Data for Good: Driving Social and Environmental Impact with Data Analytics (Cloud Next '18)

my name is Elise Roche and I lead our data for social and environmental impact initiatives here at Google cloud and today we're here to talk to you a bit about what Google cloud is doing for nonprofit organizations today and also to welcome some panelists onstage as well to talk about their work in the data for good space so to begin I'd like to start with a quote from Jake poor way who is the founder and executive director of data kind and if you're unfamiliar with data kind they are a nonprofit organization that helps to match pro bono data scientists analysts and designers with mission driven organizations to apply big data solutions to real-world challenges and the quote is as follows we see so many opportunities for data science to change the world but so many organizations don't have access to that technology and so we can notice that there is a gap here but what if we could use Big Data solutions to cure Alzheimers code food and even solve unemployment at Google cloud we have been working with nonprofit organizations for quite some time in order to identify ways to empower them to achieve their missions at scale by using cloud computing solutions starting in 2007 with Google Earth outreach we've been able to empower nonprofit organizations with access to premium apps api's so they can bring their stories to life in 2011 G suite for nonprofits launched which enabled nonprofits to collaborate and innovate together and in 2016 we actually launched GCP public datasets which helped to democratize access to planetary scale datasets and actually allows anyone to query that data up to one terabyte per month for free we have datasets from organizations like the World Bank the EPA NOAA just to name a few and in 2018 here we are next and so naturally the question is what is next we realize that Big Data solutions in particular can empower nonprofit organizations to achieve a variety of things some of which are just listed here on this slide we can help nonprofit organizations scale their data to analyze data at the scale of the entire web with bigquery to tap into artificial intelligence and machine learning algorithms and to also visualize their data to tell compelling stories that help to drive action and so with that we're announcing a new initiative that we have launched today at next called Data Solutions for change with data solutions for change we're really looking to continue to empower nonprofit organizations to achieve their missions at scale and we've been working with several nonprofits for quite some time in order to conduct qualitative research and to work together in a hands-on capacity to actually determine what do they really need and how can we truly help them and so we've been able to essentially develop these core benefits that you can see on the slide here we understand that nonprofits often face financial and technical barriers and so we are now offering nonprofit organizations and NGOs the opportunity to apply for a six month grant period during which time they'll receive up to $30,000 in a cloud credit grant need-based self training resources through quick labs and also hands-on support with Google cloud customer support at the developer level we are also working with Accenture and encouraging nonprofit organizations to reach out to them to learn more about support options so we're really looking to address again the financial and the technical enablement here with this program and with that we'd like to share just a couple of highlights from some of the organizations that we've been working with today one of them is the foundation for precision medicine so the foundation for precision medicine is actually using bigquery and machine learning algorithms to better detect alzheimers disease there's currently no cure for the disease so the best way to actually get ahead of it is to literally get ahead of it by diagnosing the disease months or even in advance of a traditional diagnosis that way they're actually able to explore preventative treatment options with the open agriculture foundation they're using bigquery and IOT core in order to build more innovative and sustainable food systems they are an extension of MIT s open agriculture initiative where they're building food computers these proprietary devices where they're actually able to identify plant code and actually shift a variety of different variables in order to optimize plant production and the Harambee youth employment accelerator who's using bigquery and machine learning to better match unemployed youth in South Africa to jobs across South Africa and so with that we would like to share a short video Harambee means we win when we pull together we started Harambee to take on the challenge of youth unemployment in South Africa we have six million unemployed youth there's a whole lot of exclusionary barriers for young people trying to break into the economy we decided to work with Google cloud because we needed to operate a large scale across the population I didn't know how to do your CV because at school we didn't – computer the internet was very foreign for me I remember the first series which I made I had to get a paper and just write it with my audience because I didn't even have money to go and print out a CV my ramiz had 1.3 million candidate in our database that's resulted in over 50,000 opportunities what machine learning allows us is the ability to innovate and create new solutions for solving youth unemployment in South Africa before we had our data in many different locations it wasn't managed very well as we migrated our data into Google cloud then we were able to understand employers and candidates bigquery allows us access our data for our candidates Hermes approach to job matching is to include unemployed youth previously excluded from the labor market so we use a lot of data points and competencies skills that don't traditionally get used in the world of employment what we're building is a pathway platform that looks to bridge gaps of young people and nudge them closer to opportunities in the economy the data we're gathering allows us to shine a light on all the great things that they can be and are I am finding my purpose way I want to be Peron B has given me hope I am confident enough to conquer the world and to make a difference out there [Applause] so that was a short clip that we created with Harambee who's actually here with us today that's very exciting to work with them together on this video we also created another video with the foundation for precision medicine also here today and you can see both of these online I'll share a link at the end of this presentation so with that in addition to the nonprofit program we also started thinking well what are the opportunities to actually inspire the next generation of data scientists and analysts and designers to join this growing data for good movement and to drive real change and so we're also happy to announce today the visualized 2030 contest whose name essentially is a nod towards the UN's 2030 agenda so with visualize 2030 we're talking about data stories for the SDGs we're asking students at the college and graduate level to apply and submit a data story that actually tells a story around at least two of the sustainable development goals as a bit of context there are 17 of them and they range from anything from preserving biodiversity to ending poverty and we're asking them to tell the story around these SDGs how they influence each other and how we can reach them by 2030 together and so we are also offering a prize well five prizes for the top five submissions the top five submissions will receive a $10,000 cash prize and announcement during the UN world data forum taking place in Dubai and also publication by Google cloud and this is particularly exciting because we're not doing this alone part of the data for good move is collaboration a huge part of it is collaboration and so we're working together with the World Bank the United Nations Foundation and also the global partnership for sustainable development data and with that I would like to welcome the panelists to the stage to share their work their experience and generally speak to how we can leverage big data solutions to drive real change today [Applause] welcome everyone so to get started I thought it would be great for them to just go through a round of introductions briefly and share who they are which organizations they work for and what their role is hi there I'm Jennifer Oldfield I'm the communications director of the global partnership for sustainable development data it's such a mouthful but you can just think of us as data for SDGs oh thanks hello everyone my name is Davis undiano I'm the Africa regional manager for the global partnership for sustainable development data based out of Nairobi Kenya great pleasure to be here hi my name's Andrew Rigby I'm a data scientist at the World Bank so most people have heard of the bank but generally speaking we're a development finance institution so we make concessional loans to fund development projects in countries around world thank you my name is eine wallah and the chief data scientists at foundation for precision medicine and we are in an intersection of AI and healthcare good hi Ron I'm Evan Jones I'm the chief information officer for Harambee youth employment accelerator and have the privilege of leading the team on the on the video that you saw a few moments ago hi everyone my name's Ian Lobo I'm one of the managing directors with Accenture development partnerships which is our social impact business within Accenture which focuses on providing arts core services to the international development sector but then also representing the broader Accenture business and other ways for us to collaborate for social impact wonderful thank you so to get started with the first question in your opinion what is the role or potential of data analytics in the humanitarian and public sector don't all jump in at once yeah big question hopefully short answer I know we don't have the whole afternoon really when you look at the broad spectrum of the sustainable development goals as you've said which is the United Nations and all of our countries transformative agenda that is at the core of that agenda because we are talking about having a Data revolution doing things differently finding new approaches new methods making new connections collaborations catalyzing innovation and finding new ways really to connect the dots among as different data communities beat government through official statistics the private sector we have citizen generated data that comes from civil society and other community groups all these different varieties of data when you look at the whole data value chain from production to the effective use require some form of analytics and without this it becomes very problematic to derive insights often we talk about making that available or producing it or opening it up but from our experience and what we've seen really the problem is in the use of that data often decision makers are faced with big questions they either have the gaps they don't know you know they don't have the right data to tell them what's really happening and even if they do have that data there many of the factors that influence the decisions they make so we have to effectively invest in methodologies that allow us to draw insights that speak to a number of different audiences even ordinary citizens want to understand what's happening so that they can take action in their own communities and I think they're all of analytics and and new methodologies around machine learning I can really help us bridge that gap and help us to really define what we think may be the more appropriate ways to do things differently to achieve the stds thank you so when we think about the size of the problem in South Africa it's six million unemployed youths and if you think about the size of the problem in Africa our generation will see close a billion youth out of work we whilst we have big operations actually see ourselves as more labour market change agents ecosystem managers and if we really want to shift the dial on unemployment issue certainly in South Africa we have to shift government I mean there's you know they've got Fiskars that's being spent on skills development we have to enable other operators Harambee will never ever operate at the sort of scale of the problem and in doing so we see data at the core of solving this you know we have the largest data set on unemployed use them in South Africa how can we make this data available to many not just to Harambee how do we use our intelligence for many not just for our army how do we use our data to shift policy for government and that's the direction we're heading and and using technology to enable that system change big bets system change and that's the role that is playing in our world you know I think I can build on that a little bit um but the power of data we can actually see the entire population now maybe we were not able to do that and and you know for a nonprofit work you want to have viewer services available to everyone you know inclusivity is very important to our work so I think with data you you're able to see those populations that you didn't maybe we're able to see before I think we're preaching to the choir here because everybody in this room knows about the of data for social change but I guess just to make that point that in the public sector and in government's the kind of skills-based can be pretty low in the understanding can be pretty low so the competition that we're doing today is about sort of raising awareness and trying to you know explain why it's important in layman's terms and one last piece I think to complement what's been said is it's about bringing all the sectors together so a lot of the work that you know we do within Accenture with our private sector clients they're facing the same challenges about data visibility on the ground same for government same for not-for-profits communities and beyond so I do think this is an unprecedented opportunity for us you know through collective action through initiatives like data for SDGs and beyond to really work together share the data and actually be able to tackle this problem at the size and scope and complexity to actually enable that type of systems change and just if I can pick up on something Jennifer said I mean a lot of the sessions I've been to at this conference it's clear that like people are working in startups or online businesses where like not having up data is not a problem right you have huge amounts of data on all of your users but anyone who's worked in government will know that government is not necessarily that organized and doesn't have a huge amount of data on on who their citizens are and in certain parts of the world there's very little data on you know who the people are that government and government programs are trying to serve or about the places about the environment that kind of thing so I think there's there's like a huge potential just by collecting small amounts of data to really better understand the problems that we're trying to solve and that's kind of what the data agenda for this is a neural development goals is really trying to do thank you so another question for you what is your background or area of expertise and how do you bring that to data for social and environmental change I guess I'll start because I'm among the least technical of the group I'm a communications director so my background is in I used to literally run the prime minister of the UK's Twitter account so it wasn't that technical and it was quite fun but through that I got to see the kind of two-way communication between and government and I became very inspired to get into sort of asking people directly in open consultations and then citizen generated data so that's a kind of an expanding area of interest for government and then the communications is really important I think yeah I've sort of adapted a technical and one a technical heart over the last couple of years but my background really is design and architecture and I went into development work around eight years ago basically driven by the passion that you know development projects deliver impact on on the lives of people and for me that's that's the key that's a key that's a key driver I would say or the incentive ultimately I had today in another session someone saying you know we we have to connect those we we have to put people or ensure that the dots that are represented really represent people and the voices of people so for me my background really is trying to bridge the gap between different data communities and defining strategies that then help them to produce better data that speaks to the needs of people at the sub-national level at the national level and connecting to the regional and the global levels and the reason why we have to do this is because the global policy agenda is set for example at the UN headquarters in New York but people living real lives at the community level in different countries and often you find that there is a huge disconnect between what is being discussed at the global level and what is really happening in people's lives including the stds different countries are struggling because people don't really understand them so we have to figure creative ways and for many creative ways means you know finding ways in which we can think outside the box you know think also the box and not only define what it actually looks like to achieve impact often that is vague but also bring citizens voices into the conversation in a way that truly matters to them and is transformative in in their lives um yeah so my job title is data scientists and I happy to embrace that kind of personality um my original background was in computer science and economics and that was computer science before machine learning was cool again so um it's being kind of interesting to go through you know it kind of moved over to economics and did a PhD in econometrics with the kind of mathematical stats bent to it but it's interesting out to be in this world where like data is cool and machine learning and AI and all these kind of buzzwords are out there now and also doing really cool things that weren't possible awhile ago so I've kind of long been a daily user and now I'm sort of in a position at the World Bank where we're a big data producer we collect a lot of data we we sort of add value and then we publish it to the world and so it's it's kind of nice to be sort of involved in that that pipeline of making datasets available that people find really useful absolutely yeah my background is also in data science and machine learning and I have a team of data scientists and statisticians that I work with on daily basis I think the difference between me and maybe some other data scientist is that or at least what people think as the more I understood about machinery and data science the more I I appreciate the domain knowledge and the domain expertise that other people bring in and I found out that you know everything is numbers to us but you know you have to go talk to people who have been doing that kind of work for years before to put some context to what you know now we have data on and we can bring our own expertise to it and you know fix the problem from a different angle so four years ago I got given the title of CIO by my CEO and I thought she was crazy because I have no technical background whatsoever but I have the great privilege of a very competent technical team that's actually with me here today our architects and developers I actually worked at a large system integrator for many years working in business process offshoring and outsourcing into South Africa and just a little bit of background on that South Africa has just been ranked number 2 preferred offshoring destination in the world taken to India it's a high high high growth sector there is a a lot of lobbying amongst government and business to really grow the sector over the next couple of years and I was very much involved in that and got very excited by the idea of job creation in a country that desperately needs it with such high unemployment rates and that was really my hook into her army and and and the big question on our mind four years ago was how do we scale our impact you know we're very proud of the 55,000 young people we placed into the economy but it's a scratch on the surface and really working with our executive to think about in a woman for scale and we've we've walked a technology journey and sourced a number of technology giants such as the Google cloud platform in helping us enable that vision and from my perspective I come at this from a business strategist perspective which is focused first on how are we going to solve this problem about the scale the complexity and beyond how do we kind of use technology to our favor how do we use data to our favor so it's kind of going beyond cool prototype what could be happening localized pilots into fundamentally you know how do we target the value and unleash it and I think another piece of that is around partnerships so you know for the last 15 years been working on cross sector and intra industry partnerships to really figure out ways for us to work together share the competencies get that momentum going to really drive that type of change so I see very much you know it's a CEO level conversation executive direct their level conversation within the not-for-profits to kind of make that shift and then of course you know using technology as one of the one of the enablers for that strategy rather than just kind of bottom up here's some of the tack around it here's some of the data if you're not getting that kind of absolute intricate realization of your leadership team that this is important this is what we need to do to be successful as a not-for-profit if it's not going to actually upper optimize the way our organization's working the way you're doing programs that's a bit of a lost cause so I think this top-down bottom-up approach is the right way to beyond I guess just to add to that we worked with Accenture at the global partnership for sustainable development data to help us strategize around engaging the private sector and they had a great approach that we sort of discussed called the octopus approach where basically this should be connectors at every level you know the CEO should know the CEO and the communications should know that communications and through that way you can really kind of get that holistic approach rather than that just solely kind of thinking of it as a sort of CSR only kind of connector Thanks great thank you so another question you know given your experience and everything that you just shared what are creative applications of data analytics and machine learning that you've seen and what made that particular example inspiring or particularly effective for us the the kind of most exciting expanding area of data is around satellite and earth observation data for SDGs and there are so many different potential applications we've just launched a Africa regional data cube so a sort of open infrastructure for the governments that we work with to access that and to kind of develop algorithms that can then be shared but some of the most exciting developments that you know it's very early days but basically they're looking into things like illegal not predicting illegal mining in Ghana sort of mapping deforestation in Kenya were sort of investigating possible use of radar data into intersected with Landsat data to look at landslides in Sierra Leone and you know understanding that depth of different buildings so we've only just scratched the surface but there are so many creative applications and there's a lot of kind of inter country sharing as well as working with the different partners like NASA the Committee on earth observations the group on earth observations you know whole host of others who are getting around the table to achieve that multi partnership stakeholder partnership one of the examples that you know we've seen the most kind of momentum happening quite quickly was with a large international NGO that is one of the biggest child sponsorship not-for-profits in the world and the challenge they were facing was around how do we quickly kind of analyze the photographs to kind of create that intimate relationship between a child and the sponsor and what we're able to do very very quickly like within a matter of weeks was develop a prototype using artificial intelligence to do a photo analyzer solution to automatically detect you know are these appropriate pictures make sure we're matching it correctly and then of course minimize the amount of human intervention required and I guess coming back to you know the stripes that I shared just now in the lens that I bring is what was exciting was it was yes it was very quick we proved that you can use artificial intelligence and a not-for-profit in weeks not months or years and with significant amount of investment it was focused on a big problem they were facing and then what was kind of piggybacking on top of that was the business case how do we actually put the financial case of saying you can actually save X amount of dollars if you actually look at rolling this out across regions so you know with that actual financial case kind of underpinning a proof of concept it was able to quickly go from prototype and to scale across multiple offices you can I don't quickly that we work on projects that are you know from personalized medicine that have you know social impacts factors and within healthcare we saw a need in Alzheimer's disease and we started this initiative and Alzheimer's disease is one of the you know six it's a sixth leading cause of death in us and there is no method of early detection for it similar to cancer or our things and we saw that you know we have millions of patients records do you identify patient records and we know you know which ones got Alzheimer's disease so we wanted to see you know can we predict the disease in patients early on and by the way you know the can we make that available for everyone so they can punch in their information and see what they get you know in us we have access to expensive MRI machines and those things that you know if you want to you really can get to the bottom of it but in underdeveloped countries you're not able to do that so if you have an algorithm that looks into medications and historical diagnosis and give a chances of Alzheimer's to patients who are you know so anyway 80 years old and are able to access that anywhere in the world that have internet so the World Bank's goal is to eliminate poverty in the world and one of the kind of contradictions I find in my role is that the poorest countries are often also that the data poor countries right so traditionally the bank would spend a lot of money on sort of on the ground survey collection and stuff like that before preparing for a project so so we're coming what Jennifer said I think Earth Observation data satellite data is exciting because it's something that you can cover the whole world and so potentially applying some of the the stuff that's being done in kind of image recognition to apply that to top-down satellite imagery has interesting applications so one I saw recently the bank was a team working on identifying infrastructure from above so identifying electricity transmission lines from from what you can just see above you can sort of see the towers in high resolution satellite imagery so that kind of thing I think is has a lot of potential to sort of reduce the expense of making those investments which benefits people and just a build on your point and I think what I really see is opportunity opportunity for machine learning for artificial intelligence because the rate at which technology is moving is quite fast and there's always the potential to leave a lot of people behind and we are talking about leaving no one behind with it with the stds as soon as you think you've learned and mastered something and you had the bingo stage somebody's knocking on the window or the door with some new technology which is fancier you know is better is you know is doing amazing things that's a great opportunity really of our time but the challenge is that if you have to look at people in low-income countries or developing countries you find that the investments they make in systems in infrastructure in software you know people want to inversed ones and hopefully that serves them for five years and within five years the rate at which we are moving technology really transforms very fast so there is an opportunity really to start thinking about the more sustainable ways in which we can extract it we can extract learning and I think machine learning AI really provide that opportunity to work with governments that potentially may struggle in in some of these areas to develop the capacity of civil society organizations to bring in academia because I think that's potentially one really big area if we institutionalize a lot of capacity within institutions of learning universities high schools you know even starting at kindergarten now in Kenya we are providing tablets to primary school kids you know in a bid to introduce them to technology and this is provided free of charge the government because there is that recognition that unless we really adopt technology and start figuring out how we can effectively use these devices then it's going to be difficult and we'll be left behind as a country so that's that's really the opportunity I see and I think by working together with the private sector with government and many other data communities really we can achieve it wonderful thank you so moving onto another question and building off of what you were talking about there on collaborating and working together so I'd love to hear some examples and just you know some short stories from you all on how you and your organization have worked with another organization and a different industry or sector and what was the result of that maybe to start and not to take to talk too much as the global partnership for sustainable development data really our our role is to is to convene is to connect and to catalyze in in Ghana for example with when we held national data roadmaps workshop which is essentially a convening that brings together different stakeholders to look at how data can be harnessed for sustainable development out of that emerged a partnership between Vodafone Ghana statistical services and flow minder which is basically looking at how they can leverage telco data or mental core data to look at a number of indicators that can support the delivery of the sustainable development goals around epidemiology you know predicting patterns of disease and that's a conversation that's that that's currently happening in in Ghana also and Sierra Leone what we are doing is to facilitate engagements between different ministries that are looking at for example environment climate and the Environmental Protection Agency's looking at the Ministry of mining Ministry of lands including the ICT ministries all have come together and what we aim to achieve there is number one to define frameworks for data sharing because we realize that there's a lot of duplication of efforts everybody's collecting data on the same indicators and all this data is telling different stories so collaborating on data sharing frameworks and also figuring out what are the big policy questions that they need to answer what is the enabling environment for other actors to participate because it's no longer about government it's about bringing in other civil society and other and other groups really to participate how can donors for example better target their resources to interventions that then catalyze better development for people so there's a number of different initiatives that we are seeing coming up and of course we'll be happy to share yeah we are a nonprofit in in a non profit world it's it's very important to be able to collaborate with volunteers and be able to do that very quickly we get volunteers all the time that they're interested in motivated to work on your data and an add value but you know for the top talents it's you know that comes by every now and then you know you you really have to take their attention and be able to collaborate data very quickly that was one of the main reasons we move our data to Google cloud and we were able to just share our data with it simple Gmail access just like a Google Doc and so once we you know do a simple agreements they are able to look at the data and and move very quickly and based on that we were able to actually get a lot more volunteers and and work with universities and in a couple of medical fest facilities but much much faster otherwise so part of our mission which actually includes working in partnership so we have a whole ecosystem dozens of partners are good sites examples of transport data concise examples of its just many many many examples working with coding organizations and funneling candidates through but one in particular that jumps to mind is we've got an initiative with our president's office at the moment youth unemployment is the number one strategic agenda for the country so they've set a target of absorbing over a million youth over the next three years into the into into the economy and what we've done essentially is we've signed an agreement with with the presidency to act as a clearinghouse for the country so that many can access our candidates many other organizations not just a rum B so I think really adopting more of an open source principle to solving this challenge rather than trying to own it and deliver it ourselves yeah it's hard to pick one partnership because it's like picking your favorite child but I guess two quick ones are with we're like anomic forum well we've been working with them and a couple of geographies around how do we do cluster development so out in East Africa it was really trying to bring together the best of the UN government to private sector NGOs to develop an agriculture backbone for a for both local development local economic opportunities and beyond and then also out in Asia we're doing something very very similar I think you know some of the insights for that is there's so many layers doing the partnerships and working together is one thing and I think you know that's important to have us there but I do think you know coming back to the focus topic here you know having the data platform and the ability to share data between organizations starts to move us away from endless pilots endless prototypes and beyond into how do we get towards interoperability how do we get into sharing data between organizations and we've seen very very positive signs on the private sector side where you know we've had sessions with very senior executives from rabid competitors within consumer goods companies saying that actually we can work together in a pre-competitive space for greater Goods and public goods so I do think that there's an appetite I'm not saying it's no complex but I do think even at at that level and looking at the private sector with companies that could potentially be competitors there is massive massive opportunity a massive realization that the public goods were trying to create on the social impact side no one organization can do on its own so how do we actually you know use data how do we use the platforms how do we use sharing an interoperability to really fast track progress that we can make wonderful so in the spirit of collaboration we would also like to open to the audience questions if you'd like to ask a question of any of the panelists here today we actually have a mic that's there in the middle section so yes please ask us questions you have a question um so for pretty much all of you your work is propelled by some kind of urgency to address a social problem right I'm wondering if you can speak a little bit about how this urgency affects the pace with which you feel you need to you need to work and whether that creates any tension in your work for example between developing something quickly versus securely you're developing something quickly versus cheaply I would imagine that would happen at times and wondering if you could elaborate that's such a good question so as you know we've set ourselves this target of the sustainable development goals by 2030 and three years have elapsed what you know just before the firing pistol has gone off people haven't even really got going so there is that tremendous pressure that tremendous tension and then you know we hear people like the Deputy Secretary General of the UN saying some of the data about the sustainable development goals is five years old like that's impossible you can't make decisions on that so you know all of the different sectors have a lot to add but I think that we can learn from that kind of iterating and moving fast while putting in place the kind of infrastructures that Davis was talking about and others talking about earlier to sort of share knowledge on privacy frameworks security that sort of thing in order to enable us to move more quickly it's a great question and we're actually grappling with exactly that so I'm flying here over the weekend you know we have a list of priorities a backlog of development asks from our government partners from our social investors from around the world and we actually cannot get to all of it you know we cannot capacitate are not for ourselves fast enough to get to all the priorities and we're having tough discussions around what's important and what's urgent what's suitable for our future vision for scale and what isn't and it's a it's a tough tough trade-off and we presented to a group of social investors around our strategic priorities and one of them was how do we as an organization prioritize for the future much much much more effectively but job is definitely an increased demand on on us from multiple people yeah it's interesting question I mean honestly no one would ever accuse the World Bank or other kind of international organizations of moving really really quickly I mean it's it's kind of us you know if you if you live in San Francisco and you work for some startup it's it's a slow-paced relative to that um but you're right the problems are a kind of urgent but I think it's more important really that we make kind of steady progress towards these things and so that's where I think like I mean setting a goal for 2030 again is might sound crazy in a place where like your company could be out of business in six months that's your own way or whatever um but at the same time I think you know there needs to be steady year-on-year progress to achieve those things and that has been that's kind of been the past history right is that you know global poverty has been declining year-on-year for quite a long time now so um it's it's kind of like urgent but at the same time um like a kind of medium term goal I also think just to build on your point Andrew these there's the pace at which you know the global development discourse is moving but still you have significantly large proportion of the global population that's in extreme poverty so there is a gap between the path of GDP growth for example in our country the rate at which the economy is growing so you often hear the narrative that you know the economy is growing but a certain proportion and which tends to be quite a large proportion of that of that economy still struggles so the urgency is how do we try to equalize how do we try to bring those are the lowest levels to a level where at least they can start and they can be in a position really to sustain whatever are the economic activities or other activities that they're really engaged in so access to essential government services access to financial services access to technology offers offers that opportunity and and that's the agency what you need is capital you know to do something and that's as urgent as yesterday so it's not it's not an issue of of today those who are looking for a meal you know it's it's today you know if you don't eat then there's a big is a big problem that those who are starving there those who are seeking medical services so the scale of all those problems is is quite urgent so the cost benefit is to think about how do we how do we prioritize and how do we better target our efforts to make sure that we collaborate live really solve some of the big questions we are faced with great thank you so we have time for one more question and then the lightning round of answers so go for it hi so I work for an Innovation Lab for the local government and I appreciate your presentation because we're dealing with a lot of the similar issues like around unemployment of local youths and just a local and streak I'm growing so fast and the residents that are born and kind of grow up here can't afford to stay because they're not able to kind of keep up with skills in the economy that's changing so fast so I really kind of wanted to ask some practical advice around well what best practices have you found that worked in terms of data sharing or even collecting data because in government like those things are like very gnarly things that people don't want to talk about and there's a lot of kind of privacy shoes like when you're like working with foster kids and just like situations where like people are very kind of protective of data so how do you even like breach that kind of conversation and what kind of best practices around establishing new policies and systems can you recommend to help things go keep get going Thanks it's such an important question so just briefly I'm excited about the potential of sort of sub national data and local movements and recently we've seen a number of mayors of cities really kind of growing in profile and sort of harnessing that local expertise the other thing is you know you have local NGOs on the ground who who know a lot more about the area and they're able to kind of tailor solutions so some of the kind of recording of data at that local level and feeding it back up the chain is really important in Kenya we worked with an organization called The Open Institute who proactively work with the local administration to collect data and this data is collected by the community themselves using mobile phone devices on a number of indicators and just a year ago they collected data on health on agriculture and education and it emerged that their top priority really was a health center in one of the called local districts and they actually used this data and the resulting analysis to petition the county government to construct a health center for them and the the county government responded and as we speak the health center is near is near completion so there is an opportunity really to use citizens and to work with the local administration and to use evidence because at the end of the date has to be about evidence what's the impact of not having that health center if you demonstrate that then potentially something can be done about it yeah it's a great question because I think these the sort of norms around privacy and consent and so on are constantly shifting I'm not sure I have any succinct practical advice other than to say um just because we work in the forward space doesn't excuse us from from thinking about those issues just the same as you know a Google or a Facebook or some other commercial entity great well thank you for that and next steps so first I would encourage you if you're a nonprofit organization or know a nonprofit organization to check out Google for nonprofits they do incredible work to the programs I mentioned earlier today Google Earth outreach and G suite for nonprofits are available through there among other programs so definitely check that out and also please look at data solutions for change and visualize 2030 the websites are there the websites are live we're very excited to share that with you all and thank you

One Comment

  1. Ekene Olatunji said:

    Very helpful conversations.

    May 22, 2019

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