This series highlights the key insights and lessons from our Digital Leadership series of podcasts. We spotlight the important takeaways from our interviews in an accessible format. The following insights come from Alfonso Velosa, Research VP for IoT at Gartner. Stay tuned for the full podcast interview with Alfonso, in the meantime, take a look at our full library of podcasts.
Tell us a little bit about your background and what has led you to the work you are doing in technology.
I came to IoT from a mix of a semi-conductor/photovoltaic perspective, because that’s part of the industries I grew up in. So, that brought one sensibility, the physical asset itself, an implementation of it in particular starting with smart cities but also fitness monitors and things like that. So, that came and eventually it really led to much more of a focus on the software, because right now how we’re implementing IoT really is about taking the data out of an asset and actually using it. So, for me it was sort of a journey on looking at how to connect to things, but then more particularly how to use the data there.
So that’s one part of it, but the other way that I would highlight it to you, the second path for me is this battle I have internally, which is the battle between the engineer in me and the MBA in me, because I, like you and like everybody else, fall in love with ‘Oh, this cool technology, dude! Oh, it’s so cool!’ But there’s plenty of times when it’s also, ‘But why are we doing that, how is there a business outcome for that specific use of the technology?’ Or, alternatively, do we have a plan so that we can get our team, our culture, on board with that use of the technology? So, for me it’s that sort of ongoing balancing act between where the technology is going, and where the business is going.
Its six years now since GE had come out with its trillion-dollar economic value-add calculations, and then Cisco has started talking about the Internet of Everything, but vendors were over-selling how imminent the promise of the realization of some of these ideas would be. As you look at the market today, how far have we come recovering from the trough of disillusionment that followed?
I still think we’re heading into the trough, we haven’t fixed certain things in terms of the ecosystem, or lack of a structured one there, lack of standards, lack of really consistent business stories from the vendors that then drive implementation. So for me part of the challenge I have with the IoT environment as a whole is, most vendors still are struggling to clearly lay out their business value proposition and show how they can scale for their customers. It doesn’t matter what sector we’re in, whether its water, utility, hospital, retail establishment, this still remains a challenge. So, from my perspective that’s one thing.
If you look at the vendors, Cisco and GE offer interesting lessons to the market. How do you make sure you set proper goals for a business unit? How do you set it up so that salespeople are trained and incentivized to sell a particular solution that aligns to the overall business, but also how do you make sure that your customers are ready for this set of capabilities they're bringing to the table? So, there’s a whole host of challenges that most of the technology and service providers in the IoT arena still lack, that I think will give us pause for at least another couple of years. That’s why I think we’re still heading a little bit more into the trough of disillusionment, even though I will also say in parallel, why we see more revenue for technology and service providers.
If you look at industries or companies at the vanguard, are there some that you would see operating are well ahead of others, and others that are still wrestling with some of the basic concepts?
Well I would say once an enterpriser achieves a certain scale, especially multinationals, the left-hand’s not talking to the right-hand, so we often see cases where one part of the business is doing cool stuff, and other parts don’t even know how to spell security in IoT, so there’s only that challenge. What I would say to you is sector-wise there’s the heavy assets folks who are further along on the journey, and this would cover any of the extractive industry folks, oil and gas, mining, and those guys, they would cover electric utilities, water utilities, and they’ve been exploring this area for quite a while, plus they have their OT systems heritage, and particular skater systems, so manufacturing would be the third area.
Transportation is one of the areas where actually we do see some consistent solutions, particularly in fleet management. In fact, fleets in the telemetry for that is the only really crystal clear, multi-billion-dollar market that we can all point to, just to also emphasize that, but they’re not fully shifting over enough to the business side of that, on that. Then I’ll close that with retail and their focus on a bit of location-based service, but also trying to serve that customer a little bit better.
What are some of the opportunities out there, and as you look at big challenges and risks ahead, what are top of mind for you?
Well, the risks, obviously it’s about security. So, for me, have they really thought about the challenge they have here? That’s definitely one of the core problems I have is that folks pay lip-service to security, but then they don’t implement it appropriately, or they just do a fig leaf, ‘Hey, we have a CISO a Chief Information Security Officer, ah that takes care of everything!’ So, there’s quite a bit of challenges like that. So, for me I really do worry quite a bit that there’s going to be a pushback on IoT in general, just because it will get a bad name from a few bad apples. The typical example, a baby monitor where they didn’t really put in too much security and they don’t update it, so it’s easier to hack into. Or, alternatively cars, we’ve seen enough cases where they’ve been hacked into, and so what’s the catch and update strategy that we see in the market, that’s still something in evolution.
So, those are some of my concerns, but what I would say is I still think of this as the way of the future, so I am excited about IoT in general because I see the business outcomes across sectors, whether its fleet management, oil and gas, retail, I’m seeing folks both lower costs, but also find new revenue opportunities.
The second thing though, and what really gets me excited about this is that IoT and other digital trends in general have really pointed to the rise of digital twins, so for me digital twins that’s really the next wave. There’s so much potential here and we’re just now starting to explore what it means to us as companies, as a society. So, that’s the most exciting thing, how do we digitize and create digital representations of people, processes and things, so we can make better decisions about them?
How would you define a digital twin? I’ve heard a number of different approaches, but what is special about the digital twin in your view?
There’s a couple of ways to think about the digital twin based on that question. The first thing what I would say is, digital twins aren’t new, NASA built them. So, it’s been 50-60 years since NASA would put a room full of PhDs and what they back then called super computers in a room, and then they would also build mock-ups of something. But that was 50-60 years ago, and like everything else gotten faster, better, cheaper, there’s been a whole ton of investment, the Department of Defence was looking at it for aircraft, Siemens, GE, and PTC have all been looking at it, a whole bunch of companies. Aviva right now is in the process of merging their 3D capabilities with some of the eco-structure capabilities to look at new use.
So, what’s happened over time is, it went from being this high-fidelity physics model, to an environment where you’re looking very specifically again, person, process or thing, to make a business decision. And to do that what you do is, you have a model or a template of the business decisions you want to make from that, you populate that model with data and with the unique identity of that thing, person, or process, and then you figure out a way to connect it, to monitor it. So, let me give you two examples.
The typical example most people will see of a digital twin today in their lives is either,
- The Customer 360. It’s not a perfect digital twin but the Customer 360 is one approach where you think about how the retailer both online and real world is trying to send you coupons or influence your shopping behaviour based on the model, the data, your identity, and how they monitor your activity. So, that’s one example, how do they influence you using that model view.
- Alternatively, my current favourite digital twin, although there’s many, but my favourite digital twin right now is the digital twin of my stereo. It tells me which stereo units are around me, what’s their battery status, what’s their volume, and if they are connected to my music collection then I can control them appropriately.
So, digital twin has gone from this high-fidelity physics thing that NASA built way back into the data, to now a variety of different approaches where you can either have one of a person, a thing, or a process, to drive a business decision.
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