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[00:00:00] Kimberly Boyd: Welcome to Pragmatism in Practice, a podcast from ºÚÁÏÃÅ where we share stories of practical approaches to becoming a modern digital business. I'm Kimberly Boyd, your host for today's deep dive into the strategic modernization of data inspired by the latest findings from the MIT Technology Review Insights report. In our digital age, businesses are grappling with the complexities of data utilization like never before, but what does it take to truly modernize data practices and unlock its potential?

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Joining us today is James Morgan, Chief Data Officer at The Crown Estate, and Danilo Sato, Global Head of Technology for Data and AI at ºÚÁÏÃÅ. With a diverse portfolio ranging from agricultural lands to commercial properties, James and his team are at the forefront of leveraging data for value creation. Together, we'll delve into their experiences and strategies for navigating the evolving terrain of data modernization. Welcome, James and Danilo, to Pragmatism in Practice.

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[00:00:57] James Morgan: Thank you.

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[00:00:58] Kimberly: James, maybe to start us off can you give our listeners an introduction to yourself and what The Crown Estate is, and a little bit about what you do there as chief data officer?

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[00:01:10] James: Yeah I'm James Morgan. I've been in The Crown Estate for nearly a year now. Spent about 25 years in the data industry doing analytics data roles, and at The Crown Estate, the chief data officer role here is all around really helping and supporting the company to become more data and digital-focused and really helping to drive and support the overall company strategy from a data perspective.

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[00:01:41] Kimberly: Maybe could you tell us a little bit about The Crown Estate as a non-Brit here? I know a little bit, but not a ton, so it'd be great to hear a little bit more.

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[00:01:51] James: It is a really amazing organization. Our history goes back to 1760. We've been set up by an act of parliament and we inhabit an interesting space, which is somewhere between the commercial and government sectors. Basically, we are the stewards of the assets that the Crown owns. We do that through a charter that really drives forward a really important thing about juggling the responsibilities of environmental, social, and economic benefit from the assets that we hold.

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Those assets are land-based, the estates, things like the Windsor Great Park, farmlands. We also have regional assets, things like shopping centers and ports. We also have a number of business parks and distribution centers, but also we have some of the most fabulous iconic central London properties around Regent Street, and St. James's Market, and Haymarket. The bit that most people don't know about us is that we are custodians of the seabed and we have ownership out to 12 nautical miles and then wider responsibilities further out to the edge of the continental shelf.

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We look after all of those so that they're utilized for the best interests of the country. Our purpose there is to create lasting and shared prosperity for the nation.

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[00:03:40] Kimberly: I think this is the first organization I've ever heard of that has responsibility out to the continental shelf. Definitely an interesting mix in your portfolio there. Danilo, some of our listeners might remember you from a previous episode, but it would also be great if you could give us a little refresher of who you are and what your role is at ºÚÁÏÃÅ.

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[00:04:00] Danilo Sato: Sure. I'm Danilo Sato. I moved into a new role actually. Last time we recorded, I was running our UK data and AI services, and since last year, I moved into a new global role. I'm the head of technology for our data and AI service side, and I support most of our global data and AI strategic clients. I'm happy to be here for this conversation.

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[00:04:23] Kimberly: Well, wonderful. Let's dive into it then. James, I know recently you were interviewed for the MIT Technology Review Insights report that focused on data. In that, you mentioned the importance of having a vision for your data strategy really before you embark on anything in the data space. Hoping maybe you could talk a little bit to us about how The Crown Estate aligns its data strategy with its kind of myriad, an interesting array of portfolio holdings. How do you really bring that together for a cohesive data strategy?

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[00:05:02] James: The most important thing about any data strategy is that it is there to drive and support the outcomes for the wider organization. The first starting point for any vision is our overall purpose as an organization, and then the strategies that the company has created and is implementing to deliver against that purpose. That's always the starting point, I take, for a vision around data.

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When you start to unpack and unpick that, what's really important is that we start to put that data in the hands of the organization. Part of the vision is all around how we democratize that data and we empower the organization to use the information.

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When you're talking about empowering an organization to use data, obviously, they have to be data literate to do that. Part of the vision, again, is about becoming more data-literate and more mature as an organization. Fundamental to anything we do anywhere in data, and certainly fundamental as the world goes on this AI, generative AI journey, is the quality of that data. Part of the vision is around making sure that we've got the highest quality of data and really rich data so that we can turn it to the purpose we need it.

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Also, there within the strategy is all around the necessary skills, knowledge, agility, and diversity to leverage that data. That's absolutely vital as well. Diversity is something really important to the whole of The Crown Estate and very key when you are dealing with data because data is such a varied field, we need all different types of personalities, mindsets, and also different backgrounds to get the best and drive the best from our data solutions.

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What we also talk about is the fact that data is really an asset for the organization. It is about treating that asset as you would any other asset in organization, investing in it, developing it, growing it. Part of the vision then, again, is how do we realize the full potential of that brilliant asset. What we're blending to bear together in the vision and obviously the actual vision itself is a bit punchier than I've laid out in those terms but is that linkage to business outcomes, that taking and involving the whole organization on a journey and how we've really got to make sure we treat this valuable commodity as an asset.

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[00:07:44] Kimberly: It sounds like it's very much of a painting a picture. You talked about the things that you really want and need to have in place. We want our folks to be data-literate. We want the data to be democratic. We want it to be diverse. So you're almost thinking of your end state and then it sounds like backing into, great, how do we build the path to get there? Is that fair to say?

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[00:08:04] James: Yes. I've reflected back a few times on my career in data and a huge amount of it has been about actually that transformation. Data and digital are fundamental to the whole transformation journey of organization. It is, as you've very clearly put there, about identifying that picture you are aiming for. I sometimes think about that nice, lovely picture that we could get to, then the fact that it's a bit like a jigsaw puzzle. What you're doing is filling in the gaps and building that jigsaw puzzle out towards that nice Nirvana.

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Actually, it's quite interesting because that picture keeps on changing as the business evolves and as time evolves and technology evolves, then you slightly readjust what the picture looks like. It's all about that and transformation is a massive element of any data journey.

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[00:08:55] Kimberly: I also really like what you said about treating it as an asset, especially given that The Crown Estate has a number of assets. It's probably helpful to think of it that way and then you can bring that rigorous operational and strategic mentality to data just like you would for anything else that you're looking after in the portfolio.

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[00:09:17] James: Yes. It's a really interesting one, and over the years, I've followed the story of, "Can you actually put it on the balance sheet? Is it that kind of asset?" Actually, we are going through an exercise at the moment to understand that question within The Crown Estate. We're not an organization, say, like a first-party customer organization where we hold lots of information about somebody or something that our primary objective is to sell and make money out of that. That's definitely not what we're about. What we are about is thinking about what you can turn that data to.

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Can you turn it to that value creation? Can you turn it to something where you can create benefit for society, for the environment, and economically as well? We look at it through a lens of what can it catalyze, what can it enable, what can it unlock.

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There is obviously value intrinsically in the data that we hold, but I think the value is more about how we bring lots of different data together and what we then do with that data to deliver outcomes. Those outcomes, like we say, can be focused on the organization, but often they're focused on part what we're trying to achieve as an organization.

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We're focused really hugely on leading the energy transition and improving UK's energy security. If you think about that, the information and data is absolutely critical in terms of that transition of the whole country to more secure forms of energy, to more environmentally-friendly forms of energy, and as net zero targets as a country and an organization. Really much about how you leverage the data for the greater good rather than just potentially seeing that as an asset in its own right that you would treat the singularity.

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[00:11:29] Kimberly: The nature of it being an asset is its ability to unlock additional insights and connections.

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[00:11:37] James: For us, a huge amount of it is can we make the best decisions? As an organization, what we really want to do is optimize our decision-making and also to be able to evidence our decision-making. We want to make sure that we can justify the decisions we make when we are dealing with a finite resource, and that's what we've got to remember about all of our resources be it the seabed or on the land. They're a finite quantity or finite scale.

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You have to look at those and go, "What's the best decision for the utilization of this fabulous, diverse resource that we have the great fortune to manage?" Looking at it and going, "Yes, we made what we feel is the right decision from that."

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[00:12:24] Kimberly: Danilo, I'd love for you to chime in here and get your thoughts. I know we're talking about James's journey with setting the data strategy for The Crown Estate, but you've also had the opportunity to see data strategy development across a number of organizations. Is there anything else you would add when it comes to developing a data strategy or other best practices to keep in mind?

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[00:12:49] Danilo: I think I would reinforce James's point about starting with idea and the idea of getting the data to the hands of the people who are making those decisions. They're oftentimes not data literature to be able to actually manipulate the data on their own. Part of that value unlock or I talk about that last mile of activation, it's really understanding what decisions are they making, where are they making those decisions, could we bring the data to life where they are already? If you can answer that question like that, not only as the value articulation, but it also helps with the transformation because it's easier for people to engage with that strategic process rather than trying to build it somewhere else.

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A lot of that where the data comes alive, I guess, it's quite important as you're thinking about developing a data strategy. This is why, when we did the reports as well, we tried to explore how data strategy connects to the business strategy because I've seen some that are actually the other way around where it's easy to build a strategy as about collecting all the data because we believe on the inherent value, but we don't know exactly how it's going to get unlocked versus if you force yourself to try to answer the value question first and figure out who are the people that will engage with it. Then part of the problem is already solved in a way, then it becomes easier to justify building all the technology infrastructure and the other things that you need.

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[00:14:19] Kimberly: Start first with the value in mind, not the data, you can go back. [crosstalk]

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[00:14:22] Danilo: Yes. The data is valuable, but you need to be able to explain why.

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[00:14:26] Kimberly: Maybe that touches on another point talking about the democratization of data, getting the organization data-literate, getting it really into folks' hands. It almost seems like there's a step before that before getting folks upskilled and getting the data right in front of them so they can use it day to day is really getting them to understand what the vision is for data in an organization.

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James and Danilo, I think this is a question for you both— How have you ensured that that data vision effectively gets communicated across the business?

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[00:15:07] James: Basically getting out and about speaking to people. The most important thing in a role like mine is to spend a huge amount of time out with your stakeholders. Listening to them about what's keeping them awake at night, understanding what they're trying to achieve as an organization. Then when you are talking to them about what you are doing in data, you're talking to them in their terms about their roles. You're not talking necessarily about systems and capabilities.

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What you're talking about is-- of their strategies and their outcomes, or even their daily tasks it can help support and enable. It's just actually one-to-one time with people, getting to know them, speaking to them, spending time with them when they're talking about their organization, zeroing in on problems that they're trying to solve, and seeing how you can help them.

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There's then a bit more of a broadcast side of it. We hold events in the organization and part of one of our most recent leadership team days was basically a whole day focused on data and talking to people about the vision, the strategy, but also bringing it into more business terms and running some exercises. Interestingly, in the afternoon of that event, we did practical exercises that were fun and interesting based on real business problems but it wasn't about somebody stood on stage presenting one at the time. This was about working through in small groups of 15 to 20 on practical gamified versions of business problems.

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That really resonated with people because it really brought it alive and it was real. Then another thing that we've done is created some communities of practice in the organization where groups of like-minded people get together to talk about the analytical work they're doing or some of the reporting they're doing.

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Also, we are working to roll out the data literacy program. This is a whole series of cohorts throughout a year going through and learning about the fundamentals of data. Looking at how to practically build some simple reports, understanding the concepts of ownership and governance of data, and really taking it out in a form that is sensible and understandable, rather than somebody just talking about these as abstract concepts. It's actually getting people to learn the fundamentals and feel comfortable with them, and that's one of the real first steps to something like literacy. A lot of work in different forms to communicate and engage people at different levels in different ways.

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[00:17:59] Kimberly: I think you hit the nail right down the head there. I think data can seem like this big vague, abstract thing, like, "Oh, great, our company's embarking on a new data strategy." When you get down there human to human and what are your problems, maybe what data would help make them a little easier, I really love that approach. I think that makes it much more consumable for everyone.

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[00:18:21] James: I think you've got to be humble as well. If I came in saying, "Oh, I know how to run property management," or "I know how to run marine." One, that would be daft of me, but it would be very wrong as well. These are experts in their business, and what you are trying to work out is how you can help and support them or maybe put a different lens on something.

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I think going in quite rightly, being humble, and learning what they're trying to do in their day jobs is really important because coming in with it through a data lens and data can solve everything, is really the wrong approach.

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[00:19:01] Danilo: Yes, I agree. We do a lot of these collaborative workshops as well when we are exploring things. You get people along some of that, like you said, gamifying the process. If we've got workshop that we'll do which you have, for instance, you put on cards, all the core data assets you may have. We've got cards that explain different-- we call it toolkits, but it's really different types of data algorithms or techniques that you could apply.

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Then through the process of combining data sets with tool kits ideas, we came up with scenarios or some of them might be impossible things or here's the brainstorm activity but what you get is people to engage with that process of combining. They might learn a little bit about some of those techniques. Maybe they haven't heard about what you can do with optimization or with forecasting.

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By trying to apply to the fictitious scenario, some of them actually turn out to be real use cases that you can actually further explore but that collaborative approach, you tend to bring the people that have those different complementary skill sets together. The other thing that's very useful is, oftentimes, like you said, data might be a bit overwhelming.

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I've seen a lot of dashboards full of information and charts and graphics that people don't understand. Just stating like a user experience, user design approach to, actually, what is the person looking at this dashboard trying to do? What are the questions they're trying to answer? What are the decisions they're trying to make?

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We can simplify that a lot, like, what is the key piece of information that they need to make that decision, and not only makes it easier to consume, then it makes it less overwhelming for someone that's not technical to understand.

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[00:20:51] Kimberly: Someone who's not technical, I would much prefer to join in a gamified data exercise versus you dumping a huge data set on me. I think anytime you're trying to win hearts and minds and get people engaged in something, that's a great way to do it.

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James, one thing that's been on my mind ever since hearing you talk about the diverse portfolio that The Crown Estate has is, how do you design your data infrastructure to serve the needs of such a diverse business? You've got seabeds and commercial property in the heart of Central London. How do you think about solving for the whole when the needs really are so different, or I guess I'm assuming they're so different from business to business?

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[00:21:42] James: The interesting thing is the outcomes and the specific detailed solutions are very different. What I've found here and over the years is that, actually, the underlying capabilities are quite similar. If you take an example of what we do spatially modeling and planning the seabed, the tools and tech are similar to a land-based set of capabilities.

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The data inputs are different and the sectors and industries are different, but essentially, what you are creating is a mapping or a model, and then you are trying to run a multitude of scenarios past it to deliver certain outcomes. When you boil it back to what is that thing trying to do, you actually can create common capabilities for the whole organization. It is the specific application that makes them different.

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What we don't necessarily have to do here, which I've had to do in other organizations, is join everything up in the same way. In certain organizations I've worked in, the customer is the same customer for lots of different products. For us, our customers, say, residential in London are very different from our customers and partners that we work with on the seabed. Obviously, you don't have to join technically everything up in terms of a data model.

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We can do slightly different things in those spaces and there allows us to create more of a domain-based approach in terms of how we link our data and how we bring it together. However, when you look at the underlying different forms of data that we have, you do center around some very common areas of actually physically there still is a customer, even though they're different customers, there are products, there is a finance domain, there is always something around locational place.

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The principles of data I've found, over the last 20-odd years doing this, are very similar in all organizations. The nuances of how you apply it are what makes it very different, and that's what makes it fabulously interesting is you're trying to work out how you can apply what's been learned over many years to these very specific intricate problems.

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[00:24:34] Kimberly: Well, it helps, I think, then, listening to what you just said, that there's commonalities in it and it's in the application where there's the difference. That does allow you to have unique use cases in the different areas, and sounds great probably just for ease that you don't have to join them all up and can think about them a little bit more discreetly.

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Danilo, I'm curious if this is consistent with patterns you've seen in other organizations as well because I'm sure there's other businesses, other industries that have a pretty diverse portfolio and how they think about treating the approach to data across a really diverse mix.

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[00:25:16] Danilo: Yes, it is similar in the sense there are these domain particularities that different parts of the business will need from the data AI. A lot about common platform. The thing to unlock that actually is you could have different domains own and operate maybe how they use the data in a different way. What you might need is the interoperability.

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If someone finds value in a data set that is owned by maybe another domain, then if they are siloed completely and it's locked down in a completely different place, it makes it hard for you to explore those cross-domain use cases, but if you've got a platform that enables, it's just the product by a domain, but there is space to connect things together, if you find the right use case, then you can still have it.

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Those platform capabilities tend to be the commonality. Then how complex, I guess, the platform needs to be because, to James' point, it might be diverse types of data that you're all seeing and the tooling to use is different, and to store and to query them is different. You can get complex, the technical implementation, but from a business perspective, you want to have the platform enable this cross-domain interoperability.

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[00:26:37] Kimberly: Also, going back to one thing you had mentioned a little earlier in the conversation, James, you've got sea, and wind, and land, but you also mentioned having a really robust mix of priorities and considerations for The Crown Estate. You're looking at the financials, the societal, the environmental impact. How are you, in your role as a data leader in The Crown Estate, really looking to leverage data to make informed decisions that equally balance amongst all those priorities?

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[00:27:11] James: We're working alongside some colleagues in the organization, so I definitely can't take credit for the leading on this. One of my colleagues is working on what's called a value creation framework, which helps us think whenever we're doing a piece of work in the organization, have we got the balance of that right? It's about being able to look at and evidence where this comes out on in terms of its financial outcome, where this comes out on its societal impact, and where it comes out in terms of the environmental piece.

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The driver behind that is that link back to our purpose and values and being able to then say, "We've made these investments based on these principles, and it fits into the framework in this way." All the major programs and activities that go through are adhering to that value creation framework. What we're working on is making sure that we can actually gather the data that enables you to measure through those different lenses and empirically prove it.

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An example area where we're investing as an organization at the moment is, how do you really measure nature recovery and be able to truly evidence it alongside the other activities you're doing. Part of the pieces of work we're looking at the moment is, what are the right data sets to bring in. What are the right frameworks to adhere to when we're looking at nature recovery? Fundamentally, able to focus on not just what we're doing, but is it really driving change. Is it really driving impact?

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Being able to look at it through those different lenses. Also in these, I say, modeling tools that we're creating, some of those levers that we can look at or the lenses we can apply to say, "Within this model, can you optimize for environmental benefits? Can you optimize for societal?" What you can then overlay is to say, what does it look like? Are there any areas where if we take a certain approach, you can deliver environmental benefits, you can deliver societal benefits, and you can actually deliver financial benefits?

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It's tricky to get the measures on those in consistency because, obviously, they're measured in very different ways, but that's part of what we're able to do here is to provide the tools and thinking and data to help that process and then allow those people making the decisions to have the evidence behind it and say, "Yes, this is the right recommendation. This is the right way forward." A really important role.

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[00:29:58] Kimberly: Now you got me curious, how do we track nature recovery? What are some of the things that can be measured?

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[00:30:05] James: You can use satellite imagery or drone imagery to actually look at an area and see what the coverage changes over time. You can obviously see the evidence of nature doing its thing and regenerating itself, or it being helped to regenerate. That's one of the ways of looking at it, is through imagery. Very interestingly, you can also do similar with sound, and that's something where you can put a microphone in somewhere, and actually, if you're in somewhere like woodland, you can hear the differences in birds--

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[00:30:48] Kimberly: Measuring the level of bird song and then-- oh, wow.

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[00:30:52] James: Well, absolutely. It's often about when it appears in a year. When was the first time you heard this type of bird? When was the last time, in a certain year? When something's in an area or not in an area. There's all that kind of thing. The coverage, what is the diversity there as well, also start and end dates. It's fascinatingly interesting the things that you can measure and start to record in that space, and it's a growing area.

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One of the spaces, again, where some of the newer capabilities around AI and generative AI can really bring something to the fore.

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[00:31:33] Kimberly: Yes, absolutely.

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[00:31:34] Danilo: I actually learned through some of the work we've done with the Natural History Museum, you can measure eDNA, its DNA from the environment, if you collect the sample, they can learn about the species that live in that environment and that is measurable, and you could track how that changes over time.

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[00:31:52] Kimberly: Sounds like we need a whole nature and data podcast to dig in.

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[00:31:57] James: I think it would be really interesting because it's such an important area, biodiversity is something that's vital to all of us. I would say this is one of the biggest growing areas in the data field, is being able to properly understand the impact that something is having on an area and biodiversity. Certainly, anything that we can do to drive nature recovery is absolutely vital.

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[00:32:28] Kimberly: Yes, absolutely. The point you made, too, is identifying it, but then also saying, "This is something that we know we can have an impact on," because you can gather all of that, and if you're, like, "Well, I can't really have an impact on how the birds are singing, then it probably doesn't make sense to capture that."

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[00:32:44] James: That's one of the problems with some of the frameworks at the moment. You can do a benchmarking, but a benchmarking will only tell you where you're at based on some very quite high-level variables when you're trying to do a more localized intervention that wouldn't actually be able to change that benchmark. You have to think about where is it right to use frameworks and benchmarks at quite a high level, where you need to really be able to truly see an impact of a change that might not fit or completely align with an overall industry-recognized benchmark, and we're working our way through that. I'd love to say we cracked it all, but it's such an evolving field.

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[00:33:28] Kimberly: It sounds like it's changing all the time in such a-- you're trying to understand what you're capturing as you capture it, and what the longer term is for that. Well, maybe that ties to one of the next things that I was pondering. We're talking about this data modernization journey and focused on setting the strategy and thinking about beginning with the end in mind, thinking about the value and all the things that you need to build, putting that puzzle together like you mentioned.

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Once that's in place, then it's also about thinking about the quality of the data that you're gathering as an organization, and that becomes critical to your ability to modernize. How have you approached governing for data quality, and what are you doing to ensure that you're maintaining those high standards?

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[00:34:23] James: Data quality is absolutely vital to everything you do, in just the most simple reporting all the way through to anything you want to do in the more complex modeling spaces. We've created a set of principles, and policies, and frameworks around how to manage and govern our data. What we're also-- and this is absolutely vital, is working with those business owners of those processes to understand, do we have the right documented processes in place. Have we got the right checks and balances at the point where process creates data and that data needs to get entered into a system.

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A couple of the teams we've put on the ground to really work on specific areas of the business where we want to make sure we are really high quality and it's really well looked after. The great thing is, I came off of a meeting earlier which is our data improvement working group, where we've got people from the property teams, people from the marine team, people from our finance teams, who are there where we're talking about where we've got opportunities to improve it, and there's real desire for ownership and to work collaboratively across the organization.

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The biggest failing is thinking that a data team can solve the data quality problems. They really can't. The solving of them comes where business teams are working together with data teams, with finance and technology teams to create solutions together and work end to end through a process or a capability or a report, to say, "Where are all the touch points? Where's an opportunity that something could go wrong? How do we put the governance in place around that?" Definitely a massive focus because, certainly, as we go on this data journey, we have to be able to trust the data that we are looking at and trust its accuracy.

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[00:36:33] Kimberly: Also, it sounds like an element of democratization of the data quality. You're having to put it in the hands of folks and have that trust.

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[00:36:41] James: It's whole a piece around ownership and stewardship. Again, that's going to be part of our data literacy work is getting people to understand that it's their business outcome, it's their data. It is absolutely around that democratization. What we are there to do is to help them facilitate and provide the best practice because, often, you'll find a business person wants their data to be good, they want to be able to manage it, but they just don't know how. They've not had that background, not had that training. It's easy to just sit there and criticize, and say, "Well, that's your problem." Well, no, we are there to really help them with how to manage it correctly and how to do it better.

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[00:37:25] Kimberly: James, I know you mentioned, I think you've been at The Crown Estate for around a year or so.

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[00:37:33] James: Just about.

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[00:37:34] Kimberly: Just curious as you're embarked on this data modernization journey, how are you thinking about defining and measuring the success of it? I know you just mentioned some of, I think, the green shoots and positive benefits you've seen, but curious about how you're thinking about it holistically.

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[00:37:53] James: We're thinking it through a multifaceted lens. One is about how many people have been through our data literacy program and what's their experience on that. That's part of the improvement process. The other pieces are actually as I was talking about nature recovery is, have we got that data and are we able to support the reporting that we want to produce by the end of the year?

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There are specific measures around the quality of our data and not just improving the quality of it, but going back to the previous point of, "Do we have well-governed, well-managed processes." Looking and saying, "Right across our key processes, are they well documented? Have we got the governance steps in place and have we got the checks and balances?" There's some of the parts of ways that we're measuring it.

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The other ways that we are looking at it are how much of our information we've been able to make available and brought into our systems so that we can provide it for reporting and analytics. Then there's harder measures about actual-- the benefits of those improvements. Have we been able to actually add true value? Some of those are, say, with our marine spatial planning, that's about how many more scenarios are more efficient we can get at running those scenarios because the actual true benefit and outcome is supporting a process that helps deliver our net zero ambitions, that helps deliver energy security. Those are very large outcomes that the whole organization is working together to achieve.

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What we are able to say is that, one, we're intrinsic part of that process, but two, actually, we're able to run more complex, different scenarios and we are able to run those scenarios much more quickly with much richer data. The measure is a better, quicker decision and more complex scenarios that further prove or provide the options we need to make decisions on.

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It is really through huge amount of different lenses that we're trying to measure the improvements.

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[00:40:04] Kimberly: Makes sense. You got to have a good mix of data to measure the success for your data modernization. Just one more question, James and Danilo, for you both, is, final thoughts or advice you'd have for folks who are listening and are in the middle of, or about to embark on their own data modernization journeys, things you've learned or observed that you'd like to share with others who are in a similar boat?

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[00:40:36] James: One, really engage the organization. Find people who are trying to deliver great outcomes or solve meaty problems and really work with them to try and help them achieve their outcomes. It's all about value creation, value focus. Then the other thing is just to really focus on iterative delivery. Don't try and go big bang in a long period of time. What are the small wins you can get on a really iterative basis?

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It's about just delivering outcome, outcome, outcome as quickly as you can, but in a controlled and coordinated way. Then a big thing around the team. Take the team on a journey. Really, really get great people together to solve problems together and lots of the answers come from within a team. It's brilliant what you can achieve when you get a room full of people together and work through a problem.

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[00:41:36] Danilo: Maybe one thought from me to contribute, it might be a good time now to actually use the AI hype to look at your data foundations, because a lot of executive boards are interested in AI for everything. What we are seeing is that actually, you need a good foundation to be able to build all the things you want with AI. If you got a good platform, if you got a good governance process in place, then it's a matter of extending it to support what AI needs, the extra capabilities, versus trying to build something completely new or just try to buy a lot of things from the outside.

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I'd say join the hype right now, it's a good excuse or a good reason to bring up and try to tie up everything that everyone is doing to modernize the data state as a foundational piece to do all the AI work.

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[00:42:29] Kimberly: A conversation isn't complete these days unless we're talking about AI in some dimension. Yes, getting a great group of people in a room to solve a joint problem that they're passionate about, that's probably something The Crown Estate's been doing since 1760, James. You got a good legacy there. I'm also excited, I'm going to head to the UK in a few weeks for a holiday, so I'll probably be adding to The Crown Estate's data set as I'm going across some of your properties.

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[00:42:59] James: Go and visit Windsor Great Park. It's fabulous location, and we've got trees there that are around 1,000 years old, so come and visit the most amazing diverse part of the nation.

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[00:43:23] Kimberly: I definitely have to check that out. Thank you so much for both joining me today. I really enjoyed our conversation and learning a little bit more about The Crown Estate and the data journey that you're on.

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[00:43:34] James: Thank you. Great to talk.

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[00:43:36] Danilo: Thank you.

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[00:43:37] Kimberly: Thanks so much for joining us for this episode of Pragmatism in Practice. If you'd like to listen to similar podcasts, please visit us at thoughtworks.com/podcasts, or if you enjoyed this show, help spread the word by rating us on your preferred podcast platform.

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[00:44:01] [END OF AUDIO]