The changing data landscape: How the data revolution and the fight against COVID are changing UK stats forever

In the third episode of Statistically Speaking we talk to Professor Sir Ian Diamond, the UK's National Statistician, and Dr Louisa Nolan, Chief Data Scientist at the ONS Data Science Campus about the past, present and future of stats. We explore how the pandemic has been transformative for the use and understanding of public data and how the data revolution and the fight against COVID are changing UK stats forever. Transcript:

MILES FLETCHER Welcome to Statistically Speaking: the podcast where numbers talk and we talk to the people behind them. In this third episode, we meet professor Sir Ian Diamond, UK National Statistician and Dr Louisa Nolan, Chief Data Scientist at the ONS Data Science Campus. We explore how the pandemic has been transformative for the use and understanding of public data and how the data revolution and the fight against COVID are together changing UK stats forever. But to begin I asked Sir Ian what led him to a life of stats SIR IAN DIAMOND Okay, well, I'm going to be absolutely honest Miles: genetics. I have no idea why I was always interested in numbers and statistics but I always was. And so something in my genes said I like numbers. Something else in my genes said I like numbers but numbers which have an application and a practical application. And that led me to not only be interested in statistics, but to study statistics and then to work as a statistician in academia for some decades, but always interested in numbers and their application to policy and to improving the lives of people. And if you take that as a starting point, then it's what I've always done, and led me to at times work in partnership with different government departments. And that led me to partnerships with ONS, which has led me here. MILES FLETCHER A lot of people sort of regard statistics as numbers on a page, something that can seem quite abstract, but they exist of course to help people make important decisions. Can you think of an example in your pre-ONS career, your pre-National Statistician life, where you first used numbers and statistics to actually help solve a real-world problem? SIR IAN DIAMOND Well, yes, I mean, if I go back to the very early 1980s, at that time, the observation was made, that there had been a decline in the number of children born in the UK. That was going to be a decline of around 30% in the number of 18-year-olds, and it was suggested that therefore there would be a reduction in the demand for higher education. Working initially with Fred Smith and then subsequently on my own, I was able to project the future demand for higher education, on the basis of some assumptions that the number of women going into higher education would increase, that there would be social mobility in the country as a whole. And also, that there would be an increase in what we now call widening participation. When you bring all those things together, you get a very, very different number for the demand for higher education than from simply following the number of births. And that had an impact alongside work that other people did on influencing policy for higher education. MILES FLETCHER So a busy, very successful academic career is followed then by stint as National Statistician. You're in the job, what six months last March, just as the pandemic, as we as we came to know, was starting to break. At what point did you realise that it was going to be as big as it turned out to be and that a very special response was going to be required from the statistical system, the UK statistical system, ONS, and all the statisticians in government departments, the system that you're responsible for? SIR IAN DIAMOND I mean, I think early in 2020 Miles. We identified, very sadly, the first death from COVID at the beginning of March 2020. We now think there might have been one earlier but, you know, I think very early on we at ONS recognised that this was something that the statistical community needed to really step up for, not least working with the wider international community to define a cause of death as being due to COVID. I'd say March 2020 is when we really became aware there was going to need to be some really fast and accurate estimates of all kinds of things around the pandemic, whether it was impacting on the economy, or indeed the pandemic itself, and that led to us in April to putting together a survey which estimated both prevalence but also the level of antibodies, and subsequently now of course, issues around vaccination. MILES FLETCHER So it was a very important decision point where it was realised that the traditional, if you put it that way, the main data sources that ONS and others in government were producing were not going to be enough to measure a very, very important factor in this, that's actually how many people have got the virus at any at any one time. What point did that arise and what happened next? SIR IAN DIAMOND We had a conversation early in April. We said ONS could use our ability to be able to design nationally representative surveys and to pivot some of those designs into collecting the biomedical data that are important in order to be able to identify both prevalence and antibodies, but we will only do so in partnership with other experts. And so we very, very quickly set up partnerships with the University of Oxford, the Wellcome Trust, and the Department of Health and the Office of Life Sciences. We were able to set up a team that in one week, was able to move from a decision to go for it, to design, to ethics to the first field workers collecting some data. MILES FLETCHER And it was mounting, what was by anybody's standards, a huge field operation, as you say, in very short order to get around households up and down the United Kingdom eventually, when the survey was running at full scale. To do that very, very quickly, a huge operation… SIR IAN DIAMOND Two stages Miles: the first of which is we stood it up as a nationally representative sample, which would make estimates for England. And, you know, it takes a lot of things at pace. So getting from the field workers getting the swabs to the laboratories, getting the tests, getting them back, doing some really quite sophisticated statistical analysis to make estimates. Getting all that done requires a lot of logistics, and I think the team deserves an enormous pat on the back for so doing. And then that success led to the scaling up. So that we can make original estimates so that we can make age-specific estimates. And we were able to do that. But then that was a huge scale up in September of 2020 and I think again, the logistics of scaling that up was incredibly challenging, but successful. And at the same time working with our colleagues in Scotland, Wales and Northern Ireland, to be able to produce estimates for those administrations too was something that I'm very proud of. MILES FLETCHER And the record shows exactly what was achieved during those pressured early months of the pandemic. And of course, right at the start there were plenty of people around who doubted whether the statistical system, whether the ONS and others were really capable of doing that job. Was it satisfying to confound those critics? SIR IAN DIAMOND I didn't hear them, I just got on and did it, to be absolutely honest, Miles. I knew what we could achieve in terms of both the survey which was able to measure prevalence and antibodies, but also the social survey because you need to know how people are feeling about the restrictions. You need to know how people are feeling about the pandemic. Were they anxious or not? And then as people started to talk about, for example, face coverings. What were people's attitudes to those things and, and were people adhering to the restrictions? So, there was a social survey, that was producing weekly estimates as well. That was incredibly important, and we were producing economic statistics, as well. So I have to say it wasn't a question of was the statistical system standing up and delivering a survey to estimate prevalence of the pandemic. But it was addressing a whole set of other questions, which required not only statistical collection, but in some cases, further analysis, and data linkage and a whole range of sophisticated statistical methods to be able to provide information for the government and for the population so that they understood exactly where we were at any time. MILES FLETCHER And what do you think that all that has done for the general trust the public have in the statistics that they see from us or from the media? SIR IAN DIAMOND ONS has always been a very trusted organisation. I mean, one of the important things that we have in the UK is the independence of the ONS and I think that's incredibly important and the public in all the surveys that we have done over many years have demonstrated great trust in the statistics that we produce. And I think that the public has continued to show that trust over the pandemic. And I hope although at this stage I stress I'm hoping, that the public will feel that the ONS has delivered during the pandemic and therefore will be prepared to continue to trust the ONS in the future. MILES FLETCHER Talking about the public and involvement, coinciding with this pandemic has been census of course in England and Wales and we asked every household once again to complete the census. Again, at the beginning, some said it couldn't be done because of the pandemic and others even more said it shouldn't be done because of the cost. How has it all gone? And will it tell us what we now urgently need to know about our population?

SIR IAN DIAMOND We had a really very good and very strong response. We're now in the process of doing the analysis so that we can produce really accurate results and that's going to be incredibly important. Should we do a census? Well, I think a census is a statement of great confidence from a country that is prepared to say that on one day, this is a picture of what that country is and how many people there are and their characteristics. And that is so important for all kinds of reasons. So yes, it was incredibly important I think that we did. Yes, it was incredibly important that we did it at the time of the pandemic, because we needed to know where we were at that time. Of course, we will be working very hard to update our statistics over time to really understand the post pandemic world. I'd have to say also that you know, the cost is high, no question. And we will be working very, very hard over the next 18 months or so, to produce a set of recommendations as to the future of population data collection. Do we need another census or can we do things that administrative way. In 2014 we thought about this with regards to 2021 and a really good report done by the late Chris Skinner, together with John Hollis and Mike Murphy, recommended that this census that we've just done, digital first census, should go ahead, but we should aim to make a recommendation about the future. And that's what we're planning to do. It will require support from many other parts of government. I'm confident that we will get that support. And the one thing I can say Miles is that over the next 18 months or so we'll be working flat out to be able to make a recommendation that is extremely tight and extremely evidence based. MILES FLETCHER Now this whole question of whether there should be another census, actually it chimes with a reaction that we saw coming back from the public, and we did certainly get a good response rate. We reckon 97 percent of households did take part in the census and that's as good a response as there's ever been - perhaps there was a certain advantage to holding it during lockdown even - but some people asked why they have to fill in this census because surely the government should already have all this information to hand by now. How far are we down the road to be able to gather all the information from other sources already as many countries do. SIR IAN DIAMOND Well other countries do and other countries for example, particularly those in Scandinavia require a Population Register where you have to if you leave the country, come back into the country, you have to register that you are there. And if you move you have to register. We don't do that. So we do not require you to register that, for example, you have moved house or register with the Office for National Statistics. You may register with the land registry but if you don't, if you just move, we don't require you to register that. Interestingly, there is no one source for occupation in this country other than the census. So, while you may think that data are held everywhere, Miles, they actually aren't. And so, while there are a lot of government data, there are no single sources which cover a lot of the things that a Census does and also there are one or two questions that one has in the census which are attitudinal, for example. So, you ask about well being. Well the only way you can ask people about wellbeing is to ask them, so you actually need to collect those data on a census. So there's a whole set of things that we ask on the census that very simply we don't ask elsewhere. And therefore, it's important, I think that we do get those data. MILES FLETCHER And of course data has to be fast to be effective now, or certainly faster. During the pandemic again we've seen advances in how new data sources have been used: anonymised credit card data, traffic camera data, mobile phone data, shipping data to provide these really fast readings of economic impact. Novel and brought in, in some cases, and as a specific response to the urgencies of the pandemic. But will these last now? SIR IAN DIAMOND One hundred percent. I think one of the things we've seen over the last few years has been the increase in born digital data, and we need to recognise the potential benefits of those data for our understanding of society and the economy, and indeed the environment and we need to be using them at pace in every way possible. And asking the question, do they replace things that we always have? Or are they in addition? And if they are, in addition, are they really adding value? Very easy to get involved in what you might call a data deluge. Yeah, there's loads of data out there so we'd better have it. I think you have to be very, very focused on whether any particular data add value and insight to the subject under study. If they do, then I think that it's important for us to use them and to access them. If they're just simply adding some more data then we do not need to follow them up. So data for insight, not data for data's sake. MILES FLETCHER So we've had two years driven mainly, but not wholly by the pandemic, but two years of incredible progress in our statistical system. Looking to the next decade, what comes next, what do you think we're going to see in statistics and data, how it's going to be used and what sort of issues are we going to be addressing? SIR IAN DIAMOND We will be able to process ever bigger datasets and to do so ever faster. So all the kinds of things we have been talking about, about more digital data, analysis of texts, as well as numbers and data produced at speed and at pace will be the norm. But that doesn't stop us wanting to continue to collect some pretty important data, for example, GDP or inflation data and to do so, perhaps, in a new way. In the last year we've calculated GDP using some innovative data sources, but in a way which enables those long time series that we started talking about at the beginning of this conversation Miles, to be maintained. I think it's incredibly important that we do maintain time series while at the same time produce evermore exciting and new data sources. And I return finally to the point that we will still want attitudes. If you want attitudes, we'll need to continue to do surveys. So I think it's an exciting time, one of the other areas that I think we will see, real progress is improved data visualisation and improved interoperability with people. And I think that's important when it comes back to trust, if people are able to go on and manipulate the data themselves very, very easily, then again, the transparency and the openness and the use of data will be something that will remain at the heart of what we do. MILES FLETCHER That's Sir Ian Diamond, the National statistician. Now if there was one single development that made the ONS and perhaps the whole of the UK statistical system ready to cope with the pandemic, it was arguably the ONS Data Science Campus. Established in 2017 its mission is to work at the frontier of Data Science and Artificial Intelligence, building skills and applying tools, methods and practices it says, to create new understanding and improve decision making for the public good. So what does that all mean in practice, and what has the campus achieved in its first four years? Questions I put to Dr. Louisa Nolan, its chief data scientist. Louisa to take it from the top as it were: tell us, what is the data science campus and what are you out to achieve? LOUISA NOLAN The data science campus was set up four and a half years ago, and our mission is to explore new types of data, new types of technology, new techniques in data science, to make sure that we're making the most out of the data that's available, the ever increasing types of data that are available to us. And we also build capability in data science not just in ONS but across government and the wider public sector as well. So data science is really about the analysis of that data, getting that data together. But we need to get hold of the data. We need the right tools and platforms to use that data, particularly big data. It's about testing those technologies and how we do that to build those insights as well. MILES FLETCHER And when does data that you harvest, when does it become statistics? LOUISA NOLAN That's a really interesting question. And different people probably would give different answers. Statistics, I would say is a summary. So it's a summary, it might be the average the mean, or it might be a trend, it's looking at the overall picture, whereas data might be your input. So the satellite picture or the information somebody's given on the census, and statistics really is turning it into something that we can then understand broadly, what's going on and why those things are going on. MILES FLETCHER And it's your job then, in essence, to find how best to use that, those mountainous volumes of data and transfer them into usable, useful statistics and insights. LOUISA NOLAN Absolutely, and there's the technical part of that the techniques but also understanding those new types of data, understanding their quality and their bias and how we can best use them so that we produce something that's useful for decision making and not misleading. MILES FLETCHER The data science campus has been around for just a couple of years really, but what have you achieved in the time since it's been running? LOUISA NOLAN We've achieved a lot. So on the capability side we've set up data analytics apprenticeships, the graduate data science programme, the data masterclass, which is about teaching senior leaders data literacy, we've delivered face to face training, we've trained more than 600 analysts across government to be data scientists in that time. We've built data science community activities, and then we've also delivered a vast range of projects, including things around faster indicators, counting cows from space, text analysis to help automate and understand big government consultations. So it's been a really wide range of stuff. MILES FLETCHER What have you been doing, for example, with economic statistics? LOUISA NOLAN So we've been doing some really interesting stuff with economic statistics. Back two years ago, seems like it was longer ago but I think it was only two years ago, we were asked to see if we could find faster indicators which would help to kind of test the health of the economy much earlier than our GDP and official outputs. And this isn't as a replacement for GDP, just to get some faster information a bit earlier. So we had a look at what was available. And we wanted to make sure that we had data that was high frequency and low latency, obviously, if we want to understand what's going on bit quicker. But also to make sure that it had some kind of relationship to economic concepts. In the past people have looked at things like lipstick sales, or men's pants sales or… MILES FLETCHER Counted cranes? LOUISA NOLAN Counting cranes! Counting cranes is maybe slightly better, but not all of these are very robust, and actually they're terribly subjective. And if you look at them over the long term, they don't really work. So we wanted things that really related to economic concept, even if they weren't the same as GDP. We're not trying to measure GDP. So we had a look at the various datasets that were available and the first set of faster indicators that we produced covered three different datasets, all of them really interesting in their own right. So the first one was creating a diffusion index from VAT returns. So a diffusion index just tells you the proportion of businesses whose turnover have gone up since they last reported, and obviously if that starts to drop off, that's a bit of a warning signal and you might want to go and have a bit more of a look and see what's going on or why that's happening. The other two were really different. We've used VAT data before, but the other two were really different for ONS. Firstly, road traffic data. So this comes from sensors in roads, particularly used for active traffic management, and it counts the number of vehicles passing those sensors and you can also tell how big the vehicles are, so you can separate out cars from HGVs. And we think this ought to be quite a good indicator of what's going on in the economy. Because the amount of stuff moving around the country, people travelling to and from work, quite interesting and you'd expect that to be related to economic health and the movement of people and goods. And then the last one was perhaps the most interesting dataset because it's the biggest. It's a global dataset on shipping. Every ship has a tracker. When it's in motion, if it's above a certain size, when it's in motion, it has to say where it is every second and then when it's at rest it needs to say where it is every couple of minutes. So this is an amazing dataset that tracks all the big ships. So we had a look at ships coming into UK ports, the number of visits, the type of ships coming in and how long they stayed there for. We created, I think it was about 300 different time series from these and published them very quickly. The first time that ONS had done something like this, possibly the first time in the world that this kind of faster indicators had been published by a national statistics institute on a regular basis. Really interesting data. And I think that kind of set the scene. So we've gone from those initial three datasets. Over COVID, huge appetite for faster information because things were happening so rapidly, lots of changes in the economy that were unpredicted two years ago. And so both data science campus and ONS have built on that initial faster indicator output. There's now a suite of I think more than ten different faster indicators based on things like job vacancies, footfall, traffic, camera information, all kinds of things that are feeding into that picture of what's going on very rapidly. High frequency, not much delay between the data and the reporting. MILES FLETCHER To what extent has the pandemic then hastened the pace of progress in the data science campus, and to what extent have the indicators that you produced been corroborated or vindicated by the subsequent classical data that ONS produce? LOUISA NOLAN And so as COVID hit, obviously, there was a huge desire to know what was going on how well people were complying with restrictions. Were people really moving about or have they complied and stopped moving about, and also understanding the impact of that on the economy. So the campus was well placed because of our skills and the way we're set up to rapidly pick up some new datasets and have a look at them. So we very quickly got some mobility dataset. So this is about how the bulk of the population is moving about to look at how well people were, not individuals, but how well the population was complying with restrictions. And I should say here that we're we've never been interested in tracking individuals. It's all about the bulk movements, what goes on. So we very quickly got that managed to quickly stand up someregular outputs. At one point we were reporting daily on what was happening because things were happening so quickly. And as time has gone on, I think it's fair to say that the narrative from some of those faster datasets has been broadly correct. But obviously as you get the more detailed information and more of the breakdowns, the information in, you can have a more robust, accurate measurement, not just the "well it looks like it's falling really rapidly", or "it looks like it's coming back up again" kind of interpretation. MILES FLETCHER In terms of speed, the delay between data creation and data analysis is getting ever and ever shorter. How fast can this get at what point will we be able to be able to read daily readings of the economy for example, daily readings of population shift? LOUISA NOLAN I think that it's becoming possible. I don't think you'd ever, I don't think you would have daily GDP because there's so many elements in GDP that you couldn't collect on a daily basis. The question is, particularly around the economy: How useful is having daily outputs on the economy? If you knew GDP daily, how would that help your decision making? But for population if you know what population density and how that changes over a day that might be really useful because that will tell you something about where there's high density areas, how people are travelling about how people are not travelling about , over COVID. And that would help with things like your local planning, with managing big events and so on, and help us to spend money more effectively because we know where people are and we've got a better and quicker understanding of where populations might be both in the short term over the timescale of a day and in the longer term. MILES FLETCHER You mentioned observing cows from outer space as well. I've got to ask you what that involved? LOUISA NOLAN Oh, counting cows, we love this. We have a data science hub that's embedded with the Foreign, Commonwealth and Development Office in East Kilbride. They focus on supporting the UK's mission to support developing countries around the world. And one of the projects that our team is doing, our team there is doing, is counting cows. So in South Sudan, where agriculture is a much bigger percentage of GDP, a huge part of GDP for them than it is in the UK. And cattle is really important, but it's quite difficult to go out and count all the cows is a huge country. Not great roads. They've had various different issues with weather and conflict there as well. So the question was, can we get a good picture a good census of the cattle in South Sudan using satellite data? And actually, it's quite it's quite promising. We have ever better quality of satellite data, higher resolution. You can see where the camps are and you can make some estimates around the number of cows there. Getting hold of your ground truth data to check whether your estimate from spaces right is probably the hardest part of that, but it's quite exciting. And of course, if this works, what else can we do with satellite data that's helpful and means that you don't have to send individual real people out over these vast areas to count things. MILES FLETCHER That's operating on the global scale as well, but you've also been working on ways of minutely examining documents that are submitted to government in very large numbers and bypassing human intelligence to use artificial intelligence to interrogate those documents and draw conclusions from them. LOUISA NOLAN That's right. I mean, one thing government is good at is having lots of words and documents and turning those documents from data, if you like, into information and insights is a big part of what we do. So we use natural language processing to do text analysis, and we worked with the Department for International Trade on one of their big consultations, they had more than 400,000 responses. And we were able to automate that to identify themes and topics in the responses in a faster way than you can do by hand. They also covered this in the traditional ways so we were able to compare our results with the manual approach as well. Certainly the automation is faster. And I think sometimes when you've got that much information, you can get different insights, new insights from automating. But when we look at AI and approaches like that, you really want to take the human in the loop approach. So you run the things that are automated, for the bits where it makes sense, where you can find out things, you can make things go faster. But if there's something which is difficult for the AI to come to a conclusion on, that's when you bring your human in to go, oh what does that look like? Where should that sit? How should we interpret that? And it's that combination of automation, getting humans to do the bit humans are good at that's really powerful. MILES FLETCHER So the campus is a campus in both senses really. It's a campus and that it has projects and enterprise and things getting started up, but it's also a campus in the academic sense as well. And you're training people some of whom have no background in in these sorts of disciplines at all. Tell us about what's been achieved there. LOUISA NOLAN So our capability team were set a task to train 500 data scientists by March 2021. Well, we far exceeded that we trained 680 something in that time through a range of different programmes that we run. These include the MDataGov, the master's in data science for government, which we run in partnership with four universities. The graduate programme, the apprenticeship programme, face to face learning and our accelerator mentoring programme, which is brilliant. So this is open to everybody across the public sector. Pitch a project. If your project is successful, then you get for 12 weeks, you get a data science mental for one day a week to do that project and that project will be something that's important to your home department and also help the individual to build the skills as well. There's been a massive range of projects and departments who've taken part in this. I think we've had more than 250 people through the accelerator so far. It's great. So we're always looking for more mentors as well. So if this sounds interesting, always, always looking for people to help out with the mentoring. MILES FLETCHER And in the apprentices, you're getting people coming in from the local communities in many areas around where you're based in, in South Wales, and coming in cold in many cases with no background in working in these sort of disciplines at all. LOUISA NOLAN That's right. For the apprentices it's about enthusiasm and potential rather than anything that's happened before. We've had a range of people from a huge range of different backgrounds, a huge range of different ages from straight out of school all the way to people who've had several careers beforehand who've wanted to retrain. It's a brilliant way to get diversity into data science, and I'm hugely supportive of this approach. It's great. MILES FLETCHER And how do you go about applying then for any of these opportunities? LOUISA NOLAN So we advertise them, the best place to go is to look at the data science campus websites where we advertise all of our learning and development programmes. And also we talk about our projects and the other things that we're doing so you can find out all kinds of information there. For jobs and recruitment, like the recent round of recruitment for the graduate data science programme, that will be on civil service jobs, but the first place to come as the data science campus website. MILES FLETCHER What are the challenges that immediately lie ahead for the campus then, what are you getting your teeth into now? LOUISA NOLAN So I think one of our challenges is a good challenge, which is that data and data science has never been a higher priority. I think so we have a lot of asks on us. I think in four years things have changed. So four years ago, there weren't so many data science teams across government, there are more now. So we need to think, make sure that what we're offering is still the right level as other departments mature as well. I think the desire for ever faster information is not going to go away at all. So more of that, and also thinking about how we can use data, novel data and data science to support the government's big programmes like net zero and levelling up and also continuing to support our response to COVID. And thinking about what we learn from that, how we can use what we learn from that for other aspects of health as well. MILES FLETCHER And Will everybody be a data scientist in the future rather than just a statistician? Dare I ask? LOUISA NOLAN Oh, I don't know. That's a very controversial question that. I think data science, data scientists aren't unicorns there are aspects of data science, that is a subset, or if you imagined a Venn diagram have overlaps with statistics, with operational research, with economics, a lot of economists really interested in data science and big data. But also with the digital skills as well. So overlaps with data engineering and software engineering. So my hope, my dream, I don't have a dream data science person, it's always a team who's made up of all of those different skills. And I hope that more people will have an opportunity to build at least some of those skills, even if they don't call themselves data scientists. One of the other programmes that I'm really proud for the campus to be leading which we developed in partnership with the Number 10 delivery unit is the data senior leaders data masterclass. So this is a masterclass designed for public sector, senior leaders talking about data, why it's important, how you can use it for evidence how you can use it for evaluation, not expecting people to come out coding in Python, but having a better understanding of what's possible and what the right questions to ask are. So we rolled it out to all permanent secretaries. We're hoping to roll it out across the senior civil service. Also the fast stream and some of the future leaders development programmes across government and it's also open to senior leaders from the wider public sector as well. I'm really pleased about this because I think if we can build those skills at the top level, get people understanding what the opportunities are then that helps us build that capability, increase the number of people who can do that coding, improve efficiency and help use data better to make better decisions. MILES FLETCHER That's Dr. Louisa Nolan from the ONS Data Science Campus and before that National Statistician Sir Ian Diamond. In the next episode of Statistically Speaking we turn to the economy. With the rising cost of living on everybody's minds, how does the ONS keep tabs on inflation? Is there more to national prosperity than mere GDP? And is economic forecasting really just a way of making astrology seem respectable? Join us then. You can subscribe to new episodes of this podcast on Spotify, Apple Podcasts and all the other major podcast platforms. You can also get more information by following the @ONSFocus twitter feed. The producers of statistically speaking are Joe Ball, Elliot Cassley and Julia short. I'm Miles Fletcher, goodbye.