 Hello, everyone. Welcome to another episode of Engaging Ideas, the Parsons TKO podcast where we like to bring leaders and luminaries from the mission-driven sector to explore ideas and all kinds of things we could talk about for how to move our work lives forward. And we're excited to have you here today. We are now on all of the podcasting channels. You can access us anywhere if you have listened to the show, if you listen to this show and you enjoy it, please leave us a comment. Give us a like. It'll really help us. We'd love to hear from you and get your feedback. So without further ado, today I am joined by Jenna Slotin, who is currently serving as the Senior Director of Policy with Global Partnership for Sustainable Development Data, also known as the Global Partnership. Today, we are going to talk about the data values campaign that Jenna and her team are leading. And I wanted to read a quick about from their website, and we're going to have the website linked in the show notes, as always, with a lot of other great materials that we're going to be able to provide to you, dear listener, after this. But a little bit about that, about to help us set the context here. The data values campaign is a global movement to challenge power structures and data to ensure that we all share in the benefits from its collection and use. We're a community of hundreds of people from more than 60 countries working to create a fairer data future. The data values manifesto calls for change in how we design, collect, fund, manage, and use data. Organizations, governments, and people must act now and together to create this change. Jenna, I am so excited for this conversation. Welcome to the show. Thanks for having me, Tony. This is going to be fantastic, y'all. I think, you know, we've talked for quite a bit on this show, too, just about data equity and getting into data values. It is going to be fantastic. Jenna, can we start at a really high level? And can you tell us how you define data values? Sure. So let's start by breaking it down a bit. Data, in its simplest form, is information, facts, or statistics gathered and formatted for analysis. Now, our definition includes all data that can be used for decision making that's in the public interest, whether those decisions are being made by CEOs or statisticians, presidents, or local community leaders. The Cambridge Dictionary defines values as a person's principles or standards of behavior. So in other words, we're talking about values, which are the core beliefs that guide our attitudes and actions. So what we mean by data values is the principles and beliefs that should guide our behavior in relation to how data is designed, collected, and used in the public interest. And by our, I mean in the global development sector, are the data values campaign and manifesto is really focused at the global development sector. The data values manifesto also says data needs to serve everyone and getting it right is as much a political and social endeavor as it is a technical one. So I bring that up because we're emphasizing data values because we're trying to inject a sort of values driven orientation into a very technical space because the decisions that are made there affect people's lives in very real ways. Thank you for that. It's great to the context too. I mean you're focusing in the global development arena. You and I talked previously and for me, I think there's just a movement that we need inside of the nonprofit space in general because where are these principles going to come from? And if we are a principled part of the sector in these global economies, how can we start to provide those principles and how to really do something with the data side? Again, super excited. I'm glad you mentioned politics there too, as much as technical. I know you're talking sort of NGO politics, big country level politics, but for any listener here too, I'd say this probably can pertain to your internal organizational politics as well. The politics is almost short for the will of the people that have to get together to do this beyond the technology because we can make the technology work, but if people aren't ready to adopt and do something, it's not going to help. So I had in the intro there, we did talk about the data values manifesto, and I've pulled the five key points from the data manifesto, which is on your website as well for everyone who's listening. We'll make sure again, you get the link to that. But I was hoping we could dive into each one of the points and talk about those today. So yeah, let's hope we could dive in the first one. The first point, key point in the manifesto says to support people to shape how they are represented in data. So what does that mean in context? How would, if somebody's represented in data, what does that really mean? Just in terms of like the piece about support is really about giving people a say. In the design of data, which is when we say design, we're really talking about like what are the questions that are asked? What are the ways in which data is collected? What type of information is collected, for example, through the use of digital tools? And we're talking about data that will affect people's lives, though they may not be aware of that because it's informing decisions that affect how they receive services perhaps, how goods are marketed to them, etc. So it doesn't necessarily mean this idea of support. We're not necessarily arguing that every person is consulted on everything because that's not realistic or desirable. But what we're trying to get at is that those in power and those making decisions about what data is collected, how it's collected, whether and how it's going to be used, create avenues for consultation and participation with those who might be affected. So let me give you an example that historically indigenous peoples have, I think, quite rightly argued that they've been consistently misrepresented in data about their communities because they have different worldviews and conceptions. For example, they have different conceptions of well-being, of health, etc. So when government entities that conduct censuses or household surveys work with indigenous communities to redesign questions or data collection approaches, and we've seen this happen in Canada, in Peru, in Colombia, in New Zealand. When governments work with these communities, that's a way of supporting those communities to shape how they're represented. It goes all the way to the level of how questions are asked, to how that data collection happens, and then what kind of analysis is drawn from that information and how that then informs, for example, resource allocation across government and the allocation and delivery of services. So in that scenario, who takes the first step? I mean, is it somebody within the community that finds the researcher or the government advocate, or does the government advocate go, hey, by the way, maybe we're not doing these questions right? How does that all happen? Yeah, I think it really varies. So one of the things we're trying to do with the manifesto and the data values campaign is raise awareness across some key stakeholder groups on this. So one is to lift up the lived experience. Let's continue with the indigenous community's example, but extends far beyond to communities, persons with disabilities, and other historically marginalized groups. But where communities that have taken on that issue and advocated in their own context, in their own government can serve as demonstration for others, can work through global national, global regional alliances to help others recognize that they can take that action. So the groups, representative groups can be the first movers and kind of advocate with their governments, let's say. But we're also trying to work advocating for government entities to recognize where taking an orientation towards inclusion and equity is necessary, where there might be built in bias, and where those with the power can be the first movers and can take those steps as a result, let's say, of broader policies around diversity and inclusion or broader policies around resolving historical inequities, etc. So it can come from different quarters. And I think there are examples around the world of a diversity in that respect. Yeah, it's hopefully with the social awakening we've had over the last year and a half or two years now that maybe there will be more attention and more demand for this type of thing from our governments than anyone who is in the position of setting policy that can infect a lot of different folks. I feel like this might tie right into the second point in the manifesto too, which is invest in public participation for accountability. So I feel like we're scratching on that a little bit by trying to figure out the who in those scenarios. But what is the vision of how the public would participate in being holding, I guess, it's a government accountable for their use of data? Yeah, and this is a great one. It's complicated, right? So in the data values project, our focus is a bit more specifically on exploring how to build participatory mechanisms into or alongside more formal mechanisms of data governance, which includes things like legislation and institutions like data protection offices and privacy commissioners and other entities that would typically be institutionalized in a city level, at a national government level, etc. But the idea is to build participatory mechanisms in right inside those institutions, operating alongside, but also to strengthen the civil society and mechanisms that connect as watchdogs or sort of third party accountability. So there's a range here. So some of the examples that we'd advocate for that we've seen from our partner organizations have tried and have worked well in different contexts is, for example, establishing committees or councils that would be made up of representatives of different groups, bringing different perspectives that can be consulted or be involved in more formalized decision making, and they can help guide how decisions about data management and use are made. So this is like a proxy representation mechanism that we would include in a sort of like vision on public participation. Another approach is a very sort of deliberative democracy approach by actually holding consultations with those communities themselves. Data assemblies is a term that's been used where a government working maybe in partnership with an organization might organize, you know, consultations like church basement or high school auditorium and bringing people relevant and affected people together to be involved in some of this decision making. Obviously, then the onus is on some of those more formal actors to be presenting material in a way that is understandable and in which people can engage. It should not require a high level of expertise and we've seen success in the health sector and elsewhere where you can bring people in and have those consultations. But I do want to add a caveat here and I mentioned this before. It's not always realistic or desirable to have full participation all the time. We don't live in ancient Greece. Women and minorities weren't involved anyway, but it's not sort of one person, one vote on everything. It's not possible, right? What is possible and you know what I think is also important to remember is that the types of consultative mechanisms that I just highlighted have their own pitfalls, right? A real dilemma around tokenism, a real dilemma around if you open up the possibility for many members of a diverse community to participate, there's a risk of capture. So the people who have more means, resources or time to attend the consultation will attend. Higher levels of education, for example, will better enable them to participate. And so there are certainly actions that can be taken to enable people to participate, to make resources available for that. But we have to recognize that it's important to also be mindful of those pitfalls, so it was not to just create the illusion of participation, but not genuine participation. But on the whole, there's a lot of evidence that as imperfect as these participatory approaches are, that they create more equitable and trustworthy outcomes and sort of better decision-making as defined by greater equity, more representative of what people want and what those affected would want to see happen in terms of the way their data is collected and then used for decision-making. And so public participation is not perfect, but it's I think an important goal to strive for in many contexts. Oh, absolutely. Two quick follow-up for you. One, I was thinking about fear, and I don't know if that's what you meant when you used the word capture, but I could imagine certain populations being afraid to come into a conversation about what's being collected about them because that's scary, I think. Is that what you meant by capture or is there... Well, I did mean something different, but I'm happy to respond to that. Just briefly in terms of capture, I think one of the really common thing you'll see is there are historic examples in urban development, where in a particular geographic area that has both low and high earning homeowners and renters, there might be deliberations around where the highways should be and where the parks should be, and those people with higher income and more resource who are homeowners might come to those community meetings and dominate the meeting about what should be put where, whereas lower income residents have less power in those conversations. And that's what I mean by capture. Capture by those, say, resourced elements of a community, and you could imagine how that would happen in terms of some of these data discussions that we're talking about. But the fear is a huge one. So it's also important to take several steps back, and we try to do this in the white paper that accompanies the data values manifesto and recognize that data means very different things to different people. So we had a speaker at the launch event for this manifesto who represents, who's a homelessness advocate in New York City, and when he said was, well, in the communities I work in, data means surveillance. And then we had a youth advocate from Uganda who said, you know, when you talk about data to people, the first thing that they think about is, you know, the data, the credit you buy for your mobile phone so that you can make calls. Right. The point is that if you come to the conversation and you think the conversation is about whether and how you're being surveilled, it's a very different attitude than if your view or your expectation is, I want to tell you what you think so that you deliver, you pick up my garbage on time, and you build a health clinic where I need it and you improve the school that my kid goes to because I expect services from the government or what have you. So those are very different conceptions of whether and how the data might be used. There are many communities who will not want for very good reason and experience of harm to be giving up their data. On our data values website for anyone who wants to go take a look, we have some first person stories around data values and how data affects them. And we have a really powerful piece from a Ugandan LGBTQI advocate who is talking about the tension and as you listeners may know in Uganda, homosexual acts are criminalized. And the challenges around being represented or not being represented in the data, because of course being represented generates massive fear and has been and has created very real experiences of persecution. However, not being represented means a real lack of services as far as healthcare and other needs that those communities might require and where decision makers need to know where those people are, what they are experiencing in terms of health or economic effects, etc. His testimony is really interesting because he's really advocates with the community that he's a part of to demand to be represented and be heard while also pushing for the necessary protections. And it's tough. We cannot underestimate I think the real fear that that brings about. Thank you for all that. I think for anyone listening too, it's just really important to go back to the opening there that it's the politics and getting people involved. Like the technology is always say this over everything, but technology is easy. People are hard and there are real hard things we've got to work through that have nothing to do with normalizing data streams. It's this other side of the work that has to happen. I was curious because we were talking about public participation for accountability on the input side. But are there talks or things that you're always thinking about on the once it's out, can I put feedback in at that point? Like, hey, this came out, but here's what I think was missing this time. If I maybe if I didn't make it to the initial inputs, I can come back around in a feedback loop. One of the things that we are focused on is around exactly feedback mechanisms, which relates to, as you were saying, feedback or engagement in data governance decisions that were already made. And on that piece, to get accountable data governance, there needs to be a kind of iterative cycle of discussion, audit, third party checking and review. Typically, a decision is rarely a one-off. So whether it's an AI application or a kind of public health decision, the data are being used and reused and collected and updated with a kind of recurrent decision making purpose or a recurrent marketing purpose or a recurrent and so data governance is not like a cannot be a one-off. In some of the work we did in the consultative period to develop the data values agenda and manifesto, we heard some really interesting experiments from in the UK, for example, around health data where they were talking about this conception of the Welcome Trust and Ada Love License Institute, the sort of concept of a learning data governance, that there would be a sort of iterative process and that data governance could sort of learn, and that would be a kind of feedback mechanism. The other piece about getting value from the use of data, so one thing that we see in global development a lot historically, whether we're talking about household surveys or censuses or much more digitally enabled work is that you have underserved communities and surveyors who show up in their town, go door to door, ask them a bunch of questions and then go away and never talk to them ever again about what happened with that data and what decisions were made on the basis. Then they come back again and different people come back and they go door to door and they ask for their time and they ask them and in many cases the same questions and sort of again and again and again with very little feedback when if one takes time to look at what information was collected across health, education, environmental management, land use, agriculture, that information would be very relevant at a community level for very basic decisions for farmers, small, medium-sized enterprises, you know, schooling, etc. And it doesn't reach back to those communities. So there's something about that kind of value coming back to the communities who gave the data. I'm smiling a little bit there because it's such a universal problem even just beyond getting data for public services. I think it's just burnout that people have experienced in the nonprofit sector from too many systems changing all at once and they're like, didn't I just answer these questions about something else before and you never came back? It's just everywhere. So it's almost like we have to get better bigger at that. So the third component here, democratized data skills for greater equality. Love it, but I'm wondering, you know, is there a basic set of data skills that's a starting point or how would we begin with something like this? So that's a great question. We've had a lot of discussions around data literacy and have actually moved from the idea of data literacy to talking more about a concept of data confidence. And this was shared with us by a partner, Bronwyn Robertson at an organization called Data for Change. So rather than focusing on like specific coursework or trainings or what have you, the idea here is to emphasize a concept of being data curious, which focuses on exploring and investigating, being data critical. So interpreting and evaluating what you see and then data creative, which is around experimenting and communicating with data. So this doesn't perfectly answer your question, I get it, but it helps us reframe or change our orientation toward practical solutions for different audiences, right? What do the leaders of civil society organizations or community-based organizations need in terms of skills and an ability to sort of read a graph or a chart or look at information within their organization? What do community advocates need if they are going to hold their government accountable in a particular sector or in relation to data governance decisions? What do individuals need to understand how their data is being extracted and used in the digital economy? Like how do we make sense of terms and conditions and basic privacies and the location, you know, apps on our phone and all of these things? What information do we need? What skills do we need? And how do we make that more widely available for different people engaging in different settings so that they can feel not just confident to engage on the numbers, so to speak, or to read a newspaper article with a critical eye, but also to be confident to question the decisions that are made by people in power, right? Is it always okay to track my location data? Well, what other information do I need to answer that question for myself in terms of what I'm concerned about? In terms of skill development, right? Yes, there are some basic courses in elementary school, high school, and then college university. I think we're seeing education systems go, you know, very much in this direction of STEM quite rightly and to engage in the data economy and hopefully, you know, our children will be way better set up than we are. And then there's, you know, there are some basic trainings to think about for sort of civil servants in the public sector, for social impact organizations. But I think we need to be thinking about that differentiation. And then also, and this also came from our consultation and some insights from colleagues that connected by data, that it's not all about the skills that ordinary people or leaders in organizations need to get. It's not all about everyone on that side getting up to speed. But those who are working with data, those who are making decisions with data, those who are presenting data can do it in a way that's more understandable to a lay audience. There's more work to be done on that side of the equation as well. That's less about the skills of the receiver and more about the that improving the ability to communicate of those presenting or using data. And the last point reminds me of during the beginning of the pandemic, when I was R not, what's the R not? What's the actual transmissibility of this? And like, of course, I know what it is now and it's going to stick forever. But I remember listening to someone, they kept talking about the R not and they go, what are they saying? They're R not what? But I think you're right, there is a way to bring it down. And I'm, yeah, I like the point to your makings. And when you were talking about that, I'm like, people having the freedom to be able to question the data that is taken and what it was used for. I really liked that getting people empowered there. And it was, I'm curious too. I mean, it feels maybe in the US, at least, maybe we're waking up to the fact that we have given away so much personal data for a free app. But I'm wondering if it's the rest of the world has kind of done that too. And what's the behavior change to undo that? I'm just looking at it, because I live in the US, and I've seen it for years, what we do. And to your point, the terms and conditions, who reads those, they just click because you're like, I need access to this app because my friends are on it. I feel like there's a huge behavior change just here in the US. And is it the same in the developing economies, you know, have, are they at that level too, or is it different? So I can't make super well-formed like generalization. I can say that I think, by and large, at an individual level, there's a much greater awareness around data privacy in Europe in the rest of the year. So that's thing one. There are definitely places where the people are further along. I think there are pockets of that awareness amongst different communities for different reasons in Latin America, Africa, Asia. I think it is also important to remember and to bear in mind that there's a very real trade-off for ordinary people that it's the manifestation of what you just said about, I need that app on my phone because all my friends have it. It's also like, on my feature phone in a small town in Malawi, my only access to the internet might be via Facebook or Twitter, especially under present circumstances. You know, my, the, my, I don't have a bank account. And the only way that I make payments is via the digital payment system in my economy. And that is an essential piece of infrastructure that is life-changing for me and my business. And the fact that in the background, the bank or the manager that that data is being harvested is really does not compare in terms of the value I get from that electronic payment system. So, you know, it's not about do I go on or do I not go on. It's the conversation around who is holding, where's the accountability, who's in charge, you know, who's consulted, to what extent is that an issue even in public discourse, how to bring it into public discourse, say through the media, through journalism, et cetera, and start to have that conversation. So, the trade-offs for different people in different places are really variable. Another important consideration is a cultural one and a history. In cultures where personal, where that are more individualistic versus more communitarian, you're going to have different views about invasions of personal privacy. In countries and regions with strong histories of authoritarian government that has used surveillance and not just digital, but in decades and centuries past, then the ingrained experience with surveillance is going to be very different. And how that manifests with digital tools is going to be very different and the reactions are going to be very different. So, that's not determinative, it's just a reality. And so, a solution that works in one part of the world isn't always going to work in another part of the world. And by that, I mean a technical solution, as well as a governance solution is not always going to work and be replicable. Thank you. I'm writing some thoughts down that I might pull back later, but I want to get to the fourth point because I could stuff down really nicely to where we just ended there. But the fourth key point here in the manifesto is create cultures of transparency, data sharing, and use, which feels very fitting to where we were just talking about there. And then the first thought I had when I read that for you was how do you see those cultures coming together to transparency, data sharing, and use in these different places? Yeah, so we brought this idea of a culture of data use into the discussion and data values, because one of the things that we found when we were digging into the question of how do you actually get sustained data use in public institutions, this again is much more human than it is technical. In other words, even when the quality of the data are good, even when the data gaps have been filled, even when the data are interoperable, even when the data are accessible and available, there's a kind of complicated mix of political economy incentives that affect whether that data is actually used for decision making. I think we see that in US politics all the time. A culture of data use is one in which leaders and decision makers are oriented toward that. You get rewarded for doing that. So examples that you can find around the world are having performance management in people's contracts around the extent to which you're really using data for making decisions. You can be rewarded as a politician or a bureaucrat by the public, for example. So if there's a high expectation that decisions are being made based on data, then a vibrant civil society will demand to see that data or demand to see evidence of it or call into question decisions that haven't been made based on data. But building that culture, it's an organizational culture question. Anyone who's in an oversight position asking a subordinate, well, what's the evidence for that decision? Why are you delivering the program in those five communities and not those five? What do you know about the difference between those communities and why that's happening? It's about thinking about what the incentives are and whether the environment really rewards that kind of decision making. Yeah, definitely. And then there was a subtext here, too, I noticed on your website for getting the use of data as a public good, which I totally love. I think it's needed and it just feels right on many levels. And it was kind of tied into what I was writing down when you were talking before. Nothing will ever be applicable directly to each culture individually or ways it can be put out, but are there ways to start thinking about if it's a public good? Is there an open-sourced methodology around it? If it's not the open-sourced technology around it, where you can take it and apply and we saw this framework work for this type of group, now how could it maybe work locally over here? But yeah, what are the, what we need to start doing to start really thinking about data as a public good rather than, especially here in the US, it feels very much like a corporate good more than anything else. So there's a lot of really good work going on in this area around data as a public good and the concept also of digital public goods and whether you would put data under that umbrella. The one thing I do want to say, though, is that as much as we're emphasizing a kind of culture of data sharing in news, not all data can or should be shared all the time. And we've talked a lot about all the reasons why earlier in this conversation, there are good reasons for eliminating accessibility or establishing parameters for how and when data is shared to protect people's rights, safety, privacy, etc. So the idea of data as a public good or for the public good is valuable because I think it helps us think about who's benefiting and who's not. And that's where the kind of should more people or more organizations have access to particular data sets to achieve aims that are in the social interest and are the benefits coming back to the so-called beneficiaries of those decisions. So that's the kind of public good angle. And what I think that then helps us think about is not about all data sharing all the time or this is a public good and therefore it should be open to everyone all the time, but rather just as you think about like public land as a public good, then it needs to be stewarded in the public interest. So we borrow these ideas from economics and bring them in and begin to talk about the concept of data stewardship. So I don't want to be super esoteric here, but this is basically just the idea that there is a person or an entity or a function that has a responsibility for thinking about how that data is handled and managed and used and then considering some of these questions around consultation and participation. This is a concept that's very present in the public private sector, chief data officers, chief information officers, etc., having certain stewardship functions, it's becoming more and more common in governments and in international organizations. And so it's about the notion that there needs to be a function that is thinking about when and how to maximize the benefits of data and share it and ensure it is used and reused as much as possible in the public interest, but also to minimize or mitigate risks. I was glad my head started going to, I'm in these conversations a lot where my wife works in the creative arts field and what's the jobs in the next 10 years, 15 years, but your kids be studying. I don't know, the whole world's changing super fast, but it sounds like it sounds like you're hitting on one right here, like where's the data ethicist who's the data, you know, chief data officer, but yeah, someone, what are those roles and where does she come from? What does she study and how do we make that happen? But yeah, what other roles are there? I mean, what are your thoughts on that? Like, is this something we have to, because you don't hear about these people a lot as a role that is funded and supported, especially in the nonprofit, let alone maybe the government space. Is it time to really say, hey, look, this is the type of role we need. And here's what it looks like. Can we start planning for this rather than piecemealing it from a few people who cared enough, someone had a little bit of technology, someone had a little bit of the social side and cared, and then they're like, we'll come up with a plan. Yeah, I think that's a really good point. I think it's a really good idea. It's interesting because some colleagues over at the NYU GovLab have been working on this concept of data stewardship for a long time and arguing that it's sort of a profession and needs to be built in private and public sector. I think the other piece of it, which is less about the role itself, is that there is no doubt, I have a daughter who's going into middle school next year, and every middle school is pitching their technology, the data piece, the STEM piece. Data science is very buzzy, and that is a kind of set of skills or exposure that there's big focus on kids getting, coding, et cetera. There may be also a value in starting to build some of these, I'll call them values or governance related considerations, into that learning incrementally over time, so that from the beginning, people who are developing their expertise in data science or analysis or visualization are actively considering the impacts of the decisions, the biases and frameworks that they're able to bake into that when they design and when they do analysis. The biases part is, yeah, just hear about it all the time, a lot of the AIs that are developed and everything that was going, and it's blind biases sometimes. You don't even realize you're putting it in, and then I think to the accountability pieces you talk to, how do we get enough diversity into these roles that the accountability starts to be built in from the start, rather than trying to then in hindsight go back and be like, oh my god, this AI that we unleashed and has been running for five years has these incredible biases that we overlooked, like you cannot do it at that point, you're starting over. And if we can get it upstream instead of trying to work on it downstream, yeah, that's actually how a daughter is about to be going into middle school next year too, and yeah, the coding thing is the toys they have now, even just for getting kids to start on coding and building robots is interesting. We'll see how the feature goes for that part of it. So the fifth key point in the manifesto says fund open and responsive data systems so that all people share in the benefits of data. I feel like we've been talking a bit about that for most of this conversation, but is there something you'd want to add there and define and talk about what an open and responsive data system would look like? Is there a vision that you could help to start painting for us and we could see it with you and maybe start moving the world there? Yeah, I'm not sure I can give you a perfect depiction of it. I think a lot of the themes we've already talked about around various forms of consultation and participation that allow people to weigh in, raising levels of awareness and even sort of education or skills or data confidence amongst the sort of user or affected community side as well as on the side of the sort of data analyst and decision maker. I think there's an institution's piece and this is the piece we haven't focused on very much. Just because in the data values sort of campaign and manifesto, we're really focused on this participatory piece because it's underdeveloped. The formal pieces are incredibly important and there are a lot of parts of the world where we're really not there yet including here in the United States. Legislation, institutions, oversight mechanisms are incredibly important and need to be developed. There are basics around infrastructure and connectivity, etc. At the risk of recommending a very dense piece of work on this, the World Bank in 2021 published their World Development Report which is one of the flagship publications and it focused on data and it's called Data for Better Lives and in that they have a chapter on at the end on what they call an integrated national data system and I think that's trying to give this sort of full picture of what a data system needs to cover. I think that they, on the piece about participation and inclusion, it's certainly there. I think it could be even stronger but I would refer listeners to that if they really wanted to get a kind of full picture. Well, that's wonderful. Yeah, I mean one of my questions here as we're getting closer up and up is just if people are really interested and I think they are. I mean a lot of people I talk to really care about data values, they care about data ethics, they care about what are we doing and how do we move this forward in a much more inclusive and equitable manner than has been previously done. How do we build it in from the beginning? Where do they, so it sounds like the World Bank Report would be a good start. Where else could they get started? I mean hop on your website and yeah tell us, tell our audience where they should go to start getting into the into the work with you. Certainly I would recommend the Data Values site which is a sort of a hub on the global partnership website and there there's a lot of material, there's the manifesto and there's a campaign toolkit for advocacy and a possibility to get in touch with us and to work with us on this. Also there I would recommend, I'd be remiss if I didn't recommend the white paper that accompanies the manifesto. The manifesto is obviously extremely short and easy to read and easy to digest. The white paper is much longer but also digestible and useful because it unpacks a lot of these concepts in a much more detailed way and brings a lot of examples from across our partners hundreds of organizations working around the world that brings those ideas to life and then it has some more concrete recommendations and a lot of references. So we worked with a lot of partners to put that together. It doesn't so much represent our thinking in the global partnership so much as it does a consolidation and a compilation of all the inputs we got from partners and so the references are incredibly important because you can find a lot of incredible work in great organizations doing fantastic work around the world in that way. It's wonderful. Thank you. I mean it's always having a tool and a resource like that to find what's been done, who's been saying what instead of always feel so often everyone's trying to start from zero or started over from scratch instead of building on some of this great like the great work you're doing. How do we build on and keep moving forward instead of trying to find some new starting point? So thank you for that and anyone who's listening we're going to have links all over the show note page which will be on the part of the DKO website. Those show notes should transfer out to anywhere you are listening to us and grabbing this podcast from. To anyone who's been a regular listener of our show, you know we always end with the same question and that is as we've been building a Spotify playlist that you can also enjoy to help you get through your workday, maybe you run or you're walking the dog. But Jenna, what is your go-to song when you need a boost and why? Well I'm going to choose Hallelujah by Leonard Cohen. So it's not a beat but and it's a little bit mournful but it's also I think quite hopeful in a lot of ways and I find it it's really a touching song. I am Canadian as well as American and so I do love Leonard Cohen as a poet and a lyricist and a songwriter and I think the music is really beautiful and so it just helps me like both to feel and think. It's wonderful. Thank you phenomenally touching song. I appreciate you sharing that with us. I end your time today and answering all my questions and walking us through you know the data values manifesto and how it can start to get better at being more participatory and getting to open data that can help everybody. So thank you so much for joining us today. Thanks a lot for having me. Bye Jenna.