Podcasts about data office

Step into the future of policing where data is a mission-critical asset. Cindi Howson talks with Aimee Smith, Director of Data at the Metropolitan Police, about the Met's bold data transformation—from digitizing records dating back to 1829 to using AI and cloud tech for smarter, faster decisions. Hear how initiatives like the V100 program and real-time analytics help improve city safety. Key Moments: Genesis of the Met's Data Strategy (03:35) - The Met's data strategy's origin is traced to former Commissioner Cressida Dick's leadership, who envisioned leveraging data to transform policing, leading to a program building data capabilities and broadening analytics use beyond traditional intelligence and performance applications.  Mission with Data and AI (13:34): The Met's overarching mission to use data and AI for precise decision-making is articulated, acknowledging the complexity of policing's multiple goals: crime prevention, incident response, organized crime intervention, victim service, and custody safety.  Infrastructure Evolution (15:18): The transformation of the Met's data infrastructure over 5 years, from 8 separate operational systems to an integrated one with cloud technology adoption, is described, enhancing analytics and data science capabilities.  V100 Initiative (19:58): The V100 initiative, a data and analytics effort to reduce violence against women and girls by prioritizing individuals with a history of harm, is explained.  Concert Security Powered by Analytics (27:50): The use of ThoughtSpot by frontline officers is illustrated with a sergeant's innovative application for analyzing crime data around events like the Taylor Swift Eras tour to improve policing plans.  AI Agent Development (36:37): An innovative project to build an AI agent that assists frontline officers at crime scenes by providing real-time guidance is outlined, aiming to improve public protection and investigative outcomes.  Key Quotes:“So if an officer wants to start being able to do their own searches, creating their own sort of planners, thinking about doing their own trend analysis essentially, of crime data, which is great, isn't it? I mean, that's just exactly how you want ThoughtSpot to be used. Every officer has access to that.” - Aimee Smith"I like to think of it as a utility belt—you know how cops wear their utility belt? Well, hanging on there is this ThoughtSpot tool. A sergeant invented a way to use it for planning major events, concerts, to make sure our presence is right. And now that's replicable by other people who want to do the same thing." - Aimee Smith"One of the 5 principles of our business strategy for London to keep it safe is to be more precise in the use of data for decision making. So it's a high-level principle of our strategy. That makes data and analytics much harder, because there aren't enough data specialists and too many data parts to point at all those missions in one go.” - Aimee SmithMentionsMet Police's V100 InitiativeMet Police Develops an Open Data Strategy with the Open Data InstituteMet Police's Concert Preparation for Taylor Swift's Eras Tour Cressida Dick Reflects on Public Trust in the Digital Age The Data Protection ActGuest Bio Aimee Smith's distinguished career in the Metropolitan Police Service (MPS) spans almost a quarter-century, truly a testament to her profound dedication to integrating robust data into the very core of police decision-making. She embarked on her journey in 2001 as an Intelligence Analyst, steadily rising through the ranks. By 2014, her leadership capabilities led her to head UK Policing's largest Confidential Intelligence Unit. A pivotal "light-bulb moment" crystallized for her the critical importance of effective data management in driving operational outcomes, inspiring her to passionately spearhead the comprehensive MPS data transformation program. In a landmark achievement, Aimee was appointed as the first Director of Data for the MPS, where in 2019, she successfully established the inaugural Data Office within law enforcement, fundamentally reshaping how the service leverages its information. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Dans cet épisode de Morning Data Chat, Emmanuel Vignon, Directeur de l'IA chez Theodo, reçoit Christophe Bonnefoux, Global Chief Data Officer chez BNP Paribas One Factoring. Ensemble, ils discutent du rôle stratégique du CDO, de l'importance de responsabiliser les métiers sur la qualité des données et du rôle du Data Office comme catalyseur de transformation.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Dany Srage est Lead Data chez Defacto, la startup FinTech qui propose des prêts aux PME. Dans cet épisode, il nous parle de leurs projets de Data Science.

Adrien Vesteghem est AI Program Director chez BNP Paribas, la banque leader mondial que tout le monde connaît. Aujourd'hui, il va nous parler de son plus gros challenge de ces dernières cette année, à savoir lancer le programme IA de la BNP.

Mickaël Kuentz est expert Data et IA et est également Directeur Data chez KPC, le cabinet de conseil spécialisé sur la data et l'IA qui connaît une croissance fulgurante.On aborde :

"Heterogenität ist das Schlüsselwort bei der Fraport AG"

  With the public release of large language models like Chat GPT putting Artificial Intelligence (AI) firmly on our radar, this episode explores what benefits this technology might hold for statistics and analysis, as well as policymaking and public services.  Joining host, Miles Fletcher, to discuss the groundbreaking work being done in this area by the Office for National Statistics (ONS) and across the wider UK Government scene are: Osama Rahman, Director of the ONS Data Science Campus; Richard Campbell, Head of Reproducible Data Science and Analysis; and Sam Rose, Deputy Director of Advanced Analytics and Head of Data Science and AI at the Department for Transport.  Transcript MILES FLETCHER Welcome again to Statistically Speaking, the official podcast of the UK's Office for National Statistics. I'm Miles Fletcher and, if you've been a regular listener to these podcasts, you'll have heard plenty of the natural intelligence displayed by my ONS colleagues. This time though, we're looking into the artificial stuff. We'll discuss the work being done by the ONS to take advantage of this great technological leap forward; what's going on with AI across the wider UK Government scene; and also talk about the importance of making sure every use of AI is carried out safely and responsibly. Guiding us through that are my ONS colleagues - with some of the most impressive job titles we've had to date - Osama Rahman is Director of the Data Science Campus. Richard Campbell is Head of Reproducible Data Science and Analysis. And completing our lineup, Sam Rose, Deputy Director of Advanced Analytics and head of data science and AI at the Department for Transport. Welcome to you all. Osama let's kick off then with some clarity on this AI thing. It's become the big phrase of our time now of course but when it comes to artificial intelligence and public data, what precisely are we talking about? OSAMA RAHMANSo artificial intelligence quite simply is the simulation of human intelligence processes by computing systems, and the simulation is the important bit, I think. Actually, people talk about data science, and they talk about machine learning - there's no clear-cut boundaries between these things, and there's a lot of overlap. So, you think about data science. It's the study of data to extract meaningful insights. It's multidisciplinary – maths, stats, computer programming, domain expertise, and you analyse large amounts of data to ask and answer questions. And then you think about machine learning. So that focuses on the development of computer algorithms that improve automatically through experience and by the use of data. So, in other words, machine learning enables computers to learn from data and make decisions or predictions without explicitly being programmed to do so. So, if you think about some of the stuff we do at the ONS, it's very important to be able to take a job and match it to an industrial classification - so that was a manually intensive process and now we use a lot of machine learning to guide that. So, machine learning is essentially a form of AI. MILES FLETCHERSo is it fair to say then that the reason, or one of the main reasons, people are talking so much about AI now is because of the public release of these large language models? The chat bots if you like, to simpletons like me, the ChatGPT's and so forth. You know, they seem like glorified search engines or Oracles - you ask them a question and they tell you everything you need to know.  OSAMA RAHMANSo that's a form of AI and the one everyone's interested in. But it's not the only form – like I said machine learning, some other applications in data science, where we try in government, you know, in trying to detect fraud and error. So, it's all interlinked.   MILES FLETCHERWhen the ONS asked people recently for one of its own surveys, about how aware the public are about artificial intelligence, 42% of people said they used it in their home recently. What sort of things would people be using it for in the home? What are these everyday applications of AI and I mean, is this artificial intelligence strictly speaking?  OSAMA RAHMANIf you use Spotify, or Amazon music or YouTube music, they get data on what music you listen to, and they match that with people who've been listening to similar music, and they make recommendations for you. And that's one of the ways people find out about new music or new movies if you use Netflix, so that's one pretty basic application, that I think a lot of people are using in the home.  MILES FLETCHERAnd when asked about what areas of AI they'd like to know more about, more than four in 10 adults reported that they'd like to know better how to judge the accuracy of information. I guess this is where the ONS might come in. Rich then, if I could just ask you to explain what we've been up to, what the Data Science Campus has been up to, to actually bring the power of artificial intelligence to our statistics. RICHARD CAMPBELLThanks Miles. Yeah, a few things that ONS has been doing in this very broad sphere of artificial intelligence, and it's really in that overlap area that Osama mentioned with data science, so I'd pick out a few sorts of general areas there. So, one is automation. You know, we're always keen to look at how we can automate processes and make them more efficient. It frees up the time of our analysts to conduct more work. It means that we are more cost effective. It means that our statistics have better quality. It's something we've done for years but AI offers some new opportunities do that. The other area which Osama touched on is the use of large language models, you know, we can get into the complexities of data. We can get much more out of data; we can complete tasks that would have been too complex or too time consuming for real data scientists. And this is good news, actually, because it frees up the data scientists to add real valuable human insights. Some of the places we've been using this. So, my team for example, which is called reproducible data science and analysis, and we use data science and engineering skills to develop computer systems to produce statistics where the data is a bit big, or what I tend to call a bit messy or a bit complex for our traditional computer systems. We use AI here through automation, as I mentioned, you know, really making sure that we're making systems as efficient and high quality as possible. Another thing we're interested in doing here is quite often we're doing something called re-platforming systems. So, this is where we take a system that's been used to produce our statistics for years and years and look to move it on to new technology. Now we're exploring with Osama's team the potential for AI to do a lot of the grunt work for us there to sort of go in and say, right, what is going on in this system? How is it working, how we can improve it? One other thing I'll mention, if Osama doesn't mind me treading on the territory of his team, is the Stats Chat function that we've used on the ONS website. So, this is using AI to enable a far more intelligent interrogation of the vast range of statistics that we've got, so it no longer requires people to be really knowledgeable about our statistics. It enables them to ask quite open questions and to be guided to the most relevant data.  MILES FLETCHERBecause at the moment, if you want to really explore a topic by getting into the depths of the data, into the granular data, you've really got to know what you're looking for haven't you? This again is an oracle that will come up with the answers for you and just present them all ready for your digestion.  RICHARD CAMPBELLThat's right. And I tend to think of these things as a starting point, rather than the whole answer. So, what it's enabling you to do is to get to the meat of the issue a lot quicker. And then you can focus your energy as a user of our statistics in doing the analysis that you want rather than thinking “how do I find the right information in the first place?” MILES FLETCHEROsama, that sounds like an intriguing tool. Tell us precisely how it works then, what data does it capture, what's in scope? OSAMA RAHMANSo the scope is publicly available documents on the ONS website. And there's a specific reason for that. So, these AI tools, you can have it look at the whole internet, you can have it look at subsets of data, you can point it to specific bits of data, right? And what's important for us is actually the work of the ONS, that statistics we produce are quality assured and relevant. And by providing these guardrails where you know, Stats Chat only looks at ONS published data, we have a degree of assurance that the data coming back to the user is likely to be of good quality and not based on who knows what information. MILES FLETCHERBecause when you use, to name one example, ChatGPT for example, the little warning comes back saying “ChatGPT can make mistakes, consider checking important information.” And I guess that's fundamental to all this isn't it. These tools, as intelligent as they might be, they're only as good - like any system - as the information that's going in the front end.  OSAMA RAHMANThat's absolutely correct, which is why we have these guardrails where, you know, the functionality on Stats Chat is focused on published ONS information.  MILES FLETCHERThat does mean that something that's offered by an organisation like the ONS does have that sort of inbuilt potential to be trustworthy and widely used. But of course, you might say, to have a really good tool it's got to be drawing on masses of information from right across the world. And it's interesting how, and you mentioned that it's open-source data, of course, that's most available for these tools at the moment, but you're seeing proprietary data coming in as well. And this week, as we're recording this, the Financial Times, for example, has announced that it's done a deal with one of the big AI firms to put all of its content into their database. Do you think there's scope for organisations like the ONS around the world to collaborate on this and to provide you know, really powerful tools for the world to exchange knowledge and data this way? OSAMA RAHMANSo there is collaboration going on. There's collaboration, both within government - we're not the only department looking at these sorts of tools; there's also collaboration internationally. I think the difference you know... our information on our website is already publicly available. That's why it's on the net, it is a publication. But there's a difference in situation with the FT where, you know, a lot of the FT information is behind a paywall. MILES FLETCHERYeah, it has a sort of democratising tendency that this publicly available information is being fed into these kinds of sources and these kinds of tools. That's big picture stuff. It's all very exciting work that's going on. But I'll come back to you Rich just for a second. What examples practically, because I think that the Stats Chat project is still a little way off actually being available publicly, isn't it? RICHARD CAMPBELLYeah, I think it is still a little way off. So, I think the key thing that we're doing at the moment and something we've done for years, but AI is helping is the use of automation principles. Just making things quicker. Now in a data science context, this might be going through very, very large data sets, looking for patterns that it would take an analyst a huge amount of time and probably far too much patience than they would have to find. MILES FLETCHERSo for example, in future then we might find that - and this is one issue that recurs in these podcasts - obviously about the limitations of official statistics is they tend to lag.  This is another way of making sure that data gets processed faster. And therefore, the statistics are more timely, and therefore the insights they provide are really much more actionable than perhaps they might be at the moment.  RICHARD CAMPBELLYeah, that's spot on. There's potential in there for pace of getting the statistics from the point that the data exists to getting it into published statistics. There's potential there for us to be able to combine and bring more sources together. There's also some behind the scenes stuff that helps as well. So, for example, quite often we are coding up the systems to produce new or improved versions of official statistics. And we're looking at the possibility of AI speeding up and supporting that process, perhaps for example, by giving us an initial draft of the code. Now, why does that matter for people in the public, you know, does anybody actually care? Well, what it means is that we can do things quicker and more to the point we can focus the time of our expert data scientists and other analysts in really helping people understand the data and the analysis that we're producing.  MILES FLETCHEROkay, so lots of interesting stuff in the pipeline there. But I'd like to bring in Sam now to talk about how AI is actually being used in government right now. Because in your work Sam at the Department for Transport, you've actually been working on some practical projects that have been gaining results in the real world. SAM ROSEWe have - we've been doing loads actually, and my poor team probably haven't had any time to sit still for the last 18 months or so. And I think like most ministerial departments, we're doing lots and lots of work to automate existing processes, so much like Rich has alluded to in your space, we're looking at the things that take up most of the time for our policy colleagues and looking at how we can automate those. So, for example, drafting correspondence, or automating policy consultation processes, or all of that kind of corporate memory type stuff. Can we mine big banks of data be it text or otherwise and summarise that information or generate new insights that we wouldn't have been able to do previously? But I think slightly more relevant maybe for you guys, is the stuff we're doing on creating new datasets or improving datasets. So, a few things. We're training a machine learning model to identify heavy goods vehicles from Earth observation data. And that's because we don't have a single nationally representative data set that tells us where these heavy goods vehicles park or stop outside of existing kind of service stations, and what we want to understand is where are those big areas of tarmac or concrete where they're all parking up as part of their routine journeys, so that we can look at when we're rolling out the green infrastructure for heavy goods vehicles, we're looking at where the important places that we need to put that infrastructure are. And that data doesn't exist at the moment. So we're using machine learning to generate a new dataset that we wouldn't otherwise have. MILES FLETCHERAnd how widespread are these kinds of projects across government in the UK now? SAM ROSESo I think that there are loads of different things and I wouldn't be able to speak on behalf of everybody but I know lots of different areas of government are looking at similar kind of automation and productivity projects like our kind of drafting all of the knowledge management area. I think there's things like Osama alluded to where DEFRA for example, I think they're using Earth observation data to assess biodiversity for example. So, there's lots of stuff that's common between lots of government departments, and then there's lots of stuff that's very specific to individual departments. But all along the way there's lots of collaboration and working together to make sure we're all learning continuously and where we can collaborate on a single solution that we are. MILES FLETCHERI guess one of the central public concerns about the spread of AI once again that it will cost jobs, that it will do people out of the means of making a living that they've become used to. And I guess from government's point of view, it's all about doing much, much more with the resources that we have and making government much more effective.  SAM ROSEYes, absolutely. And it's not necessarily - and I think Rich mentioned this earlier - it's not necessarily about doing our jobs for us. It's about improving how we can do our jobs and being able to do more with less, I think, so freeing up the human to do the bit that the human really needs to do and enabling the technology to do their very repeatable very automatable parts of the job. And indeed, in some instances, this technology can actually do the work better than humans. So be it identifying really complex patterns and datasets, for example. Or a good example from us in transport is we've trained machine learning model to be able to look at images of electric vehicle charge point installations and be able to identify that similar or the same image that has been submitted more than once. Now that's estimated to have saved over 130 man years of time, you know, that's not a task that we would have been able to do with just humans. MILES FLETCHERAnd you would have to be pretty alert as a human and have a very high boredom threshold to process all that material yourself and spot the fraudsters. SAM ROSEYeah, well, quite. And that's, I think, a really nice example of where again, it's not taking our jobs, but it's enabling us to do something that we wouldn't have been able to do previously and improve the service that we're providing. MILES FLETCHERNow, our ability collectively, whatever sort of organisation we're involved in, our ability to make the most of AI depends on of course having the right skills, and Osama I guess this is where the Data Science Campus comes in as the government's Centre of Excellence for data science, principally, but I guess also in this context, artificial intelligence as well. What work have you been involved in to make sure that the supply of those skills and knowledge is on tap for government? OSAMA RAHMANSo firstly, I would say we are a (one) centre of excellence within government. I think you know, what's been brilliant to see since the campus was set up has been that actually more and more government departments have excellent data science, AI teams. Sam leads one at DfT. There is, of course, 10DS (or 10 Data Science) at number 10 [Downing Street]. There's a Cabinet Office team. So, there's lots of teams that now work in this area. Some of the stuff we've been doing is we have various training programmes that we have run. We have senior data masterclasses so that actually, senior leaders within government can understand better the power of data. 10DS, Sam's area, have all been running hackathons, which actually improve skills as well. So, it's no longer just us who are building capability. I think it's great to see that across government and across departments there are teams improving skills within their departments, bringing in others from outside to work with them. So, there's a lot going on there. SAM ROSEJust really quickly, it's important to think that skills are not just skills of data scientists, but skills of everybody's ability to use this kind of technology. There's a lot of work going on at the moment looking at what we need to do both internally to government, but also out there in all of our sectors to make sure that our workforce has the skills it needs to be able to more rapidly kind of adopt and be able to take advantage of all the benefits that this technology brings to us. I mean from a very personal point of view, and I don't really know all of the answers to this, but you know, I'm thinking about what actually, if large language models can help us to generate efficient code, then actually, what skills do I need in my data scientists? If it's not writing code, is it actually the analytical thinking and being able to understand how to apply these kinds of technologies? So, I think it changes what we need in the workforce that we have.  MILES FLETCHERInevitably, though, if we're talking about this kind of technology being rolled out across government and thereby increasing the power of government to know more about more people, then concerns obviously, about the ethical use of data come in...  RICHARD CAMPBELLMaybe if I can just come in on that one Miles. Using data safely and responsibly - it's built into our very DNA in ONS and across government. And our keenness to sort of learn how to do new tools new techniques is always going to be tempered by our need to ensure that we are responsibly using the data that's been entrusted to us. And I think we need to sort of strike a balance here. We need to ensure that we don't take this responsibility as an excuse to not try and adopt new technology such as AI, but it also means we have to do so with care and responsibility and to do it at an appropriate pace. The key thing, I think, for me is ensuring that we can retain control of the data that we've been entrusted with. And so, understanding what AI is doing with that data, considering what data we're giving access to it, what data is being processed, and what data is being generated. And this is really at the forefront of our minds and our collective use of this. I think our approach - and Osama touched on this earlier - is to sort of be novel and start with open source and non-sensitive data first, so that will help us learn how we can effectively use it before we go on to some of the more sensitive data that we hold. SAM ROSEWe have to have ethics and data protection at the heart of everything we do, which then does have the tendency I think necessarily to reduce the pace of our ability to roll things out a little bit. But as government we do, I think have more responsibility. We can't have those kind of oops moments that some of the big tech companies have had when they're trying to reverse engineer the data to remove bias and that you know, things like that that then fundamentally undermine the output of their models. I think when you're doing a job that affects individual people, and providing services that affect citizens then we don't really have the luxury of getting it wrong like that, and we have to try to make sure we get it right first time. So, all of the things that Richard said about starting with, you know, safer datasets and working our way up before we deploy these models is kind of fundamental to how we're going to learn and ensure that we're doing it safely and securely MILES FLETCHEROsama what's your take on the ethics question?  OSAMA RAHMANFirst of all, I would echo everything just said. You know the Statistics Code of Practice is an annex to the Civil Service Code, it applies to all of us not just statisticians - I'll point that out. It is I think, not just in the ONS, I think for analysts and data scientists and specialists across government, this is kind of built into their DNA. Central Digital and Data Office has put together guidance and circulated it across government on the safe use of AI within government. So, within government, we do take this quite seriously. And then actually in terms of the use of some of these techniques, I think pointing these tools at data and information that we know is accurate is an important starting point - so having those guardrails. If it's going to be used for decision making, then having a human in the loop is quite important to make sure that the use is ethical. So, there's a bunch of safety checks that we do put in which I think allow for us to have some assurance that the use of these tools will be safe and ethical.  RICHARD CAMPBELLI think just as one additional point is you know; this isn't a new challenge for us. It's a different flavour of a challenge that we faced in considering new technology in the past. So, we can think in fairly recent times the use of cloud technology to securely and safely store data. If we go further back the use of the Internet, go back further, again, the use of computers to hold data. And what I think we've demonstrated time and time again, is that we do approach these things responsibly and maturely. But we do find opportunities to use all of them to improve the quality of statistics and analysis and the service that we offer the public. MILES FLETCHERLooking to the future then, and this is a very fast-moving future of course, I'd like to get your takes on also where you see us in five years' time in 10 years' time with this. I mean starting with the Office for National Statistics – Osama and Rich particularly on this. How will we start to see the published statistics and the big key topics, but also the granular insights that we provide on all kinds of areas. How will we see that changing and developing do you think? Where are you going to put your money? RICHARD CAMPBELLI think predicting the future in this way is quite a dangerous game. I'm thinking back to you know, if we had this podcast in the year 2000 and we asked ‘'how would the internet form part of our working lives?' We would have predicted something which would have been quite different from the impact that it had. Saying all that I think it will make a fundamental difference to the way that we work. I see that it will be integrated in the day-to-day tasks that we do in a similar way that we used computers to speed up and change the way that we produced statistics. I think it will enable our users to far better interact and engage with data and analysis. So, it will be less of us producing a specific finalised product for them, and more for them to be able to sort of get in ask questions, probe and really, really interact. And I think lastly, it will give us more potential to work and analyse data because one thing, and I think this is really important to say, AI will give more opportunities for analysts. It won't take them away. It will give them more space, more tools to work with to produce better, more complex, more useful datasets and analysis for ONS and for its users. SAM ROSEI was just going to add that I think it will fundamentally change the nature of what we do. A little bit like Rich said, the sort of work that we do will be different, but really critically, I think in a few years' time we won't really notice that change. I was thinking that most people have forgotten that 10 or more years ago before you left the house to go somewhere new, you would have consulted your map. Whereas actually nobody, or very few people, do that anymore. So, I think we're going to forget very quickly that lots of what we will be doing will be AI driven. MILES FLETCHERSo it's a big evolutionary step forward, if not quite a revolution. Do you agree with that Osama? OSAMA RAHMANAbsolutely, because some of us have actually been using sort of transformers-based models, which is what these large language models are based on for... My team has been working with those for at least the last eight years. But I wanted to just pick up on what Rich just said. And it is an evolution right. And you can't separate the tools from the data. And one of the things we're getting now is data that is much more granular and of much higher velocity than the data we were used to. So that allows us to look at things at a more local level, at a more timely level. What I do completely agree with Rich on is actually a lot of these tools and methodologies allow the technical production of statistics to get more efficient, which then allows you to produce more statistics at a disaggregated level - at a regional level or local authority area level or looking at different sub populations. It allows us to update statistics more frequently. But then also what it allows us to do, because it's not just about the production of the statistics, it's about what those statistics actually tell you is going on. And I think it allows the people we have at the ONS and other government departments to spend more time on the real value added which is “what does this mean?”  MILES FLETCHERIt's interesting if you're researching a particular topic, it must be good to sort of evolve your methodology quickly and to refine your processes on the run as it were to explore a particular topic. One thing of course we need in statistics is consistency of methodology and approach. Does that limit do you think, either of you, the ability for statistics to get more insightful to get more germane to issues because we have to stick to accepted methodologies to provide that consistency over the long run? RICHARD CAMPBELLI don't think it does Miles. I mean, you're right there. There's always a challenge for us in that, that consistency is really important, that comparability in a time series. Equally, users do want us to look for improvements, more detail, whether that's granularity or whatever else. And actually, we've got a really good successful track record of both maintaining the consistency of our statistics, while at the same time introducing new and improved methods. We do it with GDP. We do it with inflation, we do it with population, it's something that we do time and time again. And, actually, I think automation AI offers up some really exciting opportunities here in terms of methods that can be applied. There's actually an element of it, which will help us in the understanding and documentation and consistent application of the methods as well. It's perhaps one of the less – if you don't mind me using the word - “sexy” applications of AI but using it to ensure that our documentation is absolutely spot on and done quickly. To ensure that we are applying methods really quickly and consistently. I think AI offers us potential to do that even better. MILES FLETCHER“Sexy” in the particular way that we refer to progress in data science. RICHARD CAMPBELL Yes, quite. OSAMA RAHMANCan I just come in on this? And it's possibly worth using a specific example of putting out statistics on prices. In the old days you'd basically have people going out into the field, and that's where you'd find a basket of goods, and using pen and paper would collect prices. Now where a lot of national statistical organisations are going to is actually getting scanner data, because most things when you pay for them nowadays in many parts of the world, it's scanned first, electronically rather than rung up through a cash register of some sort. So, scanner data provides a lot of information about what is being purchased, and at what price it's being sold at various retail outlets. And so, you have this data which again is much more granular and has much higher velocity then price data you can collect through surveys, and you know, how you integrate that into the production of pricing statistics and other economic statistics is really, you know, a really interesting question and work that a lot of national statistical organisations are working on. So, there's still the basic methodology remains the same. It's you know, kind of defined a basket of goods, but you expand the scale of the basket, we'll get prices on at what each of the elements are those baskets are being sold at, and then produce a price measure an inflation measure, right. But these tools and the increasing quantity of data allow us to do that. But you know, the basic methodology is kind of the same, but actually the increase in this data allows us to do that in kind of a different way. It's an evolution. MILES FLETCHERIt does all suggest though, that perhaps the survey might finally be replaced - the big social surveys that the ONS runs. Do you think that the surveys days are numbered, therefore because of AI?  RICHARD CAMPBELL No. OSAMA RAHMAN No. [LAUGHTER] MILES FLETCHER A resounding no. RICHARD CAMPBELLThat was a resounding no, and it's not a pre-rehearsed one. And maybe I'll just take us back Miles. So, if we went back about the best part of 10 years, everyone was talking about big data. You know, the days of a survey was gone. What we needed was these big, complex, sometimes quite messy data sources that were collected for a variety of other reasons, and that we could utilise those to sort of answer all of the statistical questions that we had. Now, what we found out actually is that yes, these data sources can give us a lot of potential; data science is helping us make the most of them; AI is helping us make even more from them. What we also learned though, is that they work best when they're complementing the surveys, rather than trying to replace. Think of it a bit as horses for courses. Actually, though, I want to give an example of where AI might be able to help us improve the response rates on surveys. So, AI might be able to help respondents navigate through some of the surveys, helping them understand what it is that they're being asked. Helping them answer a bit more efficiently. So that might actually remove a barrier that some people, some businesses have to respond to surveys. So, you never know we might see a bit of an uptick in response rates with a bit of AI's help.  OSAMA RAHMANAnd I think the other thing I would add is what surveys are particularly good at is getting information on the extremes of the distribution. It's great if you think everything's going to be generated through digital footprints data and online services, but actually not everyone... some people... apparently dumb phones are coming back into fashion. Or there are groups that you know, for whatever reason, are not picked up in other forms of data. And actually, surveys are really important for accessing, getting information about hard to reach groups at the end of the distribution. MILES FLETCHERI think that's kind of reassuring that for all the promise of AI in this brave new world, that we hope won't be a dystopian future, but whether it will deliver all those things that we've been talking about in terms of better insights, faster statistics, and all that. It's still good to hear though, isn't it, that there is no substitution from speaking to real human beings directly.? OSAMA RAHMANI agree entirely. MILES FLETCHERWell, that's it for another episode of Statistically Speaking and, in summary, I suppose the use of AI feels like a natural evolution with a number of potential benefits, and potentially huge benefits, but with its adoption we need as always to be thoughtful and ethical. So, thanks to all our guests: Sam Rose, Osama Rahman, and Rich Campbell, and of course, thanks to you as always for listening. You can subscribe to future episodes of this podcast on Spotify, Apple podcasts and all the other major podcast platforms. You can also follow us on X - formerly known as Twitter - via the @ONSfocus feed. I'm Miles Fletcher and from myself and producer Steve Milne. Until next time, goodbye.  ENDS

This week's episode of The TreppWire Podcast was recorded early, so the team used this time to provide our listeners with a breakdown of the topics the market has been focused on lately: maturities, modifications, CRE risk pricing, cap rates, and more. In our usual fashion, we also dive into the CRE property types, but today, we share delinquency, DSCR, debt yield, and other important data points to note for each sector. Episode Notes: - Extend and pretend? Background and history (2:13) - Is there a silver lining for office properties that can extend? (6:48) - How have changing rates related to property values? + Cap Rates (9:28) - Risk pricing within CRE, Gordon Growth Framework (13:44) - Size of the CRE Universe (25:25) - Deep Dive into the Data- Office, Multifamily, Retail, Lodging (28:10) - San Antonio market (42:35) - Debt yield and underwriting standards (47:39) - Multifamily debt yield (49:24) - Freddie Mac multifamily (51:28) - Shoutouts (1:01:06) Please take our listener feedback survey: www.surveymonkey.com/r/BMPXLHG Questions or comments? Contact us at podcast@trepp.com. Follow Trepp: Twitter: www.twitter.com/TreppWire LinkedIn: www.linkedin.com/company/trepp

Today's guest is Puravee Bhattacharya, Data Product Owner - Group Data Office at Energia. Energia is the longest supplier of 100% green electricity on the island of Ireland. They are engaged in a wide range of projects including bio-energy, energy storage and solar energy. Through strategic initiatives and investments, Energia are decisively addressing the challenges of energy provision in a world that is grappling with climate change.  Puravee is a Data and Analytics professional with 10 years of Product and Consulting experience in advisory, regulatory and investigatory roles within the Data Science landscape. She has a track-record of delivering large-scale Project and Program Management in Business Intelligence, Business Transformation, Technology enablement and Regulatory driven change/implementation projects. Topics include: An overview of her role with Energia The energy sector's shift to renewables, sustainability and traceability How Data drives efficiency in sustainable energy transition Adaptability in tech, reskilling and data accessibility A Consumer-led sustainable energy shift via decentralisation and digitalization How Smart meters enable informed consumption for efficient energy use

Lebensläufe sind spannend – denn sie zeigen oft auf, was man für unterschiedliche Abbiegungen nehmen kann, um an ein Ziel zu gelangen. In der neuen Folge von MY DATA IS BETTER THAN YOURS spricht Jonas Rashedi mit Anna Louise Schröder, die das Data Office der AXA Deutschland leitet. Das Data Office verantwortet die Data Governance und zentrale Datenstrategie und arbeitet als CoE innerhalb des Data Capability Tribes eng mit operativen agilen Squads zusammen, um Daten für verschiedene Domänen zentral und harmonisiert bereitzustellen. Anna ist stolz darauf, dass die cross-funktionale Zusammenarbeit zwischen technischen und fachlichen Experten im Rahmen der agilen Transformation der AXA so operationalisiert werden konnte. Der kulturelle Wandel, der für so ein organisatorisches Data-Setup notwendig ist, kommt nicht über Nacht. Die Frage, um die es im Podcast aber auch geht, ist: Braucht man die Erfahrung aus der Praxis, um im Data Bereich eine Management-Position einzunehmen? Denn Anna hat „Stallgeruch“ an sich, sie hat Ökonometrie und Statistik studiert und über 10 Jahre in Analytics und Data Science gearbeitet. Von dieser Erfahrung profitiert sie immer noch – vor allem im Bereich Machine Learning. Doch auch hier gibt es viele verschiedene Herangehensweisen! Anna bringt ein Beispiel dazu, welche mathematischen Herausforderungen es im Versicherungssektor gibt. Stellen wir uns eine Produktion in der Textilindustrie vor, in der es brennt. Als Gewerbeversicherer solltest Du Muster erkennen können, um Deine Risiken richtig zu berechnen und auch Deinen Kunden Tipps zur Risikominimierung geben zu können. Dafür gibt es Risikoingenieure, die schon seit Jahren in diesem Bereich aktiv sind und die sich die wenigen Fälle sehr genau anschauen. Diese Expertise wird gepaart mit Daten bspw. zur Brandgefahr, z.B. bei Leuchtmitteln oder einer Vielzahl anderer Faktoren, was durch generative Modelle technisch formalisiert und umgesetzt werden kann. Ihr Fazit: Du musst die Data Science Methode an das Businessproblem anpassen und wo möglich von den fachlichen Experten lernen, um deren Knowhow in Data Lösungen maximal einzubringen! MY DATA IS BETTER THAN YOURS ist ein Projekt von BETTER THAN YOURS, der Marke für richtig gute Podcasts. Zum LinkedIn-Profil von Anna: https://www.linkedin.com/in/almschroeder/ Zur Webseite von AXA: https://www.axa.de/ Zu allen wichtigen Links rund um Jonas und den Podcast: https://linktr.ee/jonas.rashedi AXA sucht Data Talente! Zum Beispiel für unsere zentrale Datenorganisation: Data Consultant -https://careers.axa.com/de/de/job/AXGRGLOBAL230009CIYESTALEODEEXTERNALDEDE/-Senior-Data-Consultant-m-w-d Data Architect/Engineer -https://careers.axa.com/global/en/job/AXGRGLOBAL22000BEJYESTALEODEEXTERNALENGLOBAL/Data-Architect-Engineer-m-w-d Data Platform Engineer -https://careers.axa.com/global/en/job/AXGRGLOBAL23000747YESTALEODEEXTERNALENGLOBAL/Platform-Engineer-m-w-d Data Scientist Venn Diagram: https://commons.wikimedia.org/wiki/File:Data_scientist_Venn_diagram.png Tool Box Daten Kompetenz: https://beta.toolboxdatenkompetenz.de/ 00:00:00 Intro und Begrüßung 00:01:10 Vorstellung Anna Louise Schröder 00:02:29 Organisationsaufbau bei Axa 00:06:49 Der Werdegang von Anna 00:12:48 Statistik oder Machine Learning zuerst? 00:14:58 Risiken in der Versicherungsbranche 00:20:12 Zusammenarbeit mit der Praxis 00:21:41 Data Manager aus der Praxis 00:24:40 Kommunikationsfähigkeit 00:30:13 Toolbox Datenkompetenz 00:31:04 Anna's Data-Game

Jochen arbeitet nun seit über 15 Jahren im Retail Bereich und fühlt sich bei ALDI SÜD am wohlsten. Mit Kai spricht er über Datenakzeptanz und Use Cases in einem globalen Umfeld. Was du in dieser Folge erfährst: - Was bedeutet es, bei ALDI SÜD zu arbeiten? - Wie wird bei ALDI SÜD mit Daten gearbeitet? - Welches Potenzial gibt es für Use Cases in den Läden? - Welche Grundlagen sind entscheidend? - Welche Technologie wird genutzt? Als Director des Central Data Offices ist Jochen bei ALDI Data & Analytics Services unter anderem für Data Strategy, Data Governance, Data Management und Data Compliance verantwortlich. Damit leistet das Data Office einen wichtigen Beitrag für das operative Business von ALDI und bildet das Fundament für jeden neuen Analytischen Use Case. Wo liegen Daten? Wer ist der Data Owner und wie kann Daten Qualität erhöht werden? Jochen hat über 15 Jahre Retail-Erfahrung und ist seit über 7 Jahren bei ALDI SÜD. Privat ist Jochen verheiratet, hat 2 Kinder und ist ein großer Podcast-Fan.

Better use of data is key to more effective government. Across government, teams are doing fascinating work with data. But those projects don't get the attention they deserve. Data Bites aims to change that. This event was the 41st in our series, where the speakers presented their work in an exciting, quickfire format. Each speaker had eight minutes, followed by eight minutes of questions from the audience. This months speakers were: Sandrine Balley, Geographic Information Lead at the London Borough of Hackney, on how Hackney developed a webmap template to open up spatial data (and how you can use it too) Kathleen Caper, Head of Data Maturity and Governance at the Central Digital and Data Office, on Data Maturity Assessment for Government - more than a tool for the data function Dan Jeffery, Chief Information Security Officer and Deputy Chief Information Officer at NHS Blood and Transplant, on how NHSBT secure the supply of Blood, Organs, and Tissues services to the NHS from cyber threats Clara Clark Nevola, Group Manager (Technology) at the Information Commissioner's Office, on Privacy Enhancing Technologies, how they relate to data protection requirements and how they can be used in practice The event was chaired by Gavin Freeguard, Associate at the Institute for Government.

Welcome to the Data Culture Podcast, Xavier Lagardere! He is Chief Data Officer at Lufthansa and in this newly created function, Xavier's role is to promote data analytics and data culture within Lufthansa. In this episode, you will learn about the scope of his core team, and that a total of over 1,000 people work in the data team at Lufthansa. Xavier discusses his biggest success and the challenges he faces in trying to support the organization's various business units with data. You will also get an overview of how data is processed at Lufthansa. Xavier believes that "Data Culture" is not the best term to describe what he and his team is installing. As Chief Data Officer, he is committed to driving mindset and behavior with data across the company so that better results can be achieved. Want to get more insights on Lufthansa's data transformation journey? Xavier is part of the #DATAfestival online as a Speaker & YOU can be also part of it. Get your free ticket for the festival here: data-festival.com/tickets-october-2022/ Follow Carsten Bange on LinkedIn. Follow Xavier Lagardere on LinkedIn. Don't miss any news from BARC via LinkedIn. BARC website: www.barc.de Looking to take part in an episode or just say hello? Reach out to info@barc.de Did you like this episode? Share it with your friends and colleagues!

How is your curiosity manifesting? Cultural Strategist and Quantitative Futurist, Sarah Davanzo is back, talking about the four modalities of curiosity: Think, Feel, See & Do. It's a wild ride of a conversation, and we loved every minute of it. Sarah DaVanzo is a Cultural Strategist and Quantitative Futurist with >70% accuracy (a.k.a. "superforecaster") who has helped over one hundred businesses future-proof. Her futurized insights have featured in Time, The New York Times, Forbes, Fast Co., The Guardian, Wired and two TEDx talks.  After living/working internationally and cross-culturally for two decades in 22 countries, today Sarah leads the Data Office (insights, intelligence & foresight) for one of the world's most interesting sustainable companies, Pierre Fabre.  When she is not teaching "tomorrowing" methods she is promoting diversity, equity, inclusion & access (DEI&A) in the futures field via the CURIOUS FUTURES project. She also produces handmade collectible art zines, such as Insight Alchemy.  Her book showing how foresight DEI&A can prevent f@*ked futures, synthesizing seven years of original research and experiments, will be published in 2023. Live Curiously!   Follow Sarah on LinkedIn   Mix It Up with Us  Terri on Instagram  Melissa on Instagram  Join the Mixing Up Midlife Discussion Facebook Group Email Us: MixingupMidlife@gmail.com  Visit the Mixing Up Midlife website Maybe Find Us on TikTok     RESOURCES AND SOURCES : Daniel Kahneman Thinking Fast And Slow delves into the two systems in our brain and how they can affect us on our daily judgments and decisions.   Leave us a review if you enjoyed the podcast. As always, thanks for listening to Mixing Up Midlife, a podcast for women over 50 who are adventurous, curious and want to have fun.

We're joined today by Sarah DaVanzo, a Cultural Strategist and Quantitative Futurist, who at the age of 57 took a leap with her own future and pivoted to follow her calling for a purpose driven life. Sarah is a super forecaster who has helped over one hundred businesses future-proof. After living/working internationally and cross-culturally for two decades in 22 countries, today Sarah leads the Data Office (insights, intelligence & foresight) for one of the world's most interesting sustainable companies, Pierre Fabre.  When she is not teaching "tomorrowing" methods she is promoting diversity, equity, inclusion & access (DEI&A) in the futures field via the CURIOUS FUTURES project. She also produces handmade collectible art zines, such as Insight Alchemy.  Her book showing how foresight DEI&A can prevent f@*ked futures, synthesizing seven years of original research and experiments, will be published in 2023. Her futurized insights have featured in Time, The New York Times, Forbes, Fast Co., The Guardian, Wired and two TEDx talks. Follow Sarah on LinkedIn Mix It Up with Us  Terri on Instagram  Melissa on Instagram  Join the Mixing Up Midlife Discussion Facebook Group Email Us: MixingupMidlife@gmail.com  Visit the Mixing Up Midlife website Maybe Find Us on TikTok

Dagens to gæster var end ikke født, da the LEGO Group påbegyndte den digitale transformation for over 30 år siden. Glæd dig til at møde to af data-kometerne fra det topambitiøse Data Office i the LEGO Group, Anders Butzbach Christensen, Head of Data Engineering, og Oliver Reinholdt, Senior Data Engineer.  Lyt med, når Anders og Oliver deler erfaringerne med at få 27.000 medarbejdere til at træffe bedre, datadrevne beslutninger og deles om data uden at skele til 'dit og mit'. Som en bonus kan du i dagens episode blive klogere på både Hub and Spoke-distributionsmetoden ført ud i livet og ikke mindst, hvordan syntetiske data kan hjælpe dig til at bevise kommercielle usecases allerede på forhånd, fx adfærden op til og på Black Friday! Som altid er dagens gæster drevet af passion, og der er virkelig gode erfaringer at hente i den måde, the LEGO Group driver deres stærke dataplatforme på ude i de enkelte produktteams!  God fornøjelse i Dataklubben!

Better use of data is key to more effective government. Across government, teams are doing fascinating work with data. But those projects don't get the attention they deserve. At this month's event, the 33rd in our series, the speakers will present their work in an exciting, quickfire format. Each speaker has eight minutes, followed by eight minutes of questions from the audience. This month's speakers are: Claire Eadington, Head of Data Portfolio at the Central Digital and Data Office, on CDDO's strategic roadmap for data Alexis Castillo-Soto, Deputy Director for Digital and Data in the Department for Business, Energy and Industrial Strategy, on how its Data Management Service (DMS) provides a flexible, scalable solution that can be reused to support existing/future digital services Anna Price, Statistics Regulator, Health and Social Care Lead at the Office for Statistics Regulation, on Reproducible Analytical Pipelines in government Matt Kerlogue, former Head of Data Innovation, Cabinet Office Analysis & Insight, on three things he's learnt being a “data person” in government The event was chaired by Gavin Freeguard, Associate at the Institute for Government. #IfGDataBites

Richard Morton, Head of Accessibility for Government, Central Digital & Data Office Richard talks about his early work with usability testing that also exposed him to the opportunities working with accessibility. He describes the community and knowledge-sharing in the UK. Now he works for the UK Digital and Data Office where the team support accessibility in government organizations. 

As companies grow and their market expands, their systems and processes become more complex. Managing data assets can be overwhelming without proper knowledge, organization, and mastery. This is where the concept of a data office comes in handy. In a time when data is more valuable than ever, it is imperative that a company understands how to make it work for them. It might be time for you to consider forming a data office within your company, particularly if your company: is in a position of rapid growth, onboards employees daily, is expanding their market, and/or deals with systems and processes that may be out of date. In this episode, Stijn Christiaens, Founder and Chief Data Citizen at Collibra, joins us to discuss the importance of handling data and starting a data office. He explains what inspired him to begin creating the data office within Collibra, and how this concept may pave the way for future companies.  Tune in to the episode to further understand how to build a data office for your business.  Here are three reasons why you should listen to this episode: Understand the importance of data in a growing company.  Identify if your company needs to build its own data office. Learn from Collibra's journey of building a data office. Jay Militscher: “It meant to me that data and facts inform whatever point you're making at the moment. Are you making a recommendation to buy something? Where's the chart? Meaning data, to back up that decision. Are you delivering a critique on something? Again, where's the data to back up an otherwise subjective opinion.” Episode Highlights[07:47] More People Means More DataCollibra experienced rapid growth in its company, onboarding more people than the system could handle. As more people filter into the company, more data is added to the system. This increase in staff also implies an increase in customer interactions.  [08:19] An Expanding MarketFor Collibra, rapid internal growth meant growth in the market.  They needed to streamline their transition from data governance to data intelligence. Stijn believes that the growth in people and in the market means growth in data, which needs to be mastered. Stijn Christiaens: “And, in that sense, we also said, okay, if we set up a data office now because we need it, right? Because systems and processes will also have the added benefits if we do this right to continue to lead our customers. And then you start to experience, really, also what some of your customers experience, right?”  [09:58] Leading the WayStijn took on the challenge of accepting the new role of becoming the “data boss” to lead the way not only for Collibra but for future organizations.  “Data Office 2025” is realized by Stijn and his team for future organizations that will face similar challenges as Collibra is experiencing.  This includes dealing with new data technology and new tools for data stakeholders across the business. Stijn Christiaens: “All organizations, over time, we need to get better at mastering data assets. So all organizations, just like they have a chief financial officer. They will have a data boss or somebody responsible for data and maybe a data office just like their finance and HR, let's say. So, we saw a trend, and then we said, okay, we can actually do this.” About StijnStijn Christiaens is the co-founder and current chief data officer at Collibra. He's been involved with the company for 15 years and spearheaded the creation of the data office for Collibra. Enjoyed this Episode?If you did, be sure to subscribe and share it with your friends!  Post a review and share it! If you enjoyed tuning in, then leave us a review. You can also share this with your friends and family. This episode will inform them of the importance of data and building a data office for your company's future. Have any questions? You can connect with us on https://www.linkedin.com/in/jay-militscher/...

Join Jay Militscher, Head of Data & Analytics at Collibra, as he explores some of the hottest topics in the industry from building a data office, to ESG, to the ethical use of data, and beyond. In this five-part series, industry leaders from around the globe share their best practices, learnings, and predictions for the future.  But it doesn't stop there…we also have five bonus episodes in this series that feature our very own Collibrians. These “Inside Collibra” episodes show how Collibra's very own Data Office is tackling these complex issues.  Welcome to The Data Download!

Better use of data is key to more effective government. Across government, teams are doing fascinating work with data. But those projects don't get the attention they deserve. At this month's event, the 27th in our series, the speakers presented their work in an exciting, quickfire format. Each speaker had eight minutes, followed by eight minutes of questions from the audience. This month's speakers were: Tina Mermiri, Head of User & Data Insight at Government Digital Service, on data, trends and change on GOV.UK Hannah Spiro, Head of Public Attitudes, and Holly Clarke, Public Attitudes Researcher, at the Centre for Data Ethics and Innovation (CDEI), on the findings of the CDEI Tracker Survey which monitors changing public attitudes to data and AI Charles Price, Deputy Director of the Knowledge Assets Team at BEIS, on public sector knowledge asset management Kathleen Caper, Senior Policy Adviser at the Central Digital and Data Office, on the Data Standards Authority and why data sharing governance is key to its plans. The event was chaired by Gavin Freeguard, Associate at the Institute for Government. Find out more about Data Bites: https://www.instituteforgovernment.org.uk/data-bites

In this episode, Sarah talks to Sam Roberts about why he cares about engaging people with government data. From the early days of open data, to the current more mainstream landscape, Sam shares his insights, experiences and hopes for the future. He talks about his approach in sharing the benefits of open data; the impact of data around the pandemic and how the rapid development of technology might influence the future, as well as concerns over disinformation.  Sam is the Head of Open Data and Open Government Policy in the Central Digital and Data Office in the Cabinet Office. He also heads up the UK's engagement with the Open Government Partnership and sits on the bureau of the Open Government Working Party for the OECD. Links Sam on twitter @SammyR21 Swirrl on twitter @swirrl Central Digital and Data Office Swirrl Website Open Government Partnership Organisation for Economic Co-operation and Development (OECD) UK National Action Plan for Open Government 2021-2023

In this episode Dave, Mark and Mike talk about the all important and hot topic of sustainability. After doing a lot of reading and writing about sustainability and cloud compute and through the influence of COP 26 the team discuss the how sustainability is now at the top of the agenda.  Even Simon Wardley called for AWS to bring out carbon cost per lambda execution preInvent. Mark feels it's one of the big wins that all Cloud Providers can easily put out there especially with Microsoft Azure calculator and GCP and their carbon footprint capabilities.  Mark sees cost as a proxy for carbon footprint up to now, but it's a big assumption.  But with a serverless first approach, sustainability is not for free, but you're getting it as a side effect.  Dave talks about the 4 basic models of compute and how hard it is for Cloud providers to report on all cases. But the first step is to get to the cloud and the second step is to think about your energy utilisation.  They also discuss a recent report that AWS is around 80% more efficient than running your own data centre Mark also talks about the fact that teams who have delivered solutions in a serverless first way will be able to adopt any new carbon footprint features that within minutes or hours to satisfy Board/C Suite Level mandates. And they discuss the UK government sustainability guidelines for the Central Digital and Data Office. Serverless Craic from The Serverless Edge theserverlessedge.com @ServerlessEdge

Better use of data is key to more effective government. Across government, teams are doing fascinating work with data. But those projects don't get the attention they deserve. At this month's event, the 25th in our series, the speakers presented their work in an exciting, quickfire format. Each speaker had eight minutes, followed by eight minutes of questions from the audience. This month's speakers were: Natalia Domagala, Head of Data Ethics at the Central Digital and Data Office, on algorithmic transparency Henry Duquemin, Real Time Indicators (RTI) Dashboard Lead, Advanced Analytics at the Department for Business, Energy and Industrial Strategy, on the use of real time indicators to monitor the economy Ben Henshall, Head of Analysis in 10 Downing Street, and Mallory Durran, Head of Analysis and Levelling Up in the No.10 Delivery Unit, on dashboards speaking truth to power Michael Birtwistle, Senior Policy Adviser and AI Barometer Lead at the Centre for Data Ethics and Innovation, on the second iteration of the AI Barometer The event was chaired by Gavin Freeguard, Associate at the Institute for Government.

The Defense Information Systems Agency is taking a hard look at its budget and program plans over the next year. Agency officials say they want to make sure they're making the right investments, especially as flat defense budgets loom on the horizon. For the latest, Federal News Network's Justin Doubleday.

Richard is Head of Accessibility at CDDO (Central Digital and Data Office). He focuses on building accessibility capability and culture in central government and the wider public sector through Accessibility Empathy Lab sessions, clinics, training, talks and webinars, and manages the cross government accessibility communities.Until April 2021 the accessibility monitoring and capability functions were within GDS (Government Digital Service), and this work now continues through CDDO. 

In this episode, Sarah talks to Charles Baird about why he cares about the API community. From engaging with API users and producers right now, to making APIs discoverable in the future, Charles shares his insights, experiences and plans. He has a genuine enthusiasm for the API community as a place where people can really listen to each other's needs, barriers and ideas as well as being a collective which can enable and develop good practice, and he's keen to get people involved across sectors. Charles is a Data Architect at the Data Standards Authority, which is a part of the Central Digital and Data Office in the Cabinet Office. Links Charles on Twitter @charlesbaird api.gov.uk Data Standards Authority NHS Digital API Catalogue Central Digital and Data Office

Do you enjoy the GDS Podcast? Help us to make it even better by completing our short, anonymous survey. Vanessa Schneider:  Hello and welcome to the Government Digital Service podcast. My name is Vanessa Schneider and I am Senior Channels and Community Manager at GDS. Today I'm joined by the Chief Executive Officer for GDS, and that's Tom Read.    Tom, thank you so much for taking the time to be here today. I know that you joined GDS back in February, which in these times feels like an eternity. But could you please introduce yourself and what do you do to our listeners?    Tom Read:  Sure. And thank you for having me. So I'm Tom. I'm the Director General and Chief Executive of the Government Digital Service. As you said, I've been here just over 3 months now. So effectively my job is to set the strategy for the Government Digital Service, work out how it aligns with ministerial priorities, how much money we've got, what we're currently working on, and then keep out of people's way as much as possible and let people get on with delivery. That's sort of what I'm here for, I think.    Vanessa Schneider:  OK, I hear it's not your first rodeo at GDS: do you mind sharing how this experience is different?    Tom Read:  Yeah. So I was, I was at GDS from for about 2 years in 2013 to 2015. Back then, I mean, everything was quite different. I worked in Liam Maxwell's area, which was the sort of, the more, the tech area than the digital area, and I was brought in to run a technology transformation programme in the Cabinet Office itself, plus DCMS [Department for Digital, Culture, Media and Sport]. It was great fun, really good fun.    How is it different? I don't know. It's... GDS back then was was smaller, much more sort of a scrappy start-up. It had this sort of triumvirate of real heavy hitters in Mike Bracken and Liam Maxwell and the Minister, Francis Maude, now Lord Maude. And so it had a really, it sort of felt very much on the bleeding edge and it was very much going out and trying to push down some doors to get people to-to let it exist and let it really make a difference. I think a lot of that spirit is still, still here in GDS. But there's a little thing I've written in-in our new strategy, which is we're not in start-up mode anymore. And I think that's it's quite important to recognise, we-we've, we've done that phase and now we're sort of maturing a little bit. So it's slightly different. But the spirit is the same.    So after 2015, I basically I did 2 years of just like super intense work, like it was just, you know, really, really fun. So much fun but incredibly tiring. And I basically sort of said, right, that's, that's it. That's my little tour of duty in government done. And I-I went off and joined a consultancy and about 3 months in working for the consultancy, which was a lovely place, really lovely place, great people. I suddenly thought, ‘ack, I'm not done, actually. I-I-I really miss government already’.    So later that year I applied for a few roles and I was successful in a role as the Chief Technology Officer at the Department for Business, as was. And I'd worked there with amazing people like Emma Stace, Mark O'Neill and other people, it was just - Andrew Greenway - it was, it was a really great team. And we really started to create a digital movement in that weird department because it's like a small policy department with loads of arm's length bodies. And it was good fun and we really got going.    And then there was the machinery of government change. So energy and climate change came in, education went out so universities and things went out to education. And I don't know if any of our listeners have been through machinery of government changes, they're like mergers acquisitions in the private sector. I kind of saw the writing on the wall. I thought that there isn't space for, for 3 directors in what was to become BEIS [Department for Business, Energy & Industrial Strategy].    And so I started to look around government and it happened. There was a vacancy coming up at the Ministry of Justice [MoJ] working for Sir Richard Heaton, who I worked to when I was at GDS, he was the Perm Sec[retary] of the Cabinet Office and one of my all time sort of heroes in government. And so I was sort of managed moved across to MoJ. And that's where I've been for the last 4 and a half years. Up until now, by a long way, the best job I've ever had in my career. It was just this incredible, meaningful work of helping some of the most vulnerable people in society to fix their lives and get an education and get their lives back on track. It was brilliant. So yeah, I've been, I've been in a few departments.    Vanessa Schneider:  Well, they tend to say, don't meet your heroes, but it seems to have worked out really well for you. I also wanted to give a shout out to Emma Stace because the Department for Education Digital and Technology team has just launched their first podcast episode with Emma in it.    Tom Read:  Oh, awesome. Oh, well, fantastic. Well, listen to that one. She'll be amazing.    Vanessa Schneider:  [laughs] But also listen to us!   Tom Read:  Obviously listen to us!   Vanessa Schneider:  So it's clear to me, just listening to you that you're passionate about digital government, always coming back to it as well and looking at your resume in general. But I was wondering why that was. What is the power of digital?    Tom Read:  What is the power of digital? That's a really good question. So the thing that's unique about digital teams in government, but also outside government, is we just have a relentless focus on users and how they work. And I know a lot of bits of government do that as well - it would be a bit insulting to policymakers to suggest we're the only people who do that.    But any bit of digital design, whether you're working for a supermarket or a retailer or a bank or government, you have to design around how users use things because otherwise they don't use them. And then you're wasting everyone's time, right? In government, I think we've used digital, now more the word data, user needs, these sort of things, kind of as stalking horses, they're, they're ways of expressing designing things around how users work. And I just think that's a great opportunity.    I also think government itself is fascinating because some some bits of government have been around for hundreds of years and some bits have been around for a thousand years. And without being simplistic, some of the processes haven't changed very much in that time. And so you can stick a website over it. But really, you need to look at the whole, you know, policy through to what outcomes you're trying to get, the process, and then digitise that. And I think that's really missing from how we talk about digital in a lot of cases.    Vanessa Schneider:  So, you mentioned obviously that you've been here for 3 months and some people make a big deal out of it - the first 100 days somebody has spent in a new job, especially in a leadership position. Is there anything that you're keen to share that you've learnt in this time, or maybe you found something that surprised you?    Tom Read:  Yeah, I mean, just so much. It's quite weird hearing it's been 3 months actually, because, in the nicest possible way, it feels like a lot longer. And I do mean that in a positive way. I've learnt a lot. There's, there's a lot. GDS is a funny place because everybody's got an opinion about GDS just anywhere in government. And beyond actually, everyone's got an opinion about what's good, what's bad. There's a whole set of people on Twitter who seem to spend most of their lives just commenting on what on what GDS is doing. And it's really peculiar. And so coming in, or sort of back in, but, but into this role from a department has been fascinating.    So it's sort of off the top of my head, a few things I've I've learnt. One is I think the, GDS is just completely full of, like, super intelligent, incredibly civic-minded people who care. And I think, yeah, I don't want to go on a soapbox rant about this, but that's probably the thing that people really miss when they're judging GDS, is just how much people care about, you know, service design and, you know, the underlying technology and content design, accessibility, all these things that really matter. It just, it really infuses everything when you're speaking to people. And there are people who have been here for like 7 or 10 years who just still have the same absolute passion for improving public services, which is amazing. I mean, I've got a short attention span, so a lot of respect for those sort of people.    On the, on the, sort of, the more complex side. I think, the first, we still sort of hark back quite a bit to sort of the first 5 years of, of GDS, which I don't think is uncommon in a sort of quote unquote start-up. You hark back to the early days - I was speaking to a friend who works at Monzo recently. And he was saying everyone still talks about when there were 30 of us and we were trying to build from scratch. We're not like that anymore. So I think, I think a lot of people still look back at where we had all this support and we were crashing down doors and building things. And it was busy and we were on stage a lot. And then there were 5 years of much quieter GDS over the last 5 years - still doing very important work, but taking much more of a collegiate view. And I think one of the things I've been puzzling through over the last 3 months is how do you get the happy balance between those 2? I think maybe we need to get back a little bit into the setting direction and pushing delivery as well as working together.    Vanessa Schneider:  Yeah I mean, I think one of the things that people remember when they hearken back to those good old days is also the mottos that sprung up. There's a lot of stuff that we say at GDS that has spread beyond, that's really been used a lot. For instance, doing the hard work to make it easy for the user. So obviously our ambition is to make dealing with government easier. Where do you think we are in this mission?    Tom Read:  That's not what I thought you were going to ask me. So I think we're at a really interesting point. So thing, things that have been done well over the last 10 years, we talk a lot about the really good services. There are lots of services in government that are better than you would find in the private sector. And I think that the narrative that government's never going to be quite as good as the private sector: I've worked in the private sector. It's just not true.    We're all roughly trying hard, dealing with legacy, dealing with complexity, competing demands, that kind of thing. So there are a bunch of things that have been done just incredibly well. So, you know, the passport service is just an exemplar. There are amazing things in digital tax. There's stuff we were doing at MoJ, there's there's stuff at DWP [Department for Work and Pensions], which is really, you know, pushing the boundary and properly, you know, micro-services, architectured services that will last and stand the test of time.    Equally, I think I think we just declared victory way too early. So it's one of the first things I was sent when I joined GDS was, I was like, we've got a list of the the paper forms in government, you know, the, the services that have never been touched. And I was sent a spreadsheet with with 4,000 lines, and each line is a PDF or a Word doc, that a user has to download, fill in, so they need a printer, then they can fax or post it. So you either need a fax machine - I genuinely don't know how that, how that technology works in the digital age - or you go to the Post Office and I think it's just not good enough.    So I think from that perspective, we've done a lot. We've embedded amazing digital talent across government. GOV.UK is standing firm and is still a really excellent sort of front end of government. But we've got a lot more to do. And I also think we're slightly, we have still been thinking in the context of 10 years ago, where it was a publishing layer and then individual silo transactions, I think we need to move beyond that now. We'll probably talk about that a bit more later. But I think we need to move beyond what was a good idea 10 years ago and iterate - use some of our, use some of our own secret sauce for that.    Vanessa Schneider:  I am so curious. Where did you think I was going to go with that question? [laughs]   Tom Read:  I thought you were going to ask me about some of the mottos [laugh from Vanessa] and whether they still stand up. So, you know, ‘the strategy is delivery’ and you've got on your laptop ‘Make things open. It makes things better.’ In fact, I've got it on mine as well. I-I thought you were going to ask about some of those things.    Vanessa Schneider:  Do, I mean, if you want to riff on that, go for it. [laughs] [laugh from Tom]   Tom Read:  There is a lot to be said for the, the memory that goes with catchy, meaningful slogans like ‘strategy is delivery’. It's great because the strategy was never delivery. Right. The strategy was deliver something quickly and make it so good that once people come to tell you stop doing it, they'll look like idiots because you built something brilliant, fast and cheap. It's not-- the delivery isn't the strategy. Strategy is let's not talk about it. Let's let's deliver something and then we'll have something to show for it, which is great and similar with, you know, the talk about user needs, not government needs. It's still government needs. It's just if you build it around how users work, then the service is cheaper and it'll actually be used online. It's it's sort of proxies for for what we're trying to do. Big fan of that sort of proper marketing.    Vanessa Schneider:  So I was wondering if you wanted to reflect on the mission of GDS now and for the next 3 years in context of the 5 points that you outlined in your blog post?    Tom Read:  Yeah, absolutely. So the first thing we're trying to do is we need to kind of say, what are we really going to focus on? Because it's, I don't just want a shopping list of what we're busy with. It's like what can we uniquely do in GDS? We've got this, like, incredibly privileged position of being in a centre of government. We're reasonably funded at the moment. Good ministerial support. What are we uniquely able to do in that position? Let's let's leave the departments to do, to do what they do.    So we've we've we've come up with with 5 points, as you say, and I'll sort of rattle through them, but sort of explain why I think they matter. So the first and kind of most important one is we have to keep the things that we're already running running. So we, GOV.UK is a obviously fundamental part of what we do. We need it up to date, we need the publishing tools to be modern. We need to be iterating some of the design patterns around finding content, around exploring, sort of navigating content. And we need to re-platform it. It sits on a tech stack in the cloud. But but that's coming out of support. So so keeping things running, it's not always sexy, but it is the most important - if we do nothing else we'll keep GOV.UK running.    The second thing we really want to go to is, focus on is, is kind of what I meant earlier around moving the dial from just doing transactional services. So we want to focus on what we're calling whole, whole services or solving whole problems for users. So an example. And we're not sure which examples we're going to use, right. But an example that that we're looking at at the moment is around having a baby.    So if you if you are a person and you're having a baby, I've made a list here. Things you might need to know about, that government can help you with are: maternity pay, shared leave, maternity allowances, registering the birth, getting child benefits, getting tax credits, finding childminders, getting nursery places. And at the moment, you need to understand all of those things individually. Then you need to apply for each to work out whether you are eligible. It's, well, well-intentioned nonsense. And really what you should be able to do is what you would expect in a commercial transaction where you would go on, you would have your details already stored and it would say you are eligible for these 5 things. One click and we'll sort it out for you.    And I think that's, maybe that's pie in the sky, there's so many reasons why that might not work. But that's what we're going to aim for. So so we're going to go hopefully for, as I said, really early days. And a lot of people have thought about this before. We are not unique in this, but we're going to look at maybe 5 or 10 ideas and try to push them through to delivery and work out: does GDPR stop us doing this, does money stop us doing this? Does the fundamental structure of government and accounting officers accountable to Parliament stop us doing this? I don't know, but we’re gonna have a good crack at it.    Vanessa Schneider:  I think I saw on social media, because that's part of my role as well behind the scenes, that there has been work on that previously by the government, I think it was in the days of Directgov and Business Link, that life services was actually already a concept. So will it be resurfacing that kind of work? Are you going to look back at the old material and see what learnings you've made since?    Tom Read:  Probably, yeah. So Jerry Fishenden, formerly of this parish, blogged about tweeted about it. I think it was before Directgov actually, that that that screenshot. So that was kind of based around life events. So having a baby is one. I think, I think some of them aren't life events. Some of them are whole, just just whole problems, like going abroad isn't really a life event. But you do need to think about what - particularly now - you need to think about passports, COVID[-19], political unrest. You need travel insurance. You need, yeah, vaccinations, you need visas. You know, that's not real life experience. It's more a collection of whole problems to solve one thing, which is the person wants to go abroad and needs government help. So we'll definitely look back on on on on that thinking. There's very little new under the sun. But equally, we haven't done it yet. So we need to, we need to press on and deliver.    Vanessa Schneider:  No it's that agile principle of iteration, isn't it?    Tom Read:  Right, exactly. [laugh from Vanessa]   Vanessa Schneider:  All right. You've obviously mentioned that we're looking at areas that maybe aren't being captured by government departments and also haven't had that attention previously. So I was wondering if there are still opportunities for us to learn from other departments in that area. I know, obviously, like the thing that you were mentioning with the forms, those are sort of those low-usage services, is that right? Will we be leaning on government departments that own those services a little bit or will it be solely in our purview?    Tom Read:  It's a really good question, we cannot do, there are bits that we can do ourselves from the centre, but they are quite limited. I talk to, I keep talking about the getting the balance right between centralisation and working with government departments, things like the long tail of digital forms in government. That's something we can't force people to do. The, we kind of have a two-part strategy here.    So you'll be aware that there's a new bit of Cabinet Office called the Central Digital and Data Office. And basically that's set up to take the the strategy, policy, capability, those sorts of bits, and also the spend controls and like the mandate. And so they will be looking at which departments, which agencies, which bits of government still have a lot to do. And flagging that, being, you know, I don't know, a scorecard or something, but some way of measuring progress.    We're ‘good cop’ in GDS. So our job is to build platforms, continue the work of government, so platforms, so Pay, Notify, we're going to build a way of submitting information in forms. And there may be 3 or 4 others that we're looking at. And the idea is if departments haven't digitised their simple lower transaction services, we'll give them everything that they need to do that, and we'll give them some help if they need some help to do it, and kind of slowly remove all the possible reasons why you wouldn't digitally transform. So we're the, we want to be the oil, the enablers to to help the long tails transform across central government primarily, but but also local government.    Vanessa Schneider:  And if you're interested in any of those products that Tom mentioned, we have a couple of podcast episodes that could be of interest [laughs]. So is there any chance that you can share more about what's happening at GDS right now with that focus?   Tom Read:  So we're in planning stages, is what I'd say. So we've got some some platforms that are really quite mature now, so GOV.UK Notify, I don't have the data with me, but GOV.UK Notify has an awful lot of organisations using it. We're going live with the alert cell broadcast system. And other platforms we're in planning stages. It's really looking at what are the barriers to adoption. And then we're also going to spin up a team to look at what are the next 5, what are the next 5 things that should be done centrally, may have already be done in 5 departments. So can we bring those together and package it and offer it back as a service, or do we have a federated approach to the platforms? We need to look at those different options over the next 3 months.    Vanessa Schneider:  Yeah, just to add in, it's been 2.9 billion messages sent since May 2016 when Notify started up. So honestly, hats off.    Tom Read:  It's cool.    Vanessa Schneider:  And a shout out to Pete Herlihy. I hope he's enjoying New Zealand. [laughs]   Tom Read:  I'm sure he is.    Vanessa Schneider:  Yeah. So I was also wondering, I think there might be some work on single sign-on and personalisation. I was just wondering if you wanted to give a sneak preview on those?   Tom Read:  Yeah, sure. So a single sign-on for government and a way of verifying your identity. So fundamental parts of our strategy for the next few years. We've got money this year. We've got a lot of political support for this. The, some of the most brilliant people I've ever worked with anywhere, worked on Verify over the last sort of 6 or 7 years, genuinely, just utterly brilliant technologists, designers and that sort of thing. And, and Verify worked, right. It's branded as like, that didn't work. It worked for millions and millions of people.    Equally, there are some design patterns that that that that haven't quite worked. It didn't work for for certain sets of users in government. And we are now in a position where we take all of that learning and we're going to effectively build a new set of services that allow, as I said, a single sign-on for any services that need them across government and a way of proving your identity to government regardless of your social situation.    I'm really excited about this. I'm genuinely excited about this for a couple of reasons. One is we've got all that learning from Verify that we can pick up on. Secondly, a load of governments around the world have done this now, they've they've they've gone out and built on what we did and they've built their own. Thirdly, we've got proper buy-in from across government, real buy-in from ministers and senior officials in DWP, HMRC [HM Revenue and Customs], Home Office. Everyone's kind of on board for this. They know this is needed and our new sort of, very sort of collaborative approach that we're taking is-is hopefully going to bear fruit.   Vanessa Schneider:  It's great to have those big hitters on board. Those are the services where users will find themselves logging in, in order to access the information that is specific to them, which I think brings us quite neatly onto personalisation, no?    Tom Read:  Sure. Yeah. You'll probably be getting the feel for this, that a lot of what we're talking about is interdependent. These aren't completely sort of separate silos of delivery.    Vanessa Schneider:  Then what is in government, right?    Tom Read:  Well, exactly. So the way to imagine this, we're not simply building a portal, that's first thing to say. I know that’s sort of a bogey word in government and or digital design in general. GOV.UK for a lot of people is just there to get information from. And that's fine. That's fine. For for for other people, for whom government is very important because they don't access it 4 or 5 times a year, they need to go in quite regularly because they need a lot of help from government or they’re going through something quite complex in their lives.    The concept is that you will use single sign-on to log on to a GOV.UK account. And from there, you will be able to access services ideally with one click, as I mentioned previously, you could have one click access to things you're eligible based on what we already know about you, or you can change your data. So the the great mythical beast in government is this thing called Tell Me Once. Right. So we we don't have a single register of citizens in UK government, but we have hundreds of them. We have, you know, our addresses, our names, dates of birth, addresses will be in a lot of databases across government. And if we move, I don't move very often because I'm at that stage in life, young folks move a lot and it's likely that most of those bits of data are wrong across government.    So that's sort of, a by-product of a personalisation is we should be able to update that data and push it out to other parts of government in a really seamless way. And what's really exciting about personalisation, though, is there are, there's so much information on GOV.UK and so many services. You kind of need a Ph.D. in Government Studies to be able to to know how what you're what you're eligible for, what's out there. If you could personalise it by saying, you know, so for me, I'm in my 40s, I have children, I travel sometimes, I earn a certain amount. The amount of information on GOV.UK will shrink right down to, I'm making up numbers here, but 5, 10 per cent of that information and I should only be offered services that are relevant to me.    And I think from that you're doing, you know, that old adage of - it's written on your laptop - doing the hard work to make it simple. We're doing the hard work of trying to get information about a person and yes, shrink down the complexity of government to what, to what is relevant. And equally, we're not going to mandate this, right? That's really, really key to remember. If people don't want to do that, you will be able to go into your GOV.UK account and, you know, show what data we're linking and and de-link it. If you don't want to do even that, you know, you can continue accessing services how they are now and certainly we’ll always have an assisted digital method for people who don't want to or can't access services in the ways I'm describing. But I think personalisation is-is the big, our big play over the next few years. I think it will be transformational for a lot of citizens.    Vanessa Schneider:  Yeah, you mentioned the next few years. Obviously currently you're in post the next 3 years, am, is that right?    Tom Read:  Well, no, that's that's kind of artificial. I think, I'm here forever. Right. So what I've been trying to say to people, I think because GDS has had quite a lot of change at the top, I'm just trying to make it clear that I'm not going anywhere anytime soon. I think if I'm still here in 5 years, you know, maybe somebody should start to say: ‘you should probably freshen up soon’. Equally, I'm certainly not staying less than 3 or 4 years. I mean, we've got a lot to do. I'm already enjoying it.   Vanessa Schneider:  I was going to say, this is this is what you're doing for 2021 to 2024, is that right?    Tom Read:  Yeah. I've, I've, I've tried to-to sort of focus on the current Parliament cycle.    Vanessa Schneider:  Right, but it's a lot. [laughs]   Tom Read:  It's a lot. It's a lot. And we don't do anything. I also didn't, I sort of think it's slightly artificial sometimes to say, you know, here's our 10-year strategy. Who knows what on earth is going to be happening in 10 years in terms of maybe they'll be tech innovations or maybe they'll be - more likely - machinery of government changes or something else. So I want us to focus on, you know, more than a year, less than 5 years. So our Parliamentary cycle, it also slightly secretly sharpens the focus for colleagues in the Treasury and so on for for the upcoming spending review.    Vanessa Schneider:  Very strategic, I see. I know why they hired you. [laugh from Tom] Do you want to dabble in a bit of future casting? What happens beyond, or you know, say we achieve everything that you set out? What can we do after?    Tom Read:  I have absolutely no idea, I don't think. So, I think - what do I think? - The, the, the-I'm sort of stepping into areas of the Central Digital and Data Office here rather than GDS, I think. But.    Vanessa Schneider:  It will influence our work. No doubt.    Tom Read:  We work hand in glove already. It really will influence our work. I mean, things that I'm really interested in long, long-term is the there is still a relatively low digital literacy across senior policymakers and ministers, you know, with some notable exceptions across government. And I think that will change organically. I think that is changing already. But I'd quite like to see, yeah, without wanting to be hyperbolic, I think fundamentally the way we do policymaking, it's not wrong. But it's the way we've done it for a lot of time.    What what what slightly worries me about that way of doing it is 2 things. One is we've never properly stopped and really understood what are the most important policy changes for users, for people out there. You know, really, would this policy change your life or is there something else that we could do for the same amount of money with the same ambition that would change your life more? And I think we need to, the very qualitative, but I think we need to do more of that when we're doing policymaking right at the beginning. That's one.    Two: We tend to use data to prove hypotheses rather than than to suggest policy ideas. Really, I think we should be, you know, the really good work that Alison Pritchard is doing over at the Office for National Statistics around creating a data analytics platform that takes government data from all departments. That that's key because you should be able to look at the data, use, you know, authentic machine learning or similar, or just complex algorithms and say ‘find the connections’ that we don't quite know. What is that group, that for some reason they share a set of character traits or share a set of socio-economic situations? And then later on, they are the people who end up in prison or big users of the NHS or similar. And let's create some policy initiatives from the data. I think that would be spectacular. So anyway, so once we fix, once we've fixed all of the long tail of government and we've made GOV.UK personalised and we've done a digital identity service, we've moved all the legacy technology in the government to the public cloud, we've made everything secure. Yeah, that's where we'll go next, I think.    Vanessa Schneider:  Obviously yeah, that-that's some amazing work to look forward to, I hope. But I think we should finish on the hardest-hitting questions that I have for you today.    And we'll start off with Marmite. Yes or no?    Tom Read:  Uh, yes.    Vanessa Schneider:  Working from home or working on location?    Tom Read:  Both.    Vanessa Schneider:  Jam before cream or cream before jam on a scone?    Tom Read:  Oh, well, my mum lives in Devon, so I'm going to get this the wrong way around and she'll be very upset. But jam and then cream.   Vanessa Schneider:  Ooh, that's the Cornish way.    Tom Read:  Damnit.    Vanessa Schneider:  Early bird or night owl?   Tom Read:  I'm a night owl. I'm not good at morning's.   Vanessa Schneider:  Morning coffee or gin o'clock?   Tom Read:  [laughs] Both! That's healthy isn’t it?   Vanessa Schneider:  We've been stalking your Twitter feed. [laugh from Tom]   Planes, trains or automobiles?    Tom Read:  Well, I'll get in trouble with climate folk won’t I? Look, I really care about it. It's...I really miss travelling. I really miss travelling.    Vanessa Schneider:  You're allowed to say cycling, walking, canoeing.    Tom Read:  Yeah, a bit of that. Bit of, I don't really canoe. I really miss travelling on-on planes. I do live near a flight path and I'm quite enjoying not having planes going over. So I'm a hypocrite like everyone else.    Vanessa Schneider:  Totally understandable. And this is quick fire isn't it.    So Batman or Superman?    Tom Read:  Sup--Batman.    Vanessa Schneider:  All right. All about the journey or the destination?    Tom Read:  [laughs] I don't know!    Vanessa Schneider:  Too, too airy fairy for you, that's OK, no worries.    What about crunchy or smooth peanut butter?    Tom Read:  I don't eat peanuts, so neither.    Vanessa Schneider:  Allergic?    Tom Read:  No, just don't like them.   Vanessa Schneider:  Fair enough. And finally, what do you think of the idea of an office cat? I know this one's hot on people's minds.   Tom Read:  So. I'm a big fan of an office cat. I think we should have an office cat. I don't know if it's practical. We talked about an office dog when I was at MoJ with a, with a little you know, pass on its collar that was quickly squashed by our DGs [Director Generals].   Vanessa Schneider:  Yeah I feel like I've put a cat among the pigeons by mentioning this. So [laughs] [laugh from Tom] there's always, there's always chatter amongst the staff, ‘Oh, can we please have an office cat?’. But unfortunately, because we share this building with other tenants, it's not been, not been an option, apparently, especially with cat allergies. I don't know how they get away with it, with Palmerston and FCDO [Foreign, Commonwealth & Development Office], for instance, you know, there's probably going to be people with cat allergies. But if you can put in a word, the cat people will be very grateful.    Tom Read:  OK, here's my most political statement of this whole interview. I will look into whether we can get an office cat. I think it's a great idea.    Vanessa Schneider:  Oh, fantastic. Well, I've run out of quickfire hard-hitting questions for you.    Thank you so much, Tom, for coming on today and sharing with us what you see as GDS's new mission and how that's going to be achieved. If you want, you can listen to all the episodes at the Government Digital Service podcast on Apple Music, Spotify and all other major podcast platforms. And the transcripts are available on Podbean.    Goodbye.   Tom Read:  Goodbye.   ---------------------- Do you enjoy the GDS Podcast? Help us to make it even better by completing our short, anonymous survey.    

The Institute for Government was delighted to welcome the three newly appointed leaders of data, digital and technology in government. Paul Willmott, Chair of the Central Digital and Data Office, Joanna Davinson, Executive Director of the Central Digital and Data Office, and Tom Read, Chief Executive Officer of the Government Digital Service, was in conversation with Alex Thomas, Programme Director at the Institute for Government.The Central Data and Digital Office was established earlier this year, and its work will complement that of the Government Digital Service, which is approaching its tenth anniversary. The event explored Paul, Joanna and Tom's visions for the next phase of digital delivery and transformation in government, and their respective priorities for the coming year.#IfGdigitalThe Institute for Government would like to thank Oracle for kindly supporting this event.​ See acast.com/privacy for privacy and opt-out information.

The Institute for Government was delighted to welcome the three newly appointed leaders of data, digital and technology in government. Paul Willmott, Chair of the Central Digital and Data Office, Joanna Davinson, Executive Director of the Central Digital and Data Office, and Tom Read, Chief Executive Officer of the Government Digital Service, was in conversation with Alex Thomas, Programme Director at the Institute for Government. The Central Data and Digital Office was established earlier this year, and its work will complement that of the Government Digital Service, which is approaching its tenth anniversary. The event explored Paul, Joanna and Tom’s visions for the next phase of digital delivery and transformation in government, and their respective priorities for the coming year.

Iniciamos muy ilusionados la 2ª temporada del podcast Data Stand-Up! en español. Y lo hacemos con una llamada a cuatro en la que conversamos con Carlos Buenosvinos y Diego Villuendas de SEAT. Jesús y Luisma hablan con el equipo de datos de la compañía sobre los retos a los que se enfrentan y otros muchos temas de interés.Carlos Buenosvinos es el responsable de la nueva oficina de datos de la compañía (SEAT + CUPRA) y además es también el CEO y CTO de SEAT Code. Carlos tiene una dilatada experiencia en el ámbito tecnológico y de desarrollo en proyectos como Emagister, Atrápalo o Xing.Diego Villuendas es Responsable Global de Datos, Analítica y CRM también de SEAT y CUPRA. Entró a trabajar en la compañía en 2016 y ha pasado por distintos puestos dentro de la empresa de Martorell siempre vinculados con el ámbito del dato y la analítica de negocio. Es Dr. Cum Laude por la Universidad de Barcelona donde también estudió Físicas.

Martin Treder is one of the foremost data management experts globally, and helps organisations set up and run a Data Office. He is currently the Information Domain Owner at Boehringer Ingelheim.