 Rhaid, we've got about 25 minutes to do some group conversation, experience sharing. We did have an exercise that we were going to go through but I don't think we've got quite enough time to do that. So what I suggest is perhaps as groups in the tables, you throw out some ideas in terms of what are the key areas that you think are important in pushing forward in building data literacy or increasing the usage of data. Between us we've identified some key areas there in terms of developing partnerships, working on how do you change culture and behaviour in terms of usage of data in advocacy, looking at capacities, the issue of peer review and quality assurance. How do you ensure that the quality of analysis of data is such that you can actually make claims of accountability? Perceive value of different data. I think we've seen in some of the published what you pay work that there were assumptions about the perceived value of mandatory disclosure data by certain coalitions. What other issues are there in relation to that? And also learning how do you support people to learn to use data? How do you support people to learn to use data effectively and kind of push this on? So in groups what I'd like to do is have a leader on the table who will facilitate the table. Yes? Self-organising? So as tables if you could select a key area you think is important, have a conversation about it. If we do that for about 20 minutes we can then come back to plenary and share our three key takeaways from our group discussion. Yeah? Brilliant. And we'll kind of circulate around. Okay, we'll circulate around. Exactly. I missed the first part. I got a lot of thoughts on this. Exactly. I run something called Spice of Data. I run everything. Nothing that's good. Writing. I don't know, I started typing when I was like six so I never had the right problems. I'm writing a field. I used to come back from my day. I always thought that in other places I can say that using data information is some nice tool to product something that's not human. I've never actually thought about it. I'm not really scared. I've gone too far to talk about tech and being. Don't use the D word. You've never used data science and what you mean is the cell? Yeah. So I was thinking that scaring of cells in it and making people don't feel they can even... I think it's two basic questions. How do you make bridging back that to bars? You've seen very good world techie. People just have a basic question about something they want to be honest with. So it's going to be right now to come forward to solve the data problem. I'm going to mystify what it is to do data work. I think we all point to that. I'm going to start next class on the way to the trauma class. It's going to be cool to hear that today. I say that most of my job is talking about legacy. They convince themselves because they've gone through some things and they think they need to work hard. Cell is getting this. Yeah, exactly. I'm going to read a blog about cell. Why you should stop talking to me about your data problem. Because we have thousands of roles that's good at work. It's fine. And meeting people where they are. Not trying to be like, oh, you're going to find this new tool. If you do your school of data sessions and you've got great training on the day and they leave the room all enthused on that day, where do they go? Two weeks later they finally get around to doing that one thing on a Friday afternoon. Because they've gone back to their day job. How do you keep that fresh and excited? You can meet little tools like video clips. Do you remember what a me look up was? Do you remember what we do? You talked about this different format. It's just something about friendly, really usable toolkits. And then people they can speak to, humans they can speak to. So I think data analysis is something important. The way we are starting to structure it and we've been thinking about this a lot is we get professional society all the time. Can you teach us to be data scientists? I have three days. I'm like, great. Six months, nine months. So a thing that we recently did is to transparency international. The secretary had had some money to train their chapters. Whatever they called them. Chapters. And this one was specifically their European chapters. So I think eight of their chapters signed up and we said you have to send two people. Because people move on. It's really to get them to think about teams. Data teams as opposed to individuals within an organization that do the data thing. So they had to send two people. We did a survey beforehand. The whole plan was to do really basic training but the people that the European chapters were sending were doing some decently advanced data work within their individual chapters. So we did do some R and stuff like that. We did a one week very intensive training where the idea was to push them out like their company stuff a week. So it was five days. It took me nine months to negotiate this. It was a tiny, tiny contract. And then we added to it. By the end, they had to, with their chapter, come up with a project that they were going to be working on. And then following that, we built in a mentorship thing. So the two trainers that we had were available four hours a week for the different chapters to help them work through. So they go back to their office and they're actually working on a data project. It's a time and availability and follow-up. I'm also from school of data, but the German chapter. And what we do, we actually take quite more time so we don't do three-day training or stuff like that because you can do it but it does not really have any impact on the long run. So we also collaborate with the civil society organisation over three or four months and then we try to get them really engaged in their topics so we try to identify what kind of data they have and then they get really excited about working actually on the topics and on the data they already collected. And they try to better understand them. And then we kind of try to give them small tasks like cleaning data and so on. So we try to identify also very specific topics where they want to improve certain skills and then they contribute to an overall project. So at the end they kind of own the project and they can build up on top of that and also do advocacy work within governance and so on. So this is how we kind of approach that. And do you promote them? When you've done that training about hard work and all the new stuff, do you promote them having a dedicated contact with the organisation to take that work? Yeah, definitely. We work with the entire team. So we try to get the management on board, the CEOs and the team that is actually doing their everyday work. And we found out that this is like the best mixture because you have the support from the top and you have the enthusiasm for the bottom. That's what I desire from that meeting in the middle, doesn't it? If you don't have that, for me, a kind of key question is do you have a digital strategy as far as I do the answer is not? You know you don't really have that senior understanding to buy in because otherwise it's quite bottom up. It's not like a very keen person who tries to push this stuff through and I think if you don't have a CEO or a senior manager you understand how to fix it. It depends on who is saying the answer. I don't know. Before you start the work. Yeah, definitely. Because I think it's sometimes, I mean from the experience that we had in Germany, was that many NGOs they just really don't know where to start. They don't know, they should not proceed. They don't, you know, have to kind of open the door. So that's not very much a basic map. The thing about, like you were saying, the burden of being on community to be in the future is in a way, sure, we have to understand what you're giving us. Also can you just write it in a way that it's clear? Yeah. The whole point is that anybody should be able to tap into it and be like, I know exactly what this means. But, you know, if they give you the data, and they're like, yeah, the response to it every time is that it's going to be actually because they're a bit of a provider, because if it's kind of what it was before, for example, they have a data standard and they can be like, this is what we expect, university, but we ask them to open the data like this exactly what it is. And if they don't meet that standard, it's not our fault that we can't read it but I think within, again, once we have the information, it's that translation to make it useful so that so much so much to do. And then, yeah, the end of it, you're like, maybe that'll give it a closer like that is for our cases. We're like at the very end of the whole. Yeah, it's part of it, but you, your partner, don't know if it makes a difference. You know, we can spend so much time that we've met a couple of cases that demand data, demand information and God, I'm thinking like, oh, that wasn't actually my fault. I think it's not the issue. I do think that that's where that's where they're coming into this whole effort on but I do think it's more of a tinnitus like this where a range of people coming at some different perspectives is really helpful to support from where I come at it a lot and usually data isn't, and the data is necessary but it's way insufficient and it's not actually the hardest thing to get people to do it together and it's people are not dark so just don't do that every day it's part of their normal life and I think it's, you know, if what you really want to make happen is engagement by people who've got some kind of legitimate right coming as theirs and you've got to treat them as knowledgeable subjects once you both best understand how the governance process works in their area of origin so all of those things need to be kind of valued in the process and their understanding of, for example, the message that they might be able to do is that sort of thing if they start to make a complaint on the message of the day to help out. I think we're going to take it off to at least see where the data's really vital in what's in part of it. In the UK, where we haven't ever been able to find a data like we've never been able to say as a user I need this because there's nothing so generalisable as that and people have to see it as a sort of we just need the data there and we'll work out you know, it hasn't it hasn't rolled in the other another user journey later on What does DataGov do to try to pull that off? They've been doing workshops for like the last three years and they keep trying to talk to people about and why do you need the data I just might or I'm satisfied whether they haven't been able to find somebody outside Yeah, the phrasing of that as well Do you want data on who supplies the electricity to the street number in your street? But there's a different way of framing that about people's real life I don't know how it's really They have people who are much more tactful Yeah I think in Korea many public publishers didn't know about why it's not data why we should why should we open up our data to our private sector so I think the people and governments didn't know about important open data so did you have an experience of covering how it was a lot of information later which people are not really using to get through advocacy and transparency so you realised we have the local budget we have the national budget we also have the financial statements we also have the budget of financial statements so those are some of the difference you share if you can hear me clap 2 times if you can hear me clap 3 times very much welcome back so we're going to have everyone report out we're going to go one group by one group you have one minute but make the question please sure thank you Joyce so Joyce could you tell us about your key questions that you have thank you Joyce so Joyce could you tell us about your key takeaways your group talked about for data literacy and data use ok so one thing that we were mainly discussing and also that came out was the issue to do with you know when Duncan presented that at times government is not really open or maybe responsive with regards to whatever the citizens, communities would have asked assessed or analyzed so it also came out that you know when also we are doing such kind of work it's not only that we need to deal with this demand side like building the capacity of the communities or civil society to demand whatever change they want to see but it is also important that we also work with the supply side like building the capacity or training also government departments it might also be lease letters like the members of the parliament the various government departments have just been giving examples of the kind of stakeholders we work with in Zimbabwe like we have the environmental management agency we have the parliamentarians so it takes both sides for them also to understand like the raw district councils they know they are supposed to do consultations and budgeting but at times they don't but also just to try and make them understand the importance of such kind of processes so we both deal with the demand and the supply side that would also make impact so that also we don't get the communities frustrated with their asking for information or demanding for transparency and asking them to be accountable for various processes so that it's also a true process of some of the things we've been discussing thank you so much so we didn't have an organized discussion but I think one thing that really struck us from the presentations and that goes a little bit beyond the question you asked was the very strong and very encouraging developments around Mell and monitoring and learning indicators for open contracting so particular these things around like both savings perceptions of trust like there's really now a strong set of indicators that I think other sectors could really benefit from to discuss that a bit it's also striking I think from across the presentations how much we keep coming back to understanding the contextuality of data needs and data demands we can get national or global disclosures but very often there's a community or even a local context that means that we need additional data and I think building that into programs I think that's another another strike I think anyone wants to add one quick comment so I just want to point out that in new partnerships we should take where we recognize showing the importance of having viewed in the capacity of the media as well because we view these great tools we do the advocacy but they can tell the stories in the way that we can and they can also reach a wider audience so the role of the media is very important great thank you thank you very much so I should leave now from the ten events to the global change surrounding my people who are much more dedicated and literate than I am for years marking the presentation so I think we don't have a massively structured discussion but some key things that came out were around the user experience so when agencies are trying to better understand what their different user groups are and therefore understand what different presentations of data are actually useful to those user groups and actually it sounds like we can get in a twist about those types of things so we had groups who are trying to present for media outlets and journalists versus groups who are trying to present for politicians versus donors and each group has a different need and then on top of that understanding where the technology is going and so making sure that all that data is machine readable can paint quite a complex picture about where this is going I also think we had a discussion around cultural shifts so particularly when working with governments we seem to be in a world where things are much more I think easier when you're in your institutional silos and so we talked about great case studies and various institutions of data being a driver for government decision making or massive changes of massive savings in government but that those case studies aren't necessarily then shared across different ministries and the lessons aren't necessarily applied the data education isn't then implemented afterwards so you might have different groups in government who are really good publishing data they're also asking for so what and dealing with that question but not necessarily across whole governments and then from my own experience around data literacy I think a lot of our work takes place in sub-Saharan Africa trying to explain how data is really going to be useful to people who work in different government departments and what it's necessarily going to unveil is a really difficult process particularly when you don't have those case studies to hand and you can't feel the reality within your individual work stream so doing more of the sharing and learning is a really big focus for us but the first point of call will be the data literacy we had a really good conversation here about data use and data usage and approaches to it and how good it is to see the strong focus on data use built into all of the examples that we talked about and we had three points that we wanted to share the first one was the sector in a way that part of the transparency and accountability and openness field that's focused on natural resources and budget issues and issues to do with public revenue has been until now the recipe recently both supply driven very supply side focused and also very data centric and we were discussing how it's contained a production that citizens want to engage and all they lack is the data and how that's a big problem when you start to see through the years of practice and failure that citizens are not engaging you have to start asking big questions about the assumption a second point that we talked about related was that while data is clearly necessary it's not at all sufficient and we talked about what I'm interested to hear in the presentations about how the approach was very much about understanding where people are at why people are the key actors in making use of the data because they're the ones who've got the legitimate claim in principle anyway, the legitimate claim on their government to get their government to use the resource revenue for what they're supposed to spreading public funds and what they're meant to or for sharing information about budget allocation execution so there the data might be actually a relatively small part of the picture and what's the bigger part of the picture is the community mobilisation the organising the sort of the same power to stick at it over years among people who are probably by definition time poor because they're probably better simply for marginalising in their own society because they're needing to do this because people do have certain limitations like that and then we've talked about data literacy and recognised that the concept of data literacy is tending to put the onus on the citizen, the expected user or hopeful user of the data that's opened up not on the provider and just thinking that actually so there has been lots of discussion about accessible data in the open data field but the definition of accessibility tends to be around open data standards rather than around the kind of cultural and socioeconomic conditions which actually define what makes data useful for people in rural Zimbabwe or for people in poor neighborhoods where we're in Ukraine so a big need to kind of shift the perspective on data literacy and think less about open data standards and more about the kind of technological look at the realities of the people who would have to use this data if combined and followed by other strategies to mobilising, galvanising organising, advocating, arguing doing public interest litigation or whatever it might be a tough, it's a good effect to anybody on time Last group. We primarily focused on improving data literacy and talked a lot about improving data literacy in civil society organisations so one of the first things that we talked about and I think that this community is really guilty of is sometimes when we talk about tech and data we use overly complex terms so I talked to a lot of people who talk about data science and what they mean is making a pivot table in Excel and that's great when you're trying to upsell the sexiness of your work it's a problem that excludes a lot of people it makes this seem more complex than it actually is not that pivot table is the best term but these things can be simpler and we can use simpler language and kind of meet people where they are and the language that they already understand so that's kind of one of the things we talked about we talked about demystifying a little bit what it is to work with data that again it doesn't have to be this overly complex you need to be a computer programmer and these kind of things that can be really simple things really simple tools that everyone already knows how to do with kind of like basic knowledge of math so again it's kind of about meeting people where they are we also talked about the time and availability and the resources when we're working with civil society organizations they have many many priorities I get asked a lot to train people to become data scientists in a weekend and I cannot do that so making sure that people understand what can be done in different timeframes and where we can get and really be intentional in how we use people's time and the skills that we are actually trying to build one of the things that we talked about a little bit was thinking about data civil society is needing to think about data as a strategic function and not an operational function again when I work with a lot of organizations the data and tech people who know about technology sit in the operations team and so it's kind of separated from the people who are doing work on the ground and how do you build those skills across the organization so it's not just seen as a monitoring and evaluation or an operations function we talked about the need to do needs assessments when we're working with organizations understanding what they need and then building data skills from there so it's not a one size fits all approach to every organization it's thinking about okay the thing that you are most interested in doing is telling a story so we need to be able to get you to use data that might exist or to collect your own data but the thing we need to focus on is how do you present that data to the audience that you want to reach and again that doesn't mean making a data visualization that could mean telling a story using that data over the radio working with people on how to present the data to the audience that you want to reach so that goes back to problem definition like a lot of people are like oh we want to use data to do this and it's like well data's not going to solve that problem so really getting them to hone in on what is the actual problem you're trying to solve and you might not need tech skills you need to just go out and talk to people and we can come later when you get the data that you're talking about and then again we talked a lot about the buddy system of like it not just being this one tech person lone tech person within the organization I think that's been the failure of when we tried to embed people and be like oh we've done this with government we've done this a lot we embed a new person into an organization and they're supposed to transform this culture and that's obviously magical thinking but it can also be very alienating because people don't know how to interact with that tech person so how do you kind of build broad based data skills across the organization basic data skills and then if there is a need for someone to be more