>> Live from San Francisco, it's theCUBE. Covering IBM Think 2019. Brought to you by IBM. >> Welcome back to Moscone North, this is IBM Think 2019. You're watching theCUBE, I'm Stu Miniman, and we're going to dig into a segment talking about the cognitive enterprise. And helping me through that, I have one returning guest and one new guest to theCUBE, so furthest away from me, the returning guest is Jesus Mantas, who is the managing partner strategy for the digital platforms and innovation in the IBM Global business services. Jesus, welcome back. >> Thank you >> A little bit of a mouthful on the title. And Mani Dasgupta, CMO of the same group, the IBM Global business services. Thanks so much both for joining us. Alright, so cognitive enterprise. We're going to play a little game here first. Buzzword Bingo here, you know, can we talk about, what cognitive is, where you can't say AI, ML, platform, or enterprise in there. So do we start with the CMO first? >> Sure, I can go. Cognitive enterprise, those are two bing bing right there. What's your core competitive advantage, is what I would say. As a company, do you know why you exist? And once you get to that, how do you then take it to your clients, in a way that would help you grow, and sustain growth in the future. That truly is the future of a smart business, what we call the cognitive enterprise. >> So, Jesus, data is something we talk about a lot, at all the shows, we hear all the tropes about it's the new oil, the rocket fuel that are going to drive companies. You've got strategy and innovation in your title, I'd love you to build off as to where this cognitive enterprise fits in to those big trends of AI that we were talking about. Jinny was just on the keynote stage, talking about Watson, talking about all those pieces, so where does that fit with some of these megawaves that we're talking about. >> I think it's the way that we define this new, smarter organizations that use data to the fullest extent. And I think the way that we define it is, one is this reuse of data, your own data, the external data, and the way you aggregate it, the way that you apply AI or other things to use that. But the technology itself is a means to an end, it's not the end, so these organizations change the way the work flows, and they also train people to make sure that they understand how to operate in a world where they have more information and they can make better decisions with that data that they could before. All of that is what we are labeling. It's more than digital, it's more than AI. It is this concept of a cognitive enterprise. It's a smarter way to do what a company does. >> Okay, I'd love if you could give us a little bit of a compare, contrast. You know, the wave of big data was, there's massive amounts of data, we're going to allow the business practitioner, to be able to leverage that data. Was a great goal, unfortunately when we did research, at least half the time it wasn't really panning out there. Doesn't mean we didn't learn good things, and there weren't lots of great tools and business value generated out there. So, give us, you know, what's the same and what's different, as to this new wave. >> This is how do you make that data work for you, really. It is about, when you talk of data, you think of data that's out there, but 80% of the data today, is owned by you. And by you, I mean a business, right, you own your customers' data, you know your customer better than anybody else. So what do you really do with it? And we are at an inflection point right now, where these technologies that you just talked about, be it blockchain, be it internet of things, be it AI. You can truly bring the power of these technologies, to start making sense of that data that you own, and use it to create, what we call, your competitive advantage, your business platform. So, think about it, I can break it down. Would you just be a retailer of clothes? Or, would you be a fashion expert? And which one would have long-term success for you? Or if you think of a completely different industry, would you be an insurance provider, you sell insurance products, or would you be a risk management expert? That decision to be who you want to be, is really at the heart of the cognitive enterprise, and what we are proposing to the clients here. >> Alright, help frame for us your group, where that fits in. IBM sells hardware, software, has a huge services organization. What are the deliverables and the services and products involved in your group? >> Sure, we are the services organization of IBM, and one of the core reasons why we exist is to help our clients solve their toughest business problems. And so, if you think about it, you think about it as different puzzle pieces, but they don't quite always fit together. We exist to sharpen the edges, to sometimes round the edges, make it customized, make it right for you, so that at the end of the day, you're able to deliver results for your customers and be closer to them than ever before. >> The balance we look at in this multi-cloud world, it'd be nice if you have a little bit more standardization, but of course we know when we talk with businesses, every company is different and is challenging. So, where are the architectural engagements? What are the design criteria? Where is some of the hard work your group gets involved in? >> Yeah, I think we've been spending a lot of work and a lot of time on understanding how to get clients, most clients have done a lot of experimentation. But they rarely figured out how to get that experimentation into real production, at scale, with impact. So that's where we've spent a lot of the time. Fundamentally it has to do with, not only understanding Agile as a method, but being able to combine that with taking that journey all the way through to production, actually integrating with compliance requirements that, if you're in a regulated industry, you have to do, and do that in a way that doesn't become a digital island. I think what we have learned is, when companies see this big divide between, that's the legacy world and that's the new world you can never put those two together. So we came up with this concept of IBM Garage, which is the way in which our team, the services side, can actually bring it all together, and it gets massively enhanced and improved, with technology like containers, like Kubernetes, because now you can actually open up architectures, without reinventing them, and connect them with new technology, and do that synchronously. So you can basically be modernizing your legacy, you can be creating new innovation, in the form of new platforms, but you can do it at the same time, and as you do that through cycles, you also change the skillsets that you have in your company, because if you don't change that skillset, you're always going to have a problem scaling. That's what we do, that's what we help the clients do. >> Yeah, skillsets are so critical. Something we've been hearing over and over is, that whole digital transformation, this isn't some 18 to 24 month going to deploy some software, bring in a lot of consultants, they go and do it, hopefully it works and then they walk away. We're talking about much faster time frames, usually agile methodology, talk about skillset-changing. How do we help customers move fast and accelerate, because that's really the faster, faster, faster, it's just one of those driving things we hear. >> I was talking to one of the clients this morning, and what she said is, it's so helpful to have a framework, just to know where to start, and also to know, sometimes it's there in their mind, but they want to see it in front of them, how to break a problem down into smaller components, so that you can get to value faster, so we have actually a seven-step process, of the cognitive enterprise. So we start with, what is your core platform? In fact, Jesus coined this term, he calls it the digital Darwinism. Do you want to talk about the digital Darwinism, Jesus? >> Yeah, I think it reflects very well this urgency. In the analog world when most businesses are based on how clients choose you based on proximity, based on convenience, based on brand, based on trust, based on price. Even if you're not great at it, you have enough friction in an analog world, that the clients will keep coming. All of us and more of our things that we do every day, are in our phone, and they are digitally accessible, all of that friction disappears, and what happens then is, the people that are very good at something becomes, everybody goes to them, and the people that are not the best. I call it, they either thrive or they die very quickly. So in the digital world, being really good at something is a lot more important than in the analog world. You can survive being average in the analog world. Once you get to the digital world, it's transparent. Everyone will know, you're the best, you're not the best, and nobody would pick you if you're not the best, so it's really important to reconfigure yourself, and understand the trust and your brand, understand how digitally you translate what you are, and then make sure that your clients will keep choosing you in a digital world as much as they were choosing you in an analog world. >> I tell you, that resonates really well with me. The old line you used to hear is, if you want to get something done, give it to someone who's really busy, because they will usually figure out a way to do it. I spent a handful of years in my career doing operations, and what I did when I was in operations, when I talked to people in IT, is tell me next quarter and next year, do you think you're going to have more or less work more data to deal with, more thing thing, and of course the answer is, we all know that pace of change is the only thing that's constant in this industry. So, if I don't figure out how I automate, change, or get rid of the stuff that I'm not good at, we're just going to continue to be buried. Are there commonalities that you see, as success factors or how do you help measure, what are some key KPIs that customers walk out of, when they go through an engagement like this? >> Yeah, just carrying on from where Jesus left off, the second step is very close to what you were just saying. It's about the data and how you're using that data. So some of the key success factors would be, what is the output of it, and it's not in the proof of concept phase anymore. It is real-time, it is big, people are doing it at a grand scale. I think, Jesus, maybe we take it through the seven steps, and then the key success criteria comes right at you, right after that. So after you do the workflow, after you do the data for internal competitive advantage, we go to the next step, which is all about workflows. You want to talk a bit about that? >> Yeah, I think one of the advantages that artificial intelligence brings to companies is, the fact that you can now, I mean as a human, there is only so much data that you can ingest. There is a limit, and most businesses try to optimize what that is and how you make decisions. But, artificial intelligence becomes this aid that will read and summarize things for you. So now you can take into account, into workflows, massive amounts of information, to optimize, or even not having to do things you had to do before, at a scale that, as a human you cannot do. This idea of inserting AI into workflows is the real idea. I think we talk a lot about AI as a technology, but that's just a means to an end. The end is a workflow that is embedded with blockchain, with AI, with IOT, and then people that are trained to engage with those workflows, so you actually change the output. And I think that's the big idea, that step of, it is workflow that is embedded with AI, it's not just about the technology, it's the combination of the main industry, and the technology that actually creates that >> And where does it sit, right? Where does it sit? Your tech choices, the architecture choices are also important. And we joked about this, like if you really like Netflix, and you're watching something and something is coming up after three seconds, how does it know what you really like? But it does, but think about this. This wouldn't be possible on a 1950s television set. So you've got to think about what's your tech platform of choice, how do you upgrade that, and what's the architecture look like? >> I want to give you both the final word. Lots of users here at the show. What are you most excited about? Give us an insight on some of the conversations you've been having already. >> Amazing conversations so far. The really aha-moment was, people really like to share within their peer set, so this morning I was at the business exchange, and people were having conversations, but just to bounce it off someone, who is facing the same issues that you do, across different industries, was a really aha-moment, and we have the IBM Garage actually right behind us on the other side of Moscone. We set it up so that clients can come in, and unpack their problems, and we helped them think it through, used design thinking, help them think it through. We are hoping in the next couple of days, we get lots of brilliant ideas, come from the sessions like that, and really putting the customer at the core of what you want to do. >> It's a recurring theme of all the client conversations, this idea of, they all want the speed and agility of a startup at the strength and scale of an enterprise. That's what they're asking us, as the services organization of IBM, to do is, help us not just experiment, that was good before, not good enough now. Help us do that with agility, with new technologies, but we want it to mean something at scale, globally implement it, create an impact. And I think again, the way in which hybrid multi-cloud can play into that, the way in which IBM Garage can combine the legacy world with the new world and moving people into new platforms is a really exciting method and approach that is resonating a lot with clients. >> Really appreciate you both sharing updates and absolutely as you painted a picture, just as in 1950 we didn't have the tools to run Netflix, now in 2019, we have the tools for customers to be able to help build the cognitive enterprise and not only test but get into real-world deployment at a speed that was really unheralded before today. Thanks so much for joining. We'll be back with more coverage here from IBM Think 2019. I'm Stu Miniman, and thanks for watching theCUBE. (upbeat techno music)

>> Hi, I'm Peter Burris and welcome to another Cube Conversation. We're broadcasting from our beautiful Palo Alto studios and this time we've got a couple of great guests from SWIM. And one of them is Chris Sachs, who's the founder and lead architect. And the other one is Simon Crosby, who's the CTO. Welcome to the Cube, guys. >> Great to be here. >> Thank you. >> So let's start. Tell us a little bit about yourselves. Well, Chris, let's start with you. >> So my name's Chris Sachs. I'm a co-founder of SWIM, and my background is embedded in distributed systems and bringing those two worlds together. And I've spent the last three years building software from first principles for its computing. >> But embedded, very importantly, that's small devices, highly distributed with a high degree of autonomy-- >> Chris: Yes. >> And how they will interact with each other. >> Right. You need both the small footprint and you need to scale down and out, is one thing that we say. People get scaling out in the cloud and scaling up and out. For the edge, you need to scale down and out. There's similarities to how clouds scale and some very different principles. >> We're going to get into that. So Simon, CTO. >> Sure, my name is Simon Crosby. I came this way courtesy of being an academic, a long time ago, and then doing startups. This is startup number five for me. I was CTO and founder at XenSource. We built the Xen hypervisor. Also at Bromium, where we did micro-virtualization, and I'm privileged to be along for the ride with Chris. >> Excellent. So guys, the SWIM promise is edge AI. I like that, down and out. Tell us a little bit about it, Chris. >> So one of the key observations that we've made over the past half decade is there's a whole lot of compute cycles being showered on planet Earth. ARM is shipping five billion chips a quarter. And there's a tremendous amount of computing, generating a tremendous about of data and it's trapped in the edge. There are physics problems, economic problems with back on it all to the cloud, but there's tremendous, you're capturing the functionality of the world on these chips. >> We like to say that if software's going to eat the world, it's going to eat it at the edge. Is that kind of what you mean? >> Yes. >> That's right. >> And you start running into, when you decide you want to eat the edge, you run into problems very quickly with a traditional way of doing things. So one example is where does your database live if you live on the edge? Which telephone pole are you going to put at your database node in? >> Simon: How big does this need to be? >> There are a number of decisions that are very difficult to make. So SWIM's promises, now, you have some advantages as well in that billions of clock cycles go by on these chips in between that work packets. And if you can figure out how to squeeze your software into these slop cycles between network packets, you can actually do, you actually have a super computer, a global super computer on which you can do machine learning. You can try and predict the future of how physical systems are going to play out-- >> Hence, your background in distributive systems because the goal is to try to ensure that the network packets are as productive as possible. >> Chris: Exactly. >> Here's another way of looking at the problem. If you count top down, it's reasonable to think of things in the future, all sorts of things, which have got computer and maybe some networking in them, presenting to you a digital twin of themselves. Where's the thing come from? >> Now, describe digital twin. We've done a lot of research on this, but it's still is relatively novel concept. GE talked about it. IVM talks about it. When we say digital twin, we're talking about the simulacrum, the digital representation of an actual thing, right? >> Of an actual thing. There are a couple of ways you can get there. One way is if you give me the detailed design of a thing and exactly how it works, I can give you all of that detail and maybe (mumbles) can help use that to find a problem. The other way is to try and construct it automatically. And that's exactly what SWIM does. >> So it takes the thing and builds models around it that are-- >> Well, so what do things do? Things give us data. So the problem, then, becomes how can I build a digital twin just given the data? Just given the observations of what this thing is seeing, what its sensors are bleating about, what things near it are saying. How can I build a digital twin, which will analyze itself, tell you what its current state is and predict the future, just from the data? >> All right, so the bottom line is that you've got, you're providing a facility to help model real world things that tend to operate in an analog way and turning them into digital representations that then can be a full member, in fact, perhaps even a superior member in a highly distributed system of how things work together. >> Yes. >> Got that right. >> A few key points is digital twins are in the loop with the real world. And they are in the loop with their neighbors, and you start with digital twins that reflect the physical world, but they don't end there. You can have physical twins. You can have digital twins of concepts as well and other higher order notions. And from the masses of data that you get from physical devices, you can actually infer the existence of twins where you don't even have a sensor. >> It's making it real. So you could have a digital. If you happen to be tracking all of the buses in downtown San Francisco, you can infer PM10 pollution as a virtual sensor on a bus. And then you can pretty quickly work out something which is a value to somebody who's trying to sell insurance, for example. And that's not a real sensor on every bus, but you can then compose these things, given that you have these other digital twins which are manifesting themselves. >> So folks talk about the butterfly effect and things like chaos theory, which is a butterfly affecting the weather in China. But what we're talking about is locality really matters. It matters in real systems. And it matters in computers. And if you have something that's generating data, more than likely, that thing is going to want its own data because of locality. But also, the things near it are also going to want to be able to infer or understand the behavior of that thing, because it's going to have a consequential impact on them. >> Correct, so I'll give you two examples of that. We've been using aircraft manufacturing facility. The virtual twin here is some widget which has an RFID tag in it. We don't know what that is. We just know there's a tag and we can place it in three ways because it gets seen by multiple sensors we triangulate. And then, as these tags come together makes an aircraft sub-assembly. That meaning of an aircraft sub-assembly is kind of another thing but the nearness, it's the locality that gets you there. So I can say all these tags came together. Let's track that as a superior object. There's a containment notion there. And suddenly, we're tracking will assemblies instead of widgets. >> And this is where the AI comes in, because now, the AI is the basis for recognizing the patterns of these tags and being able to infer from the characteristics of these patterns that it's a sub-assembly. Have I got that right? >> Right. There's a unique opportunity that is opened up in AI when you're watching things unfold live in that you have this great unifying force to learn off of, which is causality. It's the what does everything have in common? It's that data that you've lost through time. And what do you do when you have billions of clock cycles to spare between network packets? Well, you can make a guess about what your particular digital twin might see next. So you can take a guess based on what you're state is, what the sensors around you are saying, and just make a guess. Then you can see what actually happens. You see what actually happens. You measure the error between what you predicted would happen and what actually happened. And you can correct for that. And you could do that just add in an item. Just trillions of times over the course of a year, you make small corrections for how you think. Your particular system will evolve, whether it's a street of traffic light trying to predict when it's going to change, when cars are going to show up, when pedestrians are going to push buttons, or it's a machine, a conveyor belt or a motor in a factory, trying to predict when it might break down, you can learn from these precise systems that very specific models of how they're going to evolve and you can play reality forward. You learn a simulation. And you can play your own, predict your own future. >> And there's a very cool thing that shows up from that. So instead of say, let's take a city and all of its lights. Instead of trying to gather all that data from the city and go then solve a big model, which is the cloud approach to doing this, big data in cloud approach, essentially each one of these digital twins is solving its own problem of how do I predict my own future? So instead of solving one big model, you'll have 200 different insections all predicting their own future, which is totally cool, because it distributes well in this fabric of space CPU cycles and can be very efficient to computers. >> And a consequence of that is, again, you can get these very rich patterns that then these things can learn more from and each acting autonomously in individual as groups. >> Even more than that. There's an even cooler thing. Imagine I set you down by an insection and I said, "Write me a program for how this thing is going to behave." First of all, you wouldn't know how to do it. Second, there aren't enough humans on planet Earth to do this. What we're saying is that we can construct this program from the data, from this thing as it evolves through time. We'll construct the program, and it will be merely a learned model. And then you could ask it how it's going to behave in the future. You could say, "Well, what if I do this? "What if a pedestrian pushes this button? "What will the response be?" So effectively, you're learning a program. You're learning the digital twin just from the data. >> All right, so how does SWIM do this? So we know now we know what it is. And we know that it's using, it's stealing cycles from CPUs that are mainly set up to gather, to sense things, and package data up and send it off somewhere else, but how does it actually work? What does the designer, the developer, the operator do with SWIM that they couldn't do before? >> So SWIM is a tiny, vertically integrated software stack that does all, has all the capabilities you'd find in an open source cloud platform. You have persistence. You have message dispatch. You have peer-to-peer routing. You have analytics and a number of other capabilities. But SWIM hides that and it takes care of it, abstracts over what you need to do to, rather than thinking about where do you place compute, it's when you think "What is my model? "What is my digital twin? "And what am I related to?" And SWIM dynamically maps these logical models to physical hardware at run time and dynamically moves these encapsulated agents around as needed based on the loads and the demand in the network. And in the same way that-- >> In the events? >> Yes, in the events. And in the same way that you, if you're using Microsoft Word, you don't really what CPU core is that running on? Who knows and who cares? It's a solved problem. We look from the ground up and the edge is just one big massively, multi-core computer. And there's similar principles to apply in terms of how you maintain consistency, how you efficiently route data that you can abstract over and eliminate as a problem that you have to be concerned about as a developer or a user who just wants to ingest some data and get insights on how-- >> So I'm going to make sure I got that. So if I look at the edge, which might have 200, might have 10 thousand sensors associated with it, we can imagine, for example, level of complexity like what happens on a drilling platform on an oil field. Probably is 10 thousand sensors on that thing, all of these different things. Each of those sensors are doing something. And they're sending, dispatching information. But what you're doing is you're basically saying we can now look at those sensors that can do their own thing, but we can also look at them as a cluster of processing capability. We'll put a little bit of software on there that will provide a degree of coordinated control so that models can-- >> So two things. >> Build up out of that? >> So first off, SWIM itself builds a distributed fabric on whatever computer's available. And you can smear SWIM between an embedded environment and a VM in the cloud. We just don't care. >> But the point is anything you pointed at becomes part of this cluster. >> Yes, but the second level of this is when you start to discover the entities in the real world. And you begin to discover the entities from that data. So I'll get all this gray stuff. I don't really know what it means, but I'm going to find these entities and what they're related to and then, for each entity, instantiate when these digital twins as an active, essentially the things that microservice. It's a stateful microservice, which is then just going to consume its own real world data and do its thing and then present what it knows by an API or graphical UI components. >> So I'm an operator. I install. What do I do to install? >> You start a process on whatever devices you have available. So SWIM is completely self-contained and has no external dependencies. So we can run as the (mumbles) analytics box or even without an operating system. >> So I basically target swim at the device and it installs? >> Chris: Correct. >> Once it's installed, how am I then acquiring it through software development? >> Ultimately, in this edge world, there is, you've asked the key question, which is how the hell do I get ahold of this stuff and how does it run? And I don't think the world knows the answer to all these questions. So, for example, in the traffic views case, the answer is this. We've published an API. It happens to be an (mumbles), but who cares? Where people like Uber and Lyft or UPS can show up and say what's this traffic light can do in the future. And they just hit that. What they're doing is going for the insides of digital twins in real time as a service. That's kind of an interesting thing to do, right? But you might find this embedded in a widget, because it's small enough to be able to do that. You might find that a customer installs them in a couple of boxes and it just runs. We don't really care. It will be there, and it's trivial to run. >> So you're going to be moving it into people who are building these embedded fixtures? >> Sure. >> Yes. >> Sure, but the key point here is that I know you, particularly in the Cube, you're hearing all these wonderful stories about DevOps and (mumbles) and all this guff up in the cloud, fine. That's where you want those people to be. >> Don't call it guff (laughs). >> But at the edge, no (mumbles). There aren't enough humans to run this stuff so it's got to be completely automatic. It's got to just wake up, run, find all the compute, run ceaselessly, distribute load, be resilient, be secure, all these things that just got to happen. >> So SWIM becomes a service that is shipped with an embedded system. >> Possibly, or there is a potential outcome where it's delivered as software which runs on a box close to some widget. >> Or willed out as a software update with some existing manufacturers. >> In this particular case of traffic, we should be on 60 thousand insections by the end of this year. The traffic infrastructure vendor, the vendor that delivers the traffic management system, just rolls up an upgrade and suddenly, a whole bunch of new insections appear in a cloud API. And an UBER or a Lyft or whatever, it's just hitting that thing and finding out what they are. >> Great, and so but as developers, am I going into a SWIM environment and doing anything? This is just the way that the data's being captured. >> Simon: So we take data. >> That the pattern's being identified. >> Take data, turn into digital twins with intelligent things to say and expose that as APIs or as UI components. >> So that now the developers can go off and use whatever tools they want and just invoke the service through the API. >> Bingo, so that's right. So developers, if they're doing something, just hit digital twins. >> All right, so we've talked a couple. We've talked a little bit about the traffic example and mentioned being in an oil field. What are some of the other big impacts? As this thing gets rolling, what is it going to, what kind of problems is this going to allow us to solve? Not just one, but there's definitely going to be a network effect here, right? >> Sure, so the interesting thing about the edge world is that it's massively diverse. So even one cookie factory's different from another cookie factory in that they might have the same equipment, but they're in different places on planet Earth, may have different operators in everything else. So the data will be different in everything else. So the challenge in general with the edge environment has been that we've been very professional services centric people bring in (mumbles) people and try and solve a local problem and it's very expensive. SWIM has this opportunity to basically just show up, consume this gray data, and tell you real stuff without enormous amounts of semantic knowledge a priority. So we hae this ability to conquer this diversity problem, which is characteristic of the edge, and also come up with highly realistic and highly accurate models for this particular thing. I want to be very clear. The widget in chocolate factory A is exactly the same as the widget in chocolate factory B, but the models will be 100% different and totally (mumbles) at either place, because if the pipes go bang at 6 a.m. here, it's in the model. >> And SWIM has the opportunity to reach the 99.9% of data that currently is generated and immediately forgotten, because it's too expensive to store. It's too expensive to transport. And it's too expensive to build applications to use. >> We should talk about cost, because that's a great one. So if you wanted to solve the problem of predicting what the lights in Palo Alto are going to do for the next five minutes, that's heading towards 10 thousand dollars a month in AWS. SWIM will solve that problem for a tiny fraction, like less than a 100th of that, just on stranded CPU cycles lying around at the edge. And you have say, bandwidth and a whole bunch of things. >> Yeah, and that's a very important point, because the edge is, it's been around for a while. Operational technology. People have been doing this for a while, but not in a way that's naturally, easily programmable. You're bringing the technology that makes it easy to self-discover simply by utilizing whatever cycles and whatever data's there and putting a persistence, making it really simple for that to be accessed through an API, and ultimately, it creates a lot of options on what you can do with your devices in the future. Makes existing assets more valuable, because you have options in what you can do with it. >> If you look at the traffic example, it's the AWS scenario is $50 per month per insection. No one's going to do that. But if it's like a buck, I'm in. And you can do things, 'cause then it's worthwhile for UBER to hit that API. >> All right, so we got to wrap this up. So one way of thinking about it is, I'm thinking. And there's so many metaphors that one could invoke, but this is kind of like the teeth that are going to eat the real world. The software teeth that's going to eat the real world at the edge. >> So if I can leave with one thought, which is SWIM loosely stems from software and motion. And the idea is that teeth edge. You need to move the software to where the data is. You can't move the data to where the software is. The data is huge. It's immobile. And the quantities of data are staggering. You essentially have a world of spam bots out there. It's intractable. But if you move the software to where the data is, then the world's yours. >> One thing to note is that software's still data. It just happens to be extremely well organized data. So the choice is do you move all the not-particularly-well-organized data somewhere where it can operate or would you move the really well organized and compact? And information theory says move the most structured thing you possibly can and that's the application of the software itself. All right. Chris Sachs, founder and lead architect of SWIM. Simon Crosby, CTO of SWIM. Thank you very much for being on the Cube. Great conversation. >> Thanks for having us. >> Good luck. >> Enjoy. >> And once again, I'm Peter Burris. And thank you for participating in another Cube conversation with SWIM. Talk to you again soon.

>> Live from Boston, Massachusetts, it's the Cube, covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back to the Cube's live coverage of the IBM CDO Summit here in Boston Massachusetts. I'm your host, Rebecca Knight, along with my co-host Dave Vellante. We are joined by Caitlin Halferty. She is the Chief of Staff IBM Data Office, and also John Backhouse, the chief information officer and senior VP at CareEnroll. Thank you both so much for coming on the Cube. >> Great to be here. >> Thank you, good to see you. >> So before the cameras were rolling, John, we were talking about how you have this very unique vantage point and perspective on the role of the CIO and CDO. Can you tell our viewers a bit about your background? >> Sure. I started off in the military. I was in the army for 12 years as a military intelligence officer. I then moved to the NHS, which is a national health service in England and where I wrote the Clinical Care Pathways for myocardial infraction and diabetes pre-hospital. I then moved to the USA and became Chief Data Officer for Envision Healthcare, one of the largest hybrid providers of insurance and clinical care. And then I became a CIO for a multi-state Medicare program. >> So you've been around, so to speak (laughter) But the last two roles, CIO and CDO, so how would you describe them? I mean obviously two different places, but is it adversarial? Is it cooperative? What is the relationship like? >> I think its, the last couple of years, CDO role has matured, and it's become a direct competition between a CIO and a CDO. As my experiences I've been fighting for the same budget. I've been fighting for the same bind, I've been fighting for the same executives to sponsor my programs and projects. I think now as the maturity of the CDO has stepped out, especially in health, the CDO has a lot more power between the conduit between the business and IT. If the CDO sits in IT he's doomed for failure because it's a direct competition of a CIO role. But I also think the CIO role has changed in the way that the innovation has stepped up. The CIO role used to be "Your career is over, CIO." (laughter) Now it's the innovational aspect of infrastructure, cloud cognitive analysts, cognitive solutions and analytics so that the way the data is monetized and sold and reused, in the way that the business makes decisions. So I see a big difference. >> How much of that, sort-of authority, if I can use that term, of the chief data officer inside of a regulated company versus you're in the office of the chief data officer in an unregulated company, compare and contrast. >> Well, the chief data officer's got all the new regulatory compliancies coming down the GDC, the security, safe harbor, and as the technology moves in to cloud it becomes even harder. As you get PCI, HIPPA and etc. So, everything you do is scrutinized to a point where you have to justify, why, what, and when. And then you have to have the custodian of who is responsible. So then no longer can you say, "I got the data for this reason." You have to justify why you have that information about anything. And I think that regulatory component is getting stronger and stronger. >> And you know, we've often talked about the rise of the CDO role and how it's changed over the last few years. Primarily it started in response to regulatory and compliance concerns within financial services industries as we know banking and insurance, healthcare. And we're seeing more and more retail consumer products. Other industries saying look, "We don't really have enterprise-wide management of data across the organization" Investing in that leadership role to drive that transformation. So I'm seeing that spread beyond the regulated industries. >> Well Caitlin, in the keynote you really kicked off this conference by reminding us of why we're all here and that is to bring chief data officers together, to share those practices, to share what they've learned in their own organizations. Hearing John talk about the fight for resources, the fight to justify its existence. What do you think, how would you tease out the best practices around that? >> The way we've approached it, you know, I've mentioned this cognitive enterprise blueprint that we highlighted and released this morning. And this has been an 18-month project for us. And we've done it in close partnership with folks like John, giving a lot of great insight and feedback. And essentially the way we see it is there's these four pillars. So it's the technology piece and getting the technology right. It's the business process, both CDO-owned processes as well as enterprise-wide. And then the new piece we've added is around data, understanding the data part of it is so important. And so we've delivered the blueprint and then taking it to the next level to figure out what are the top used cases. How do we prioritize to your question, where prioritized-used cases. >> So, come back to the overlap between the CIO and CDO. I remember when I first met Ender Paul, we had him on the Cube and he's seared into my brain he's five points that the CDO has to do, the imperative. And three were sequential two were in parallel. One was figure out how to monetize, how you're data can contribute to the monetization of your company. Second was data trust and sources, third was access to that data and those were sequential >> Right. Processes and then he said "Line of business and skill sets were the other two that you kind of do in parallel, >> Absolutely. forge relationships with a lot of businesses and re-skill. Okay, so with that as the Ender Paul framework for what a CDO's job was... I loved it, I wrote a blog about it, (laughter) I clipped it. >> That's very good >> But the CIO hits a lot of those areas, certainly data access, of trust and security, the skill sets. Thinking about that framework, first of all do you buy it? I presume it's pretty valid, but where do you see the overlap and the collaboration? >> So I think that the framework works out and what IBM has produced is very tangible, it means you can take the pieces and you can action them. So, before you have to reflect on one: building the team, getting the right numbers in the team, getting the right skill sets in the team. That was always a challenge because you're building a team but you're not quite sure what the skill set is until you've started the plan and the math and you've started down that pathway, so with that blueprint it helps you to understand what you're trying to recruit for, is one aspect, and then two is the monetization or getting the data or making it fit for purpose, that's a real challenge and there's no magic wand for this, you know it depends on what the business problem is, the business process and understanding it. I'm very unique cause not only have I understand the data and the technology I actually give it the clinical care as well, so I've got the translations in the clinical speak into data, into business value. So, I can take information and translate it into value very quickly, and create a solution but it comes back to that you must have a designer and the designer must be an innovator, and an innovator must stay within the curve and the object is the business problems. That enables, that blueprint to be taken and run with, and hit the ground very quickly in an actionable manner. for me information in health is about insights, everybody's already doing the medical record, the electronic record, the debtor exchange. It's a little immature in health and a proper interoperability but it there and it's coming it's the actually use of and the visualization of population analysis. It used to be population health, as in we knew what we were doing after the fact, now we need to know what we are doing before the fact so we can target the outreach and to move the right people in the right place at the right time for the right care, is a bigger insight and that's what cognitive and the blueprint enables. >> So Caitlin, it feels like these two worlds are really coming together, you know, in the early days it was just really regulated businesses. >> Correct. >> Now with GDPR now everybody is a regulated business, >> Right. >> And given that EMR, and Meaningful Use and things like that are kind of rote now. >> Yeah. >> Regulated industries are really driving for that value holy grail. >> Yeah. >> So, I wonder if you could share your perspectives on those two worlds coming together. >> Yeah I do see them coming together, as well as the leadership. >> Right, yeah. >> Across the C-sweep, it's interesting we host these two in-person summits, one in the spring in San Francisco one here in Boston in the fall and we get about 120 or so CDOs that join us. We pull for, what are top topics and we always get ones around data monetization, talent, the one again that came up this year was changing nature of to the point on building those deep analytics partnerships within the organization, changing the relationships between CDOs and C-sweep peers. We do a virtual call with about 25 CDO's and we had John as our guest speaker, recently >> Yeah. And it was our best attended call, (laughter) it was solely focused on how CDOs and CIOs can partner together to drive business critical cross-enterprise initiatives, like GDPR in ways that they haven't in the past. >> Yeah. >> It was a reinforcement to me that building those relationships, that analytic partnership piece, is still top of mind to our CDO community. >> Yeah, and I think that the call itself was like sun because I invited the chief of their office and now he's the innovator and the chief information officer used to be the guy who kept the lights on, that's no longer the fact. The chief information officer is the innovator of the infrastructure, the design, the monetization, the value, the business and the chief in their office now has become the chief designer of information to make it fit for purpose, for presentation, for analytics, for the cognitive use of the business. Those roles now, when you bring them together, is extremely powerful and as the maturity comes of these chief there officer roles with the modern approach to chief information then you have a powerful, powerful dynamic. >> Well let's talk about the chief innovator, it reminds me of 1999. (laughter) >> If you want to be a CEO you've got to go the CEO's office and then Y2K on the whole thing blew up. (laughter) >> What's different now though, is the data >> Yeah. - [Caitlin] Absolutely. >> There certainly was a lot of data back then but not nearly like it is today and the technology underneath it, the whole cloud piece, but I wonder if you could talk about the innovation piece of that a little bit more >> Sure. and it's relationship to the data. >> So, I mean we've always been let's all go to the data warehouse, let's have a data lake, let's get the data scientist to fix the data lake. (laughter) >> Yeah. >> And then he's like " Whoa, well what did he do?" "Does it do anything? Show me." And you know now that physical massive environment of big service and big cages and big rooms with big overhead expenses is no longer necessary. I've just put 91 servers for an entire state's data and population in a cloud environment, multiple security levels with multiple methods of new innovational cloud management. And I've been able to standup 91 server in six and a half minutes. I couldn't even procure that... (laughter) - Right. >> Before >> I'd be months, and months >> Yeah, to put physical architecture together like that but now I can do it in six and a half minutes, I can create DR rapidly, I can do flip over active-active and I can really make the sure of it. Not only can I use the infrastructure I can enable people to get information at the point where it's needed now, far easier than I ever did before. >> So talking about how the technology has moved and evolved and changed so rapidly for the better but yet there is still a massive talent shortage of the people who, as you said - [John] Yeah >> Who can speak the language and take the data and immediately translate it into business value. What are you doing now about this talent shortage? What's your take on it and what are we doing to fix it? >> Yeah >> I would say, in one of the morning keynotes, Jim Cavanaugh our SVP for transportation operations got that question around how do you educate internally what it means to be a cognitive enterprise when there are so many questions about what does that really mean? And then how do you access skill against those new capabilities? He spoke about some of the internal hackathons that we did and ran sort of an internal shark tank-like to see how those top projects rise, align resources against it and build those skills and we've invested quite a lot internally as I know many of our clients have around what we call cognitive academy to ensure that we've one: figured out and defined what it means in this new...what type of new skills and then make sure that we're able to retrain and then keep and retain some of our new talents. So I think we're trying that multi-prong approach to retrain and retain as well. >> You guys use the term cognitive business we use the term digital business cause we can't use IBM's terms (laughter) But to us there the same thing >> Why not? >> Cause it's all about... (laughter) >> Cause were independent - [Caitlin] Dave's upset here >> But to us it's all about how you leverage data >> Yeah. >> And how you use data to >> Yeah. >> Maintain and to get and maintain costumers. So since we're playing CX bingo >> Yeah right. >> Chief digital officer, Bob Lord >> Right >> Bob Lord and Ender Paul Endario are two totally different people and there roles are quite different, but if it's all about the data and you buy that premise what is the chief digital officer do? they are largely driving revenue >> Absolutely >> That's understandable but it's part of your job too >> Right >> Or former job as a CDO and now as an innovation officer. Where do those roles fit? >> I think there's a clear demarcation line and especially when you get into EIM solutions as in Enterprise Information Management. And you start breaking those down and you've got to break them down into master data management and you start putting the domains together, the multi-master domains, and one of them is media, and media needs someone to own it, be the custodian, manage it, and present it to the business for consumption, the other's are pure data driven. >> Yeah. >> Master patient, master member, master costumer, master product, they all need data driven analytics to present information to the business. You can't just show them a sequel schemer and say "There you go." >> Yeah. (laughter) >> It doesn't work so there is different demarcations of specialist skills and the presentation and it got to be that hybrid between the business and IT. The business and the data, the business and the consumer and that is, I think the maturity of way this X-sweet is going these days >> Yeah. >> One thing we've seen internally to that point, I agree there's a clear demarcation there, is when we do partner with the digital office it can be to aid say digital sellers so we have a joint project going where we are responsible for the data piece of it >> Yeah. >> And then we are enabling our digital sellers, we're calling it cognitive sales advisor to pull dispersed pieces of costumer data that are currently housed in cylos across the organization, pull that into a digital, user friendly app, that can really enable those sellers, so I think there's some nice opportunities just as there are CDOs and CIOs to partner, for a data officer and a digital officer as well. >> One of our earlier guests was talking about some of the things that he's hearing in the break out sessions and he said "You know they could have been talking about the same stuff ten years ago, these intractable organizations that aren't quite there yet." What do you think we will be talking about next CDO summit? Do you think there will come a point where were not talking about is data important? Or does data have a role in the organization? When do you think that will happen? (laughter) >> Every time I say we're done with governance right? >> Yeah >> We're done and then governance >> Comes right back - Top topic (laughter) >> If you get the answer to that can I have the locker notes? (laughter) >> Sure >> Exactly, Exactly >> I think in the next ten years we're not going to ask anymore about what did we do, we're going to be told what we did. As in we're going to be looking forward, thing are going to be coming out and saying this is the projected for the next minute, second, hour, month, year and that's the big change. We are all looking back, what did we do? How did we do? What was the goals we tried to achieve? I don't think that's going to be what we ask next month, next year, next week. It's going to be you're going to tell me what I did and you're going to tell me what I'm doing. And that's going to change, and also the healthcare market, the way that health is prescriptive, they're not prescribed anymore. They way that we diagnose things against the prognosis, I think that the way we manage that information is going to change dramatically. I would say too, I've been working quite a bit with a client in Vegas, a casino, and their current issue or problem is they have all this data on what their guest do from the moment they check in, they get their hotel key, they know where spend, where they go to dinner, what type of trip they're on, is it business is is pleasure. Are the kids in town, different behaviors, spending patterns accordingly. >> Yeah. >> And the main concern they relate to us is I can't do anything about it until my guest has exited the property and then I'm sending them outreach emails trying to get them back, or trying to offer a coupon. >> Yeah. >> You know post - [John] Yeah, yeah. >> And they're gone. >> And what if I could do some real time analysis and deliver something of value to my guest while they are on site and we are starting to see some of that with Disney and some other companies. - [John] Yeah. >> But I think we will see the ability to take all this data that we already have and deliver it. >> In real time. -[John] Yeah. >> Influence behavior >> Right >> And spending patterns in real time that's what I'm excited about. >> Yeah and these machines will actually start making decisions, certain decisions for the brand. >> Yeah >> Right >> At the point where it can affect an outcome. >> Right, right, Which I think is hard >> It's starting >> Yeah >> No question, you certainly see it in fraud detection today, you mentioned Disney. >> The magic bands >> Right >> And the ability to track >> Yeah >> Where you are and that type of thing, yeah >> Great >> We're starting cyber security cause cyber security, an aspect of user log, server log, network, are looking for behavioral patterns and those behavioral patterns are telling us where the risks and the vulnerabilities are coming from. >> Thing that humans >> Yep >> Would not see that >> People don't see the patterns, yep. >> You're absolutely right, >> right >> They just wouldn't see the patterns of the risk. >> Excellent, well John, Caitlin, thanks so much for coming on the Cube it's always a pleasure to talk to you. >> Thank you - Great, thank you. >> I'm Rebecca Knight for Dave Vellante we'll have more just after this.

(upbeat music) >> Announcer: Live from Orlando, Florida it's theCUBE. Covering Grace Hopper Celebration of Women in Computing. Brought to you by SiliconANGLE Media. (upbeat music) >> Woman: (clears throat) Here today. >> Welcome back to theCUBE's coverage of the Grace Hopper Conference here in Orlando, Florida. I'm your host, Rebecca Knight. We are joined by Josie Gillan and Laurel McLay. They have just launched a new collaboration, Twinovate. Tell our viewers about Twinovate. You are identical twins, I first of all should say his. >> Yes, we are. So Twinovate one in, what it is is Laurel and I are actually mirror twins and I'm left brain, I'm right-handed. Laurel's right brain and she's left-handed. So what I am is, I'm in my previous background is engineering leadership. I've worked at companies like Salesforce, Atlassian, Cloudera. But Laurel and I saw an opportunity with our diverse viewpoints to start a collaboration together. So I'm the left brain twin. I'm logical, I'm problem-solving, and I love nothing more than to get that code compiled. >> And I'm the right brain twin, so I'm creative, language, any of that messy human emotional stuff. I'm a career coach back in New Zealand. And so I love nothing more that helping people with their identity, their uniqueness, and looking at some of the behavioral challenges which might be holding them back. So we looked at the two of us together and we thought, wow, we've got some great stuff and what are we truly passionate about? We're truly passionate about women, particularly in STEM, being able to contribute themselves fully in a way that works for them. To not only their own legacy, but the legacy of who they're collaborating with. >> Now you are here at Grace Hopper, you're running a workshop, and before the cameras were rolling, you were talking about an apology epidemic. Explain what you mean by that. >> Well, if you think about an epidemic, it's something that spreads, and often it spreads without people even realizing it, before it's too late. And so what we realized was that women, and particularly when you're using language like just, I'm sorry, it's only me. If someone gives us a compliment we say, oh, I bought this, this old thing, I got it on sale. And what we realized was the message of that was saying was I don't count, I'm invisible, please put yourself before me. And the challenge about this epidemic is a lot of people don't realize they're saying it. >> Yeah, and some great examples. This is really resonating with people. So I'm actually on a moms in tech Facebook group, and I asked for some stories. And one woman talked about softball practice. And she practices at the same field where men practice. And what she noticed is every time the women dropped a ball or missed a pitch they would say sorry, sorry, and she turned around and looked at the males and the males never, never did that. So why are we apologizing? >> And we have created this cool little sheet we call Apology Bingo that's available on our Facebook page, and it helps people to look at the many times that they might say these words. One of the words that I have realized I say all the time is actually. And even though actually may not sound apologetic in itself, it's absolutely. >> It's a qualifier, it's, right. >> It's qualifier, exactly. And so what we're talking about apologizing, over-explaining and qualifying. >> And that makes you appear a lot less confident, and really can have career-limiting impact. >> Well, I want to talk about the career-limiting impact, but I also just want to ask you about so it's one thing to understand and acknowledge and become aware that you are using this kind of language. How do you eradicate it from your vocabulary? >> So what we talk about in the workshop is little shifts and big calls. So the little shifts are those small things that you can do to catch yourself. And that's at the language level. So for example, there's a Gmail app called Just Not Sorry. >> It's a Chrome plugin. >> And so what you do is, you add that to your Gmail and it will show and underline some of the language in each email which is apologetic. But then I call it the big calls. And that's really two things. The first thing is do you want to start a revolution? Because let's face it, when you turn up previously apologetic and maybe not too troublesome, let's just say, and you start kicking out your unapologetic language, there are going to be potentially some people around there who don't take kindly to that. And they may call you angry or uppity. >> Or even worse (laughs). >> Or even worse, exactly. So I feel it's about people learning and doing some personal development work on themselves to get the courage to that. Not saying that everyone needs to start a revolution, but for those who feel inspired to do it. And for everyone I believe it is a symptom of the I'm not good enough self-worth and we have an interesting take on self-value, don't we, Josie? >> We do. Being an identical twin is very interesting because what we've found is I might get really quite snippy at Laurel and she said to me, well, why are you so snippy at me? And it was like, well, I see things in you that I don't like in myself. And so we have decided let's turn it around. I want to acknowledge in Laurel things I do like in myself and accept the things that, the bad with the good. >> Right, right and we could all learn from that. I mean, it's just a lesson in humanity. >> And one other point I want to make though, with the people might not appreciate this. We're not dropping manners here. Clearly we are not suggesting that you're no longer courteous. What we want to say is save sorry for when it really counts. >> Rebecca: For when you need to apologize. >> Right. >> Absolutely. >> So in terms of the career-limiting factors that we were talking about, what are sort of the unintended consequences of this apologetic behavior? >> Well, I can talk to that. In some of my roles in the past as an engineering leader, I've really focused on maybe more building up my team, collaboration, and sometimes my management may not agree with the way that I'm doing it, right? Now, rather than having a healthy dialogue about why I'm doing it this way and maybe coming to some kind of general agreement, I have in the past tended to say I must be wrong, he or she must be right. And the ironic thing is, with my experience, I meant to bring that in. I meant to bring my experience in. I've heard in reviews that you don't have enough of an opinion. So really I think that was certainly career-limiting for me and something I'm learning how to do much better. >> So at Twinovate you are empowering women in STEM, you are making sure that they feel included, making sure that they feel like they have a voice at the table, making sure that they are, as you said, not apologizing for being women in the workforce. Do you go in and do you work with individuals? Do you work with companies who say we need to help our workforce deal with these issues? >> Absolutely. So in this workshop we just had an hour and it was a packed audience, it was fantastic. So something that I'm really clear about is it's such a privilege being in front of a room, so we want to make sure that it's just not the talking heads, that people look at their own situation, and we give them examples, both professional and personal, because let's face it, that's a big part of it, isn't it? When people are apologetic in their own worlds. And so they all work together at the table to be able to come up and discuss, and we share that as a room. And the workshop capacity is something that we will deal with people one-on-one because that's when I've done this the whole. I think that one of the reasons I am good at uniqueness and identity is because I'm an identical twin. And so I can work with people and nail their specific challenge in a heartbeat. So for me it's about sharing that power of group but also giving the individual attention so people can walk away knowing the stuff that's particularly relevant to them. >> Okay, alright. So how, I mean I think one of the other questions I would have for you is that you're based in Silicon Valley, you're based in Auckland. Is the tech industry similar? How would you describe the different tech industries in your respective countries? >> Look, it's been so interesting, because I do quite a lot of work in New Zealand and Australia, and not just in technology, but also in engineering, which is the other part of STEM, of course. And it's more flipped the other way because I understand the challenges in new Zealand and Australia, I've been having wonderful conversations on the floor here in the last couple of days, and saying, is it true that when you turn up or someone turns up to your offices that they immediately assume you're the receptionist? And they just go, oh my goodness, absolutely. You know, is it true that you have sometimes direct reports who don't like what you say and they'll literally say it's because you're a woman? And they'll go, yes. So I feel that this is a global epidemic. >> It's a challenge, >> It's a challenge, yes. >> They're facing it everywhere. So what is next for Twinovate? Where do you go from here? I mean you're here at Grace Hopper, which is obviously a receptive audience, a vast audience for the message, but what's next for your collaboration? >> Well, as Josie said, we were really quite surprised about how strongly it resonated here today, and we've got some great feedback. We're both got children, but we're both lucky enough to have fathers of those children that are very, very supportive, and so, hey, we've got this great opportunity to see more of each other. I'm coming back in March, we're coming back next year for Grace Hopper, so I'll be coming to the states twice a year and Josie's coming down to New Zealand and Australia at least once a year. And we're just having very limited partnerships with people who want to work with us and we'll look at some public stuff too. >> And maybe a book in the works? >> So I've already written a book. >> Okay. >> But I wrote it about, I was being unapologetic at the time, and this is what I'm really passionate about. So by the time I come back in March, my unapologetic book, which is literally about unapologetic careers and lifestyles will be in our hot little hands. And Josie's contributing to that with a particular Twinovate chapter that we've been working on. >> Excellent, well, Josie, Laurel, thanks so much for joining us. It's been a pleasure having you on the show. And Josie, you're a Cube alum I should have said, too. >> There you go, yes, exactly. >> Great to see you again, Rebecca. >> Well, best of luck to you both. >> Thank you so much. >> Thank you. >> We will have more from the Grace Hopper Conference just after this. (upbeat music)

>> Announcer: Live from Las Vegas, it's theCUBE, covering VMworld 2017. Brought to you by VMware and its ecosystem partners. (electronic music) >> Chad: Thank you. >> Welcome back to VMworld 2017 here in Las Vegas. I'm Dave Vellante with my co-host Peter Burris. This is Day 2. Chad Sakac is here. He's the president of Dell EMC, long-time CUBE guest, CUBE alumn. Chad, we were talking about eight years >> Yeah, it's crazy, man, it's been great. >> Dave: And you were one of the first. >> It's been great. I was thinking about it. What I love is it's always a great events where there's great things happening. And you guys ask killer, right to the point questions, and I always try to give killer, right to the point answers. >> Dave: All right, well let's get into it. Your ascendancy, personally, kind of coincided with VMware's explosion. You are down and dirty with the customers. So, first of all, congratulations on-- >> Thanks, dude. >> Dave: that role and being the president at Dell EMC. Awesome. >> I have a passion for it, Dave. Passion is the key, right? >> Okay, so we're all talking about cloud, right? Cloud first is something that you hear from customers all the time. I want to be cloud first. What does that mean to you, to Dell EMC, VMware? >> I think the first thing is is that to all of us, cloud is much more about an operating model than a place. And people have started to internalize that it's about changing the way that they operate their business, both for some of their traditional apps, as well as how they build cloud-native apps. That's the first thing. Operating model, not place. The second thing is, I think I'm seeing the customers in the market get a little more, I don't how to say this without sounding pejorative, but a little more mature in their view that the answer is going to have to be a hybrid model based on data and data gravity, based on elasticity of workloads, based on governance factors, that lead to hybrid being the answer. And if you think about what we've seen just in the last two days, VMware on AWS, Google partnering with VMware and Pivotal around Pivotal Container Services. Those are all about hybridizing both traditional IT and how people build new cloud applications. So operating model, not place. Hybrid is the answer. And the third thing is that it's multi. I still occasionally encounter somebody that says, "It's all going to be one cloud." And I'm like, "Okay, just out of curiosity, "does your definition of cloud include SalesForce.com?" "Well, yeah." "Does it include Office 365?" "Well, yeah." "Does it include AWS?" "Well, yeah." "Does it include your own private cloud?" "Well, yeah." "So, what are you talking about?" >> Peter: Where's the one? (laughs) >> Where's the one, right? And I think that there's a... Things start to matter once they move out of hyperbole and into pragmatism. >> Well, and when you paid attention, let's say four or five years ago, when you listened to Andy Jassy speak, the notion of hybrid cloud, or hybrid IT, or private cloud was not in his lexicon. And then yesterday, we saw him up on stage with Pat Gelsinger. They were talking about hybrid and private cloud. >> Chad: Well, by the way, I don't want to throw the dart at Andy. I think if you talked to Vmware or Dell EMC four or five years ago, public cloud wasn't in our-- >> Dave: Absolutely. >> (laughs) in our vernacular either. >> Dave: Absolutely, so those worlds have come together with the customer reality that says, "Well there isn't just one cloud. "And I can't just bring my business, "reform my business for the cloud." So what do you make of the fact that we've evolved as an industry and as a vendor community? >> I think it's time to get on to the brass tacks of solving the problems for the customers, ma'am. And I see actually that happening in the industry more and more. People are solving problems that they can't solve in their private clouds using public clouds. They're figuring out that the best place to put a dollar is to rebuild their applications using cloud-native principles. But they're also realizing that sometimes that it's not even a legitimate choice or option. And they're trying to figure out also, at the same time, "How do I support some of those "more traditional application stacks "and make them more automated and cloud-like, "even if they're not going to be cloud-native maybe ever." >> Peter: Let me jump in here for a minute. >> Dave: So this gets to the promise that you've got to make. And please jump in. >> Yeah, because in many respects, what we're really saying, let me test this with you, Pat, is that, ultimately, it may be one cloud, but that one cloud is going to be defined by the business and not by a particular vendor. >> Chad: It's a higher-order function. >> Peter: That's right. So what we like to say is we like to say, "You're not going to take your business to the cloud. "You're going to bring the cloud to your business." And your business attributes and your business characteristics, where you operate, how you operate, how you use data, who your customers are, how are you going to reach them, all those different things, the physical realities, the legal realities, the IP realities, all that's going to shape the architectural choices that you make regarding cloud. And you have to have a strategy for that becoming consistent and coherent for your business. So a lot of piece parts, but it becomes your cloud. Does that make sense to you? >> It makes sense to me. It means that it's an answer that involves a little more sophistication and nuance for the customer, because they've got to think about what it means for them. And the answer is not the same for every single customer. However, there are common base elements in that formula. Number one, digital transformation always starts with applications that are written using cloud-native principles, often using data fabrics that are modern distributed data fabrics. That's one piece. There's a consistent piece that says, "I'm going to leverage public and private cloud models." And the definition of which workload goes to one or another, like you said, is very much driven by data gravity. Compute tends to co-locate with the data against which it's computing. Governance rules, which is not security. Public and private clouds are equivalent. In some cases, one or the other is more secure than the other. But those are common elements. There's one other common element that I've learned over the last four years of being on the journey myself with many of our customers, which is that the only way that the on-premises part of cloud stacks work is through radical simplification and deploying their on-premises infrastructure using design and automation principles that look a lot more like the public cloud than they look like their most traditional IT. >> No, you're absolutely right. And I think that's a crucial point, that ultimately the physics of all this. >> Chad: Mm-hmm. >> And I agree, cloud is not a We like to say the cloud is not a place, it's a time. >> Chad: Yeah. >> Because at the end of the day, all this is defined by the realities of your data. >> Chad: Yep. >> And if your data can't If you don't have time to move the data or it's too expensive to move the data, that's going to dictate where the process actually runs. And I like the way you've redefined, I'll say redefined data gravity. Most people think data gravity, "Oh, once you put data in place, "it's going to accrete more gravity." And you're saying, "No, that's not the way to think about it. "It's going to accrete more function." >> Chad: Right. >> 100% agreement. And I don't think a lot of people are talking about things that way. >> By the way, the linkage to physics, I don't know how many of the viewers basically were physics majors, but it's actually related to quantum physics and mechanics. Information inherently can't simultaneously be in two places at once. That's a law of physics, right? >> Data, okay, keep going, keep going. >> If any bit, any information, basically, is connected between two points at the speed of light, that's not a function of vendor technology until someone-- >> Well, let me we get into quantum entanglement, nano >> Yeah. >> But I think where you are, where you're absolutely correct is that ultimately, that there is a cost to moving data. >> Chad: Bingo. Bingo >> And we have to start When we think about digital transformation, our approach is the difference between a digital business and a business is a digital business treats data as an asset and builds strategic capabilities to treat data as an asset and apply data as an asset. And one of the beauties of what you were saying earlier with simplification, is for example, the idea that if I build around data, >> Chad: Yup. >> then I can use hyper-converged, I can use converged, I can use Flash, I can use VC, and I can use all these different things to treat my data differently. >> And do it as simple as possible. The thing that I think I'll give you an example from this morning. I was meeting with a customer that's in the finance and insurance vertical, right? And they're pretty advanced down there, use of both public and private clouds. They've got a software-defined data center. And they're trying to basically redefine how they're using mobile apps and customer intelligence. They provide a ton of services. They're a great, great customer and a partner. But again, to highlight that cloud is a place, or a time, to use your vernacular, as they build their mobile app, sets of assets are running in a public cloud, the data was born in the public cloud, the compute is running in the public cloud, it's built around a cloud-native app principle using PCF and Kubernetes. Great! When that person is using that application, there's a moment when they go in and do a transaction where literally it's hitting a mainframe running DB2 in their core data center. >> And there's nothing wrong with that. >> And there's nothing wrong with that. In fact, that pattern is universal, right? And so it highlights basically that you've got to be a little practical about how you do this stuff. >> Okay, so in the limited time we have remaining, give us the the quick why Dell EMC, Dell EMC, VMware, why you guys Maybe talk a little bit about the portfolio. >> So if you look at this week, the one thing that jumps out, at least to me, I'm probably a little close to it, Dave, but I hope it jumps out to the viewers, right, is while we maintain an open ecosystem, Dell EMC has its open ecosystem, VMware has its ecosystem, Pivotal has its ecosystem, we're becoming much more opinionated, right? And that matters because customers want clarity. So I'll give you clarity. Clarity that jumped out on stage and it came out of the mouth of Pat, not me, was the easiest way to deploy VSAN is on VxRail. The easiest way to deploy VMware Cloud Foundation is on VxRack SDDC. Period, full stop. Now, why are we saying that so emphatically? It's because if you don't have a good foundation for an SDS and SDC, or SDC, SDS, and SDN, so software-defined network and compute and storage, in the case of VMware Cloud Foundation and VxRack SDDC, then your whole underpinning is just way too complex, right? So there's a very clear opinionated point of view that says hyper-converged infrastructure that's being built by the combined team is the way forward for customers who have standardized on vSphere. >> Well, and you nailed it earlier. If you're going to bring the cloud model to on-prem, to the data, it's got to be simple. >> Chad: Mm-hmm. >> Peter: That's the cloud model. >> That is the cloud model, right? And and without it, you can't fulfill that promise. With it, you can. >> I'll give you a second example. For the last four years, we've been supporting our customers with the Enterprise Hybrid Cloud. We've learned more about what does it take to lifecycle, manage, and deploy vRealize Suite on top of HCI, where we own the lifecycle, right? At the same time, VMware has been learning about, "What does it take to take vRealize and run it "in the VMware cloud on AWS, not as software, "but as a service?" And that's all about simplification and lifecycle management. What we're doing between VMware and Dell EMC is taking that knowledge and saying, "HCI is the foundation, and on top of that, "here's how you build your IaaS "for your traditional applications, "and the foundation for what's coming next." And then the last part that we saw today loud and clear is a strongly opinionated point of view that says PCF, Pivotal Cloud Foundry, is the best structured PaaS in the market, and a full embrace of Kubernetes, Pivotal Kubernetes Services, Pivotal Container Services using Kubernetes, is going to be the best way to build container as a service. How do you deploy it best? On vRealize. How do you deploy it best? On top of VxRack SDDC. It's pretty clear. >> Covered all the bases, we could go all day with you, but we're out of time. >> Yeah. >> Chad, thanks very much for coming on theCUBE yet again. Really appreciate it. >> It's my pleasure, guys, thank you. >> All right, keep it right there, everybody. We'll be back with our next guest right after this short break. This is theCUBE. (electronic music)

(techno music) >> Announcer: Live, from San Francisco, it's theCUBE, covering AWS Summit 2017. Brought to you by Amazon Web Services. >> Hi, welcome back to theCUBE. We are live in San Francisco at the AWS Summit. We have a great day so far. I'm Lisa Martin, with Jeff Frick, and we're really excited to be joined next by ThingLogix, Carl Krupitzer from ThingLogix, welcome to theCUBE. >> Carl: Thank you. >> Tell us all about ThingLogix What do you guys do? And how do you work with AWS? >> Sure, so we're an IoT platform and solutions company. So we've actually helped customers design, develop, and deploy, and bring to market, IoT solutions and connected products. >> How long have you been, and tell us a little bit about your history. There's an Amazon tie in. >> Carl: There is. >> That kind of predates ThingLogix. >> Carl: Right. >> Give us a little bit of insight about that. >> So we were actually the services and solutions group with inside of a company called 2lemetry. And that was eventually purchased by Amazon and became the AWS IoT platform. So our DNA of our company goes back to the very beginnings of what is now the AWS IoT service. >> Excellent, and so you were founded in 2014? >> 2014, we spun out from 2lemetry. And we did so because we were working with a few big customers that really, we saw an opportunity to help companies really kind of figure out what to do with IoT and accelerate their adoption of IoT inside of the enterprise. >> So there's a consulting arm as well as a technology lead. >> Right, right. So we have our professional services, and our advisory services group that works with customers, really to get them through the idea phase, and then we offer a technology platform that is ThingLogix's foundry, that really is a platform that sits top of all the underlying AWS serverless compute resources. >> So IoT's a big space. GE's in it, everybody's in it. You're a little company. >> Carl: Yeah. >> So what's interesting is, both from an entrepreneurial point of view, as well as just, you know, punching above your weight, how does working kind of in the AWS eco system, both as for your own infrastructure, but also as for go to market and partnership, enable you guys to really do punch above your weight. >> You know, it's a big challenge when you start getting into a partner eco system, like AWS. The thing that sets us apart, really, is that we are very much a pure play, serverless, computing company. From the ground up, we built our own infrastructure that way, we built our own platform that way, and it allows us to be a lot more agile and creative with our customers. It allows us to move much faster and more cost effectively than a lot of other system integrators. >> Right, and you said before we turned on the cameras, that too, it also though, gives you these partnership opportunities with less pure plays. >> Carl: Correct. >> To insert you into potentially a bigger project for that piece that you guys can deliver better than anybody else. That's a pretty unique opportunity. >> Right, yeah. So us partnering with some of the bigger systems integrators is pretty standard practice for us because we can come in and we can work with the the business on really prototyping and innovating quickly. Get us, getting the rapid application development side of things done, and then transition that over to the more managed services oriented firms to take on board. >> Right. And can you imagine trying to do what you're doing without a big infrastructure provider, a big marketplace partner? >> No, it would be nearly impossible. Just to, IoT is fast-moving technology trend. It's been around for a while, in the M to M space. Typically, it's been controlled by the engineering side of house. What we're seeing now is that it's migrating more over to the product management and marketing folks. So they're expecting the same agility that you get with platforms like Salesforce, platforms like Workday. They want that same thing in their product development lifecycle. We've been able to help customers take projects from concept and prototype, through to actually in stores, in the market in about nine to nine weeks, nine to twelve weeks. >> Jeff: Wow. >> So I was just thinking, as you guys were chatting about what the consulting services are like. Give us an example of a typical customer, and you kind of just did, where they, are you talking to retailers that have IoT products to sell, you mentioned, kind of more of a bind center, maybe within products and marketing. So I was just wondering kind of, what is that typical customer like, and what sort of questions have they come to you with? Is it more of an idea that we need to get to market, or is it more of a, we have all of these devices at the edge that we sort of need to-- >> It's a combination, right? We deal a lot with consumer product companies that are trying to enable or connect an existing product or an existing line of products. And they're doing so, not for the engineering purposes, but more to get a better customer experience, and a more timely customer experience, right? Being able to connect with their customers in new and different ways. We're also seeing quite a bit of migration from legacy systems like Exeda or In-House Solutions to the AWS cloud. Really this idea of cloud first architecture, has taken root in the enterprise. And it's been happening over the last 10 years, and I think it's really starting to pay off because companies are looking for a reason not to go to the cloud, versus a reason to go to the cloud. And IoT with the AWS platform and serverless compute resources, really, it takes away all those reasons why you wouldn't. >> [Convention Intercom] Ladies and Gentlemen, don't forget to stop-- >> Lisa: Oops, we'll pause for a-- >> The big voice from above, right. >> Pause for an announcement. >> [Convention Intercom] Get a t-shirt. >> Get a t-shirt. >> Oh, a t-shirt. >> Get a t-shirt. >> I don't want to miss out on that. So just wanted to ask you, give us some ideas of how customers are using the services. I was looking at your webpage, I'll open it back up, and as a pool owner, I though, oh, pool energy. I think I need that. Give us an idea of a company like that. Was this an idea that has really been enabled by what you provide? >> Sure, we've seen companies really try to evolve some of the products, some of their commodity products into more of a smart service, right? When AWS IoT launched, we led with a company called Sealed Air. And they were actually investigating, they make commodity chemicals and cleaning equipment, and things like that. And they were looking for new and different ways to really add value to their products. So we came out, helped them prototype and come out with a connected hand soap dispenser, which seemed kind of silly at the time, but when you start looking at the secondary uses of the data, it allowed them to really start to hone in on hand sanitation compliance, and really kind of start to wrap a reduction of foodborne illness around this one connected device. And as we started to extend that, we started to get into auto-replenishment, we started to get into consumption billing, so they can actually, companies can now take a piece of equipment, put it out to a customer with less capital investment, and charge per hour of use, or per thing that happens on that machine, right? So we're seeing a lot of evolution of business models. People trying to do different things. And it comes down usually to make money or save money, right? >> Jeff: Right, right. >> Companies that want to make money are going down a path of really that enhanced customer experience, companies that want to save money are really looking for efficiencies in field service and warranty claims, and in waste reduction. >> Right. I'm curious though, on kind of the secondary value of the data. >> Carl: Right. >> Was that something they kind of thought about ahead of time, that maybe we'll be able to get? >> Carl: No, no. >> Or was it something that kind of came along. Because clearly, auto-replenishment, right, that's a easy, and billing by consumption, that's not brain surgery. But it's the secondary stuff that really becomes the essence of digitizing your business. >> Carl: Right. >> And I think the hand sanitizer's a really interesting example, because who would ever think there's a digital play beyond the obvious in hand sanitizer. >> Right, right. And what it allows them to do is focus in on behaviors of people that you could never measure otherwise. It would be very difficult to sit in the deli all day long and watch whether or not every employee washes their hands a correct amount of time, but we can really easily take a look across an entire supermarket chain and pick out who the outliers are, and then focus the efforts on training those individuals, and really enhancing the compliance of that. >> So does it pick up their ID tag when they're in proximity to the hand sanitizer? >> Carl: Well, see there are a lot of privacy concerns. >> Right, right. >> The use would be more, take a look at the aggregate of the data and just say, "That one is completely out of norm from the others. >> Jeff: Right, right. That's great, though. >> That's amazing, you again, wouldn't really think of that, but to your point, that does really kind of underscore just one of the important elements that businesses need to consider when digitizing. It's new business opportunities, new revenue streams, cost optimization, and that is a really, kind of a, I don't know, maybe it's not a unique example, the hand sanitizer example, of the other elements in which that business was able to get into by having this secondary look, or maybe a completely different look at the data. >> Yeah, and it's, as IoT really starts to serve those other masters besides the engineering and R and D folks, the marketing people are asking completely different questions than the technology people have been asking, which is why we're being pressured to move so quickly, beause as the creativity starts to enter in to this technology trend, they're expecting results immediately versus having to wait nine months, and spend millions of dollars-- >> It was interesting, in Andy's fireside chat, Buzzword Bingo, he said the buzzword that's delivering on its promise the fastest, in his opinion, was IoT. I was totally caught by surprise. Of all the different things, I would never have guessed that he would pick IoT, but you're right at the leading edge of this stuff, and it's moving faster than probably people probably give it credit for. >> The tough part about IoT is it's so huge, right? >> Jeff: Right. >> There's so many different flavors of it. GE has the industrial IoT that they're chasing after, the consumer products tends to be, right now, it's a trend. They connect everything from toothbrushes to whatever. But the idea being that having this connected product, can either enable new customer experiences, drive new business models, or help drive efficiencies in an organization, is really the fulfillment of that promise. >> Jeff: All right. >> From the culture perspect, I'm just curious, you're small right now, one of the things, too, that Andy talked about that I thought was interesting, was he was starting to talk about the culture of AWS. One of the things that they've been very vocal about is, they're very customer centric. They rarely talk about competition. How is that being a partner and being in the marketplace, with one of the announcements today, that's making it even simpler. Do you feel that, as a partner with them, that being in this marketplace, does their culture kind of permeate through that and help you open doors, like we talked about a minute ago, with other partners? >> Oh, they're fantastic. It's a great partner program just because they're super collaborative with even small partners like us. We had, maybe a little bit different experience coming into Amazon, because we ame with a little bit of knowledge of what they were already dealing with, but they've been really responsive and helpful, and it's, being in the marketplace is going to change the game for us because it offloads a lot of the things that we don't want to do, as we make the move more toward providing a platform as a service. They will take over the billing, and the distribution, and the management of, and customer, more so, than a small company like us would be able to do. So I think it enables a small company to get a greater reach than it would for normal, normally distributed. >> Excellent. Well, Carl, thank you so much for joining us-- >> Carl: Thank you. On theCUBE today, and sharing with our audience, a little bit about ThingLogix. We wish you continued success. >> Thank you. >> In connecting more and more devices globally. >> Carl: Thank you. >> For my co-host, Jeff Frick, I'm Lisa Martin. You've been watching us live on theCUBE, at AWS Summit, San Francisco. Stick around, we'll be right back. (techno music)

>> Announcer: Live, from San Francisco. It's The Cube, covering Oracle Open World 2016. Brought to you by Oracle. Now, here's your host, John Furrier and Peter Burris. >> Hey, welcome back everyone, we are live here in San Francisco. This is SiliconANGLE Media's The Cube. It's our flagship program, we go out to the events and extract the signal from the noise. I'm John Furrier, the CEO of SiliconANGLE Media, joined by co-host Peter Burris all week. Three days of wall-walk of day three. He's the head of research at SiliconeANGLE Media Inc., as well as the general manager of Wikibon research. Our next guest is Roddy Martin, VP of SC Supply Chain Cloud Product Marketing at Oracle. Welcome to The Cube. >> Thank you very much for the opportunity. I look forward to the discussion. >> Thanks for coming on. Really want to hear your thought leadership around the supply chain transformation, because it might be a little bit bumpy depending upon your perspective. But is a huge opportunity going on in every single theater of where software used to be a point solution. The cloud is now an opportunity for customers to think differently, and is a catalyst for essentially a business model change as well as a fundamental data-driven change. Your thoughts on this? What do you see going on? What are the key inflection points? >> So a very interesting part of my background is I came out of the brewing industry in South Africa. and then I led the supply chain practice at AMR Research, which today is Gartner. And we did a lot of studies on, what are companies doing to lead this transformation? Because it's a transformation of the interim business operating model of a company. This is not stitching data together in the traditional supply chain system sense. So one of the very first foundations that is really fundamental, and Gartner has done a great job of carrying the search forward, is the idea that every company progresses to an interim operating model in five stages of capability, and every one of those builds on the other. So they're either reacting in stage one's problem and never saw the shortage coming and ran out of product. Stage two is I performance improve around projects. Stage three is I drive functional excellence. And stage four I start working as an engine outside an operating model. In other words, I'm driving the business from what's happening in the market and I'm making sure that supply is matching demand. So it's very interesting and it's very important to consider that as the base foundation for this whole discussion. >> So that outside is interesting, we've heard this before, a lot of people are going that way, but there's no shortcuts. Can you talk about, cause you talk about the endpoint is then outside-in. >> Right, when you're operating as a demand-driven interim supply channel operating model, you can't run out of supply, right? So if you saw a change happening in the marketplace but there's nothing to supply, you've really just messed up the business. And so, each of these stages builds on every other stage. So functional excellence is: Am I good at planning? Am I good at product management? Am I good at logistics? Because those are the foundations for operating in the interim business model. This is why the Oracle's blanching in the cloud, in fact all of Oracle's developments in the cloud are so important because you're effectively building a new process oriented operating model that spins the entire business. If I started off with ERP systems and then I put logistics in place and tied it together, there's all sorts of disconnects in the business. When you pick it up in cycle times, you pick it in disconnect sometimes, they don't see changes to the marketplace for weeks. So, this overarching end to end supply chain operating model in the cloud is a fundamental enabler. >> So how do you gauge a customer? First of all, I buy everything that you said, but I want to bring up a point, because it seems to me that the theme of Oracle OpenWorld that traditional applications and I won't say, I'll just say the word Silo just to use it as a point, has been a specific domain specific thing. But to be end to end and be outside-in, which is the end game, you have to know how to talk and integrate with other systems which might have been a problem if you built the most badass end to end system. >> That is a part of the challenge and in fact, a lot of companies that I've worked with over the 15 years I've been researching this, they get stuck for that very reason. In other words, this is a re-engineering of the whole IT infrastructure versus having a thousand consultants come in and tie all my data together over a question of four years and move 15 instances of whatever system you want to one. >> So, if I question on the journey thing, you mentioned thousands of consultants, which customers are now seeing. They want faster mile posts, they want to see faster agility but a lot of the customers actually outline the journey for the customer. So they're saying, here's your journey and they shorten the mile posts for the deliverables. But they're the one getting paid for it so is that the right model, should they be outlining the journey for the customer? >> And they are. It's been very interesting because I was a partner with a major global consulting company for four years and I've been mixing with them here, they suddenly recognizing that this path to the cloud is something they've better get on the bandwagon because they're not going to have a thousand consultants deploying whatever ERP system you talk about as the future of IT. So, what's happening is the business is having much more of a say in this fast deployment, fast time to value, putting these new-- >> So they're driving the journey for parameters? >> They are gearing up for this new journey, the consultants are. >> So, let's get to the fundamentals behind all this and ask a question about it. At the end of the day, digital technologies give customers an option to do their journeys very differently whether in a B2B sense or a consumer sense. And as they use digital technologies, they're also giving data up and so we have now a combination where customers are getting something out of digital, they are demanding it as part of the engagement model. They are giving up data along the way, and the technologies for sensing and doing something with that data in business are now, we're not figuring out how that impacts business design, process design, and offering design. >> So, that's stage 4S, what we talk about is people, process, and technology versus, in the past, when you had stage one, two, and three. People as one set of projects, process as another set of projects, and technology as another set of projects. >> Yeah, I may or may not take some middlings with the model you put out, but it does matter. At the end of the day, what is driving this increasingly is that it used to be that the dominant consideration in, I think, and I'm testing you, the dominant consideration was assets. Where is the physical asset, where are the materials, where is the machine, and we'll focus our returns on this things and then presume that there's a demand for it and now we're getting all this data about demand and that is having an impact on how we talk about arranging the assets. >> That is the inside-out to outside-in. So, let me give you an example without mentioning companies. A major retailer and a major pharmaceutical company. They share pollen data, they share weather data, they mine Facebook to find out what are people saying about allergies, let's say in New England. And the ragweed's busting and they say, do we have the right levels of inventory, and they're moving inventory to make sure that people who aren't on Facebook are saying we can't buy this particular product. They're moving inventory, that's the difference. >> So, they're sharing data amongst themselves. >> Yes, and they're collaborating between retailers. >> Arguably a similar example, and a retailer that's actually not moving inventory but moving pointers and offering new channel options so that someone decides may not, that they know somebody's going to come into the store, the size may not be there but they can still get it to them that day. >> So, it's very interesting, Procter and Gamble, who I did a lot of work with, and this is public domain information, the CEO drove two fundamental transformation messages in the business. And they called it the two moments of truth. He said, we will always have our product when we say we've got a product. So, if we promote a new product, the consumer goes to the shelf, it will be there. Moment of truth number two, we understand why consumers choose and use our products. And you don't fix number two until you fix number one because if I wanted a small tube of toothpaste and I went in and there were only big ones, it's the wrong buying signal. So, what you're seeing is that whole flip to measuring what the market's looking for and shaping their demand and then making sure that the assets and the supply system is geared to deliver. >> Right, I want to ask you a question. First of all, I love that point, I love your point about the data, but here's the question: cause supply chain has been very instrumentation drive, okay, and that certainly is transforming but now you mention Procter and Gamble. We are living in an era where, in the history of business, you can actually now potentially measure everything. So how does that impacting the reconfiguration of the business model? I mean, Procter and Gamble has those moments of truth, every company will have a moment of truth which is, everything is now measurable so, advertising to employee things and everything. >> So let's take the asset story versus the on shelf thing, right, so when I have assets and I'm getting all the data out of my assets, what am I doing with all of that data, right? Because it's not connected to demand. What I got to know is what demand data do I really want to be able to move my assets to the right place. >> Peter: By the way, the shelf is an asset. >> Of course it is, yes. It's a sensing point and it's an asset. They own it, they replenish that shelf. So the point is, data is everywhere and now these, the consulting and the BPM organizations supporting and companies doing their own business process manner, they got to know what data is really important and what data from the outside-in is going to allow me to leverage a new operating model for my business and become digital. >> So, this is really awesome, I was talking with an Oracle executive last night at one of their customer parties and we had a conversation around this data sharing. This is a new, different behavior. This is a theme of the show that no one's really talking about but it's in plain sight which is there is a data sharing aspect of systems and vendors and companies. >> Roddy: That's why the cloud is so important. >> John: This is now impacting everything. >> Everything. >> How do companies go forward and do this? What are you seeing, is there a best practice, is there a starting point? Is there a five step process on that? >> Well, first of all, these transformations are being lead by the C level executive team in a business. This is now longer somebody who decides to buy a new IT system and plug it in to the business. So, the business is saying, how do we change the operating model of the way we work, right? So, and then, what are the capabilities, and this is where that five stage model comes in, what capabilities do we need to look at building over the next three years so that we can operate in this intent way because you can't wake up tomorrow and go from an inside-out asset driven business to an outside-in demand driven business in two weeks. It ain't going to happen. >> So what's the progression? What's the progress bar look like when you have that moment of an epiphany and say, you know, I'm the CEO-- >> What's the earning point of the business? If it's Procter and Gamble, I want X number of one billion dollars brands. If you're a pharmaceutical company, you want to launch brand new drugs and you want to do it at half the price and half the speed that you're used to. It's the business articulating, this is why the leadership teams are so fundamental, articulating what's the burning platform and then translating that back into the capabilities-- >> So you get a reverse engineer. >> Outside-In. >> Outside-In, I love it. >> The way our research says it, and it's very similar but I want to test this because it's, we say start with context. >> Yes. >> What are you going to do with your customer that you have to do better than everybody else? And then identify the community that you're going to do it with and identify the capabilities that are going to delight that community. So it's context, community, and capabilities. >> Now here's the context, further piece to context. If context changes, how quickly do I sense that change and how fast can I respond to that change? Because if I've got all my asset capabilities and my supply capabilities locked into one set of context and that changes and I now have to re-engineer my whole business, I may lose the whole show in the process. I got to see those changes as they are happening, literally in real time. This is where the internet of things, this is where demand shaping, demand sensing, retailers collaborating, supplies connected into supply chain, everybody sharing that information and the fact that not many people, they don't know how to do it. The culture of business is not yet at the points-- >> That's why the measurement thing I brought up, I mean Procter and Gamble, they used to say to their agencies, we know that 50% of our advertising is good, we don't know which half. So now they can measure it all just like in every other aspect so this is where the business model-- >> You also have to be careful about whether or not, again going back to context changes, measurements change, data can blow you away. You have to be very smart about how you do it so a lot of these intelligent things, machine learning, how the models get built, how the insides get delivered, all become very very important. Very quickly, I have two quick questions for you. One is really approximate to the conversation, one less so but the approximate one: IOT. IOT is, has many many applications. Certainly turning analogue data into digital data so you can build models is a crucial piece of it. But it also has another implication in how you enact the output of that model back into the real word. How does supply chain and IOT come together? >> So if you look at the studies that are being done by Oracle and Gartner et cetera on what's important to the supply chain, two things come up. One is visibility and the other is analytics. Right, so there's tons of data available, to your point just now. That data could cause massive noise to the business unless you know what you're looking at. I know companies that will say, 95% visibility of changes on their demand side is good enough but I'm good enough on the supply side to be able to adjust. But you got to know which data to look at. So I'm looking at on shelf. I'm looking at what consumers are choosing and using, I'm looking to see what of my contract manufacturers-- >> Peter: Analyze key constraints. >> Bingo, so it's not about, I think what we're all going to have to learn in the internet of things is we need, again, a cloud based internet of things platform that does the analytics. >> Because we can rewire things faster. >> Exactly, you can adjust the business to new scenarios based on what you're reading from the demand side and what you're reading from the supply side. >> So you're a great foil for my second question. My second question is you look back at the history, or the recent history let's call it, of strategy, very asset based, Porter said pick the industry that has the best returns, pick your position in that industry, then choose your games based on the five factor analysis that you want to play to get to that position. Very asset oriented, we're in control, that's going to dictate how things change. What you just suggested was a very very different way of thinking about strategy. >> Same fundamentals. It's the same fundamentals but it's allowing yourself to adjust those fundamentals based on what's happening in the market place. >> Peter: But you're not going to base it on just the assets. >> No, we're not going to base it on the assets unless you've focused on, like if you're an engineering company and that's all you make is machines, you can't suddenly start producing toothpaste, for example. There are, that's why I say it's a reconfiguration of those same principles but flexible enough to meet demand. >> So how does, how does the world of design and the world of strategy start to come together in C suite? >> Fundamentally, because it's the voice of the customer that starts to count. It's the voice of the customer that dictates the strategy. So if my customers don't want green Guinness for Saint Patrick's Day, don't make any, because it's going to hang around and get thrown away, right? So, the voice of the customer determines what's happening on the demand side and the supply side has to be agile enough to meet that need. >> So, I would suggest keep Guinness the way it is because it's damn good the way it is, so personally I would agree on the Guinness comment. No green Guinness. >> So, what's the South Africa beer? >> Castle Lager. Well, SAB, South African Brewery, has been bought by Anheuser-Busch InBrev, a massive big giant. >> We love beer and if there's any beer sponsors out there, we're happy looking for our Budweiser. We want a, maybe an IPA in there. Roddy, thanks for spending the time, coming in with you, appreciate it. Some thought leadership here on Reconfiguration and looking at some of the nuances that are really going to impact the buyers here on The Cube. Oracle Open will be back with more live coverage from SiliconANGLE's The Cube after this short break.