Data for Development: Strengthening Partnerships and Financing for the SDGs

Excellencies, esteemed delegates, colleagues and friends, uh thank you for joining us uh for this HLPF site event. Uh my name is Miriam Rabi. I'm the director of SGS today at the sustainable development solutions network and I'll be uh helping with the moderation of today's uh session. uh feel free to introduce yourselves and let us know where you're joining us from in the chat. We'd love to hear from everyone. Um and behalf on behalf of all of the co-organizers, uh so uh the UN ECA, NORAD, UND uh and uh our team SGS today uh we welcome you to uh today's uh discussion. So I've had the pleasure of collaborating with all of my dear colleagues uh on uh today's panel and as part of today's session uh on various initiatives trying to tackle issues related to building capacity uh data innovation to support decision making uh or discussing uh financial models that can support uh data ecosystems. Um and one of the the topics that we always come back to uh is uh different sustainable partnership models uh specifically one that focuses on engaging universities and academia more broadly. Um earlier this year um at the UN statistical commission uh we launched um a report and published the findings from a stud study that we conducted as part of the data for now initiative um that sheds light on some of the challenges and opportunities related to partnerships uh between universities and national statistical offices. Um so as a continuation of that conversation and as part of uh other initiatives like the FFD4 and future of data initiative that uh we are um many of us are a part of uh we wanted to create a space today uh so that for the next hour or so uh we can discuss uh various topics uh related to uh this issue uh learn from uh our experts and esteemed panelists um and also hear from you. So there will be opportunities uh through menty meter for all the participants to share their input on some of the topics that we'll be discussing uh today. So thank you again uh for joining us and without further ado I'm going to hand it over to my colleague Miss Vbecki Nielsen who is a senior adviser at NORAD to help set the scene for today's discussion. Vbeck, over to you. Uh, you're on mute, Vbeck. Sorry, too many things to organize at once. Thank you very much, Miam. And I'm very al also very pleased to be here at at this webinar uh today and and to be able to share and discuss with with many excellent uh people on on the work that is going on and and the opportunities for the future. I've prepared a small slide deck that I'll be sharing now um to maybe help visualize some of the things I will be talking about. First of all, uh since I am a bit of a scene setter for today's session, uh I thought I'll I'll put us all um a bit at at the starting point, uh saying that uh the work on on the STGS and and the whole process around the 2030 agenda has given us more data and more statistics, but there's still large gaps. Um we see that uh many statistical offices and in some cases the wider statistical community um and data ecosystem have worked a lot at national level to to strengthen their systems and we see increased availability at regional and global level. But at the same time there's still large gaps both when it comes to data availability uh and when it comes to quality and and use. And this is particularly the case when you look at more granular level. Um there has been a lot of exploration and and use of non-traditional data sources. But we also see that this takes time and and in many countries still is in early stages. This includes use of uh administrative data also in the global south. Uh we know that there are a lot of data in different ministries and agencies that are there but that need uh to to be used to be published but also need quality improvements and sometimes innovation in terms of digitization etc. We also see that national data ecosystems are fragmented that many work in silos. um that is something that that is uh present at national level and it is also something that seems to be reinforced by development partners because a lot of the engagement is sectoral as is much of the STG work. We also see uh a challenge in limited user engagement. Many statistitians are not trained in how to engage with different user groups or how to engage with different um partners. Um and uh at the same time we see it low data literacy with with data users not necessarily knowing how to engage with data, how to question quality of data and and be aware of what data uh are the reliable ones versus the the less reliable ones. So many challenges uh still uh and uh some of the learning has been that that some of the solutions to this can be to strengthen the partnerships across sectors and disciplines to ensure sustainability and and as Marian was saying the focus today is on the collaboration between the statistics community and and academia. But there are many other partnerships that also could benefit from strengthening within government, with civil society, with media um and uh internationally as well. One uh aspect of of collaboration and and working beyond the the more traditional data and statistics community has been the engagement of the the the statistics community in the financing for development discussions and particularly around the fourth international conference on financing for development which took place in Sevilla last summer. Uh the outcome document of of that conference, the Sevilla commitment uh shows a clear change from previous outcome documents in both the recognitions and commitment to strengthen national data and statistical systems. uh and uh committing to increasing financial support to and and enhancing investment in data collection and statistical capacity building of national systems. Here on the screen is an extract of of two of of uh of a whole paragraph and and an small extraction of of a much longer paragraph in in the case of 63C. Um that gives you an indication of of some of the the specifications that are made in the sevilla commitment. But we also know that many of these commitments are high level uh they're often negotiated um by representatives uh in uh in missions to the UN and New York and they're not necessarily trickling down to country level. And in this context um the the financing for development uh agenda also launched so-called Sevilla platform for action initiatives. There are many of them uh but one that that Norway together with Colombia and and the UK are co-leading with many partners involved including um most uh colleagues on the panel today is uh the SBA uh that is called FFD and the future of data. We work together, many of us, uh, to try and further identify both the the challenges and opportunities and and also to see how we can help operationalize and and make the recommendations more concrete and how they can be implemented by member states and partners. This trickle down to four specific recommendations. Uh, one is to consider data and statistics as a public good. This relates very much to a lot of data sitting in different um agencies, ministries, but also private sector, civil society, etc. Data that can be used um to increase efficiencies, to increase information and knowledge, increase accountability and transparency. Uh and and there are many lowhanging fruits there. if awareness is increased and and u sharing of knowledge and experiences are also uh included. The second one is to improve national data and statistics system coordination. As I mentioned there is a lot of silo thinking and silo approaches. So working more together having more working groups across the the system learning from each other and helping each other is an important element that that we included as a recommendation. The third one is to strengthen data governance and innovation. Um this is of course something that is uh in focus in in many different contexts and and not surprising. Uh but maybe the data governance element is worth highlighting saying that we shouldn't have innovation without ensuring that the quality standards um and systems are in place that allow innovation to be sustainable and also uh taking into account uh protection uh and security elements. The last recommendation is to invest in national data and statistics capacity both at national level by partners etc. Um aiming at focusing on on this rather than supporting parallel mechanisms or at least supporting um mechanisms that that work together and and enhance a sustainable national data ecosystem. Uh I've included a few links. I'll put them in the chat as well for those of you who want to explore where you can read more about these recommendations uh as well as concrete implementation options and examples and stories of success as well as challenges that that have been experienced in different countries and and by different partners that were involved in this work. Last just more specifically on NSOS and academia uh I think uh all of us will agree that there's a lot of potential mutual benefits uh from collaboration where this is not already in place and it is uh made use of where it is in place. Collaboration around data quality is is an important element. I think many researchers uh working very much in in depth with with micro data will have an possibility to provide feedback to statistics offices on quality concerns that they have on challenges that they experience helping statistical offices improve their data. Many researchers and academia work on innovation and can help statistical offices with innovation approaches uh through collaboration and and statistics offices on the other hand can potentially also provide relevant innovative experiences with academic. um data literacy is a joint interest I would say and something that could be collaborated around as well as the importance of independence and accountability where um helping each other in maintaining this in a context now where where we see that there is more and more backlash of of democracies. this could be a partnership that that both could benefit from and working together on. And then there is always potential of further exploring also how this can be done effectively, how they how the collaboration also can reduce um use of resources and explore comparative advantages as well as also exploring what each partner needs uh so that the s the collaboration remains sustainable and not just a one-off thing. There are many more on the panel now that that have a lot more practical experience than I have. So I will stop here and I will hand over to um uh Mrs. Yongi Min uh who is the acting chief of development data and outreach branch at UNESA statistics division. Thank you very much and and over to you. Thank you Rebecca and uh good morning, good afternoon, good evening to everyone and thank you for joining uh this event. Uh so for this panel discussion we will have uh two uh academics and three uh NSO or international government statistician representatives who will respond to the prompts in a debate style format uh with a for and against perspectives to make the uh discussion more engaged. So let me uh briefly introduce them. Uh first um first we have professor uh Samuel Aname and uh Samuel is the director of the Africa center for statistics from United Nations economic commission for Africa and uh then we have uh uh Dr. Macdonald uh Macdonald is a director general from Kenya National Bureau of Statistics. He is also the co-chair of high level group for partnership coordination and capacity building for statistical for the 2030 agenda for sustainable development. And then we have uh Dr. Carlos Alberto Gazun uh associate professor of economics from university data mea Nova Granada. Um uh we also have uh Mr. Camilo Andre Mandates uh the coordinator of partnerships and international affairs uh from the office of the director general from uh Colombia at Denny and lastly we have uh Dr. Phoebe Kuri, the professor of environmental and natural resources uh economics and economatric from Athens University of Economics and Business. Uh so for each prompt uh we also have the mantime meter as to invite you to uh express your opinions. uh for uh we will have invited three uh of the fi three to four of the five um panel member uh to express uh their opinion. We didn't assign which one for and against but we like to hear uh their thoughts on the prompt. So for the first prompt is with limited resources financing for data and statistical should prioritize long-term investments in partnerships capacity strengthening and data use rather than primarily supporting the production of new data. So first I would like to maybe invite Phoebe um to uh have your perspective. >> Thank you. Thank you very much. I have put on these slides just to make the um discussion a bit more explicit with the visualization. So I am the chair of the uh global sustainable development report. This uh report is assigned by the general secretary of the UN and it comes out every uh four years and it's basically an assessment of assessments of the progress with regards to the uh implementation of the STGS and it is also uh the flagship science policy society um uh report of the UN on which the deliberations of the summit for sustainable development that will take place in 2027 uh will focus it will partly decide the framework for the beyond 2030 time period. Now, what kind of data do we need in order to support the integrated approaches that are needed in order to implement the SDG framework? Well, for us the group that I chair, the independent group of scientists that I chair that we are drafting the global sustainable development report and I should tell you that now we are at zero draft level. We uh have the following narrative. We think a lot has been achieved with regards to the STGS. 196 countries signed 193 submitted over 440 uh voluntary reports policy frameworks have aligned with the SG we have on the ground impermentation of an average of 20%. However, while the SGS provide direction there is no robust structure explicitly tasked with driving their implementation. So we believe in order to really accelerate implementation we need operational global commerce for creating the capacity the uh transparency the accountability and achieving effective and fair coordination on the ground to uh upscale the implementation of the STGS and this as you know is a very very data country and sciencehungry process. So what we have as a structure is a three-step approach. The first step is about STG continuous measurement, monitoring and assessment. And this of course is a very data hungry exercise that needs to make different sources of data, spatial internet of things, time series data, um econ um socioeconomic data and so on interoperable. We need aggregated levels. We need um uh participatory diagnostic. This is step one. Step two is the integrated approaches. So in step one we know where we stand with regards to the SGS. We know where we want to go and in step two we bring system science and stakeholder code design in order to find those pathways that will align nature, infrastructure, economy and society on a sustainable path. And in step three from that modeling exercise which is a mathematical modeling exercise a data in intensive exercise we uh create the portfolios of investments and identify how they can be financed using blended finance public and private finance in order to achieve implementation of the ground. This is a a a an explicitly stakeholder and science engagement co-design process that focuses on evidence-based datadriven solution pathways that are equitable, fair and inclusive and creates in continuous low capacity building accountability and transparency. So and what's more it also serves it also serves the beyond GTP framework. So all we do in the digital twin step two of the um structure that we have where we co-design the sciencebased integrated pathways we use a beyond GDP uh approach that is wellbased growth measurement which is not just reporting like usual GDP measure measurements what we produce, but it reports the stocks and flows of comprehensive wealth that sustains human being across generations. Meaning not just produce capital but also human capital, social capital, institutional capital, natural capital. Again a very data intensive process. So if you have limited resources then financing for me should prioritize sustainable data ecosystems. ecosystems that are strategically deciding new data production that will help facilitate create the capacity for long-term partnerships and effective data use. In order to implement DSSGS that are humanbased and science um valid, we need a lot of data in functioning data ecosystems. So our focus should be on integrated long-term data systems and this should be invested upon as soon as we um seriously do the analysis for identifying data gaps that limit policy actions and include provisions for in institutional and technical capacity interoperability and data governance long-term maintenance partnerships and knowledge transfer. uh disagregator disagregation and quality assurance open and responsible access and analytical capacity and policy update. One important point that we should keep in mind is that new data should also be viewed as investment enabling infrastructure. New data can reduce uncertainty in the projects of in the portfolio of projects and be used to demonstrate project viability by measuring risk and risk returns. In this way, we can decrease projects and crowding substantially graded volumes of private finance. So public finance can be devoted to identifying and acquiring the data we use and make it usable and accessible and then this data will allow the risking portfolios of projects and attracting private finance. So good data is a financial has a financial multiplier effect. Thank you. Thank you so much uh Fab for gave us a a glimpse view of the upcoming uh JSDR report. Um also share your view on the uh with limited resource how to invest in the long-term strategies. Um let me maybe invite Sam and uh to share your view um on this prompt. >> Thank you very much yi colleagues. Good morning good afternoon um good evening. So, I'm going to argue that financing for data and statistics should prioritize long-term investment in partnerships, capacity building, and data use rather than production of new data. I would want to underscore the fact that in as much as I'm arguing for that, I do not underestimate the need that there is data nonavailability especially on the African continent. So we're not relegating the need for new data production um altogether but rather we are saying that given the change that we need in this statistical landscape it is important we look at it from a long-term perspective especially in the areas of partnership capacity building and data use and I'm going to highlight these um points from the perspectives that some have been shared earlier by Vbecki and Phoebe the first point that I want to highlight is that with what is happening in the statistical landscape now we completely need an end to end change in mindset and what I mean by an end toend change in mindset is not from the usual production to policy use but now changing the narrative away from starting with production to begin to think about what the policy needs are and then ending with policy. This requirement of a change in mindset would require us as a community to revisit the fundamental principles of official statistics which is heavily aligned to production of um statistics with a couple of them leaning towards co coordination and to an extent use. But why the change in the theory of change requires an endto-end change in mindset is that going beyond statistical accuracy would enable us to think about two things. the trade-off between statistical accuracy and what I call policy relatability. And here I give a specific example in the case of unemployment rates that internationally we publish about the African continent which says unemployment is 6.3%. This is statistically accurate. But from a policy point of view, if you want to push that unemployment realistically in Africa is 6.3%, why then do you invest more in job creation? because we have 6.3% of unemployment rate with working poverty in excess of 30 percentage points 30% the highest across the regions. So the first thing we need to think about is aligning statistical accuracy with what I call the policy um relatability. The second reason why I say we need to think about an end-to-end mind mindset change is the conversation around beyond statistical accuracy and public trust. The national statistical systems, the regional statistical systems and the global statistical systems have focused on the use of statistics only for purposes of government. But this is around this is time around. this the time around that we need to go beyond statistical accuracy and begin to think about how to sustain public trust and this has become a necessity for different reasons. One the fact that the independence of national statistical systems is being compromised. Artificial intelligence is not making national statistical offices now the interface between the public and the statistics that they need. So when we talk about partnerships from the perspective of capacity building, we need to rethink how we handle for instance statistical literacy which in a number of countries in a number of regions it starts only at the tertiary level. So the public that you expect them to have trust in the data that you are putting out there are partially knowledge knowledgeable about it. So if we really want to build partnerships, if we want to have long-term investment from a capacity building point of view, then we need to begin to think about a holistic statistical literacy which would come in at different levels right from the private primary level or to the um statistical literacy among policy makers in terms of changing mindset. The third point that I want to highlight in terms of supporting um long-term investment in the area of partnerships is all this conversation of how does measurement comes in and our collaboration with with with um academia measurement would only come in when there's a clear sense of theory of change. So if the link between the theory of change and measurement is weakened then statistics that would have to guide policy choices, policy monitoring and policy evaluation would also be weakened. So we really need to go back to the drawing board to begin to think about what I call curing the non-statistical data gaps. And what I mean by the non-statistical data gaps is a lot of things are happening on the front of validation. A lot of things are happening on the front of aligning context with international comparability which goes beyond countries which goes beyond regions and requires a whole community of practice of statisticians to begin to think about how do we address the trade-off between context and international comparability. How do we address the trade-off between ensuring that leadership, governance, data stewardship are much more adhered to in line with the data that we are producing. And lastly, Yongi, the point I want to highlight is what I refer to as making statistics an accountability tool. and Vbecki, thank you for align averting our minds to the outcome document of the FFD4 which really begins to get us to think about data and statistics beyond monitoring and more importantly for informing policy choices. But the part that is missing and we need to hone in more on is the account accountability part and whether national statistical systems do really engage in what I call the statistical profiling of policy documents which really as I started with it is not part of of the fundamental principles of official statistics. But if we want data and statistics to touch on policy design, policy monitoring and policy evaluation, then statistical offices should begin to think about what I refer to as statistical profiling of policy documents which would make data and statistics an accountability tool. I end here. Thank you. Thank you so much and Sam and forgive this strong argument of how why we need to prioritize long-term investment particular on the change of mindset my for make the data is accurate directly response to the policies and to public trust and I would like to maybe invite Macdonald uh to also uh uh share your view on this. Thank you so much uh Yongi for the for the chance and uh uh good morning, good afternoon, good evening wherever you are uh uh here in Kenyat is uh getting to evening. uh now the uh mine is to really uh go for a situation where we are not uh uh talking of either this or that choice but uh I would like to look at the sustainable the long-term sustainability of of of the process that uh for us to have a sustainable impact that that we can deal with then there has to be some balance there has to be some element of sequenced investment in uh in both data and uh and partnership and so production of uh of statistics is a continuous uh thing. Now I look at it that statistics will get still very fast depending on the type of that you're talking about. You do it today and tomorrow it will be irrelevant and you require another set of data. And so that means that continuation of the data that we are collecting uh would be very critical. But as we do that, we also uh need to look at it that uh how do we then bring uh together the the partnership and especially with the with the academia uh so that uh we have uh on one side we have fresh data uh which is well disagregated and we also have the uh which would also meet the quality the standard that we require and we also uh ensure that it is linked to the policies that uh we are uh we are meant to come up with based on the data that we have. And uh then the the other point is u is about the so much uh data that is already in existence but not properly utilized not utilized in the sense that uh proper data mining is not done. This is something that with the collaboration through partnership with the with with with the universities would really help in ensuring that the significant data that is sitted and uh not well utilized and not well accessed can now be put into good use because then we'll now identify the high value use use cases and also improve on the dissemination tools uh like now use of you know through the the portals that we are having having using the the API so that we can read the data from from across uh checking on the element of visualization of the data that we have. Uh this will also help in building the the user capacity and also create uh feedback loops that can sharpen our future production priorities. And so uh I'm looking at it that it is something that we need to really continue uh using I mean producing and also uh engaging in our partnership that would be very helpful. And uh I I look at the case of Kenya where uh here at the Bureau of Statistics we have engaged a number of universities uh in in a in a in in a in a in a in a collaboration both public and and private universities so that uh we put our strengths together looking at uh what is it that we could do uh in terms of data production? What is it that you can what how would we be able to use it properly including the the data that is required by the students so that they can also do their research and uh it is proving to be very useful. In fact, even the element of benchmarking is also something that uh we appreciating with the students. You know, we have students who are coming to to visit us, get to to see what we are doing. There are those concepts that are not taught at the university. you'll be able to get them outside and this is where now we come in handy also to support the universities as the universities are also channeling out uh students with the knowledge that would be able to provide uh uh uh uh I mean we'll have that continuity of production of statistics so I look at it that uh the resources that we have limited as they are we are portion uh we are portion u part of it to to go for the core surveys, censuses and administrative data that we are collecting. While on the other side, we are also reinforcing the portion that would would help us in partnership platforms so that uh uh the technical working groups are able to work the data literacy programs are able to run and the uh the and the many aspects that are required of uh the data that we're producing to support SDGs uh would also be uh produced uh at the same time. And so it is a collabor collaborative effort that uh uh should be able to to help us run get fresh data uh utilize uh uh the the the existing data through data mining and also chart the way forward in terms of policy making uh and and and utilization of such kind of statistics. I would want to stop at that point. Thank you. Thank you so much McDonald's and also thank you for share your experience uh of the benefits of working with universities. Maybe let's move on to the second prompt. Also [clears throat] invite the participant also share your view uh through the the mentions and we come back to the Q&A during the Q&A session. And in the second prompt uh we ask one partnering uh with national statistics office uh universities should prioritize policy impact over academic uh publications. I would like to maybe invite Carlos uh to express your view on this. >> Okay. Thank you. Thank you very much for inviting me. I'm very happy to be here. Well, um well first at first this question the this question uh tries to to divide the the the research and the and the data production. I think that I I think that scientifically rigorous and relevant investigations are important for decision making in uh for decision making in the in in government. I think that uh academic academic research can't be divided by just uh knowledge uh creation. Uh it's it's more it's more a matter of of a partnership between government and and academia. Uh I think I would not frame it as a choice between policy impact and academic publications. I believe more that universities should prioritize generating high quality evidence that is both scient scientifically rigorous and relevant for public decision making. Um I also think that acade academics make the most important policy questions uh that perhaps governments and perhaps policy makings or perhaps uh statistical national statistical office have not the the the time or the or or or the the ability to do it. I I think I think that uh in order to achieve the sustainable development goals is not uh only a political commitment. I think it's also a academic commitment and the academia plays a critical role in developing rigorous methodologies to measure progress evaluate effectiveness of public policies and identify unintended consequences. And these questions, this main questions comes from academia because we have um perhaps we have more time to do it. [clears throat] I I I worked for 20 years in the government and I know that we don't have too much time to think deeper in some problems that and in the daily life we are running running solving things and we don't have the time to to to do that. Now as a full-time professor I I I I realize that we have more time to think on on on these main questions around around the policy making uh issues and and my my third opinion is and and I think the last one is I think that closer partnerships with national national offices can also stimulate innovation in theical operations. s in universities. Universities have have the opportunity to make good questions and good uh data sets. Data set that perhaps is are not relevant for national discussions. Data set that perhaps are not uh relevant for for for big randomized control trials or for bigger uh impact evaluation. But uh uh our our extent is not is not like this. Our extent is to solve some some specific aspects of of the of these policy implications and these policy questions. But I think I do think that we have the the ability to promote panel data following and national statistical offices uh they they have some restrictions to budget to make this uh this uh data this panel datas. Uh I think that we can both learn and we with partnerships we we we can uh have make this rise this to a better level of of of knowledge of transparency of methodology innovation of data data data access of uh I think it's a it's a winwin partnership in my last in my last job I was the secretary of the uh of the human development capability approach association and we found that in academia we have many many small data sets that are very relevant for our our our questions but are not that relevant for policy discussions and I think this is my participation on this topic and thank you very much. Yeah, thanks so much uh Carlos and based on your experience that you you can demonstrate the leverage universities for uh policy like uh making and also for innovations. Um Camilo, what do you think? Thank you very much um Yangi for for the question and perhaps before going into direct prompt and just following some the intervention by Phoebe for me is uh astonishing to keep having the debate of if we have good and bad data for me it's very important uh to share that we have high quality we have low quality you have consistent data we have ethical data we have accessible data transparent and relevant data but keep talking as of today of good and bad data. I think that doesn't convey the technical message of what producing data means. So I think that it's quite clear uh to go back to those principles that lead the statistical production function. Um now going into the prompt uh I think that one important things is to go back to the study that that we saw uh from SDGs today and it's seeing that um upon its findings it was astonishing that out of the 52 respondents only 11 of them had previously worked with an NSO. So and the main argument that they gave uh for the lack of cooperation was not uh because of technical reasons. it was not because they were not interested in the data but it was because there was uh institutional barriers. So of of course for example from the NSO perspective uh we want um the academia to focus on policy impact. We want to have a voice because we are a technical body and we want the academia to convey that policy urgency and policy relevance to the different stakeholders. But we also as national statistical offices we need to understand which are the metrics that are used by the academia to measure success and for them the measurement of success comes directly linked with academic publications. So going into this uh debate I think that I agree very much with what uh professor Samuel was saying and is that we need to rethink our partnership model is not just assume what we think that the other might want but truly come to an agreement and what better place for the UN to play than reaching those agreements and really brokering those partnerships working with the UN country teams to find those incentives to provide all that framework. So for us um a key example for this uh comes with the work on on the UN with the UN data that we did two years ago in which we in which we identified a common problem from the city from the a national perspective and we involve the academia not only saying do this but let's build this problem together and in that way we aim to uh achieve sustainable solutions. So now coming back to to to just the the ending of this I think that the main uh idea of this intervention it's really focusing of of data being the basis of partnership brokering that we have and really going back into what Bbeca showed I think it's important that we focus on how expensive high quality data is we have around of 2,34 indicators of the Each indicators can cost up to $120,000 >> so how do we invest in this data, how do we manage to work together in order to have this high quality data that enables decision making and also sustainable partnerships. So yeah, back to you. >> Yeah, thanks so much Camila for for um sharing ourselves. Maybe I'll invite Macdonald. >> Thank you again. uh uh the actually the the the most powerful partnership uh uh would be to to have the uh the the the delivery that uh would uh meet uh the purposes of the uh of uh what is happening at the universities and the NSOS in terms of policym uh you know traditionally university career would depend on uh uh the funding uh I mean the the their progression uh would depend on uh how well they have uh peer they have peer reviewed their their publications at the citation that is also impacting on the level funding and ranking. uh but from the side of the NSO is about uh uh the need to apply the uh the the analysis that would fill the the data gaps that uh that are in existence and also help in validating the citizen generating generated data uh and of course then it supports the uh SDG reporting uh which of course uh as well as has been mentioned before there's element of the voluntary and national national reporting which we are part of and uh uh if you are just to go on one-sided then uh you'll be able to lose something that is very important in terms of uh uh evidence-based planning and if you are just to look at the side of the academics the the incentives that is just on that side might misalign with the national priorities and timeliness if not well controlled so I'm looking at a situation where uh we are able to ensure that uh uh the the the priorities are running together I mean running the same same direction. So when you when you if you can co-design projects with explicit uh dual deliverables that is policy briefs uh or contributions of the national SG reporting alongside the peer-reviewed papers uh that will help and this joint we can also have the the joint this is supervision on topics that will be aligned to the official statistic priority. I remember at one time we were struggling with my supervisor uh that I need to bring a a real case situation so that we can now tackle it in in a project which then at the end of it becomes very beneficial. You'll get your your papers, you get the citation but you also solve a real problem. So the uh in terms of recognition recognition mechanism any source can issue impact letters or co-author official publications while universities can also can can can can value uh [clears throat] that is the engagement of the of the engage scholarships so that we can now have SGS that are having impact metrics in promotion criteria. So in that case if if you have a dual way of approaching this it becomes easier rather than just saying that let us the universities prioritize more on the uh policy uh policy impact over the academic publication they would need to really run together. uh I'm looking at it that uh uh some of the some of the research funders should also uh require to incentivize NSO's collaboration in in terms of grant so that those joint uh curriculum development and blending that with academic rigor uh with official statistics will help in ensuring that quality frameworks of statistics are are applied based on uh the international standard that we keep on getting from time to time. So let us be I think I look at it that we can create platforms for uh uh this uh kind of dialogue to ensure that uh we remain relevant in terms of policy formulation uh citation I mean academic publication so that at the end of it all we are able to meet the standards that we require on the side of the the universities and on the side of the NSOS to meet uh all our requirement through a joint effort where we we can collaborate and work together as a team uh and that will be very beneficial. Thank you. >> Thank you McDonald. I think it's your point on the co-design and the co- benefits uh is very important. U let me move on to the last prompt um because we're running out of time but we'll maybe invite uh three uh panel member to express their view on this one u maybe for two minutes each. The third prompt is building stronger data ecosystems through collaboration among governments, academia and other stakeholder is more critical for SDG progress than investing solo in expanding data production. I would like to invite Sam first. >> Yangi, thank you very much. Let me try and put a context to the question in terms of expanding data production in the context of the SDGs. It's important we keep in mind that there are three dimensions of data gaps that we have with the SDGs. One has to do with the frequency of the data. So there are instances where countries reported on let's say um poverty since 2017 and since then they've never reported on that. And there's the issue of some SDGs that we are not reporting on on at all and also some SDGs that will require that we integrate different data sources. So really if you want to expand data production we need to put it in the context and ask ourselves how best can we expand um data production and from my point of view irrespective of the three sources that I've talked about the collaboration among government institutions with academia really is a prerequisite in addressing any of these and I can give you and I would give you very specific quick examples of that in the case of frequency of data the academic community really is helping the statistical practice to think through how we can for instance use synthetic data, use modeling, use estimates to fill some data gaps and this is a partnership that would help improve on the frequency of data once we have the base data available on the front of integration. I have always argued that an inflative data source and I think Vbecki or one of the earlier speakers did talk about it and as we see in the Scandinavian countries once you get your national ID system right and you see it as a statistical product once you get your civil registration and vital statistics data right once you get your health management information system right your education management information system right and your address geospatial systems right then that would help you get all the data that you you need to have within a country from an an integrated point of view. So if you really want to expand and you know the value of data integration from a data sharing point of view as I've explained then certainly this is something that you can you can you can easily do. So I really argue that we need to think about those collaborations those partnerships as a real prerequisite for data um expansion. In the interest of time let me pause here for others to intervene. Thank you. Thank you so much and uh uh I think this is uh really important and emphasize the this is very important for through the collaboration to address uh the different dimensions of data gaps. U maybe I would like to invite Carlos to express your wish. >> Yes. Yes. Well, I at the beginning I thought that the question was different but if the question is how to expand those those statistics uh uh so some ideas comes to my mind the first is um uh I think that a key partner in the ecosystem is the is the group of institutions that produces that produce administrative data particularly governmental ministries and agencies that manage uh uh very granular information. Um I I don't see clearly how to expand uh many many many statistics in in in uh from the statistical offices. Uh because I I have two ideas in my mind. The first one is the national statistical uh uh report system. In Colombia, we have a national statistical system where it where we collect all the statistical operations, administrative uh service, sensus, everything is listed in this in this document uh from the from the uh social issues, from the environmental issues, from uh every every different topic in in in the country is in this in this system. and and and I think the first step to expand all those uh data is is to know how much data we have in the country, how much data we have in the country from the statistical office, from the ministries, from their administrative registries, from the universities and collect them in a national statistical system. This is one of the things that comes to my mind. The second one is that in Colombia we have another another uh tool which is the poverty table. The poverty table is a very a very good mixture. It's a very good partnership between academia, government and and and and private sector. We have in this uh poverty table the the experts in poverty that help us to develop better questions, better surveys, better data uh in order to keep keep reading and keep measuring poverty uh by income uh by multi-dimensional and and and the idea of this this table is this year uh is like I I think this table is like 10 years So this I think or more perhaps that this this table is the main is the main uh institution to to guarantee the quality of the data innovation quality and all the things that I said before. Um I think that this is the process to guarantee a very good expansion of the data having good quality and and innovative data. >> Thank you so much. uh uh Carlos and and then then lastly I would like to invite Camilo uh to express your thought. >> Thank you very much Yangi. So on the prompt from uh my perspective as an NSO um I think that here the objective is not to produce more data for its own sake but to produce trusted data that adhere to this fundamental principles of social statistics. Um, and it's also important to acknowledge that today's data landscape is fundamentally different from what we had a decade ago. And national statistical offices do not hold neither the monop the the monopoly nor work in an environment in which they can afford to u consider themselves the sole data producer. We have to engage further work with the academia, civil society and the private sector as Carlo was saying. And for this um we as as Danny had advanced towards that direction. Uh we recently launched for example the citizen data framework allowing to further collaboration with the csos but also are strengthening our uh institutions. Sclo mentioned one which is the expert committee round table in which we ask uh experts to to say how better to measure multi-dimensional poverty and um extreme poverty. But we also have within our national certific um advisory council um and we need to strengthen the collaboration with academia uh posing better questions and also seeing ways in which we can better collaborate with them. Um through these experiences we we demonstrate that collaboration is not an end itself but a mechanism through which official statistics can generate uh public value and this is the vision that underpins the initiatives of the FFD4 and the future of data initiatives in which invest in which we seek to position that investing in data should not only be limited to financing statistical production but should strengthen the institutions and the partnerships as well as the governance arrangements that enable countries to build resilient national data ecosystems. Um so in here u from what we see the the future of Minnesota is not defined by producing more data but leading to trusted data ecosystems that can transform these high quality statistics into better policies and better decisions for uh policy makers and decision makers. Back to you Yi. Yeah, thanks so much Camilo to emphasize the importance of building this uh uh new like data system to incorporate more partnership but produce trusted data for policym. Uh I would like to hand over uh to Mariam and to to for the Q&A session. Over to you Mariam. >> Thank you so much Yungi for moderating such an insightful discussion. It was very uh exciting to follow um uh everyone's input and thank you to all the panelists uh for their contributions. Uh we are at time but I quickly just wanted to share my screen and share um some of the input that our participants have shared uh about the prompts that we just heard our panelists speak about. So just very quickly um showing everyone uh that the majority that did participate um in uh in the menty meter uh survey um agreed with the first pro prompt on limiting funding should prioritize partnerships and capacity uh over producing new data. Um and then moving on to the next prompt. Again, the majority agreed um that partnerships with NSOs uh um universities should prioritize policy impact over publications. I think there's uh a lot more we wish we could have heard from the participants on their thoughts uh regarding these prompts. And then again uh the majority um agreed um with the prompt that collaboration across governments, academia and others is more important than expanding uh data production. Again, I know there's a lot more to unpack and it's not a simple um kind of for against discussion regarding some of these problems, but I thought it would be interesting to show the participants uh input uh on uh some of these um topics. Um, we do have some great questions uh in the Q&A. My colleague Vbecki has been trying to go through and respond to some of them. We might not have time to um uh go through uh a live Q&A uh session today, but uh there will be um another event on July 28th um that will be tackling um uh some of these topics and that could be an opportunity for us to maybe uh take some of the questions from the Q&A box and address them uh in this upcoming event. If you're interested in participating and following along, you can find the registration link on the World Data Forum website uh and join us uh for that event. And hopefully we can um unpack and address some of the great points that you have been making in the Q&A um uh box um uh over the next couple of weeks. So um I again want to thank uh all of our participants from joining us from all around the world and taking the time to be part of this uh discussion. Uh thank you to uh Yungi and Fabbeck uh for their contributions and thanks to all of the panelists for the very interesting and insightful contributions that they have made to today's uh discussion. Um, I'm uh I hope you all have um a very productive rest of your HLPF. I know it's a busy time for everyone and I look forward to connecting with everyone in future discussion and and events. Thank you all and have a good day.