 Live from Las Vegas, it's theCUBE. Covering IBM Think 2018, brought to you by IBM. Welcome back to theCUBE. We are live at IBM Think 2018, our inaugural event. I'm Lisa Martin with Dave Vellante. We're joined by another Vegas veteran as we all are. First time guests of theCUBE, Brett Greenstein, the VP of Watson IoT offerings. Brett, welcome to theCUBE. Thank you very much, exciting to be here. Yeah, so this is the inaugural Think 2018 event. 40,000 plus attendees expected over 10 keynotes, lots of cool stuff. Speaking of cool stuff, IoT. What is happening in IoT this year? So we've been here in Vegas several times over the last several years talking about the internet of things, but what's really pivoted, it's really changed as people are talking about applied IoT. How they're using it to get business outcomes, something different happening. And I think when we all started with the internet of things, we talked a lot about connecting stuff and devices, but really it was always about the data and the effect that data had on changing business, changing user engagement, changing outcomes. And so here on stage, you're going to see people talking about how their businesses have been changed, how their customers are changing as a result of IoT. Yeah, so I mean, I've always felt like IoT is the intersection of devices, data and machine intelligence. How are those sort of three things coming together? What's the data model look like? Data model is every type of data. I think what people really didn't expect was it wasn't just machine data coming off, sensors, temperatures, vibrations, it's all this unstructured data coming in from connected things that are everywhere in our lives. So sensors with cameras, for example, being able to see, that's not just recorded images, but it's information, tons of information that you need AI systems and other systems to interpret. So we're able to take all of that data, unstructured data, numeric stuff coming off of devices and sensors, but images and sound and vibration, even emotional content and people's dialogue, all of that is relevant to the internet things. What's the conversation like with customers? They remember what I was saying, okay, what physical assets do we have that we can, instruments, parking meters and whatever, okay. What physical assets don't we have that we should have? How can we leverage our existing data? What's the conversation like in terms of transformations that are going on? I think the conversations have shifted a lot. Over the last couple years, people were talking about, we want to connect our thing, whatever the thing is, whether it's an elevator, a car, whatever. We want to connect it, what does that mean? And that's shifted very quickly to customers who are coming in talking about information, data and insights, and they want to know, what should I do to get more of those insights? So I'm seeing customers now with chief data officers or heads of digital transformation, totally new roles that didn't exist before, and they're coming in with a data-centric view, they're saying we're going to be a digital business. We need to understand all this live data about our customers and our things and our business process, help us do that. And so that's much more than just instrumenting the individual devices now. And I find that conversation is really, really focused on the value of the data. What about the industry impact in this context? Do you see, as IBM's perspective, is IoT, it's certainly transformative, but is it disruptive, or is it sort of, the guys with infrastructure are going to evolve to it? Is it more evolutionary? Is it more disruptive? How do you see it? No, I think there's room for both. Obviously, traditional players are going to instrument their business process. They're bringing in connected cars and all that, but you could also look at those same industries and say there's new players emerging who are coming in with software-defined products that are digital by design, and they can come in and suddenly become leaders in their field. I don't think people would have expected companies like Tesla to be so disruptive in automotive, but coming in as electric changes the game without having to build on 100 years of mechanical design. You're building on some new principles. And now we see some new players coming into automotive who've never built cars at all before. Like Dyson, for example, that recently announced they were working on electric cars. So I think a digital platform, a digital way of thinking also creates opportunities for new entrants in every market. I think automobiles is actually a great example because it's an industry that hasn't been, largely hasn't been disrupted, but then you use an example of Tesla, which is extremely innovative. You could actually pretend disruptions coming down and you're seeing whole ecosystems form around that. Right, right. And I think what was so powerful about the effect they had was it's a software-defined product. The software in it is upgraded constantly, sometimes you buy the car, the next day you get a new feature you didn't even expect. And this is the way we've come to appreciate experience through mobile and everything else. Software that continues to improve, products that get more valuable over time, not less valuable over time. So let's talk about Watson and IoT and I'd also love to maybe take a slice on how IBM is helping customers that have been around, maybe the flip side of a Tesla that have been around a long time. How are they leveraging Watson and IoT to transform their businesses? But kind of start with kind of what's new with Watson and IoT over from there. So I'd mentioned before that there's a whole bunch of many data types now that previously were very hard to interpret through traditional analytics, but AI and machine learning gave you the ability to absorb and consume some of that data. Unstructured sound, images, video, vibration, all of that stuff is now able to be part of a business process. So even traditional companies that have been around a long time can start to look at the data coming off of cameras, visual inspection and manufacturing, sound and voice, for example. We work with Jefferson Hospital where they brought Watson into patients' rooms so you could ask questions like visiting hours or set the temperature, put the patients in control of their experience in a hospital. That takes a traditional experience like a hospital recovery room and turns it into something AI-driven, IoT-powered and puts the patient at the center. So very big changes can occur when you do that. How far do you see us being able to take AI in this whole world of IoT and how far should we take it? I think we have to start to become more appreciative of the power of machine learning to drive outcomes that are not as easily prescribed with code. And so all of us, all of our business processes, all of our businesses will be enhanced with AI. And we shouldn't look at that in any other way as a better tool to understand data in a way that's different than the way that we interpret data. And so it wasn't long ago where big data just meant writing an algorithm and looking across large volumes of data. But now we literally have algorithms whose job is to find patterns, whose job is to understand data from training and deliver an outcome that you couldn't have prescribed before. And so those types of problems, if it just opens up a class of problems, we can all solve them out that we couldn't before. Well, and you're seeing a whole set of digital services emerge. I mean, the link where Franca is changing, it's sense here, see, respond, optimize, fix. Right. And all that comes from comprehending. So having a system that can look, for example, I have a camera aimed outside my window at my house and every once in a while, I feed the images into Watson to see what it sees. When I first did it, it would say truck. But later, as we've made Watson better, now it says FedEx truck or UPS truck. It can read the writing, it can see the patterns. Every camera should know what it sees, whether it's in a car or in a home or somewhere else, because it's much more valuable than just taking a picture and letting a human being interpret it later. So cameras should know what they see, machines should know what they hear, machines should tell us when they're about to break based on vibration or sound. And so this is possible with machine learning. So you're seeing machines actually take on a whole new set of human-like activities. Digital twins is an example. What's your perspective on, let's start there, digital twins? Yeah, digital twin to me represents sort of the evolution of IoT in that it's digitizing things. And so a thing that has no connectivity and very few sensors is just a thing, just a box, a block. But as you start to put sensors on it, start to understand its behavior, its motion, its vibration, its location, any of the mechanisms, the angles, all this stuff, then you've had a virtual representation of that thing. And if you can do that of all the things in your business, you can start to look for patterns, you can start to assess what's working and what's not working. So I think it just represents a true digitization of a business, of a class of objects in your business. Does IoT make security a do-over, in your opinion? I know, but it certainly raises the bar. And so when we all started connecting our computers to the internet, I remember everyone being panicked. If you put a disk in your machine, you might get a virus, right? Then we connect them to the internet, we all panic, but the tools evolved. And we start to get things that can help detect zero-day problems and stuff, all kinds of... In the case of IoT, you've got these software-defined products that are connected. That are inherently vulnerable because they're in the real world, they can be touched by other things. So it raises the bar in the expectation of monitoring normal behavior for things, monitoring all kinds of different threats and stuff. So companies like IBM that focus so much on security and security services, we build that right into our platform so we can keep an eye on that. And also, when things occur, be able to push out new software that is protected. So firmware updates, keeping the products live and current is a huge security protection. Brett, how would you describe the ecosystem, IBM's point of view on the ecosystem, that you've got to form and catalyze in order to succeed in IoT? What does that look like? Yeah, so there are so many things for people to do in the world of IoT that IBM doesn't prescribe to do all of them at all. There's things we're really, really good at. We're certainly good at our cloud infrastructure and the analytics and the platforms that enable this and deep industry knowledge, but the ability to apply that in businesses to take our machine learning algorithms and make it work on the thousands of classes of machines in manufacturing requires a huge partner ecosystem. So we work very openly on contributions to standards and open source. We certainly work with partners who build a lot of value around our stuff. So for example, on stage this week, we have several partners who are going to be up there. One of them is Harman who builds all kinds of things including infotainment units and cars and the professional equipment that goes into hotels and buildings. So we work with them to build great integrated value together and they do things that they're experts in and we do what we're experts in. So from an IoT perspective, what are some of the cool things that are here at IBM Think 2018 that those that are attending are going to get to see and feel and touch and smell? Well, there's the things I can talk about and things I can't. So tomorrow we have some very exciting announcements coming up that'll talk a lot more about Watson and IoT coming together. I just saw all I can say about that. But you'll also see physical representations of things. There's a Jaguar Land Rover out here on the floor to look at where we've contributed significantly to the engineering and the software development inside these kinds of products like JLR. So they're going to be up on stage talking about some of the things we're doing together. You'll hear ABB here talking about some of the work we're doing around manufacturing techniques and helping manage wind turbines. So all kinds of really cool industrial use cases. It's really exciting and I think working in IoT is great because not only do you get to talk about technology and the analytics and the data, but you actually get to see things. So it makes all of this feel very real when you can walk up to and see a thing that's infused with IoT and made better because of IBM. What inning are we in? What's that? What inning are we in? Oh, it's still early. It's still early, third inning still. Mostly because so much of the market is still working to figure out how to take advantage of the data and the insights about this to transform their business. I think if you thought of the dot com era and how long it took for companies to emerge to be truly digital e-businesses, on-demand businesses, the IoT businesses, the AI driven businesses of the future, it's still very early. Some of them probably you don't even know their names yet but they're going to be the leaders of this time. Do you think it'll happen faster because there is an internet or not so much because of the physical infrastructure that has to get built out? No, the infrastructure is actually not the gate at all. The real gate is the cultural difference of having people who are data driven, data thinkers, having a leadership role in our clients. And so if you can think about it, mechanical things have dominated for 100 years. Software engineers are still not even the most senior people in most of the companies that build physical things. But to have the data scientists, have the data leaders have a strong enough role to define business process, it's really the readiness and maturity of those data leaders. Yeah, so the culture of a mechanical engineering, culture that says don't touch my things. Right. I'm not going to let a software engineer come in and mess with it because it works, it's secure. I trust it. So that's the cultural one of the cultural dimensions. Yeah, it's to look at what the data might mean, whether you want to just understand how your users use your things, or if you want to understand what they're doing with those things somewhere else, or even what the value of the insights of your users are and building entirely new ecosystems of the data of IoT. All right, so we're in the third inning. We'll say the top of the third. Okay. But one of the things that you share with us is that you're excited about is that this is about applied IoT to get business outcomes. Yes. Shared some examples that attendees of the event are going to hear from ABB, you mentioned, you mentioned the land member that's here, right, Harman as well, and maybe some best practices for how to advise companies to get through some of those cultural hurdles, we'll say, to start embracing the opportunities that are within the IoT space. Yeah, I think the best thing people can do is to start to really, I'm going to say it again, put value on data science. It doesn't mean everyone has to be a data geek, but it does mean you have to have a certain value on the skills and the insights that come from a data-driven business. What does it mean to make decisions in real time based on your customers? For 100 years when companies shipped a washing machine, it went into someone's house and sat for 10 years, and they never heard from the person ever again until they bought another one 10 years later. But now when you ship a washing machine, you want people to connect it to the Wi-Fi. You want to know the features that are used, suddenly as a manufacturer of things, you have to respect the data coming off of those things because they inform you on how to design better, how to deliver better service and value, which means those engineers who are the experts in washing machines now have to be the experts in the data of washing machines and the data of their users. So I would say focus on the education, the recruitment, the enablement, the empowerment of people who are data centric by nature and who are looking for the transformation of a digital business from a physical business. Brett, thanks so much for stopping by theCube and sharing your insights. Good luck tomorrow with your presentation and we are going to be waiting on the edge of our seats for those Watson IoT announcements. Very exciting. Okay, thank you so much. You can watch all of our good stuff on theCube.net live, of course, as we are now, as well as the interviews that we've already done and those that we'll be doing for the next two days as our coverage continues at IBM Think 2018. Also check out siliconangle.com, our media site for all the near real-time coverage of this event and others. For Dave Vellante and Brett to Vegas Veterans, I'm Lisa Martin. Stick around, Dave and I are going to be right back after a short break.