Placeholder Image

字幕列表 影片播放

  • June 2010.

    譯者: 易帆 余 審譯者: Amy H. Fann

  • 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!

  • Très magnifique.

    (笑聲)

  • Thank you.

    (法語)太棒了!

  • (Applause)

    謝謝。

June 2010.

譯者: 易帆 余 審譯者: Amy H. Fann