June 2010. I landed for the first time in Rome, Italy. I wasn't there to sightsee. I was there to solve world hunger.
2010eko ekaina. Lehenbizikoz nintzen Erroman, Italian. Ez nintzen turismoa egitera joan. Munduko gosea konpontzera baizik.
(Laughter)
(Barreak)
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."
Hori da. 25 urteko dokotegaia nintzen nire unibertsitatean sortutako tresna baten prototipo batekin, eta Munduko Elikagai Programan laguntzera nindoan. Egoitza nagusira sartu nintzen eta NBetako bandera ilarari begiratu bat bota nion, irribarre egin eta pentsatu nuen "ingeniaria hemen da."
(Laughter)
(Barreak)
Give me your data. I'm going to optimize everything.
Emaizkidazue zuen datuak. Guztia optimizatzera noa.
(Laughter)
(Barreak)
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.
Esaidazue ze janari erosi duzuen, nora joan behar duen eta noizko, eta esango dizuet, motzena, azkarrena eta merkeena den bidea, janaria eramateko. Dirua aurreztuko dugu, atzerapenak eta eragozpenak ekidingo ditugu, eta azkenik, bizitzak salbatuko ditugu. Ez horregatik.
(Laughter)
(Barreak)
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."
12 hilabetetan egina izango zela uste nuen, ados, agian 13 hilabetetan. Baina ez zen horrela izan. Proiektuan pare bat hilabete nindoala nire nagusiak esan zidan "Mallory, ideia ona da, baina zure algoritmoetarako behar dituzun datuak ez daude hemen. Ideia ona da, baina une txarrean, eta ideia ona une txarrean ideia txarra da."
(Laughter)
(Barreak)
Project over. I was crushed.
Proiektua amaituta. Apurtuta nengoen.
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.
Orain atzera begiratzean, Erromako lehen uda hartara, eta azken sei urteetan zenbat aldatu den ikusten dut, erabateko eraldaketa izan da. Mundu berri bat da informazioaren eta laguntza humanitarioarentzat. Kitzikagarria da. Inspiratzailea. Baina oraindik ez gaude hor. Eta adi exekutiboak, hor jardungo naizelako, konpainiak errudunen aulkian ipintzen, euren eginbeharra bete dezaten.
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.
Erroman izan nituen esperientziek erakutsi zidaten informazioa baliatuz bizitzak salba zitezkeela. Beno ez lehen saiakeran, baina halako batean lortu genuen. Utz iezaidazue irudia deskribatzen. Pentsa gosaria, bazkaria eta afaria planifikatu behar direla 500.000 pertsonentzat, eta horretarako aurrekontu bat daukazuela, demagun 6.5 milioi dolar hilean. Zer egin beharko zenukete? Zein da kudeatzeko modu egokiena? Arroza, garia, garbantzoak, olioa erosi beharko lirateke? Zenbat? Sinplea dirudi. Ez da. 30 elikagai desberdin dituzue, bost aukeratu behar dituzue. 140.000 konbinaketa baino gehiago lirateke. Gainera, elikagai bakoitzeko, zenbat erosi erabaki behar da, non erosiko den, non gordeko den, zenbat denboran eramango den hara. Garraiobide guztiak aztertu beharko dira. 900 milioi aukera baino gehiago lirateke. Aukera bakoitza segundu batez kontsideratuko bazenute, 28 urte beharko zenituzkete. 900 milioi aukera.
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.
Hortaz, erabakiak hartzeko tresna sortu genuen 900 milioi aukerak aztertzeko egun gutxi batzuetan. Oso arrakastatsua izan zen. Irak-eko operazio batean, kostuaren %17 aurreztu genuen, eta honek 80.000 pertsona gehiago elikatzea suposatzen zuen. Guztia datuen erabilerari esker, eta sistema konplexuak modelatzeari esker.
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.
Baina ez genuen bakarrik egin. Erroman lankide izan nuen unitatea paregabea zen. Lankidetzan sinesten zuten. Mundu akademikoa ekarri zuten. Konpainiak ekarri zituzten. Eta gosetea bezalako arazo handietan benetan aldaketak egin nahi baditugu, guztion lankidetza behar dugu. Erakunde humanitarioetako datuak aztertzen dituzten pertsonen gidaritza behar dugu, behar diren bezalako loturak ezarriz akademikoekin eta gobernuekin. Eta bada talde bat beharko lukeen bezala jokatzen ari ez dena. Asmatu duzue zein? Konpainiak.
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.
Konpainiek gure munduko arazoak konpontzen paper garrantzitsu bat dute. Duela bi urtetik hona sektore pribatuan nago. Konpainiek egin dezaketena ikusi dut, baita egiten ari ez direna ere, eta uste dut hiru modu daudela hutsune hori betetzeko: datuak dohaintzan emanez, erabaki hartzaile zientifikoak emanez eta teknologia emanez, data iturri berriak bilatzeko. Hau datu filantropia da, eta etorkizuneko korporazioen ardura soziala da. Gainera, ikuspuntu enpresarialetik ere zentzua du.
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.
Egun, enpresek datu kantitate handiak jasotzen dituzte, beraz, egin dezaketen lehen gauza datuak ematea da. Konpainia batzuk jada egiten dute. Adibidez, Telecom konpainiak. Senegalen eta Boli Kostan euren datuak ireki zituzten eta ikerlariek zera aurkitu zuten, telefonoen antenetako errepikapenen patroiak behatuz gero, jendea nora doan ikusi daitekeela. Eta hainbat gauza esan ditzakezula, adibidez malaria nora hedatu daitekeen, horrekin iragarpenak eginez. Edo, demagun satelite konpainia berritzaile bat. Euren datuak ireki eta eman zituzten, eta horrekin zera kontrolatu daiteke: lehorteek elikagai ekoizpenean duten eragina. Horrekin krisia gertatu aurretik laguntza martxan jar daiteke.
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.
Hori hasiera bikaina da. Aurkikuntza handiak daude konpainien datuetan giltzapetuta. Eta bai, oso kontuz ibili behar gara. Pribazitatea errespetatu behar da, adibidez datuak anonimizatuz.
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.
Baina uhateak irekiko balira ere, eta konpainia guztiek datuak emango balituzte akademikoek, GKE-k eta erakunde humanitarioek erabiltzeko, ez litzateke nahikoa izango datuen eragin osoa aprobetxatzeko helburu humanitarioak betetzeko. Zergatik? Datuek ezkutatzen dutena ikusteko, erabaki hartzaileak behar dituzu. Erabaki hartzaile zientifikoak ni bezalako pertsonak dira. Datuak hartu, garbitu, eraldatu eta algoritmo erabilgarrietan sartzen dituzte hori da aukerarik onena egin beharrekoa egiteko. Laguntza humanitarioaren munduan, erabaki hartzaile gutxi daude. Gehienak konpainietan daude. Beraz hori da konpainiek egin beharreko bigarren gauza. Datuak emateaz gain, erabaki hartzaileak eman behar dituzte.
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.
Konpainiek zera esango dute: "Eh! ez kendu guri erabaki hartzaileak. beraien denbora guztia behar dugu." Baina bada modu bat. Konpainia bat erabaki hartzaile baten denbora kopuru bat ematera badoa, zentzu gehiago izango luke kopuru hori barreiatzeak denbora tarte luze batean, esaterako 5 urtetan. Horrela agian hilabeteko pare bat ordu soilik lirateke, konpainian ez luke gehiegi eragingo, baina horrela zera lortuko litzateke: epe luzeko asoziazioak. Epe luzeko asoziazioek erlazioak sortzea ahalbideratzen dute, datuak ezagutzera heltzeko, benetan ulertzeko, eta erakunde humanitarioak dituen beharrak eta zailtasunak ezagutzeko. Erroman, Munduko Elikagaien Programan luze jo zigun, 5 urte. Lehen hiru urteak, beno hau ezin da murriztu. Horren ostean bi urtez tresna hobetu eta martxan jarri Irak eta beste herrialdeetako operazioetan bezala. Ez dut uste planifikazio hori errealista ez denik operazioetan datuak erabiliz aldaketak egitean. Inbertsio bat da. Pazientzia eskatzen du. Baina sor daitezkeen emaitzak ukaezinak dira. Gure kasuan milaka pertsona gehiago elikatzea izan zen.
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.
Beraz datuak ematea, erabaki hartzaileak ematea, eta konpainiek lagundu ahal izateko 3. modu bat ere badaukagu: datu iturri berriak lortzeko teknologia ematea. Hainbat gauza ditugu zeinaren oraindik daturik ez dugun. Oraintxe Siriar errefuxatuak Greziara iristen ari dira, eta NBetako errefuxatuen agentziak esku bete lan du. Egun jendea jarraitzeko modua arkatz eta paperezkoa da, horrek zera esan nahi du, ama bat bere 5 haurrekin kanpamentura sartzean, egoitza nagusiak ez duela jakingo. Guzti hori aste gutxi barru aldatuko da, sektore pribatuaren kolaborazioari esker. Paketeen jarraipeneko teknologian oinarritutako sistema berria egongo da nik lan egiten dudan konpainiak emandakoa. Sistema berri honekin, datuak jarraitu ahalko dira, zehazki jakiteko ama eta bere haurrak kanpamendura noiz iritsi diren. Are gehiago, hornigaiak izango dituen jakingo dugu hilabete honetan eta hurrengoan. Informazioak eraginkortasuna dakar. Konpainientzat, teknologia erabiltzea datu garrantzitsuak lortzeko ogia eta gurina bezala dira. Urteak daramatzate hortan, eta operazio hobekuntza eraginkorrak ekarri ditu. Imajinatu zure edari konpania gustukoena euren inbentarioa osatu nahian eta apaletan zenbat boteila dauden jakin ezinean. Absurdua da. Datuek erabaki hobeak dakartzate.
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.
Konpainia baten ordezkari bazara, eta idealistaz gain pragmatikoa bazara, zeure buruari ariko zara "Ok, hau ederki dago, Mallory, baina zertarako sartuko naiz horretan?" Gauza batengatik, publizitate onaz gain, laguntza humanitarioak, 24 bilioi dolar mugitzen ditu, eta 5 bilioi pertsona baino gehiago, agian etorkizuneko bezeroak, garapen bideko herrialdeetan bizi dira. Datuen filantropian sartzen ari diren konpainiak euren datuetan ezkutatutako gauza berriak aurkitzen ari dira. Demagun kreditu txartelen konpainiak zentro bat ireki duela akademiko, GKE eta gobernuentzat egoitza bezala lan egiten duena, guztiak batera lan eginaz. Kreditu txartelen irakurketetako datuak bilatzen dituzte eta datu horiek erabiltzen dituzte jakiteko Indiako etxeetan nola bizi, lan egin, irabazi eta gastatzen den. Mundu humanitarioarentzat, honek datuak ematen ditu jendea txirotasunetik nola atera asmatzeko. Baina konpainientzat, honek ezagutza berria dakar bezero eta Indiako bezero posibleen inguruan. Denek irabazten dute. Nire kasuan, datuen filantropiatik liluragarriena iruditzen zaidana -- datuak, erabaki hartzaile zientifikoak eta teknologia ematea -- ni bezalako profesional gazteentzat duen esanahia da, konpainietan lan egiten dugunontzat. Ikerketek diote belaunaldi berrietako langileriak euren lanak inpaktu handiagoa izateagatik arduratzen direla. Gauzak desberdin egin nahi ditugu, eta datuen filantropia bidez, konpainiek erabaki hartzaile zientifiko batzuk manten ditzakete. Eta hau garrantzitsua da eskaera altuko profesio batean.
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.
Datuen filantropiak zentzua du negozioetan, eta mundu humanitarioa iraultzen lagundu dezake. Planifikazioa eta logistika koordinatzen baditugu prozesu humanitario nagusienen ezaugarri guztietan, ehundaka milaka pertsona elikatu, jantzi eta babestu genitzake, eta konpainiek aurrerapausua eman eta joka dezaketen papera jokatu behar dute iraultza hau burutzeko.
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.
Ziurrenik entzuna duzue "jana pentsamenduen truk". Hau literaki pentsamenduen truk jana litzateke. Azkenean ideia zuzena da, une egokian.
(Laughter)
(Barreak)
Très magnifique.
Très magnifique
Thank you.
Mila esker.
(Applause)
(txaloak)