June 2010. I landed for the first time in Rome, Italy. I wasn't there to sightsee. I was there to solve world hunger.
2010 年六月, 我第一次前往意大利羅馬。 我不是去觀光的, 我是去解決世界飢餓問題的。
(Laughter)
(笑聲)
That's right. I was a 25-year-old PhD student armed with a prototype tool developed back at my university, and I was going to help the World Food Programme fix hunger. So I strode into the headquarters building and my eyes scanned the row of UN flags, and I smiled as I thought to myself, "The engineer is here."
沒錯。 我當時是 25 歲的博士生, 我帶著在大學期間開發的原型工具, 準備幫助世界糧食計劃署 解決飢餓問題。 我大步走進他們的總部大樓, 映入眼簾的是一整排的聯合國國旗, 我開心地對著自己說: 「工程師來了!」
(Laughter)
(笑聲)
Give me your data. I'm going to optimize everything.
「拿出你們的數據, 我要優化所有資料。」
(Laughter)
(笑聲)
Tell me the food that you've purchased, tell me where it's going and when it needs to be there, and I'm going to tell you the shortest, fastest, cheapest, best set of routes to take for the food. We're going to save money, we're going to avoid delays and disruptions, and bottom line, we're going to save lives. You're welcome.
「告訴我你們已經購買的食物, 告訴我要送到哪裡、什麼時候需要, 我就會告訴你們最短、最快、 最便宜的食物運送路徑。 我們會節省很多錢, 我們可以避免延遲和中斷, 最後,我們還可以拯救世人。 不用客氣!」
(Laughter)
(笑聲)
I thought it was going to take 12 months, OK, maybe even 13. This is not quite how it panned out. Just a couple of months into the project, my French boss, he told me, "You know, Mallory, it's a good idea, but the data you need for your algorithms is not there. It's the right idea but at the wrong time, and the right idea at the wrong time is the wrong idea."
我在想這大概需要 12 個月的時間來實現, 好吧,可能要 13 個月。 但事情並沒有想像中的簡單。 當我加入這個專案幾個月之後, 我的法國老闆,他告訴我: 「馬洛里,妳知道嗎? 妳的點子是不錯啦! 但要符合你演算法的數據並不存在。 點子是對的,但時機不對, 而對的點子在錯誤的時機出現…… 就是一個錯誤的點子!」
(Laughter)
(笑聲)
Project over. I was crushed.
專案結束! 我超傷心的。
When I look back now on that first summer in Rome and I see how much has changed over the past six years, it is an absolute transformation. It's a coming of age for bringing data into the humanitarian world. It's exciting. It's inspiring. But we're not there yet. And brace yourself, executives, because I'm going to be putting companies on the hot seat to step up and play the role that I know they can.
現在當我回頭去看 從羅馬的第一個夏天到現在, 我看到在這六年來, 真的是完全轉變了。 把數據帶入人道世界的時代來臨了。 這真是令人興奮、鼓舞人心的。 但是我們還沒有達到。 現場的各位主管,請仔細聽好了, 我準備要把你們的公司推上火線, 因為我知道你們辦得到。
My experiences back in Rome prove using data you can save lives. OK, not that first attempt, but eventually we got there. Let me paint the picture for you. Imagine that you have to plan breakfast, lunch and dinner for 500,000 people, and you only have a certain budget to do it, say 6.5 million dollars per month. Well, what should you do? What's the best way to handle it? Should you buy rice, wheat, chickpea, oil? How much? It sounds simple. It's not. You have 30 possible foods, and you have to pick five of them. That's already over 140,000 different combinations. Then for each food that you pick, you need to decide how much you'll buy, where you're going to get it from, where you're going to store it, how long it's going to take to get there. You need to look at all of the different transportation routes as well. And that's already over 900 million options. If you considered each option for a single second, that would take you over 28 years to get through. 900 million options.
我在羅馬的經驗告訴我, 運用數據,你可以拯救生命。 的確,不是一試就能成功, 但最終我們還是能辦到。 讓我來解釋一下。 想像一下,你準備要為 50 萬人準備早、中、晚餐, 但你的預算有限, 比如說,每個月 650 萬美元。 你要怎麼做?最好的方式是甚麼? 你需要買米、小麥、鷹嘴豆和油嗎? 要買多少? 聽起來很簡單,但做起來很難。 你有 30 種可能的食物, 你必須從中挑選五種。 那樣就會有超過 14 萬種 不同的食物組合。 你挑選的每樣食物, 你要決定準備買多少、 去哪買、 買來後要存放在哪、 運送到目的地要多久的時間。 你還需要查看所有不同的運輸路線。 而這樣已經超過九億種選擇了。 如果你每個選項都需要思考一秒, 那你要花超過 28 年的時間 才能把它們全過一遍。 九億種選擇啊!
So we created a tool that allowed decisionmakers to weed through all 900 million options in just a matter of days. It turned out to be incredibly successful. In an operation in Iraq, we saved 17 percent of the costs, and this meant that you had the ability to feed an additional 80,000 people. It's all thanks to the use of data and modeling complex systems.
所以我們創建了一個 只要花幾天的時間,就可以讓決策者 解決九億種選擇的工具。 果然非常成功。 在伊拉克的一次任務中, 我們節省了 17% 的成本, 也就是說,你還有能力 能餵飽另外的八萬人。 這一切都要感謝數據 和複雜的建模系統。
But we didn't do it alone. The unit that I worked with in Rome, they were unique. They believed in collaboration. They brought in the academic world. They brought in companies. And if we really want to make big changes in big problems like world hunger, we need everybody to the table. We need the data people from humanitarian organizations leading the way, and orchestrating just the right types of engagements with academics, with governments. And there's one group that's not being leveraged in the way that it should be. Did you guess it? Companies.
但這並不是我們獨自完成的。 我們在羅馬合作的單位, 他們真的很棒。 他們相信合作的力量。 他們把學術界帶入這個領域, 把企業帶入這個領域。 如果我們希望能在像世界飢餓 這種大問題上做出改變, 我們需要每一個社會成員的加入。 我們需要來自人道組織的數據人員 引領道路, 並組織學術界及政府部門 好好地參與合作。 還有一種群體沒有被充分利用。 猜猜是誰?公司企業。
Companies have a major role to play in fixing the big problems in our world. I've been in the private sector for two years now. I've seen what companies can do, and I've seen what companies aren't doing, and I think there's three main ways that we can fill that gap: by donating data, by donating decision scientists and by donating technology to gather new sources of data. This is data philanthropy, and it's the future of corporate social responsibility. Bonus, it also makes good business sense.
公司在解決世界的大問題方面 扮演了重要的角色。 我在私人公司已經工作了兩年。 我見識到了企業的能力, 以及他們沒有充分做到的部分, 我認為有三個主要方式, 可以填補這個空缺: 藉由捐贈數據、決策科學家及科技 來收集新數據的技術。 這是數據慈善事業, 是企業的未來社會責任。 好處就是,對公司的形象有幫助。
Companies today, they collect mountains of data, so the first thing they can do is start donating that data. Some companies are already doing it. Take, for example, a major telecom company. They opened up their data in Senegal and the Ivory Coast and researchers discovered that if you look at the patterns in the pings to the cell phone towers, you can see where people are traveling. And that can tell you things like where malaria might spread, and you can make predictions with it. Or take for example an innovative satellite company. They opened up their data and donated it, and with that data you could track how droughts are impacting food production. With that you can actually trigger aid funding before a crisis can happen.
如今的公司,收集了一大堆數據, 所以他們可以做的第一件事 就是捐贈數據。 有些公司已經在做了。 舉例,以某一家大型的 電信公司為例。 他們開放了位於塞內加爾 和象牙海岸的數據, 研究人員發現, 如果你觀察手機傳送到 基地台的數據圖形, 你可以觀察到人們到哪裡活動, 像這樣的數據能告訴你, 瘧疾可能傳播的地方, 你可以用它做預測。 或者拿另一個創新的衛星公司為例, 他們開放並捐獻了數據, 使用那些數據,你就能夠追蹤 乾旱是如何影響糧食產量的。 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。
This is a great start. There's important insights just locked away in company data. And yes, you need to be very careful. You need to respect privacy concerns, for example by anonymizing the data.
這是一個好的開始。 在公司的數據中, 禁錮著許多重要的信息。 是的,你需要非常小心。 你需要尊重隱私問題, 例如可以用匿名化數據解決。
But even if the floodgates opened up, and even if all companies donated their data to academics, to NGOs, to humanitarian organizations, it wouldn't be enough to harness that full impact of data for humanitarian goals. Why? To unlock insights in data, you need decision scientists. Decision scientists are people like me. They take the data, they clean it up, transform it and put it into a useful algorithm that's the best choice to address the business need at hand. In the world of humanitarian aid, there are very few decision scientists. Most of them work for companies. So that's the second thing that companies need to do. In addition to donating their data, they need to donate their decision scientists.
但即使所有的管道資料都開放了, 即使所有的公司都捐贈出他們的數據 給學術界、非政府組織、人道組織, 光有這些資料,仍無法達到 人道主義的目標。 為什麼? 要解開數據中的信息, 你仍需要決策科學家。 像我這樣的決策科學家。 他們拿到數據,會稍作整理, 把資料轉換後, 帶入有用的演算法裡。 這才是解決問題的最佳選擇。 但在人道援助的領域裡, 決策科學家很罕見。 他們大多數都為私人企業工作。 所以,公司要做第二件事, 公司除了捐贈他們的數據以外, 他們還需要捐贈他們的決策科學家。
Now, companies will say, "Ah! Don't take our decision scientists from us. We need every spare second of their time." But there's a way. If a company was going to donate a block of a decision scientist's time, it would actually make more sense to spread out that block of time over a long period, say for example five years. This might only amount to a couple of hours per month, which a company would hardly miss, but what it enables is really important: long-term partnerships. Long-term partnerships allow you to build relationships, to get to know the data, to really understand it and to start to understand the needs and challenges that the humanitarian organization is facing. In Rome, at the World Food Programme, this took us five years to do, five years. That first three years, OK, that was just what we couldn't solve for. Then there was two years after that of refining and implementing the tool, like in the operations in Iraq and other countries. I don't think that's an unrealistic timeline when it comes to using data to make operational changes. It's an investment. It requires patience. But the types of results that can be produced are undeniable. In our case, it was the ability to feed tens of thousands more people.
但公司會說: 「啊!別帶走我們的決策科學家, 我們分分秒秒都很需要他們。」 但是有一個辦法, 如果說一家公司決定貢獻出 它的決策科學家的部分時間, 那我們就把這些時間分散到 長期使用,這樣才行得通, 比如說,五年的時間。 這樣分配之後,每個月 可能就只需要幾個小時, 對於一家公司來說不足掛齒, 但產生的效果是很重大的: 長期的夥伴關係。 長期的夥伴關係能促進建立友誼, 對資料更理解, 而且可以更深入地了解到 人道組織的需求及 目前所面臨到的問題。 在羅馬,我們在世界糧食計劃署, 花費了五年時間,五年。 前三年,沒錯,我們在 討論解決不了的問題。 然後我們又花了兩年時間 去更新、完善我們的工具。 就像我們在伊拉克 和其他國家的行動一樣。 當涉及到使用數據 進行營運修改的時候, 我不認為這樣的時間安排 會有甚麼不妥。 這是一項投資,我們要有耐心。 但產生的效果是不可否認的。 以我們的個案而言, 它可以養活好幾萬人。
So we have donating data, we have donating decision scientists, and there's actually a third way that companies can help: donating technology to capture new sources of data. You see, there's a lot of things we just don't have data on. Right now, Syrian refugees are flooding into Greece, and the UN refugee agency, they have their hands full. The current system for tracking people is paper and pencil, and what that means is that when a mother and her five children walk into the camp, headquarters is essentially blind to this moment. That's all going to change in the next few weeks, thanks to private sector collaboration. There's going to be a new system based on donated package tracking technology from the logistics company that I work for. With this new system, there will be a data trail, so you know exactly the moment when that mother and her children walk into the camp. And even more, you know if she's going to have supplies this month and the next. Information visibility drives efficiency. For companies, using technology to gather important data, it's like bread and butter. They've been doing it for years, and it's led to major operational efficiency improvements. Just try to imagine your favorite beverage company trying to plan their inventory and not knowing how many bottles were on the shelves. It's absurd. Data drives better decisions.
所以我們需要捐獻數據, 我們需要捐獻決策科學家, 實際上公司還有 第三種方法可以提供協助: 透過捐贈技術來取得數據的新來源。 你看,還有很多地方, 我們都沒有數據。 目前,敘利亞難民正湧入希臘, 而聯合國的難民機構, 他們也忙得不可開交。 目前的難民跟進系統 是用紙和筆來作業, 意思就是, 當一個母親帶著她的五個孩子 走進難名營時, 總部基本上根本看不到。 在未來幾周中, 這一切都將會改變, 這要感謝私人機構的合作。 我合作的物流公司, 即將捐贈一套全新的追蹤科技系統。 有了這個新系統,數據就能被追踪, 所以當一位母親 帶著她的孩子走進難民營時, 你就會知道這件事。 甚至,你還可以知道 這個月及下個月她是否能得到支援。 數據的能見度驅動了效率。 對公司而言,利用技術收集重要數據, 就像奶油和麵包一樣基本。 他們多年來都在從事這件事, 並帶來了卓越的效率提升。 試想一下,你最喜歡的飲料公司, 將要計劃下一批生產, 卻對正在貨架上的飲料數量毫不知情, 這是很荒謬的。 數據驅使我們做出更好的決策。
Now, if you're representing a company, and you're pragmatic and not just idealistic, you might be saying to yourself, "OK, this is all great, Mallory, but why should I want to be involved?" Well for one thing, beyond the good PR, humanitarian aid is a 24-billion-dollar sector, and there's over five billion people, maybe your next customers, that live in the developing world. Further, companies that are engaging in data philanthropy, they're finding new insights locked away in their data. Take, for example, a credit card company that's opened up a center that functions as a hub for academics, for NGOs and governments, all working together. They're looking at information in credit card swipes and using that to find insights about how households in India live, work, earn and spend. For the humanitarian world, this provides information about how you might bring people out of poverty. But for companies, it's providing insights about your customers and potential customers in India. It's a win all around. Now, for me, what I find exciting about data philanthropy -- donating data, donating decision scientists and donating technology -- it's what it means for young professionals like me who are choosing to work at companies. Studies show that the next generation of the workforce care about having their work make a bigger impact. We want to make a difference, and so through data philanthropy, companies can actually help engage and retain their decision scientists. And that's a big deal for a profession that's in high demand.
現在,如果您代表一個公司, 你很務實,不是那種只會空想的人, 你可能會說:「沒錯, 是很偉大,馬洛里, 但為什麼我要參與?」 其實,就一件事,提升公司形象, 人道援助是一個 240 億美元的事業, 有超過 50 億人口住在發展中國家, 很有可能你的下一個客戶就是他們。 此外,從事數據慈善事業的那些公司, 他們正在挖掘 禁錮在數據當中的新信息。 例如,以某家信用卡公司為例, 他們建立了一個數據中心樞紐, 將學術界、非政府組織和政府 組織起來一起工作。 他們透過刷卡紀錄, 觀察到一般的印度家庭 他們如何生活、工作、賺錢和消費。 對人道組織而言,這裡面隱含著 如何使人們擺脫貧困的資訊。 但對公司來說, 這就是向他們提供了 在印度的用戶和潛在用戶信息。 這是一個三贏的局面。 而對我而言,我發現 數據慈善事業是令人振奮的── 數據捐贈、決策科學家捐贈 及科技捐贈── 對我這樣年輕的專家而言, 這就是我們選擇待在公司的原因。 研究表明,下一世代的 勞動人口關心的是 他們的工作能不能為世界帶來影響。 我們想要改變, 所以透過數據慈善事業, 公司更容易留得住他們的決策科學家, 特別是對於這種高需求的 職業來說尤其重要。
Data philanthropy makes good business sense, and it also can help revolutionize the humanitarian world. If we coordinated the planning and logistics across all of the major facets of a humanitarian operation, we could feed, clothe and shelter hundreds of thousands more people, and companies need to step up and play the role that I know they can in bringing about this revolution.
數據慈善事業 能創造良好的商業形象, 它同時也能夠為人道主義事業 做出巨大變革。 如果我們可以協調規劃 並支援所有人道主義各方面的後勤, 我們就可以為成千上萬的人 提供食物、衣服和住所, 為了這場改革, 公司需要站出來扮演其中的角色, 因為我知道你們辦的到。
You've probably heard of the saying "food for thought." Well, this is literally thought for food. It finally is the right idea at the right time.
各位也許聽過「值得思考的食物」。 (英文意思是:值得深思的問題) 而字面意思就是「想想食物」。 我終於在對的時間找到對的方法了!
(Laughter)
(笑聲)
Très magnifique.
(法語)太棒了!
Thank you.
謝謝。
(Applause)
(掌聲)