>> As to the adoption challenges, I wasn't clear on where that should go. I mean, I'm happy to just throw it out there. >> You'll again punch it back to me, right? >> Okay. >> Question comes to me and I'm going to pass the ball to Greg to connect the thread on one backbone is needed. Emphasizing Cat 9K that we just talked about. >> And same thing for the last question. The routes to market? >> Yes. >> Okay. >> Yes. >> Great. So we'll use that program for everything. Perfect. >> Masum, could you... Yeah, right there. So mark your place and try not to move that seat. That's it. Now, come forward just a tad, just a tad. There we go. Yeah. Okay, that's fine. Okay Alex, we're good. >> Okay. So Leonard don't leave after this 'Cause I'm going to do my outro. I'm going to do that as a separate asset, okay? >> You bet. >> Okay, great. So guys just it'll be five, four, three, silent, two, one. And then just follow my lead, okay? All right, Alex, you're ready? Masum and Greg, you're ready? >> Ready. >> Ready. >> Okay, here we go on me. On Dave in five, four, three, (beep). Okay, we're back. Digging into the infrastructure to make hybrid work possible. High performance, cost effective, scalable, and secure. That's what it's all about. And so far, we've covered the rapid migration to Wi-Fi 6E technology, and the role that switching is going to play. And now we're going to get into Private 5G and to do that, let's welcome Masum Mir, who is Vice President, and General Manager of Mobile, Cable and IoT business at Cisco. And Greg Dorai who is the Vice President of Product Management for the networking experiences group at Cisco. He's responsible for Catalyst access, that whole portfolio, Enterprise 5G, Cisco DNA Spaces, Cisco ISE, a lot of stuff there Greg. And gentlemen, welcome. >> Dave thank you for having us. >> Yeah, our pleasure. Masum let's start with you on the topic of Private 5G. What do we need to know about that? And more specifically, what's unique about Cisco's Private 5G? >> So most importantly, delivering Private 5G in enterprise terms, that's super important to look at 5G. Many of our peer groups might have got it wrong. We're looking at Private 5G with the lens of enterprise, what enterprise really needs. Is 5G going to come and displace a lot of existing technology, or is it going to help augment the technology that enterprise. It has an excellent the digitization journey. I wanted to start Dave with the basic premise of hybrid work. And what hybrid work really means. Is it only for knowledge worker, or is it for all workers? So we strongly believe hybrid work needs to empower all workers. It's not only connecting remote workers but also bringing people, things and space together. And we strongly believe the combination of Wi-Fi 6 and 5G for private network is going to accelerate that journey bringing people, things and space together in a very, very cohesive way. Why our offer is so unique? We are going to create a continuum. Enterprises don't have to make a hard choice. They will be using Wi-Fi technology and 5G technology hand in hand without creating a disruption on their policy and identity systems. They don't have to rethink, "Do I have to go and build a new backbone?" Is a common backbone that will support both Wi-Fi as well as 5G. Most importantly, delivering this entire offer as a service with the ease of consumption, ease of operation, and a trusted environment that they can put their mission critical workload on. >> Now, I like it. So a couple takeaways there. I mean, it's inclusive of all workers not just knowledge workers, non disruptive, everybody loves to hear that. And of course, it has service model as key Masum, let me stay with you. I mean, we can't wait for 5G, right? It's lightning fast, it got super low latency, very high bandwidth. So that's what everybody's excited about. The question though is, 5G gets introduced, yeah it's going to power things like IoT networks. Is that going to replace Wi-Fi and legacy wired broadband? >> Absolutely not. So we see Private 5G as an augmentation to the enterprise on top of Wi-Fi. Wi-Fi as you heard in the previous conversation, Wi-Fi is bringing more capability with Wi-Fi 6 and Wi-Fi 6E. And 5G is going to be yet another augmentation. Wi-Fi and 5G will coexist within enterprise for many years to come. I would like my friend, Greg to talk a little bit about this continuum. Greg? >> Yeah, I think it's sort of like, I like to say it's an and not an or. Because there's enough use cases out there which require spectrum. And you know, spectrum is a constraint. So you have Private 5G, your Wi-Fi 6, and both offer opportunities. So for example, in an indoor carpeted setting where you're basically connecting your phone for basic browsing, or connecting your laptop, Wi-Fi is sufficient. But if it's a process automation factory where you need seven nines of reliability, Private 5G is the better technology. Similarly outdoor, large areas, it's probably Private 5G, right? 'Cause you can have easy handoff between public and private. So it's use case driven. And once it's use case driven, it's going to be an or because there's so many next-gen use cases. Whether it's AR VR, drones, you know, self-driving cars you name it, right? And so I think these two technologies, 5G and Wi-Fi 6E is going to work hand in hand to deliver awesome outcomes for our customers. >> Yeah. And just the data volumes are going to be incredible. We always talk about the data volumes. You ain't seen nothing yet is what I always say. But the thing is every new tech that's introduced into the enterprise, you can almost be certain that it's going to bring adoption challenges. And not only that, it also is going to bring changes in the way you do things. And that brings new complexities from an operational standpoint. So my question is, how are you addressing this with the introduction of 5G? >> Dave, this is a fantastic question. And this is why we have spent, me and Greg have spent tremendous amount of time to create continuum. I'll start with the foundation first, backbone. So we have been building this enterprise backbone supported with wired connection as well as Wi-Fi connection. We wanted to make sure that as Private 5G comes within enterprise, you don't have to rethink and reimagine your backbone. It's the common backbone that will support what Wi-Fi, Wi-Fi 6, Wi-Fi 6E, as well as Private 5G. You're rest assured that it is the same backbone that we have heard in the previous section on the Cat 9K that will also support a Private 5G access. The second aspect of Private 5G is as you build any new technology into enterprise often time we get into this trap. To get to an outcome, we move fast and we create a silo. And then that silo operation creates barriers to mainstream it. So upfront, we have to think about not creating another silo. And how we are doing it. Number one, is a device that can connect into Wi-Fi network or a Private 5G network. You don't have to reimagine or rethink how I'm going to manage the identity. We'll create continuum with a common identity across the Wi-Fi access or 5G access in the same environment. The second aspect of that is how are we going to retain all our staff? Our enterprise staff is well trained with Wi-Fi technology and wired technology. Now 5G comes with tremendous amount of value and benefit. But it also comes with inherent technology complexity, learning curve problem. This is where our simple to consume, simple to operate model of SaaS comes to play. That we're going to take all those complexity away. It is a cloud delivered service. So enterprise don't have to go through this massive learning curve adopting this technology. Last but not least, on how we are going to manage your capital. Any new technology and enterprise often time, you need huge amount of upfront investment to adopt the technology to get to the other side of getting the outcome. So again, our business model of SaaS will allow enterprise to adopt this new technology and pay as your grow model to meet with enterprise needs. Finally, I also wanted to pass to Greg to touch a little bit more on how we are thinking about this common identity across any access in the enterprise. Greg, to you. >> So we thought about it in two different ways. One is, a lot of enterprises today use our identity and secure management platform. We call it ISE, Cisco ISE platform. And so, years and years of policy and identities, and which access servers, radio servers they use et cetera, are plugged in already into our ISE, right? So, if you can share that with this Private 5G as a service infrastructure that Masum's been building, we think we'll be able to create that bridge. Because we are not forcing enterprises to create new identities or new policies. So that's sort of step one to make it easier. We also thought through so something where in the case of a public 5G network, for example. It's very convenient because you take your phone out of your pocket and it's connected to the network, right? Versus for wifi, you have to log into an SSID in your hotel, or in your home, and in home, it's automatic. But that's that login process that creates friction. And that's a problem because then you can't be seamless. So we initiated what we call as open roaming, right? Like that's a identity federation that we first created between identity owners. Could be carriers, could be anything, right? Anyone who owns an identity. And they will share with venues. And so if the sharing happens, then that onboarding can be automatic. And once onboarding is automatic, then it's easy to pass off between Wi-Fi and 5G. And so that's again, another way in which you can lower the adoption barriers 'cause you share across public Private 5G and Wi-Fi networks. So these are two concrete examples of how we thought about lowering the barriers of adoption as we enter into this heterogeneous world. >> Nice, I can't wait. Let's talk about how this thing, scales in the go to market. What are the most likely, or maybe preferred, or obvious routes to market for Private 5G from Cisco? >> So Dave stay tuned right when they announce more about it. But I can also assure you that access to this spectrum is a challenge for many enterprises when it comes to cellular technology. In some countries there are more spectrum accessible by enterprise. In many countries, that's not the case. So we have thought through very carefully that how do we bring this offer to the market partnering with many service providers and mobile operators. Where in countries where you don't have direct access to the spectrum, our partnership with mobile operators, that you will hear more about as we come to Mobile World Congress, is going to allow our enterprise to consume this technology. even if they don't have the spectrum. In the places where the enterprise might have spectrum access, we'll also in our manage service providers to hide the complexity of the new technology on top of our SaaS services, or cloud delivered services. This is the augmentation with the partnership with manage service providers and mobile operators that will ease this journey for enterprises. Our most important primitive in this journey is to keep it simple for enterprise, make it intuitive, and trust it from day one. >> Outstanding. Okay, Masum, Greg, thanks so much. It was great to have you guys on. I really appreciate your time. >> Thank you. >> Thank you. >> In a moment, I'll be back with some closing thoughts and an opportunity for you to actually see this technology in action and talk to the experts directly. Keep it right there.

>> Live, from Barcelona, Spain, it's the CUBE, covering CISCO Live 2018, brought to you by CISCO, Veeam, and the Cube's ecosystem partners. Welcome back, I'm Stu Miniman, and this is the CUBE's coverage of CISCO Live 2018 in Barcelona. You know, I'm a networking guy by background, but there's certain people in the industry that really I've gone to to learn, been really thrilled that I've had the opportunity to get to know, and every once in awhile I get to bring them to our audience, and really happy to bring back to the program Ivan Pepelnjak, from Slovenia, blogger, author, webinar, generally, you know, that network guy that those of us who watch the industry know. >> That grumpy networking guy. >> Ah, ya know, aren't most networking people at least online at least a little bit grumpy? And when you meet 'em in person, though, it's a slightly different experience, so thanks for joining us. >> Ah, thank you for inviting me. >> All right, so 2013 was the last time that we actually go to do one of these in person. >> Ivan: That's true. >> So networking, it's all the same, right? >> Ivan: It is. >> I mean we're probably still working on 10 gig rollout, ah, maybe 25 or 50 gig, speeds and feeds, and, ya know, oh, okay, ya know, IPV 6, I think we're kind of getting there, lots of other acronyms. We could talk awhile. But really, what's been some of the big things that you've been looking at? What are customers actually doing, and what are customers thinking of that you've been playing with? >> Well, it's amazing how little has changed. >> People are still talking about SDN like that's the big thing. No one has delivered on that apart from some point products like VMware, NSX, or CISCO ACI. Cloud is still the thing that will happen next year to most companies. We hear how 90% of all the companies that participate in some survey are using Cloud. And then the next question I'll ask is, "Well, is this Office 365, "or is this something more?" And they go like, "Well the survey "didn't differentiate on that." So thank you. >> Yeah, but, yeah look. The SDN, a friend of mine said SDN stands for Still Does Nothing. That being said, ACI, NSX, there's customers using it. >> Ivan: Oh, absolutely. >> It has not totally transformed the industry like they said. Cloud, I've yet to find a company that's not doing some SAS, and unless you have some regulation or things like that, you at least have some sandbox that you're doing some public Cloud. >> Ivan: Absolutely. >> But, absolutely people, they still have data centers, despite... >> Ivan: Well, it's a... >> It might not be their own building anymore. I was just talking to a service provider and the like, but yeah, I mean, the more things change the more things stay the same, right? >> Absolutely. Well yeah, we do see people moving to colos or, they would build their stuff somewhere else, or whatever, but it's amazing how much interest I am still getting in data center design courses, so there are still zillions of people who think that that is important, and yes, we all know we'll go to the Cloud, but everyone has his own hurdles, and so I think that eventually everyone will get to some sort of hybrid Cloud, where some stuff will be there, and some stuff legacy whatever will be here, and we'll have to live with that forever. >> Yeah, I mean, those of us we think back, I remember when this wave came, it was like, well, remember the XSPs in the 90s? There were two reasons why it failed. Number one, there wasn't enough network, and number two, ah, security. Well, you fast forward to, you know, two decades, and the network's gotten way better. I've got great speeds, and stuff like that, but you know physics is still a factor- >> Ivan: Well, yeah. >> And security is even more of an issue today than it was 20 years ago, I think. >> This started as a joke, but it is becoming more and more true. If you move to the Cloud, your security actually improves. >> Stu: Right. >> Because they have some security and you had none before. (both laugh) >> I at least get to rethink my security. >> Yeah. >> When I make some transformation. >> No, and they have the basics right. >> Right. >> Like physical access control, multi-tenant separation, encryption, trunk authentication. They get those things right, because otherwise they would be out of business. >> Okay, so we spent like more than a decade with how virtualization in networking. Have we gotten most of that at least reasonably well now? >> There are still people who don't get that ethernet was designed to be used on a single cable. So they still think that stretching a single ethernet across wide distances is a great idea, and everyone is still letting them get away with that. >> Yeah. >> Fortunately the Cloud vendors aren't buying. So if you want to move to Amazon, Google, whoever else, you have to redesign your applications and make them work correctly. So, eventually this thing will die, but it's like COBOL and mainframes, it will be there forever. >> Yeah, I mean, we've been saying for a few years on the Cube now that the challenge of our time is really distributed architectures, and of course they have a huge impact on networking, so how's the industry doing? How would you rate, you know, say we're here at CISCO Live, you know, how are they doing helping customers with these challenges? >> Most of them don't. >> I mean, if you look at a typical enterprise application, it still isn't developed for a distributed environment. Yeah, they use three tiers of servers, like always, but then they try to cope by solving all the problems in the ops phase, when they deploy stuff. And that's the biggest problem we are facing today. We are not changing the development processes and paradigms. >> Well, we're actually here in the Dev Net zone. I mean, I give CISCO kudos. Last time I came to CISCO Live was 2009. There weren't, we didn't talk about developers. >> Really? >> Everybody was, you know, doing plug fests, and getting their latest certification, but they're trying to embrace the developers more. There seem to be more of them here. >> Yeah. >> That boundary between network operator and developer, do you see? You know, is there communication, or are the network guys still stuck in a closet somewhere not talking to anybody? >> Well, there are two types of challenges. The first type of challenge is that the network guy in particular, but ops teams in general, are still not invited to the table when new stuff is discussed. So, the application developers dream up something based on their best knowledge. I mean, they're not evil or anything. They just don't know the operational impact of their decision. And because the networking security virtualization people are not at the table, then they have to cope with whatever these guys dream up in isolation. I'm never blaming them, because, you know, we should education them, and we are not doing that. >> Yeah. >> So anyone who manages to bring security, networking, and storage people in when the application architecture is being designed is my hero. But there are only few of them. And the other challenge is that the networking people don't realize that their world has changed. That they can't manually provision VLANs the way they've been doing for the last 20 years, and it's amazing once they get it, once they start simple automation stuff, how creative they become. What types of problems they solve. They don't have the shackles of CLI anymore. I shouldn't be saying that. (both laugh) I'm the old CLI junkie. But it's amazing how much can be done once you realize that you don't have to do everything manually. >> Yeah, CISCO's, you know, not shy about putting out strong visions. Marketing is definitely part of what they do. And the keynote this morning said it's a new era, and new infrastructure, powered by intent, informed by context. It sounded like a nice message, but this whole intent-based networking, what's your take on it? Is this, you know, are we going to come back five years from now and talk about intent based like we did SDN? Or, you know, what's your take? >> Well, let's keep in mind that this is all hype. What we're really talking about is an orchestration system with an abstraction layer. 'Cause first, it's really hard to define what intent based is, because there's no good definition. But there is a definition in programming which differentiates between declarative programming and imperative programming. And if we use declarative programming as something which could be intent based, that thing says, well, I don't tell the machine, or whatever, the system, how to do things. I just tell it what to do. And if you take a look at that from that perspective, then you figure out that every device configuration is an expression of your intent. >> Right. >> You never tell the device how to work. >> Yeah. >> You just tell the device what to do. >> Right. It's interesting, Ivan. I think back, you know, we used to manage individual boxes. Then we kind of created a little bit more pools, and the challenge they see right now is with the explosion of device, we're not going to have time to talk about all the IOT edge piece and everything, but there's no way an admin or a team of admins are going to be able to help there, so I need to infuse, I hate that, the ML, AI, choose your buzzword of choice, though, the machines need to be able to manage that a little bit more, you know, autonomous networks or something they (mumbles). I understand you're skeptical, so how do we get there, or, you know, otherwise, this whole label crap. >> There are two, there are three totally different things here. The first one, I totally agree with you that we should view networks as a single entity. Configuring boxes is stupid, and it's like, these admins don't do that. Well, some still do. They get the results they deserve. So, we should start thinking about network-wide data models which are then translated into device intent, which is really device configuration. And that makes absolute sense. But remember what I said. This is just a glorified orchestration system with an abstraction layer. The second problem is machine learning. Some of the things we are dealing with have physical limitations, like the speed of light, or the number of things you can put into a hardware forwarding table. Once you're faced with those physical limitations, it's like, you know, self-driving cars, yeah, they are self driving, but they cannot go 300 miles per hour because laws of physics. So, it's one thing to say, well, I have these infinite resources and I can learn how to play Go in eight hours. >> Right. >> And it's a completely different thing to say now I will figure out how to deal with my network, which has this physical limitations. And also, you know, whenever I hear about these autonomous distributed thingees, we have routing protocols. They have been autonomous and distributed and self healing for decades, and we didn't call them machine learning or artificial intelligence. And, finally, once you get to the bottom of it, and you're faced with all those physical limitations, and now, let's say you want to solve a simple problem, which is, how do I optimize the use of my network? You do some research. You figure out that this problem has been solved 20 years ago. There are companies with commercial products that have solved this problem. It's just that no one is using them because they are too expensive, because what you can save by using them doesn't offset the cost that these people had to invest into R and D to make this work. So, machine learning, yeah. Can you make it cheaper? I don't think so. >> All right, so, Ivan, I want to give you the last word. >> Mm-hmm. >> Grumpy networking, what do you look forward to the most at this show, and any final anecdotes you want to share, before we have to wrap? >> Well, the one thing I am looking forward is to see people to start automate their networks. To jump over that mental barrier, and when they break through it, it's amazing how many success stories you get. So I know a number of networking engineers who were on my automation course, and six months later, they write me saying, "Now I have this thing in production, "and we cut down the site deployment "from three days to five minutes." When I read emails like that, it's like, "You're my hero." >> Excellent, well I love it. For a grumpy person, you sure sound a little bit of an optimist about what some of the people come in and get this. Maybe a realist is more right. Ivan Pepelnjak, really appreciate you joining us. We'll be back with lots more coverage from CISCO Live 2018 from Barcelona. I'm Stu Miniman. You're watching the Cube. (the Cube jingle)

(upbeat music) >> Hi we're at the Palo Alto studio of SiliconANGLE Media and theCUBE. My name is George Gilbert, we have a special guest with us this week, Veeru Ramaswamy who is VP IBM Watson IoT platform and he's here to fill us in on the incredible amount of innovation and growth that's going on in that sector of the world and we're going to talk more broadly about IoT and digital twins as a broad new construct that we're seeing in how to build enterprise systems. So Veeru, good to have you. Why don't you introduce yourself and tell us a little bit about your background. >> Thanks George, thanks for having me. I've been in the technology space for a long time and if you look at what's happening in the IoT, in the digital space, it's pretty interesting the amount of growth, the amount of productivity and efficiency the companies are trying to achieve. It is just phenomenal and I think we're now turning off the hype cycle and getting into real actions in a lot of businesses. Prior to joining IBM, I was junior offiicer and senior VP of data science with Cable Vision where I led the data strategy for the entire company and prior to that I was the GE of one of the first two guys who actually built the Cyamon digital center. GE digital center, it's a center of excellence. Looking at different kinds of IoT related projects and products along with leading some of the UX and the analytics and the club ration or the social integration. So that's the background. >> So just to set context 'cause this is as we were talking before, there was another era when Steve Jobs was talking about the next work station and he talked about objectory imitation and then everything was sprinkled with fairy dust about objects. So help us distinguish between IoT and digital twins which GE was brilliant in marketing 'cause that concept everyone could grasp. Help us understand where they fit. >> The idea of digital twin is, how do you abstract the actual physical entity out there in the world, and create an object model out of it. So it's very similar in that sense, what happened in the 90s for Steve Jobs and if you look at that object abstraction, is what is now happening in the digital twin space from the IoT angle. The way we look at IoT is we look at every center which is out there which can actually produce a metric on every device which produces a metric we consider as a sense so it could be as simple as the pressure, temperature, humidity sensors or it could be as complicated as cardio sensors and your healthcare and so on and so forth. The concept of bringing these sensors into the to the digital world, the data from that physical world to the digital world is what is making it even more abstract from a programming perspective. >> Help us understand, so it sounds like we're going to have these fire hoses of data. How do we organize that into something that someone who's going to work on that data, someone is going to program to it. How do they make sense out of it the way a normal person looks at a physical object? >> That's a great question. We're looking at sensors as a device that we can measure out of and that we call it a device twin. Taking the data that's coming from the device, we call that as a device twin and then your physical asset, the physical thing itself, which could be elevators, jet engines anything, physical asset that we have what we call the asset twin and there's hierarchical model that we believe that will have to be existing for the digital twin to be actually constructed from an IoT perspective. The asset twins will basically encompass some of the device twins and then we actually take that and represent the digital twin on a physical world of that particular asset. >> So that would be sort of like as we were talking about earlier like an elevator might be the asset but the devices within it might be the bricks and the pulleys and the panels for operating it. >> Veeru: Exactly. >> And it's then the hierarchy of these or in manufacturing terms, the building materials that becomes a critical part of the twin. What are some other components of this digital twin? >> When we talk about digital twin, we don't just take the blueprint as schematics. We also think about the system, the process, the operation that goes along with that physical asset and when we capture that and be able to model that, in the digital world, then that gives you the ability to do a lot of things where you don't have to do it in the physical world. For instance, you don't have to train your people but on the physical world, if it is periodical systems and so on and so forth, you could actually train them in the digital world and then be able to allow them to operate on the physical world whenever it's needed. Or if you want to increase your productivity or efficiency doing predictive models and so forth, you can test all the models in your digital world and then you actually deploy it in your physical world. >> That's great for context setting. How would you think of, this digital twins is more than just a representation of the structure, but it's also got the behavior in there. So in a sense it's a sensor and an actuator in that you could program the real world. What would that look like? What things can you do with that sort of approach? >> So when you actually have the data coming this humongous amount of terabyte data that comes from the sensors, once you model it and you get the insights out of that, based on the insight, you can take an actionable outcome that could be turning off an actuator or turning on an actuator and simple thngs like in the elevator case, open the door, shut the door, move the elevator up, move the elevator down etc. etc All of these things can be done from a digital world. That's where it makes a humongous difference. >> Okay, so it's a structured way of interacting with the highly structured world around us. >> Veeru: That's right. >> Okay, so it's not the narrow definition that many of us have been used to like an airplane engine or the autonomous driving capability of a car. It's more general than that. >> Yeah, it is more general than that. >> Now let's talk about having sort of set context with the definition so everyone knows we're talking about a broader sense that's going on. What are some of the business impacts in terms of operational efficiency, maybe just the first-order impact. But what about the ability to change products into more customizable services that have SLAs or entirely new business models including engineered order instead of make to stock. Tell us something about that hierarchy of value. >> That's a great question. You're talking about things like operations optimization and predicament and all of that which you can actually do from the digital world it's all on digital twin. You also can look into various kinds of business models now instead of a product, you can actually have a service out of the product and then be able to have different business models like powered by the hour, pay per use and kinds of things. So these kinds of models, business models can be tried out. Think about what's happening in the world of Air BnB and Uber, nobody owns any asset but still be able to make revenue by pay per use or power by the hour. I think that's an interesting model. I don't think it's being tested out so much in the physical asset world but I think that could be interesting model that you could actually try. >> One thing that I picked up at the Genius of Things event in Munich in February was that we really have to rethink about software markets in the sense that IBM's customers become in the way your channel, sometimes because they sell to their customers. Almost like a supply chain master or something similar and also pricing changes from potentially we've already migrated or are migrating from perpetual licenses to service softwares or service but now we could do unit pricing or SLA-based pricing, in which case you as a vendor have to start getting very smart about, you owe your customers the risk in meeting an SLA so it's almost more like insurance, actuarial modeling. >> Correct so the way we want think about is, how can we make our customers more, what do you call, monetizable. Their products to be monetizable with their customers and then in that case, when we enter into a service level agreement with our customers, there's always that risk of what we deliver to make their products and services more successful? There's always a risk component which we will have to work with the customers to make sure that combined model of what our customers are going to deliver is going to be more beneficial, more contributing to both bottom line and top line. >> That implies that your modeling, someone's modeling and risk from you the supplier to your customer as vendor to their customer. >> Right. >> That sounds tricky. >> I'm pretty sure we have a lot of financial risk modeling entered into our SLAs when we actually go to our customers. >> So that's a new business model for IBM, for IBM's sort of supply chain master type customers if that's the right word. As this capability, this technology pervades more industries, customers become software vendors or if not software vendors, services vendors for software enhanced products or service enhanced products. >> Exactly, exactly. >> Another thing, I'd listened to a briefing by IBM Global Services where they thought, ultimately, this might end up where there's far more industries are engineered to order instead of make to stock. How would this enable that? >> I think the way we want think about it is that most of the IoT based services will actually start by co-designing and co-developing with your customers. And that's where you're going to start. That's how you're going to start. You're not going to say, here's my 100 data centers and you bring your billion devices and connect and it's going to happen. We are going to start that way and then our customers are going to say, hey by the way, I have these used cases that we want to start doing, so that's why platform becomes so imortant. Once you have the platform, now you can scale, into a scale, individual silos as a vertical use case for them. We provide the platform and the use cases start driving on top of the platform. So the scale becomes much easier for the customers. >> So this sounds like the traditional application. The traditional way an application vendor might turn into a platform vendor which is a difficult transition in itself but you take a few use cases and then generalize into a platform. >> We call that a zone application services. The zone application service is basically, is drawing on perfectly cold platform service which actually provides you the abilities. So for instance like an asset management. An asset management can be done in an oil and gas rig, you can look at asset management in power tub vine, you can can look at asset management in a jet engine. You can do asset management across any different vertical but that is a common horizontal application so most of the time you get 80% of your asset management API's if you will. Then you can be able to scale across multiple different vertical applications and solutions. >> Hold that thought 'cause we're going to come back to joint development and leveraging expertise from vendor and customer and sharing that. Let's talk just at a high level one of the things that I keep hearing is that in Europe industry 4.0 is sort of the hot topic and in the states, it's more digital twins. Help parse that out for us. >> So the way we believe how digital twin should be viewed is a component view. What we mean the component view is that we have your knowledge graph representation of the real assets in the digital world and then you bring in your IoT sensors and connections to the models then you have your functional, logical, physical models that you want to bring into your knowledge graph and then you also want to be able to give the ability of search visualize allies. Kind of an intelligent experience for the end consumer and then you want to bring your similation models when you do the actual similation models in digital to bring it in there and then your enterprise asset management, your ERP systems, all of that and then when you connect, when you're able to build a knowledge graph, that's when the digital twin really connects with your enterprise systems. Sort of bring the OT and the IT together. >> So this is sort of to try and summarize 'cause there are a lot of moving parts in there. You've got you've got the product hierarchy which, in product Kaiser call it building materials, sort of the explosion of parts in an assembly, sub-assembly and then that provides like a structure, a data model then the machine learning models in the different types of models that they could be represent behavior and then when you put a knowledge graph across that structure and behavior, is that what makes it simulation ready? >> Yes, so you're talking about entities and connecting these entities with the actual relationship between these entities. That's the graph that holds the relation between nodes and your links. >> And then integrating the enterprise systems that maybe the lower level operation systems. That's how you effect business processes. >> Correct. >> For efficiency or optimization, automation. >> Yes, take a look at what you can do with like a shop floor optimization. You have all the building materials, you need to know from your existing ERP systems and then you will actually have the actual real parts that's coming to your shop floors to manage them and now base supposing, depending on whether you want to repair, you want to replace, you want an overall, you want to modify whatever that is, you want to look at your existing building materials and see, okay do I first have it do we need more? Do we need to order more? So your auditing system naturally gets integrated into that and then you have to integrate the data that's coming from these models and the availability of the existing assets with you. You can integrate it and say how fast can you actually start moving these out of your shop, into the. >> Okay that's where you translate essentially what's more like intelligent about an object or a rich object into sort of operational implications. >> Veeru: Yes. >> Okay operational process. Let's talk about customer engagement so far. There's intense interest in this. I remember in the Munich event, they were like they had to shut off attendance because they couldn't find a big enough venue. >> Veeru: That's true. >> So what are the characteristics of some of the most successful engagements or the ones that are promising. Maybe it's a little early to say successful. >> So, I think the way you can definitely see success from customer engagement are two fold. One is show what's possible. Show what's possible with after all desire to connect, collection of data, all of that so that one part of it. The second part is understand the customer. The customer has certain requirements in their existing processes and operations. Understand that and then deliver based on what solutions they are expecting, what applications they want to build. How you bring them together is what is, so we're thinking about. That Munich center you talked about. We are actually bringing in chip manufacturers, sensor manufacturers, device manufacturers. We are binging in network providers. We are bringing in SIs, system integrators all of them into the fold and show what is possible and then your partners enable you to get to market faster. That's how we see the engagement with customer should happen in a much more foster manner and show them what's possible. >> It sounds like in the chip industry Moore's law for many years it wasn't deterministic that you we would do double things every 18 months or two years, it was actually an incredibly complex ecosystem web where everyone's sort of product release cycles were synchronized so as to enable that. And it sounds like you're synchronizing the ecosystem to keep up. >> Exactly The saxel of a particular organization IoT efforts is going to depend on how do you build this ecosystem and how do you establish that ecosystem to get to market faster. That's going to be extremely key for all your integration efforts with your customer. >> Let's start narrowly with you. IBM what are the key skills that you feel you need to own starting from sort of the base rocket scientists you know who not only work on machine learning models but they come up with new algorithms on top of say tons of flow work or something like that. And all the way up to the guys who are going to work in conjunction with the customer to apply that science to a particular industry. How does that hold together? >> So it all starts on the platform. On the platform side we have all the developers, the engineers who build these platform all the video connection and all of that to make the connections. So you need the highest software development engineers to build these on the platform and then you also need the solution builders so who is in front of the customer understanding what kind of solutions you want to build. Solutions could be anything. It could be predictive maintenance, it could be as simple as management, it could be remote monitoring and diagnostics. It could be any of these solutions that you want to build and then the solution builders and the platform builders work together to make sure that it's the holistic approach for the customer at the final deployment. >> And how much is the solution builder typically in the early stages IBM or is there some expertise that the customer has to contribute almost like agile development, but not two programmers but like 500 and 500 from different companies. >> 500 is a bit too much. (laughs) I would say this is the concept of co-designing and co-development. We definitely want the ultimate, the developer, the engineers form, the subject exports from our customers and we also need our analytics experts and software developers to come and sit together and understand what's the use case. How do we actually bring in those optimized solution for the customer. >> What level of expertise or what type of expertise are the developers who are contributing to this effort in terms of do they have to, if you're working with manufacturing let's say auto manufacturing. Do they have to have automotive software development expertise or are they more generically analytics and the automotive customer brings in the specific industry expertise. >> It depends. In some cases we have RGB for instance. We have dedicated servers, that particular vertical service provider. We understand some of this industry knowledge. In some cases we don't, in some cases it actually comes from the customer. But it has to be an aggregation of the subject matter experts with our platform developers and solution developers sitting together, finding what's the solution. Literally going through, think about how we actually bring in the UX. What does a typical day of a persona look like? We always by the way believe it's an augmented allegiance which means the human and the machine work together rather than a complete. It gives you the answer for everything you ask for. >> It's a debate that keeps coming up Doug Anglebad sort of had his own answer like 50 years ago which was he sort of set the path for modern computing by saying we're not going to replace people, we're going to augment them and this is just a continuation of that. >> It's a continuation of that. >> Like UX design sounds like someone on the IBM side might be talking to the domain expert and the customer to say how does this workflow work. >> Exactly. So have this design thinking, design sessions with our customers and then based on that we take that knowledge, take it back, we build our mark ups, we build our wire frames, visual designs and the analytics and software that goes behind it and then we provide on top of platform. So most of the platform work, the standard what do you call table state connections, collection of data. All of that as they are already existing then it's one level above as to what the particular solution a customer wants. That's when we actually. >> In terms of getting the customer organization aligned to make this project successful, what are some of the different configurations? Who needs to be a sponsor? Where does budget typically come from? How long are the pilots? That sort of stuff so to set expectations. >> We believe in all the agile thinking, agile development and we believe in all of that. It's almost given now. So depending on where the customer comes from so the customer could actually directly come and sign up to our platform on the existing cloud infrastructure and then they will say, okay we want to build applications then there are some customers really big customers, large enterprises who want to say, give me the platform, we have our solution folks. We will want to work on board with you but we also want somebody who understands building solutions. We integrate with our solution developers and then we build on top of that. They build on top of that actually. So you have that model as well and then you have a GBS which actually does this, has been doing this for years, decades. >> George: Almost like from the silicon. >> All the way up to the application level. >> When the customer is not outsourcing completely, The custom app that they need to build in other words when when they need to go to GBS Global Business Services, whereas if they want a semi-packaged app, can they go to the industry solutions group? >> Yes. >> I assume it's the IoT, Industry Solutions Group. >> Solutions group, yes. >> They then take a it's almost maybe a framework or an existing application that needs customization. >> Exactly so we have IoT-4. IoT for manufacturing, IoT for retail, IoT for insurance IoT for you name it. We have all these industry solutions so there would be some amount of template which is already existing in some fashion so when GBS gets a request to say here is customer X coming and asking for a particular solution. They would come back to IoT solutions group to say, they already have some template solutions from where we can start from rather than building it from scratch. You speed to market again is much faster and then based on that, if it's something that is to be customizable, both of them work together with the customer and then make that happen, and they leverage our platform underneath to do all the connection collection data analytics and so on and so forth that goes along with that. >> Tell me this from everything we hear. There's a huge talent shortage. Tell me in which roles is there the greatest shortage and then how do different members of the ecosystem platform vendors, solution vendors sort of a supply-chain master customers and their customers. How do they attract and retain and train? >> It's a fantastic question. One of the difficulties both in the valley and everywhere across is that three is a skill gap. You want advanced data scientists you want advances machinery experts, you want advanced AI specialists to actually come in. Luckily for us, we have about 1000 data scientists and AI specialists distributed across the globe. >> When you say 1000 data scientists and AI specialists, help us understand which layer are they-- >> It could be all the way from like a BI person all the way to people who can build advanced AI models. >> On top of an engine or a framework. >> We have our Watson APIs from which we build then we have our data signs experience which actually has some of the models then built on top of what's in the data platform so we take that as well. There are many different ways by which we can actually bring the AM model missionary models to build. >> Where do you find those people? Not just the sort of band strengths that's been with IBM for years but to grow that skill space and then where are they also attracted to? >> It's a great question. The valley definitely has a lot of talent, then we also go outside. We have multiple centers of excellence in Israel, in India, in China. So we have multiple centers of excellence we gather from them. It's difficult to get all the talent just from US or just from one country so it's naturally that talent has to be much more improvement and enhanced all the wat fom fresh graduates from colleges to more experienced folks in the in the actual profession. >> What about when you say enhancing the pool talent you have. Could it also include productivity improvements, qualitative productivity improvements in the tools that makes machine learning more accessible at any level? The old story of rising obstruction layers where deep learning might help design statistical models by doing future engineering and optimizing the search for the best model, that sort of stuff. >> Tools are very, very hopeful. There are so many. We have from our tools to python tools to psychic and all of that which can help the data scientist. The key part is the knowledge of the data scientist so data science, you need the algorithm, the statistical background, then you need your applications software development background and then you also need the domestics for engineering background. You have to bring all of them together. >> We don't have too many Michaelangelos who are these all around geniuses. There's the issue of, how do you to get them to work more effectively together and then assuming even each of those are in short supply, how do you make them more productive? >> So making them more productive is by giving them the right tools and resources to work with. I think that's the best way to do it, and in some cases in my organization, we just say, okay we know that a particular person is skilled is up skilled in certain technologies and certain skill sets and then give them all the tools and resources for them to go on build. There's a constant education training process that goes through that we in fact, we have our entire Watson ED platform that can be learned on Kosera today. >> George: Interesting. >> So people can go and learn how to build a platform from a Kosera. >> When we start talking with clients and with vendors, things we hear is that and we were kind of I think early that calling foul but in the open source infrastructure big data infrastructure this notion of mix-and-match and roll your own pipeline sounded so alluring, but in the end it was only the big Internet companies and maybe some big banks and telcos that had the people to operate that stuff and probably even fewer who could build stuff on it. Do we do we need to up level or simplify some of those roles because mainstream companies can't have enough or won't will have enough data scientists or other roles needed to make that whole team work >> I think it will be a combination of both one is we need to up school our existing students with the stem background, that's one thing and the other aspect is, how do you up scale your existing folks in your companies with the latest tools and how can you automate more things so that people who may not be schooled will still be able to use the tool to deliver other things but they don't have to go to a rigorous curriculum to actually be able to deal with it. >> So what does that look like? Give us an example. >> Think of tools like today. There are a lot of BI folks who can actually build. BI is usually your trends and graphs and charts that comes out of the data which are simple things. So they understand the distribution and so on and so forth but they may not know what is the random model. If you look at tools today, that actually gives you to build them, once you give the data to that model, it actually gives you the outputs so they don't really have to go dig deep I have to understand the decision tree model and so on and so forth. They have the data, they can give the data, tools like that. There are so many different tools which would actually give you the outputs and then they can actually start building app, the analytics application on top of that rather than being worried about how do I write 1000 line code or 2000 line code to actually build that model itself. >> The inbuilt machine learning models in and intend, integrated to like pentaho or what's another example. I'm trying to think, I lost my, I having a senior moment. These happen too often now. >> We do have it in our own data science tools. We already have those models supported. You can actually go and call those in your web portal and be able to call the data and then call the model and then you'll get all that. >> George: Splank has something like that. >> Splank does, yes. >> I don't know how functional it is but it seems to be oriented towards like someone who built a dashboard can sort of wire up a model, it gives you an example of what type of predictions or what type of data you need. >> True, in the Splank case, I think it is more of BI tool actually supporting a level of data science moral support on the back. I do not know, maybe I have to look at this but in our case we have a complete data science experience where you actually start from the minute the data gets ingested, you can actually start the storage, the transformation, the analytics and all of that can be done in less than 10 lines of coding. You can just actually do the whole thing. You just call those functions then it will the right there in front of you. So in twin you can do that. That I think is much more powerful and there are tools, there are many many tools today. >> So you're saying that data science experience is an enter in pipeline and therefore can integrate what were boundaries between separate products. >> The boundary is becoming narrower and narrower in some sense. You can go all the way from data ingestion to the analytics in just few clicks or few lines of course. That's what's happening today. Integrated experience if you will. >> That's different from the specialized skills where you might have a tri-factor, prexada or something similar as for the wrangling and then something else for sort of the the visualizations like Altracks or Tavlo and then into modeling. >> A year or so ago, most of data scientists try to spend a lot of time doing data wrangling because some of the models, they can actually call very directly but the wrangling is actually where they spend their time. How do you get the data crawl the data, cleanse the data, etc. That is all now part of our data platform. It is already integrated into the platform so you don't have to go through some of these things. >> Where are you finding the first success for that tool suite? >> Today it is almost integrated with, for instance, I had a case where we exchange the data we integrate that into what's in the Watson data platform and the Watson APIs is a layer above us in the platform where we actually use the analytics tools, more advanced AI tools but the simple machinery models and so on and so forth is already integrated into as part of the Watson data platform. It is going to become an integrated experience through and through. >> To connect data science experience into eWatson IoT platform and maybe a little higher at this quasi-solution layer. >> Correct, exactly. >> Okay, interesting. >> We are doing that today and given the fact that we have so much happening on the edge side of things which means mission critical systems today are expecting stream analysts to get to get insights right there and then be able to provide the outcomes at the edge rather than pushing all the data up to your cloud and then bringing it back down. >> Let's talk about edge versus cloud. Obviously, we can't for latency and band width reasons we can't forward all the data to the cloud, but there's different use cases. We were talking to Matasa Harry at Sparks Summit and one of the use cases he talked about was video. You can't send obviously all the video back and you typically on an edge device wouldn't have heavy-duty machine learning, but for video camera, you might want to learn what is anomalous or behavior call out for that camera. Help us understand some of the different use cases and how much data do you bring back and how frequently do retrain the models? >> In the case of video, it's so true that you want to do a lot of any object ignition and so on and so forth in the video itself. We have tools today, we have cameras outside where if a van goes it detect the particular object in the video live. Realtime streaming analytics so we can do that today. What I'm seeing today in the market is, in the transaction between the edge and the cloud. We believe edge is an extension of the cloud, closer to the asset or device and we believe that models are going to get pushed from the cloud, closer to the edge because the compute capacity and storage and the networking capacity are all improving. We are pushing more and more computing to their devices. >> When you talk about pushing more of the processing. you're talking more about predicts and inferencing then the training. >> Correct. >> Okay. >> I don't think I see so much of the training needs to be done at the edge. >> George: You don't see it. >> No, not yet at least. We see the training happening in the cloud and then once a train, the model has been trained, then you come to a steady, steady model and then that is the model you want to push. When you say model, it could be a bunch of coefficients. That could be pushed onto the edge and then when a new data comes in, you evaluate, make decisions on that, create insights and push it back as actions to the asset and then that data can be pushed back into the cloud once a day or once in a week, whatever that is. Whatever the capacity of the device you have and we believe that edge can go across multiple scales. We believe it could be as small with 128 MB it could be one or two which I see sitting in your local data center on the premise. >> I've had to hear examples of 32 megs in elevators. >> Exactly. >> There might be more like a sort of bandwidth and latency oriented platform at the edge and then throughput and an volume in the cloud for training. And then there's the issue of do you have a model at the edge that corresponds to that instance of a physical asset and then do you have an ensemble meaning, the model that maps to that instance, plus a master canonical model. Does that work for? >> In some cases, I think it'll be I think they have master canonical model and other subsidiary models based on what the asset, it could be a fleet so you in the fleet of assets which you have, you can have, does one asset in the fleet behave similar to another asset in the fleet then you could build similarity models in that. But then there will also be a model to look at now that I have to manage this fleet of assets which will be a different model compared to action similarity model, in terms of operations, in terms of optimization if I want to make certain operations of that asset work more efficiently, that model could be completely different with when compared to when you look at similarity of one model or one asset with another. >> That's interesting and then that model might fit into the information technology systems, the enterprise systems. Let's talk, I want to go get a little lower level now about the issue of intellectual property, joint development and sharing and ownership. IBM it's a nuanced subject. So we get different sort of answers, definitive answers from different execs, but at this high level, IBM says unlike Google and Facebook we will not take your customer data and make use of it but there's more to it than that. It's not as black-and-white. Help explain that for so us. >> The way you want to think is I would definitely paired back what our chairman always says customers' data is customers' data, customer insights is customer insights so they way we look at it is if you look at a black box engine, that could be your analytics engine, whatever it is. The data is your inputs and the insights are our outputs so the insights and outputs belong to them. we don't take their data and marry it with somebody else's data and so forth but we use the data to train the models and the model which is an abstract version of what that engine should be and then more we train the more better the model becomes. And then we can then use across many different customers and as we improve the models, we might go back to the same customers and hey we have an improved model you want to deploy this version rather than the previous version of the model we have. We can go to customer Y and say, here is a model which we believe it can take more of your data and fine tune that model again and then give it back to them. It is true that we don't actually take their data and share the data or the insights from one customer X to another customer Y but the models that make it better. How do you make that model more intelligent is what out job is and that's what we do. >> If we go with precise terminology, it sounds like when we talk about the black box having learned from the customer data and the insights also belonging to the customer. Let's say one of the examples we've heard was architecture engineering consulting for large capital projects has a model that's coming obviously across that vertical but also large capital projects like oil and gas exploration, something like that. There, the model sounds like it's going to get richer with each engagement. And let's pin down so what in the model is sort of not exposed to the next customer and what part of the model that has gotten richer does the next customer get the balance of? >> When we actually build a model, when we pass the data, in some cases, customer X data, the model is built out of customer X data may not sometimes work with the customer Y's data so in which case you actually build it from scratch again. Sometimes it doesn't. In some case it does help because of the similarity of the data in some instance because if the data from company X in oil gas is similar to company Y in oil gas, sometimes the data could be similar so in which case when you train that model, it becomes more efficient and the efficiency goes back to both customers. we will do that but there are places where it would really not work. What we are trying to do is. We are in fact trying to build some kind of knowledge bundles where we can actually what used to be a long process to train the model can ow shortened using that knowledge bundle of what we have actually gained. >> George: Tell me more about how it works. >> In retail for instance, when we actually provide analytics, from any kind of IoT sense, whatever sense of data this comes in we train the model, we get analytics used for ads, pushing coupons, whatever it is. That knowledge, what you have gained off that retail, it could be models of models, it could be metamodels, whatever you built. That can actually serve many different customers but the first customer who is trying to engage with us, you don't have any data to the model. It's almost starting from ground zero and so that would actually take a longer time when you are starting with a new industry and you don't have the data, it'll take you a longer time to understand what is that saturation point or optimization point where you think the model cannot go any further. In some cases, once you do that, you can take that saturated model or near saturated model and improve it based on more data that actually comes from different other segments. >> When you have a model that has gotten better with engagements and we've talked about the black box which produces the insights after taking in the customer data. Inside that black box there's like at the highest level we might call it the digital twin with the broad definition that we started with, then there's a data model which a data model which I guess could also be incorporated into the knowledge graft for the structure and then would it be fair to call the operational model the behavior? >> Yes, how does the system perform or behave with respect the data and the asset itself. >> And then underpinning that, the different models that correspond to the behaviors of different parts of this overall asset. So if we were to be really precise about this black box, what can move from one customer to the next and what what won't? >> The overall model, supposing I'm using a random data retrieval model, that remains but actual the coefficients are the feature rector, or whatever I use, that could be totally different for customers, depending on what kind of data they actually provide us. In data science or in analytics you have a whole platora of all the way from simple classification algorithms to very advanced predictive modeling algorithms. If you take the whole class when you start with a customer, you don't know which model is really going to work for a specific user case because the customer might come and can say, you might get some idea but you will not know exactly this is the model that will work. How you test it with one customer, that model could remain the same kind of use case for some of other customer, but that actual the coefficients the degree of the digital in some cases it might be two level decision trees, in others case it might be a six level decision tree. >> It is not like you take the model and the features and then just let different customers tweak the coefficients for the features. >> If you can do that, that will be great but I don't know whether you can really do it the data is going to change. The data is definitely going to change at some point of time but in certain cases it might be directly correlated where it can help, in certain cases it might not help. >> What I'm taking away is this is fundamentally different from traditional enterprise applications where you could standardize business processes and the transactional data that they were producing. Here it's going to be much more bespoke because I guess the processes, the analytic processes are not standardized. >> Correct, every business processes is unique for a business. >> The accentures of the world we're trying to tell people that when SAP shipped packaged processes, which were pretty much good enough, but that convince them to spend 10 times as much as the license fee on customization. But is there a qualitative difference between the processes here and the processes in the old ERP era? I think it's kind of different in the ERP era and the processes, we are more talking about just data management. Here we're talking about data science which means in the data management world, you're just moving data or transforming data and things like that, that's what you're doing. You're taking the data. transforming to some other form and then you're doing basic SQL queries to get some response, blah blah blah. That is a standard process that is not much of intelligence attached to it but now you are trying to see from the data what kind of intelligence can you derive by modeling the characteristics of the data. That becomes a much tougher problem so it now becomes one level higher of intelligence that you need to capture from the data itself that you want to serve a particular outcome from the insights you get from is model. >> This sounds like the differences are based on one different business objectives and perhaps data that's not as uniform that you would in enterprise applications, you would standardize the data here, if it's not standardized. >> I think because of the varied the disparity of the businesses and the kinds of verticals and things like that you're looking at, to get complete unified business model, is going to be extremely difficult. >> Last question, back-office systems the highest level they got to were maybe the CFO 'cause you had a sign off on a lot of the budget for the license and a much much bigger budget for the SI but he was getting something that was like close you quarter in three days or something instead of two weeks. It was a control function. Who do you sell to now for these different systems and what's the message, how much more strategic how do you sell the business impact differently? >> The platforms we directly interact with the CIO and CTOs or the head of engineering. And the actual solutions or the insights, we usually sell it to the COOs or the operational folks. So because the COO is responsible for showing you productivity, efficiency, how much of savings can you do on the bottom line top line. So the insights would actually go through the COOs or in some sense go through their CTOs to COOs but the actual platform itself will go to the enterprise IT folks in that order. >> This sounds like it's a platform and a solution sell which requires, is that different from the sales motions of other IBM technologies or is this a new approach? >> IBM is transforming on its way. The days where we believe that all the strategies and predictives that we are aligned towards, that actually needs to be the key goal because that's where the world is going. There are folks who, like Jeff Boaz talks about in the olden days you need 70 people to sell or 70% of the people to sell a 30% product. Today it's a 70% product and you need 30% to actually sell the product. The model is completely changing the way we interact with customers. So I think that's what's going to drive. We are transforming that in that area. We are becoming more conscious about all the strategy operations that we want to deliver to the market we want to be able to enable our customers with a much broader value proposition. >> With the industry solutions group and the Global Business Services teams work on these solutions. They've already been selling, line of business CXO type solutions. So is this more of the same, it's just better or is this really higher level than IBM's ever gotten in terms of strategic value? >> This is possibly in decades I would say a high level of value which come from a strategic perspective. >> Okay, on that note Veeru, we'll call it a day. This is great discussion and we look forward to writing it up and clipping all the videos and showering the internet with highlights. >> Thank you George. Appreciate it. >> Hopefully I will get you back soon. >> I was a pleasure, absolutely. >> With that, this George Gilbert. We're in our Palo Alto studio for wiki bond and theCUBE and we've been talking to Veeru Ramaswamy who's VP of Watson IoT platform and we look forward to coming back with Veeru sometime soon. (upbeat music)