Dr. Andreas Schroeder, Reader for Information System at Aston University and Digital Lead for the Advanced Services Group Joins Sarah to discuss what it takes to succeed in delivering data-led services and to offer his advice on the best approach for monetization of data.
Sarah Nicastro: Welcome to the Future of Field Service podcast. I'm your host, Sarah Nicastro. Today we're going to be talking about using data to drive your services-led business model. I'm very, very excited about today's conversation because the opportunity that exists with better leveraging data, both inside and outside the organization, is a huge, huge topic and major area of interest. I have a great expert here with us today to shed some insight on this topic, who is Dr. Andreas Schroeder, Reader for Information Systems at Aston University and Digital Lead for the Advanced Services Group.
Sarah Nicastro: Andreas, I'm going to call you Andy for the sake of this podcast, because I know that's your preference. So Andy, welcome to the podcast.
Andy: Thank you. Thank you for having me.
Sarah Nicastro: Absolutely. You all, as listeners, may remember seeing Tim Baines’ name pop up on the podcast a couple of times, who is also with the Advanced Services Group. You may have heard Tim speak about some of the research that the Advanced Services Group has done around the journey and path to Servitization, some of the trends that they're seeing, and some of the considerations for how to differentiate between a business model and a revenue model.
Sarah Nicastro: Andy is with us today to build upon and bring a different lens to some of those conversations and the Servitization trends by talking about how data plays such a critical, critical role. So again, super excited for the conversation. Before we dig into the content, Andy, why don't you spend a couple moments telling our listeners about yourself, anything you want to share about your background, and a little bit more about what you do at Aston and the Advanced Services Group.
Andy: Yeah. My name is Andreas Schroeder, I'm originally from Germany but now I'm located in the UK. Originally from background, I'm from engineering, process engineering, then went toward the IT direction. And now since around seven years, I'm getting pulled more and more into what we call advanced service, or Servitization. That leads me to looking at IT and digital on its own, to now I'm being forced to look at digital and IT within the context of a business model, or a approach to doing business.
Andy: For me, this is quite a journey. I've seen a lot of different sides of the equation, but also I think that gives me an opportunity to bridge two sides. I think this is what our organization today is all about. We don't want to dig into the depth of IT, but we want to see how IT and data can be used to inform and improve the business model, or make it even work.
Sarah Nicastro: Mm-hmm (affirmative). I'm curious, Andy, how did you initially make that move from the IT side to the advanced services and the business side? What interested you about it? To me, it's almost a reflection of how things have evolved in the sense of they can't really be separate. They've become more integrated. But curious how you made that transition.
Andy: Part of the equation is that in IT we looked for a long time at outsourcing. IT is a major outsourcing domain, and a lot of IT research and IT thinking is about how can we manage outsourcing of IT development or hosting or whatever is on the other side? So when I looked at advanced services, I realized that this is to some extent an element of outsourcing. Then I looked at all my previous research and readings, and I realized that the digital role or digital perspective on outsourcing can play a major role. The more I looked into it, the more I realized that the missing ingredient for a lot of what my colleagues talked about when they talked about advanced services and Servitization, was the digital side. The idea of being able to manage the business model, manage the risk of the business model.
Andy: From my point of view, that is what the IT component does. Servitization, advanced service as a business model, has been theoretically around for eons, 20-30 years. But it’s been perceived just to be too risky, because you don't know what happening with your product. So when you servitize, when you're providing advanced services as a manufacturer, you take your product, you hand it over to your customer, but you're still responsible for the product. The customer will only pay for use of the product, or value that they get out of the product.
Andy: Without having any kind of oversight, what value the product creates on the customer side, or if it even works or doesn't work, it's a very risky proposition. That is the reason it has taken a long time to take off, but when the internet of things and everything took off on a major scale, this is the application area where it now comes all together.
Sarah Nicastro: Very good. Okay, cool. Let's talk a little bit about... There's a lot of things to dive into. The first thing I want to talk about is this point that when you're thinking about the different ways you need to use data on the path to Servitization, it's critical to lead with strategy versus technology. That's an important point that you make, and it's one that I wholeheartedly agree with. But let's dig into that and talk about why that's so important and how people need to approach this the right way.
Andy: When I started to... You mentioned Tim Baines before has done previous podcasts with you. We work very closely together. When we started to work together, we came very much from the different ends of the scale. Tim was very much focusing on the... just seeing it as a business model, everything else a distraction. I was always seeing it as a digital project.
Andy: I think we have both moved quite closely to the center over time. That's what happens when you work together. The question then becomes... We all agree that Servitization is tightly linked to digitalization. I would even go the other way around and say that Servitization is the way to monetize digitalization. A lot of the IoT investments that we're doing, that we're seeing, may not pay off on their own unless they're wrapped into a business model that takes advantage of being able to monitor something at distance.
Andy: We'll come to this probably later, but Servitization is this business model that makes it happen, or that can make it happen if it's done well. It's, of course, not easy. Then the question is what should come first and comes second. To some extent, I give in and say the strategy should come first, whatever the advanced services are that the organization envisions. Then define the scope of what digital needs to do to provide this.
Andy: The digital needs to follow, of course in practice digital also leads, to some extent. What we're seeing a lot with companies coming to us to talk about Servitization, quite a number of them come from a digital project. They have put a lot of sensors on their product, they have created data, created connectivity. They're now wondering what else they could do with this.
Andy: We have quite a number of companies starting from the digital end, and then looking for the business model together with us. Of course we have others who recognize that the business model is a starting point, and then they engage with us early and then start to look at how digital can inform the business model.
Sarah Nicastro: Mm-hmm (affirmative). Okay. They're tied closely together, but to summarize, why is it best to lead with strategy rather than technology?
Andy: The way I look at it, and I know it's not... Not everybody shares this, but it works for me. Technology or data, information technology is a way to answer questions. The questions that need to be answered are defined by the business model. So if my business model, for example, says I provide heating as a service, then the question is, for example, how much heat does my customer need, how much comfort does he or she need? This is a question, and then we can look at how we can use technology to answer this question.
Andy: It's a bit of a demystification of it, and there's a lot more to it, but on the high level, this is the way I see the link.
Sarah Nicastro: Yeah. I think if you look at the... I'm probably going to get the terminology wrong of what you call it, but the progression or the roadmap that you guys have developed on the journey through beginning to Servitization fruition, if you will. I don't see ever really an end point. I think you go back through that and look for the next wave of innovation or the next new area to develop.
Sarah Nicastro: If you look at that journey, and if you had... I mentioned before we started recording how many conversations I've had with people in these roles over many, many years. You develop a deep respect for how complicated it really is, and you start to understand yes, the concept of Servitization has been around for a very long time. But the reason it's still such a relevant, timely topic is because that shift takes a lot of things and a lot of steps and a lot of iterations and a lot of factors coming together to really progress through that journey.
Sarah Nicastro: I think the idea of leading with strategy versus technology, I love the way you said it. Technology answers a question. You need to know what question you're trying to answer. To me it's just a way to organize yourself to achieve faster time to value than if you start with technology, because you're ultimately starting with functionality instead of the value that your customers need. While you may be able to guess quite well what some of that technology is that you need to deliver the value, if you define the value first, i.e. the question you need to answer, it just organizes you better on the rest of that journey to be working toward the goal you need to work to.
Sarah Nicastro: I don't think it is strategy versus technology, I don't think it is strategy first and technology five years later or anything like that. I think they're so, so close but knowing what that question is, which in my mind is what do your customers need from you? What is the challenge you can alleviate or the opportunity that you can provide to them? And then what technology do you need to equip yourself to be able to deliver that?
Andy: One thing to keep in mind, there is no single answer to any of these given questions. The answers can be of different quality. Let's go with the example of heating as a service. My first question might be, "How much heat does my customer want?" The second question I can derive from there, "Why does he or she need this kind of heat?" So we look at maybe we need data on insulation to explain variations in heating needs or value of heating that we create.
Andy: Or I might want to have an even finer-grain answer and I can say, "This old-age lady requires higher heat because of a health concern, while the young entrepreneur is not in his house anyway all day long, so I don't need to focus on this kind of heat provision at this point in time."
Andy: I can then, once I understood the generic question, I can look at sub-questions that give me larger granularity to better understand the customer, but I also can look on the other side of different ways to answer the question. I can start eyeballing some charts and get some insights, but I can also look at, for example, predict heating needs at some point in time by just looking at the demographics of a certain area where I provide heat.
Andy: This is, as you mentioned, it's a long-term giving here, taking there, figuring out better ways. We create these incremental gains by being able to answer the questions even better by adding more technology pieces. There's a role for technology pieces, but it is to answer more difficult questions or the existing questions in a more refined way.
Sarah Nicastro: Mm-hmm (affirmative). Yeah, that makes sense. I was just thinking, again we're still on question one and we're going to run out of time if I don't stop free styling here. I'm enjoying the conversation. The other thing that made me thing is, just thinking back on a lot of the conversations I've had over the years, I think if you look at this from a psychology perspective, the strategy part is really hard and really taxing. It's multi-faceted. It requires alignment among a lot of different stakeholders. It can be fun if you have the right personality, but especially for someone maybe in a service role within operations, it can also be just very overwhelming.
Sarah Nicastro: Whereas technology is the cool, sexy part of all of this where everywhere you look it's AI this and augmented reality this. I think it's easy to get off track a bit or distracted by all of the options that exist. There's that cool factor, and everyone telling you... Whereas Servitization is still in some ways this ambiguous term that can be hard for people to really internalize and think about how it applies to their organization if that feels overwhelming to them. Technology is so easy to say, "Oh my gosh, I saw this video or this white paper on this new AI tool. We need it!" Right?
Sarah Nicastro: And then you go down this path because you're trying to innovate. Everyone's trying to innovate, but it's... I don't know. That was the other thing I was thinking about, because I've seen people just catch onto what they think is the latest, greatest technology because they're being messaged to that that's what they need to achieve success. And it may be, right? It may be part of the answer to that question. But then when you don't do the work of setting that strategy or asking the question first, you go down these rabbit holes.
Andy: I know we want to move onto the question, but I still need to go back to this. Might have to do a second podcast.
Sarah Nicastro: We may.
Andy: You mentioned the struggle of setting up or expanding the service portfolio in the organization. You didn't use the word, but you hinted at the political dimension of it. I mean, at some point in time, it sounds like, "We had a good run for 30 years. We are producing really good products and selling them. But we don't do this anymore."
Sarah Nicastro: Mm-hmm (affirmative).
Andy: From now on we don't do this. This is something that's a huge misconception. It's a huge misconception. Servitization is not a statement of, "We are not good at producing products." It's a statement of, "We can provide our product as a service because we have the best products around, because we can provide products that we can put a lifelong commitment to instead of our competitor who is more or less happy when they don't have to be involved with that product long-term."
Andy: It is really... Only companies who are confident about the quality of their product and the quality of their manufacturing of these products, should be in the game of providing the product as a service. For everybody else, this is not the right strategy.
Sarah Nicastro: Yeah. The other thing that that made me think of is, you talk about... Servitization is not the idea of the service function of the business just improving its sale of contracts. It's not some incremental gain. It's really a fundamental shift in how these companies deliver value, right? Not eliminating product, but evolving that and adding to it.
Sarah Nicastro: I think, in some of the companies that I talk to, that strategy really has to be bought into at the top. Because what you run into, I think part of what I just said about the technology part seeming easier is because if you have someone that understands the value of this vision, but isn't in control of the strategy at large, they can feel the only part they can control is to look at new technologies and make those incremental improvements because they're not... There isn't a cohesive move at the top level of the company toward Servitization yet.
Sarah Nicastro: That's just one thing I've run into, where there's pockets of the business that see it as potential, but it's not a shared vision yet and that creates a lot of frustration within those organizations.
Andy: And it's really an unnecessary frustration. I mean, every organization is political, and there's a lot at stake for everybody's turf and reward structures and so on and so on. But in theory, Servitization is something that embraces what manufacturers do, as I mentioned before. It's not moving away from being a top-notch manufacturer. It's rather the opposite, of putting really long-term commitments around that.
Sarah Nicastro: Okay. All right, so I want to move on to the next point. I'm going to change this a little bit in real time, because I think... When I think of the criticality of data on this journey, there's really two major categories of that. There's the internal use of data in terms of how do you gain the visibility you need into your assets? How do you leverage that visibility along with a lot of other real-time information to really optimize the productivity and efficiency and move toward more of a proactive, predictive model, et cetera.
Sarah Nicastro: And then there's also a huge interest in how do you use data externally as a potential source of new revenue, right? What data can you glean that your customers may have a vested interest in understanding? How can you use it to position yourself in a way that you have the knowledge to help them improve their business or their operations, et cetera.
Sarah Nicastro: Let's just talk about both of those sides, and how people need to be thinking about each, that sort of thing. And then I want to dig into the external part and monetizing data successfully on the external side. But maybe let's start with any input or thoughts on how do you get the data right internally, so that you can have success with the monetization of that externally?
Andy: I mean, one of the aspects that I'm quite focused on is a long-term strategy around data. To be meaningful in the way you can interpret data, you need to... There's these how many terabyte and so on and so on. This is not necessarily the major point. The major point is that you have some longitudinal data, okay?
Andy: If I, for example, want to have a good understanding of how, again let's say heating of my trucks or something, whatever I have as part of my service proposition. If I want to have something good understanding of what kind of repairs will be needed, some predictive statements about what will be the upcoming events that will go through this, I need to have looked at quite a number of previous installations of my installed base, over a period of time, to get any sense of being able to predict something. Because we're looking for patterns.
Andy: First, before we can identify... before we can make use of the knowledge of patterns, we need to understand the patterns. The patterns need to be established across different product lines. We have a lot of companies that have quite diverse product lines. They not necessarily behave in the same way. Most likely, they don't behave in the same way, because they're different generations of products that are running side by side.
Andy: I have different customers. Some may use a product in the right way, some may abuse the product by using it outside the envelope. I might have different geographies. With some products it's humidity, dust, whatever it is. All of these factors will be important to make any kind of predictions about the timeline of repairs required. If I have just volumes of data without longitudinal data, and if I just have volumes of data without having diversity of use-cases that are covered by these data, whatever level of granularity we want to have, it doesn't really tell me much.
Sarah Nicastro: Mm-hmm (affirmative).
Andy: So just having big amounts of data doesn't give a lot of answers to questions. If the question is, to come back to the starting point, when will this machine break next? I need to have a granular understanding of how the context of this particular machine actually affects the lifetime of the machine.
Sarah Nicastro: Mm-hmm (affirmative), mm-hmm (affirmative).
Andy: To do this, I need year, yes.
Sarah Nicastro: Right. Okay. What I did, Andy, you're probably... I mean, just in terms of our outline, I kind of combined the two questions and we're working on both at once. But that's fine.
Sarah Nicastro: If we talk about this though before we talk about external use of data, you need a longitude of data, and you need data that's representative of all of your use-cases. You need years of this at a time to really be able to have a strong predictive model.
Andy: Yeah. Let me correct this. The more you have, the more accurate the predictions.
Sarah Nicastro: The better it is, yes.
Andy: ... the more accurate your answer will become. Of course you start off with some low-hanging fruits. You may be able to, within a limited amount of data or just lab-based data, you may be able to do some kind of predictions. But the accuracy of your prediction will depend on the diversity of the data, the longitudinal nature of the data, and your understanding of what the data actually means when you open up the machine later on.
Andy: Because that's another thing. You can have a lot of predictive models that can tell you something about the certain rate of deterioration of your product. Until you come to the product, you open it up, and can confirm that your prediction actually is anywhere close to the truth, you don't have a good control over the accuracy of your predictions.
Sarah Nicastro: Mm-hmm (affirmative), okay. Okay.
Andy: You put two questions together and I took them apart again. I think what you are looking for is the monetization.
Sarah Nicastro: Right. And I was just looking at it as internal and external. They are kind of correlated in that way, but yes. Let's talk about the monetization and that area.
Andy: The monetization.
Sarah Nicastro: Yes.
Andy: Yes. Monetization. If we take the narrow term of monetization, that means money for data in the way we can interpret this. We found that in a lot of companies that we work with, they come with ambitions of being able to monetize the data on their own. Some make investments in development platforms and so on and so on, to be able to communicate data and insights to the customer. Very, very few are managing to convert this into actual business propositions that are being taken up by the customers.
Andy: What we find is that a lot of times, the customer is not willing to pay for the data. There might be different reasons, there might not be enough value in the data, or the customer might say, "I buy your product and you want to charge me to tell me when my product will break? I mean, I'm your customer, why don't you just tell me? Why would we have to have a different loop, why do you want to charge me extra for it?"
Andy: These two things come together. It's not enough value in the data, or the customer finds it difficult to understand how much more value there is, or there's a request for the customer to have this all bundled up into one big proposition, instead of having different chunks of transaction.
Sarah Nicastro: Mm-hmm (affirmative). Okay. That's the challenge. How do we solve that challenge?
Andy: That's what I started with the definition. We take the narrow definition of saying, "We want money for data." There's a struggle. I mean, we have a few instances where it worked. Quite curious instances. I don't think I can share them with you, but they were very, very specific needs of the customer. There wasn't something that could easily be scaled up. Okay?
Andy: The way to monetize, in the wider context, data is to make this part of the entire service proposition.
Sarah Nicastro: Right, right.
Andy: To be honest, the customer doesn't really care to know when the machine breaks in a service context. In a service context, you're contracted to make sure the machine doesn't break, and there's a penalty associated with the downtime of the machine. Why would the customer want to know? For curiosity reasons, yes, but it is your responsibility.
Andy: By folding this into a service proposition, that is one way of monetizing the data. Not as a direct cash-for-data, but as a way to make value, create value, substantial value from the data.
Sarah Nicastro: That makes sense. That was one train of thought I had, was it isn't something... I see a lot of companies do exactly what you're saying, which is try and sell the data itself in terms of... In the way you mentioned, or even sell a tiered service program, right? But what happens is, to your point, why should a customer care? If you have additional capabilities to provide better service, then they're going to expect that you just provide better service. So when you introduce this as the concept of, "We can provide you better service! Will you pay more?" It's, I think, human nature to say, "Great. Congratulations on innovating and please keep providing me the best service you can offer, because that's what we want from you."
Sarah Nicastro: I think if you can, to your point, instead look at how do you essentially build the revenue you want from the data into the service cost and value proposition as a whole, that's, I think, the appropriate way to think about it. That being said, the other area that I have seen that I wanted to ask you about is... I'm trying to think of the right way to generalize this without giving those particular examples. But basically in certain industries or applications where there is a change happening within the customer base, and therefore an unmet need, I've seen organizations be able to create a net new value proposition for those customers using data that they may already collecting to inform their own service operations.
Sarah Nicastro: But for instance, I was on a webinar, or a web session a couple weeks ago with some folks in the diagnostic space. They were talking a lot about the shift in that industry toward what they referred to as the dark lab, where there are no human beings running the lab, it's all automated. But there's a lot of things in terms of data and insights and knowledge that their customers need to be able to bring that reality to fruition, and so that's one example.
Sarah Nicastro: I've seen other examples where it can become an additional revenue stream if you can, not offer just data, like here's a bunch of data. But turn that data into insights that your customers find value in outside of your own service proposition.
Andy: So far, I've seen... Not in our work but outside in the media and so on, I've seen also some areas like this where, in some cases, even a separate consulting proposition is being created, or a separate consulting business. One thing is fascinatingly clear, that a lot of manufacturers will, because they are looking at comparable business processes across the globe, an airline, Rolls Royce, they look at... I think 30,000 engines flying at any point in time.
Andy: They will have a lot better understanding of engine, engine management, fuel efficiency, takeoff angles, and so on and so on, than any of the airlines individually. That's just by the sheer scale of numbers. If they are able to make use of this additional, not just the data, but the scope of application areas they can look at and derive value from this, there's an opportunity.
Andy: The companies we work with, it hasn't been... that hasn't taken place, and I would argue that it's probably more the exception than the rule just on the basis of data, significant value can be exchanged. Value is being created, but the question is, who pays for it and how is it being used.
Sarah Nicastro: Right. I think it may be a real area of future opportunity, though. I mean, the thing is again, if you look at this journey, you have to not get ahead of yourself, right? So if you aren't yet using the data the way you need to internally, if you haven't yet determined how you're going to monetize the data in a way that works for both your business and your customer base, then you don't need to go creating some net new value proposition.
Sarah Nicastro: But I think the companies I'm thinking of that have progressed through a certain level of success with the core value proposition that are starting to look at these adjacent opportunities for revenue, I mean, I've heard enough examples of it to where I think that there's some real significance to what's possible if you're taking the approach of really understanding your customers' businesses and operations and identifying real areas of opportunity. Not just trying to sell something for the sake of...
Andy: I see we are completely abandoning the script now. Let me get back to you on this. Yes, valuable data are being created, but the next question is who will actually create this value? If you start to create value with data, purely based on data, and you get more value the more data you have and the more you can link different data piece together. Entire shop floors instead of your machines, and so on and so on.
Andy: Then at some point in time, it's the Googles of the world that will move and take their skills into the space, and not the individual manufacturers. One of the things that the manufacturers are actually getting worried about is that they're just becoming a data feed mechanism for something bigger that is coming around the corner. We have seen purchases of Google buying into hardware around the households and so on and so on.
Andy: There is a significant opportunity for them to move into any kind of manufacturing environment, applications, and apply their skills. So yes, but it might be... The fear is there, but it might not be the manufacturers who can create value, monetize the data on their own. It might be somebody else.
Sarah Nicastro: Yeah. That's a good point. Okay, Andy. We have gotten off track, but I think it's been a really interesting conversation. Let's talk about... I am thinking we need a part two to this for sure.
Sarah Nicastro: Let's talk about one more thing before we break, and then we'll plan on scheduling another session to tackle... Because we had a question in here around common missteps, and I'm just going to assume we can probably do a whole other episode on common missteps around this.
Sarah Nicastro: But when you talk about the, not only volume, but longitude and variety and all of the things you need to consider in terms of the data you're collecting, it could just be me because I'm not a data person, but it feels very overwhelming in terms of getting the right systems in place to collect that data, having so much to weave through and determine what's valuable, that sort of things. I know that one of the things that you said is, "Honesty is the path to refinement." I was hoping you could talk a little bit about what that means and how people can use that as a filter to maybe ease some of the overwhelm.
Andy: Yeah. I think the important part is, I mean I come back to my simplistic view of the world. There's questions, there's answers, and it's a matter of beginning these two together. The way I think that is most valuable to think about for service provider or service managers to think around data needs is literally going back to basic principles. What questions do we have? What answers can we get from somewhere? How can we combine maybe different data points to create different answers?
Andy: The point of overwhelming comes into play when you work the other way around. I have had several meetings with people who show me the data they have and ask me, "What can we do with this?" Often I could just not see what you could actually do with this. Thank you for sharing your data, but there is not much that I can see that can be done with this.
Andy: Looking the other way around and taking off what are the most urgent questions, prioritizing the questions, and saying after prioritizing, looking at what can we answer to which extent and are we happy with the answer? Or could we add additional data points, would that help us with certain investments to refine our answer?
Andy: I think what you're referring to is what I said, but also be honest about what you don't know and be able to really say, "We don't know this. There's a big unknown. We can either live with the unknown or we can put steps in place to reduce this," and being honest to yourself about what you actually can and what value you can create from the data, and what answers you can get from the data.
Sarah Nicastro: Yeah.
Andy: One additional point to bring in is that the longitudinal view of data that I tried to push early on in our conversation makes the whole thing quite a bit more complicated. Because literally, apart from asking the questions now, you have to wonder what kind of questions you have in five years' time, and plan for being able to answer these questions. Because it all depends on accumulating the data to get reasonably good answers for whatever questions you may have.
Andy: If you arrive in five years' time at questions that you haven't foreseen, then it's quite an acrobatic to recreate data or recombine data in a way that it starts to approximate anything near an answer. This kind of long-term view... And of course nobody knows exactly what will be happening in five years' time. We're now all locked down in our houses. So nobody knows. But getting reasonably good understanding of what the business will be doing and literally what kind of questions I will have to answer to manage this business, I think is the best starting point to be focused and not be overwhelmed in this discussion.
Sarah Nicastro: Yeah. I think that comes back to our very initial point, which is you need to know the question, i.e. the strategy. You need to have that strategy to have an idea of not just what questions are critical today, but where do we see the business in five years, and therefore what questions will we need to have answered then. If you're lacking that, then it would be really hard to guesstimate what you might need to know.
Sarah Nicastro: All right, well Andy, it's been fun. I've enjoyed the conversation. I hope you have, too. I know we got a little off script, but to be honest, we do that a lot because I think it's more fun than following a script. But we did have some other great talking points on our outline, so let's schedule another session and have you back to dig back in and talk through those. I would love to do that if you're game for that.
Andy: Of course I am, yeah.
Sarah Nicastro: Awesome.
Andy: Thank you very much, Sarah. Really enjoyed this.
Sarah Nicastro: Thank you. Thank you for being here.
Sarah Nicastro: You can find more on Servitization, use of data, and all other things by visiting us at futureoffieldservice.com. You can also find us on LinkedIn as well as Twitter, @thefutureofFS. The Future of Field Service podcast is published in partnership with IFS. You can learn more about IFS service management by visiting www.ifs.com. As always, thank you for listening.