data in Senegal and the Ivory Coast

舉例，以某一家大型的電信公司為例。

and researchers discovered

他們開放了位於塞內加爾和 象牙海岸的數據，

that if you look at the patterns in the pings to the cell phone towers,

研究人員發現，

you can see where people are traveling.

如果你觀察手機傳送到 基地台的數據圖形，

And that can tell you things like

你可以觀察到人們到哪裡活動，

where malaria might spread, and you can make predictions with it.

像這樣的數據能告訴你，

Or take for example an innovative satellite company.

瘧疾可能傳播的地方， 你可以用它做預測。

They opened up their data and donated it,

或者拿另一個創新的衛星公司為例，

and with that data you could track

他們開放並捐獻了數據，

how droughts are impacting food production.

使用那些數據，你就能夠追蹤

With that you can actually trigger aid funding before a crisis can happen.

乾旱是如何影響糧食產量的。

This is a great start.

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。

There's important insights just locked away in company data.

這是一個好的開始。

And yes, you need to be very careful.

在公司的數據中， 禁錮著許多重要的信息。

You need to respect privacy concerns, for example by anonymizing the data.

是的，你需要非常小心。

But even if the floodgates opened up,

您需要尊重隱私問題， 例如可以用匿名化數據解決。

and even if all companies donated their data

但即使所有的管道資料都開放了，

to academics, to NGOs, to humanitarian organizations,

即使所有的公司 都捐贈出他們的數據

it wouldn't be enough to harness that full impact of data

給學術界、非政府組織、人道組織，

for humanitarian goals.

光有這些資料，仍無法達到

Why?

人道主義的目標。

To unlock insights in data, you need decision scientists.

為什麼？

Decision scientists are people like me.

要解開數據中的信息， 你仍需要決策科學家。

They take the data, they clean it up,

像我這樣的決策科學家。

transform it and put it into a useful algorithm

他們拿到數據，會稍作整理，

that's the best choice to address the business need at hand.

把資料轉換後，帶入有用的演算法裡。

In the world of humanitarian aid, there are very few decision scientists.

這才是解決問題的最佳選擇。

Most of them work for companies.

但在人道援助的領域裡， 決策科學家很罕見。

So that's the second thing that companies need to do.

他們大多數都為私人企業工作。

In addition to donating their data,

所以，公司要做第二件事，

they need to donate their decision scientists.

公司除了捐贈他們的數據以外，

Now, companies will say, "Ah! Don't take our decision scientists from us.

他們還需要捐贈他們的決策科學家。

We need every spare second of their time."

但公司會說， 「啊！別帶走我們的決策科學家，

But there's a way.

我們分分秒秒都很需要他們。」

If a company was going to donate a block of a decision scientist's time,

但是有一個辦法，

it would actually make more sense to spread out that block of time

如果說一家公司決定貢獻出 它的決策科學家的部分時間，

over a long period, say for example five years.

那我們就把這些時間分散到長期使用， 這樣才行得通，

This might only amount to a couple of hours per month,

比如說，五年的時間。

which a company would hardly miss,

這樣分配之後，每個月 可能就只需要幾個小時，

but what it enables is really important: long-term partnerships.

對於一家公司來說不足掛齒，

Long-term partnerships allow you to build relationships,

但產生的效果是很重大的： 長期的夥伴關係。

to get to know the data, to really understand it

長期的夥伴關係能促進建立友誼，

and to start to understand the needs and challenges

對資料更理解，

that the humanitarian organization is facing.

而且可以更深入地了解到

In Rome, at the World Food Programme, this took us five years to do,

人道組織的需求及 目前所面臨到的問題。

five years.

在羅馬，我們在世界糧食計劃署，

That first three years, OK, that was just what we couldn't solve for.

花費了五年時間，五年。

Then there was two years after that of refining and implementing the tool,

前三年，沒錯，我們在 討論解決不了的問題。

like in the operations in Iraq and other countries.

然後我們又花了兩年時間 去更新，完善 我們的工具。

I don't think that's an unrealistic timeline

就像我們在伊拉克 和其他國家的行動一樣。

when it comes to using data to make operational changes.

當涉及到使用數據進行操作修改的時候，

It's an investment. It requires patience.

我不認為這樣的時間安排會有甚麼不妥。

But the types of results that can be produced are undeniable.

這是一項投資，我們要有耐心。

In our case, it was the ability to feed tens of thousands more people.

但產生的效果是不可否認的。

So we have donating data, we have donating decision scientists,

以我們的個案而言， 它是可以養活好幾萬人的。

and there's actually a third way that companies can help:

所以我們需要捐獻數據， 我們需要捐獻決策科學家，

donating technology to capture new sources of data.

實際上公司還有 第三種方法可以提供協助：

You see, there's a lot of things we just don't have data on.

透過捐贈技術來取得數據的新來源。

Right now, Syrian refugees are flooding into Greece,

你看，還有很多地方，我們都沒有數據。

and the UN refugee agency, they have their hands full.

目前，敘利亞難民正湧入希臘，

The current system for tracking people is paper and pencil,

而聯合國的難民機構， 他們也忙得不可開交。

and what that means is

目前的難民跟進系統 是用紙和筆來作業，

that when a mother and her five children walk into the camp,

意思就是，

headquarters is essentially blind to this moment.

當一個母親帶著她的五個孩子 走進難名營時，

That's all going to change in the next few weeks,

總部基本上根本看不到。

thanks to private sector collaboration.

在未來幾周中， 這一切都將會改變，

There's going to be a new system based on donated package tracking technology

這要感謝私人機構的合作。

from the logistics company that I work for.

我合作的物流公司，

With this new system, there will be a data trail,

即將捐贈一套全新的追蹤科技系統。

so you know exactly the moment

有了這個新系統，數據就能被追踪，

when that mother and her children walk into the camp.

所以當一位母親 帶著她的孩子走進難民營時，

And even more, you know if she's going to have supplies

你就會知道這件事。

this month and the next.

甚至，你還可以知道

Information visibility drives efficiency.

這個月及下個月 她是否能得到支援。

For companies, using technology to gather important data,

數據的能見度驅動了效率。

it's like bread and butter.

對公司而言，利用技術收集重要數據，

They've been doing it for years,

就像做奶油麵包一樣（簡單）。

and it's led to major operational efficiency improvements.

他們多年來都在從事這件事，

Just try to imagine your favorite beverage company

並帶來了卓越的效率提升。

trying to plan their inventory

試想一下，你最喜歡的飲料公司，

and not knowing how many bottles were on the shelves.

將要計劃下一批生產

It's absurd.

卻對正在貨架上的 飲料數量毫不知情，

Data drives better decisions.

這是很荒謬的。

Now, if you're representing a company,

數據驅使我們做出更好的決策。

and you're pragmatic and not just idealistic,

現在，如果您代表一個公司，

you might be saying to yourself, "OK, this is all great, Mallory,

你很務實，不是那種只會空想的人，

but why should I want to be involved?"

你可能會說， 「沒錯，是很偉大，馬洛里

Well for one thing, beyond the good PR,

但為什麼我要參與？」

humanitarian aid is a 24-billion-dollar sector,

其實，就一件事，提升公司形象，

and there's over five billion people, maybe your next customers,

人道援助是一個240億美元的公營事業，

that live in the developing world.

有超過50億人口住在發展中國家，

Further, companies that are engaging in data philanthropy,

很有可能你的下一個客戶就是他們。

they're finding new insights locked away in their data.

此外，從事數據慈善事業的那些公司，

Take, for example, a credit card company

他們正在挖掘 禁錮在數據當中的新信息。

that's opened up a center

例如，以某家信用卡公司為例，

that functions as a hub for academics, for NGOs and governments,

他們建立了一個數據中心樞紐，

all working together.

將學術界、非政府組織和政府

They're looking at information in credit card swipes

組織起來一起工作。

and using that to find insights about how households in India

他們透過刷卡紀錄，

live, work, earn and spend.

觀察到一般的印度家庭

For the humanitarian world, this provides information

他們如何生活、工作、賺錢和消費。

about how you might bring people out of poverty.

對人道組織而言，這裡面隱含著

But for companies, it's providing insights about your customers

如何使人們擺脫貧困的資訊。

and potential customers in India.

但對公司來說， 這就是向他們提供了

It's a win all around.

在印度的用戶和潛在用戶信息。

Now, for me, what I find exciting about data philanthropy --

這是一個三贏的局面。

donating data, donating decision scientists and donating technology --

而對我而言，我發現 數據慈善事業是令人振奮的 --

it's what it means for young professionals like me

數據捐贈、決策科學家捐贈及科技捐贈--

who are choosing to work at companies.

對我這樣年輕的專家而言，

Studies show that the next generation of the workforce

這就是我們選擇待在公司的原因。

care about having their work make a bigger impact.

研究表明，下一世代的勞動人口關心的是

We want to make a difference,

他們的工作能不能為世界帶來影響。

and so through data philanthropy,

我們想要改變，

companies can actually help engage and retain their decision scientists.

所以透過數據慈善事業，

And that's a big deal for a profession that's in high demand.

公司更容易留得住 他們的決策科學家

Data philanthropy makes good business sense,

特別是對於這種高需求 的職業來說尤其重要。

and it also can help revolutionize the humanitarian world.

數據慈善事業 能創造良好的商業形象，

If we coordinated the planning and logistics

它同時也能夠為人道主義事業 做出巨大變革。

across all of the major facets of a humanitarian operation,

如果我們可以協調規劃

we could feed, clothe and shelter hundreds of thousands more people,

並支援所有人道主義各方面的後勤，

and companies need to step up and play the role that I know they can

我們就可以為成千上萬的人提供 食物、衣服和住所，

in bringing about this revolution.

為了這場改革， 公司需要站出來扮演其中的角色，

You've probably heard of the saying "food for thought."

因為我知道你們辦的到。

Well, this is literally thought for food.

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）

It finally is the right idea at the right time.

而字面意思就是「想想食物」（要如何分配）

(Laughter)

我終於在對的時間找到對的方法了！