 What do you think the answer is? How many? Six? Six hundred? Ten million? How many thousand? One thousand? Five thousand? It's funny how we don't know the answer to stuff like this. Forty one thousand eight hundred and thirty five and an ash bit and a loan there are six hundred. Which region has the highest rate of twenty to twenty four year olds? So that group of people that you really hope are starting to get on with their lives and making decisions about what they want to do that aren't doing anything in education, employment or training. What region has the highest rate do you think that is? East Coast? Northland? Auckland? And what percentage do you think it would be for the region with the highest rate? Like we're talking ten percent, fifty percent? Thirty percent? Sixty percent? So twenty eight point three, so almost not quite a third of those in their age group in that region aren't really doing anything and in Canterbury the answer is about ten point three percent. I was going to say this is the last one but I'll probably ask you more just because I like it. So this is my favourite data point and if you follow me on Twitter don't yell out the answer because it's really fascinating what some people first guess at. So there's currently about four point six million New Zealanders and of course there are caveats with data collection and definition here but bear with me the magnitude is about right. How many do you think there have ever been? So the data set goes back to about 1855 if you were to add up all of the people that have ever kind of felt like they are New Zealanders. How many do you think there have ever been? Twenty five million? Eight? Seven? Ten. Ten? So only when you ask teenagers this question they say things like ah probably like three hundred billion and it's really really fascinating actually and even some adults have said things like two hundred million and just it's so fascinating that we just have no idea. For someone got it right about seven million and I personally guessed between kind of twenty and thirty million so this is a surprise to me and then as soon as you think about it it's like well of course. Because we only got to a million what like 1940s, 1950s or something and most people were born since then are still alive. But the most extraordinary thing for this from my perspective is it means that two thirds of New Zealanders are alive today. And like that seems really important to remember and to reflect on in terms of when we think about how responsible we are for kind of shaping our future and the values of our country it's still in our hands. So Kia ora koutou, kula li and greys, toku ingwa. I run figure NZ and we are working to create a data democracy and to get everyone able to use numbers in their thinking. So I'm going to give you a bit of a background about like the why and the bigger kind of co-pup around why we exist and before getting into what we're doing and then where I see it going and how I think it can be useful. So I've been around the tech sector probably for I don't know maybe only ten years or so. I actually started my career as a high school phys ed teacher and but since then I've been doing a bunch of things and I used to work at a company called massive software artificial intelligence kind of stuff for visual effects and movies. And in the last 10 years like it's been really interesting to see how much is changing in terms of technology and also how we're usually only looking at the thing that's right in front of our faces. So if it's new devices or new software and new apps and things, most of the population just are seeing what's kind of looming at us in the immediate future. But when you kind of pop your head up on the time horizon and think what is actually changing, like what's actually changing in terms of what's what are the real shifts in the way that society can run that technology is leading to. And I think there's two or there's two big things that surface for me. So until about 10 years ago until the use of the internet was was more widespread for all of time. It's been hard to share information widely and it's been hard to communicate to lots of people in multiple directions. And for the first time ever, those two things are now easy. And when you think of most of the apps that we have, most of the devices and everything, that's basically what they're enabling. They're enabling us to share information widely and enabling us to communicate to lots of people in multiple directions. And so when you think about what the world was like before this was possible, so in all of our living memory, when only a few people, when it only made sense for a few people to get information, the best future was created by having a few great leaders. And that makes sense, right? Like it's really hard to share all the information. Then you kind of need to have a place to send it to and smart people to digest it and to think about what should be done and then kind of mobilise with actions and policies and whatnot. So whether that's about the way that companies were run or the way that countries were run, that's the model that we've been using for a long time, not in all not in all cultures, but in ours for a while. And now it can be done differently. So for the first time, we can look at how we can make decisions in different ways, how we can restructure, how we govern ourselves, who we listen to, who is responsible for sharing information, who has the rights to access information. And it kind of just changes everything about what we can do as a society. But when you really think about it, we are only just at the very, very start of this. It's only been kind of 10 years that this has even been a possibility. And so much of the systems and things that we are still working with were created before this time was even dreamed of. And the reason I think it's important to remember that we're at the start of it is because so many people that have their hands on levers to change things are scared to change because it means admitting that they've been doing something wrong before. Whereas I consider it like we've just never been able to do this stuff before. We've never been able to share before. We've never been able to look at making decisions in a more kind of community basis in this kind of way. And so it's just exciting. But we need to think a big part of what we do with Figure NZ is try and create spaces where people actually feel safe through this transition, rather than like they're going to be found out for having done it wrong until now. And so when I talk about data and there's lots of different definitions of data in ways to think about it. So I consider what we think about kind of big data is what is enabling kind of automated decision making and telling you what bus to get on, etc. When it comes to Figure NZ when I'm talking about data, I'm talking about the kind of numbers like in the earlier quiz questions, the numbers that help humans make decisions or help humans see the world around them. And often I use the word numbers or figures because if you go around the country and you start talking about data to people, most people say, ah, isn't that what you buy to top up your phone with? And it literally happened again last week. I was in the car with a friend and she had her three kids in the back between kind of nine and 14 and she was trying to describe what I did. And when she said I'd like have a distance stuff with data and they're like, oh cool, is that like the Wi-Fi? And you're like, yeah, so interesting how the language we use immediately cuts out people from understanding what we're talking about even though they're very capable of understanding the concepts themselves. If you imagine what society was like when not everyone could read. So people like society used to believe that not everyone was capable of reading, that there was just a small portion of the population that was involved in that knowledge sharing of information and it was quite contained and not everyone was able to participate. And we don't accept that model now, right? We say absolutely everyone can learn to read and we completely achieve that standard. Everyone learns to read in our society and if someone says, ah, I'm not very good at reading, you say you can learn. You don't say, ah, that's not for you. And we do that by, you set the standard and say everyone can learn to read and then you build the way that you teach it and the way that you serve the information up and the way that you take people on the process of reading until everyone can do it. But we haven't made that transition with the use of numbers in our thinking yet. We still say it's for experts and analysts and intermediaries to give us all the insights from numbers and something like this kind of situation where there's an analyst up the front telling a group of people what the insights are from data is something that is quite typical in terms of what we get used to seeing. And if someone says, ah, I'm not very good at numbers, we say, ah, that's all right, it's just for the geeks in the corner. And we don't have yet that state where we believe everyone is capable of using numbers in their thinking. So figure NZ, we're the first organisation globally to actually to assert everyone is capable of using numbers in their thinking and to try and achieve that standard. And it's really important because it doesn't mean that everyone is going to be able to be an analyst or to do complex mathematics but everyone should be able to participate. So the numbers, you know, like of how many, how many Labradors there are, people, everyone is capable of understanding what that means. If I say to you how many mushroom farms are in New Zealand, how many do you think there are? Not enough. Any idea of the number? 20, pretty close, 15. So everyone is capable of understanding what that means. And so there's two things with that. When we learn to read and write, we don't expect that everyone's going to be able to read and write a PhD but everyone being able to participate makes the whole kind of system work better together. It almost makes those that are reading and writing PhDs more valuable because they have a place within the whole of society that everyone can kind of understand a little bit more. And in the same way when it comes to using numbers and data, if everyone is capable of using numbers, it doesn't suddenly mean that analysts, et cetera, aren't valuable. It almost makes them more valuable because it means that people then understand the value of the space that they're operating in. And it was interesting, I was talking to a primary teacher the other day and she said that young children actually find it easier to learn, to think using numbers than to learn the English language when they're first starting out. You know, you think of counting oranges and stuff. But it's just that really quickly we tie the use of numbers into mathematics and it suddenly becomes too hard for some people because it's a different way of thinking. But so we kind of talk about numbers as a language that's holding a lot of our stories and it's a language that very few people are literate in. But it holds stories about our people and our environment and our economy and our businesses and yet very few people are actually able to get their hands on it and understand it. And so when you think about, so why don't we use numbers in our thinking? Why is it something that very few are actually using today in terms of understanding the world around us? And so when I started Figure NZ, and I'll talk more about what we actually do and what it is in a moment, but I had quite a weird kind of curly background and I didn't know anything about even what data was or what open data was, etc. And I started working at the New Zealand Institute, which is a think tank and we were looking at social economic and environmental research about what can really shift the dial for New Zealand. And when I started there, I had no idea about how to be a researcher and analyst and I had never used data in my thinking at all. And I remember opening up the stats NZ database for the first time and the OECD database and I was like, what is this? There are just literally thousands of stories sitting in there that when you surface them, especially into like a simple graph or something, you suddenly understand, you suddenly can see something. And I was kind of gobsmacked about how much was just sitting there, just like incredible resources that I had actually really never heard of or never used. And interestingly, because of coming into that space with no experience or background, that's what almost made me realise how rich it was and how not well used it is, compared to if I'd, like I talked to others who were friends about it and they said, oh, but I can always access data that I want because they grew up, they just thought it was normal that people knew that those things existed. So none of my family probably still know that places like the OECD database or statistics New Zealand, et cetera, exist. And so if you don't know it, you can't find it. And so it was just gobsmacking to me to realise how much is there and has been collected for decades. And so when we were at, when I was at the New Zealand Institute, we would always start by looking at a topic and understanding the metrics around it. So say we were looking at internationalising tech companies or youth disadvantage. We would always say, what are the numbers? What are the metrics? How do we get our first kind of series of questions? And we would look at, so what should the metrics be that we look at? How is New Zealand currently performing? How has that changed over time? How does that compare to other countries? And then we'd say, what countries used to perform poorly that now perform well? And then we would go and look at the literature and or just the activity that had been occurring and say, is that something that's happening in that country that could be relevant to New Zealand? Is that some kind of stuff that could help us move where we are? And so we'd always start framing up using numbers, which was really new to me. And then we would go out when we had some kind of ideas. We would go out and share a lot of these really simple graphs, simple like line charts, column charts, things that didn't move. And we'd go to lots of different communities and take them through our thinking. And it was amazing seeing the reactions that people had to seeing some of these numbers for the first time. And time and time again, it was just surprise, was the first reaction. People had just literally never seen some of these stories. And I still remember there as I was up in Whangarei and I was talking to a group of people who worked directly with youth. And I put up a chart that shows that New Zealand has the highest rate of reported youth suicide out of all advanced economies. And they were just flawed. And one of the women there said, that graph is literally going to make me work harder at my job because I had no idea that was the situation that we were dealing with. And having lots of those kind of experiences, I was like, this is so interesting because it's not like I used to think people were complacent about getting to know Aotearoa and caring about its future and wanting to be involved. And I've been 100% proven wrong in the last six or so years. Haven't met any person that doesn't care about our future and that doesn't wish that they could be a bit more effective in terms of helping it. But hardly anyone has any idea about how to be effective and where to be effective. And so this made me to thinking, so if there's lots of data there, that's really great resources that people have been spending a lot of time and effort on for decades. And there's people that actually want to use numbers in their thinking. Where's the gap? Like what's going on? And just saw that there's such a difference between when something is technically available and when something is actually truly usable from a real user-centred kind of approach. And so I started kind of like backing in to understand a bit more about the data space because I started knowing literally nothing at all. And so I kind of got to know a few government agencies and went and sat next to people and that were, you know, someone that owns like the cancer data set in New Zealand, et cetera. And I was like, why is everything so fragmented? Because we have literally tens of thousands of data sets spread over literally hundreds of websites and all of them are in different formats and adhere to different standards. And so once I kind of really thought about this because I also realised that most of the people that are working with the data care heaps about it and they really want people to know about it because it's literally the area that they care about most often and it's the area that they're working in. And then you think about the history. So you think about one of the earliest slides about, you know, 10 years ago, we weren't able to share information widely and you think about what that means for our data sets. And most of New Zealand's data was, or not most, a lot of New Zealand's data has been collected as a by-product of a service. So if you're a government agency, you're providing a service, whether you're distributing benefits, whether you're a local council and you're registering dogs and you're just counting how many you're doing and saying what breeds they are. And so a lot of our data sets were established before the internet existed. And so they were also set up before anyone imagined ever having to share them. And so there was, of course, no drive to get together and say, so what standard should we be using or how should we be doing this? It was literally just not thought about. And so you fast forward, suddenly the internet is there and people with information are now expected to and made responsible for opening it and sharing it, which is awesome. But it's also terrifying for those that have been collecting the data sets because they didn't set them up to be shared. And so it's almost like if you had been doing all your books offline and like a messy little spreadsheet on your computer and you suddenly asked to publish that or send it directly through to someone and it kind of freaks you out because you're like, oh, yeah, no, I wasn't thinking we were going to have to do that. And so it's really interesting understanding that whole landscape and the motivations and the amount of fear that is around making some of our data available. And so now the first steps when people do make their data open is usually to put it on their website. And so that's all good, well-motivated steps to make. But when you think of it from a user's perspective, what we have as New Zealand's data is organised by source. You have to know who collected something. You have to know the name of the data set before you can find the content. And when you understand the history of it, it makes perfect sense. But when you imagine it from the perspective of a user, it's as silly as having a dictionary that's ordered by the country of origin of the words within it, where you open it up and you're like, too hard. I'm not even going to bother. And so, but it's really interesting, I think, and valuable to understand the motivations of. It's not because everyone just wants to keep people away from it. It's just that we've been evolving. We've never been able to do this before. And when you think about that, when you think about the fact that, you know, even within one government agency, there might be four different definitions for family because people have been collecting data and establishing these definitions separately. When you think about everyone collecting data and having them on their websites, there are some other interesting kind of statements that can be made. So, for example, New Zealand's data isn't archived. It's not, right? Because we don't even know what we've got. We can't even, like, we don't even find it. We don't know where the gaps are. We don't know where the duplicated effort is. So we don't have our arms around what we even have, let alone able to then archive it and make sure it's always protected in the future. And it makes sense, but when you say statements like that, it's like, oh, probably should be. And so when people try and use data now, this is what they usually look like, right? You, as someone, like, we get asked all the time for help finding data. And it's like, people know what they want and they just literally have no idea how to get it. And it's just simply too hard. I'm just gonna get some water while you read these questions. Is that one used? Oh, yep, it's used. That first one is interesting, eh? So in the 1950s, New Zealand had about 129,000 hectares of sand dunes. What do you think the number is now? Maybe just think on your inside voice then, if you don't want to yell it out. Right? So so many of our stories are held within these data sets. And when you use the word stories or when you use numbers in this form, it's relevant for most people, compared to if you show someone an Excel spreadsheet. Like most people in our country, like not always these specific three questions, but we'll find this interesting in some way. And if you think about how data can be used and who can use it and the different applications of if all of our stories were easy for people to find and use, what does that even mean? And one of my favorite examples is the tow truck driver in Hawkes Bay, so which is actually my brother. So we did a bunch of research going out to ask people like, what data do you use? How do you think about it? Where do you get your data from? What would make it easier to access? And I think 29 out of 30 businesses that we spoke to, we're just like, what are you talking about? Definitely don't use data, I don't think this is for me. I'm like, well, it's good at least to know where our starting position is. And but I talked to my brother and who's been running a tying and transport business for about 10 years. And I said a few things, I was like, but would you like to know how many accidents occur in Hawkes Bay and where they occur and what time of year they occur? And he was just like, flawed. He's like, what is that like a thing? Like, is that, can I know it now? And I was like, ah, so I was like, okay, so that's data, right? And it was in every single interview, it became apparent that when we, so when those of us who knew what data could exist that would be relevant. When we were able to play it back to them in their words, every single person was excited by it. And then I said to him, is there something that took you a few years to learn that if you had seen a graph on day one, you would have been able to operate differently? And he said that it took about three years for him to realize the seasonal effect on his business. And you think about it, you start up a business and you know, you come out of the gate and a few months later, your business starts going up, you're getting more work and you're thinking, awesome, growing my business, hire more people, get some more trucks or whatever. And then a few months later it starts going down and you're like, oh heck. And so you're kind of scrambling and you do that a few times and next minute you realize, oh, this happens every March. Okay, this is a seasonal thing. And so the implication of that is if you knew that, then you would, I never take a holiday in the busy season, but you would also then get contractors rather than employees or things like that you might hire in another truck rather than buying one. And just simple decisions that would make a massive impact on things like small businesses. And lots of other examples. There are lots of philanthropic foundations who might care heaps about certain issues, so youth that aren't doing much. And if you don't know what region that's occurring in and are there 50 of them or are there 5,000 of them, you can't really target what activity you're doing. And so the numbers I always consider like graphs and numbers and data, that they almost never have the answers in it. What they actually do is they let you know, they help you find what question to ask next and who to ask the question of. And there's applications for that everywhere. And so despite this, despite the numbers, how many stories are held within our data sets and despite how useful they can be to pretty much everyone, most people don't use data and most of our data isn't used. So figure NZ, our mission is to help everyone make sense of data so that they can see New Zealand clearly in a way that inspires them forward and encourages them to do something about it. And so we're a charity. And the reason we're a charity is because if you think of something like Wikipedia, it just wouldn't work. In my view, if it was a commercial entity or if it was a government entity, it has to be a place that people that have content feel safe with how it's going to be treated and a place where people that use it trust the motives of what people are doing. So that our co-papper is front and central for us every single day of is this going to help people use numbers and their thinking so that they can understand the world around them better. And it's interesting because there's, I mean, when I say that we're the first organisation in the world to do this, like definitely there are many awesome things happening with data around the globe and lots of cool stuff in New Zealand. But the difference is the assertion that everyone can use numbers. And when you're thinking, when you're building a system that when you're constantly saying, how would a seven-year-old find this? How would a seven-year-old interpret this? How would my mum use this? That's really different from starting with, you know, how do we get data out for experts so that they can do modelling and analysis and stuff, which is also really important, but just fundamentally different way of thinking. And we have two main functions. It was interesting, it was earlier this year, that I realised I spend most of my time talking about where we want to go and I don't always talk about the actual what we actually do. So we do two main things. So we take New Zealand's kind of disparate data and we standardise it and turn it into machine-readable form and then we make it easy and compelling for people to find the news. And so this is kind of how the system works. So probably many of you know most of our data sets are held in things like Excel spreadsheets and PDFs and CSV files. Our Excel spreadsheets might have meaning attributed to merge cells or to bold, et cetera. There might be a really important note down in cell A362 that says, by the way, all these values are per 100,000. And so they're all different. And so we've built a platform that allows us to extract all of these, all of this data, all of these values, and to standardise it. And by standardise, I mean it could be as simple as one data set says March quarter 2010 and another has Q1 2010 and we kind of align that and strong type it so that the system understands that it's the same thing. So when you're a user, you don't have to keep reinterpreting that. And when we extract it, we also, we're building a whole set of relationships between the data itself. So for example, Waupokaro is a small town in Central Hawkes Bay, which is in Hawkes Bay, which is in the North Island, which is in New Zealand. And typically, when you look at a data set, say, in an Excel spreadsheet or in like a bespoke kind of database, there's no real relationship or understanding in the system of where a point for Waupokaro sits within the other geographical kind of areas. And so we're building those models up so that from a user perspective, if you search for data and you say Waupokaro and there's none there, then it will show you Central Hawkes Bay or it will then show you kind of, you can go up and down through that. And we have other relationship models that are to do with industries, et cetera, just so that the world of data and being able to navigate through it becomes easier for people to use their own language in. And so from there, we turn it into machine readable form and then we make it available in three different ways. So we care heaps about low sophisticated users, but we also totally want to enable high sophisticated users at the same time. And so when we publish in three different ways, we publish into simple visual form. So we've got about 30,000 graphs and maps on just free for anyone to take a news on figure.nz. We have CSV files of the standardized kind of content that anyone can take a news and do whatever they like with. And we have a public API that we're redesigning at the moment. And that's because we don't care about people using our website. That's not our purpose. We care about people using numbers and their thinking. So if someone wants to take from our API everything we've ever done, including the relationship models and things, then that's awesome, that success for us if they're going to take it and use it. And then we care about it actually getting used. And so just having people take the content from the site in various forms also isn't sufficient. And so we work hard to find existing channels where we can make our content kind of turn up. So for example, we recently partnered with the spin-off where so now every Monday we do like, Charter for the week and every Friday we do a little quiz of the week that I think about a thousand people take at the moment. And working with, say, Business New Zealand, they've got 72 different industry body groups that kind of sit under their umbrella. So we're going, right, what content can we be sharing through your channels so that people are able to access it, relevant stuff in their environments rather than having to go somewhere else. And in terms of how we make money, because everything is free for everyone to use, we believe that charging for, like that data shouldn't be the currency, it's what comes next, that's the added value. And so instead we charge those that have data to make their data usable. So for example, we have customers like the Treasury or Department of Internal Affairs or Department of Prime Minister and Cabinet. Recently the Defense Force, and so they pay us to take their data sets as they currently are without having to do anything and to turn them into usable form. And from their perspective, what that means is they don't have to build and invest in a system, they don't have to become data usability experts. And they also know that their data is more likely to be in front of more people because most people don't know to go to their website. But then it means that when we start doing things, so one of the things I'm personally, I get quite excited about the quiz kind of side of things. And we are likely going to be able to start running quizzes on trains in Auckland. And like that kind of stuff is just so delightful to think, how do we get numbers in front of people? And when you're sitting there from a government agency perspective, it's like, well, there's very little likelihood that anyone's going to be able to spend the time to build that whole channel out. And so we do that once and suddenly everyone that's published with us can take advantage of those channels. And just quickly about what it actually looks like and this, like there's definitely things to improve here. But it's kind of like a Pinterest style where you can search using your own words for graphs and for thumbnails. You can save them to boards like Pinterest. And you can just do a little check. And when you click on one of the thumbnails, you see the graph in detail. And it's so interesting because my team and I, like we love tech and we love pushing the boundaries of what can be done. But all of the way that we display data at the moment is very simple and it's static and it doesn't move and sometimes it makes us cringe for that because we know what's possible. But when you go out and about and you show people data, it's when it doesn't move that it suddenly becomes a little bit safer. It's when you don't have to manipulate variables before you see something that people feel comfortable engaging. And so we're starting in a way that includes everyone. And you can save the graph, you can download the graph or the data set behind it and share it and things. And really importantly is all the metadata that flows through the system with every single piece of content. And I didn't actually used to understand the value of metadata at all. And I just, I really didn't. Sorry. And just in the last few years, like it's just so in your face now for myself and for my whole team about how just totally critical it is. And what's been interesting is because we work in a central way of using lots of different data sets. We get to see all the different ways that people have added or not added metadata. And there'll be data sets that we take that have acronyms that there's literally just no definition for anywhere. And so we will engage with the people because we're not usually experts on the data, we're experts on how to make it usable. And so we'll engage back with the people that collected it and say, what's this mean? And they're like, oh, it just means that. How do you not know that? And it's amazing how in our bubbles we get. And then so we know that at the moment to use our site, you still have to know what to search for. And that's also a little bit too harsh for lots of people because people aren't used to using data, they don't even know what exists. And so we've started down the path of so what other user experiences do we need to make so it becomes even simpler. And the first cab off the rink for this is business figures, which is a different user experience which we'll wrap back into the site properly. But when we did the interviews with businesses, we realized actually the only things that businesses know to do with data is they know what they do and they know where they are. And so we built this as a way of, we partnered with Statistics New Zealand and with ASB to do this. And so businesses can or anyone can just say what they do. And I think there's about 15,000 kind of selections for that and where they are right down to quite a small area. And immediately all of the content starts to get filtered through those things and then you can dive into, I just wanna know about my industry. I want to know about people in my employment kind of catchment area. And it's totally limited by how much data we've processed. So if we haven't processed data, it's not gonna show up and some industries work better than others. But one of the things that we're doing is to build some manual connections between data sets. So for example, if you are a florist in Nelson and you were to look at data on the florist industry, that's typically where you would find florist related data. It's not actually as like that relevant compared to if you were to see what is the trend for funerals in my town and what does that mean for my business. And so we're connecting things like that to surface up data that's relevant even if it's not what you would naturally think of as fitting into a specific industry. And our current, so we've got goals. We've got goals specifically to 1.57 p.m. on the 24th of February, 2022. And that's because it'll be 10 years exactly from when I first had the idea. And we want to get by then 80% of New Zealand's public aggregate data processed. And we want to be having about two million people engage with our content. Both things that are actually really hard to measure because how do you count data? But we're kind of working on those things. And our best, these are our best attempts at guessing where we are at the moment. And that's just an ongoing measurement. So we think we have about 15% at the moment. And although I did get a message from, I think it was some of you might know Chris McDowell and he's one of my team on a part-time basis. And he messaged me and was like, I think it might actually be closer to 10% now. They've just figured out a better way of measuring it. So it's probably a total lie, what we currently think. And our whole, like so the whole way that we work is to play well with others. And we rely on everyone else to do what we do. And we're just trying to play like a neutral platform kind of in the center. And we really care about, so it's interesting because we really separate our functions from our purpose. So our purpose is the whole reason we're there. And it's where I guess being a charity, we have almost the luxury of being really staunch and true to that and not getting swayed by other motives. What that means is that in terms of the functions, we kind of expand and contract depending on what others are doing. So if somebody else was to do something else that like endure better in terms of some of our functions, we'd just go, oh, sweet, you do that and we'll connect into you in a different way. And so we really care about, so what's the current state of play and how can we connect in to what people are doing and how can we serve what people are doing in the best way. And it seems to be working. It's amazing actually, it feels like such a privilege of a space to work in, that you literally never say to someone, oh, we're making all the data available for people to use and for them to turn around and go, oh, that's a rubbish idea. No, I don't want that. And it's, so it's quite an amazing space and we're super grateful for those that work with us and that trust us and back us in what we're doing. And we've got some, we've had some amazing partnerships, those stats NZ are one of our strategic partners and ASB are a cornerstone sponsor, but it's seeing data out and about and being used by people that have never used data before that is just the most heartwarming thing. One of my favorite talks was to a group called the University of the Third Age in Helensville. There were about 15 women over 65 and I said, I'm gonna talk about data for an hour and they were like trying to back out of the tea room and I was like, director, so trust me. And I put up a couple of graphs and the first one was the number of dogs registered in Helensville by breed and they were sitting there like, one woman was like, is my border collie in that line? And I was like, yeah, if it's registered, absolutely. And you could just see on their faces, they're like, oh, is that what data is? And then the other delightful moment from that was there's, there was a graph that showed number of farms by type. I think it was in the Kaipata area and there was, and there was like number of horse farms or something and one woman said, oh, but what does that mean? Because we don't technically have any horse farms and I know because I'm in the horsing area and she was being quite forthright about it and I was like, that is the perfect question to ask. And that is the same rigor that you should be looking at all data through is because everything is based on definitions and often it's only when we see something that we're familiar with that we realize how flawed data collection is and but it's never perfect and so it was a really nice way of going, that's totally how we should all be looking at. Don't be afraid to question and to dive in and figure out the definitions. And importantly, it is so not really about the data. People have started asking me if we can help them create a data-driven organization and I really thought about it and I was like, that's totally the wrong way to think about it because data is an input, right? It is only one way of seeing the world and the most important thing is that data shows us something that we didn't know before and we work in cultures or we operate in environments where we aren't encouraged to identify the things that we don't know, typically. We're encouraged to promote the things that we do know. So far more important than going, oh, how do we alert and create a data-driven organization? I think people are under the illusion that you can connect some pipes and that magic insights will turn up and it's like actually the more important thing is how can you create a culture that encourages people to identify what they don't know and to seek the answers using data as an input and that's what we're really about. Thank you.