 We're so glad you're here. You have made us to the right place. This is TechSoup inviting you to a virtual briefing about democratizing access to data and incorporating local knowledge. We are so excited for this webinar on Data Commons and to share with you today. Please continue to input into the chat where you're joining us from. We're so glad that you're here. Also, if you're able to turn on your camera, that would be fantastic. We love seeing your faces. A couple of housekeeping notes. Please keep your microphone muted when you're not talking. Again, we love to see your face. We know that's not possible for everybody, but it always helps. And then use the chat to participate. We would love to hear from you in the chat as much as possible. We're tracking that and we'll answer as much as we can. I wanted to note that closed captioning is available. So turn on with the closed caption button located in your Zoom menu. And just to note that this session will be recorded. And this is the first of two. There is another session tomorrow. That's the exact same time. And that session is a little bit more focused on the application with food security organizations so that organizations can understand how that can really be hugely beneficial. So we encourage you to come to that one as well. And we see this as the beginning of a conversation. So I wanted to now turn it over to Marni Webb, our Chief Community Impact Officer at TechSoup. Go ahead, Marni. Hi, everyone. It's great to see you all virtually. And it is, as Lizzie said, it's wonderful to see all the places that you're calling in from. I am Marni Webb, as Lizzie said. I'm the Chief Community Impact Officer for TechSoup. I'm also the CEO of our Product Development Division, Caravan Studios. And I am here with some wonderful panelists. We're going to spend today talking about how civil society organizations can access, use, and contribute to public data, how they can use that data to tell stories, how they can use that data to illuminate issues within their community, and also how they can see the places where data is not. The dark spots in the data that we need to go in and maybe do some data collection and some work so that we can help understand what's going on. The object of this work is a Google project, a Google initiative called Data Commons. We have been funded by google.org to do this. And I'll go through it a little bit and describe how the product works and what it is and give a little bit more detail there. But before I go any further, I wanted to give my co-panelists an opportunity to introduce themselves. And Oya Bisi, maybe we'll start with you. Just say a little bit about who you are and the organization that you represent. Yes, thank you so much. I am Oya Bisi, and I work with the Nigerian Network of Engels. The Nigerian Network of Engels is a membership-based organization and with members spread across the 36 states of the federation. So good to be here. Welcome. Ramina. Hi, Marnie. Good morning, everyone. I am Ramina Farias. I'm Research Director of SAMEFI, which is a membership organization also based in Mexico City. Wonderful. And your colleague Julio is here with us. Yeah, hi, everyone. I am the researcher analyst. I would like to be here. Thank you for the invitation. I'm glad being here with Ramina, with Oya Bisi, with Ingrid. And well, I'm excited to start this. Wonderful to have you here. You're late addition, which is why your smiling face is not on there on the panelists list. We'll fix that in the deck as we send it out. So folks know everyone that was talking. And then Ingrid. Hello, everyone. My name is Ingrid. I'm from Makaya. I'm from Colombia. I'm working with Makaya today. And we are a social organization that works with innovation, technology, and cooperation here in Colombia and Latin America. Happy to join today in this conversation. For me personally, it's wonderful to have all these panelists here. Some of these organizations we've worked with for many years. And some we're in new relationships with. But in this project, we had an opportunity to work together quite closely and look at data. And I will say, for my part, that I learned so much. I know our team here at TechSoup learned so much in getting to do this, not just the technology part of the project, but the working with the partners part of the project. So thanks for participation and thanks for being here today to talk about what we've done. So one of our colleagues, Corey Halbert, is staffing the slide. So I will very subtly ask him to move to the next slide now. Thank you. So today's agenda is welcome and introductions. That's what we've been doing. We're going to talk a little bit about what Google's data comment actually is. And then we're going to dive in. Most of the conversation today is going to be how we use data to tell stories and how we go in and get data and organize it and think about it as a tool for not just uncovering a fact, but actually helping to provide context for those facts so that we can understand how we change the things that we wish to change. And so with that, we will dive into what is Google's data comments. Actually, I'm wrong. We will dive into a little bit more about what is TechSoup. So the TechSoup Global Network is a group of organizations that are working with civil society organizations around the world to make sure they have access to the resources that they need to be able to, the technology and digital resources that they need to be able to do their jobs. And what those folks that know us, what they're most likely to know us for is the global technology marketplace that we run where organizations can come and get donations and discounts on technology products. Some of that happens on our platforms. Some of that is happening on the back end when our corporate partners such as Box or Slack may be providing a donation or a discount to organizations. The thing that people may not know as much about that we do is actually work a lot with those organizations on how they manage their digital stack and use it. And for us, data is an increasingly important part of that digital stack, not just the data that the organizations have, but the data that's in the world around them so that they can look at that in combination with their own data and understand what change they're aiming for and whether or not they're progressing towards those changes, the change that they wanna make. So this project, this project that we're gonna be talking about today fits very much into that scheme, right? It fits very much into the space where we're saying, well, it's not just about getting the technology, it's not just about learning the skills, it's about coming together as a community to do these kinds of sense-making activities that help us be able to better understand and tell the stories of our communities in ways that resonate and are backed by data and driven by data. So if we can go in and jump to the next thing, now I'm gonna talk about what is Google's data commons. So Google's data commons basically allows anyone with an internet connection to be able to access, use and contribute to public data. If you imagine for just a second what it may be like in your own organization when you get a spreadsheet from one person and it's got a bunch of data in it organized in rows and columns, you get a spreadsheet from another person, also organized in rows and columns and you wanna make that into one spreadsheet. Even in your own organization, it's likely that you gotta make changes to the two spreadsheets to get them into one that allows you to look at it and make a graph and use both sets of the data. What Google's data commons has done is taken away the barrier of doing that kind of normalization across different data sets. And they've actually put together, and I'm gonna talk about the main three elements of it, they've put together a system really that allows you to be able to interrogate data from a variety of sources, be able to download data, interact with data and actually have a framework for publishing your own data. So the three main elements of Google's data commons is first, Google's public data commons. This is what you see if you go to datacommons.org. Google has taken data sets from hundreds of sources where sources are like the UN and the World Bank and the European Union and the Scottish Space Agency. And they've taken these wide variety of data sets and their data engineers have done all the work of being able to combine and join those data sets to be able to normalize them so you can look across them. So it's a lot of heavy lifting and then they've put an AI layer, an artificial intelligence layer on top of it, not generative artificial intelligence that makes up answers it gives back to you, but artificial intelligence that takes your question, the question you have about the data and says, okay, based on the question you're asking, this is the data that I think you wanna see and it shows you that data in numbers and graphs with sources so that you can interrogate the data so that you can change your question if you didn't get your question quite right. What that means is that you don't have to have a data engineer or a data scientist on staff to be able to do, be curious about the data and to be able to put in queries and get responses to it. So it's a big bit of work that has helped jumpstart this particular ecosystem. The second thing that they've done, the third part that I am geekily most excited about actually is provided a framework for publishing data. This is based on an old, a standard, an existing web standard called schema.org that lets you talk about what different things, what different elements are. So you can identify that something is a first name or a last name and that it's a street address or what kind of data point it is, right? And it's a very robust mechanism for describing what different data points are. And it's a very, it's a very extensible mechanism. So it can have, it's not domain specific. It can have a lot of different kinds of data. And why I think this is so exciting is, is when I said Google's data commons allows anybody to be able to access, use and publish data. It's this, the framework for data publishing that allows civil society organizations to not just be consumers of public data but to be contributors to public data and so that it can be joined with these other data sets in the same way that Google has worked on with these things. Finally, they have produced a suite of tools that allow you to interact with this data. One is a set of software that, that is data commons software that allows organizations to be able to set up their own instances of data commons. That's what we've done at data commons.techsoup.org. That's our own instance of data commons. We're curating the data that's in there. We can use the framework for data publishing to add data into that. But also it has tools in it that allow you to embed charts on your website a little bit harder than embedding a YouTube video but the same kind of concept. You know, it takes a little bit of extra code but the same thing. It also allows you, and this feels miraculous to me, it allows you to go in and pick the data points that you're interested in and assemble them into a single CSV file, a single spreadsheet even if they came from different sources. That's because of the work that's happened normalizing it. That means that if I want to make charts and manipulate data in the program that I'm most familiar with on my own computer I can go in and say I want these different data points and I can download it and then I can work with it versus having to download 20 different fold data sets to get at each of those data points. And it comes in with provenance. And by that I mean you simply know where the data comes from, right? Where each of those data points come from. So that's part of what you get whenever you use the download tool and you can find that on data commons.org in Explorer, the download tool is one of the options there. The key thing behind all of these things is that you can take data sets and you can easily join them across the internet so that you can analyze different pieces of data together with again, out having to do all the data engineering work to normalize it. What is required is that to participate in that way is using the set of standards that have been made available. It's not about using actually the exact tools. It's more about the schema, that's what allows it to be joined in these kinds of ways. I think if you just jump to the next slide so just quickly, Google's public data commons is that bottom layer there. That's where they've done the work in ingesting it. Other people can set up their own data commons as we've done with the TechSoup data commons that allows us to pull from data in the Google public data commons but also add our own data and data sets to it and then you can continue to grow that across other data commons instances. The combination of the tooling, the APIs and the schemas allow people to join data across those instances. You end up with like a hyperlinked web of data. Not just of web pages but it's data that you can interrogate and manipulate and put together in a variety of ways. Data commons has, as I said, made a bunch of tools that make it easy for you to explore this. They have a statistical variable explorer. This quite simply lets you look at the vast number of the vast amount of data they have in there and say, okay, I want to see data about demographics and then you can open that up and go through and pick what demographics you want to explore and examine. A place explorer that allows you to dive into the data that's available in a region, in a country. It can go very far down in the United States. It goes all the way down to the census track level. A map explorer, which actually allows you to do that kind of exploration but literally against a map so that you're looking at the map and picking out certain sections you want to look at more deeply. And then finally a timeline explorer. Allows you to take the data that you might have looked at in the place explorer, the map explorer and spread it out over time. So I'm not just seeing the number of fires in Chile, let's say, right now, but I'm able to see how fires have changed over time and see that against a timeline explorer. That's what it's called. So that's the best word to use. All of this comes together so that you can explore the data and get in and start telling stories with it. And that's the part with that little bit of background and do feel free to drop any questions into the chat that you might have. But what we want to do now with that little bit of background on what Data Commons is, is get in and show you how these three different groups used it to accomplish some goals and to share some data and dive into it. And OUBC, let's start with you. So much for that background. And I think for Ross at the Nigerian Network of Angels, this platform is a good way of democratizing evidence for development. So you find that we now have data for all of us, like Mary explained, for all of us to bring in data and also to see what already exists. And that was what we did while we were working on thinking around what progress have we made around resource-enabled development goals in Nigeria. We had a pool of data from the Data Commons platform. And we started interrogating them. Interrogating them for us to be able to evidence the progress but also to begin to think more as to what may have helped us to get here, what is injuring us, what are the gaps. So when you look at the page itself, we started looking at the population of the country, then bringing it back now into issues around the health, issues around maternal mortality. And after we've gone all of that, we weren't concluding. We were more or less trying to interrogate what the data was telling us. And it helps us in being able to bring data to critical stakeholders. And for these, it helps also our diplomacy engaging the estuaries from the point of lens of what has Nigeria done and what progress have we made. The beauty of this platform is that for the shadow voluntary national reports that we write as civil society organizations, we can now evidence data from civil society, evidence data from private sector, evidence data from the UN system and also from other organizations that may have pulled up data for all of us to say, okay, what exactly is happening with education, what is happening with health, what is happening with population and what is happening with that sustainability and what future does this tell us? What does the president also tell us and how can we use this information to plan for the attainment of the sustainable development goals. These is one of the best ways for us to ensure that again, that data can be trusted. I always say that data can be political or technical. The technical side of data is what we now have, where we're using Google's facility to be able to bring everything together in ways that are accessible and also in ways that we can have the right information. Then bringing multilateral data that may have been gone through rigorous processes as well into the poll. Then for us as civil society organizations to then begin to look at that data in ways that provide us evidence and ways that help us to be able to sit at the table and tell government what is working, what is not working, not necessarily with emotions, but with what the facts that we're seeing is telling us. That's what we've done with what you see on your screen at this time. It's very excellent. It got to a time where we were also looking at the gaps in data. This also speaks to what Mary said around sometimes the data we are missing and we have to ask ourselves why is this data missing? How can we get this data that we can begin to use that also to evidence the progress that we're making towards the estuaries? You would recall that the estuaries itself requires a lot of evidence so that we can show progress and also see where we need to advance or need to accelerate. That's what we've done with the Nigerian page. Very interesting assignment. You don't necessarily have to have all of the answers but what this goes to you helps you to have a mind-wide open when you get into the data commerce pool. Mind-wide open. Of course you can begin to now start asking questions around the data you are seeing and using that to think through what progress should look like or at the moment what gaps are there and how those can be addressed. Over. I'm talking but on mute. You think I'd have learned by now. Back to the screen for just a second. I wanted to point a couple things out and link to the general description of data commons. If you have questions for OEBC or for us in general, feel free to drop them into the zoom chat. We'll have a bunch of times for questions at the end. Will you scroll down again? I think it was to the fertility and births section and then pause there for just a minute. This is the timeline explorer that I was talking about. It allows you to take the data and you can examine it right now but you can also see it against the timeline. I also want to note a couple of things. One of these, this is on data commons.techsoup.org. This is the instance of data commons that we put up and worked with and we're pulling data from Google's public data commons and choosing the visualizations that allow us to tell the story and to examine the data because the local expertise is what's necessary to understand the context of the data that you're looking at and I think the distinction OEBC that you made between the technical and political part of data is great and I might extend that to also say there's a social part of data because it's telling our stories in aggregate as a collection of people that are engaging in different ways. So I think this is a great example of being able to pull data out of the data visualization and then be able to provide context and talk about it and I think if you go just go ahead and stay there rather than change. I also want to just point out that you'll see that you know where the data is coming from. Both of these have the provenance. You don't need to do it now Cori but clicking through on those links will take you back to the source and you can do some of what OEBC was talking about and say how much do I trust this data. You can also export the data so that you can get at it and you can investigate it and say what other stories you might find in it than the story that was chosen here. So this is a robust tool to expose the data but then actually allow you to do more and more sophisticated data exploration you know relatively easy from and I'm just going to scan to see if we don't have any open questions right now again drop them in and we'll have plenty of time for questions at the end. Thank you for describing that and Cori thanks for bringing that back up. Let's go ahead and go to Ingrid with Micaiah. Thank you my name. Well first of all I would like to talk a little bit about Micaiah and tell why we actually are in a partnership with TechSoup to talk about 10 stories of data and well first of all Micaiah is part of the synchase capacity for society and developed that through three things cooperation, technology and innovation and we believe that technology actually is a strong platform to get into innovation and cooperation between the civil society organizations in whole Latin America. We see these as an increase of opportunities for them to transform and of course be a part of the transformation of their surroundings and context. We've worked through lines of action one of them is if we go to the next slide please. One of them is the technology for the social change. So we bring actually technology closer to individuals and organizations and we friendly try to go into a proven methodology to explain the capabilities, access opportunities and knowledge and we also improved capacities through our line of mobilization and current funding into the national and international resources for social impact through our platform. This is some figures about our social contribution Latin America and Colombia. We've worked in around 24 departments out of the 36 that we have in Colombia and we work in 18 countries in Latin America. So this is a little bit about us to talk about why we have the pleasure to work near TechSoup for this project in the data comments. So if we go now I would like to talk something that an approach that we have here in Makaya when we talk about our project from the Google data comments and it was very exciting for us to talk about data because these challenges to take something that it actually is taught a lot like the data and the open data mainly for governments we see today a barometer even with the global open data barometer where it actually followed the improvement of governments using data. This information is actually very good for the government but not for civil society so whenever we started to talk about data for civil society we faced our face challenge and it was like it was a lack of information for civil society to talk about different approaches besides the government sites. Google data comments actually helped us to understand the information that was already available in some different sources and it was very good for us to start asking ourselves how we can tell a story that can be comprehensive for everyone because sometimes data is not democratized because of the comprehension of it it's just made for experts sometimes we see and we need even in our teams people that is expert in data analyst or an expert in some of the topics that we want to talk but as you know and I think this is a problem that all civil society organizations face which is the lack of people you know like team that could work or could even participate as an expert in this kind of projects so we start talking about something that could be related for everyone not just in Colombia but also in Latin America that is talking about an economical growing this is something that sometimes can be very hard to understand for civil society because the root grass organizations they are mainly working day by day in the small projects and small things in the territory but how we can use for example the good and big data of economic growing to show them that actually civil society organization are adding to this economical growth and I think that sometimes we have we haven't shown you know like search for this information because sometimes they don't acknowledge how to use the data but also they don't know how to share this data and as we know and we see as a part of the civil organization we also have this responsibility to talk about the examples that we have out there from organizations like the MAF or like the energy or MACAIA to talk about this importance and show the civil society the importance of the data that they have because sometimes and I know this happens in a lot of countries addressing our Latin America governments had a very weak arm to take to get into the territory and the organizations that were there are actually that they have this familiarity with the territory and the community and they can get this information that sometimes is hard for governments to take are also some of the communities or or territories they are not formalized so the government actually doesn't count on them because they are not inside the political or the territory or organization of the country or the city so we have a very strong opportunity there that can show both things first of all how it's important for civil society to acknowledge that they have a good source of power and data that can be used for other organizations and even the government to take a better action or decisions to public policy and also could take could take a little bit of a good challenge for civil society and it is to understand that the data that they collect can be also be talked in a very friendly story that stories can be made out of data and this data can tell strong stories for everybody in the example that we are showing right now here for the data comments in the text of Columbia we started actually to think about the data that we have available in the data comments so we can talk and do this cross-reference source to see what a story we can tell that actually help to show that this is possible that data can be easily shown speak or understood so we started talking about an economic growth because apparently it is something that everybody sees as an important thing in a country because of the investing or market or because of the economical growth of the region and we started to use this data and also be related with for example an exciting thing that we seen that it is not actually shown when we are talking about the economical growth of course when we are talking about the economical growth we see things about life poverty line or education or going into education or birth or but we also started watching that something that if you could go into the second chapter right there the story blocks we could see we could see that actually homicides were also a variable that is not very talk a lot when you are talking about the economical growth because apparently there are two variables that are things separated because security is not actually talked together with the economical growth but we also saw a connection there that as Colombia has improved their economical growth also in security and the number of homicides in Colombia during the last year has dropped as the GDP growth. So this is very exciting for us because it actually supports our theory of talking stories that are not you know like in the surface but we also could talk about stories that were grounded in the data but with these for example different different forms of view like you can draft and you can also check and cross different variables we could also see this good information and allowed us also to have a little bit of hope that going and to the economical growth in Colombia we are making we are making it more secure and we also are making civil society more empowered of their informations to show that just the information that are available from the government it is important but also what other initiatives from Colombia from another countries we can use as an example to you know enhance and contribute to the data commons and the use of open data in general. That's great thank you and again we'll have plenty of time for questions later but just quickly one of the things that you saw as we were going through that is the context around the data you know the story that you were telling us Ingrid is you're able to put on the page right and illustrate with charts but getting to that spot where you've been able to curate the data that helps tell you the story requires the ability to investigate those data you know and I think the point you made about being able to look at data that is not normally shown together you know is a huge part of what this tool has helped us do right because otherwise you would have had to have thought of finding both of those data sets find them and then get them into a format to just even ask the question right and I think that's what it's pushing us towards so that we can then take that insight that there's a correlation between lowering homicides and increased GDP and say well does that play out in the region does it play out in other countries you know can we look at homicide as lowering homicide as a leading indicator of an improved GDP right and what might that mean to civil society organizations working in violence prevention in their communities you know to be able to tell their story about why their work is important their work is important to keep people safe and secure but maybe this other attachment to the economic security of their region is something we don't talk about enough and so I think the ability to get to that story is key and then to test it and see somebody listening to that can say I can say does that play out where I live in California and go in and look to see if the same data is available to me right it makes it easy for me to take your lesson and apply it to a different community and see if it holds true or not and I think that's a great part of the insight that comes from this and what this exploration starts this on so that's great so the last two stories we saw were built on data commons itself right it was using the visualizations that are in data commons to be able to show and tell these stories was using data that's already inside of data commons and it creates these topic pages that allow us to provide some context as both of these pages did around a set of visualizations but there's also an opportunity to use the data that's in data commons in more visually sophisticated ways connecting via the API and tell a more visually rich story and with Romina and Julio that's that's what we're going to dive into now well thank you many and team tech sub team for letting us being part of this experience and working in this new way of looking and looking and use that I think that that has been a very important in some sectors of decision making for some long time ago but in the social sector I think that we haven't used it as much as we could maybe because it's not easy to understand it because traditionally we are more used to qualitative information so in our experience this was a new level of understanding data first of all I would say like choosing a sustainable development goal it was complicated especially since SEMEFI does not directly address any of these issues but rather to support organizations that are actually working on the field so the commitment of working on it on an SDG became a process of internal debate all of them are important they are a common floor of facing problems so finally we would choose Sierra of hunger and then I will say that is to understand the dimensions that the concept of hunger is related for example it could be approached from the perspective of nutrition or quality food or under nutrition or foreign security so it has many ways to understand what that means that made us aware of the complexity of the hunger issue then I will say that another important thing that we have to in the way of making this story was the availability of the information to tell a story you basically need to have that disaggregation to understand and to put on perspective the problem because sometimes just like national data is just not like if you take the average of the country for example it's not as much as useful or the best way to connect with the problem so we explore different sources of information at the national level and some were not comparable so that was when we had to the decision of how to manage different sources to start looking for the data so food insecurity for example before we arrived to hunger and gender in Mexico we tried so many hypotheses that we were thinking for example like the difference between food insecurity in men and women for example food insecurity in women in rural and urban areas for example food insecurity among people who faces disabilities or indigenous people or communities so by the end I mean even if all these data that we were like trying to understand and to interpret were alerting we think that it was not reflecting the impact that we consider necessary to get a deep reflection of these issues and I remember that by the time we were participating in a forum of access to water and it was argued for example that the issue that an issue that contributed to domestic violence was the lack of water so that gave us like an idea of exploring the relationship between food insecurity and domestic violence and we started looking for this possible relationship because there were different sources actually one looking for food insecurity and the other for domestic violence in women so we started looking also for academic articles and all of them addressed that this connection was but in a more qualitative manner than really thinking about data so we began just like the task of cross-checking data to verify the relationship with these issues as Ingrid was saying there was just two different problems but we just trying to put them together and trying to think what was behind that and we observed that and maybe you can jump into chapter 2 is it possible in the story because I think that that's where actually you can see more about the information in chapter 2 because in chapter 2 for example we really started to understand or to observe that in many states of the country the data of hunger of domestic violence hinted like a direct relationship so for example then you have there the information about the state so when you actually click there in some states you can actually see how do they are moving and in some states when violence was decreasing then hunger was decreasing and so on it really depends on the state that you were actually looking so that was something interesting now how do they were moving these two viral votes directly in some states were more clear than in the others but it was the thing then after we see all of these we subsequently look to organizations that were focused directly on the problems of food insecurity and violence and talking to them there was just like a whole learning process about their work experience this exercise was really really important for us because even though the data was already suggesting a relationship between these two problems the people that actually was working in these organizations confirm us that in practice these food insecurity in women was very common to be part of these violence that they were facing like violence so it was interesting also to have that and talking to them we also realized that violence and food insecurity were not the only problems that were related for example such as lack of education home care informal work poverty like so many issues that we were not able to put on this story where just like a whole thing a whole panorama they're working on the work of the organizations so I would say that for this reason in this story we really wanted to make more visible because based on their experience they have designed integral solutions in their models of attention that allows them to not only attack one problem but also to set conditions that lead to more visible effects on food insecurity we also realized with exploring all these different sources of information and data that the information of NGOs were not really included in any place it was something that it was inside of organizations and as Ingrid says I think that data commons will help us to get more data for the people who is actually working on the field and who is actually producing evidence of how the problems are being faced and how the problems are being doing with that so that was something absolutely important for us and even though the chapter one was just more about like playing a still like a base of what is the problem then on the next chapters we were just able to explore or to give more elements to the story to make a storytelling and make more connection with the people so that was basically the story behind this story of course we will talk maybe later about some other issues that we were facing on getting this in this view but also like to understand how that works to say or to tell a story that's great thank you and we're starting to get a few questions coming in so please feel free to drop them either in the Q&A section or write into the chat whatever you prefer you know just before we jump in to the more general questions Romina and Julio I wonder if you can talk a little bit about more about how you think organizations can use data and build up the sources so that they're getting at what they care about with their causes one of the things that was OEBC talked a lot about the political part of data and the necessity to be able to interrogate the data source and determine if you trust it and to identify places where there may be political reasons that data isn't being shared with the people of granularity you also talked about when you were just talking about validating the data with community organizations so that they're helping provide even more hyperlocal context you know and talking about the differences you saw in correlations in this data at the state level right and so I wonder if you can talk a little bit about how you see organizations being able to plug into this kind of data assets to achieve some of their own goals yeah right maybe I'm going to to talk a little bit about our experience the thing is that traditionally from our team we were used to create narrative from the data and for this experience we took a reverse approach identifying the story we want to tell and then support it with the data so trying to answering the question you are making now I think that we still have a long way to go in order to make NGOs let's say that they can work with data but not only for making decisions but to tell quite good stories in order to communicate effectively their causes to the public so I think that well how organizations can use the data to build stories and strengthen their causes is can be summarized in this phrase is not the same thing to have the data that to know how to tell them we have seen many organizations that gather a lot of data public data and inside data related to issues that they address however they are not necessarily useful when they want to communicate their work and especially when they want to invite other people to be aware of their causes and to invite them to work in order to create a better situation for the population so one challenge I think NGOs have is that the nonprofit sector at least in Mexico has only recently began to develop this culture of working with data and there is a lot of public data but it is not necessarily connected nor is the knowledge to upload it properly so we believe that platforms like this one like Data Commons are going to play a key role in giving giving people more access to direct data to visualize it and also I think that at least with us in our experience working with TechSoup does a great job for us in adding experience they help us a lot to connect with this project and to discover how do we want to tell the story how to use the data to tell a good story that at the end of the day I think that this kind of help from a partner that teaches how to tell and to stretch a story give us the capacity to generate impactful narratives and it also allows us to get involved in this culture of data in a better way I think so that's in my opinion I think this is how organizations can use data and at the end we need to learn how to to not only use them but how to tell it to the world to cause this positive impact when you one of the other things that I just wanted to ask about is you were going through the process of deciding what you wanted to explore the process you described early on Romina of picking an issue area and then saying okay how is it that we want to be able to look at this data what are we looking at it in combination with you found data sets that were not in data commons that you chose to use and are on there and I wonder if you can just talk a little bit about what made you say actually and that's part of the reason we told the story in this format right because it allowed us to pull in some non data commons data sets and I wonder if you can talk just a little bit about what caused you to pull in that additional data set or what you saw as limiting in some way with the data that was in data components. Yeah we when we jump like I don't know it was just like I guess we started this program this this thing in May I think it was May from May to now data commons has changed a lot it is right now more accessible I think it's easier to go in first time and we start looking for a specific SDG with was food insecurity and women in women then we start looking that all of the information was at the national leverage and as we talk sometimes the national leverage is just doesn't really focus on the population on the issues that you want to address so it was very important for us to get a little bit more of what was going on every state for example on another group that we were trying to focus on so I think that another thing is that Mexico we were more actually familiarity with information that is in Mexico producing Mexico so I think that for example in data commons something that is missing at least in Mexico for the information in Mexico for hunger it was just that the information was not not necessarily complete for what we wanted to tell then for example when we saw the rest of the stories of Nigeria and Colombia it was very interesting how they managed to build a different story with the information that was available there I think that in that moment when we jump into the story and we try to really focus on more state level the information was not ready there but the important thing that was that we were able also to contribute to put data on data commons so it can be used for you know different organizations or different people who were actually trying to look for more specific ways to problems domestic violence and hunger I will say that yeah and I know that part of the goal of this grant work is to identify data sets that should and could be ingested in data commons and by ingested I mean normalized so that people can explore them using these tools I mean I think the thing we saw is that there's always still a link back to the source of the data because the part of the goal is surfacing it so that community members can interrogate the data in all of the ways you all have described whether it's about trusting the source or validating with local communities or understanding how it may be differentiated and we see different trends in different places and then get the insights from community members on why they believe that to be true the somebody in the comments said collaboration is key and I think collaboration yeah exactly is very key to us understanding the insights and I think the role we all have is capacity building organizations is not to say what is and is not true or necessarily why something is or is not happening but it's to help surface the content so that people that are closer to the context can have the conversations that are necessary to say what is our understanding of this and what do we want to do about it right I think just diving into some of the questions that we've grabbed here I mean let me start with sort of talking a little bit about how you see again I want to dive more into how you see civil society being able to use this data when we talk about it when I talk about it I usually talk about three ways of civil society organizations using the data one is just so that they can understand it and they can compare their own data to it and they have a benchmark or a reference spot right the second place is to identify places where data is missing local or national level to be able to organize that data so that it can be included in these data sets how do we make civil society we saw in all those demos the World Bank and the UN how do we make civil society be as authoritative a source of data and what are the projects that we do about that and then also using that data for advocacy using that data as a way of having maybe political conversations or the policy conversations that allow us to say where does policy need to influence the providing influence on that and OUBC that's something that you talk about quite a bit right data as a tool in diplomacy and to be able to surface issues so that you can have hard conversations without necessarily being oppositional right and I wonder if you can talk about that a little bit and how you see civil society using it in some of those kinds of ways I think the data commands platform helps us to be able to balance confrontation with collaboration meaning that when we see the pool of data that we have and I will give you an example with Mike of Texas we were looking at the data and we're saying to ourselves what exactly is missing here okay now that we've seen what is missing what exactly is the data that we have at this time telling us and how do we use this to begin to jump start conversations that can help us all as stakeholders to come to the table and start reasoning as to what progress needs to be made changes would are impacting on that progress and how do we collectively come together and work to address all of this so civil society and the beauty of this platform is that you only need to be able to play with numbers and be grounded in the context of your local community for instance when the data on fertility comes up you read it you look at it and say to yourself what does this mean 10 years ago and what might this mean for the future of education or what might this mean for the use of resources in the country in ways that are sustainable then you go back to that data and interrogate some of those metrics that probably will give you the picture of what you're trying to look at civil society organizations now have an authoritative source so I'm imagining someone reading the page that we have developed from Niger and saying what's your source then I can say this is data that has been collectively pulled from government data from UN data and you know we have all of that in the central pool and we're just playing with it for us to get a sense of what progress looks like for the ASTG then that moves away from the conversation of at this data cannot be trusted or this hasn't been scientifically proved because we've brought all of the methodologies of this world together into a pool that helps us to say this is the authoritative source and when you cross check this with other data whether it is government administrative data or the UN administrative data the margin of error might come to something like 1.2% or 2% which is negligible that some society organizations can sit in the room using this data to tell stories like colleagues have done in Colombia or bringing it back to interrogate what the picture and progress looks like then someone asks how can non-profit organizations how can we use data commons to show the work of non-profit organizations when you look at the Nigerian page at the end of that we spotlight some organizations working in those key areas that we have identified and data commons also helps so that where we are able to do an atlas of organizations working on those issues so for instance we've looked at education and we're saying this is the trajectory of education we can say these are non-profit resources that can help us address these challenges and these are government resources that are also helping us to challenge that at the end of the day we are even able to track goal 17 of the STGs which looks at partnerships and also looks at you know collaborations that have happened for the STGs over to you man yeah we did a very small sort of pilot for a pre not even a pilot just a very small example of what you're talking about in California where we took predictive data about global warming identified the areas of the US state of California that we're going to get hot in a way that made it hard to get jobs or you know have food security and then we looked at the number of non-profit organizations that could be offering pantry or other meal services to the community members and said okay in preparation for this future event we're going to need to activate these organizations so that they're prepared to offer a service because we can reliably predict they're going to need to offer that service you know in the next five years and I think you know that gets at some of what you were talking about right how do we identify the trajectory of something and say these are the organizations that can influence that trajectory how do we get the resources to them you know so that we're helping influence that and how do we use data as a way of having that conversation right this this thing that we're all looking at you know and we're talking about what the data means instead of whether or not we agree with each other you know and that's so much of what I hear and what you describe in terms of using the data to be able to have these what could sometimes be harder conversations you know I think I think all of you have also touched on the idea of you know inherently that in some places at least nonprofit organizations civil society organizations may hold data about their community that nobody else does I think Ingrid you talked about this most clearly right because you're talking about a country post with a peace process you know that has territories and areas that have not received government services for the lifetime of like a 50 year old citizen and so you know what does it mean so that we can expand our understanding of Columbia and the needs of Columbia to include these territories that the government does not have data on and the role of civil society potentially in helping collect and organize some of that data and I wonder if you can talk about that a little bit more right how we make visible these communities that have been inside the border of Columbia but outside of government services for so long Yeah I think you touched on a very important point there because I think sometimes data as it is collected in the urban areas and the main cities or through a population you actually receive that results from a certain part of the country and the population of that country so whenever you start to you know going into these isolated territories and you start collecting data you can also see a tendency there you know like you can start showing up different needs or different point of views or different even services that they actually would need as we have you see talked important to find what is missing there but if we are just searching through the data of the part of the population we maybe like ignoring need that is already pleased in this part of the population but it's not necessarily come to the other part of the population and of course these isolated territories they have needs that may be in the urban areas that we don't have because for example internet connection is something that we struggle a lot in our rural areas and we started talking about collecting data with papers or with you know other things rather than technology so what can we do to make this data come into the table and start taking you know like big conversation and difficult conversations even with those data that are missing in the technological world so we have also these kind of responsibility to take these data into the conversation and trying to you know put these organizations into the technological world and started talking why it is important for them because sometimes they acknowledge you know like having these data is important for them and they try to do the best for the community but whenever it comes to talk about what they do inside the community outside the community it is difficult for them to you know let them the others know why they do what they do because sometimes when we are far away from territory we we try to know what they need because we have sort of the same you know if they are hungry we need to make community you know community lunches or community diner rooms or something like that but maybe they will have a little bit of information that is unknown for us that will you know support a different decision that it is I don't know obvious for us but not for the people in the territory so whenever we come with the data outside of the territory and start understanding their context in their ways in their you know like way of living or the point of view they have we can make we can start to make better decisions and start you know spreading widely our actions like for example Makaya we work in the twenty twenty four of the thirty six departments that had here in Colombia but whenever we start working in the new department which is sort of a state in the United States we start you know like talking to locals with organization with civil society organization in the local area just to understand what data they have that we don't to make you know improved our services for them not because we are working in Colombia and Colombia it is a country everybody is the same actually we are a very diverse country and we have many different territories as well as Mexico and Latin America of course Africa would have also inside their own countries like a small countries it's held so these actually make us improve our services as well so I think there is the importance to acknowledge and also to empower and also to learn about these included and including data from territories outside the government side I think that you know it's interesting because you've been we have a question in here that's about how Data Commons helps your region but what I've heard is all three of you all three groups have talked about you know how Data Commons how Data Commons can can sort of amplify the work of civil society organizations that it's not actually Data Commons it's actually civil society Data Commons becomes an insight tool that civil society is using to accomplish their goals and find things I mean Data Commons it's an important tool because it does make the data accessible to organizations that would never be able to afford to have a data engineer scientist on staff right and I think as I said at the beginning the framework for publishing the data means that civil society organizations can be a contributor to the data conversation and not just a consumer of the data conversation you know but it's really the work that you're talking about is getting close to the organizations that understand the context of that data so that we have a way of validating our insights and then using that data to get a seat at the table so that we're able to share that context with other policymakers I'm wondering you know as you look at the region you all are sort of representing in some way Latin America Africa very broadly speaking I just wonder do you see a regional application of data commons we've gone into this sort of the more hyper local right getting close to the context so that we can validate our understanding seeing the way that that context changes even within a country at a state by state level within a country where we have territories that may not have been included in that definition but let's go the other way for a minute and say go through each of you maybe Romina and Hulu starting with you too and just saying do you see a regional application here well every application it's obviously we face some problems in the region I will say that but also for example something that maybe a lot of countries in Latin America I mean not only in Latin America but in some of the countries are facing for example is that during a disaster for example now so right now for example what we are looking with some of the for example the Huracan right now in Guerrero which is one of the poorest countries in the world not in the world sorry in Mexico it's that the information of NGOs is getting absolutely basic so I think that one approach could be in general I mean in some countries will be like getting for example data commons in getting or maybe helping organizations to improve their abilities their capacities to put data during emergencies for example that could be absolutely helpful because right now we are looking that there are a lot of organizations working but we are missing data we are missing for example who is doing where who is helping with that and maybe that the commons could be like a place where actually that could help and I'm talking right now in Mexico I think that natural disasters could be something that all around the world is just happening so it could be a way of making for example more easy for organizations to use and put that over there to then try to improve our actions in a more effective way I will say that just jump into my head that's great Julio anything you want to add to that actually I was about to say something like that yeah I think data commons could help a lot when we talk about regional data the only in fact one of our challenges working in this project was realizing that at least now Google have only national data but they have the sensitive to accept that they could receive more data more focus on a specific geographical area so if if data commons continue that way I think I don't know no more than a year maybe two years could become a super super tool that could help visualizing data even in real time maybe helping NGOs in natural disasters like Romino already said this hurricane in Guerrero maybe could be helpful like in issues like just OUBC told us about political issues maybe could help us on what just show us about violence about well at the end of the day I think that if Google continue having this sensitive to receive data from other sources that made that they didn't haven't mapped yet yeah data commons could become a greater tool to the NGOs to help working to empower them in their projects and so many things Ingrid anything to add to that yeah I would like to say that also as you were saying earlier organizations they they don't have sometimes they don't have these expertise inside because social organizations they aren't able to employ data engineer or data analyst or something like that and I feel like Google data commons in that way could help a lot to understand and manage data in a very easy way for people to understand it and play with it you know like maybe get around some tools like easy ones that they could see their data being worldwide it is also that that could empower these organizations to go more into this data thing because of course data and data analysis and everything is going like very famous right now because everything start about data you know like opinions are there but now data has a more powerful you know like force into the conversation worldwide so I think that when organizations see that they can actually manage data understand data and they could transfer this data to their beneficiaries or to their stakeholders in general they could be capable to be in power of the tool because I think that more more than they to see their data on the website itself is to play with it and see how useful is the data that they put on there to the other people so I think that that data comes in that way could actually democratize it very well because they have a very easy tool to share to search and cross different variables that actually we can relate in our heads you know sometimes hungry and gender they couldn't be related but with this tool actually we could cross and link it there and they could also you know like contribute to these insights because sometimes great like the World Bank or the United Nations and everything they are the only ones that actually get insights from the information because they have a lot of people working on it but whenever a small organization could get an insight that we haven't seen because they have not just the data but also the context of the territory there will we have a more empowered civil society yeah it's interesting because that's one of the places where I think lowering the bar to asking questions of the data you know that you were just talking about it does well on the other side it lets you pluck out the answer I mean this is what I was saying they have something which we didn't demonstrate in this that allows you to embed something so if you imagine embedding a YouTube video onto a website you can do that with a graph because then the organization can say this is the piece I got the insight about and share it on their own website and provide context right for that sharing and I think that also that ability to pull it out and say the context is what's important here you know I'm not going to send you off to do the exploration on your own you can to validate what I said or to see if it applies to you but I want to use this piece of it to tell my story I think that's a hugely valuable part OEBC if you you want to chime in on how you see this playing out maybe at a regional and not just a national level yes I mean understanding that national progress makes regional progress that means that we can begin to use data on data commands to peer review governments across the region in ways that helps us to map best practices but also helps us to show regional progress for instance what does progress looks like in Africa and what those are the gaps that we're seeing how we move together as a region in ways that helps us to contribute to global development as it were so it's also a tool for peer pressuring governments using evidence and also a tool for global diplomacy as it were so in Latin America region bringing in the dynamics the African region bringing in the dynamics the American region bringing in the dynamics then we all come together at the United Nations and say this is what progress looks like in terms of the work that we have done civil society organizations using pooled or elective data this is what the data is telling us that this is where we're not making progress this is where we have made progress for countries that have made progress what lessons can we learn from that what competitive best practices can we bring together in ways that helps others to compare the notes but also puts more pressure on countries that have the same access to the same resources but that belong to the same continent of the same region this is another value had for us when we make use of this platform yeah and I think also as we have an increasing number of existential issues that aren't defined by our country borders like climate change and migration that happens because of war or regime change or climate you know and the ability to look at data you know people aren't people aren't staying within their own borders because they need to travel and it helps us be able to talk about those kinds of issues as well I think with a voice that crosses national boundaries and I think that also is a way to think about it you know at the regional level I love this stacking that I've heard from you all of the hyper local context so we're validating like is this what you see the ability to go to organizations and say if you don't see yourself reflected in this data let's talk about that right is it because your data is not in here and then we're working on a data organization and collecting project is it because this data doesn't resonate with you well let's let's interrogate you know the data source and understand what that means you know at the national level the ability to use this data to talk about hard problems and say okay we need policy to influence this this can't just be the work of local organizations trying to lower homicide or you know increase food security or provide for education we also need policy support so that we can get the real benefits of these activities and we can see that correlation and the ability of the data to support this kind of conversations and then for us to be able to talk about the issues that are not going to be contained but by our national governments because they are about things like climate change or because they are about migration due to natural disasters or war or regime change you know whatever it may be and and our ability to say okay how are we supporting the our fellow humans on this planet so that we can continue to be fellow humans on this planet and have access to resources that allow us to thrive in our communities I just want to say thank you to all of you not just for presenting today it was wonderful to be able to ask you questions and do a deep dive into your stories but also for the commitment and work that you do every day and just the thoughtfulness that you bring to all of your work and that you bring to this project so thank you for participating in the whole project and to those of you that attended today thank you very much we'll be sending out a recording of the session we're also going to write up just a short article that does an overview of what we talked about and learned to make it easy for you all to share with colleagues or for us to share with other people we'll share this list of links I know that we've been sharing it throughout in the chat as well and then there were a few questions that I answered typing we'll pull those out and share them broadly too for folks that missed them some of my answers were great question I don't know we'll talk to the data comments team and look at it and get a little bit more information but with that I think we're two minutes to time so thank you all very very much thank you thank you thank you, a pleasure to be here thank you