Click the graphic to listen to the full podcast.
Chris McNamara, Smart Industry editor in chief: Hello, and welcome to the podcast. My name is Chris McNamara, editor-in-chief with Smart Industry. We're thrilled to have you join us. Today, we're talking to Ali Nicholl, head of engagement with IOTICS. We're looking at the concept of boundless data ecosystems. We talk about data all the time in this world, but as our knowledge of and application of data analytics matures, it branches off into new concepts. So, I'm very excited to dive into boundless data ecosystems. Ali, how are you?
Ali Nicholl, IOTICS head of engagement: I'm very well. Thank you, Chris. Very well this morning.
Chris: Thanks for joining us. Let's get to know you a little bit. Kind of a fun question, what's a hobby of yours? Outside of work, what's a passion of yours?
Ali: Well, so a passion of mine would be rugby. Apologies to all the U.S. folks. I realize American football is obviously a vastly superior sport. But a passion of mine is rugby. Although, in truth, when it comes to hobbies, I have two children under four. So, there is not a great deal of time for anything at the moment other than looking after them, especially with the crazy two years that we've had going on. There's been a lot of children around the virtual office.
Chris: Yeah. Yeah. Your hobbies are picking up after children and washing clothes.
Ali: Exactly so. Exactly so. Hearing the plot to "Frozen" 7,000 or 8,000 times a day
Chris: And where's home base for you?
Ali: So, at the moment, I'm based just outside of London in the UK.
Chris: Okay. And tell me who and what is IOTICS and tell me about your role there.
Ali: Yeah. So I lead engagement, which is a sort of euphemistic term for being a cheerleader here at IOTICS alongside partners and channel, really driving this concept of boundless data ecosystems. And what IOTICS is, is we're a startup. We've been around for a little over eight years looking at creating a next-generation data architecture. And what I mean by that is what IOTICS is enabling right now is the secure, selective sharing of right-time data between multiple parties. And this is really where that kind of concept of the boundless data ecosystem comes in. It's this idea about how do we share the right data at the right time with the right people, all the while making sure it's secure, it's protected, and that everyone involved in that ecosystem is confident that they are only sharing with the people they wanna share with and only sharing what it is they wanna share.
Chris: Yeah. Lofty goals. Let's get into the specifics there since we're using this terminology. What do you mean by the boundless component there? You know, we're all pretty familiar with the data ecosystem in this area, but what do you mean by boundless?
Ali: So, I think the real distinction is that it's about decentralization rather than centralization. So, most architectures, whether we're talking within an organization or across a data ecosystem system, are fundamentally centralized, the management of the infrastructure, the governance, the access policy, whatever it might be. A boundless data ecosystem isn't one that is unprotected or open, but it's one where those boundaries, be they internal based on, you know, governments or regulatory constraints, or whether they're across the supply and demand chain, aren't an impediment to the interoperability of the data. So, a boundless of data ecosystem is one that grows with need. And what we are seeing across utilities, transport, manufacturing, government, connected places, built environment is that people are rapidly realizing that the artificial boundaries in our technology, you know, is it this sector? Is it this purpose? Is it about assets or is it about people? Actually aren't how we work. They're not really how we need to do business. And so, boundless data ecosystems are enabling the autonomous interoperability across those boundaries.
Chris: So, that interoperability, what's the next step? What does that lead to? What capabilities does approach of boundless data ecosystem offer, what are the wins? What are the applications?
Ali: Sure. Absolutely. So, the most important thing is about the cooperation it enables and the ability to develop cooperative services. And I use cooperative deliberately rather than collaborative because in collaborative, there is still that center, right? You and I agreed on what it is we're gonna do, how we're gonna do it, and what it is we're going to achieve. What we see in cooperative systems is that we agree that we need to interoperate, we need to share data, but we don't have to agree on the same outcome, but we learn more from each other than we can do independently. So, in a closed system, you have to know everything, you have to own everything. So, the applications you can build are limited by the data you have, the access you have, and your ability to control identities. In cooperative spaces, you know, some things and someone else knows some things.
So, let's use a transport example here. We worked with Rolls-Royce Power Systems who manufacture, well, power systems for large platforms, so, trains, submarines, tanks, etc. And so, they generate power units for trains, but those power units are installed on trains that are owned by Hitachi. The trains that are owned by Hitachi are then operated by train operating companies and run on infrastructure that's owned by a fourth organization. Now, each of those organizations, in turn, has their own data and their own visibility of what they're doing. But by creating, in this case, a boundless rail data ecosystem, they're able to share little bits of information with each other, which meant for the very first time, they were able to do real-time recognition of where specific trains were, not the 720 from Boston to New York, but actually, where is the specific train unit number that needs servicing? Has it gone off schedule? Is it gonna be in the right place? And independently, those organizations don't know all that information. So, their ability to share suddenly means they can deliver services.
We see the same thing in the built environment when you start looking at how do you bring blue light services, and healthcare, and water companies, power companies, how do they start sharing information with each other to deliver services to protect vulnerable customers in the event of extreme weather events, for example, or with the rise of new green fuel system, you know, electric vehicles, it's fundamentally changing the nature of how energy systems need to be able to interoperate. And it's no longer a monopoly, right? It's no longer an organization at the center saying, "We own everything. We run it all. We know it all." So, if they don't know it all and they don't own it all, how are they gonna deliver the service? That means that you can identify which rural EV charging point you need for your particular vehicle in the Midwest as it goes cross country from coast to coast.
Chris: Yeah. So, you know, I always think about hurdles to, you know, concepts like this and challenges that are in play. And one of the first things that jumps to mind is you just described the setup there is these four parties, let's say. The train was a great example. You know, it sounds great. It's probably easier to apply internally within the enterprise. And as you add a second stakeholder organization or a third or a fourth, that complexity has got to befuddle some of these efforts or the team's willingness to engage with this stuff or the maturity of understanding of these approaches and concepts and technologies. What are some of the main hurdles with kind of broadening the hands in the data pot like this?
Ali: Yeah. I think that's a really, really interesting point, Chris. And I think one of the pieces is that there is a recognition that you need to do this progressively. I was actually listening to your podcast earlier, and your guest from Deloitte talking about, "Well, start with an asset. You know, don't start with the whole factory as your starting point." And the same is true for ecosystems. So, start with your internal data ecosystem, which is already bounded, right? I mean, in most organizations we talk to, your systems for managing people are different from the systems for managing assets, for example. So, you start there. And what that helps with is then the evolution of trust. You start to see, "Okay. I can share and I can see how that works." But it's also then about recognizing that you get value instantly from that ecosystem. And the way we do it at IOTICS is we base it on digital twins, you know, so we virtualize everything, which is then what enables the security aspect. So, start with digital twins of small assets and see what they can share and progressively share more. And really, the bit that I love is that this is just a learning from how the world works, right? I mean, like...
Chris: What do you mean?
Ali: Well, and you and I are chatting here and I don't tell you everything about myself in our first meeting, you know? So, what I progressively do is I progressively share information with you and I see how you use that information and what you do with it and how you use it. And that enables me to progressively add more. And that's what we see as the way of overcoming that internal barrier. Because you're absolutely right for...well, I mean, as long as you like, really, but certainly for the last three decades, the focus has been on don't share, you know, don't let out of your organization, don't give it to other people. It is only secure if we control it. Well, the only way...
Chris: This runs counter to true connectivity.
Ali: Absolutely. So you can only get round that if you kind of progressively start saying, "Well, look, share something that isn't particularly commercially sensitive but is something that you have that the other person doesn't." And then you will start seeing a benefit almost instantly from suddenly saying, "Oh, actually, if we just...we won't share how the engine is performing and everything else. We'll just share the GPS location of it." Okay. Well, we'll combine that with us knowing the train schedule for maintenance. Okay. Well, those are two pieces of information, which truthfully, in most systems, people are already sharing, right? They're already sending each other CSV files and emails saying, "Where is that for gonna be? What's your delivery schedule for this part or this component?" That information's already being shared. It just isn't being shared in a way that's trackable and interoperable. So, if you start there, I think that helps you develop that evolution. That's certainly what we've seen with our customers, is there's a recognition that you start seeing value, you start developing trust in the other people in your ecosystem without needing to go all in. So, at all times, you retain control of what you share and with whom. So, if you are ever uncomfortable, you can just cut them off.
Chris: Right. Interesting. We're gonna switch gears a little bit here onto a topic that has been really top of mind for the past couple of months here in our universe with our contributors in our community, metaverse. What role does the metaverse play here in this concept of boundless data ecosystems and how is the metaverse being employed? You know, do you have clients who are already reaping wins with this or is it still kind of kicking the tires on this, or where we're at?
Ali: So, I think from our perspective, the metaverse is still fairly nascent, you know. So, I know there's lots of good rhetoric about it. But, truthfully, what I think we're seeing in the absence, frankly, of these kind of boundless data ecosystems, or the other buzzword doing the rounds is the kind of cyber-physical fabrics and that kind of thing, is that in the absence of those, what you've actually got a meta world. You know, I can go into our system with our set of data and our visualization and I can look at something in isolation. I think where people are starting to explore, which really excites me is moving from a kind of souped-up virtual reality... I don't want to be overly dismissive, but a kind of souped-up virtual reality of, "Look, I can look at my factory floor." To actually saying, "Well, look, if I can enable other people to come into this world, I can actually create more of a verse, right? That Neil Stephenson's universe, this meta virtual universe, which we can now navigate around and suddenly, I can see how their products, or their supply, or their components are working within my virtual factor and where does my delivery systems come into my virtual factory?" And that's really exciting. I mean, it's early stage, but that is some really exciting uses of metaverse and some real justification for why the virtual aspect to it, because at that level, you can enable people to play, but across your supply and demand chain. And, I mean, yeah, that's something that I think could be transformative.
Chris: Yeah. It's very exciting stuff. And we haven't seen, you know, at least people openly talking about too much investment or knowledge of these approaches, but a great hunger and interest in seeing where the opportunities exist there. So, it's gonna be very interesting to see in the next month, in six months, in a year or five years how that takes hold. Another phrase in your universe is this open-world approach. What is the open-world approach? How do you use that phrase and approach to what?
Ali: Yeah. So, I mean, the open-world approach is really that piece around collaboration. It's recognizing that you don't know everything. So, a closed world approach says, "I know everything, and as a result, I can model." You know, if I'm doing my simulation in my factory or whatever else, I can model it because I know all the variables, I know everything that's needed. I control it all. I can validate all the data. I can validate all the models. I can validate all the outcomes. That's a classic closed world. And what we saw is people doing that. I mean, by necessity, doing it for factories, doing it for testing of infrastructure and assets. And then there was a recognition that actually what they needed was an open-world approach where you said, "Well, I know some things, but the absence of unknown doesn't mean a negative, essentially," is the open-world approach. So, you don't say, "Oh, because I don't know this, it doesn't exist or it isn't true." You say, "I just know these bits. Who out there might know some other bits?" And what that helps you do is continually refine your models. And we are doing some work at the moment in the UK with an organization called DigiTwin who have spent three years developing incredibly sophisticated computational models for doing things like ground testing of vibration from jets, that kind of thing, who are just starting to say, "Actually, what we really need to do is we now need to apply those models flexibly and learning from each other longitudinally and on real-time data securely across boundaries." Well, that requires an open-world approach. That requires you to say, "Well, what's available now? What can I learn now?" And then start to fill in the gaps as you go, as you learn, you know, as you look at the outliers.
Chris: It seems like there's a lot, and I'm sure you'd agree with this, overlaps in all these concepts. An open-world mindset, you know, is critical to applying the metaverse in manufacturing, and, you know, that replicates the concepts of boundless data ecosystems, just connectivity, and secure openness, and visibility, and transparency across stakeholders and across the supply chain.
Ali: For sure. For sure.
Chris: Interesting.
Ali: And it's a necessity born of the rise of new challenges, right? I mean, so we've seen the need for supply chain resilience. We saw with the Panama Canal the need for supply chain reprogramming. We're seeing a lot of sustainability and net-zero challenges emerging where, as you say, all these pieces are coming together because what you really need, you know, is not just the openness and the collaboration, but it's got to have an element of autonomous interoperability, right? I mean, I was just today talking to an organization in utility sector that, for governance reasons, has to do sustainability targets across their business, across their stakeholders. It's taken them six months to put that information together for a yearly report.
Chris: Wow.
Ali: Yeah. Because it's in such a number of silos. It's so diverse. They have to go to different people. I mean, that's...
Chris: So they're looking to automate and streamline that process.
Ali: Yeah. That's not sustain... You know, the irony of the sustainability report not being sustainable is not lost, you know. So, how do you automate that? How do you automate it when it's lots of different heterogeneous data types? And suddenly, if you can start doing autonomous interoperability, because that's really what we're talking about. If you have an open-world approach, you're talking about a data model that is concerned with the metadata, the description, which means that you can start making autonomous, you can start leveraging the visualization of the metaverse, the insights from AI and ML, and the cooperative benefits you get from being able to share bests practice with people that you are already working with. And I think we are... You know, if the last two years, this horrendous period we've been through has taught us anything, is that we need autonomous solutions for the holistic nature of what we're trying to do because individuals just can't cope.
Chris: Yeah. And we hear that across the board. Autonomous efforts and automation of OT assets and remote, you know, all these capabilities, it really is a perfect storm and there's, you know, a lot of it is born out of negative events, but, you know, there is that silver lining of accelerating some of these things, not only just the pandemic, but, like, the issue in the Panama Canal you mentioned, you know, and a greater demand from clients and customers who want this transparency and they demand, you know, customization capabilities, and speed, and sustainability promises from their providers and things like that. Interesting stuff all coming to a head. Last question for you, let's talk bigger picture. What most excites you in the broader world of digital transformation? You know, kind of big picture, back things up a little bit. What do you find most intriguing coming down the road in the next six months, or year, or five years?
Ali: So, I think the most exciting thing is that...and, actually, it goes back to your observation about the metaverse, is that I think what we're starting to see across a whole slew of technologies and platforms and approaches is a digital world that isn't simply a mirror of the world that we live in now. And that's really exciting to me because that's reminiscent of when the worldwide web became the force that it is today. You know, in the early stages of the worldwide web, you had a kind of your digital shop window, your business's website just replicated anything you might have in your shop window. These are our opening times. This is our phone number. This is what our stock looks like. And then what you started to see is people saying, "Well, hold on. Actually, this is a different abstraction of... What else could we do? Could we offer new services? Could we do new things?" And I think within the data world, we're just at the beginning of that journey, and some of it, as you say, born of the necessity of things like sustainability and so on. But the rise of decentralized financing and so on has meant that people are starting to look at, are there different models that data and the digital transformation will enable, not just how do we streamline what we do today physically, but actually the kind of, what if we could, why can't we do something different? Why can't we look at the way data operates in a different way? And I think that is unbelievably exciting. And I think that we're just starting to see people actually looking at what that could mean for their business.
And what's really exciting to me is that I no longer feel that I have conversations with people that start at first principles about, "Why do you need digital transformation? Why does this matter?" Like, yeah, that has been swept aside. The question is now, "Do we just recreate everything we were doing physically in a digital space?" You know, people can't come into the office now, they can't visit the construction site. Do we just mimic what we were doing? And what's really exciting is, I think, we're starting to people...people asking the question. No, because we had people, because that was how our physical space worked, we had to work in these ways. Actually, if we don't have a physical space, how might we work? So some of the stuff work that Fujitsu's doing around co-creation and their services in the metaverse is the beginning of that for me, of kind of, "Hold on. Let's not just get digital post-it notes and digital whiteboards and, you know, replicate all the things that are born of the limitations of the physical world." Actually, are there better ways of doing this? Cranfield University is doing some amazing stuff using digital to change timelines and scale things so that you can look at physical assets and manufacturing systems in a way that you've never been exposed to it before. And I just think as that comes together, we'll see some really creative, new businesses and services that are not just the kind of current big four technology providers, but are genuinely groundbreaking and possibly from left field.
Chris: Yeah. It's exciting stuff. Boy, you're a good cheerleader for the larger world of digital transformation, not just IOTICS. Ali Nicholl with IOTICS, thank you for joining us here today in the "Remaking Industry" podcast.
Ali: Chris, it's been an absolute pleasure. Thank you very much for having me.
Chris: Very interesting stuff. Cool concepts. And for our listeners, as always, we encourage you to go out and make it a smart day.
