WEBVTT 00:00:00.640 --> 00:00:04.200 This is Government’s Future Frontiers, the podcast from Deloitte 00:00:04.200 --> 00:00:07.200 that asks questions today to help create tomorrow. 00:00:07.560 --> 00:00:10.840 I’m Bill Eggers, Deloitte’s executive director of the Center 00:00:10.840 --> 00:00:12.480 for Government Insights. 00:00:12.480 --> 00:00:16.040 This episode comes to you live on location from Barcelona, 00:00:16.440 --> 00:00:21.400 at the Smart City Expo World Congress, an amazing event which brings together 00:00:21.400 --> 00:00:23.760 key players from all over the world. 00:00:23.760 --> 00:00:26.400 So it’s an ideal place to explore this episode’s 00:00:26.400 --> 00:00:31.560 subject: predictive digital twins and engineering tomorrow’s infrastructure. 00:00:32.200 --> 00:00:35.520 A digital twin is a live virtual representation 00:00:35.520 --> 00:00:38.120 of a real physical asset in the world. 00:00:38.120 --> 00:00:42.160 It could be a building, a power grid, or even an entire city. 00:00:42.840 --> 00:00:46.320 Creating a digital twin provides the ability to try things out 00:00:46.320 --> 00:00:50.360 in the virtual space before actioning them in the real world. 00:00:50.760 --> 00:00:54.160 So let’s get into this subject with two fantastic guests: 00:00:54.640 --> 00:00:59.280 Justin Anderson from Connected Places and Nick Holmes from ServiceNow. 00:01:00.960 --> 00:01:03.960 Yeah, well, first of all, thank you very much for having me. 00:01:03.960 --> 00:01:07.600 Awesome to be here back in Barcelona, as always. 00:01:08.000 --> 00:01:08.800 Yes. 00:01:08.800 --> 00:01:10.240 So, my name is Nick Holmes. 00:01:10.240 --> 00:01:13.200 I’m a veteran of government. 00:01:13.200 --> 00:01:17.400 I’m working with government agencies for about 23, 24 years or so. 00:01:17.800 --> 00:01:20.920 I work at ServiceNow. I’m based in Dubai. 00:01:20.960 --> 00:01:24.880 I’m a global public sector director of sustainable infrastructure 00:01:24.880 --> 00:01:26.200 and transportation. 00:01:26.200 --> 00:01:27.760 So I spend a lot of time 00:01:27.760 --> 00:01:30.200 at conferences like this, really trying to understand 00:01:30.200 --> 00:01:33.240 what are the leading practices, what are the things that our platform, 00:01:33.240 --> 00:01:37.440 from a ServiceNow standpoint, can do to help our customers solve their problems? 00:01:38.040 --> 00:01:40.080 Terrific. Justin. 00:01:40.080 --> 00:01:42.160 Well, again, thank you very much for having me. 00:01:42.160 --> 00:01:46.320 I’m Justin Anderson, managing director at Connected Places Catapult. 00:01:46.800 --> 00:01:50.400 The Catapult’s a part of a network that is funded by UK 00:01:50.400 --> 00:01:53.480 governments, and we’re here running the UK pavilion. 00:01:53.720 --> 00:01:54.560 It’s great to be back. 00:01:54.560 --> 00:01:56.480 I think this is my 12th year here. 00:01:56.480 --> 00:01:57.360 I love this show. 00:01:57.360 --> 00:01:59.240 It’s a great place to meet 00:01:59.240 --> 00:02:02.080 and see people that you haven’t seen for a year, often. 00:02:02.080 --> 00:02:07.560 This is a great environment to see the trends and the changes that have happened. 00:02:07.920 --> 00:02:10.960 Sometimes not that much happens over the course of a year 00:02:10.960 --> 00:02:13.760 other than bigger stands for some countries, 00:02:13.760 --> 00:02:19.000 and a few advances in technology. Connected Catapult’s 00:02:19.080 --> 00:02:25.080 focused on bringing together cities so that they can learn from each other. 00:02:25.440 --> 00:02:30.640 And we have a very strong focus on ensuring that we can align 00:02:30.920 --> 00:02:36.640 the policy intentions of the UK government with the country and with the markets. 00:02:36.640 --> 00:02:42.600 So we sit in this interesting space that makes sense of the policy and helps 00:02:42.600 --> 00:02:46.120 guide industry so that they can make the most of it. Wonderful. 00:02:46.680 --> 00:02:48.320 Well, let’s get into the subject. 00:02:48.320 --> 00:02:50.520 And Nick, it’s great to have you here. 00:02:50.520 --> 00:02:54.120 We collaborated on a big 250- 00:02:54.120 --> 00:02:59.520 city survey, AI-powered cities, which I think is the largest of its kind 00:02:59.880 --> 00:03:03.480 in the world and hope to be doing round two very soon. 00:03:04.160 --> 00:03:06.800 Now, the subject here is digital twins. 00:03:06.800 --> 00:03:09.960 Both of us have been walking around, and it seems like 00:03:10.080 --> 00:03:14.840 you can’t go more than about five yards without seeing a different digital twin 00:03:14.840 --> 00:03:17.440 or hearing about it. And the conversation, 00:03:17.440 --> 00:03:20.880 everything from wildfire prevention to transportation planning 00:03:20.880 --> 00:03:22.840 to workforce planning. 00:03:22.840 --> 00:03:25.320 Just so many different digital twins. 00:03:25.320 --> 00:03:26.200 And it really 00:03:26.200 --> 00:03:29.960 seems to be one of the big themes and trends of this year’s conference. 00:03:29.960 --> 00:03:32.880 Tell us a little bit more about digital twins 00:03:32.880 --> 00:03:36.360 and why we’re seeing such an explosion at Smart City Barcelona. 00:03:36.560 --> 00:03:39.520 Yeah, I think that’s a very true observation. 00:03:39.520 --> 00:03:42.520 Like you said, as we walk around, we do see so many different examples. 00:03:43.000 --> 00:03:45.240 And I think it really comes down, in my mind, 00:03:45.240 --> 00:03:47.960 why are we seeing such a prevalence of them right now? 00:03:47.960 --> 00:03:50.960 I do think it comes down to our good old friend, the data. 00:03:51.000 --> 00:03:54.520 Right? To drive a digital twin, which is really just sort of 00:03:54.520 --> 00:03:57.560 like a replica of— it could be your environment, 00:03:57.560 --> 00:04:00.480 it could be your building, it could be your city, you name it. 00:04:00.480 --> 00:04:02.880 Like you mentioned, a whole bunch of different use cases. 00:04:02.880 --> 00:04:05.280 You need really good data to be able to do that. 00:04:05.280 --> 00:04:08.160 And the idea, I think, is that you can run what-if scenarios. 00:04:08.160 --> 00:04:11.200 So you can play around and you can start thinking about, 00:04:11.200 --> 00:04:13.960 well, what if this happens or what if that happens? 00:04:13.960 --> 00:04:16.960 I think that’s very powerful when it comes down to decision-making, 00:04:17.040 --> 00:04:19.080 because we’re not just making decisions in the dark. 00:04:20.360 --> 00:04:23.320 I think that the inflection point and why we’re seeing right now 00:04:23.320 --> 00:04:26.320 that digital twins are becoming more popular is just, one 00:04:26.320 --> 00:04:29.520 is that accessibility of data, being able to capture the data. 00:04:29.880 --> 00:04:31.640 When I first started out doing 00:04:31.640 --> 00:04:35.160 smart cities, you know, we were very, very early stage. 00:04:35.400 --> 00:04:39.560 We didn’t really have that many IoT sensors. Really, the 00:04:39.800 --> 00:04:44.680 big pivot point there was the cost and also the battery life of all things. 00:04:44.680 --> 00:04:45.360 Right? 00:04:45.360 --> 00:04:48.840 If you had a sensor and you deployed that sensor and it ran out of juice, 00:04:49.080 --> 00:04:50.760 it stopped communicating, 00:04:50.760 --> 00:04:52.320 stopped giving the data, right? Yeah. 00:04:52.320 --> 00:04:53.360 And now I think what’s happened 00:04:53.360 --> 00:04:56.400 is we’ve figured out those issues and those problems. 00:04:56.520 --> 00:05:00.320 Now we’ve got great sensors providing a lot of data, a lot of information. 00:05:00.600 --> 00:05:01.920 And now I think we’re into the next phase, 00:05:01.920 --> 00:05:03.480 which is, well, what do I do with that information? 00:05:03.480 --> 00:05:05.480 How do I process that information? 00:05:05.480 --> 00:05:08.960 Because if you’re not making a decision from the digital twin, 00:05:09.240 --> 00:05:11.320 then why are you really doing that? 00:05:11.320 --> 00:05:12.400 Right? 00:05:12.400 --> 00:05:13.920 And then, as we were talking a little bit before, 00:05:13.920 --> 00:05:16.960 I feel like we’re at that same sort of stage where a couple of years ago 00:05:16.960 --> 00:05:20.440 we’ve just talked about AI, AI, AI— everything was AI-oriented. 00:05:20.440 --> 00:05:23.760 Right? And we sort of got that. 00:05:23.760 --> 00:05:26.760 And the study actually came out that you were alluding to. 00:05:27.000 --> 00:05:30.680 You know, one of the findings was people were doing AI just for AI’s sake. 00:05:30.880 --> 00:05:31.880 Right? Right. 00:05:31.880 --> 00:05:36.120 At ServiceNow we have a digital workflow platform 00:05:36.720 --> 00:05:39.240 that has AI baked into everything that you’re doing. 00:05:39.240 --> 00:05:40.840 So, you know, really thinking about consciously, 00:05:41.000 --> 00:05:43.880 hey, I’m doing an AI addition onto this. 00:05:43.880 --> 00:05:45.040 But similarly, you know, 00:05:46.000 --> 00:05:47.360 could there be a process fix, 00:05:47.360 --> 00:05:51.000 could there be an organization change, could there be some sort of other way 00:05:51.000 --> 00:05:54.600 that you can actually sort this out versus jumping down the AI route? 00:05:55.080 --> 00:05:59.040 And I wonder if that’s also the case here with digital twins, right? 00:05:59.040 --> 00:06:02.600 I mean, I think there’s very, very good use cases, very sound, solid use cases. 00:06:03.120 --> 00:06:06.360 But does everyone need to have a digital twin in their back pocket? 00:06:06.480 --> 00:06:06.920 Right? 00:06:06.920 --> 00:06:09.920 I don’t know. Are we at the top of that Gartner 00:06:10.440 --> 00:06:11.200 hype cycle? 00:06:11.200 --> 00:06:15.160 Now, Justin… I’ll just pick up on that. 00:06:15.160 --> 00:06:17.520 I think we’re past the top of the hype cycle. Oh, we are? 00:06:17.520 --> 00:06:19.720 I think we’re going down the slope. Okay. 00:06:19.720 --> 00:06:20.800 I think when we get back up 00:06:20.800 --> 00:06:24.240 the slope, it’ll be much bigger than we could possibly imagine. 00:06:24.600 --> 00:06:27.840 One of the questions that I often get Asked is what is a digital twin? 00:06:27.840 --> 00:06:31.080 And the first thing I’ll say is, it’s not a noun, it’s a verb. 00:06:31.520 --> 00:06:34.800 This is a journey that many organizations 00:06:34.800 --> 00:06:36.600 have been on over a period of time. 00:06:36.600 --> 00:06:39.600 You can’t go out and just… If you do, 00:06:39.600 --> 00:06:40.440 you’re making a mistake. 00:06:40.440 --> 00:06:42.920 You can’t just buy a digital twin. 00:06:42.920 --> 00:06:45.480 There is no one-size-fits-all. 00:06:45.480 --> 00:06:47.520 There are many, many different flavors. 00:06:47.520 --> 00:06:49.800 And herein lies the real challenge. 00:06:49.800 --> 00:06:53.040 It’s that you have to make them to fit the purpose and the use case 00:06:53.360 --> 00:06:56.560 that you’re trying to build those scenarios and decisions 00:06:56.880 --> 00:06:59.880 and the what-if statements you’re looking to try and answer. 00:06:59.880 --> 00:07:04.960 Now, you mentioned, Justin, that you think that the future 00:07:05.320 --> 00:07:09.240 is going to be a lot of use cases and things that we 00:07:09.240 --> 00:07:12.360 maybe can’t even imagine, that are going to be very transformational. 00:07:12.360 --> 00:07:15.280 Could you give us a quick peek into what they are? 00:07:15.280 --> 00:07:17.120 I’ll try. 00:07:18.320 --> 00:07:19.120 Well, 00:07:19.120 --> 00:07:21.240 first of all, one of the challenges is 00:07:21.240 --> 00:07:24.800 that we’ve been building digital twins in silos using different standards 00:07:25.040 --> 00:07:29.480 and frameworks. And a twin that Nick might be building 00:07:29.480 --> 00:07:33.120 and a twin that I’m building may or may not easily communicate. 00:07:33.280 --> 00:07:34.840 And it’s not just the technology 00:07:34.840 --> 00:07:37.840 that is the challenge, it’s the governance that sits around this. 00:07:38.080 --> 00:07:42.040 So we don’t know yet what will happen when we start to bring these twins 00:07:42.040 --> 00:07:45.280 together as autonomous decision-making agents. 00:07:45.960 --> 00:07:50.840 Once we start to connect different data sets that essentially represent 00:07:51.000 --> 00:07:54.720 different sectors or different ideas and merge them together, 00:07:54.720 --> 00:07:58.120 we will create something that is difficult to comprehend. 00:07:58.120 --> 00:08:01.280 What I would suggest, though, is that as we build 00:08:01.280 --> 00:08:04.240 that digital nervous system around the planet, 00:08:04.240 --> 00:08:06.840 what will emerge on top of that would be something 00:08:06.840 --> 00:08:10.160 that would be like looking back a hundred years and seeing horse and 00:08:10.160 --> 00:08:13.600 carts on the streets as to where we are today. 00:08:13.960 --> 00:08:14.400 Right. 00:08:14.400 --> 00:08:16.600 Well, Nick, I’d love to get your thoughts on that, 00:08:16.600 --> 00:08:20.280 given that ServiceNow is doing exactly what Justin is talking about. 00:08:20.520 --> 00:08:25.480 Yeah, I think I’m very much in favor of this sort of concept 00:08:25.480 --> 00:08:29.320 of creating a library, a library of digital twins, like you said, right? 00:08:29.320 --> 00:08:30.720 I mean, we’ve got to get the governance right. 00:08:30.720 --> 00:08:33.520 We’ve got to be interoperable. But wouldn’t it be really good? 00:08:33.520 --> 00:08:35.240 I mean, I spend a lot of time— 00:08:35.240 --> 00:08:37.200 I’m based in Dubai, but I spend a lot of time in Africa. Right. 00:08:37.200 --> 00:08:40.720 And we’re talking about how Africa is growing and Africa is growing so fast. 00:08:41.160 --> 00:08:42.640 One of the terms that I hear a lot, 00:08:42.640 --> 00:08:46.560 and I’m sure you do too, in Africa, is the concept of leapfrogging, right? 00:08:46.640 --> 00:08:47.840 Wouldn’t it be cool 00:08:47.840 --> 00:08:49.920 when Africa is able to have all the infrastructure pieces 00:08:49.920 --> 00:08:52.400 they can leapfrog so much ahead because they don’t have 00:08:52.400 --> 00:08:54.040 all the legacy things to do? 00:08:54.040 --> 00:08:56.720 Well, what if we could have a marketplace with digital twins 00:08:56.720 --> 00:08:59.360 and playbooks that we could pull down off the shelf? 00:08:59.360 --> 00:09:01.040 It’s not going to be an exact fit, 00:09:01.040 --> 00:09:03.440 but it would be a close enough fit to get started. 00:09:03.440 --> 00:09:05.640 And, you know, suddenly that blank sheet of paper— 00:09:05.640 --> 00:09:06.200 Yeah. 00:09:06.200 --> 00:09:08.640 —you’re moving on from that. I get excited when we start thinking 00:09:08.640 --> 00:09:10.680 about the interoperability pieces like that. 00:09:10.680 --> 00:09:14.560 I 100% agree. The UK government has committed 00:09:14.560 --> 00:09:17.880 100 million pounds to building what we call a national data library, 00:09:18.240 --> 00:09:20.560 and that will be all about discoverability. 00:09:20.560 --> 00:09:23.680 And whether it’s actually discovering a full twin or the data sets 00:09:23.680 --> 00:09:26.680 that are available to make it easier for that connection 00:09:26.680 --> 00:09:30.280 is recognized as an important enabler for our industrial strategy. 00:09:30.640 --> 00:09:33.480 What kind of data are we talking about 00:09:33.480 --> 00:09:38.040 that are needed to build the digital twins? IoT data, 00:09:38.040 --> 00:09:40.800 obviously, what are some of the other kinds of data 00:09:40.800 --> 00:09:43.600 and how do you build those into simulation? 00:09:43.600 --> 00:09:47.160 The other thing I’ve heard a lot about is if the data is bad, 00:09:47.160 --> 00:09:50.960 you’re going to have a lot of very bad predictions in terms of the twin. 00:09:50.960 --> 00:09:54.080 So let’s talk a little bit about that data piece. 00:09:54.080 --> 00:09:57.560 So, I mean, there’s a plethora of data that feeds digital twins. 00:09:57.560 --> 00:10:01.640 That might be static reference data that is historical in one format. 00:10:01.640 --> 00:10:03.840 So maybe you’re pulling it out of a system 00:10:03.840 --> 00:10:05.880 that’s been running for a while— a legacy system— 00:10:05.880 --> 00:10:09.560 you have to extract that, supplement it with IoT data. 00:10:09.560 --> 00:10:10.480 It could be sensors, 00:10:10.480 --> 00:10:13.760 it could be video that is pulling information 00:10:13.760 --> 00:10:16.600 straight out of the streets, and we’ve got to merge that. 00:10:16.600 --> 00:10:21.320 There’s a lot of work right now that’s being done in AI around VLMs. 00:10:21.560 --> 00:10:24.840 Which I think, you know, is one of my big takeaways from the show, 00:10:24.840 --> 00:10:28.200 is how we understand the analytics behind it. 00:10:28.200 --> 00:10:30.800 And VLMs, could you explain those? Visual language models. 00:10:30.800 --> 00:10:32.560 So think about ChatGPT. 00:10:32.560 --> 00:10:35.440 But rather than it being about text, it’s all the visuals 00:10:35.440 --> 00:10:40.160 and how you interface and interact with real images in real time 00:10:40.160 --> 00:10:43.720 and creative scenarios that you can play out very, very rapidly. 00:10:44.520 --> 00:10:48.360 And simulating the real world off the back of us. You see what happens. 00:10:48.360 --> 00:10:50.040 You see how people move around. 00:10:50.040 --> 00:10:53.080 Let’s just take an example. Let’s say we have, 00:10:53.080 --> 00:10:56.400 some sort of an event that requires an evacuation. 00:10:56.760 --> 00:11:00.720 We can simulate the evacuation, recognizing the people in the city, 00:11:00.920 --> 00:11:04.600 movement of the people across transport networks, through the roads and, 00:11:04.960 --> 00:11:09.280 which cross trains, working out how perhaps the power is going to be impacted 00:11:09.280 --> 00:11:12.840 as a result of the evacuation. Start bringing that data together, 00:11:13.120 --> 00:11:16.680 simulate that using the visual language models 00:11:16.680 --> 00:11:19.680 to watch what’s happening and feed it back and analyze it 00:11:19.680 --> 00:11:24.720 and in real time, make decisions about how we should deploy our emergency services. 00:11:25.040 --> 00:11:27.120 I think that’s one of my big takeaways as well. 00:11:27.120 --> 00:11:31.240 I mean, you know, back in the day when we were trying to create those sort 00:11:31.240 --> 00:11:34.920 of like visual models, the amount of training data, 00:11:34.920 --> 00:11:37.360 the amount of custom tagging you had to do. 00:11:37.360 --> 00:11:40.000 And then, you know, I was looking at some of the demos we’ve seen here. 00:11:40.000 --> 00:11:42.240 I mean, there’s VLMs. 00:11:42.240 --> 00:11:44.720 It’s just plug and play, off 00:11:44.720 --> 00:11:45.600 you go to the races. 00:11:45.600 --> 00:11:47.200 I mean that is such a game changer. 00:11:47.200 --> 00:11:48.960 It’s such a game changer in my mind. 00:11:48.960 --> 00:11:51.360 And I do think, going back to the question that you asked, right, 00:11:51.360 --> 00:11:55.440 we obviously want to have the best data we possibly can. I think about it 00:11:55.480 --> 00:11:56.640 in a couple of different ways, right? 00:11:56.640 --> 00:11:59.040 One is like if you have a platform like ServiceNow, 00:11:59.040 --> 00:12:02.400 we can very much tie into all of those legacy systems. 00:12:02.400 --> 00:12:05.400 We’re not necessarily looking to replace your entire legacy 00:12:05.400 --> 00:12:08.400 infrastructure, your legacy application real estate. 00:12:08.760 --> 00:12:09.960 That’s just too complicated. 00:12:09.960 --> 00:12:12.680 People spend too much time, too much effort. 00:12:12.680 --> 00:12:15.000 No one wants to be told that their babies are ugly, right? 00:12:15.000 --> 00:12:16.480 And that you can’t use the data. 00:12:16.480 --> 00:12:20.720 So being able to use existing data sources, plugging in those new models, 00:12:20.720 --> 00:12:24.080 which I think can smooth all of that to a large extent, a lot of what’s going 00:12:24.080 --> 00:12:28.200 on, I think that becomes absolutely critical to what we’re looking to do. 00:12:28.560 --> 00:12:32.240 So what about some real use examples we might go through? 00:12:32.240 --> 00:12:35.040 One of the things that I’m really interested in 00:12:35.040 --> 00:12:36.560 is we’ve got the Winter Olympics. 00:12:36.560 --> 00:12:38.200 We’ve got the World Cup coming up, 00:12:38.200 --> 00:12:43.640 and then we’ve got Summer Olympics in Los Angeles, where I used to live. And sports 00:12:43.640 --> 00:12:46.640 have been a leader over the years 00:12:46.640 --> 00:12:49.760 in digital twins and using vision, 00:12:51.240 --> 00:12:53.280 computer vision, and so on. 00:12:53.280 --> 00:12:56.120 But with the Olympics, you both have the athletes, 00:12:56.120 --> 00:13:00.160 the user experience, you’ve got emergency management 00:13:00.160 --> 00:13:02.680 areas, you’ve got security infrastructure, 00:13:02.680 --> 00:13:06.480 so many very complex things that all have to come together. 00:13:06.680 --> 00:13:09.680 Would that be a great use of digital twins? 00:13:09.800 --> 00:13:12.320 So it’s funny you say that and this is a shameless plug 00:13:12.320 --> 00:13:15.320 for your booth and what you’re basically showing over there 00:13:15.640 --> 00:13:18.280 because, a colleague of mine, Mick, and I saw that, 00:13:18.280 --> 00:13:20.720 and we were thinking on very much the same lines. 00:13:20.720 --> 00:13:22.480 Right. We’ve got the events, 00:13:22.480 --> 00:13:25.560 the events management, of course, the in-stadium experience. 00:13:25.560 --> 00:13:29.880 Now ServiceNow works very closely with the NBA, with the Hockey League, 00:13:29.880 --> 00:13:31.560 you know, and all these big sports things. 00:13:31.560 --> 00:13:33.720 I know you guys do too at Deloitte 00:13:33.720 --> 00:13:37.800 with the IOC and what we’re thinking is, to your point, 00:13:38.360 --> 00:13:39.600 it’s the visitor experience. 00:13:39.600 --> 00:13:41.240 it’s the fan experience. 00:13:41.240 --> 00:13:43.440 But it’s also the employee experience. 00:13:43.440 --> 00:13:46.200 I mean, I think the statistic I had for an Olympic games is 00:13:46.200 --> 00:13:49.960 the temporary staff is around about 40,000 temporary staff 00:13:49.960 --> 00:13:53.040 that need to be attracted, 00:13:53.040 --> 00:13:56.040 retained, hired, trained, 00:13:56.080 --> 00:13:59.080 put into right position, told what to do, told how to do it. 00:13:59.400 --> 00:14:02.760 You know, some massive, massive, massive complicated headache for someone. 00:14:03.040 --> 00:14:04.240 What if I could simulate that. 00:14:04.240 --> 00:14:04.360 Yeah. 00:14:04.360 --> 00:14:06.840 What if I could simulate, like the causes and effects, 00:14:06.840 --> 00:14:08.120 and that’s just one area, right? 00:14:08.120 --> 00:14:10.680 So on your board where you can move all the pieces around, 00:14:10.680 --> 00:14:14.200 just thinking of all those different areas that you would have—transportation, 00:14:14.200 --> 00:14:17.920 customs and borders, police, the tourist experience. 00:14:17.920 --> 00:14:20.160 I mean, there’s just so many verticals 00:14:20.160 --> 00:14:23.720 that would be one really, really good digital twin, I think. 00:14:23.720 --> 00:14:27.520 Can I just add to that thinking, which is, 00:14:27.520 --> 00:14:30.520 yes, the cause is a fantastic 00:14:30.560 --> 00:14:34.360 opportunity to deploy state-of-the-art digital twins 00:14:34.640 --> 00:14:37.640 and the technology that will sit underneath that, 00:14:37.640 --> 00:14:40.320 but also an opportunity to ensure that 00:14:40.320 --> 00:14:43.320 public and private sectors work well together. 00:14:43.440 --> 00:14:46.760 Because if you’re bringing a large number of visitors into a city, 00:14:46.920 --> 00:14:49.920 then the city itself needs to react to that. 00:14:50.080 --> 00:14:53.080 The stadium is there to be able to bring together 00:14:53.080 --> 00:14:57.000 those visitors, and managing that visitor experience is absolutely key, 00:14:57.480 --> 00:15:00.400 but you want to make sure that the safety around the city as well. 00:15:00.400 --> 00:15:00.640 Yeah. 00:15:00.640 --> 00:15:04.400 And that interplay of data between the city and the stadium, 00:15:04.400 --> 00:15:08.400 which may well have a number of different private 00:15:08.400 --> 00:15:12.360 entities managing different parts of it, is key. 00:15:12.520 --> 00:15:15.960 And too often you’ll find that the city has got data sets 00:15:16.240 --> 00:15:18.920 about the transport systems, 00:15:18.920 --> 00:15:22.880 about its services that don’t connect into the private sector. 00:15:22.880 --> 00:15:27.440 So I think where you’ve got that opportunity, it’s a critical mass 00:15:27.440 --> 00:15:32.160 to create something that really needs to be delivered in an exceptional way. 00:15:32.400 --> 00:15:36.000 It also creates that tension that will drive that public- 00:15:36.120 --> 00:15:39.880 private sector interoperability that is key for us. 00:15:40.320 --> 00:15:43.440 I have another really good use case that I’d heard a while ago 00:15:43.800 --> 00:15:46.080 and talk about digital twins, I think is an interesting one, 00:15:46.080 --> 00:15:49.080 where that interplay between private sector and public sector, 00:15:49.240 --> 00:15:51.720 and that’s in the world of government procurement. 00:15:51.720 --> 00:15:53.240 Yeah. Right? 00:15:53.240 --> 00:15:55.120 We know government procurement is long and lengthy, 00:15:55.120 --> 00:15:57.000 especially when we talk about infrastructure projects 00:15:57.000 --> 00:15:58.680 and those kind of things. 00:15:58.680 --> 00:16:02.840 What if you had a digital twin that then enabled the stage 00:16:02.840 --> 00:16:07.720 gates of payments and completion timelines or completion deadlines? 00:16:07.720 --> 00:16:08.640 Right. 00:16:08.640 --> 00:16:12.400 That would, I think, you know, we all get frustrated when we drive down the road 00:16:12.720 --> 00:16:15.480 and the road is closed because there’s work going on, 00:16:15.480 --> 00:16:16.880 but you don’t actually see any workers. 00:16:16.880 --> 00:16:19.880 And of course, as citizens, we’re sitting there saying, well, what’s going on? 00:16:19.880 --> 00:16:21.240 But there’s an interplay there. 00:16:21.240 --> 00:16:23.520 What if we could do a digital twin of the procurement 00:16:23.520 --> 00:16:27.400 and then make sure that the vendor is delivering on that? 00:16:27.560 --> 00:16:29.400 Yeah. I think that would be a good use case. 00:16:29.400 --> 00:16:34.440 I like that. One of the challenges that I think we face 00:16:34.440 --> 00:16:37.560 is that there is this drive to want to share data 00:16:37.920 --> 00:16:40.920 or provide access to others to use that data 00:16:41.040 --> 00:16:44.280 so that we can create something better than the sum of the parts. 00:16:44.640 --> 00:16:47.880 But the problem often is I don’t want to share my data. 00:16:47.880 --> 00:16:49.080 You don’t want to share your data. 00:16:49.080 --> 00:16:52.960 The last time anybody around here shared that data, there was too much risk, 00:16:52.960 --> 00:16:54.320 and we had a problem. 00:16:54.320 --> 00:16:58.320 So we’re operating in an environment where in fact we’re deincentivized 00:16:58.320 --> 00:16:59.400 to do that. Right. 00:16:59.400 --> 00:17:02.640 So some kind of mechanism 00:17:02.640 --> 00:17:06.120 that allows for better procurement and incentives to be aligned. 00:17:06.160 --> 00:17:06.360 Yeah. 00:17:06.360 --> 00:17:09.360 You know, some kind of data exchange that ensures 00:17:09.360 --> 00:17:12.440 that when I do actually there’s an upside to this. 00:17:12.680 --> 00:17:14.480 That transaction is recorded. 00:17:14.480 --> 00:17:17.240 It can be assured and audited. 00:17:17.240 --> 00:17:21.760 And we can build the trust in the system that then builds 00:17:21.760 --> 00:17:25.760 the critical mass to the level where, of course, I’m going to be sharing. 00:17:25.760 --> 00:17:27.160 I’m quite happy to do it. 00:17:27.160 --> 00:17:31.120 You know, now we’ve built that trust that lets the data flow. 00:17:31.360 --> 00:17:34.360 And if we create additional value, 00:17:34.520 --> 00:17:39.640 and I was the one that had the source data, I may be rewarded 00:17:39.640 --> 00:17:41.560 at the later stage. 00:17:41.560 --> 00:17:43.280 Yeah. Far too often that doesn’t happen. 00:17:43.280 --> 00:17:44.880 I couldn’t agree more. 00:17:44.880 --> 00:17:48.720 And my most recent book is on public-private collaboration. 00:17:49.080 --> 00:17:52.640 We have a whole chapter on data is the new language, and it’s around 00:17:52.640 --> 00:17:56.080 sharing the data between public and private sectors, universities. 00:17:56.080 --> 00:17:59.440 Because we talk about the data silos within government, 00:17:59.640 --> 00:18:02.160 but the private sector has so much of the data now, 00:18:02.160 --> 00:18:06.600 and we have to figure this out. Now with all the talk about digital twins, 00:18:06.600 --> 00:18:08.520 question for both of you: 00:18:08.520 --> 00:18:13.840 In what cases does it not make sense to have a digital twin, to go down that route? 00:18:13.840 --> 00:18:16.280 Are there any or no? 00:18:16.280 --> 00:18:19.800 So let me just jump in with one digital 00:18:19.800 --> 00:18:23.320 twin in the UK, which is a fantastic digital twin. 00:18:23.920 --> 00:18:26.560 It’s Kraken, owned by Octopus Energy, 00:18:26.560 --> 00:18:30.520 that now is responsible for balancing 50 GW of energy. 00:18:30.840 --> 00:18:36.280 It has 60 million customers in 26 countries around the world, and the twin 00:18:36.280 --> 00:18:39.960 that’s being created by the company is arguably worth more than the company. 00:18:40.360 --> 00:18:43.120 How so? Because of the value of data. 00:18:43.120 --> 00:18:45.120 Exactly the point you were making. 00:18:45.120 --> 00:18:48.200 It’s because they are able to drive those connections, 00:18:48.200 --> 00:18:51.200 but importantly, help balance the grids. 00:18:51.320 --> 00:18:54.320 And so whilst there’s bits and bytes that are moving around, 00:18:54.560 --> 00:18:57.560 they are related to energy. 00:18:57.600 --> 00:19:01.560 And it’s that relationship to the electric currents that is key. 00:19:01.800 --> 00:19:04.000 The value sits there. It models it. 00:19:04.000 --> 00:19:06.000 It works out how to get it flowing 00:19:06.000 --> 00:19:09.000 and therefore the economic model that underpins it, 00:19:09.000 --> 00:19:12.000 it drives the adoption and drives the use. 00:19:12.200 --> 00:19:13.800 I think I’ll keep it really simple. 00:19:13.800 --> 00:19:17.240 I think it boils down to, you know, the return on investment that you’re going 00:19:17.240 --> 00:19:20.880 to be getting by building your digital twin, like you said, right? 00:19:20.880 --> 00:19:25.480 I mean, sometimes you don’t need such a big, complicated 00:19:26.280 --> 00:19:29.280 initiative to solve a simple problem. 00:19:29.440 --> 00:19:30.520 If you’re getting the output 00:19:30.520 --> 00:19:32.040 that you really want to be getting from it, 00:19:32.040 --> 00:19:35.760 and it’s not costing you that much to figure out 00:19:35.760 --> 00:19:37.800 how to build that digital twin, maybe you can resurface 00:19:37.800 --> 00:19:39.840 some of the information that’s already available, 00:19:39.840 --> 00:19:43.320 maybe pull down one of those playbooks that’s already existing, and sure, 00:19:43.360 --> 00:19:44.480 go for it. Right. 00:19:44.480 --> 00:19:48.960 But I do think that to the point that we were making before and the hype cycle, 00:19:49.880 --> 00:19:52.560 just before you jump in the water, just make sure that you really know 00:19:52.560 --> 00:19:55.320 how deep that water is going to be and in how long you’re going to get there, 00:19:55.320 --> 00:19:56.040 and that it doesn’t 00:19:56.040 --> 00:19:59.040 distract you from the actual goal that you’re trying to accomplish. 00:19:59.480 --> 00:20:00.840 Because if you’re doing that, leading you to 00:20:00.840 --> 00:20:02.760 spending cycles, you’re going to get frustrated. 00:20:02.760 --> 00:20:04.040 You’re going to lose your stakeholders. 00:20:04.040 --> 00:20:06.280 The change management aspect goes out the window. 00:20:06.280 --> 00:20:08.280 You’re just going to not look so great. 00:20:08.280 --> 00:20:11.920 So if we look out five years from now, and we’ll end with this, 00:20:12.120 --> 00:20:17.120 what is the, maybe, wildest example you can think of a digital twin? 00:20:17.120 --> 00:20:20.200 Well, we each have our digital twins of ourselves. 00:20:20.200 --> 00:20:24.680 So maybe not with all their varying weights, but is that something realistic? 00:20:25.440 --> 00:20:27.400 You’ve got a smartwatch on there. 00:20:27.880 --> 00:20:30.320 That’s already the beginning of your digital twin, right? Yeah. 00:20:31.560 --> 00:20:35.360 It’s monitoring your vitals and it’s building up a picture 00:20:35.360 --> 00:20:39.480 over time. It’ll look at the trends, it’ll predict whether or not, in fact, 00:20:39.480 --> 00:20:42.480 it’s time for you to get up or do something that you need to do. 00:20:43.360 --> 00:20:47.080 Yes. But if I was to look ahead, I think the key thing is that today 00:20:47.080 --> 00:20:48.600 we still think of digital twins 00:20:48.600 --> 00:20:52.680 a little bit as a mirror of the real world. 00:20:52.840 --> 00:20:56.240 We’re able to look in the mirror, we’re able to see what’s going on. 00:20:56.400 --> 00:20:58.400 We’re able to use it to predict the future 00:20:58.400 --> 00:20:59.760 to a certain degree. 00:20:59.760 --> 00:21:05.040 I think we’ll see that shift from a twin being a representation of the world 00:21:05.320 --> 00:21:09.560 to a decision-making, autonomous piece of a broader 00:21:09.560 --> 00:21:13.320 digital nervous system that wraps itself around the planet. 00:21:13.680 --> 00:21:17.120 And those twins are responsible for different parts of the planet 00:21:17.120 --> 00:21:21.200 and the connection, and most importantly, to pull in the data, 00:21:21.200 --> 00:21:25.000 make sure that it’s well organized, make sure that the hygiene of the data 00:21:25.000 --> 00:21:29.160 is properly considered before it is then passed into some part of the cortex. 00:21:29.160 --> 00:21:30.080 It feels like 00:21:30.080 --> 00:21:32.240 I’ve read a lot of science fiction books around that, 00:21:32.240 --> 00:21:35.720 where things might go wrong in that kind of a case. 00:21:35.720 --> 00:21:37.440 Right? Once again, 00:21:38.640 --> 00:21:41.160 I’m really a big believer in keeping the human in the loop. 00:21:41.160 --> 00:21:41.520 Right. 00:21:41.520 --> 00:21:45.000 So when we talk about these autonomous systems, I mean, 100%, right. 00:21:45.000 --> 00:21:48.400 If I’m doing something relatively simple, straightforward, you know, 00:21:49.040 --> 00:21:52.200 it’s a completely autonomous tier-one IT helpdesk, for example, 00:21:52.200 --> 00:21:54.880 password resets—sure, go for it. 00:21:54.880 --> 00:21:55.840 But when you start talking 00:21:55.840 --> 00:21:58.840 and when you start talking like that, getting into the cortex and like, 00:21:59.000 --> 00:21:59.280 yeah, that’s 00:21:59.280 --> 00:22:00.960 when I get a little bit nervous and I’m like, 00:22:00.960 --> 00:22:03.520 are we sure that’s exactly the direction that we want to be going. 00:22:03.520 --> 00:22:06.760 So I do think those safeguards become very important 00:22:06.800 --> 00:22:08.120 as we rightly start doing that, 00:22:08.120 --> 00:22:11.120 not to slow us down necessarily, but just to make sure that, you know, 00:22:11.400 --> 00:22:14.120 we’re not having bad actors who, of course, are out there 00:22:14.120 --> 00:22:17.120 all the time trying to disrupt what it is that we’re trying to do. 00:22:17.760 --> 00:22:21.080 I think, I mean, one of the things, it was several years ago, 00:22:21.080 --> 00:22:22.720 we were talking about the omniverse. 00:22:22.720 --> 00:22:26.360 And so I was wondering, like, you know, and that was, to me, another fad 00:22:26.840 --> 00:22:28.440 type thing, a little bit. Right. 00:22:28.440 --> 00:22:31.920 But I do wonder and as we’ve said, the technology is there. 00:22:32.880 --> 00:22:35.120 The data is getting to where it needs to be. 00:22:35.120 --> 00:22:36.720 It becomes a bit more of a human— 00:22:36.720 --> 00:22:39.760 does the human want to entertain this idea and the human want to do this? 00:22:40.480 --> 00:22:43.800 I wonder if, you know, we will be walking around in this virtual universe 00:22:44.440 --> 00:22:45.920 and actually seeing what it’s like? 00:22:45.920 --> 00:22:50.400 You know, perhaps we’re driving our cars around just to see how well 00:22:50.600 --> 00:22:54.000 did it work when we had an interchange where there were all these road accidents? 00:22:54.320 --> 00:22:56.760 We’ve been able to fix that. We’ve been able to model that out. 00:22:56.760 --> 00:23:00.640 All those good use cases that if they have very positive human impacts, 00:23:01.480 --> 00:23:05.720 can we mirror those in a bit more of a graphical user interface? 00:23:06.720 --> 00:23:09.720 Can I pick up on the going wrong piece because this is a really 00:23:10.080 --> 00:23:13.960 the going wrong piece, you know, could this all go wrong> And once 00:23:13.960 --> 00:23:16.880 we start to have decisions that are being made, 00:23:16.880 --> 00:23:19.920 perhaps a little bit outside of our day-to-day 00:23:20.240 --> 00:23:21.840 purview. 00:23:21.840 --> 00:23:24.960 I think this is where ensuring the system is key at every level. 00:23:24.960 --> 00:23:27.120 We’ve got to have the right standards that underpin it. 00:23:27.120 --> 00:23:30.560 We’ve got to have the continuous learning, continuous monitoring 00:23:30.560 --> 00:23:33.560 to make sure the models aren’t drifting in the wrong direction. 00:23:33.720 --> 00:23:34.000 Yeah. 00:23:34.000 --> 00:23:35.960 And then at the top level, we’ve got to make sure 00:23:35.960 --> 00:23:39.880 that we can assure the whole system, but it’s not going to be a single system 00:23:39.880 --> 00:23:41.000 that rules it all, 00:23:41.000 --> 00:23:43.080 I hope. It must be federated. 00:23:43.080 --> 00:23:48.000 It must have federated governance that ensures that we can still have a person 00:23:48.000 --> 00:23:51.600 in the loop, ensuring that we can control whatever it is 00:23:51.600 --> 00:23:53.200 that we’re going to be building. 00:23:53.200 --> 00:23:56.440 But governance is key and we’re going to need governance 00:23:56.440 --> 00:23:59.520 to move at the speed of data. Yes. 00:23:59.760 --> 00:24:01.240 Well, our time is up. 00:24:01.240 --> 00:24:03.760 Let’s leave it with that fascinating conversation. 00:24:03.760 --> 00:24:04.840 Thank you two so much. 00:24:04.840 --> 00:24:06.920 And thank you for joining us today. 00:24:07.880 --> 00:24:09.360 My thanks to Nick and 00:24:09.360 --> 00:24:12.720 Justin for their fascinating insights about digital twins. 00:24:13.240 --> 00:24:17.040 If you like to hear more conversations from Government’s Future Frontiers, 00:24:17.280 --> 00:24:21.240 you’ll find all our previous episodes wherever you get your podcast. 00:24:21.640 --> 00:24:25.040 And to make sure you don’t miss new episodes, be sure to follow the show 00:24:25.040 --> 00:24:27.200 on your podcast platform of choice.