The Cube Presents UI Path Forward five. Brought to you by UI Path. >>I Ready, Dave Ante with David Nicholson. We're back at UI Path forward. Five. We're getting ready for the big guns to come in, the two co CEOs, but we have a really special analyst panel now. We're excited to have Daniel Newman here. He's the Principal analyst at Future and Research. And Andy Dai, who's the Vice president and Principal Analyst at Constellation Research. Guys, good to see you. Thanks for making some time to come on the queue. >>Glad to be here. Always >>Good. So, >>Andy, you're deep into ai. You and I have been talking about having you come to our maor office. I'm, I'm really excited that we're able to meet here. What have you seen at the show so far? What are your big takeaways? You know, day one and a half? >>Yeah, well, so first of all, I'm d AI because my last name has AI and I >>Already talk about, >>So, but, but all jokes aside, there are a lot of good things I heard from the conference, right? I mean, one is the last two years because of the pandemic, the growth has been phenomenal for, for a lot of those robotic automation intelligent automation companies, right? So because the low hanging through position making processes have been already taken care of where they going to find the next growth spot, right? That was the question I was looking answers to. And they have some inverse, one good acquisition. They had intelligent document processing, but more importantly they're trying to move from detrimental rules based RPA automation into AI based, more probabilistic subjective decision making areas. That's a huge market, tons of money involved in it, but it's going to be a harder problem to solve. Love to see the execut. >>Well, it's also a big pivot for the, for the company. It started out as sort of a a point product and now is moving to, to platform. But to end of the macro is not in UI pass favor. It's not really in any, you know, tech company's favor, but especially, you know, a company that's going into a transition transitioning to go to market cetera. What are you seeing, what's your take on the macro? I mean, I know you follow the financial markets very closely. There's a lot of negative sentiment right now. Are you as negative as the sentiment? >>Well, the, the broad sentiment comes with some pretty good historical data, right? We've had probably one of the worst market years in multiple decades. And of course we're coming into a situation where all the, the factors are really not in our favor. You've got in interest rates climbing, you've got wildly high inflation, you've had a, you know, helicopters dumping money on the economy for a period of time. And we're, we're gonna get into this great reset is what I keep talking about. But, you know, I had the opportunity to talk to Bill McDermott recently on one of my shows and Bill's CEO of ServiceNow, in case anybody there doesn't know, but >>Former, >>Yeah, really well spoken guy. But you know, him and I kind of went back and forth and we came up with this kind of concept that we were gonna have to tech our way out of what's about to come. You can almost be certain recession is gonna come. But for companies like UiPath, I actually think there's a tremendous opportunity because the bottom line is companies are gonna be looking at their bottom line. A year ago it was all about growth a deal, like the Adobe Figma deal would've been, been lauded, people would've been excited. Now everybody's looking at going, how are they paying that price? Everybody's discounting the future growth. They're looking at the situation, say, what's gonna happen next? Well, bottom line is now they're looking at that. How profitable are we? Are you making money? Are you growing that bottom line? Are you creating earnings? We're >>Gonna come in >>Era, we're gonna come into an era where companies are gonna say, you know what? People are expensive. The inflationary cost of hiring is expensive. You know, what's less expensive? Investing in the cloud, investing in ai, investing in workflow and automation and things that actually enable businesses to expand, keep costs somewhat contained fixed costs, and scale their businesses and get themselves in a good position for when the economy turns to return to >>Grow. So since prior to the pandemic cloud containers, m l and RPA slash automation have been the big four that from a spending data standpoint have been above the line above all kind of the rest in terms of spending momentum up until last quarter, AI and RPA slash automation declined. So my question is, are those two areas discretionary or more discretionary than other technology investments you heard? >>Well, I, I think we're in a, a period where companies are, I won't say they've stopped spending, but you listened to Mark Benioff, you talked about the elongated sales cycle, right? I think companies right now are being very reflective and they're doing a lot of introspection. They're looking at their business and saying, We hired a lot of people. We hired really fast. Do we need to cut? Do we need to freeze? We've made investments in technology, are we getting a return on 'em? We all know that the analytics, whether it's you know, digital adoption platforms or just analytics in the business, say, What is all this money we've been spending doing for us and how productive are we? But I will tell you universally, the companies are looking at workflow automations that enable things. Whether that's onboarding customers, whether that's delivering experiences, whether that's, you know, full, you know, price to quote technologies, automate, automate, automate. By doing that, they're gonna bring down the cost, they're gonna control themselves as best as possible in a tough macro. And then when they come out of it, these processes are gonna be beneficiary in a, in a growth environment even more so, >>Andy UiPath rocketed to a leadership position, largely due to the, the product and the simplicity of the product relative to the competition. And then as you well know, they expanded into, you know, platform. So how do you see the competitive environment? A UI path is again focusing on that platform play Automation Anywhere couldn't get to public market. They had turnover at the go to market level. Chris Riley joined a lot of, lot of hope left Microsoft joined into the fray, obviously is having an impact that you're certainly seeing spending momentum around Microsoft. Then SAP service Now Salesforce, every software company the planet thinks they should get every dollar spent on software. You know, they, they see UI pass momentum and they say, Hey, we can, we can take some of that off the table. How do you see the competitive environment right now? >>So first of all, in in my mind, UI path is slightly better because of a couple of reasons. One, as you said, it's ease of use. >>They're able to customize it variable to what they want. So that's a real easy development advantage. And then the, when you develop the bots and equal, it takes on an average anywhere between two to maybe six weeks, generally speaking, in some industries regulated government might take more so that it's faster, quicker, easier than others in a sense. So people love using that. The second advantage of what they have in my mind is that not only they are available as a managed SA solution on, on cloud, on Azure Cloud, but also they have this version that you can install, maintain, manage any way you want, whether it's a public cloud or, or your own data center and so on so forth. That's not available with almost, not all of them have it, Few have it, but not all of the competitors have it. So they have an advantage there as well. Where it could become useful would be one of the areas that they haven't even expanded is the government. >>Government is the what, >>Sorry? The government. Yeah, related solutions, right? Defense, government, all of those areas when you go, which haven't even started for various reasons. For example, they're worried about laying off people, worried about cost, worried about automating things. There's a lot of hurdles to overcome. But once you overcome that, if you want to go there, nobody's going to use, or most of them will be very of using something on the cloud. So they have a solution for version variation of that. So they are set up to come to that next level. I mean, I don't know if you guys were at the keynote, the CEO talked about how their plans to go from 1 billion to 5 billion in ar. So they're set up to capture the market. But again, as you said, every big software company saw their momentum, they want to get into it, they want to compete with them. So >>Well, to get to 5 billion, they've gotta accelerate growth. I mean, if you do 20% cer over the next, you know, through the end of the decade, they don't quite get there. So they're gonna have to, you know, they lowered their forecast out of the high 20 or mid twenties to 18%. They're gonna have to accelerate that. And we've seen that before. We see it in cloud where cloud, you know, accelerates growth even though you got the lower large numbers. Go ahead Dave. >>Yeah, so Daniel, then how do we, how do we think of this market? How do we measure the TAM for total addressable market for automation? I mean, you know, what's that? What's that metric that shows how unautomated are we, how inefficient are we? Is there a, is there a 5% efficiency that can be gained? Is there a 40% efficiency that can be gained? Because if you're talking about, you know, how much much of the market can UI path capture, first of all, how big is the market? And then is UI path poised to take advantage of that compared to the actual purveyors of the software that people are interacting with? I'm interacting with an E R p, an ER P system that has built into it the ability to automate processes. Then why do I need 'EM UI path? So first, how do you evaluate TAM? Second, how do you evaluate whether UI Path is gonna have a chance in this market where RPAs built into the applications that we actually use? Yeah, >>I think that TAM is evolving, and I don't have it in front of me right now, but what I'll tell you about the TAM is there's sort of the legacy RPA tam and then there's what I would sort of evolve to call the IPA and workflow automation tam that is being addressed by many of these software companies that you asked in the competitive equation. In the, in the, in the question, what we're seeing is a world where companies are gonna say, if we can automate it, we will automate it. That's, it's actually non-negotiable. Now, the process in the ability to a arrive at automation at scale has long been a battle front within the nor every organization. We've been able to automate things for a long time. Why has it more been done? It's the same thing with analytics. There's been numerous studies in analytics that have basically shown companies that have been able to embrace, adopt, and implement analytics, have significantly better performances, better performances on revenue growth, better performances and operational cost management, better performances with customer experience. >>Guess what? Not everybody, every company can get to this. Now there's a couple of things behind this and I'm gonna, I'm gonna try to close my answer out cause I'm getting a little long winded here. But the first thing is automation is a cultural challenge in most organizations. We've done endless research on companies digitally transforming and automating their business. And what we've found is largely the technology are somewhat comparable. Meaning, you know, I, I've heard what he is saying about some of the advantages of partnership with Microsoft, very compelling. But you know what, all these companies that have automation offerings, whether it's you know, through a Salesforce, Microsoft, whether it's a specialized rpa like an Automation Anywhere or a UI path, their solutions can be deployed and successful. The company's ability to take the investment, implement it successfully and get buy in across the organization tends to always be the hurdle. An old CIO stat, 50% of IT projects fail. That stat is still almost accurate today. It's not 50% of technology is bad, but those failures are because the culture doesn't get behind it. And automation's a tricky one because there's a lot of people that feel on the outside rather than the inside of an automation transformation. >>So, Andy, so how do you think about the, to Dave's question, the SAPs the service nows trying to, you know, at least take some red crumbs off the table. They, they're gonna, they're gonna create these automation stove pipes, but in Automation Anywhere or, or UI path is a horizontal play, are they not? And so how do you think about that progression? Well, so >>First of all, all of this other companies, when they, they, whether it's a build, acquire, what have you, these guys already have what, five, seven years on them. So it's gonna be difficult for them to catch up with the Center of Excellence knowledge on the use cases, what they got to catch up with them. That's gonna be a lot of catch up. Just to give you an idea, Microsoft Power Automate has been there for a while, right? They're supposedly doing well as well, but they still choose to partner with the UiPath as well to get them to the next level. So there's going to be competition coming from all areas, but it's, it's about, you know, highlights. >>So, so who is the competition? Is it Microsoft chipping away an individual productivity? Is it a service now? Who's got a platform play? Is it themselves just being able to execute >>All plus also, but I think the, the most, I wouldn't say competition, but it's more people are not aware of what areas need to be automated, right? For example, one of the things I was talking about with a couple of customers is, so they have a automation hub where you can put the, the process and and task that need to be automated and then you prioritize and start working on it. And, and almost all of them that I speak to, they keep saying that most of the process and task identification that they need to do for automation, it's manual right now. So, which means it's limited, you have to go and execute it. When people find out and tell you that's what need to be fixed, you try to go and fix that. But imagine if there is a way, I mean the have solutions they're showcasing now if it becomes popular, if you're able to identify tasks that are very inefficient or or process that's very inefficient, automatically score them up saying that, you know what, this is what is going to be ROI and you execute on it. That's going to be huge. So >>I think ts right, there's no shortage of, of a market. I would, I would agree with you Rob Sland this morning talked about the progression. He sort of compared it to e R P of the early days. I sort of have a love hate with E R P cuz of the complexity of the implementation and the, and the cost. However, first of all, a couple points and I love to get your thoughts for you. If you went back, I know 25 years, you, you wouldn't have been able to pick SAP out of a lineup and say that's gonna be the leader in E R P and they ended up, you know, doing really, really well. But the more interesting angle is if you could have figured out the customers that were implementing e r p in, in a really high quality fashion, those are the companies that really did well. You buy their stocks, they really took off cuz they were killing their other industry competitors. So, fast forward to automation. Will automation live up to its hype and your opinion, will it be as transformative and will the, the practitioners of automation see the same type of uplift in their markets, in their market caps, in their competitiveness as did sort of the early adopters and the excellent adopters of brp? What are your thoughts? Well, >>I think it's an interesting comparison. Maybe answer it slightly different way. I think the future is that automation is a non-negotiable in every enterprise organization. I think if you're a large organization, we have absolutely filled our, our organizations with waste too much overhead, too much expense, too much technical debt and automation is an answer. This is the way we want to interact, right? We want a chat bot that actually gives us good answers that can answer on a Tuesday at 11:00 PM at night when we want to know if the right dog food, you know, and I'm saying that, you know, that's what we want. That's the outcome we want. And businesses have to be driven by the outcome. Here's what I'm not sure about, Dave, is we have an era where over the last three to five years, a lot of products have become companies and a lot of 'EM products became companies ended up in public markets. >>And so the RPA space is one of those areas that got this explosive amount of growth. And you look at it and there's two ways. Is this horizontally a business rpa or is this going to be something that's gonna be a target of those Microsofts and those SAPs and say, Look, we need hyper automation to be deeply integrated at the E R P crm, hcm SCM level. We're gonna build by this or we're gonna build this. And you're already hearing it in the partnerships, but this is how I think the story ends. I I think either the companies like UiPath get much bigger, they get much more rounded in their, in their offerings. Or you're gonna have a large company like a Microsoft come in and say, you know what? Buy it rather >>Than build can they can, they can, can this company, maybe not so much here, but can a company like Automation Anywhere stay acquisition? Well, >>I use the, I use the Service now as an, as a parallel because they're a company that I thought would always end up inside of a bigger company and now you're like, I think they're too big. I think they've they've dropped >>That, that chart. Yeah, they're acquisition proof. I would agree. But these guys aren't yet Nora's automation. They work for >>A while and it's not necessarily a bad thing. Sometimes getting bit bought is good, but what I mean is it's gonna be core and these big companies know it cuz they're all talking >>About, but as independent analysts, we want to see independent companies. >>I wanna see the right thing. >>It just makes it fun. >>The right thing >>Customers. Yeah, but you know, okay, Oracle buy more customers, more >>Customers. >>I'm kidding. Yeah, I guess it's the right thing. It just makes it more fun when you have really good independent competitors that >>We >>Absolutely so, and, and spend way more on r and d than these big companies who spend a lot more on stock buyback. But I know you gotta go. Thanks so much for spending some time, making time for Cube Andy. Great to see you. Good to see as well. All right, we are wrapping up day one, Dave Blan and Dave Nicholson live. You can hear the action behind us, forward in five on the Cube, right back.

(upbeat music) >> theCUBE presents KubeCon and CloudNativeCon Europe 22, brought to you by the Cloud Native Computing Foundation. >> Valencia, Spain, a KubeCon, CloudNativeCon Europe 2022. I'm Keith Towns along with Paul Gillon, Senior Editor Enterprise Architecture for Silicon Angle. Welcome Paul. >> Thank you Keith, pleasure to work with you. >> We're going to have some amazing people this week. I think I saw stat this morning, 65% of the attendees, 7,500 folks. First time KubeCon attendees, is this your first conference? >> It is my first KubeCon and it is amazing to see how many people are here and to think of just a couple of years ago, three years ago, we were still talking about, what the Cloud was, what the Cloud was going to do and how we were going to integrate multiple Clouds. And now we have this whole new framework for computing that is just rifled out of nowhere. And as we can see by the number of people who are here this has become the dominant trend in Enterprise Architecture right now how to adopt Kubernetes and containers, build microservices based applications, and really get to that transparent Cloud that has been so elusive. >> It has been elusive. And we are seeing vendors from startups with just a few dozen people, to some of the traditional players we see in the enterprise space with 1000s of employees looking to capture kind of lightning in a bottle so to speak, this elusive concept of multicloud. >> And what we're seeing here is very typical of an early stage conference. I've seen many times over the years where the floor is really dominated by companies, frankly, I've never heard of that. The many of them are only two or three years old, you don't see the big dominant computing players with the presence here that these smaller companies have. That's very typical. We saw that in the PC age, we saw it in the early days of Unix and it's happening again. And what will happen over time is that a lot of these companies will be acquired, there'll be some consolidation. And the nature of this show will change, I think dramatically over the next couple or three years but there is an excitement and an energy in this auditorium today that is really a lot of fun and very reminiscent of other new technologies just as they requested. >> Well, speaking of new technologies, we have Dave Cole, CRO, Chief Revenue Officer. >> That's right. >> Chief Marketing Officer of Spectrum Cloud. Welcome to the show. >> Thank you. It's great to be here. >> So let's talk about this big ecosystem, Kubernetes. >> Yes. >> Solve problem? >> Well the dream is... Well, first of all applications are really the lifeblood of a company, whether it's our phone or whether it's a big company trying to connect with its customers about applications. And so the whole idea today is how do I build these applications to build that tight relationship with my customers? And how do I reinvent these applications rapidly in along comes containerization which helps you innovate more quickly? And certainly a dominant technology there is Kubernetes. And the question is, how do you get Kubernetes to help you build applications that can be born anywhere and live anywhere and take advantage of the places that it's running? Because everywhere has pluses and minuses. >> So you know what, the promise of Kubernetes from when I first read about it years ago is, runs on my laptop? >> Yeah. >> I can push it to any Cloud, any platforms. >> That's right, that's right. >> Where's the gap? Where are we in that phase? Like talk to me about scale? Is it that simple? >> Well, that is actually the problem is that today, while the technology is the dominant containerization technology in orchestration technology, it really still takes a power user, it really hasn't been very approachable to the masses. And so was these very expensive highly skilled resources that sit in a dark corner that have focused on Kubernetes, but that now is trying to evolve to make it more accessible to the masses. It's not about sort of hand wiring together, what is a typical 20 layer stack, to really manage Kubernetes and then have your engineers manually can reconfigure it and make sure everything works together. Now it's about how do I create these stacks, make it easy to deploy and manage at scale? So we've gone from sort of DIY Developer Centric to all right, now how do I manage this at scale? >> Now this is a point that is important, I think is often overlooked. This is not just about Kubernetes. This is about a whole stack of Cloud Native Technologies. And you who is going to integrate that all that stuff, piece that stuff together? Obviously, you have a role in that. But in the enterprise, what is the awareness level of how complex this stack is and how difficult it is to assemble? >> We see a recognition of that we've had developers working on Kubernetes and applications, but now when we say, how do we weave it into our production environments? How do we ensure things like scalability and governance? How do we have this sort of interesting mix of innovation, flexibility, but with control? And that's sort of an interesting combination where you want developers to be able to run fast and use the latest tools, but you need to create these guardrails to deploy it at scale. >> So where do the developers fit in that operation stack then? Is Kubernetes an AIOps or an ops task or is it sort of a shared task across the development spectrum? >> Well, I think there's a desire to allow application developers to just focus on the application and have a Kubernetes related technology that ensures that all of the infrastructure and related application services are just there to support them. And because the typical stack from the operating system to the application can be up to 20 different layers, components, you just want all those components to work together, you don't want application developers to worry about those things. And the latest technologies like Spectra Cloud there's others are making that easy application engineers focus on their apps, all of the infrastructure and the services are taken care of. And those apps can then live natively on any environment. >> So help paint this picture for us. I get AKS, EKS, Anthos, all of these distributions OpenShift, the Tanzu, where's Spectra Cloud helping me to kind of cobble together all these different distros, I thought distro was the thing just like Linux has different distros, Randy said different distros. >> That actually is the irony, is that sort of the age of debating the distros largely is over. There are a lot of distros and if you look at them there are largely shades of gray in being different from each other. But the Kubernetes distribution is just one element of like 20 elements that all have to work together. So right now what's happening is that it's not about the distribution it's now how do I again, sorry to repeat myself, but move this into scale? How do I move it into deploy at scale to be able to manage ongoing at scale to be able to innovate at-scale, to allow engineers as I said, use the coolest tools but still have technical guardrails that the enterprise knows, they'll be in control of. >> What does at-scale mean to the enterprise customers you're talking to now? What do they mean when they say that? >> Well, I think it's interesting because we think scale's different because we've all been in the industry and it's frankly, sort of boring old word. But today it means different things, like how do I automate the deployment at-scale? How do I be able to make it really easy to provision resources for applications on any environment, from either a virtualized or bare metal data center, Cloud, or today Edge is really big, where people are trying to push applications out to be closer to the source of the data. And so you want to be able to deploy it-scale, you want to manage at-scale, you want to make it easy to, as I said earlier, allow application developers to build their applications, but ITOps wants the ability to ensure security and governance and all of that. And then finally innovate at-scale. If you look at this show, it's interesting, three years ago when we started Spectra Cloud, there are about 1400 businesses or technologies in the Kubernetes ecosystem, today there's over 1800 and all of these technologies made up of open source and commercial all version in a different rates, it becomes an insurmountable problem, unless you can set those guardrails sort of that balance between flexibility, control, let developers access the technologies. But again, manage it as a part of your normal processes of a scaled operation. >> So Dave, I'm a little challenged here, because I'm hearing two where I typically consider conflicting terms. Flexibility, control. >> Yes. >> In order to achieve control, I need complexity, in order to choose flexibility, I need t-shirt, one t-shirt fits all and I get simplicity. How can I get both that just doesn't compute. >> Well, that's the opportunity and the challenge at the same time. So you're right. So developers want choice, good developers want the ability to choose the latest technology so they can innovate rapidly. And yet ITOps, wants to be able to make sure that there are guardrails. And so with some of today's technologies, like Spectra Cloud, it is, you have the ability to get both. We actually worked with dimensional research, and we sponsor an annual state of Kubernetes survey. We found this last summer, that two out of three IT executives said, you could not have both flexibility and control together, but in fact they want it. And so it is this interesting balance, how do I give engineers the ability to get anything they want, but ITOps the ability to establish control. And that's why Kubernetes is really at its next inflection point. Whereas I mentioned, it's not debates about the distro or DIY projects. It's not big incumbents creating siloed Kubernetes solutions, but in fact it's about allowing all these technologies to work together and be able to establish these controls. And that's really where the industry is today. >> Enterprise , enterprise CIOs, do not typically like to take chances. Now we were talking about the growth in the market that you described from 1400, 1800 vendors, most of these companies, very small startups, our enterprises are you seeing them willing to take a leap with these unproven companies? Or are they holding back and waiting for the IBMs, the HPS, the MicrosoftS to come in with the VMwares with whatever they solution they have? >> I think so. I mean, we sell to the global 2000. We had yesterday, as a part of Edge day here at the event, we had GE Healthcare as one of our customers telling their story, and they're a market share leader in medical imaging equipment, X-rays, MRIs, CAT scans, and they're starting to treat those as Edge devices. And so here is a very large established company, a leader in their industry, working with people like Spectra Cloud, realizing that Kubernetes is interesting technology. The Edge is an interesting thought but how do I marry the two together? So we are seeing large corporations seeing so much of an opportunity that they're working with the smaller companies, the latest technology. >> So let's talk about the Edge a little, you kind of opened it up there. How should customers think about the Edge versus the Cloud Data Center or even bare metal? >> Actually it's a... Well bare metal is fairly easy is that many people are looking to reduce some of the overhead or inefficiencies of the virtualized environment. But we've had really sort of parallel little white tornadoes, we've had bare metal as infrastructure that's been developing, and then we've had orchestration developing but they haven't really come together very well. Lately, we're finally starting to see that come together. Spectra Cloud contributed to open source a metal as a service technology that finally brings these two worlds together, making bare metal much more approachable to the enterprise. Edge is interesting, because it seems pretty obvious, you want to push your application out closer to your source of data, whether it's AI inferencing, or IoT or anything like that, you don't want to worry about intermittent connectivity or latency or anything like that. But people have wanted to be able to treat the Edge as if it's almost like a Cloud, where all I worry about is the app. So really, the Edge to us is just the next extension in a multi-Cloud sort of motif where I want these Edge devices to require low IT resources, to automate the provisioning, automate the ongoing version management, patch management, really act like a Cloud. And we're seeing this as very popular now. And I just used the GE Healthcare example of that, imagine a CAT scan machine, I'm making this part up in China and that's just an Edge device and it's doing medical imagery which is very intense in terms of data, you want to be able to process it quickly and accurately, as close to the endpoint, the healthcare provider is possible. >> So let's talk about that in some level of details, we think about kind of Edge and these fixed devices such as imaging device, are we putting agents on there, or we looking at something talking back to the Cloud? Where does special Cloud inject and help make that simple, that problem of just having dispersed endpoints all over the world simpler? >> Sure. Well we announced our Edge Kubernetes, Edge solution at a big medical conference called HIMMS, months ago. And what we allow you to do is we allow the application engineers to develop their application, and then you can de you can design this declarative model this cluster API, but beyond Cluster profile which determines which additional application services you need and the Edge device, all the person has to do with the endpoint is plug in the power, plug in the communications, it registers the Edge device, it automates the deployment of the full stack and then it does the ongoing versioning and patch management, sort of a self-driving Edge device running Kubernetes. And we make it just very easy. No IT resources required at the endpoint, no expensive field engineering resources to go to these endpoints twice a year to apply new patches and things like that, all automated. >> But there's so many different types of Edge devices with different capabilities, different operating systems, some have no operating system. I mean that seems, like a much more complex environment, just calling it the Edge is simple, but what you're really talking about is 1000s of different devices, that you have to run your applications on how are you dealing with that? >> So one of the ways is that we're really unbiased. In other words, we're OS and distro agnostic. So we don't want to debate about which distribution you like, we don't want to debate about which OS you want to use. The truth is, you're right. There's different environments and different choices that you'll want to make. And so the key is, how do you incorporate those and also recognize everything beyond those, OS and Kubernetes and all of that and manage that full stack. So that's what we do, is we allow you to choose which tools you want to use and let it be deployed and managed on any environment. >> And who's... >> So... >> I'm sorry Keith, who's responsible for making Kubernetes run on the Edge device. >> We do. We provision the entire stack. I mean, of course the company does using our product, but we provision the entire Kubernetes infrastructure stack, all the application services and the application itself on that device. >> So I would love to dig into like where pods happen and all that. But, provisioning is getting to the point that is a solve problem. Day two. >> Yes. >> Like you just mentioned HIMMS, highly regulated environments. How does Spectra Cloud helping with configuration management, change control, audit, compliance, et cetera, the hard stuff. >> Yep. And one of the things we do, you bring up a good point is we manage the full life cycle from day zero, which is sort of create, deploy, all the way to day two, which is about access control, security, it's about ongoing versioning in a patch management. It's all of that built into the platform. But you're right, like the medical industry has a lot of regulations. And so you need to be able to make sure that everything works, it's always up to the latest level have the highest level of security. And so all that's built into the platform. It's not just a fire and forget it really is about that full life cycle of deploying, managing on an ongoing basis. >> Well, Dave, I'd love to go into a great deal of detail with you about kind of this day two ops and I think we'll be covering a lot more of that topic, Paul, throughout the week, as we talk about just as we've gotten past, how do I deploy Kubernetes pod, to how do I actually operate IT? >> Absolutely, absolutely. The devil is in the details as they say. >> Well, and also too, you have to recognize that the Edge has some very unique requirements, you want very small form factors, typically, you want low IT resources, it has to be sort of zero touch or low touch because if you're a large food provider with 20,000 store locations, you don't want to send out field engineers two or three times a year to update them. So it really is an interesting beast and we have some exciting technology and people like GE are using that. >> Well, Dave, thanks a lot for coming on theCUBE, you're now KubeCon, you've not been on before? >> I have actually, yes its... But I always enjoy it. >> Great conversation. From Valencia, Spain. I'm Keith Towns, along with Paul Gillon and you're watching theCUBE, the leader in high tech coverage. (upbeat music)

>>The cube presents, Coon and cloud native con Europe 22 brought to you by the cloud native computing foundation. >>Lisia Spain, a cuon cloud native con Europe 2022. I'm Keith towns, along with Paul Gillon, senior editor, enterprise architecture for Silicon angle. Welcome Paul, >>Thank you, Keith pleasure to work >>With you. You know, we're gonna have some amazing people this week. I think I saw stat this morning, 65% of the attendees, 7,500 folks. First time Q con attendees. This is your first conference. >>It is my first cubic con and it is amazing to see how many people are here and to think of, you know, just a couple of years ago, three years ago, we were still talking about what the cloud was and what the cloud was gonna do and how we were gonna integrate multiple clouds. And now we have this whole new framework for computing that is just rifled out of, out of nowhere. And as we can see by the number of people who are here, this has become a, a, this is the dominant trend in enterprise architecture right now, how to adopt Kubernetes and containers, build microservices based applications, and really get to that, that transparent cloud that has been so elusive. >>It has been elusive. And we are seeing vendors from startups with just a, a few dozen people to some of the traditional players we see in the enterprise space with thousands of employees looking to capture kind of lightning in a bottle, so to speak this elusive concept of multi-cloud. >>And what we're seeing here is very typical of an early stage conference. I've seen many times over the years where the, the floor is really dominated by companies, frankly, I've never heard of that. Many of them are only two or three years old, and you don't see the big, the big dominant computing players with, with the presence here that these smaller companies have. That's very typical. We saw that in the PC age, we saw it in the early days of Unix and, and it's happening again. And what will happen over time is that a lot of these companies will be acquired. There'll be some consolidation. And the nature of this show will change, I think, dramatically over the next couple or three years, but there is an excitement and an energy in this auditorium today that is, is really a lot of fun and very reminiscent of other new technologies just as they press it. >>Well, speaking of new technologies, we have Dave Cole, CR O chief revenue officer that's right. Chief marketing officer that's right of spec cloud. Welcome to the show. Thank >>You. It's great to be here. >>So let's talk about this big ecosystem. Okay. Kubernetes. Yes. Solve problem. >>Well, you know, the, the dream is, well, first of all, applications are really the lifeblood of a company, whether it's our phone or whether it's a big company trying to connect with its customer, it's about applications. And so the whole idea today is how do I build these applications to build that tight relationship with my customers? And how do I reinvent these applications rapidly in, along comes containerization, which helps you innovate more quickly. And certainly a dominant technology. There is Kubernetes. And the, the question is how do you get Kubernetes to help you build applications that can be born anywhere and live anywhere and take advantage of the places that it's running, cuz everywhere has pluses and minuses. >>So you know what the promise of Kubernetes from when I first read about it years ago is runs on my laptop. Yep. I can push it to any cloud, any platform that's that's right. Where's the gap. Where are we in that, in that phase? Like talk to me about scale. Is that, is that, is it that simple? >>Well, that act is actually the problem is that date while the technology is the dominant containerization technology and orchestration technology, it really still takes a power user. It really hasn't been very approachable to the masses. And so it was these very expensive, highly skilled resources that sit in a dark corner that have focused on Kubernetes, but that, that now is trying to evolve to make it more accessible to the masses. It's not about sort of hand wiring together. What is a typical 20 layer stack to really manage Kubernetes and then have your engineers manually can reconfigure it and make sure everything works together. Now it's about how do I create these stacks, make it easy to deploy and manage at scale. So we've gone from sort of DIY developer centric to all right, now, how do I manage this at scale? >>Now this is a point that is important, I think is often overlooked. This is not just about Kubernetes. This is about a whole stack of cloud native technologies. Yes. And you who is going to, who is going to integrate that, all that stuff, piece that stuff together, right? Obviously you have a, a role in that. Yes. But in the enterprise, what is the awareness level of how complex this stack is and how difficult it is to assemble? >>We, we see a recognition of that, that we've had developers working on Kubernetes and applications, but now when we say, how do we weave it into our production environments? How do we ensure things like scalability and governance? How do we have this sort of interesting mix of innovation, flexibility, but with control. And that's sort of an interesting combination where you want developers to be able to run fast and use the latest tools, but you need to create these guardrails to deploy it at scale. >>So where do the developers fit in that operation stack then? Is this, is Kubernetes an AI ops or an ops a task, or is it sort of a shared task across the development spectrum? >>Well, I think there's a desire to allow application developers, to just focus on the application and have a Kubernetes related technology that ensures that all of the infrastructure and related application services are just there to support them. And because the typical stack from the operating system to the application can be up to 20 different layers components. You just want all those components to work together. You don't want application developers to worry about those things. And the latest technologies like spectra cloud there's others are making that easy application engineers focus on their apps, all of the infrastructure and the services are taken care of. And those apps can then live natively on any environment. >>So help paint this picture for us. You know, I get got AKs ETS and those, all of these distributions OpenShift, the tan zoo, where is spec cloud helping me to kind of cobble together all these different distros I thought distro was the, was the thing like, just like Lennox has different distros, you know, right. Randy said different distros >>That actually is the irony. Is that sort of the age of debating, the distros largely is over. There are a lot of distros and if you look at them, there are largely shades of gray in being different from each other. But the Kubernetes distribution is just one element of like 20 elements that all have to work together. So right now what's what's happening is that it's not about the distribution it's now, how do I, again, sorry to repeat myself, but move this into a, into scale. How do I move it into deploy at scale, to be able to manage ongoing at scale, to be able to innovate at scale, to allow engineers, as I said, use the coolest tools, but still have technical guardrails that the, the enterprise knows they'll be in control of what, >>What does at scale mean to the enterprise customers you're talking to now? What do they mean when they say that? >>Well, I think it's interesting cuz we think scale's different cuz we've all been in the industry and it's frankly sort of boring old wor word, but today it means different things. Like how do I automate the deployment at scale? How do I be able to make it really easy to provision resources for applications on any environment from either a virtualized or bare metal data center cloud or today edge is really big where people are trying to push applications out to be closer to this source of the data. And so you want to be able to deploy it scale you wanna manage at scale, you wanna make it easy to, as I said earlier, allow application developers to build their applications, but it ops wants the ability to ensure security and governance and all of that. And then finally innovate at scale. If you look at this show, it's interesting, three years ago, when we started spectra cloud, there are about 1400 businesses or technologies in the Kubernetes ecosystem today there's over 1800 and all of these technologies made up of open source and commercial, all versioning at different rates. It becomes an insurmountable problem unless you can set those guardrails sort of that balance between flexibility and control, let developers access the technologies. But again, manage it as a part of your normal processes of a, of a scale of operation. >>So, so Dave, I'm a little challenged here cuz I'm hearing two where I typically consider conflicting terms. Okay. Flexibility control. Yes. In order to achieve control, I need complexity in order to choose flexibility. I need t-shirt one t-shirt fits all right. To and I, and I, and I get simplicity. How can I get both that just doesn't you know, compute >>Well thus the opportunity and the challenge at the same time. So you're right. So developers want choice, good developers want the ability to choose the latest technology so they can innovate rapidly. And yet it ops wants to be able to make sure that there are guard rails. And so with some of today's technologies like spectral cloud, it is you have the ability to get both. We actually worked with dimensional research and we sponsor an annual state of Kubernetes survey. We found this last summer, that two out of three, it executives said you could not have both flexibility and control together, but in fact they want it. And so it is this interesting balance. How do I give engineers the ability to get anything they want, but it ops the ability to establish control. And that's why Kubernetes is really at its next inflection point. Whereas I mentioned, it's not debates about the distro or DIY projects. It's not big incumbents creating siloed Kubernetes solutions. But in fact it's about allowing all these technologies to work together and be able to establish these controls. And that's, that's really where the industry is today. >>Enterprise enterprise CIOs do not typically like to take chances. Now we were talking about the growth in the market that you described from 1400, 1800 vendors. Most of these companies, very small startups are, are enterprises. Are you seeing them willing to take a leap with these unproven companies or are they holding back and waiting for the IBMs, the HPS, the Microsofts to come in with the VMwares with whatever they solution they have? >>I, I think so. I mean, we sell to the global 2000. We had yesterday as a part of edge day here at the event, we had GE healthcare as one of our customers telling their story. And they're a market share leader in medical imaging equipment. X-rays MRIs, cat scans, and they're, they're starting to treat those as edge devices. And so here is a very large established company, a leader in their industry, working with people like spectral cloud, realizing that Kubernetes is interesting technology. The edge is an interesting thought, but how do I marry the two together? So we are seeing large corporations seeing so much of an opportunity that they're working with the smaller companies, the latest technology. >>So let's talk about the edge a little. You kind of opened it up there. Yeah. How should customers think about the edge versus the cloud data center or even bare metal? >>Actually it's a well bare bare metal is fairly easy is that many people are looking to reduce some of the overhead or inefficiencies of the virtualized environment. And, but we've had really sort of parallel little white tornadoes. We've had bare metal as infrastructure that's been developing and then we've had orchestration technology's developing, but they haven't really come together very well lately. We're finally starting to see that come together. Spectra cloud contributed to open source a metal as a service technology that finally brings these two worlds together. Making bare metal much more approachable to the inters enterprise edge is interesting because it seems pretty obvious. You wanna push your application out closer to your source of data, whether it's AI in fencing or O T or anything like that, you don't wanna worry about intermittent connectivity or latency or anything like that. But people have wanted to be able to treat the edge as if it's almost like a cloud where all I worry about is the app. >>So really the edge to us is just the next extension in a multi-cloud sort of motif where I want these edge devices to require low it resources to automate the provisioning, automate the ongoing version management patch management really act like a cloud. And we're seeing this as very, very popular now. And I just used the GE healthcare example of that. Imagine a cat scan machine, I'm making this part up in China and that's just an edge device. And it's, it's doing medical imagery, which is very intense in terms of data. You want to be able to process it quickly and accurately as close to the endpoint, the healthcare provider as possible. >>So let's talk about that in some level of detail, as we think about kind of edge and you know, these fixed devices such as imaging device, are we putting agents on there? Are we looking at something talking back to the cloud, where does special cloud inject and help make that simple, that problem of just having dispersed endpoints all over the world? Simpler? >>Sure. Well we announced our edge Kubernetes edge solution at a big medical conference called, called hymns months ago. And what we allow you to do is we allow the application engineers to develop their application. And then you can de you can design this declarative model, this cluster API, but beyond cluster profile, which determines which additional application services you need and the edge device, all the person has to do with the endpoint is plug in the power plug in the communications. It registers the edge device. It automates the deployment of the full stack. And then it does the ongoing versioning and patch management, sort of a self-driving edge device running Kubernetes. And we make it just very, very easy. No, it resources required at the endpoint, no expensive field engineering resources to go to these endpoints twice a year to apply new patches and things like that, all >>Automated, but there's so many different types of edge devices with different capabilities, different operating systems, some have no operating system. Yeah. I mean, what, that seems like a much more complex environment, just calling it, the edge is simple, but what you're really talking about is thousands of different devices, right? That you have to run your applications on how, how are you dealing with that? >>So one of the ways is that we're really unbiased. In other words, we're OS and distro agnostic. So we don't want to debate about which distribution you like. We don't want to debate about, you know, which OS you want to use. The truth is you're right. There's different environments and different choices that you'll wanna make. And so the key is, is how do you incorporate those and also recognize everything beyond those, you know, OS and Kubernetes and all of that and manage that full stack. So that's what we do is we allow you to choose which tools you want to use and let it be deployed and managed on any environment. >>And who's respo, I'm sorry, key. Who's responsible for making Kubernetes run on the edge device. >>We do. We provision the entire stack. I mean, of course the company does using our product, but we provision the entire Kubernetes infrastructure stack all the application services and the application itself on that device. >>So I would love to dig into like where pods happen and all that, but provisioning is getting to the point that it's a solve problem. Day two. Yes. Like we, you know, you just mentioned hymns, highly regulated environments. How does spec cloud helping with configuration management change control, audit, compliance, et cetera, the hard stuff. >>Yep. And one of the things we do, you bring up a good point is we manage the full life cycle from day zero, which is sort of create, deploy all the way to day two, which is about, you know, access control, security. It's about ongoing versioning and patch management. It's all of that built into the platform. And, but you're right. Like the medical industry has a lot of regulations. And so you need to be able to make sure that everything works. It's always up to the latest level, have the highest level of security. And so all that's built into the platform. It's not just a fire and forget it really is about that full life cycle of deploying, managing on an ongoing basis. >>Well, Dave, I'd love to go into a great deal of detail with you about kind of this day two option. I think we'll be covering a lot more of that topic, Paul, throughout the week, as we talk about just, you know, as we've gotten past, you know, how do I deploy Kubernetes pod to how do I actually operate it? >>Absolutely, absolutely. The devil is in the details as they say, >>Well, and also too, you have to recognize that the edge has some very unique requirements. You want very small form factors. Typically you want low it resources. It has to be sort of zero touch or low touch because if you're a large food provider with 20,000 store locations, you don't wanna send out field engineers two or three times a year to update them. So it really is an interesting beast and we have some exciting technology and people like GE are using that. >>Well, Dave, thanks a lot for coming on to Q you're now Cub Alon. You've not been on before. >>I have actually. Yes. Oh. But I always enjoy it. >>It's great conversation. Foria Spain. I'm Keith towns along with Paul Gillon and you're watching the cue, the leader in high tech coverage.

(upbeat music) >> Announcer: From theCUBE studios in Palo Alto in Boston connecting with thought leaders all around the world, this is theCUBE Conversation. >> Well, welcome to theCUBE Conversation here in theCUBE studios in Palo Alto, California. I'm John Furrier, your host. Got a great conversation with two great guests, going to explore the edge, what it means in terms of commercial, but also national security. And as the world goes digital, we're going to have that deep dive conversation around how it's all transforming. We've got Ki Lee, Vice President of Booz Allen's Digital Business. Ki, great to have you. John Pisano, Principal at Booz Allen's Digital Cloud Solutions. Gentlemen, thanks for coming on. >> And thanks for having us, John. >> So one of the most hottest topics, obviously besides cloud computing having the most refactoring impact on business and government and public sector has been the next phase of cloud growth and cloud scale, and that's really modern applications and consumer, and then here for national security and for governments here in the U.S. is military impact. And as digital transformation starts to go to the next level, you're starting to see the architectures emerge where the edge, the IoT edge, the industrial IoT edge, or any kind of edge concept, 5G is exploding, making that much more of a dense, more throughput for connectivity with wireless. You got Amazon with Snowball, Snowmobile, all kinds of ways to deploy technology, that's IT like and operational technologies. It's causing quite a cloud operational opportunity and disruption, so I want to get into it. Ki, let's start with you. I mean, we're looking at an architecture that's changing both commercial and public sector with the edge. What are the key considerations that you guys see as people have to really move fast in this new architecture of digital? >> Yeah, John, I think it's a great question. And if I could just share our observation on why we even started investing in edge. You mentioned the cloud, but as we've reflected upon kind of the history of IT, then you take a look from mainframes to desktops to servers to cloud to mobile and now IoT, what we observed was that industry investing in infrastructure led to kind of an evolution of IT, right? So as you mentioned, with industry spending billions on IoT and edge, we just feel that that's going to be the next evolution. If you take a look at, you mentioned 5G, I think 5G will be certainly an accelerator to edge because of the resilience, the lower latency and so forth. But taking a look at what's happening in space, you mentioned space earlier as well, right, and what Starlink is doing by putting satellites to actually provide transport into the space, we're thinking that that actually is going to be the next ubiquitous thing. Once transport becomes ubiquitous, just like cloud allows storage to be ubiquitous. We think that the next generation internet will be space-based. So when you think about it, connected, it won't be connected servers per se, it will be connected devices. >> John: Yeah, yeah. >> That's kind of some of the observations and why we've been really focusing on investing in edge. >> I want to come back to that piece around space and edge and bring it from a commercial and then also tactical architecture in a minute 'cause there's a lot to unpack there, role of open source, modern application development, software and hardware supply chains, all are core issues that are going to emerge. But I want to get with John real quick on cloud impact, because you think about 5G and the future of work or future of play, you've got people, right? So whether you're at a large concert like Coachella or a 49ers or Patriots game or Redskins game if you're in the D.C. area, you got people there, of congestion, and now you got devices now serving those people. And that's their play, people at work, whether it's a military operation, and you've got work, play, tactical edge things. How is cloud connecting? 'Cause this is like the edge has never been kind of an IT thing. It's been more of a bandwidth or either telco or something else operationally. What's the cloud at scale, cloud operations impact? >> Yeah, so if you think about how these systems are architected and you think about those considerations that Ki kind of touched on, a lot of what you have to think about now is what aspects of the application reside in the cloud, where you tend to be less constrained. And then how do you architect that application to move out towards the edge, right? So how do I tier my application? Ultimately, how do I move data and applications around the ecosystem? How do I need to evolve where my application stages things and how that data and those apps are moved to each of those different tiers? So when we build a lot of applications, especially if they're in the cloud, they're built with some of those common considerations of elasticity, scalability, all those things; whereas when you talk about congestion and disconnected operations, you lose a lot of those characteristics, and you have to kind of rethink that. >> Ki, let's get into the aspect you brought up, which is space. And then I was mentioning the tactical edge from a military standpoint. These are use cases of deployments, and in fact, this is how people have to work now. So you've got the future of work or play, and now you've got the situational deployments, whether it's a new tower of next to a stadium. We've all been at a game or somewhere or a concert where we only got five bars and no connectivity. So we know what that means. So now you have people congregating in work or play, and now you have a tactical deployment. What's the key things that you're seeing that it's going to help make that better? Are there any breakthroughs that you see that are possible? What's going on in your view? >> Yeah, I mean, I think what's enabling all of this, again, one is transport, right? So whether it's 5G to increase the speed and decrease the latency, whether it's things like Starlink with making transport and comms ubiquitous, that tied with the fact that ships continue to get smaller and faster, right? And when you're thinking about tactical edge, those devices have limited size, weight, power conditions and constraints. And so the software that goes on them has to be just as lightweight. And that's why we've actually partnered with SUSE and what they've done with K3s to do that. So I think those are some of the enabling technologies out there. John, as you've kind of alluded to it, there are additional challenges as we think about it. We're not, it's not a simple transition and monetization here, but again, we think that this will be the next major disruption. >> What do you guys think, John, if you don't mind weighing in too on this as modern application development happens, we just were covering CloudNativeCon and KubeCon, DockerCon, containers are very popular. Kubernetes is becoming super great. As you look at the telco landscape where we're kind of converging this edge, it has to be commercially enterprise grade. It has to have that transit and transport that's intelligent and all these new things. How does open source fit into all this? Because we're seeing open source becoming very reliable, more people are contributing to open source. How does that impact the edge in your opinion? >> So from my perspective, I think it's helping accelerate things that traditionally maybe may have been stuck in the traditional proprietary software confines. So within our mindset at Booz Allen, we were very focused on open architecture, open based systems, which open source obviously is an aspect of that. So how do you create systems that can easily interface with each other to exchange data, and how do you leverage tools that are available in the open source community to do that? So containerization is a big drive that is really going throughout the open source community. And there's just a number of other tools, whether it's tools that are used to provide basic services like how do I move code through a pipeline all the way through? How do I do just basic hardening and security checking of my capabilities? Historically, those have tend to be closed source type apps, whereas today you've got a very broad community that's able to very quickly provide and develop capabilities and push it out to a community that then continues to adapt and add to it or grow that library of stuff. >> Yeah, and then we've got trends like Open RAN. I saw some Ground Station for the AWS. You're starting to see Starlink, you mentioned. You're bringing connectivity to the masses. What is that going to do for operators? Because remember, security is a huge issue. We talk about security all the time. Where does that kind of come in? Because now you're really OT, which has been very purpose-built kind devices in the old IoT world. As the new IoT and the edge develop, you're going to need to have intelligence. You're going to be data-driven. There is an open source impact key. So, how, if I'm a senior executive, how do I get my arms around this? I really need to think this through because the security risks alone could be more penetration areas, more surface area. >> Right. That's a great question. And let me just address kind of the value to the clients and the end users in the digital battlefield as our warriors to increase survivability and lethality. At the end of the day from a mission perspective, we know we believe that time's a weapon. So reducing any latency in that kind of observe, orient, decide, act OODA loop is value to the war fighter. In terms of your question on how to think about this, John, you're spot on. I mean, as I've mentioned before, there are various different challenges, one, being the cyber aspect of it. We are absolutely going to be increasing our attack surface when you think about putting processing on edge devices. There are other factors too, non-technical that we've been thinking about s we've tried to kind of engender and kind of move to this kind of edge open ecosystem where we can kind of plug and play, reuse, all kind of taking the same concepts of the open-source community and open architectures. But other things that we've considered, one, workforce. As you mentioned before, when you think about these embedded systems and so forth, there aren't that many embedded engineers out there. But there is a workforce that are digital and software engineers that are trained. So how do we actually create an abstraction layer that we can leverage that workforce and not be limited by some of the constraints of the embedded engineers out there? The other thing is what we've, in talking with several colleagues, clients, partners, what people aren't thinking about is actually when you start putting software on these edge devices in the billions, the total cost of ownership. How do you maintain an enterprise that potentially consists of billions of devices? So extending the standard kind of DevSecOps that we move to automate CI/CD to a cloud, how do we move it from cloud to jet? That's kind of what we say. How do we move DevSecOps to automate secure containers all the way to the edge devices to mitigate some of those total cost of ownership challenges. >> It's interesting, as you have software defined, this embedded system discussion is hugely relevant and important because when you have software defined, you've got to be faster in the deployment of these devices. You need security, 'cause remember, supply chain on the hardware side and software in that too. >> Absolutely. >> So if you're going to have a serviceability model where you have to shift left, as they say, you got to be at the point of CI/CD flows, you need to be having security at the time of coding. So all these paradigms are new in Day-2 operations. I call it Day-0 operations 'cause it should be in everyday too. >> Yep. Absolutely. >> But you've got to service these things. So software supply chain becomes a very interesting conversation. It's a new one that we're having on theCUBE and in the industry Software supply chain is a superly relevant important topic because now you've got to interface it, not just with other software, but hardware. How do you service devices in space? You can't send a break/fix person in space. (chuckles) Maybe you will soon, but again, this brings up a whole set of issues. >> No, so I think it's certainly, I don't think anyone has the answers. We sure don't have all the answers but we're very optimistic. If you take a look at what's going on within the U.S. Air Force and what the Chief Software Officer Nic Chaillan and his team, and we're a supporter of this and a plankowner of Platform One. They were ahead of the curve in kind of commoditizing some of these DevSecOps principles in partnership with the DoD CIO and that shift left concept. They've got a certified and accredited platform that provides that DevSecOps. They have an entire repository in the Iron Bank that allows for hardened containers and reciprocity. All those things are value to the mission and around the edge because those are all accelerators. I think there's an opportunity to leverage industry kind of best practices as well and patterns there. You kind of touched upon this, John, but these devices honestly just become firmware. The software is just, if the devices themselves just become firmware , you can just put over the wire updates onto them. So I'm optimistic. I think all the piece parts are taking place across industry and in the government. And I think we're primed to kind of move into this next evolution. >> Yeah. And it's also some collaboration. What I like about, why I'm bringing up the open source angle and I think this is where I think the major focus will shift to, and I want to get your reaction to it is because open source is seeing a lot more collaboration. You mentioned some of the embedded devices. Some people are saying, this is the weakest link in the supply chain, and it can be shored up pretty quickly. But there's other data, other collective intelligence that you can get from sharing data, for instance, which hasn't really been a best practice in the cybersecurity industry. So now open source, it's all been about sharing, right? So you got the confluence of these worlds colliding, all aspects of culture and Dev and Sec and Ops and engineering all coming together. John, what's your reaction to that? Because this is a big topic. >> Yeah, so it's providing a level of transparency that historically we've not seen, right? So in that community, having those pipelines, the results of what's coming out of it, it's allowing anyone in that life cycle or that supply chain to look at it, see the state of it, and make a decision on, is this a risk I'm willing to take or not? Or am I willing to invest and personally contribute back to the community to address that because it's important to me and it's likely going to be important to some of the others that are using it? So I think it's critical, and it's enabling that acceleration and shift that I talked about, that now that everybody can see it, look inside of it, understand the state of it, contribute to it, it's allowing us to break down some of the barriers that Ki talked about. And it reinforces that excitement that we're seeing now. That community is enabling us to move faster and do things that maybe historically we've not been able to do. >> Ki, I'd love to get your thoughts. You mentioned battlefield, and I've been covering a lot of the tactical edge around the DOD's work. You mentioned about the military on the Air Force side, Platform One, I believe, was from the Air Force work that they've done, all cloud native kind of directions. But when you talk about a war field, you talk about connectivity. I mean, who controls the DNS in Taiwan, or who controls the DNS in Korea? I mean, we have to deploy, you've got to stand up infrastructure. How about agility? I mean, tactical command and control operations, this has got to be really well done. So this is not a trivial thing. >> No. >> How are you seeing this translate into the edge innovation area? (laughs) >> It's certainly not a trivial thing, but I think, again, I'm encouraged by how government and industry are partnering up. There's a vision set around this joint all domain command control, JADC2. And then all the services are getting behind that, are looking into that, and this vision of this military, internet of military things. And I think the key thing there, John, as you mentioned, it's not just the connected of the sensors, which requires the transport again, but also they have to be interoperable. So you can have a bunch of sensors and platforms out there, they may be connected, but if they can't speak to one another in a common language, that kind of defeats the purpose and the mission value of that sensor or shooter kind of paradigm that we've been striving for for ages. So you're right on. I mean, this is not a trivial thing, but I think over history we've learned quite a bit. Technology and innovation is happening at just an amazing rate where things are coming out in months as opposed to decades as before. I agree, not trivial, but again, I think there are all the piece parts in place and being put into place. >> I think you mentioned earlier that the personnel, the people, the engineers that are out there, not enough, more of them coming in. I think now the appetite and the provocative nature of this shift in tech is going to attract a lot of people because the old adage is these are hard problems attracts great people. You got in new engineering, SRE like scale engineering. You have software development, that's changing, becoming much more robust and more science-driven. You don't have to be just a coder as a software engineer. You could be coming at it from any angle. So there's a lot more opportunities from a personnel standpoint now to attract great people, and there's real hard problems to solve, not just security. >> Absolutely. Definitely. I agree with that 100%. I would also contest that it's an opportunity for innovators. We've been thinking about this for some time, and we think there's absolute value from various different use cases that we've identified, digital battlefield, force protection, disaster recovery, and so forth. But there are use cases that we probably haven't even thought about, even from a commercial perspective. So I think there's going to be an opportunity just like the internet back in the mid '90s for us to kind of innovate based on this new kind of edge environment. >> It's a revolution. New leadership, new brands are going to emerge, new paradigms, new workflows, new operations, clearly great stuff. I want to thank you guys for coming on. I also want to thank Rancher Labs for sponsoring this conversation. Without their support, we wouldn't be here. And now they were acquired by SUSE. We've covered their event with theCUBE virtual last year. What's the connection with those guys? Can you guys take a minute to explain the relationship with SUSE and Rancher? >> Yeah. So it's actually it's fortuitous. And I think we just, we got lucky. There's two overall aspects of it. First of all, we are both, we partner on the Platform One basic ordering agreement. So just there we had a common mentality of DevSecOps. And so there was a good partnership there, but then when we thought about we're engaging it from an edge perspective, the K3s, right? I mean, they're a leader from a container perspective obviously, but the fact that they are innovators around K3s to reduce that software footprint, which is required on these edge devices, we kind of got a twofer there in that partnership. >> John, any comment on your end? >> Yeah, I would just amplify, the K3s aspects in leveraging the containers, a lot of what we've seen success in when you look at what's going on, especially on that tactical edge around enabling capabilities, containers, and the portability it provides makes it very easy for us to interface and integrate a lot of different sensors to close the OODA loop to whoever is wearing or operating that a piece of equipment that the software is running on. >> Awesome, I'd love to continue the conversation on space and the edge and super great conversation to have you guys on. Really appreciate it. I do want to ask you guys about the innovation and the opportunities of this new shift that's happening as the next big thing is coming quickly. And it's here on us and that's cloud, I call it cloud 2.0, the cloud scale, modern software development environment, edge with 5G changing the game. Ki, I completely agree with you. And I think this is where people are focusing their attention from startups to companies that are transforming and re-pivoting or refactoring their existing assets to be positioned. And you're starting to see clear winners and losers. There's a pattern emerging. You got to be in the cloud, you got to be leveraging data, you got to be horizontally scalable, but you got to have AI machine learning in there with modern software practices that are secure. That's the playbook. Some people are making it. Some people are not getting there. So I'd ask you guys, as telcos become super important and the ability to be a telco now, we just mentioned standing up a tactical edge, for instance. Launching a satellite, a couple of hundred K, you can launch a CubeSat. That could be good and bad. So the telco business is changing radically. Cloud, telco cloud is emerging as an edge phenomenon with 5G, certainly business commercial benefits more than consumer. How do you guys see the innovation and disruption happening with telco? >> As we think through cloud to edge, one thing that we realize, because our definition of edge, John, was actually at the point of data collection on the sensor themselves. Others' definition of edge is we're a little bit further back, what we call it the edge of the IT enterprise. But as we look at this, we realize that you needed this kind of multi echelon environment from your cloud to your tactical clouds where you can do some processing and then at the edge of themselves. Really at the end of the day, it's all about, I think, data, right? I mean, everything we're talking about, it's still all about the data, right? The AI needs the data, the telco is transporting the data. And so I think if you think about it from a data perspective in relationship to the telcos, one, edge will actually enable a very different paradigm and a distributed paradigm for data processing. So, hey, instead of bringing the data to some central cloud which takes bandwidth off your telcos, push the products to the data. So mitigate what's actually being sent over those telco lines to increase the efficiencies of them. So I think at the end of the day, the telcos are going to have a pretty big component to this, even from space down to ground station, how that works. So the network of these telcos, I think, are just going to expand. >> John, what's your perspective? I mean, startups are coming out. The scalability, speed of innovation is a big factor. The old telco days had, I mean, months and years, new towers go up and now you got a backbone. It's kind of a slow glacier pace. Now it's under siege with rapid innovation. >> Yeah, so I definitely echo the sentiments that Ki would have, but I would also, if we go back and think about the digital battle space and what we've talked about, faster speeds being available in places it's not been before is great. However, when you think about facing an adversary that's a near-peer threat, the first thing they're going to do is make it contested, congested, and you have to be able to survive. While yes, the pace of innovation is absolutely pushing comms to places we've not had it before, we have to be mindful to not get complacent and over-rely on it, assuming it'll always be there. 'Cause I know in my experience wearing the uniform, and even if I'm up against an adversary, that's the first thing I'm going to do is I'm going to do whatever I can to disrupt your ability to communicate. So how do you take it down to that lowest level and still make that squad, the platoon, whatever that structure is, continue survivable and lethal. So that's something I think, as we look at the innovations, we need to be mindful of that. So when I talk about how do you architect it? What services do you use? Those are all those things that you have to think about. What if I lose it at this echelon? How do I continue the mission? >> Yeah, it's interesting. And if you look at how companies have been procuring and consuming technology, Ki, it's been like siloed. "Okay, we've got a workplace workforce project, and we have the tactical edge, and we have the siloed IT solution," when really work and play, whether it's work here in John's example, is the war fighter. And so his concern is safety, his life and protection. >> Yeah. >> The other department has to manage the comms, (laughs) and so they have to have countermeasures and contingencies ready to go. So all this is, they all integrate it now. It's not like one department. It's like it's together. >> Yeah. John, I love what you just said. I mean, we have to get away from this siloed thinking not only within a single organization, but across the enterprise. From a digital battlefield perspective, it's a joint fight, so even across these enterprise of enterprises, So I think you're spot on. We have to look horizontally. We have to integrate, we have to inter-operate, and by doing that, that's where the innovation is also going to be accelerated too, not reinventing the wheel. >> Yeah, and I think the infrastructure edge is so key. It's going to be very interesting to see how the existing incumbents can handle themselves. Obviously the towers are important. 5G obviously, that's more deployments, not as centralized in terms of the spectrum. It's more dense. It's going to create more connectivity options. How do you guys see that impacting? Because certainly more gear, like obviously not the centralized tower, from a backhaul standpoint but now the edge, the radios themselves, the wireless transit is key. That's the real edge here. How do you guys see that evolving? >> We're seeing a lot of innovations actually through small companies who are really focused on very specific niche problems. I think it's a great starting point because what they're doing is showing the art of the possible. Because again, we're in a different environment now. There's different rules. There's different capabilities. But then we're also seeing, you mentioned earlier on, some of the larger companies, the Amazons, the Microsofts, also investing as well. So I think the merge of the, you know, or the unconstrained or the possible by these small companies that are just kind of driving innovations supported by the maturity and the heft of these large companies who are building out these hardened kind of capabilities, they're going to converge at some point. And that's where I think we're going to get further innovation. >> Well, I really appreciate you guys taking the time. Final question for you guys, as people are watching this, a lot of smart executives and teams are coming together to kind of put the battle plans together for their companies as they transition from old to this new way, which is clearly cloud-scale, role of data. We hit out all the key points I think here. As they start to think about architecture and how they deploy their resources, this becomes now the new boardroom conversation that trickles down and includes everyone, including the developers. The developers are now going to be on the front lines. Mid-level managers are going to be integrated in as well. It's a group conversation. What are some of the advice that you would give to folks who are in this mode of planning architecture, trying to be positioned to come out of this pandemic with a massive growth opportunity and to be on the right side of history? What's your advice? >> It's such a great question. So I think you touched upon it. One is take the holistic approach. You mentioned architectures a couple of times, and I think that's critical. Understanding how your edge architectures will let you connect with your cloud architecture so that they're not disjointed, they're not siloed. They're interoperable, they integrate. So you're taking that enterprise approach. I think the second thing is be patient. It took us some time to really kind of, and we've been looking at this for about three years now. And we were very intentional in assessing the landscape, how people were discussing around edge and kind of pulling that all together. But it took us some time to even figure it out, hey, what are the use cases? How can we actually apply this and get some ROI and value out for our clients? So being a little bit patient in thinking through kind of how we can leverage this and potentially be a disruptor. >> John, your thoughts on advice to people watching as they try to put the right plans together to be positioned and not foreclose any future value. >> Yeah, absolutely. So in addition to the points that Ki raised, I would, number one, amplify the fact of recognize that you're going to have a hybrid environment of legacy and modern capabilities. And in addition to thinking open architectures and whatnot, think about your culture, the people, your processes, your techniques and whatnot, and your governance. How do you make decisions when it needs to be closed versus open? Where do you invest in the workforce? What decisions are you going to make in your architecture that drive that hybrid world that you're going to live in? All those recipes, patience, open, all that, that I think we often overlook the cultural people aspect of upskilling. This is a very different way of thinking on modern software delivery. How do you go through this lifecycle? How's security embedded? So making sure that's part of that boardroom conversation I think is key. >> John Pisano, Principal at Booz Allen Digital Cloud Solutions, thanks for sharing that great insight. Ki Lee, Vice President at Booz Allen Digital Business. Gentlemen, great conversation. Thanks for that insight. And I think people watching are going to probably learn a lot on how to evaluate startups to how they put their architecture together. So I really appreciate the insight and commentary. >> Thank you. >> Thank you, John. >> Okay. I'm John Furrier. This is theCUBE Conversation. Thanks for watching. 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>>Live from Las Vegas. It's the cube covering UI path forward Americas 2019 brought to you by UI path. >>Hello everyone and welcome to the cubes live coverage of UI path forward here at the Bellagio. I'm your host hosting alongside of Dave Volante. David's so great to be here with you. I'm so excited to get into this. See Rebecca, so we were, we would use came from the keynote. A lot of high profile UI path executives and important customers were on there too, but then this is the message is it's time to reboot work. It's time to reboot your business, transformed the customer experience, transform the employee experience. I'm wondering as someone who spent a lot of time at these kinds of conferences, and here's a lot of this, these, this kind of messaging, especially in this age of digital transformation, how compelling do you find this value proposition, this, this idea that RPA, robotics, processing automation can do these things? >>The first thing I would mention, Rebecca, is to me it's all about the customers. And you know, it's rare that you see a tech show start with the customers to actually do in the intro. I've seen it before. Nutanix actually does it at his shows, but it's, but it's quite rare because you know, the vendors want to put their message out, they want to control everything, and so they're very, very cautious about that. But, so we had three customers up on stage today doing the intro, which I thought was kind of cool. Tech shows, you know, a lot of smoke, a lot of mirrors and so forth. So you have to try to squint through that. I would say this, it's very clear that the age of automation is here. You know, people have been always concerned about automation for good reason. They're afraid that automation is gonna take away their jobs. >>Having said that, machines have always replace humans. We've talked about this a lot on the cube, but this is the first time in history that machines are replacing humans with cognitive tasks. So that's got to be scaring people a little bit. But when you come back and answer your question, when you talk to customers, they're really happy about software robots because they're doing, they're automating mundane tasks that these folks don't want to do on a day to day basis and they want to do other things. They want to get their weekends back. They don't want to just manually enter data from spreadsheets into applications and back and forth. And so from that standpoint, I think it is real and it is unique. You know, the big question is how much of this is transformational and is it really a path to AI something that UI path and others are really pointing towards and we're going to explore that, >>right? I mean in what you were just saying too is that that that the company's pitch is that we are freeing people. We are liberating them from the mundane, from the drudgery, from the data entry. And as you, as you pointed out, rightfully, a lot of the customers are saying, Oh no, it's giving our time. It's giving our employees time back to focus on the higher level tasks, the more creative aspects of their job. But, but I wonder if it is in fact a w what it really is doing. Two jobs. I mean I think that there was a really telling line in that Forbes profile of uh, Daniel Dina's who is the, the CEO of this company is founder of this company. The first ever bought billionaire exactly. Um, where it was an MIT professor quoted saying, you know, we always say to the companies that we say, give, give us your data and we'll tell you if it is in fact, uh, having this job killing effect. And he said, the companies don't want to give, give that up. >>Right? So now just look at the why is Daniel didn't as a billionaire, it will here, here, here's why. >>Yeah, walk, walk us through this. >>So UI path is up to 3,400 employees. 34 50 is the actual number. Now back in 2017, two years ago, this company did $25 million in annual recurring revenue. Now, ARR is a metric that's very important because you know, even though you book, let's say you book a $12,000 deal, you recognize that $1,000 a month over the 12 month period. So ARR is a very really important metric. So 25 million in 2017 my sources indicate that they'll do over 300 million this year in ARR. So we're talking about a 12 X plus increase in a two year period. They've raised $1 billion. One of their key competitors, automation anywhere has raised similar amounts of money. So they're talking about a couple of billion dollars raised just in the last couple of years. UI past valuation in March was $7 billion. So at that kind of back of napkin, and we're talking about a $10 billion valuation, Daniel obviously owns a lot of that. >>So 20% yeah. So it's, it's pretty substantial in terms of the market impact. Now valuations, as you all know, it's a fleeting metric, right? It comes in, it goes, but so the, but the landscape is very strong right now. It's really interesting to see how much customers are glomming onto this automation tailwind. The other comment I would make is let's lay out the sort of competitive landscape. UI path has gone from kind of a clear third in the marketplace to clear number one. I mean they're kind of separating from the pack, but there are others automation anywhere, blue prism and there are a number of legacy customers as well >>that that's what I wanted to ask you too, is that we have seen a few Microsoft and Google of course are, are, are partnered in their, in their customers, but they also are moving into this area themselves. So I mean will you will let UI path be able to maintain its competitive position as these very established and frankly very smart companies move into this area. Safety's >>another one. SAP bought an RPA company. It's a good question, but, so if you look at, let me start with this sort of underlying trend. If you look at the spending data, so we have access to the enterprise technology, research spending data and it shows the entire space is gaining share relative to other technology initiatives. So when you look at the data for UI path automation, anywhere blue prism, even legacy process automation companies like Pega systems, they're all actually from a spending standpoint attracting a lot of attention. So it's this rising tide lifts all ships. It's still somewhat early in terms of this next generation RPA if you will, you I-PASS advantage is simplicity. They are totally focused on this. You see this all the time. Do we go best of breed or do we go with a suite? So if Oracle comes up with an RPA solution, they throw it in for free, you know, does a customer take that? >>I think it comes down to what the business value is and that's something we're going to explore. It's not uncommon in detect industry that there's a first mover advantage or maybe it's a second mover advantage. You know, Facebook wasn't really first mover, but the one who really gets it right is kind of a winner take most. And so that's where a UI path is going like crazy right now. Trying to scale the company, raise a bunch of money. We saw this week a bunch of bankers sort of sniffing around. All the bankers are here cause they want their business. So I would expect there's some kind of IPO on the horizon, which I think they need to do to be, to your point to be able to compete with the big guys. So bottom line is they have to do it on a better product, more openness, moving faster and getting to scale. And I think they'll be able to reach escape velocity. I don't know if there's enough room for the big three. I would expect that given the spending climate is very good for everybody right now. I would expect within the next two to three years, some consolidation in this space. >>Well. So one of the things that you had just talked about with this next generation RPA, and that is exactly where we're going because these bots have got to become more durable, more smarter and more capable of handling complex tasks. We saw a number of new product announcements today. Oh, I might to get your thoughts and what you think about them and just whether or not they will have this transformational effect. Um, so, so yes, we have some new product announcements, some, some that democratize automation building that all you have to do is know how to run an Excel spreadsheet and you too can build an automation in your company. >>Yeah. It'll take a little bit of training though. >>I know. I think a better idea for those those demos is they should just pluck someone out of the audience and say, okay, you're going to do this. >>No, they would fail. I mean, let's say said, I remember the first time you learned Excel, I'm old enough to remember slash file, retrieve, paste, copy, whatever. You had to go through some training and we went through classes back in the eighties I think it's a similar here. I mean it's not overly complex. It's gonna have a low code theme, but you're right, UI path announced the number of new products. You know, we looked at this a couple of years ago, we went, we went out and we took the big three from the Forrester wave blue prism automation anywhere in UI path and we said, Hey, let's download them and start building some, some, some automations. While the only software we could get ahold of was UI path. Because as they say, they had kind of a simpler or more open model. The other guys were like, well, talk to a reseller is spend some money. >>And we were like, no, we just want to try it before we buy it. And we weren't able to get the other guy software. Now I think automation anywhere has made some strides in that regard in terms of simplification. You know, it's a copycat industry like the NFL. But so let's remember here we're talking about automating mundane tasks. Relatively simple automations. The customers are asking for things like more complex automations. How do we prioritize the automations? How do we figure out where, what's the best bang for the buck? How do we actually have attended bots because many of these are unintended. They'd like to have the human injected into the equation and that's pretty interesting because it brings forth this augmentation scenario that's everybody's talking about in AI and that starts to move us from sort of this tactical, I'm going to save some time on a use case specific or a technology specific automation to something that's more strategic that I can scale across my organization but right now people are saving money on this as a super hot space. As I say, all the bankers are trying to get in because they know some other ideas are coming down the road and the VCs I'm sure are gonna want the air exits. >>I want to talk to you about the leadership of this company. This is Daniel Dienes and you have interviewed him many times. Do minimun has as well. He he, he seems like a different kind of CEO. I mean, first of all, he is, he's a Romanian. Uh, he grew up, uh, behind the iron curtain. Uh, he was a professional bridge player for awhile, at least play competitive bridge player play competitive bridge and now he is a company headquartered in New York city. He still spends a lot of time in Bucharest but I'm curious to hear your thoughts about his leadership style and the kind of culture he's created at UI path and whether or not, because he's made some key hires from AWS, from Google, some, some of the more established tech players, whether or not he is, whether or not he'll be able to keep that startup culture, that startup mindset as the company becomes so much bigger. Well >>I think it's a concern and something that we want to ask about when you ask Daniel about, you know, how have you been able to do this? He'll talk about the mistakes that they made, how they sort of, they had a build it and you and they shall come mentality, which is kind of kind of old thinking these days and they sort of lucked into this RPA space. He also emphasize, emphasize as humble, and he's a very humble guy. I mean, you'll, you'll, you'll meet him I think last year he came on and you know, he's a developer. He had a tee shirt on. He's a coder right now. He's a billionaire coder. So maybe, maybe he'll, he'll dress up a little bit, but you know, maybe a fancy tee shirt, I don't know. Or maybe a collared shirt that says UI path on it. We'll see. >>But so they end, they want to move fast. They believe in openness there. They believe in transparency. I think those things worked in today's marketplace. People love the guy. I mean the customers love them. The employees love them. As you said, they're pulling people in from the hottest companies. Google, AWS. We, I got a on the shoulder today from, from a gentleman and I know from Google, he was in sales at Google. It's not me. There's no, Oh, I'm day three. And so people want to be part of this, this rocket ship. And I think it's gonna move very, very fast. Like I say, I think you're going to see some moves in the marketplace. I think you're going to see some exits and consolidations. We saw some M and a today UI path announced the acquisition of company called process gold that actually competes with a partner of UI path. So it's again, people are going to be on collision courses and they recently made another acquisition of a company called step shots and we're seeing some M and a, you know, relatively small MNA, but it's all about how can they transform from this little startup to this major player. To your point that can compete with the Microsofts and the SAP and the big whales of the world. >>And what do you think is his bigger selling point? Is it that it is transforming the employee experience, which as we know that that should not be discounted because an employee who is doing less mundane tasks able to focus on the more creative interesting parts of his or her job is a happier employee, happier your employees, more productive employee. A more productive employee means a healthier bottom line. So that's now funding to discount. Also the customer experience, as you said, which is clearly a huge top priority for this company. But, but I think the question is, is this technology now is a transformative enough? >>You know, as you asked that question, it kind of reminds me in a different way of a company that we've followed for years service now. When service now first came out, it was kind of doing what people saw as help desk, improving help desk, and they disrupted an industry and they made it better, which is kind of boring. It's kind of mundane, but actually having good it where you're not constantly down and you're not complaining and stuff's not falling through the cracks actually can be somewhat transformative. Kind of boring, but really important. And I see a similar sort of pattern here now the vision is, you know, a robot for every worker and the path to AI and we'll see. But right now the trans, the transformation is we're going to take away all this crap that you hate doing all these crap locations or mundane tasks and we're going to make your life better. >>And people workers want that and it's going to be in theory, a productivity boost as a result of that. That in and of itself, I think Rebecca can be transformative because it'll, it'll help with morale, it'll help with culture, it'll allow people to shift their emphasis on more strategic work and drive more value for the companies. And so, and I think companies that invest in RPA are, are seeing returns in terms of quality, just in terms of employee morale. You'll hear that from the customers that we talked to today. So I think in that sense it can be transformative like service now was now can it take the next step or is this really just paving the cow path? Is it just taking mundane known processes, automating them as opposed to really rethinking what process automation should look like. And that's some of the criticism of RPA and the RPA hype. And you know, we're going to talk about that. We're going to talk to customers about that. We've got analysts from HFS coming on, Kathy from Gartner's coming on. So excited to hear their perspectives as well. >>Exactly. And I, I want to reiterate that point that you're absolutely right. Their question is should we actually think about redesigning the process itself rather than automating the, the the flawed process? >>Yeah, and I mean I guess part of me says yes strategically we should be doing that, but another part of me says, look, I don't have to change anything. And I think that's the big advantage of UI path and these other players is you can basically automate what you have today. You don't have to redesign the process because process redesign is a heavy lift. So if I don't have to do a heavy lift, if I can improve what I'm doing today and it works, yeah, it's the old, if it ain't broke, why fix it, but just improve it. I think that's a very powerful, I think the big question I have is, is that like a big hit of a step function or is it really transformative? I feel like today's tech is a step function, which is important. You're going to get that step function, but I think you're going to absorb that benefit fast and then people are going to say, okay, now what? >>Another good example is virtualization. When I first saw virtualization and the ability to spin up a server, my jaw dropped and went, Oh my God, I could spin up a server in five minutes and it used to take weeks, months to spin up a server. That's game changing. Nobody talks about virtualization anymore. It was a, you know, a five year absorption of productivity for it and now it's like, yeah, I've been there, done that. That's yesterday's news. I think the same thing is going to happen with today's RPA and the big question is can they cross that strategic chasm into what the gentleman from Pepsi, the executive from Pepsi was saying, this automation fabric across the enterprise as a, as a platform for automation and artificial intelligence. That's a big leap. These guys get big plans. Daniel Dienes is a big thinker, go big or go home. So I don't, I don't have the crystal ball on that, I think, but I think there's a decent opportunity given that there's enough attention on this business right now that it, that it could be transformed. >>All right, well, hopefully we'll know more at the end of these two days. Dave, I've, I'm looking forward to getting into with you. I'm Rebecca Knight for Dave Volante. Stay tuned for more. You're watching the cube.

(upbeat music) >> Woman Voiceover: From our studios in the heart of Silicon Valley, Palo Alto, California this is a CUBE Conversation. >> Hello and welcome to this special CUBE Conversation here in Palo Alto, California, CUBE Studios, I'm John Furrier, host of theCUBE, here with Sri Ambati. He's the founder and CEO of H20.ai. CUBE Alum, hot start up right in the action of all the machine learning, artificial intelligence, with democratization the role of data in the future, it's all happening with Cloud 2.0, DevOps 2.0, Sri, great to see you. Thanks for coming by. You're a neighbor, you're right down the street from us at our studio here. >> It's exciting to be at theCUBE Com. >> That's KubeCon, that's Kubernetes Con. CUBEcon, coming soon, not to be confused with KubeCon. Great to see you. So tell us about the company, what's going on, you guys are smoking hot, congratulations. You got the right formula here with AI. Explain what's going on. >> It started about seven years ago, and .ai was just a new fad that arrived that arrived in Silicon Valley. And today we have thousands of companies in AI, and we're very excited to be partners in making more companies become AI-first. And our vision here is to democratize AI, and we've made it simple with our open source, made it easy for people to start adapting data science and machine learning in different functions inside their large organizations. And apply that for different use cases across financial services, insurance, health care. We leapfrogged in 2016 and built our first closed source product, Driverless AI, we made it on GPUs using the latest hardware and software innovations. Open source AI has funded the rise of automatic machine learning, Which further reduces the need for extraordinary talent to fill the machine learning. No one has time today, and then we're trying to really bring that automatic machine learning at a very significant crunch time for AI, so people can consume AI better. >> You know, this is one of the things that I love about the current state of the market right now, the entrepreneur market as well as startups and growing companies that are going to go public. Is that there's a new breed of entrepreneurship going on around large scale, standing up infrastructure, shortening the time it takes to do something. Like provisioning. The old AIs, you got to be a PHD. And we're seeing this in data science, you don't have to be a python coder. This democratization is not just a tag line, actually the reality is of a business opportunity. Whoever can provide the infrastructure and the systems for people to do it. It is an opportunity, you guys are doing that. This is a real dynamic. This is a new way, a new kind of dynamic and an industry. >> The three real characteristics on ability to adopt AI, one is data is a team sport. Which means you've got to bring different dimensions within your organization to be able to take advantage of data and AI. And you've got to bring in your domain scientists, work closely with your data scientists, work closely with your data engineers, produce applications that can be deployed, and then get your design on top of it that can convince users or strategists to make those decisions that data is showing up So that takes a multi-dimensional workforce to work closely together. The real problem in adoption of AI today is not just technology, it's also culture. So we're kind of bringing those aspects together in formal products. One of our products, for example, Explainable AI. It's helping the data scientists tell a story that businesses can understand. Why is the model deciding I need to take this test in this direction? Why is this model giving this particular nurse a high credit score even though she doesn't have a high school graduation? That kind of figuring out those democratization goes all the way down. Why is the model deciding what it's deciding, and explaining and breaking that down into English. And building a trust is a huge aspect in AI right now. >> Well I want to get to the talent, and the time, and the trust equation on the next talk, but I want to get the hard news out there. You guys have some news, Driverless AI is one of your core things. Explain the news, what's the big news? >> The big news has been that... AI's a money ball for business, right? And money ball as it has been played out has been the experts were left out of the field, and algorithms taking over. And there is no participation between experts, the domain scientists, and the data scientists. And what we're bringing with the new product in Driverless AI, is an ability for companies to take our AI and become AI companies themselves. The real AI race is not between the Googles and the Amazons and the Microsofts and other AI companies, AI software companies. The real AI race is in the verticals and how can a company which is a bank, or an insurance giant, or a healthcare company take AI platforms and become, take the data and monetize the data and become AI companies themselves. >> Yeah, that's a really profound statement I would agree with 100% on that. I think we saw that early on in the big data world around Hadoop, well Hadoop kind of died by the wayside, but Dave Vellante and the WikiBon team have observed, and they actually predicted, that the most value was going to come from practitioners, not the vendors. 'Cause they're the ones who have the data. And you mentioned verticals, this is another interesting point I want to get more explanation from you on, is that apps are driven by data. Data needs domain-specific information. So you can't just say "I have data, therefore magic happens" it's really at the edge of the domain speak or the domain feature of the application. This is where the data is, so this kind of supports your idea that the AI's about the companies that are using it, not the suppliers of the technology. >> Our vision has always been how we make our customers satisfied. We focus on the customer, and through that we actually make customer one of the product managers inside the company. And the doors that open from working very closely with some of our leading customers is that we need to get them to participate and take AIs, algorithms, and platforms, that can tune automatically the algorithms, and have the right hyper parameter optimizations, the right features. And augment the right data sets that they have. There's a whole data lake around there, around data architecture today. Which data sets am I not using in my current problem I'm solving, that's a reasonable problem I'm looking at. That combination of these various pieces have been automated in Driverless AI. And the new version that we're now bringing to market is able to allow them to create their own recipes, bring their own transformers, and make an automatic fit for their particular race. So if you think about this as we built all the components of a race car, you're going to take it and apply it for that particular race to win. >> John: So that's the word driverless comes in. It's driverless in the sense of you don't really need a full operator, it kind of operates on its own. >> In some sense it's driverless. They're taking the data scientists, giving them a power tool. Historically, before automatic machine learning, driverless is in the umbrella of machine learning, they would fine tune, learning the nuances of the data, and the problem at hand, what they're optimizing for, and the right tweaks in the algorithm. So they have to understand how deep the streets are going to be, how many layers of deep learning they need, what variation of deep learning they should put, and in a natural language crossing, what context they need. Long term shot, memory, all these pieces they have to learn themselves. And there were only a few grand masters or big data scientists in the world who could come up with the right answer for different problems. >> So you're spreading the love of AI around. >> Simplifying that. >> You get the big brains to work on it, and democratization means people can participate and the machines also can learn. Both humans and machines. >> Between our open source and the very maker-centric culture, we've been able to attract some of the world's top data scientists, physicists, and compiler engineers. To bring in a form factor that businesses can use. One data scientist in a company like Franklin Templeton can operate at a level of ten or hundreds of them, and then bring the best in data science in a form factor that they can plug in and play. >> I was having a concert with Kent Libby, who works with me on our platform team. We have all this data with theCUBE, and we were just talking, we need to hire a data scientist and AI specialist. And you go out and look around, you've got Google, Amazon, all these big players spending between 3-4 million per machine learning engineer. And that might be someone under the age of 30 with no experience. So the talent bore is huge. The cost to just hire, we can't hire these people. >> It's a global war. There's talent shortage in China, there's talent shortage in India, there's talent shortage in Europe, and we have offices in Europe and India. There's a talent shortage in Toronto and Ottawa. So it's a global shortage of physicists and mathematicians and data scientists. So that's where our tools can help. And we see Driverless AI as, you can drive to New York or you can fly to New York. >> I was talking to my son the other day, he's taking computer science classes in night school. And it's like, well you know, the machine learning in AI is kind of like dog training. You have dog training, you train the dog to do some tricks, it does some tricks. Well, if you're a coder you want to train the machine. This is the machine training. This is data science, is what AI possibility is there. Machines have to be taught something. There's a base input, machines just aren't self-learning on their own. So as you look at the science of AI, this becomes the question on the talent gap. Can the talent gap be closed by machines? And you got the time, you want speed, low latency, and trust. All these things are hard to do. All three, balancing all three is extremely difficult. What's your thoughts on those three variables? >> So that's why we brought AI to help with AI. Driverless AI is a concept of bringing AI to simplify. It's an expert system to do AI better. So you can actually give to the hands of the new data scientists, so you can perform at the power of an advanced data scientist. We're not disempowering the data scientist, the part's still for a data scientist. When you start with a confusion matrix, false positives, false negatives, that's something a data scientist can understand. When you talk about feature engineering, that's something a data scientist can understand. And what Driverless AI is really doing is helping him do that rapidly, and automated on the latest hardware, that's where the time is coming into. GPUs, FPGAs, TPUs, different form of clouds. Cheaper, right. So faster, cheaper, easier, that's the democratization aspect. But it's really targeted at the data scientist to prevent experimental error. In science, the data science is a search for truth, but it's a lot of experiments to get to truth. If you can make the cost of experiments really simple, cheaper, and prevent over fitting. That's a common problem in our science. Prevent bias, accidental bias that you introduce because the data is biased, right. So trying to prevent the flaws in doing data science. Leakage, usually your signal leaks, and how do you prevent those common pieces. That's where Driverless AI is coming at it. But if you put that in a box, what that really unlocks is imagination. The real hard problems in the world are still the same. >> AI for creative people, for instance. They want infrastructure, they don't want to have to be an expert. They want that value. That's the consumerization. >> AI is really the co founder for someone who's highly imaginative and has courage, right. And you don't have to look for founders to look for courage and imagination. A lot of entrepreneurs in large companies, who are trying to bring change to their organizations. >> Yeah, we always say, the intellectual property game is changing from protocols, locked in, patented, to you could have a workflow innovation. Change one little tweak of a process with data and powerful AI, that's the new magic IP equation. It's in the workflow, it's in the application, it's new opportunities. Do you agree with that? >> Absolutely. The leapfrog from here is businesses will come up with new business processes. So we looked at business process optimization, and globalization's going to help there. But AI, as you rightfully said earlier, is training computers. Not just programming them, you're schooling them. A host of computers that can now, with data, think almost at the same level as a Go player. The world's leading Go player. They can think at the same level of an expert in that space. And if that's happening, now I can transform. My business can run 24 by 7 and the rate at which I can assemble machines and feed it data. Data creation becomes, making new data becomes, the real value that AI can- >> H20.ai announcing Driverless AI, part of their flagship product around recipes and democratizing AI. Congratulations. Final point, take a minute to explain to the folks just the product, how they buy it, what's it made of, what's the commitment, how do they engage with you guys? >> It's an annual license, a software license people can download on our website. Get a three week trial, try it on their own. >> Free trial? >> A free trial, our recipes are open-source. About a hundred recipes, built by grand masters have been made open source. And they can be plugged, and tried. Customers of course don't have to make their software open source. They can take this, make it theirs. And our vision here is to make every company an AI company. And that means that they have to embrace AI, learn it, tweak it, participate, some of the leading conservation companies are giving it back in the open source. But the real vision here is to build that community of AI practitioners inside large organizations. We are here, our teams are global, and we're here to support that transformation of some large customers. >> So my problem of hiring an AI person, you could help me solve that. >> Right today. >> Okay, so anyone who's watching, please get their stuff and come get an opening here. That's the goal. But that is the dream, we want AI in our system. >> I have watched you the last ten years, you've been an entrepreneur with a fierce passion, you want AI to be a partner so you can take your message to wider audience and build monetization around the data you have created. Businesses are the largest, after the big data warlords we have, and data privacy's going to come eventually, but I think businesses are the second largest owners of data they just don't know how to monetize it, unlock value from it, and AI will help. >> Well you know we love data, we want to be data-driven, we want to go faster. Love the driverless vision, Driverless AI, H20.ai. Here in theCUBE I'm John Furrier with breaking news here in Silicon Valley from hot startup H20.ai. Thanks for watching.

(energetic music) >> Live from Orlando Florida, it's theCUBE, covering Microsoft Ignite. Brought to you by Cohesity, and theCUBE's ecosystem partners. >> Welcome back everyone, to theCUBE's live coverage of Microsoft Ignite here in Orlando, Florida. I'm your host, Rebecca Knight, along with my cohost, Stu Miniman. We're joined by Lynn Lucas. She is the CMO of Cohesity. Thanks so much for coming on the program, Lynn. >> Oh, just so excited to be here with you guys and host you in the Cohesity booth for the first time at Microsoft Ignite. >> It's been a lot of fun. There's a lot of buzz around here, and it's fun to be right, to be your neighbor. Exactly. >> Great. >> So today, there's been a lot of news, some new exciting announcements of integrations with Microsoft. I wonder if you can walk our viewers a little bit through what Cohesity announced today. >> Absolutely. So, we have been partners with Microsoft for some time, and today, we announced extensions to our capabilities with Microsoft Azure and Office 365. So Cohesity now extends data protection and backup for Office 365, including granular recovery of mailboxes and granular search for discovery purposes. We also have extended our integration with the Azure data box, and we also are increasing our DR capabilities for our customers with Azure so we now have fail back from the Azure Cloud for disaster recovery purposes. So, just continuing to see tremendous growth, hundreds of Microsoft customers with Cohesity, and these new capabilities are going to expand the possibilities for them. >> Lynn, it's an interesting conversation these days 'cause, you know, in our research, and we've talked about this, data's at the center of everything, and the challenge for customers is data's everywhere. You look here at the Microsoft show, well, I've got all my traditional stuff, I've got my SaaS stuff, my PubliCloud stuff, now Edge with the data box things there. Microsoft plays across there, and it sounds like Cohesity is playing in all of these areas, too. >> Absolutely, and I thought, you know, Sacha did such a good job in the keynote yesterday of really laying out the imperative for digital transformation, data being at the heart of it, but also laying out one of the key challenges which he pointed out, which is the data silos. And, I think Cohesity is right smack in the center of that conversation because we've always been about consolidating secondary data silos. And, you know, our partnership with Microsoft, really, I think, reinforces what they've been talking about, which is also a hybrid strategy that the bulk of customers that we talk to see that their data is going to be on premise, it's going to be in the cloud, and increasingly, it's goinna at the Edge, and we span all of those locations to create this one operating environment so that things like the new open data initiative, I think, will be much easier for customers because they won't be wondering, well, is my data all in one place to be operated on? >> So, talk about the problem of the data silos, because, as you said, it's one of the biggest challenges that companies face today. They are data rich and yet, this data's here and this data's here. Can you describe a little bit about what kind of problems this is for companies, and why this matters? >> So, I think it's just something folks are starting to really get a handle on. As I talked to individual folks here at the show, you'd be surprised at how many aren't even really sure, maybe, how many islands they have, you know, so, even mapping where is all my data, I think, is a capability that many organizations are still getting their arms around. And the challenge, of course, is that in today's world, it's very expensive to move large data sets, and so you want to bring compute to the data, which is what a hyper-convergence in Cohesity is about. And, when you look at the imperatives at the board level, the CEO level, they increasingly see that data becomes really the true competitive advantage for most organizations, and yet, if they can't operate or bring compute to that data and do something with it, they're really at a handicap. We call, you know, some of the newer companies are kind of data-centric or data natives, the Air BNB's, the, maybe, Netflixes of the world, not everyone aspires to be them. As well, not everyone has the resources that those companies may have had or just stay short period of time. Most organizations have the benefit of years of data. We want to level the playing field and allow them to become competitive with their data by providing that single foundation. >> Yeah, Lynn, it's a big show here. They said thirty thousand people and a really diverse ecosystem. What really surprised me is the spectrum of customers that you have here. I mean, we know Microsoft has a long history in higher education. We spoke to one of your customers, Brown University, and of course, long history they have with Microsoft. What are some of the things that you're hearing from customers, maybe, what's different at this show than some of the other, cloud and kind of younger shows that we might go to. This show's been around about almost thirty years now, so. >> Yeah, you know, isn't it, you know, I hate to give our ages but, I think we've been doing this for a while now, right? And Microsoft has been part of the IT ecosystem in a major way, and it's great to see the vibrancy here and how they're talking about AI and ML and moving forward with it. You know, what strikes me here is that a lot of the organizations here are now really understanding the pragmatism of having a hybrid strategy of what makes sense in the cloud as well as what may continue to be on prem for them. I think we complement that well. I'm really excited, too, about the idea that we are going to be using machine learning to be doing a lot more that humans simply can't keep up with in terms of the data growth and then doing something productive with that. And I think that's a conversation that we're just tapping the surface of here at this show. >> Yeah, you've said something that really resonated with me. You know, we have people that have been in the industry a while and, I look at you, your founder, Mohit, and this isn't his first rodeo. He'd been looking at data back from a couple of generations of solutions, and people are very excited. Machine learning, as you said, we used to talk about automation and intelligence around this environment. Now, I lived in the storage industry for quite a while, and we've talked about it but it feels more real when I talk to the architects and the people building this stuff. They are just so excited about what we will be able to do today that we talked about a decade or so ago but now really can make reality for customers. >> No, absolutely, and I think, you know, we have our own investment in that. Helios, which we announced just last month, you know, provides that machine learning capability because what we hear from our customers is what they love is the ability to have simplicity because, let's face it, IT environments continue to grow in complexity. They're looking for ways to subtract that complexity so they can apply their talents to solving the primary mission, as I call it, of their organization, whether that be public sector or private sector, adoing that in a simpler way. You know, look, one of the great stories that one of our customers is talking about here is how Cohesity helped him with a standard thing that most IT organizations have, which is, we're going to do a power shut down and we've got to perform a DR failover, and this particular organization, University of Pennsylvania Annenberg, had a set of twelve websites which, the professors and the students rely on, and it was going to take them literally almost a month to try to move them, and they didn't have that kind of time, and with Cohesity, with our DR capabilities, he was able to do that literally with a few clicks, kept the community of professors and students happy, and didn't spend, more importantly, twenty days trying to rebuild websites for a standard IT event, right? That's the kind of real life story in terms of what IT gets back that they can invest in other more important focus areas for their business. >> Well, for their business and also, just for their lives giving people their time back, their weekends back, their time at night >> Weekends and nights, right? >> With their families, yeah. >> We all need that. >> Satya Nadella is such a proponent of an improving workplace productivity, even five percent, he says, can make this big difference. Can you talk a little bit about how you view that workplace productivity at Cohesity and your approach to giving people either time to concentrate on more value for their companies or just their lives? >> So, again, a super story that we have from another customer that is here at Microsoft, and is an Azure customer, and a Cohesity customer. HKS, one of the world's most respected architectural firms, designed AT&T Stadium, there's a new major pediatric hospital going in in Dubai. They operate in ninety-four countries with remote designers and architects, and because of their inefficient backup processes and archive processes, they literally were having their associates have to work weekends as well as losing time on their projects, and time is money, and they, you know, in some cases, are penalized if they don't make certain dates. And so, I think, these are really pragmatic examples. On average here, pulling some of the folks here, I've heard that they can get a day a week back, sometimes for their administrator who now doesn't have to do repetitive manual tasks anymore. >> One of the things we always love digging into is, you talk about people's jobs and some of the new careers that are happening. We talked to one guest earlier this week. He said, if you're a customer and you learn Azure as what you're doing, like, you're resume is gold. We've talked to, and the really early Edge, like site reliability engineering, he said, don't put SRE on your resume or every recruiter will be calling you up and you won't even be able to answer your phone. Cohesity, you're doing a bit of hiring also. Maybe you could talk about- >> We are! >> What are you seeing from customers and what are you looking for internally? >> We have tremendous good fortune, we grew three hundred percent in revenues year over year, we're hiring in our RTP offices, in our San Jose, in India, around the globe. You know, we look for the best and the brightest, a lot of engineering talent, marketing talent as well, really, across the board but, you know, I think to the point you just made for the IT folks that are here, looking forward as to how you are going to help your business with your data infrastructure or data flows throughout their organization is, to me, where some of the career movement is happening when you hear the talk about how important it is to so many aspects of the business. >> And what are the sort of challenges that you're having with hiring, or are you? I mean, you're a red hot company, but, are you finding it difficult to find the kind of skills, the kind of talent that you want? I mean, what is, what's the candidate pool like? >> You know, so, I think what's really interesting, we are red hot, we have a lot of applicants so, I'd say, in general, no, we're very blessed that way. I think, though, more businesses, including ours, are finding it's difficult to get, say, those data scientists, right? Some of these also front end or back end developers, you know, it's not just the technical companies that are recruiting for that anymore. It's not just the Cohesitys and the Microsofts that are looking for that talent, but it's now also the Netflixes or, you know, the eBays, et cetera, right? They are all looking for the type of talent that we are and so, in general, I think that this bodes well for young people or folks really anywhere in their career watching about, thinking about, where the talent needs are, and there's a lot of activity and interest in people with those kinds of skills. >> You know, let me just follow up on that. So, Cohesity is a Silicon Valley-based company but, as you mentioned, you've got an RTP location. We've seen quite a lot of Silicon Valley-based companies that are starting to do a lot more hiring outside 'cause it's, I'm going to be honest, really expensive to live in the valley these days. So, any commentary on that dynamic? >> Well, you know, I think you're in Boston, not the lowest cost market either in the country. >> True, it's true! >> Yeah, you know, I think with a lot of the technology that's out there, you know, people don't have to be co-located, and we certainly also look to develop and invest in other communities around the globe, so we're not looking solely in San Jose but also in RTP, we've got headquarters in Europe as well as, of course, in India. So we look for talent everywhere, and, my own personal team, you know, I have folks basically around the US as well as across parts of the globe because talent, in many cases, is what matters and where you are physically, you know, some of the great technology that's out there can help break down those barriers of time and distance. >> Finally, this conference, it's thirty thousand people from five thousand different companies around the world. What is going to be, I mean, we're only on day two, but, what's been your big take-away so far? What's the vibe you're getting here at Ignite? >> You know, the vibe has been one of energy, of excitement. I've talked to a lot of folks from around the globe. I've been actually, pretty amazed at some of the people from different countries around the globe that are here, which is fantastic to see that draw in, and I feel like there's a general sense of excitement that technology and what Microsoft's doing can help solve some of the bigger challenges that are here, in the world, and for their own businesses, and we really look forward to Cohesity helping them lay that great data infrastructure foundation, consolidate their silos and help them build a foundation for, you know, doing more with their data. >> Great. Lynn Lucas, thank you so much for coming on theCube. It was great, great talking to you. >> Thank you. >> I'm Rebecca Knight for Stu Miniman. We will have more from Microsoft Ignite and theCube's live coverage coming up in just a little bit. (electronic music)

(bright orchestral music) >> I'm Peter Burris and welcome to another Cube Conversation from our beautiful studios here in Palo Alto, California. Not a great show today. First off, being joined by my colleague at SiliconANGLE Wikibon, Dave Vellante. >> Peter >> But the real star of the show, Nick Curcuru with MasterCard. Welcome to The Cube Nick. >> Thanks for having me. >> So Nick, MasterCard, 165 million transactions an hour. A financial juggernaut. Blockchain, interesting technology, a lot of applications. How are they going to come together? >> Well, the biggest thing that we look at when we look at those two technologies: our world which is the network and you look at blockchain, is they're the challenge. And I think we have the opportunity to actually meet the challenge and those challenges are speed, transparency of the transaction itself, and actually even trying to reduce the cost of those transactions, especially when you talk cross border. You know when you're going from country to country right now blockchain has a big cost in order to let that happen. The other component is that transparency. I need to know who I am dealing with on the other side and create an auditable trail to understand how that transaction is going through, and again this is something that we do within our core business and again, we're trying to make that meet and then work on the speed. Again, one of the things we pride ourselves are on that 165 million transactions per hour, making it a smooth flow, making it seamless, making it frictionless, that we can do. So again, can we do the same now with blockchain. You know, and for us, we're experimenting now with our B2B, but we hopefully will be able to move that right into individuals as well, to the consumer level. >> So, we're a decade into when Satoshi, whoever he or she was created Bitcoin. >> Or them. >> Or them and yeah, was it the Russians? People are asking that question, so who knows? But, of course a lot of people have been facing negative comments in the press, et cetera. What was your motivation for exploring blockchain, starting to experiment with it? Take us through that if you would. >> Well you know part of what we started to see is that it started to gain traction. That was the biggest thing, and as you start to take a look at more and more people that started to use that technology, it's one of those items that in the beginning we're like okay it's nice it's a hobby right as it started to come out. But as you started to see some more heavyweights come into the place to use it and actually utilize what that technology can provide, we're like, there is something here. Again, MasterCard, our CEO has been very good to say, we need to always be thinking outside of our core. What else do we have to be able to include to allow our MasterCard stakeholders, our banks, our issuers, and everyone, the opportunities that we can continuously expand. So our CEO has been really good about that. And when blockchain started to gain some momentum, he goes, we need to actually take a look, so our guys in the labs, our smart people that sit there in O'Fallon and New York City started to explore how do we take what we know, apply it here to help with that particular way a transaction is being done, and then, can we really allow ourselves and blockchain to grow? So, that's pretty much where we started. Again, it was a little hobby, we started to see it pick up momentum, and about three years ago we were like, there is something here. We need to actually begin to think about how we can interact with this form of payment. >> So what are you actually doing? Are you experimenting, kicking the tires, trying to figure out the use cases? >> That's actually everything that we're doing. Right now, we've actually got a few patents that have just come out, which is very good for what we are trying to accomplish. Right now, we're in the B2B space because that's what we're watching mostly is being used right now is in that business to business space. So we're out there piloting. We actually have set up a whole bunch of APIs to allow people to actually put the blockchain inside, whether it's a mobile device that you want to use, or within the Internet of things. So we have developed a set of APIs that we have got out there that we are allowing our different people within B2B to use, to experiment, to start to say, hey give us feedback on how are they operating. Is it seamless, is it frictionless, are we reducing that operational time, making it efficient, reducing those costs. So that's what we're beginning to roll out. And again, our goal is, if we can do it in B2B, how do we finally get it to the consumer? Because again, that's going to be a big part of what people are going to want to do, to be able to do those transactions amongst themselves. >> When you think about things like AML and Know Your Customer KYC, do you see blockchain as having a role there or does it sort of accentuate your need to understand different ways to know your customer and fight money laundering. >> Well that's actually a big part of it. That's the whole thing we talk about being able to authorization and authentication. So there is a big thing, again, when you deal with blockchain, people, you got the wire in transit right? And there are people trying to skim off that, trying to find a way to get into your bank account, basically, because that's really what you're exposing because you're making a payment. So the question for us is okay, again, that's a core competency of ours is data in motion and securing the transaction while it's in motion before that. So for us, when you start to take a look at the way we can do the authorization and authentication becomes a big deal. And our core competency is to do that, to make sure that you can't have anti-money laundering, to make sure that you can't have fraud existing because we can verify it's you who is transacting with Dave, that you are the two people transacting, just like we do with a card, right? And when you do the pin, chip, we know it's you. Even with our new products like new data with biometrics, we know it's you. We can validate and verify and authenticate it's you. That's where we think we can provide tremendous value with the blockchain. >> So blockchain is kind of a hot new technology, but there's got to be more than just the fact that it's a hot new technology. Give us some examples of some use cases that you're envisioning that will be made possible and will be sustained with the blockchain approach. >> A lot of it is actually, if you take a look at the supply chain, the ability to make sure that when I need goods and services, not only, I don't have to wait for it. I think actually one of the best stories that we heard when it came down to the blockchain is how, actually the Defense Department has used it. So for example, if you can imagine, on an aircraft carrier, there's a plane that went down, right? That needed a part. Or I think it was a helicopter, sorry. And it needed a part. Well the question was it's in the middle of the Pacific Ocean. So how do you get the part there? Well if you go through the normal channels, to get that helicopter up and running, it's going to take you two to three months to get it there. But using blockchain, because it's anonymous and you have some privacy within it, being able to say, can you send me the specs? This particular ship had a metal 3D printer on it. So not only were they able to send the specs via blockchain in an anonymous manner so no one else could pick it up, they could actually put it on the ship. They could actually create the part, and what's really kind of cool is they actually put a flaw. They put a scratch across the part itself so that you knew the guys who sent it are the guys that you are getting it from and no one else picked it up along the way. So that's one way to be able to do it, to actually create the parts that you need when you need them in a secure manner. The other part, if you believe it or not, I was just at a sports conference, and the other thing was is can I actually use blockchain to transfer my tickets? So you're in Palo Alton. I got 9ers tickets. I'm a season ticket holder, and what I want to be able to do is send you my tickets, but you need to know it's me who has the tickets, not a fraudster, right, that's going up there saying I got two tickets for sale or whatever it may be. So I can use blockchain in an anonymous transaction You send me the funds, you know it's me, and I can send you the tickets because I am a verified, valid ticket holder. So there is another case where it is consumer to consumer. >> But coming back to the B2B examples, there are a lot of circumstances when a business realizes that entering into a transaction is signaling an enormous amount of information other than just the part that they're getting or the business activity that they're performing, and so it has the potential to be a great technology to dramatically focus the characteristics of the transaction just on the transaction and keep all the other signaling that might otherwise be picked up on out of the equation. Is that right? >> Yeah, that's absolutely correct. The other part is it creates that efficiency in that transaction itself. We're always worried about can you reduce paperwork? We did that, that's the 80's and 90's, right? And then it became into now we got these electronic transfers. But what blockchain is allowing you to do is almost in real time to be able to order those goods and services and get them delivered when you need them and be able to run those transactions. That's a big part to it. Now we're getting faster and better at what we're doing. We're not letting antiquated processes and procedures really bog us down. And again, the blockchain allows you to do that, allows an easier transfer of cash amongst the providers, a lower cost in many cases on that transfer when you're talking about the funds, more of the ability to actually interact with the consumer itself, especially if you've got artificial intelligence, because one of the other use cases in the supply chain is the auto-ordering. Right, so this thing is learning, it's understanding what's coming off the shelves, what's going on the shelves, where it needs to be. Can I actually that to help me distribute my products amongst my warehouses, amongst my stores? Blockchain is doing that. It's automating that and allows those transactions, both I need this and you sent it to me as well as actually going through and making the financial transaction happen. >> So you guys must be having some mind-melting conversations inside your company. (laughing) When you think about the examples that you gave those transactions, I presume, the ticket transaction, doesn't require a trusted third party to validate that transaction because the technology of blockchain is doing that and then yet, but MasterCard is a trusted third party. So how are you thinking about, this might change your business? You've still got amazing assets. You've got a brand, you've got a network, you've got your partnerships, you've got the relationships that you have with the suppliers and customers and consumers, et cetera. So how do you think about that notion of when you talk to the world of crypto. Oh let's find where there's a trusted third party and we can disintermediate that. So what do you think all this means for the future of financial services and companies like MasterCard? >> Well, you know for us it's not the ability to say that one is going to... for a lot of folks, their complaint is, what we hear is, blockchain is going to take over everything. Cryptocurrency is going to... no it's how you actually have to live within that, because you're going to have to have multiple ways to do that. So that's how we feel we can make that help those folks in the transition. So that trusted third party, okay you can have five trusted third parties take care of your credit cards, your debit cards, your blockchain, your cryptocurrency. Our goal is, just come to us. Let's get you that solution. We can help embed that API. We can give you some flexibility. We can give you the reach of being able to have you know 22,000 banks and issuers worldwide at your disposal if you need that. So again, that's where we see ourselves really playing a good role, and that's how it's going to change our business. >> But it's, related to that, it's we can bring the scale, we can bring your operational certainty, we can bring you all the things because at the end of the day, it's still a computer, right, and it has to stay up and it has to be auditable and it has to be backed up and that's something that there's not a lot of companies that know how to operate at the kind of scale you guys do. >> Technology platform is critical. >> Absolutely >> Yes, absolutely. And again, that's when you look at quadruple and quint- types of redundancy, not just primary and secondary. I mean we are running four or five types of redundancy to make sure those networks are up and running. >> So Nick, I got a question because one of the things that I find interesting about all this and I know that you and I have talked about this, Dave, is that a blockchain presumes that there's some sort of contract in the middle of all this, but the processes of running contracts are complex. The design of the blockchain is crucial ultimately to the behavior and the success of the blockchain. Not a lot of tools to do that. How do you think the future of blockchain design is going to evolve so that issues like scale, technological, operational certainty, et cetera, come into play? >> Well, it's almost, as you take a look at it, it's almost the way that you have to be interacting today. So you've got the edge where the transaction is happening right and you've got the core part of the business where you're using that machine learning, the artificial intelligence to help you make better decisions. And then of course, you've got the deep learning. So as you look at those technologies, it's how you're handling within that contract, where things need to be done. Right, so again, if you're looking at how we supply a shelf, well that's not going to be done potentially at the edge. That's potentially in your core. It could be part of deep learning, but then how do you bring it to the edge to make that transaction go through to make that part of blockchain? So as you think about the contracts, something that's real important with blockchain is picking the right partner to go to market with because, again, you're looking at those technologies you want to make sure are in place. >> So, you're adding to a notion of scale and operational certainty, the expertise associated with how do you design these things well so that they can be put in an operation and you don't have to, you know, the immutability issue doesn't come and bite you in the butt in six months. >> Yeah, absolutely. So again, what you're looking for is, what we always look for are those people that have the right ability for scale, have the global experience that we really need, because again, when you think about it, you're in a global economy, so you're really looking to see how those people interact and can they do it. You're looking for that partner. You're not looking for the guy who's got the coolest, latest technology. Those are always fine to know about, but again, you're always worried about scale at this point. You're looking at flexibility. You know, how do I, how can I be flexible in the way I'm making those contracts and those contracts always change. It's not like there's a template, all right? Almost with blockchain, it's almost individual companies and B2B are coming back with their own types of contracts. >> Sure. >> And that's the part that you also have to have make sure is available to you, both from a technology standpoint and being able to you know actually operationalize it. >> Peter, at the top, talked about the transaction volumes being you know limited, you were talking about Bitcoin transaction volumes. Obviously, in the near term anyway, limits some of the use cases, but I wonder how you guys are thinking about solving that problem. Do you see that as MasterCard's role or is that, is Google, a Google-like company going to solve that? Is it going to be a partnership? How do you see that shaking out? >> It is going to be, it's a collaborative partnership, so again, we have conversations with people like, the Googles of the world, the Microsofts, the Dells, and people like that. It's a collaboration now. So just like four years ago. Remember Hadoop's community? >> Yeah. >> So we see it, there is a blockchain community because we are all seeing the same issues, but what's nice is, because of the experience that we're having through being part of a community, we're helping each other solve those particular problems. Because again, Google sees a different part of blockchain. Right, we see a different part of blockchain. And when you start to bring those resources together and you start talking to them and the Microsofts and the Dells and even the Amazons of the world. When you start putting everybody into a room, we're frenemies at that point. Because we're all trying to solve the same problem. We all have different interests within the major issue, but if we can do it together, tide rises all boats, right? >> The best innovations are combinatorial. >> Correct. >> Taking a lot of folks with expertise and mature technology and bringing it together and creating something new not just because you're creating something new but because you have the social reach to actually have it happen in the marketplace. >> Absolutely. >> Nick Curcuru, MasterCard, thanks very much for being on The Cube and talking about blockchain. >> Appreciate it. >> Thank you for having me, thanks guys. (orchestral music fading out)

(electronic music) - Welcome to theCube Insights. A podcast that is typically taken from Siliconangle media's theCube interviews, where we share the best of our teams insights from all events we go to and from time to time we want to be able to extract some of our learnings when we're back at the ranch. Joining me for this segment is co-founder, co-CEO, benevolent dictator of a community, my boss, Dave Vellante. - Hey Stu. - Dave. Good to see you dressed down. - Yeah, well. Podcast, right? We got toys, props and no tie. - Yeah, I love seeing this ... we were just talking, John Furrier, who we could really make a claim to say we wouldn't have the state of podcasting today, definitely in tech, if it wasn't for what John had done back in the day with PodTech and it's one of those things, we've talked about podcasts for years but I'd gotten feedback from the community that said, "Wow, you guys have grown and go to so many shows that we want to listen to you guys as to: what was interesting at this show, what did you guys take out of it, what cool people did you interview?" We said, "Well, of course all over youtube, our website thecube.net but it made a lot of sense to put them in podcast form because podcasts have had a great renaissance over the last couple of years. - Yeah, and it's pretty straight forward, as Stu, for us to do this because virtually every show we do, even if it's a sponsor show, we do our own independent analysis upfront and at the tail end, a lot of our people in our community said, "We listen to that, to get the low down on the show and get your unfiltered opinion." And so, why not? - Yeah, Dave. Great point. I love, from when I first came on board, you always said, "Stu, speak your mind. Say what the community; what are the users saying? What does everybody talk about?" As I always say, if there is an elephant in the room we want to put it on the table and take a bite out it. And even, yes, we get sponsored by the companies to be there. We're fully transparent as to who pays us. But from the first Cube event, at the end of the day, where after keynote, we're gonna tell you exactly what we think and we're always welcome for debate. For people to come back, push on what we're saying and help bring us more data because at the end of the day, data and what's actually happening in the world will help shape our opinions and help us move in the direction where we think things should go. - I think the other thing is too, is a lot of folks ask us to come in and talk to them about what we've learned over the past year, the past six months. This is a great way for us to just hit the podcast and just go through, and this is what I do, just go through some of the shows that I wasn't able to attend and see what the other hosts were saying. So, how do you find these things? - Yeah, so first of all, great. theCube insights is the branding we have on it. We're on iTunes, We're on Spotify, We're on Google Play, Buzzsprout's what we use to be able to get it out there. It's an RSS on wikibon.com. I will embed them every once in a while or link to them. We plan to put them out, on average, it's once a week. We wanna have that regular cadence Typically on Thursday from a show that we've been out the spring season is really busy, so we've often been doing two a week at this point, but regular cadence, just podcasts are often a little tough to Google for so if you go into your favorite player and look at thecube insights and if you can't find it just hit you, me, somebody on the team up. - So you just searched thecube insights in one of those players? - Yeah absolutely, I've been sitting with a lot of people and right now it's been word of mouth, this is the first time we're actually really explaining what we're doing but thecube one word, insights is the second word I found it real quick in iTunes I find it in Google Play, Spotify is great for that and or your favorite podcast player Let us know if we're not there. - So maybe talk about some of the things we're seeing. - Yeah absolutely - The last few months. - So, right when we're here, what are our key learning? So for the last year or two Dave, I've really been helping look at the companies that are in this space, How are they dealing with multi cloud? And the refinement I've had in 2018 right now is that multi cloud or hybrid cloud seems to be, where everyone's Landing up and part of it is that everything in IT is heterogeneous but when I talk about a software company, really, where is their strength? are they an infrastructure company that really is trying to modernize what's happening in the data center are they born with cloud are they helping there? or are they really a software that can live in SAAS, in private cloud and public cloud? I kinda picture a company and where's their center of gravity? Do they lean very heavily towards private cloud, and they say public cloud it's too expensive and it's hard and You're gonna lose your job over it or are they somebody that's in the public cloud saying: there's nothing that should live in the data center and you should be a 100% public cloud, go adopt severless and it's great and the reality is that customers use a lot of these tools, lots of SAAS, multiple public Cloud for what they're doing and absolutely their stuff that's living in the data center And will continue for a long time. what do you see in it Dave? - My sort of takeaway in the last several months, half a year, a year is we used to talk about cloud big data, mobile and social as the forward drivers. I feel like it's kinda been there done that, That's getting a little bit long in the tooth and I think there's like the 3DS now, it's digital transformation, it's data first, is sort of the second D and disruption is the 3rd D And I think if you check on one of the podcast we did on scene digital, with David Michella. I think he did a really of laying out how the industry is changing there's a whole new set of words coming in, we're moving beyond that cloud big data, social mobile era into an era that's really defined by this matrix that he talks about. So check that out I won't go into it in detail here but at the top of that matrix is machine intelligence or what people call AI. And it's powering virtually everything and it's been embedded in all types of different applications and you clearly see that to the extent that organizations are able to Leverage the services, those digital services in that matrix, which are all about data, they're driving change. So it's digital transformation actually is real, data first really means You gotta put data at the core of your enterprise and if you look at the top five companies in terms of market cap the Googles, the Facebooks, the Amazons, the Microsofts Etc. Those top five companies are really data first. But People sometimes call data-driven, and then disruption everywhere, one of my favorite disruptions scenarios is of course crypto and blockchain And of course I have my book "The Enigma war" which is all about crypto, cryptography and we're seeing just massive Innovation going on as a result of both blockchain and crypto economics, so we've been really excited to cover, I think we've done eight or nine shows this year on crypto and blockchain. - Yeah it's an interesting one Dave because absolutely when you mention cryptocurrency and Bitcoin, there's still a lot of people in the room that look at you, Come on, there's crazy folks and it's money, it's speculation and it's ridiculous. What does that have to do with technology? But we've been covering for a couple of years now, the hyper ledger and some of these underlying pieces. You and I both watch Silicon Valley and I thought they actually did a really good job this year talking about the new distributed internet and how we're gonna build these things and that's really underneath one of the things that these technologies are building towards. - Well the internet was originally conceived as this decentralized network and well it physically is a decentralized network, it's owned essentially controlled by an oligopoly of behemoths and so what I've learned about cryptocurrency is that internet was built on protocols that were funded by the government and university collaboration so for instance SMTP Gmail's built on SMTP (mumbles) TCPIP, DNS Etc. Are all protocols that were funded essentially by the government, Linux itself came out of universities early developers didn't get paid for developing the technologies there and what happened after the big giants co-opted those protocols and basically now run the internet, development in those protocol stopped. Well Bitcoin and Ethereum and all these other protocols that are been developed around tokens, are driving innovation and building out really a new decentralized internet. So there's tons of innovation and funding going on, that I think people overlook the mainstream media talks all about fraud and these ICO's that are BS Etc. And there's certainly a lot of that it's the Wild West right now. But there's really a lot of high quality innovation going on, hard to tell what's gonna last and what's gonna fizzle but I guarantee there's some tech that's being developed that will stay the course. - Yeah I love....I believe you've read the Nick Carr book "The Shallows", Dave. He really talked about when we built the internet, there's two things one is like a push information, And that easy but building community and being able to share is really tough. I actually saw at an innovation conference I went to, the guy that created the pop-up ad like comes and he apologizes greatly, he said "I did a horrible horrible thing to the internet". - Yeah he did - Because I helped make it easier to have ads be how we monetize things, and the idea around the internet originally was how do I do micropayments? how do I really incent people to share? and that's one of the things we're looking at. - Ad base business models have an inherent incentive for large organizations that are centralized to basically co-opt our data and do onerous things with them And that's clearly what's happened. users wanna take back control of their data and so you're seeing this, they call it a Matrix. Silicon Valley I think you're right did a good job of laying that out, the show was actually sometimes half amazingly accurate and so a lot of development going on there. Anywhere you see a centralized, so called trusted third-party where they're a gatekeeper and they're adjudicating essentially. That's where crypto and token economics is really attacking, it's the confluence of software engineering, Cryptography and game theory. This is the other beautiful thing about crypto is that there is alignment of incentives between the investor, the entrepreneur, the customer and the product community. and so right now everybody is winning, maybe it's a bubble but usually when these bubbles burst something lives on, i got some beautiful tulips in my front yard. - Yeah so I love getting Insight into the things that you've been thinking of, John Furrier, the team, Peter Borus, our whole analyst team. Let's bring it back to thecube for a second Dave, we've done a ton of interviews I'm almost up to 200 views this year we did 1600 as a team last year. I'll mention two because one, I was absolutely giddy and you helped me get this interview, Walter isaacson at The Dell Show, One of my favorite authors I'm working through his DaVinci book right now which is amazing he talks about how a humanities and technology, the Marrying of that. Of course a lot of people read the Steve Jobs interview, I love the Einstein book that he did, the innovators. But if you listen to the Michael Dell interview that I did and then the Walter isaacson I think he might be working on a biography of Michael Dell, which i've talk to a lot of people, and they're like i'd love to read that. He's brilliant, amazing guy I can't tell you how many people have stopped me and said I listened to that Michael Dell interview. The other one, Customers. Love talking about customers especially people that they're chewing glass, they're breaking down new barriers. Key Toms and I interviewed It was Vijay Luthra from Northern trust. Kissed a chicago guy And he's like "this is one of the oldest and most conservative financial institutions out there". And they're actually gonna be on the stage at DockerCon talking about containers they're playing with severless technology, how the financial institutions get involved in the data economy, Leverage this kind of environment while still maintaining security so it was one that I really enjoyed. How about...... what's jumped out of you in all your years? - (Mumbles) reminds me of the quote (mumbles) software is eating the world, well data is eating software so every company is.... it reminds me of the NASDAQ interview that I did Recently and all we talked about, we didn't talk about their IT, we talked about how they're pointing their technology to help other exchanges get launched around the world and so it's a classic case of procurer of technology now becoming a seller of technology, and we've seen that everywhere. I think what's gonna be interesting Stu is AI, I think that more AI is gonna be bought, than built by these companies and that's how they will close the gap, I don't think the average everyday global 2000 company is gonna be an AI innovator in terms of what they develop, I think how they apply it is where the Innovation is gonna be. - Yeah Dave we had this discussion when it was (mumbles) It was the practitioners that will Leverage this will make a whole lot more money than the people that made it. - We're certainly seeing that. - Yeah I saw.....I said like Linux became pervasive, it took RedHat a long time to become a billion dollar company, because the open stack go along way there. Any final thoughts you wanna go on Dave? - Well so yeah, check out thecube.net, check out thecube insights, find that on whatever your favorite podcast player is, we're gonna be all over the place thecube.net will tell you where we're gonna be obviously, siliconangle.com, wikibon.com for all the research. - Alright and be sure to hit us up on Twitter if you have questions. He's D Villante on twitter, Angus stu S-T-U, Furrier is @Furrier, Peter Borus is PL Borus on twitter, Our whole team. wikibon.com for the research, siliconangle.com for the news and of course thecube.net for all the video. - And @ TheCube - And @TheCube of course on Twitter for our main feed And we're also up on Instagram now, so check out thecube signal on one word, give you a little bit of behind the scenes fun our phenomenal production team help to bring the buzz and the energy for all the things we do so for Dave Vellante, I'm Stu Miniman, thanks so much for listening to this special episode of thecube insights. 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(fast orchestral music) >> Hi, I'm Peter Burris, and welcome to another Wikibon CUBE Conversation. Today I'm joined by Eric Herzog, who's the CMO and Vice President of Channels in IBM's Storage Group. Welcome, Eric. >> Peter, thank you very much. Really appreciate spending time with theCube. >> Absolutely, it's always great to have you here, Eric. And you know, it's interesting. When you come in, it's kind of, let's focus on storage, cause that's what you do, but it's kind of interesting overall, the degree to which storage and business is now becoming more than just a thing that you have to have, but part of your overall business strategy increasingly because of the role that digital business is playing. Well, earlier today IBM made some pretty consequential announcements about how you intend to help customers draw those two together closely. Why don't you take us through 'em? >> So, first thing I think, with the digital business, it's all about data. And the digital business is driven by data. Data always ends up on storage and is always managed by storage software, so while it may be underneath the hood if you will, it is the critical engine underneath that entire car. If you don't have the right engine or transmission, which you could argue storage and storage software is, then you can't have a truly digital business. >> True, so tell us, what did IBM do? >> So what we do is we announced a number of technologies today, some of which were enhancing, some of which were brand new. So for example, a lot of it was around our Spectrum storage software family. We introduced a new software-defined storage for NAS, Spectrum NAS. We introduced enhancements to our IBM cloud object storage offering, also to our Spectrum Virtualize, several enhancements to our modern data protection suite, which is Spectrum Protect and Spectrum Tech Plus were enhanced. And lastly, from an infrastructure perspective, we announced a first real product around an NVMe storage solution over an InfiniBand fabric, and what we're going to do for rest year-round NVMe and how that impacts storage systems. Which are of course, a critical component in your digital data business. >> You also announced some new terms and conditions, or new ways of conceiving how you can get access to the storage, capacity storage plans you want. Why don't ya give us a little bit of inside on that. >> So one of the things we've done is we've already created, a couple years ago the Spectrum storage suite which has a whole raft of different products, file software, block software, back-up, archive software. So we added the Spectrum Protect Plus offering into that suite. We also had a back-up only suite which focuses just on modern data protection. We've put it in there and in both cases, it's at no additional fee. So if you buy the suite, you get Spectrum Protect Plus. If you buy the back-up only suite, so you're more focused on back-up only, again at no extra charge to the end user. The other thing we've done is we announced in Q4, a storage utility model. So think that you can buy storage, the way you buy your power bill or your water bill or your gas bill. So it can go up and it can go down. We bill you quarterly. We added our IBM cloud object storage on premises solution to that set of products. We had an earlier set of products built around flash we announced in Q4 of last year. Now we've added object storage as a way to consume in basically a utility offering model. >> So we talk a lot at Wikibon about the need for what we call the true private client approach which is basically the idea that you want the cloud experience wherever your data requires. And it sounds like IBM is actually starting to accelerate the process by which it introduces many of these features, especially in the storage unit. You've bought in more stuff underneath the spectrum family. You're starting to introduce some of those new highly innovative technologies like NVMe over Fabric and you've also introduced an honest utility model that allows people to have or to treat their storage capacity more like that cloud experience. Have I got that right? >> Absolutely. And we've done one other things too. For example, as you know, from a cloud perspective everyone is moving to containers, right? Our Spectrum Connect product offers free support for dockers and kubernetes. So if you're going to create a private cloud, and you're going to do that on your own, of even hybrid cloud where you're, you know, sluffing some of it into your public cloud provider. Bottom line is that dockers support, that container support is what you need to create the true private cloud experience that Wikibon has been talking about for the last year and half now. >> Well, let's talk about the kubernetes and dockers and the notion of containers as a dissociative storage. I want to take it in two directions. First off, tell us a little bit about how it works kind of dissolver oriented terms and then, let's talk about what that's going to mean to the ecosystem and how people are going to think about buying storage going forward. So why don't we start with how does this capability work? >> Sure. So the key thing we've done with the Spectrum Connect product is provide persistent storage capability to a container environment. As you know, containers just like VM's in the past can come up and come down very frequently especially if you're in a dev-ops environment. The whole point is they can spin them up quickly and take them down quickly. The problem is they don't allow for persistent storage. So our Spectrum container product allows for the capability of doing persistent storage connected to a containerized environment. >> So they way this would would work is you'd still have a server, you'd still have machine with some compute that would be responsible for spinning the containers up and down. But you'd have a storage feature that would make sure that that storage associated with that container would persist. >> Correct. >> Therefore you could continue to do the container up and down in the server while at the same time persisting the storage over an extended period of time. >> Right. So what that means is any of our customers who have our Spectrum Accelerate software defined storage for block, our Spectrum Virtualized software defined storage for block, and the associated family of arrays that ship with that software embedded. Remember, for us, our software defined storage can be sold stand alone as just a piece of software or embedded in our arrays, which for example, at Spectrum Virtualized means there's hundreds and hundreds of thousands of our software defined storage between the software only version and the array version. So for people who have those arrays, the container support is absolutely free. So if you've already bought the product and you're on our maintenance support, you just download the Spectrum Connect, boom you're off to the races, you deploy your containers for your private cloud environment and you've got it right there. If you're a brand new customer, you're going to buy let's say for example next week, you buy it next week. You get the Spectrum Virtualized, let's say for example on our Storewize V7000 F all-flash array cause that software comes with it. And you could go download Spectrum Connect at no fee cause you just type in you're a customer, put in your serial number, boom! They can just download it. And we don't charge anything for that. >> And now your storage guys and your developer guys are working a little bit more closely together as opposed to being at each others' throats. >> And saying what happened to the storage? >> There you go. >> Oh wait. I thought that was going to be... well no, it's not persistent. And in this case, it's persistent. They can take it up. They can take it down. They can do whatever they want. And that container product is free so the IT guy doesn't go, "Oh now I got to pay more money cause he doesn't." And then the guys on the dev-ops side and on the deployment application side are saying oh okay now I don't have to worry about that as an issue anymore. The IT guys took care of that for me. So you get everybody working together. You get the persistent storage that is not, you know, comes when you get a container environment. You get the exact opposite that is not persistent. And now we've offered that. And again it's a no charge for the users so it's easy to deploy. Easy to use and there's no fee. >> And so Eric, the reason I ask questions is because it's the compounding of these little annoyances that make it difficult for companies to accelerate their entree into digital business. And how they engage their customers differently and so this is one of those examples where as you said, data is the asset that distinguishes a digital business from a regular business competitor. What types of changes is this going to mean to the way the business thinks, the way the business buys, the way the business perceives storage? >> So I think the first thing is they need to realize that in a digital business, data is the oil. It is the gold, it is the silver, it's the diamonds. It is the number one entity. >> It's the value. >> It is the value of your digital business. So, you have to realize that the underlying infrastructure if it goes down, guess what? Your digital business is no longer up and running. So from that perspective, you need to have your underlying foundation from a storage perspective. In this case, think of Storage System the highly, highly available, highly, highly reliable and it needs to be incredibly fast because now you're doing everything from a digital business. And so everything is pounding on your server and storage infrastructure. Not that it wasn't a traditional data center but if certain things need to be slow, it's okay. But now that you've gone true private cloud with a full digital business, it can't be slow. It has to be resilient and it has to be always available. And those are things we've built in to both our storage software lair, the Spectrum family and to all of our storage arrays. The Storewize family, our DS family, our Flash System family. All are highly redundant, highly available and they're all flash. >> And let me add two more things to that. Cause I think it's pertinent to the direction that IBM is taking here, because data is not exactly like oil or not exactly like diamonds, in the sense that, oil and diamonds still follow the laws of scarcity. The value of data increases, and I know you've made this point, as you use it more. >> Right. >> So on the one hand, the storage has to provide the flexibility that developers can go after the same data at different times and in different ways. But still have that data be persistent and related to that obviously is that you want to ensure that you're able to drive that through-put through the system as aggressively as possible without creating a whole bunch of administrative headaches. So if we pivot for a second to NVMe, what does that mean to introduce things like NVMe to those five things we just talked about? Especially you know, the performance and the flexibility of having multiple applications and groups being able to go at the same data, perhaps do some snapshots and copies? >> So, couple things. From a software perspective that sits on top of all of our products, we've taken the approach of modern data protection. It's not let's just do an incremental back-up like in the old days. So what we do today is we have basically incessant snapshotting which is a full boat copy. What you can do is you can check those out with our Spectrum Content data manager which we didn't announce anything new on that, but we announced it last year. And with that, you can have unending snapshots. The dev-ops guys can grab a real piece of software, a real piece of data. So when they're doing their development, they're not using a faux set. And that faux set often can introduce more bugs. It doesn't get up as quickly. >> And so now you got more data, so you take the snapshot. By the way, it's self service. They can check it out themselves. Now when you look at it from the IT guy's perspective, guess what? There's a log of who's got what. So if there was a security issue, they can say, oh Eric Herzog, you're the one that had that. It looks like that leaked out from you. Even if it was inadvertent, the point is the dev-op guys can go in and grab from this new modern data production paradigm that we have. At the same time, the IT guys can at least track what is going on, so it's interesting. Then from a NVMe perspective, the key thing that NVMe has is A, all of the existing infrastructures, InfiniBand, Fabric, Fibre Channel Fabric, and Ethernet Fabric will be supported. Okay, over time, we're announcing today an InfiniBand Fabric solution, but all of the arrays that you buy today, if you for example bought a flash system V9000 and you wanted to do NVMe over Ethernet later in the year, software upgrade only. You buy the hardware now, you're done, okay? Our A9000 flash systems, Fibre Channel Connect, you buy they Fibre Channel now, you just upgrade the software a little bit later. So the key things within a NVMe configuration is A, the box is already highly resistant, highly available. Okay, they resist failures. They're easy to fix if there is a hardware failure for example, failed power supply. You know it's going to happen, okay? The smart business has an extra power supply sitting on the shelf. He pulls it out, he swaps it then sends it back to IBM. And when it's under warranty, boom, we take care of it. Okay? So that's the resiliency and the availability aspect from a physical perspective. But with NVMe, you get a better performance, which means that the arrays can handle more workloads. So as you go to a truly digital business built around the private cloud that Wikibon has been talking about now for 18 months, as you go to that model, you want to get more and apps pounding on the same storage, if you will. And with an NVMe Fabric solution, NVMe over time in the sub system itself, all that gives you more apps can work on the same set of storage. Now, do I have enough capacity, which is a separate topic. But as far as can the array handle the workload with NVMe from a Fabric perspective and NVMe in a storage sub system? You can handle additional workloads on the same physical infrastructure which saves you time, saves you money and gives you the performance for all workloads. Not just for a few niche workloads and all the other ones have to be slow. >> So Eric, you're out spending a lot of time with customers. Tell us a little about how they see their environments changing as a consequence of these and other related announcements. Are developers going to be looking at storage more as a potential source of value? How are administrators dealing with this? And give us some examples if you would. >> Sure, sure. So I think the key thing is with things like our content data manager. As we've got customers right now and they're able to check it out to all the test step guides which they couldn't do before. They're getting work done faster with real data. So the amount of bugs that come up with internal developers just like commercial developers like IBM or any other software company, the Microsofts, the Oracles, everybody has bugs. Well, guess what? In house developers got the same bugs. But, we help reduce that bug count. We make it easier for them to fix. Cause we're working on a real data set and not a fake data set, right? The IT guys love it because the dev-op guys don't say can you spin this up, spin this down? They do it on their own, right? Which accelerates them in doing their work. And the IT guys aren't bothered for it. That one concern on security, guess what? You got that long saying who's got what. >> Right, right. >> Burris has this. Herzog has that. >> That's a big deal because the IT guys ultimately, if something leaks out or there's a security issue, they get the call from the Chief Legal Officer, not the dev-ops guy. So this way, everybody is happy. The dev-op guys are happy. The IT guys are happy. The IT guys can focus on spinning up and spinning down for the dev guys. You can build it all yourself. Our copy data management and all of our storage softwares are API driven. Rest API's, integration with all of the object storage interfaces including S3. So it's easier and easier for the IT guy to make the dev-ops guys happy and give the dev-op guys self service, which, as you know, self service is one of the key attributes of the private cloud that Wikibon keeps talking about is self service. So we can give more through the software side. >> So I have one more question Eric. As we think about kind of where this announcement is, most important to businesses that are trying to affect that type of transformation we're talking about, is there one specific feature that is your conversation with customers, your conversations with the channels, since you're also very very close with the channel, that keeps popping to the top of the list of things to focus on as companies? As I said, try to figure out how to use data and assets differently? >> Well I think what the key thing from a storage guy perspective is one, interfacing with all the API's which we've done across our whole family, okay? Second thing is automation, automation, automation. The dev-ops guys like it. In a smaller shop, there may be only one IT guy who has to take care of their entire infrastructure. So the fact that our Spectrum Protect Plus for example can do VMware hyper V back-up except it can be done by the VMware hyper V guy or a general IT guy not a storage guy or a back-up admin. In the enterprise, sure there's a back-up admin in the big enterprises, but if you're at Herzog's Bar and Grill there is no back-up admin. So that ease of use, that simplicity, that integration with common API's and automating as much as possible is critical as people go to the digital business based on private clouds. >> Excellent. Eric Herzog, CMO, Vice-President of Channels at IBM storage group, talking about a number of things that were announced today as businesses try to marry their storage capability and their digital business strategy more closely together. Thanks for being here. >> Great, thank you very much. >> Once again, I'm Peter Burris. This has been a Wikibon CUBE Conversation with Eric Herzog of IBM. (fast orchestral music)

>> This is George Gilbert, we're on the ground at Demandbase, the B2B CRM company, based on AI, one of uh, a very special company that's got some really unique technology. We have the privilege to be with Seth Myers today, Senior Data Scientist and resident wizard, and who's going to take us on a journey through some of the technology Demandbase is built on, and some of the technology coming down the road. So Seth, welcome. >> Thank you very much for having me. >> So, we talked earlier with Aman Naimat, Senior VP of Technology, and we talked about some of the functionality in Demandbase, and how it's very flexible, and reactive, and adaptive in helping guide, or react to a customer's journey, through the buying process. Tell us about what that journey might look like, how it's different, and the touchpoints, and the participants, and then how your technology rationalizes that, because we know, old CRM packages were really just lists of contact points. So this is something very different. How's it work? >> Yeah, absolutely, so at the highest level, each customer's going to be different, each customer's going to make decisions and look at different marketing collateral, and respond to different marketing collateral in different ways, you know, as the companies get bigger, and their products they're offering become more sophisticated, that's certainly the case, and also, sales cycles take a long time. You're engaged with an opportunity over many months, and so there's a lot of touchpoints, there's a lot of planning that has to be done, so that actually offers a huge opportunity to be solved with AI, especially in light of recent developments in this thing called reinforcement learning. So reinforcement learning is basically machine learning that can think strategically, they can actually plan ahead in a series of decisions, and it's actually technology behind AlphaGo which is the Google technology that beat the best Go players in the world. And what we basically do is we say, "Okay, if we understand "you're a customer, we understand the company you work at, "we understand the things they've been researching elsewhere "on third party sites, then we can actually start to predict "about content they will be likely to engage with." But more importantly, we can start to predict content they're more likely to engage with next, and after that, and after that, and after that, and so what our technology does is it looks at all possible paths that your potential customer can take, all the different content you could ever suggest to them, all the different routes they will take, and it looks at ones that they're likely to follow, but also ones they're likely to turn them into an opportunity. And so we basically, in the same way Google Maps considers all possible routes to get you from your office to home, we do the same, and we choose the one that's most likely to convert the opportunity, the same way Google chooses the quickest road home. >> Okay, this is really, that's a great example, because people can picture that, but how do you, how do you know what's the best path, is it based on learning from previous journeys from customers? >> Yes. >> And then, if you make a wrong guess, you sort of penalize the engine and say, "Pick the next best, "what you thought was the next best path." >> Absolutely, so the way, the nuts and bolts of how it works is we start working with our clients, and they have all this data of different customers, and how they've engaged with different pieces of content throughout their journey, and so the machine learning model, what it's really doing at any moment in time, given any customer in any stage of the opportunity that they find themselves in, it says, what piece of content are they likely to engage with next, and that's based on historical training data, if you will. And then once we make that decision on a step-by-step basis, then we kind of extrapolate, and we basically say, "Okay, if we showed them this page, or if they engage with "this material, what would that do, what situation would "we find them in at the next step, and then what would "we recommend from there, and then from there, "and then from there," and so it's really kind of learning the right move to make at each time, and then extrapolating that all the way to the opportunity being closed. >> The picture that's in my mind is like, the Deep Blue, I think it was chess, where it would map out all the potential moves. >> Very similar, yeah. >> To the end game. >> Very similar idea. >> So, what about if you're trying to engage with a customer across different channels, and it's not just web content? How is that done? >> Well, that's something that we're very excited about, and that's something that we're currently really starting to devote resources to. Right now, we already have a product live that's focused on web content specifically, but yeah, we're working on kind of a multi-channel type solution, and we're all pretty excited about it. >> Okay so, obviously you can't talk too much about it. Can you tell us what channels that might touch? >> I might have to play my cards a little close to my chest on this one, but I'll just say we're excited. >> Alright. Well I guess that means I'll have to come back. >> Please, please. >> So, um, tell us about the personalized conversations. Is the conversation just another way of saying, this is how we're personalizing the journey? Or is there more to it than that? >> Yeah, it really is about personalizing the journey, right? Like you know, a lot of our clients now have a lot of sophisticated marketing collateral, and a lot of time and energy has gone into developing content that different people find engaging, that kind of positions products towards pain points, and all that stuff, and so really there's so much low-hanging fruit by just organizing and leveraging all of this material, and actually forming the conversation through a series of journeys through that material. >> Okay, so, Aman was telling us earlier that we have so many sort of algorithms, they're all open source, or they're all published, and they're only as good as the data you can apply them to. So, tell us, where do companies, startups, you know, not the Googles, Microsofts, Amazons, where do they get their proprietary information? Is it that you have algorithms that now are so advanced that you can refine raw information into proprietary information that others don't have? >> Really I think it comes down to, our competitive advantage I think is largely in the source of our data, and so, yes, you can build more and more sophisticated algorithms, but again, you're starting with a public data set, you'll be able to derive some insights, but there will always be a path to those datasets for, say, a competitor. For example, we're currently tracking about 700 billion web interactions a year, and then we're also able to attribute those web interactions to companies, meaning the employees at those companies involved in those web interactions, and so that's able to give us an insight that no amount of public data or processing would ever really be able to achieve. >> How do you, Aman started to talk to us about how, like there were DNS, reverse DNS registries. >> Reverse IP lookups, yes. >> Yeah, so how are those, if they're individuals within companies, and then the companies themselves, how do you identify them reliably? >> Right, so reverse IP lookup is, we've been doing this for years now, and so we've kind of developed a multi-source solution, so reverse IP lookups is a big one. Also machine learning, you can look at traffic coming from an IP address, and you can start to make some very informed decisions about what the IP address is actually doing, who they are, and so if you're looking at, at the account level, which is what we're tracking at, there's a lot of information to be gleaned from that kind of information. >> Sort of the way, and this may be a weird-sounding analogy, but the way a virus or some piece of malware has a signature in terms of its behavior, you find signatures in terms of users associated with an IP address. >> And we certainly don't de-anonymize individual users, but if we're looking at things at the account level, then you know, the bigger the data, the more signal you can infer, and so if we're looking at a company-wide usage of an IP address, then you can start to make some very educated guesses as to who that company is, the things that they're researching, what they're in market for, that type of thing. >> And how do you find out, if they're not coming to your site, and they're not coming to one of your customer's sites, how do you find out what they're touching? >> Right, I mean, I can't really go into too much detail, but a lot of it comes from working with publishers, and a lot of this data is just raw, and it's only because we can identify the companies behind these IP addresses, that we're able to actually turn these web interactions into insights about specific companies. >> George: Sort of like how advertisers or publishers would track visitors across many, many sites, by having agreements. >> Yes. Along those lines, yeah. >> Okay. So, tell us a little more about natural language processing, I think where most people have assumed or have become familiar with it is with the B2C capabilities, with the big internet giants, where they're trying to understand all language. You have a more well-scoped problem, tell us how that changes your approach. >> So a lot of really exciting things are happening in natural language processing in general, and the research, and right now in general, it's being measured against this yardstick of, can it understand languages as good as a human can, obviously we're not there yet, but that doesn't necessarily mean you can't derive a lot of meaningful insights from it, and the way we're able to do that is, instead of trying to understand all of human language, let's understand very specific language associated with the things that we're trying to learn. So obviously we're a B2B marketing company, so it's very important to us to understand what companies are investing in other companies, what companies are buying from other companies, what companies are suing other companies, and so if we said, okay, we only want to be able to infer a competitive relationship between two businesses in an actual document, that becomes a much more solvable and manageable problem, as opposed to, let's understand all of human language. And so we actually started off with these kind of open source solutions, with some of these proprietary solutions that we paid for, and they didn't work because their scope was this broad, and so we said, okay, we can do better by just focusing in on the types of insights we're trying to learn, and then work backwards from them. >> So tell us, how much of the algorithms that we would call building blocks for what you're doing, and others, how much of those are all published or open source, and then how much is your secret sauce? Because we talk about data being a key part of the secret sauce, what about the algorithms? >> I mean yeah, you can treat the algorithms as tools, but you know, a bag of tools a product does not make, right? So our secret sauce becomes how we use these tools, how we deploy them, and the datasets we put them again. So as mentioned before, we're not trying to understand all of human language, actually the exact opposite. So we actually have a single machine learning algorithm that all it does is it learns to recognize when Amazon, the company, is being mentioned in a document. So if you see the word Amazon, is it talking about the river, is it talking about the company? So we have a classifier that all it does is it fires whenever Amazon is being mentioned in a document. And that's a much easier problem to solve than understanding, than Siri basically. >> Okay. I still get rather irritated with Siri. So let's talk about, um, broadly this topic that sort of everyone lays claim to as their great higher calling, which is democratizing machine learning and AI, and opening it up to a much greater audience. Help set some context, just the way you did by saying, "Hey, if we narrow the scope of a problem, "it's easier to solve." What are some of the different approaches people are taking to that problem, and what are their sweet spots? >> Right, so the the talk of the data science community, talking machinery right now, is some of the work that's coming out of DeepMind, which is a subsidiary of Google, they just built AlphaGo, which solved the strategy game that we thought we were decades away from actually solving, and their approach of restricting the problem to a game, with well-defined rules, with a limited scope, I think that's how they're able to propel the field forward so significantly. They started off by playing Atari games, then they moved to long term strategy games, and now they're doing video games, like video strategy games, and I think the idea of, again, narrowing the scope to well-defined rules and well-defined limited settings is how they're actually able to advance the field. >> Let me ask just about playing the video games. I can't remember Star... >> Starcraft. >> Starcraft. Would you call that, like, where the video game is a model, and you're training a model against that other model, so it's almost like they're interacting with each other. >> Right, so it really comes down, you can think of it as pulling levers, so you have a very complex machine, and there's certain levers you can pull, and the machine will respond in different ways. If you're trying to, for example, build a robot that can walk amongst a factory and pick out boxes, like how you move each joint, what you look around, all the different things you can see and sense, those are all levers to pull, and that gets very complicated very quickly, but if you narrow it down to, okay, there's certain places on the screen I can click, there's certain things I can do, there's certain inputs I can provide in the video game, you basically limit the number of levers, and then optimizing and learning how to work those levers is a much more scoped and reasonable problem, as opposed to learn everything all at once. >> Okay, that's interesting, now, let me switch gears a little bit. We've done a lot of work at WikiBound about IOT and increasingly edge-based intelligence, because you can't go back to the cloud for your analytics for everything, but one of the things that's becoming apparent is, it's not just the training that might go on in a cloud, but there might be simulations, and then the sort of low-latency response is based on a model that's at the edge. Help elaborate where that applies and how that works. >> Well in general, when you're working with machine learning, in almost every situation, training the model is, that's really the data-intensive process that requires a lot of extensive computation, and that's something that makes sense to have localized in a single location which you can leverage resources and you can optimize it. Then you can say, alright, now that I have this model that understands the problem that's trained, it becomes a much simpler endeavor to basically put that as close to the device as possible. And so that really is how they're able to say, okay, let's take this really complicated billion-parameter neural network that took days and weeks to train, and let's actually derive insights at the level, right at the device level. Recent technology though, like I mentioned deep learning, that in itself, just the actual deploying the technology creates new challenges as well, to the point that actually Google invented a new type of chip to just run... >> The tensor processing. >> Yeah, the TPU. The tensor processing unit, just to handle what is now a machine learning algorithm so sophisticated that even deploying it after it's been trained is still a challenge. >> Is there a difference in the hardware that you need for training vs. inferencing? >> So they initially deployed the TPU just for the sake of inference. In general, the way it actually works is that, when you're building a neural network, there is a type of mathematical operation to do a whole bunch, and it's based on the idea of working with matrices and it's like that, that's still absolutely the case with training as well as inference, where actually, querying the model, but so if you can solve that one mathematical operation, then you can deploy it everywhere. >> Okay. So, one of our CTOs was talking about how, in his view, what's going to happen in the cloud is richer and richer simulations, and as you say, the querying the model, getting an answer in realtime or near realtime, is out on the edge. What exactly is the role of the simulation? Is that just a model that understands time, and not just time, but many multiple parameters that it's playing with? >> Right, so simulations are particularly important in taking us back to reinforcement learning, where you basically have many decisions to make before you actually see some sort of desirable or undesirable outcome, and so, for example, the way AlphaGo trained itself is basically by running simulations of the game being played against itself, and really what that simulations are doing is allowing the artificial intelligence to explore the entire possibilities of all games. >> Sort of like WarGames, if you remember that movie. >> Yes, with uh... >> Matthew Broderick, and it actually showed all the war game scenarios on the screen, and then figured out, you couldn't really win. >> Right, yes, it's a similar idea where they, for example in Go, there's more board configurations than there are atoms in the observable universe, and so the way Deep Blue won chess is basically, more or less explore the vast majority of chess moves, that's really not the same option, you can't really play that same strategy with AlphaGo, and so, this constant simulation is how they explore the meaningful game configurations that it needed to win. >> So in other words, they were scoped down, so the problem space was smaller. >> Right, and in fact, basically one of the reasons, like AlphaGo was really kind of two different artificial intelligences working together, one that decided which solutions to explore, like which possibilities it should pursue more, and which ones not to, to ignore, and then the second piece was, okay, given the certain board configuration, what's the likely outcome? And so those two working in concert, one that narrows and focuses, and one that comes up with the answer, given that focus, is how it was actually able to work so well. >> Okay. Seth, on that note, that was a very, very enlightening 20 minutes. >> Okay. I'm glad to hear that. >> We'll have to come back and get an update from you soon. >> Alright, absolutely. >> This is George Gilbert, I'm with Seth Myers, Senior Data Scientist at Demandbase, a company I expect we'll be hearing a lot more about, and we're on the ground, and we'll be back shortly.

>> Announcer: Live from Orlando, Florida. It's TheCUBE covering PentahoWorld 2017. Brought to you by Hitachi Ventara. >> Welcome back to TheCUBE's live coverage of PentahoWorld brought to you by Hitachi Ventara, we are wrapping up day one. I'm your host Rebecca Knight along with my cohosts today James Kobielus and Dave Vellante. Guys, day one is done what have we learned? What's been the most exciting thing that you've seen at this conference? >> The most exciting thing is that clearly Hitachi Ventara which of course, Pentaho is a centerpiece is very much building on their strong background and legacy and open analytics, and pushing towards open analytics in the Internet of things, their portfolio, the whole edge to outcome theme, with Brian Householder doing a sensational Keynote this morning, laying out their strategic directions now Dave had a great conversation with him on TheCUBE earlier but I was very impressed with the fact that they've got a dynamic leader and a dynamic strategy, and just as important Hitachi, the parent company, has clearly put together three product units that make sense. You got strong data integration, you got a strong industrial IOT focus, and you got a really strong predictive and machine learning capability with Pentaho for the driving the entire pipeline towards the edge. Now that to me shows that they've got all the basic strategic components necessary to seize the future, further possibilities. Now, they brought a lot of really good customers on, including our latest one from IMS, Hillove, to discuss exactly what they're doing in that area. So I was impressed with the amount of solid substance of them seizing the opportunity. >> Well so I go back two years, when TheCUBE first did PentagoWorld 2015, and the story then was pretty strong. You had a company in big data, they seemingly were successful, they had a lot of good customer references, they achieved escape velocity, and had a nice exit under Quentin Galavine, who was the CEO at the time and the team. And they had a really really good story, I thought. But I was like okay, now what? We heard about conceptually we're going to bring the industrial internet and analytics together, and then it kind of got quiet for two years. And now, you're starting to see the strategy take shape in typical Hitachi form. They tend not to just rush in to big changes and transformations like this, they've been around for a long time, a very thoughtful company. I kind of look at Hitachi limited in a way, as an IBM like company of Japan, even though they do industrial equipment, and IBM's obviously in a somewhat different business, but they're very thoughtful. And so I like the story the problem I see is not enough people know about the story. Brian was very transparent this morning, how many people do business with Hitachi? Very few. And so I want to see the ecosystem grow. The ecosystem here is Hitachi, a couple of big data players, I don't see any reason why they can't explode this event and the ecosystem around Hitachi Ventara, to fulfill it's vision. I think that that's a key aspect of what they have to do. >> I want to see-- >> What will be the tipping point? Just to get as you said, I mean it's the brand awareness, and every customer we had on the show really said, when he when he said that my eyes lit up and I thought oh wow, we could actually be doing more stuff with Hitachi, there's more here. >> I want to see a strong developer focus, >> Yeah. >> Going forward, that focuses on AI and deep learning at the at the edge. I'm not hearing a lot of that here at PentahoWorld, of that rate now. So that to me is a strategic gap right now and what they're offering. When everybody across the IT and data and so forth is going real deep on things like frameworks like TensorFlow and so forth, for building evermore sophisticated, data driven algorithms with the full training pipeline and deployment and all that, I'm not hearing a lot of that from the Pentaho product group or from the Hitachi Ventara group here at this event. So next year at this event I would like to hear more of what they're doing in that area. For them to really succeed, they're going to have to have a solid strategy to migrate up there, openstack to include like I said, a bit of TensorFlow, MXNet, or some of the other deep learning tool kits that are becoming essentially defacto standards with developers. >> Yeah, so I mean I think the vision's right. Many of the pieces are in place, and the pieces that aren't there, I'm actually not that worried about, because Hitachi has the resources to go get them, either build them organically, which has proven it can do overtime, or bring in acquisition. Hitachi is a decent acquire of companies. Its content platform came in on an acquisition, I've seen them do some hardware acquisitions, some have worked, some haven't. But there's a lot of interesting software players out there and I think there's some values, frankly. The big data, tons of money poured in to this open source world, hard to make money in opensource, which means I think companies like Hitachi could pick off to do some M and A and find some value. Personally, I think if the numbers right at a half a billion dollars, I personally think that that was pretty good value for Hitachi. You see in all these multi billion dollar acquisitions going left and right. And so the other thing is the fact that Hitachi under the leadership under Brian Householder and others, was able to shift its model from 80% hardware, now it's 50/50 software and services I'd like to dig into that a little bit. They're a public company but you can't really peel the onion on the Hitachi Ventara side, so it kind of is what they say it is, I would imagine that's a lot of infrastructure software, kind of like EMC's a software company. >> James: Right. >> But nonetheless, they're moving towards a subscription model, they're committed to that, and I think that the other thing is that a lot of costumers. We come to a lot of shows and they struggle to get costumers on with substantive stories, so we heard virtually every costumer we talked to today is like Here's how I'm using Pentaho, here's how it's affecting. Not like super sexy stories yet, I mean that's what the IOT and the edge piece come in, but fundamental plumbing around big data, Pentaho seems like a pretty important piece of it. >> Their fundamental-- >> Their fundamental plumbing that's really saving them a lot of money too, and having a big ROI. >> They're fairly blue-chip as a solution provider of a full core data of a portfolio of Pentaho. I think of them in many ways as sort of like SAP, not a flashy vendor, but a very much a solid blue-chip in their core markets >> Right. >> I'm just naming another vendor that I don't see with a strong AI focus yet. >> Yeah. >> Pentaho, nothing to sneeze at when you have one customer after another like we've had here, rolling out some significant work they've been doing with Pantaho for quite a while, not to sneeze at their delivering value but they have to rise to the next level of value before long, to avoid be left in the dust. >> You got this data obviously they're going to be capturing more more data with the devices. >> James: Yeah. >> And The relationship with Hitachi proper, the elevator makers is still a little fuzzy to me, I'm trying to understand how that all shakes up, but my question for you Jim is: okay so let's assume for second they're going to have this infrastructure in place because they are industrial internet, and they got the analytics platform, maybe there's some holes that they can fill in, one being AI and some of the deep learning stuff, can't they get that somewhere? I mean there's so much action going on-- >> Yes. >> In the AI world, can't they bring that in and learn how to apply it overtime? >> Of course they can. First of all they can acquire and tap their own internal expertise. They've got like Mark Hall for example on the panel, they've obviously got a deep bench of data scientist like him who can take it to that next level, that's important. I think another thing that Hitachi Ventara needs to do to take it to the next level is they need a strong robotics portfolio. It's really talking about industrial internet of things, it's robotics with AI inside. I think they're definitely a company that could go there fairly quickly, a wide range of partners they can bring in or acquire to get fairly significant in terms of not just robotics in general, but robotics for a broad range of use cases where the AI is not so much the supervise learning and stuff that involves training, but things like reinforcement learning, and there's a fair amount of smarts and academe on Reinforcement learning for in body cognition, for robots, that's out there in terms of that's like the untapped space other than the broad AI portfolio, reinforcement learning. If somebody's going to innovate and differentiate themselves in terms of the enterprise, in terms of leveraging robotics in a variety of applications, it's going to to be somebody with a really strong grounding and reinforcement learning and productizing that and baking that in to an actual solution portfolio, I don't see yet the Google's and the IBM's and the Microsofts going there, and so if these guys want to stand out, that's one area they might explore. >> Yeah, and I think to pick up on that, I think this notion of robotics process automation, that market's going to explode. We were at a conference this week in Boston, the data rowdy of Boston, the chief data officer conference at the Park Plaza, 20 to 25% of the audiences, the CDO's in the audience had some kind of RPA, robotic process automation, initiative going on which I thought was astoundingly high. And so it would seem to me that Hitachi's going to be in a good position to capture all that data. The other thing that Brian stressed, which a lot of companies without a cloud will stress, is that it's your data, you own the data, we're not trying to resell that data, monetize that data, repackage that data. I pushed him a little bit on well what about that data training models, and where do those models go? And he says Look we are not in the business of taking models and you know as a big consultancy, and bringing it over to other competitors. Now Hitachi does have consultancy, but it's sort of in a focus, but as Brian said in his keynote, you have to listen to what people say and then watch them to see how they act. >> Rebecca: Do they walk the walk? >> How they respond. >> Right. >> And so that's you have to make your decision, but I do think that's going to be a very interesting field to watch because Hitachi's going to have so much data in their devices. Of course they're going to mine that data for things like predictive analytics, those devices are going to be in factories, they're going to be in ecosystems, and there's going to be a battle for who owns the data, and it's going to be really interesting to see how that shakes out. >> So I want to ask you both, as you've both have said, we've had a lot of great customer stories here on TheCUBE today. We had a woman who does autonomous vehicles, we had a gamer from Finland, we had a benefit scientist out of Massachusetts, Who were your favorite customer stories and what excited you most about their stories? >> James: Hmmm. >> Well I know you like the car woman. >> Well, yeah the car woman, >> The car woman. >> Ella Hillel. >> Ella Hillel, Yes. >> The PHD. That was really what I found many things fascinating, I was on a panel with Ella as well as she was on TheCUBE, what I found interesting I was expecting her to go to town on all things autonomous driving, self driving vehicles, and so forth, was she actually talked about the augmentation of the driver, passenger experience through analytics, dashboards in the sense that dashboards that help not only drivers but insurance companies and fleet managers, to do behavioral modification to help them modify the behavior, to get the most out of their vehicular experience, like reducing wear and tear on tires, and by taking better roads, or revising I thought that's kind of interesting; build more of the recommendation engine capability into the overall driving experience. That depends on an infrastructure of predictive analytics and big data, but also metered data coming from the vehicle and so forth. I found that really interesting because they're doing work clearly in that area, that's an area that you don't need levels one through five of self driving vehicles to get that. You can get that at any level of that whole model, just by bringing those analytics somehow into an organic way hopefully safely, into your current driving experience, maybe through a heads-up display that's integrated through your GPS or whatever might be, I found that interesting because that's something you could roll out universally, and it can actually make a huge difference in A: safety, B: people's sort of pleasure with the driving experience, Fahrvergnugen that's a Volkswagon, and then also see how people make the best use of their own vehicular assets in an era where people still mostly own their own car. >> Well for me if there's gambling involved-- >> Rebecca: You're there. >> It was the gaming, now not only because of the gambling, and we didn't find out how to beat the house Leonard, maybe next time, but it was confirmation of the three-tier data model from from edge-- >> James: Yes. >> To gateway to cloud, and that the cloud is two vectors; the on-premise and the off-premise cloud, and the fact that as a gaming company who designs their own slot machines it's an edge device, and they're basically instrumenting that edge device for real-time interactions. He said that most of the data will go back, I'm not sure. Maybe in that situation it might, maybe all the data will go back like weather data, it all comes back, But generally speaking I think there's going to be a lot of analog data at the edge that's going to be digitize that maybe you don't have to save and persist. But anyway, confirmation of that three-tiered data model I think is important because I think that is how Brian talked about it, we all know the pendulum is swinging, swung away from mainframe to decentralize back to the centralized data center and now it's swinging again to a much more distributed sort of data architecture. So it was good to hear confirmation of that, and I think it's again, it's really early innings in terms of how that all shakes out. >> Great, and we'll know more tomorrow at Pentaho day two, and I look forward to to being up here again with both of you tomorrow. >> Likewise. >> Great, this has been TheCUBE's live coverage of PentahoWorld brought to you by Hitachi Ventara, I'm Rebecca Knight for Jim Kobielus and Dave Vellante, we'll see you back here tomorrow.

>> Voiceover: Live (digital music) from New York City, it's The Cube covering Lenovo Transform 2017. Brought to you by Lenovo. >> Welcome back to The Cube's coverage of Lenovo Transform. I'm your host, Rebecca Knight, along with my cohost, Stu Miniman. We are here with Kim Stevenson. She is the senior vice president and general manager of data center infrastructure here at Lenovo. Thank you so much for joining us, Kim. >> Thanks! I always enjoy my time on The Cube, so thanks for having me. >> So you've had a long and esteemed career in technology. Former CIO of Intel. Why did you come to Lenovo? What was it about Lenovo that drew you? >> Yeah, so I was very specifically looking for, to be in the data center space, because I believe our whole data center industry is changing. Right, and the incumbents actually don't possess very much value in this rapid pace of change. And I wanted to be a part of that. I've always loved big change agendas, so I was looking for that. Lenovo was clearly the underdog at the time I was making my decision. I like the underdog, I want, you know, I sort of-- it's about making impact and making progress, and being able to see that impact, and so that fit. And then I had, you know, good experiences with the management team, and I wanted to be able to leverage that, and in fact, you know, it's been a seamless transition in because I knew the management team and I understood some of the dynamics that we'd be facing together, and the challenges that we wanted to take on together. So it's been great, and I do think today's product launch is a culmination of, you know, we're not going to be the underdog for very much longer. >> Kim, I want to help you out and kind of help us unpack that changing dynamic of the data center. You had a very interesting viewpoint, coming from Intel. You know, Intel chips, we know they're going everywhere. What do you see as the role of the data center? Some people are like, oh, public cloud, and the hyperscales, and Lenovo has a, you know, a strong position playing across the board, so what do you see as data center? What does that mean to you and what does that mean to your customers? >> Yeah, so look, I think all businesses today are using technology to deliver their competitive advantage. It's the fundamental thing that's driving transformation in companies. So but if you look what's happened in enterprise tech for the last couple years, we've been consumed as IT practitioners, moving our workload to the cloud. Because there's rich functionality there, there's good economics, it's made complete sense. But that's only half the journey. And when you look at the other half, the second half of this movie, is really about why that enterprise data center becomes so much more important tomorrow than it is today. And it's because of the workloads, the differentiating workloads of your company. So when I was the CIO at Intel, our differentiating workloads were engineering and manufacturing. We got paid to engineer great products. We got paid to make them. And every industry has those kind of workloads. And then with the emergence of artificial intelligence and IOT, those workloads are going to explode, and they are going to reside in your enterprise data center. And so, but you think about what that needs in terms of network, in terms of memory, IO capability, those are much different platforms than what you think of as a legacy data center. And I simply want to be a part of making that come to life. >> Okay, and so you would include, some people would call that the edge requirement, edge computing, which, you know, sometimes the little bit of difference is, it's not my centralized data center, even if it does live out in the customer's environment. >> Some of it is edge, right? Clearly, the growth in the edge will be phenomenal. And some of that will be real-time processing that happens at the edge, and others of it will be coming back to the data center for other types of processing, right? It could be for pattern analysis over a longer time horizon. It could be for developing of new services to deploy it at a later date. But I think both of those things are going to, what do they call it, Jevons paradox, right? The more you have, the more you use, right? And that's what I think's going to happen with the value of data, being able to capture it and store it and analyze it in a very, very cost-effective manner. >> You were talking about how companies get paid to make great products, and how with the introduction or the evolution of AI and machine learning, how those workloads are going to explode, and it's not going to be enough any more to just make great products. Are companies ready for these changes? >> You know, there's a spectrum. I would say some yes, some no. I think in some cases, the rapid pace of technology scares the crap out of boards of directors and CEOs that don't come from our space. But, in the end, I do think that's going to be the winners and losers will be decided by who can deploy the most advanced technology in the fastest amount of time. And I think there's a whole generation of people that that scares. They didn't grow up like us, in the middle of it. There's a whole generation of people like me, that you know, it excites us, and it makes us want to do more. So but yeah, I think companies, especially those outside of tech, they have a longer journey than tech companies do. >> Rebecca: A learning curve. Yeah, learning curve is steeper. >> I want to talk a little about your experience at Lenovo so far. Before the cameras were rolling, you were talking about just the greater numbers of women in senior leadership positions at Lenovo, and how that changes the dynamic and the approach to teamwork. Can you tell our viewers a little bit more about what you've experienced? >> Yeah, so it's really been a pleasant surprise, because Lenovo's a very diverse company, and that diversity plays out. So in our president's staff, right, which I'm a member of, you know, half of the staff are women. And that may not sound unusual, except for it's very unusual in tech, >> Rebecca: In technology! and it's very unusual for me, as a woman, to have the opportunity to work with other women. And so, but, it's an interesting thing, because we're focused on driving more customer-centricity as a company, and particularly in data center group, and so what you see with this natural collaboration is that we're all focused on the same problem, and we're willing to leverage the strengths of one another, so there's no siloed thinking, there's no I have to be the smartest person in the room thinking, that often exists in tech companies. And I largely attribute that to the diversity of the staff. And the other thing is, it's sort of a sidetrack, but, the number of African Americans in our organization are greater than I've ever seen in any tech company. And so I've been asking some of the African American women, like, why is that, help me understand that, let's-- and they're brilliant, they're really brilliant. And so I just never subscribe to you can't find any women for these jobs, or you can't find any, you know, diverse candidates for these jobs. You just have to look in the right places, and so it's been really, really fabulous at Lenovo to work with really talented women, and learn from them, and hopefully I'm helping them learn something too, right? >> And eye-opening that it can happen, even in technology. At a time when we're hearing about the dearth of woman leaders, and the sort of bro culture in Silicon Valley, in particular. >> Yep, yep, so it's, you know, I mean I do think it takes a management team that has an open mindset to solve problems in different ways, and you know, if our core different is better, we're willing to do it differently, and look, we have a different management team and style that, you know, is much more focused on our customers. So it's been good. >> Kim, as a former CIO, I'm curious what your viewpoint is of the role of the CIO today. We talked about, you know, just the rapid pace of change that's happening in data center, a cloud needs to fit into the equation, you know, what do you see as the primary role of the CIO, and you know, how that's different today than it was, say, five years ago. >> Yeah, so, look, I think today's, the very, very best CIOs, people the top of our profession, are outstanding change agents. They're transformational leaders, they speak for the company, they don't speak for IT. They are integral in the strategic direction setting of the company, and they bring sort of a new thinking to that. Now, that said, they also have to run the operations extremely well. You do not get to sit at the table at the board meetings, or the CEO's staff, if you can't run the place really well. Because it's so pivotal that operational execution is really, really outstanding when your whole business is built on this. But the differentiators are really the ones around leadership, and speaking for the business, and strategic direction setting, and really trying to understand the capacity of change that the company can go through, and how do you keep expanding that capacity for change. >> Lenovo is number one in customer satisfaction on a slew of different rankings, and as you said, this is where you are focused, on pleasing the customer, satisfying the customer, anticipating the customer's needs before the customer even knows he has those needs. When you're number one, you can't move up. So how do you keep driving toward remaining in that top position? >> Yeah, so you know, one of the things that we're trying to do is change the expectation of the customers have of their hardware vendors, right? So obviously, customers always expect great products. It needs to be reliable, it needs to have good performance, it needs to cost the right price point. Those are always the standard. But we're trying to take it a little bit further and say, look, your expectation should be, we need to be easy to do business with, right, from the time I think about writing an RFQ, to maybe evaluate some hardware, to the time that I sunset that piece of equipment. Everything in-between, right, it needs to be easy to acquire, it needs to be easy to run, easy to manage, it needs to be easy to sunset. I want to future-proof that investment by making things upgradeable, and having, you know, instead of, I was talking to one customer today who said, hey, look, if I could have a 10 year life cycle, I can use bond money to fund my growth. If I don't, I can't. I have to use expense money, and therefore, I can do far less. And I said, well, we future-proofed this system, set of products, and here's how you can do that, right? You upgrade the memory at this cycle. You upgrade the processors at this cycle. Use the same rack, same chassis, for the next 10 years, and you get three generations of technology out of a single rack. And he's like, that's a brilliant! I mean, that's the kind of thing we're trying to help customers think through. It's more than, I always say, it's the tech and the team that get leadership, and that's what we want, to be a part of the team that drives leadership. >> Kim, your team has an interesting blend of people with long history in especially the x86 server market, and new people. Kirk and yourself, both, you know, relatively new to the position, how are you tracking, what KPIs are you measuring, to say that Lenovo's succeeding, becoming a larger, more strategic partner for customers in the data center. >> Yeah, so we're coming off, and it's no surprise, we're coming off a bad year, right? And so, we have really three dimensions that we are driving improvements in. One is sales execution. So all the classic KPIs and you know, sales productivity types of measures, and we've made the changes to get a dedicated sales force, hired very specific specialists, so that's one dimension. The second is product performance and portfolio gaps. So how well, you know, do we make, you know, our benchmarks? So we've got these 30 world record benchmarks, right? In the next generation, how well are we going to do against those? HPC benchmarks, right? So we're really external benchmarking, and it really is, like we measure ourselves externally, so we can get good view of the competition. And then we look at where our portfolio gaps are, and what do we have to do to close those portfolio gaps through focused engineering? And the third is then customer engagement and support, which is really where the satisfaction measure comes from, but we're not-- satisfaction is a rear-view mirror look at the world. We're using advanced analytics and AI to predict customer engagement drop, right? Why would that customer no longer be engaging with us? Customer field failures, and we're tracking every incident that happens out there, whether it's something we need to act on, or the customer needs to act on, so those become our important KPIs, to build this lifelong customer within a trusted relationship. >> The theme of this event is transform, and Lenovo has undergone an enormous transformation in the past few years, and now you're just starting, and sort of on the brink of this new transformation. I asked Cameron about this earlier, but just, if you could talk a little bit about where you want to be five years from now, in Lenovo, and sort of what you've accomplished in the next five years. What do you want to see? >> So I guarantee you, five years from now, I'll still be saying, we need to change, we need to grow! Because I think it's a big journey. But I believe what you'll see in the next five years is you're going to see this enormous growth in these new workloads. And you're going to have to have data center capability that effectively is seamless. So we call that hybrid cloud today, or we call it converged infrastructure, or we call it, you know, hyper converged. But the reality is, you know, IT organizations will have morphed their skill sets to be, instead of being siloed servers, network, and storage, you'll have people that span those technology skill sets, and therefore our products need to be fully integrated, right, and almost so self-healing in their process that you don't need consoles. The idea of a console, the monitor things that I know, we have one, right? You have to have it today. I don't think you need it in the future, because the machine is so self-healing, why do I need someone looking at a console? >> Rebecca: I love that concept of a self-healing machine, one that can fix itself. >> Yeah, and we've got some Lenovo research being done today on how you do that. And I know it can be done, because if you look at the equipment, individual pieces of equipment, all have now call home capability, or some intelligent care capability, but they're siloed by vendors, or type of product. So you could integrate that into a machine learning application, and use that to act, where I don't need any human interference with that. Because I've got a knowledge management database, I have all those events that have happened in the past, so you can really, truly bring self-healing, self-diagnosing, self-healing, to life, and I think that's where we'll be in five years. It doesn't take very long to get that type of capability, given that we have the foundation already. >> So Kim, now that you're on the other side of the table from your relationship with Intel, speak a little bit about Lenovo as a partner. I think about Intel, Microsoft, you know, companies that are very strategic to Lenovo, but also with your key competitors. What differentiates Lenovo as a partner? >> Yeah, so you know, one, we don't compete with our partners, right? And so it sort of depends on what kind of partners, but some of our competitors end up, you know, through acquisition, through growth, through intention, they end up competing with their partners. We're very, very clear. We don't compete with our partners. We're here to make our partners successful. And so we have deep industry partnerships with like the Intels of the world, and the Microsofts of the world, because we're so complementary. And we're after the same types of things, and so, that's really worked to our advantage. But I would even go down to, you know, our channel partners, the value added resellers and distributors, they're critically important to us, and we've made changes this year to insure that they have rich incentives to work with Lenovo. And so it really spans, you know, sort of the supply side to the delivery side, that we have a holistic view of how strategic and important our partners are to us, and that shared sense of win-win. We really want a win-win relationship with all of our partners. >> A great note to end on. Thanks so much for joining us, >> Thanks for having me. it's always a pleasure having you on the show. >> Yep, thanks for having me again, I really appreciate it. >> I'm Rebecca Knight for Stu Miniman. We will have more from Lenovo Transform just after this. (digital music)

>> Dave: The cloud. There you go. I presume that worked. >> David: Hi there. >> Dave: Hi David. We had agreed, Peter and I had talked and we said let's just pick three topics, allocate enough time. Maybe a half hour each, and then maybe a little bit longer if we have the time. Then try and structure it so we can gather some opinions on what it all means. Ultimately the goal is to have an outcome with some research that hits the network. The three topics today, Jim Kobeielus is going to present on agile and data science, David Floyer on NVMe over fabric and of course keying off of the Micron news announcement. I think Nick is, is that Nick who just joined? He can contribute to that as well. Then George Gilbert has this concept of digital twin. We'll start with Jim. I guess what I'd suggest is maybe present this in the context of, present a premise or some kind of thesis that you have and maybe the key issues that you see and then kind of guide the conversation and we'll all chime in. >> Jim: Sure, sure. >> Dave: Take it away, Jim. >> Agile development and team data science. Agile methodology obviously is well-established as a paradigm and as a set of practices in various schools in software development in general. Agile is practiced in data science in terms of development, the pipelines. The overall premise for my piece, first of all starting off with a core definition of what agile is as a methodology. Self-organizing, cross-functional teams. They sprint toward results in steps that are fast, iterative, incremental, adaptive and so forth. Specifically the premise here is that agile has already come to data science and is coming even more deeply into the core practice of data science where data science is done in team environment. It's not just unicorns that are producing really work on their own, but more to the point, it's teams of specialists that come together in co-location, increasingly in co-located environments or in co-located settings to produce (banging) weekly check points and so forth. That's the basic premise that I've laid out for the piece. The themes. First of all, the themes, let me break it out. In terms of the overall how I design or how I'm approaching agile in this context is I'm looking at the basic principles of agile. It's really practices that are minimal, modular, incremental, iterative, adaptive, and co-locational. I've laid out how all that maps in to how data science is done in the real world right now in terms of tight teams working in an iterative fashion. A couple of issues that I see as regards to the adoption and sort of the ramifications of agile in a data science context. One of which is a co-location. What we have increasingly are data science teams that are virtual and distributed where a lot of the functions are handled by statistical modelers and data engineers and subject matter experts and visualization specialists that are working remotely from each other and are using collaborative tools like the tools from the company that I just left. How can agile, the co-location work primer for agile stand up in a world with more of the development team learning deeper and so forth is being done on a scrutiny basis and needs to be by teams of specialists that may be in different cities or different time zones, operating around the clock, produce brilliant results? Another one of which is that agile seems to be predicated on the notion that you improvise the process as you go, trial and error which seems to fly in the face of documentation or tidy documentation. Without tidy documentation about how you actually arrived at your results, how come those results can not be easily reproduced by independent researchers, independent data scientists? If you don't have well defined processes for achieving results in a certain data science initiative, it can't be reproduced which means they're not terribly scientific. By definition it's not science if you can't reproduce it by independent teams. To the extent that it's all loosey-goosey and improvised and undocumented, it's not reproducible. If it's not reproducible, to what extent should you put credence in the results of a given data science initiative if it's not been documented? Agile seems to fly in the face of reproducibility of data science results. Those are sort of my core themes or core issues that I'm pondering with or will be. >> Dave: Jim, just a couple questions. You had mentioned, you rattled off a bunch of parameters. You went really fast. One of them was co-location. Can you just review those again? What were they? >> Sure. They are minimal. The minimum viable product is the basis for agile, meaning a team puts together data a complete monolithic sect, but an initial deliverable that can stand alone, provide some value to your stakeholders or users and then you iteratively build upon that in what I call minimum viable product going forward to pull out more complex applications as needed. There's sort of a minimum viable product is at the heart of agile the way it's often looked at. The big question is, what is the minimum viable product in a data science initiative? One way you might approach that is saying that what you're doing, say you're building a predictive model. You're predicting a single scenario, for example such as whether one specific class of customers might accept one specific class of offers under the constraining circumstances. That's an example of minimum outcome to be achieved from a data science deliverable. A minimum product that addresses that requirement might be pulling the data from a single source. We'll need a very simplified feature set of predictive variables like maybe two or three at the most, to predict customer behavior, and use one very well understood algorithm like linear regressions and do it. With just a few lines of programming code in Python or Aura or whatever and build us some very crisp, simple rules. That's the notion in a data science context of a minimum viable product. That's the foundation of agile. Then there's the notion of modular which I've implied with minimal viable product. The initial product is the foundation upon which you build modular add ons. The add ons might be building out more complex algorithms based on more data sets, using more predictive variables, throwing other algorithms in to the initiative like logistic regression or decision trees to do more fine-grained customer segmentation. What I'm giving you is a sense for the modular add ons and builds on to the initial product that generally weaken incrementally in the course of a data science initiative. Then there's this, and I've already used the word incremental where each new module that gets built up or each new feature or tweak on the core model gets added on to the initial deliverable in a way that's incremental. Ideally it should all compose ultimately the sum of the useful set of capabilities that deliver a wider range of value. For example, in a data science initiative where it's customer data, you're doing predictive analysis to identify whether customers are likely to accept a given offer. One way to add on incrementally to that core functionality is to embed that capability, for example, in a target marketing application like an outbound marketing application that uses those predictive variables to drive responses in line to, say an e-commerce front end. Then there's the notion of iterative and iterative really comes down to check points. Regular reviews of the standards and check points where the team comes together to review the work in a context of data science. Data science by its very nature is exploratory. It's visualization, it's model building and testing and training. It's iterative scoring and testing and refinement of the underlying model. Maybe on a daily basis, maybe on a weekly basis, maybe adhoc, but iteration goes on all the time in data science initiatives. Adaptive. Adaptive is all about responding to circumstances. Trial and error. What works, what doesn't work at the level of the clinical approach. It's also in terms of, do we have the right people on this team to deliver on the end results? A data science team might determine mid-way through that, well we're trying to build a marketing application, but we don't have the right marketing expertise in our team, maybe we need to tap Joe over there who seems to know a little bit about this particular application we're trying to build and this particular scenario, this particular customers, we're trying to get a good profile of how to reach them. You might adapt by adding, like I said, new data sources, adding on new algorithms, totally changing your approach for future engineering as you go along. In addition to supervised learning from ground troops, you might add some unsupervised learning algorithms to being able to find patterns in say unstructured data sets as you bring those into the picture. What I'm getting at is there's a lot, 10 zillion variables that, for a data science team that you have to add in to your overall research plan going forward based on, what you're trying to derive from data science is its insights. They're actionable and ideally repeatable. That you can embed them in applications. It's just a matter of figuring out what actually helps you, what set of variables and team members and data and sort of what helps you to achieve the goals of your project. Finally, co-locational. It's all about the core team needs to be, usually in the same physical location according to the book how people normally think of agile. The company that I just left is basically doing a massive social engineering exercise, ongoing about making their marketing and R&D teams a little more agile by co-locating them in different cities like San Francisco and Austin and so forth. The whole notion that people will collaborate far better if they're not virtual. That's highly controversial, but none-the-less, that's the foundation of agile as it's normally considered. One of my questions, really an open question is what hard core, you might have a sprawling team that's doing data science, doing various aspects, but what solid core of that team needs to be physically co-located all or most of the time? Is it the statistical modeler and a data engineer alone? The one who stands up how to do cluster and the person who actually does the building and testing of the model? Do the visualization specialists need to be co-located as well? Are other specialties like subject matter experts who have the knowledge in marketing, whatever it is, do they also need to be in the physical location day in, day out, week in and week out to achieve results on these projects? Anyway, so there you go. That's how I sort of appealed the argument of (mumbling). >> Dave: Okay. I got a minimal modular, incremental, iterative, adaptive, co-locational. What was six again? I'm sorry. >> Jim: Co-locational. >> Dave: What was the one before that? >> Jim: I'm sorry. >> Dave: Adaptive. >> Minimal, modular, incremental, iterative, adaptive, and co-locational. >> Dave: Okay, there were only six. Sorry, I thought it was seven. Good. A couple of questions then we can get the discussion going here. Of course, you're talking specifically in the context of data science, but some of the questions that I've seen around agile generally are, it's not for everybody, when and where should it be used? Waterfalls still make sense sometimes. Some of the criticisms I've read, heard, seen, and sometimes experienced with agile are sort of quality issues, I'll call it lack of accountability. I don't know if that's the right terminology. We're going for speed so as long as we're fast, we checked that box, quality can sacrifice. Thoughts on that. Where does it fit and again understanding specifically you're talking about data science. Does it always fit in data science or because it's so new and hip and cool or like traditional programming environments, is it horses for courses? >> David: Can I add to that, Dave? It's a great, fundamental question. It seems to me there's two really important aspects of artificial intelligence. The first is the research part of it which is developing the algorithms, developing the potential data sources that might or might not matter. Then the second is taking that and putting it into production. That is that somewhere along the line, it's saving money, time, etc., and it's integrated with the rest of the organization. That second piece is, the first piece it seems to be like most research projects, the ROI is difficult to predict in a new sort of way. The second piece of actually implementing it is where you're going to make money. Is agile, if you can integrate that with your systems of record, for example and get automation of many of the aspects that you've researched, is agile the right way of doing it at that stage? How would you bridge the gap between the initial development and then the final instantiation? >> That's an important concern, David. Dev Ops, that's a closely related issue but it's not exactly the same scope. As data science and machine learning, let's just net it out. As machine learning and deep learning get embedded in applications, in operations I should say, like in your e-commerce site or whatever it might be, then data science itself becomes an operational function. The people who continue to iterate those models in line the operational applications. Really, where it comes down to an operational function, everything that these people do needs to be documented and version controlled and so forth. These people meaning data science professionals. You need documentation. You need accountability. The development of these assets, machine learning and so forth, needs to be, is compliance. When you look at compliance, algorithmic accountability comes into it where lawyers will, like e-discovery. They'll subpoena, theoretically all your algorithms and data and say explain how you arrived at this particular recommendation that you made to grant somebody or not grant somebody a loan or whatever it might be. The transparency of the entire development process is absolutely essential to the data science process downstream and when it's a production application. In many ways, agile by saying, speed's the most important thing. Screw documentation, you can sort of figure that out and that's not as important, that whole pathos, it goes by the wayside. Agile can not, should not skip on documentation. Documentation is even more important as data science becomes an operational function. That's one of my concerns. >> David: I think it seems to me that the whole rapid idea development is difficult to get a combination of that and operational, boring testing, regression testing, etc. The two worlds are very different. The interface between the two is difficult. >> Everybody does their e-commerce tweaks through AB testing of different layouts and so forth. AB testing is fundamentally data science and so it's an ongoing thing. (static) ... On AB testing in terms of tweaking. All these channels and all the service flow, systems of engagement and so forth. All this stuff has to be documented so agile sort of, in many ways flies in the face of that or potentially compromises the visibility of (garbled) access. >> David: Right. If you're thinking about IOT for example, you've got very expensive machines out there in the field which you're trying to optimize true put through and trying to minimize machine's breaking, etc. At the Micron event, it was interesting that Micron's use of different methodologies of putting systems together, they were focusing on the data analysis, etc., to drive greater efficiency through their manufacturing process. Having said that, they need really, really tested algorithms, etc. to make sure there isn't a major (mumbling) or loss of huge amounts of potential revenue if something goes wrong. I'm just interested in how you would create the final product that has to go into production in a very high value chain like an IOT. >> When you're running, say AI from learning algorithms all the way down to the end points, it gets even trickier than simply documenting the data and feature sets and the algorithms and so forth that were used to build up these models. It also comes down to having to document the entire life cycle in terms of how these algorithms were trained to make the predictors of whatever it is you're trying to do at the edge with a particular algorithm. The whole notion of how are all of these edge points applications being trained, with what data, at what interval? Are they being retrained on a daily basis, hourly basis, moment by moment basis? All of those are critical concerns to know whether they're making the best automated decisions or actions possible in all scenarios. That's like a black box in terms of the sheer complexity of what needs to be logged to figure out whether the application is doing its job as best a possible. You need a massive log, you need a massive event log from end to end of the IOT to do that right and to provide that visibility ongoing into the performance of these AI driven edge devices. I don't know anybody who's providing the tool to do it. >> David: If I think about how it's done at the moment, it's obviously far too slow at the moment. At the same time, you've got to have some testing and things like that. It seems to me that you've got a research model on one side and then you need to create a working model from that which is your production model. That's the one that goes through the testing and everything of that sort. It seems to me that the interface would be that transition from the research model to the working model that would be critical here and the working model is obviously a subset and it's going to be optimized for performance, etc. in real time, as opposed to the development model which can be a lot to do and take half a week to manage it necessary. It seems to me that you've got a different set of business pressures on the working model and a different set of skills as well. I think having one team here doesn't sound right to me. You've got to have a Dev Ops team who are going to take the working model from the developers and then make sure that it's sound and save. Especially in a high value IOT area that the level of iteration is not going to be nearly as high as in a lower cost marketing type application. Does that sound sensible? >> That sounds sensible. In fact in Dev Ops, the Dev Ops team would definitely be the ones that handle the continuous training and retraining of the working models on an ongoing basis. That's a core observation. >> David: Is that the right way of doing it, Jim? It seems to me that the research people would be continuing to adapt from data from a lot of different places whereas the operational model would be at a specific location with a specific IOT and they wouldn't have necessarily all the data there to do that. I'm not quite sure whether - >> Dave: Hey guys? Hey guys, hey guys? Can I jump in here? Interesting discussion, but highly nuanced and I'm struggling to figure out how this turns into a piece or sort of debating some certain specifics that are very kind of weedy. I wonder if we could just reset for a second and come back to sort of what I was trying to get to before which is really the business impact. Should this be applied broadly? Should this be applied specifically? What does it mean if I'm a practitioner? What should I take away from, Jim your premise and your sort of fixed parameters? Should I be implementing this? Why? Where? What's the value to my organization - the value I guess is obvious, but does it fit everywhere? Should it be across the board? Can you address that? >> Neil: Can I jump in here for a second? >> Dave: Please, that would be great. Is that Neil? >> Neil: Neil. I've never been a data scientist, but I was an actuary a long time ago. When the truth actuary came to me and said we need to develop a liability insurance coverage for floating oil rigs in the North Sea, I'm serious, it took a couple of months of research and modeling and so forth. If I had to go to all of those meetings and stand ups in an agile development environment, I probably would have gone postal on the place. I think that there's some confusion about what data science is. It's not a vector. It's not like a Dev Op situation where you start with something and you go (mumbling). When a data scientist or whatever you want to call them comes up with a model, that model has to be constantly revisited until it's put out of business. It's refined, it's evaluated. It doesn't have an end point like that. The other thing is that data scientist is typically going to be running multiple projects simultaneously so how in the world are you going to agilize that? I think if you look at the data science group, they're probably, I think Nick said this, there are probably groups in there that are doing fewer Dev Ops, software engineering and so forth and you can apply agile techniques to them. The whole data science thing is too squishy for that, in my opinion. >> Jim: Squishy? What do you mean by squishy, Neil? >> Neil: It's not one thing. I think if you try to represent data science as here's a project, we gather data, we work on a model, we test it, and then we put it into production, it doesn't end there. It never ends. It's constantly being revised. >> Yeah, of course. It's akin to application maintenance. The application meaning the model, the algorithm to be fit for purpose has to continually be evaluated, possibly tweaked, always retrained to determine its predictive fit for whatever task it's been assigned. You don't build it once and assume its strong predictive fit forever and ever. You can never assume that. >> Neil: James and I called that adaptive control mechanisms. You put a model out there and you monitor the return you're getting. You talk about AB testing, that's one method of doing it. I think that a data scientist, somebody who really is keyed into the machine learning and all that jazz. I just don't see them as being project oriented. I'll tell you one other thing, I have a son who's a software engineer and he said something to me the other day. He said, "Agile? Agile's dead." I haven't had a chance to find out what he meant by that. I'll get back to you. >> Oh, okay. If you look at - Go ahead. >> Dave: I'm sorry, Neil. Just to clarify, he said agile's dead? Was that what he said? >> Neil: I didn't say it, my son said it. >> Dave: Yeah, yeah, yeah right. >> Neil: No idea what he was talking about. >> Dave: Go ahead, Jim. Sorry. >> If you look at waterfall development in general, for larger projects it's absolutely essential to get requirements nailed down and the functional specifications and all that. Where you have some very extensive projects and many moving parts, obviously you need a master plan that it all fits into and waterfall, those checkpoints and so forth, those controls that are built into that methodology are critically important. Within the context of a broad project, some of the assets being build up might be machine loading models and analytics models and so forth so in the context of our broader waterfall oriented software development initiative, you might need to have multiple data science projects spun off within the sub-projects. Each of those would fit into, by itself might be indicated sort of like an exploration task where you have a team doing data visualization, exploration in more of an open-ended fashion because while they're trying to figure out the right set of predictors and the right set of data to be able to build out the right model to deliver the right result. What I'm getting at is that agile approaches might be embedded into broader waterfall oriented development initiatives, agile data science approaches. Fundamentally, data science began and still is predominantly very smart people, PhDs in statistics and math, doing open-ended exploration of complex data looking for non-obvious patterns that you wouldn't be able to find otherwise. Sort of a fishing expedition, a high priced fishing expedition. Kind of a mode of operation as how data science often is conducted in the real world. Looking for that eureka moment when the correlations just jump out at you. There's a lot of that that goes on. A lot of that is very important data science, it's more akin to pure science. What I'm getting at is there might be some role for more structure in waterfall development approaches in projects that have a data science, core data science capability to them. Those are my thoughts. >> Dave: Okay, we probably should move on to the next topic here, but just in closing can we get people to chime in on sort of the bottom line here? If you're writing to an audience of data scientists or data scientist want to be's, what's the one piece of advice or a couple of pieces of advice that you would give them? >> First of all, data science is a developer competency. The modern developers are, many of them need to be data scientists or have a strong grounding and understanding of data science, because much of that machine learning and all that is increasingly the core of what software developers are building so you can't not understand data science if you're a modern software developer. You can't understand data science as it (garbled) if you don't understand the need for agile iterative steps within the, because they're looking for the needle in the haystack quite often. The right combination of predictive variables and the right combination of algorithms and the right training regimen in order to get it all fit. It's a new world competency that need be mastered if you're a software development professional. >> Dave: Okay, anybody else want to chime in on the bottom line there? >> David: Just my two penny worth is that the key aspect of all the data scientists is to come up with the algorithm and then implement them in a way that is robust and it part of the system as a whole. The return on investment on the data science piece as an insight isn't worth anything until it's actually implemented and put into production of some sort. It seems that second stage of creating the working model is what is the output of your data scientists. >> Yeah, it's the repeatable deployable asset that incorporates the crux of data science which is algorithms that are data driven, statistical algorithms that are data driven. >> Dave: Okay. If there's nothing else, let's close this agenda item out. Is Nick on? Did Nick join us today? Nick, you there? >> Nick: Yeah. >> Dave: Sounds like you're on. Tough to hear you. >> Nick: How's that? >> Dave: Better, but still not great. Okay, we can at least hear you now. David, you wanted to present on NVMe over fabric pivoting off the Micron news. What is NVMe over fabric and who gives a fuck? (laughing) >> David: This is Micron, we talked about it last week. This is Micron announcement. What they announced is NVMe over fabric which, last time we talked about is the ability to create a whole number of nodes. They've tested 250, the architecture will take them to 1,000. 1,000 processor or 1,000 nodes, and be able to access the data on any single node at roughly the same speed. They are quoting 200 microseconds. It's 195 if it's local and it's 200 if it's remote. That is a very, very interesting architecture which is like nothing else that's been announced. >> Participant: David, can I ask a quick question? >> David: Sure. >> Participant: This latency and the node count sounds astonishing. Is Intel not replicating this or challenging in scope with their 3D Crosspoint? >> David: 3D Crosspoint, Intel would love to sell that as a key component of this. The 3D Crosspoint as a storage device is very, very, very expensive. You can replicate most of the function of 3D Crosspoint at a much lower price point by using a combination of D-RAM and protective D-RAM and Flash. At the moment, 3D Crosspoint is a nice to have and there'll be circumstances where they will use it, but at the meeting yesterday, I don't think they, they might have brought it up once. They didn't emphasize it (mumbles) at all as being part of it. >> Participant: To be clear, this means rather than buying Intel servers rounded out with lots of 3D Crosspoint, you buy Intel servers just with the CPU and then all the Micron niceness for their NVMe and their Interconnect? >> David: Correct. They are still Intel servers. The ones they were displaying yesterday were HP1's, they also used SuperMicro. They want certain characteristics of the chip set that are used, but those are just standard pieces. The other parts of the architecture are the Mellanox, the 100 gigabit converged ethernet and using Rocky which is IDMA over converged ethernet. That is the secret sauce which allows you and Mellanox themselves, their cards have a lot of offload of a lot of functionality. That's the secret sauce which allows you to go from any point to any point in 5 microseconds. Then create a transfer and other things. Files are on top of that. >> Participant: David, Another quick question. The latency is incredibly short. >> David: Yep. >> Participant: What happens if, as say an MPP SQL database with 1,000 nodes, what if they have to shuffle a lot of data? What's the throughput? Is it limited by that 100 gig or is that so insanely large that it doesn't matter? >> David: They key is this, that it allows you to move the processing to wherever the data is very, very easily. In the principle that will evolve from this architecture, is that you know where the data is so don't move the data around, that'll block things up. Move the processing to that particular node or some adjacent node and do the processing as close as possible. That is as an architecture is a long term goal. Obviously in the short term, you've got to take things as they are. Clearly, a different type of architecture for databases will need to eventually evolve out of this. At the moment, what they're focusing on is big problems which need low latency solutions and using databases as they are and the whole end to end use stack which is a much faster way of doing it. Then over time, they'll adapt new databases, new architectures to really take advantage of it. What they're offering is a POC at the moment. It's in Beta. They had their customers talking about it and they were very complimentary in general about it. They hope to get it into full production this year. There's going to be a host of other people that are doing this. I was trying to bottom line this in terms of really what the link is with digital enablement. For me, true digital enablement is enabling any relevant data to be available for processing at the point of business engagement in real time or near real time. The definition that this architecture enables. It's a, in my view a potential game changer in that this is an architecture which will allow any data to be available for processing. You don't have to move the data around, you move the processing to that data. >> Is Micron the first market with this capability, David? NV over Me? NVMe. >> David: Over fabric? Yes. >> Jim: Okay. >> David: Having said that, there are a lot of start ups which have got a significant amount of money and who are coming to market with their own versions. You would expect Dell, HP to be following suit. >> Dave: David? Sorry. Finish your thought and then I have another quick question. >> David: No, no. >> Dave: The principle, and you've helped me understand this many times, going all the way back to Hadoop, bring the application to the data, but when you're using conventional relational databases and you've had it all normalized, you've got to join stuff that might not be co-located. >> David: Yep. That's the whole point about the five microseconds. Now that the impact of non co-location if you have to join stuff or whatever it is, is much, much lower. It's so you can do the logical draw in, whatever it is, very quickly and very easily across that whole fabric. In terms of processing against that data, then you would choose to move the application to that node because it's much less data to move, that's an optimization of the architecture as opposed to a fundamental design point. You can then optimize about where you run the thing. This is ideal architecture for where I personally see things going which is traditional systems of record which need to be exactly as they've ever been and then alongside it, the artificial intelligence, the systems of understanding, data warehouses, etc. Having that data available in the same space so that you can combine those two elements in real time or in near real time. The advantage of that in terms of business value, digital enablement, and business value is the biggest thing of all. That's a 50% improvement in overall productivity of a company, that's the thing that will drive, in my view, 99% of the business value. >> Dave: Going back just to the joint thing, 100 gigs with five microseconds, that's really, really fast, but if you've got petabytes of data on these thousand nodes and you have to do a join, you still got to go through that 100 gig pipe of stuff that's not co-located. >> David: Absolutely. The way you would design that is as you would design any query. You've got a process you would need, a process in front of that which is query optimization to be able to farm all of the independent jobs needed to do in each of the nodes and take the output of that and bring that together. Both the concepts are already there. >> Dave: Like a map. >> David: Yes. That's right. All of the data science is there. You're starting from an architecture which is fundamentally different from the traditional let's get it out architectures that have existed, by removing that huge overhead of going from one to another. >> Dave: Oh, because this goes, it's like a mesh not a ring? >> David: Yes, yes. >> Dave: It's like the high performance compute of this MPI type architecture? >> David: Absolutely. NVMe, by definition is a point to point architecture. Rocky, underneath it is a point to point architecture. Everything is point to point. Yes. >> Dave: Oh, got it. That really does call for a redesign. >> David: Yes, you can take it in steps. It'll work as it is and then over time you'll optimize it to take advantage of it more. Does that definition of (mumbling) make sense to you guys? The one I quoted to you? Enabling any relevant data to be available for processing at the point of business engagement, in real time or near real time? That's where you're trying to get to and this is a very powerful enabler of that design. >> Nick: You're emphasizing the network topology, while I kind of thought the heart of the argument was performance. >> David: Could you repeat that? It's very - >> Dave: Let me repeat. Nick's a little light, but I could hear him fine. You're emphasizing the network topology, but Nick's saying his takeaway was the whole idea was the thrust was performance. >> Nick: Correct. >> David: Absolutely. Absolutely. The result of that network topology is a many times improvement in performance of the systems as a whole that you couldn't achieve in any previous architecture. I totally agree. That's what it's about is enabling low latency applications with much, much more data available by being able to break things up in parallel and delivering multiple streams to an end result. Yes. >> Participant: David, let me just ask, if I can play out how databases are designed now, how they can take advantage of it unmodified, but how things could be very, very different once they do take advantage of it which is that today, if you're doing transaction processing, you're pretty much bottle necked on a single node that sort of maintains the fresh cache of shared data and that cache, even if it's in memory, it's associated with shared storage. What you're talking about means because you've got memory speed access to that cache from anywhere, it no longer is tied to a node. That's what allows you to scale out to 1,000 nodes even for transaction processing. That's something we've never really been able to do. Then the fact that you have a large memory space means that you no longer optimize for mapping back and forth from disk and disk structures, but you have everything in a memory native structure and you don't go through this thing straw for IO to storage, you go through memory speed IO. That's a big, big - >> David: That's the end point. I agree. That's not here quite yet. It's still IO, so the IO has been improved dramatically, the protocol within the Me and the over fabric part of it. The elapsed time has been improved, but it's not yet the same as, for example, the HPV initiative. That's saying you change your architecture, you change your way of processing just in the memory. Everything is assumed to be memory. We're not there yet. 200 microseconds is still a lot, lot slower than the process that - one impact of this architecture is that the amount of data that you can pass through it is enormously higher and therefore, the memory sizes themselves within each node will need to be much, much bigger. There is a real opportunity for architectures which minimize the impact, which hold data coherently across multiple nodes and where there's minimal impact of, no tapping on the shoulder for every byte transferred so you can move large amounts of data into memory and then tell people that it's there and allow it to be shared, for example between the different calls and the GPUs and FPGAs that will be in these processes. There's more to come in terms of the architecture in the future. This is a step along the way, it's not the whole journey. >> Participant: Dave, another question. You just referenced 200 milliseconds or microseconds? >> David: Did I say milliseconds? I meant microseconds. >> Participant: You might have, I might have misheard. Relate that to the five microsecond thing again. >> David: If you have data directly attached to your processor, the access time is 195 microseconds. If you need to go to a remote, anywhere else in the thousand nodes, your access time is 200 microseconds. In other words, the additional overhead of that data is five microseconds. >> Participant: That's incredible. >> David: Yes, yes. That is absolutely incredible. That's something that data scientists have been working on for years and years. Okay. That's the reason why you can now do what I talked about which was you can have access from any node to any data within that large amount of nodes. You can have petabytes of data there and you can have access from any single node to any of that data. That, in terms of data enablement, digital enablement, is absolutely amazing. In other words, you don't have to pre put the data that's local in one application in one place. You're allowing an enormous flexibility in how you design systems. That coming back to artificial intelligence, etc. allows you a much, much larger amount of data that you can call on for improving applications. >> Participant: You can explore and train models, huge models, really quickly? >> David: Yes, yes. >> Participant: Apparently that process works better when you have an MPI like mesh than a ring. >> David: If you compare this architecture to the DSST architecture which was the first entrance into this that MP bought for a billion dollars, then that one stopped at 40 nodes. It's architecture was very, very proprietary all the way through. This one takes you to 1,000 nodes with much, much lower cost. They believe that the cost of the equivalent DSSD system will be between 10 and 20% of that cost. >> Dave: Can I ask a question about, you mentioned query optimizer. Who develops the query optimizer for the system? >> David: Nobody does yet. >> Jim: The DBMS vendor would have to re-write theirs with a whole different pensive cost. >> Dave: So we would have an optimizer database system? >> David: Who's asking a question, I'm sorry. I don't recognize the voice. >> Dave: That was Neil. Hold on one second, David. Hold on one second. Go ahead Nick. You talk about translation. >> Nick: ... On a network. It's SAN. It happens to be very low latency and very high throughput, but it's just a storage sub-system. >> David: Yep. Yep. It's a storage sub-system. It's called a server SAN. That's what we've been talking about for a long time is you need the same characteristics which is that you can get at all the data, but you need to be able to get at it in compute time as opposed to taking a stroll down the road time. >> Dave: Architecturally it's a SAN without an array controller? >> David: Exactly. Yeah, the array controller is software from a company called Xcellate, what was the name of it? I can't remember now. Say it again. >> Nick: Xcelero or Xceleron? >> David: Xcelero. That's the company that has produced the software for the data services, etc. >> Dave: Let's, as we sort of wind down this segment, let's talk about the business impact again. We're talking about different ways potentially to develop applications. There's an ecosystem requirement here it sounds like, from the ISDs to support this and other developers. It's the final, portends the elimination of the last electromechanical device in computing which has implications for a lot of things. Performance value, application development, application capability. Maybe you could talk about that a little bit again thinking in terms of how practitioners should look at this. What are the actions that they should be taking and what kinds of plans should they be making in their strategies? >> David: I thought Neil's comment last week was very perceptive which is, you wouldn't start with people like me who have been imbued with the 100 database call limits for umpteen years. You'd start with people, millennials, or sub-millenials or whatever you want to call them, who can take a completely fresh view of how you would exploit this type of architecture. Fundamentally you will be able to get through 10 or 100 times more data in real time than you can with today's systems. There's two parts of that data as I said before. The traditional systems of record that need to be updated, and then a whole host of applications that will allow you to do processes which are either not possible, or very slow today. To give one simple example, if you want to do real time changing of pricing based on availability of your supply chain, based on what you've got in stock, based on the delivery capabilities, that's a very, very complex problem. The optimization of all these different things and there are many others that you could include in that. This will give you the ability to automate that process and optimize that process in real time as part of the systems of record and update everything together. That, in terms of business value is extracting a huge number of people who previously would be involved in that chain, reducing their involvement significantly and making the company itself far more agile, far more responsive to change in the marketplace. That's just one example, you can think of hundreds for every marketplace where the application now becomes the systems of record, augmented by AI and huge amounts more data can improve the productivity of an organization and the agility of an organization in the marketplace. >> This is a godsend for AI. AI, the draw of AI is all this training data. If you could just move that in memory speed to the application in real time, it makes the applications much sharper and more (mumbling). >> David: Absolutely. >> Participant: How long David, would it take for the cloud vendors to not just offer some instances of this, but essentially to retool their infrastructure. (laughing) >> David: This is, to me a disruption and a half. The people who can be first to market in this are the SaaS vendors who can take their applications or new SaaS vendors. ISV. Sorry, say that again, sorry. >> Participant: The SaaS vendors who have their own infrastructure? >> David: Yes, but it's not going to be long before the AWS' and Microsofts put this in their tool bag. The SaaS vendors have the greatest capability of making this change in the shortest possible time. To me, that's one area where we're going to see results. Make no mistake about it, this is a big change and at the Micron conference, I can't remember what the guys name was, he said it takes two Olympics for people to start adopting things for real. I think that's going to be shorter than two Olympics, but it's going to be quite a slow process for pushing this out. It's radically different and a lot of the traditional ways of doing things are going to be affected. My view is that SaaS is going to be the first and then there are going to be individual companies that solve the problems themselves. Large companies, even small companies that put in systems of this sort and then use it to outperform the marketplace in a significant way. Particularly in the finance area and particularly in other data intent areas. That's my two pennies worth. Anybody want to add anything else? Any other thoughts? >> Dave: Let's wrap some final thoughts on this one. >> Participant: Big deal for big data. >> David: Like it, like it. >> Participant: It's actually more than that because there used to be a major trade off between big data and fast data. Latency and throughput and this starts to push some of those boundaries out so that you sort of can have both at once. >> Dave: Okay, good. Big deal for big data and fast data. >> David: Yeah, I like it. >> Dave: George, you want to talk about digital twins? I remember when you first sort of introduced this, I was like, "Huh? What's a digital twin? "That's an interesting name." I guess, I'm not sure you coined it, but why don't you tell us what digital twin is and why it's relevant. >> George: All right. GE coined it. I'm going to, at a high level talk about what it is, why it's important, and a little bit about as much as we can tell, how it's likely to start playing out and a little bit on the differences of the different vendors who are going after it. As far as sort of defining it, I'm cribbing a little bit from a report that's just in the edit process. It's data representation, this is important, or a model of a product, process, service, customer, supplier. It's not just an industrial device. It can be any entity involved in the business. This is a refinement sort of Peter helped with. The reason it's any entity is because there is, it can represent the structure and behavior, not just of a machine tool or a jet engine, but a business process like sales order process when you see it on a screen and its workflow. That's a digital twin of what used to be a physical process. It applied to both the devices and assets and processes because when you can model them, you can integrate them within a business process and improve that process. Going back to something that's more physical so I can do a more concrete definition, you might take a device like a robotic machine tool and the idea is that the twin captures the structure and the behavior across its lifecycle. As it's designed, as it's built, tested, deployed, operated, and serviced. I don't know if you all know the myth of, in the Greek Gods, one of the Goddesses sprang fully formed from the forehead of Zeus. I forgot who it was. The point of that is digital twin is not going to spring fully formed from any developers head. Getting to the level of fidelity I just described is a journey and a long one. Maybe a decade or more because it's difficult. You have to integrate a lot of data from different systems and you have to add structure and behavior for stuff that's not captured anywhere and may not be captured anywhere. Just for example, CAD data might have design information, manufacturing information might come from there or another system. CRM data might have support information. Maintenance repair and overhaul applications might have information on how it's serviced. Then you also connect the physical version with the digital version with essentially telemetry data that says how its been operating over time. That sort of helps define its behavior so you can manipulate that and predict things or simulate things that you couldn't do with just the physical version. >> You have to think about combined with say 3D printers, you could create a hot physical back up of some malfunctioning thing in the field because you have the entire design, you have the entire history of its behavior and its current state before it went kablooey. Conceivably, it can be fabricated on the fly and reconstituted as a physicologic from the digital twin that was maintained. >> George: Yes, you know what actually that raises a good point which is that the behavior that was represented in the telemetry helps the designer simulate a better version for the next version. Just what you're saying. Then with 3D printing, you can either make a prototype or another instance. Some of the printers are getting sophisticated enough to punch out better versions or parts for better versions. That's a really good point. There's one thing that has to hold all this stuff together which is really kind of difficult, which is challenging technology. IBM calls it a knowledge graph. It's pretty much in anyone's version. They might not call it a knowledge graph. It's a graph is, instead of a tree where you have a parent and then children and then the children have more children, a graph, many things can relate to many things. The reason I point that out is that puts a holistic structure over all these desperate sources of data behavior. You essentially talk to the graph, sort of like with Arnold, talk to the hand. That didn't, I got crickets. (laughing) Let me give you guys the, I put a definitions table in this dock. I had a couple things. Beta models. These are some important terms. Beta model represents the structure but not the behavior of the digital twin. The API represents the behavior of the digital twin and it should conform to the data model for maximum developer usability. Jim, jump in anywhere where you feel like you want to correct or refine. The object model is a combination of the data model and API. You were going to say something? >> Jim: No, I wasn't. >> George: Okay. The object model ultimately is the digital twin. Another way of looking at it, defining the structure and behavior. This sounds like one of these, say "T" words, the canonical model. It's a generic version of the digital twin or really the one where you're going to have a representation that doesn't have customer specific extensions. This is important because the way these things are getting built today is mostly custom spoke and so if you want to be able to reuse work. If someone's building this for you like a system integrator, you want to be able to, or they want to be able to reuse this on the next engagement and you want to be able to take the benefit of what they've learned on the next engagement back to you. There has to be this canonical model that doesn't break every time you essentially add new capabilities. It doesn't break your existing stuff. Knowledge graph again is this thing that holds together all the pieces and makes them look like one coherent hole. I'll get to, I talked briefly about network compatibility and I'll get to level of detail. Let me go back to, I'm sort of doing this from crib notes. We talked about telemetry which is sort of combining the physical and the twin. Again, telemetry's really important because this is like the time series database. It says, this is all the stuff that was going on over time. Then you can look at telemetry data that tells you, we got a dirty power spike and after three of those, this machine sort of started vibrating. That's part of how you're looking to learn about its behavior over time. In that process, models get better and better about predicting and enabling you to optimize their behavior and the business process with which it integrates. I'll give some examples of that. Twins, these digital twins can themselves be composed in levels of detail. I think I used the example of a robotic machine tool. Then you might have a bunch of machine tools on an assembly line and then you might have a bunch of assembly lines in a factory. As you start modeling, not just the single instance, but the collections that higher up and higher levels of extractions, or levels of detail, you get a richer and richer way to model the behavior of your business. More and more of your business. Again, it's not just the assets, but it's some of the processes. Let me now talk a little bit about how the continual improvement works. As Jim was talking about, we have data feedback loops in our machine learning models. Once you have a good quality digital twin in place, you get the benefit of increasing returns from the data feedback loops. In other words, if you can get to a better starting point than your competitor and then you get on the increasing returns of the data feedback loops, that is improving the fidelity of the digital twins now faster than your competitor. For one twin, I'll talk about how you want to make the whole ecosystem of twins sort of self-reinforcing. I'll get to that in a sec. There's another point to make about these data feedback loops which is traditional apps, and this came up with Jim and Neil, traditional apps are static. You want upgrades, you get stuff from the vendor. With digital twins, they're always learning from the customer's data and that has implications when the partner or vendor who helped build it for a customer takes learnings from the customer and goes to a similar customer for another engagement. I'll talk about the implications from that. This is important because it's half packaged application and half bespoke. The fact that you don't have to take the customer's data, but your model learns from the data. Think of it as, I'm not going to take your coffee beans, your data, but I'm going to run or make coffee from your beans and I'm going to take that to the next engagement with another customer who could be your competitor. In other words, you're extracting all the value from the data and that helps modify the behavior of the model and the next guy gets the benefit of it. Dave, this is the stuff where IBM keeps saying, we don't take your data. You're right, but you're taking the juice you squeezed out of it. That's one of my next reports. >> Dave: It's interesting, George. Their contention is, they uniquely, unlike Amazon and Google, don't swap spit, your spit with their competitors. >> George: That's misleading. To say Amazon and Google, those guys aren't building digital twins. Parametric technology is. I've got this definitely from a parametric technical fellow at an AWS event last week, which is they, not only don't use the data, they don't use the structure of the twin either from engagement to engagement. That's a big difference from IBM. I have a quote, Chris O'Connor from IBM Munich saying, "We'll take the data model, "but we won't take the data." I'm like, so you take the coffee from the beans even if you don't take the beans? I'm going to be very specific about saying that saying you don't do what Google and FaceBook do, what they do, it's misleading. >> Dave: My only caution there is do some more vetting and checking. A lot of times what some guy says on a Cube interview, he or she doesn't even know, in my experience. Make sure you validate that. >> George: I'll send it to them for feedback, but it wasn't just him. I got it from the CTO of the IOT division as well. >> Dave: When you were in Munich? >> George: This wasn't on the Cube either. This was by the side of, at the coffee table during our break. >> Dave: I understand and CTO's in theory should know. I can't tell you how many times I've gotten a definitive answer from a pretty senior level person and it turns out it was, either they weren't listening to me or they didn't know or they were just yessing me or whatever. Just be really careful and make sure you do your background checks. >> George: I will. I think the key is leave them room to provide a nuanced answer. It's more of a really, really, really concrete about really specific edge conditions and say do you or don't you. >> Dave: This is a pretty big one. If I'm a CIO, a chief digital officer, a chief data officer, COO, head of IT, head of data science, what should I be doing in this regard? What's the advice? >> George: Okay, can I go through a few more or are we out of time? >> Dave: No, we have time. >> George: Let me do a couple more points. I talked about training a single twin or an instance of a twin and I talked about the acceleration of the learning curve. There's edge analytics, David has educated us with the help of looking at GE Predicts. David, you have been talking about this fpr a long time. You want edge analytics to inform or automate a low latency decision and so this is where you're going to have to run some amount of analytics. Right near the device. Although I got to mention, hopefully this will elicit a chuckle. When you get some vendors telling you what their edge and cloud strategies are. Map R said, we'll have a hadoop cluster that only needs four or five nodes as our edge device. And we'll need five admins to care and feed it. He didn't say the last part, but that obviously isn't going to work. The edge analytics could be things like recalibrating the machine for different tolerance. If it's seeing that it's getting out of the tolerance window or something like that. The cloud, and this is old news for anyone who's been around David, but you're going to have a lot of data, not all of it, but going back to the cloud to train both the instances of each robotic machine tool and the master of that machine tool. The reason is, an instance would be oh I'm operating in a high humidity environment, something like that. Another one would be operating where there's a lot of sand or something that screws up the behavior. Then the master might be something that has behavior that's sort of common to all of them. It's when the training, the training will take place on the instances and the master and will in all likelihood push down versions of each. Next to the physical device process, whatever, you'll have the instance one and a class one and between the two of them, they should give you the optimal view of behavior and the ability to simulate to improve things. It's worth mentioning, again as David found out, not by talking to GE, but by accidentally looking at their documentation, their whole positioning of edge versus cloud is a little bit hand waving and in talking to the guys from ThingWorks which is a division of what used to be called Parametric Technology which is just PTC, it appears that they're negotiating with GE to give them the orchestration and distributed database technology that GE can't build itself. I've heard also from two ISV's, one a major one and one a minor one who are both in the IOT ecosystem one who's part of the GE ecosystem that predicts as a mess. It's analysis paralysis. It's not that they don't have talent, it's just that they're not getting shit done. Anyway, the key thing now is when you get all this - >> David: Just from what I learned when I went to the GE event recently, they're aware of their requirement. They've actually already got some sub parts of the predix which they can put in the cloud, but there needs to be more of it and they're aware of that. >> George: As usual, just another reason I need a red phone hotline to David for any and all questions I have. >> David: Flattery will get you everywhere. >> George: All right. One of the key takeaways, not the action item, but the takeaway for a customer is when you get these data feedback loops reinforcing each other, the instances of say the robotic machine tools to the master, then the instance to the assembly line to the factory, when all that is being orchestrated and all the data is continually enhancing the models as well as the manual process of adding contextual information or new levels of structure, this is when you're on increasing returns sort of curve that really contributes to sustaining competitive advantage. Remember, think of how when Google started off on search, it wasn't just their algorithm, but it was collecting data about which links you picked, in which order and how long you were there that helped them reinforce the search rankings. They got so far ahead of everyone else that even if others had those algorithms, they didn't have that data to help refine the rankings. You get this same process going when you essentially have your ecosystem of learning models across the enterprise sort of all orchestrating. This sounds like motherhood and apple pie and there's going to be a lot of challenges to getting there and I haven't gotten all the warts of having gone through, talked to a lot of customers who've gotten the arrows in the back, but that's the theoretical, really cool end point or position where the entire company becomes a learning organization from these feedback loops. I want to, now that we're in the edit process on the overall digital twin, I do want to do a follow up on IBM's approach. Hopefully we can do it both as a report and then as a version that's for Silicon Angle because that thing I wrote on Cloudera got the immediate attention of Cloudera and Amazon and hopefully we can both provide client proprietary value add, but also the public impact stuff. That's my high level. >> This is fascinating. If you're the Chief of Data Science for example, in a large industrial company, having the ability to compile digital twins of all your edge devices can be extraordinarily valuable because then you can use that data to do more fine-grained segmentation of the different types of edges based on their behavior and their state under various scenarios. Basically then your team of data scientists can then begin to identify the extent to which they need to write different machine learning models that are tuned to the specific requirements or status or behavior of different end points. What I'm getting at is ultimately, you're going to have 10 zillion different categories of edge devices performing in various scenarios. They're going to be driven by an equal variety of machine learning, deep learning AI and all that. All that has to be built up by your data science team in some coherent architecture where there might be a common canonical template that all devices will, all the algorithms and so forth on those devices are being built from. Each of those algorithms will then be tweaked to the specific digital twins profile of each device is what I'm getting at. >> George: That's a great point that I didn't bring up which is folks who remember object oriented programming, not that I ever was able to write a single line of code, but the idea, go into this robotic machine tool, you can inherit a couple of essentially component objects that can also be used in slightly different models, but let's say in this machine tool, there's a model for a spinning device, I forget what it's called. Like a drive shaft. That drive shaft can be in other things as well. Eventually you can compose these twins, even instances of a twin with essentially component models themselves. Thing Works does this. I don't know if GE does this. I don't think IBM does. The interesting thing about IBM is, their go to market really influences their approach to this which is they have this huge industry solutions group and then obviously the global business services group. These guys are all custom development and domain experts so they'll go into, they're literally working with Airbus and with the goal of building a model of a particular airliner. Right now I think they're doing the de-icing subsystem, I don't even remember on which model. In other words they're helping to create this bespoke thing and so that's what actually gets them into trouble with potentially channel conflict or maybe it's more competitor conflict because Airbus is not going to be happy if they take their learnings and go work with Boeing next. Whereas with PTC and Thing Works, at least their professional services arm, they treat this much more like the implementation of a packaged software product and all the learnings stay with the customer. >> Very good. >> Dave: I got a question, George. In terms of the industrial design and engineering aspect of building products, you mentioned PTC which has been in the CAD business and the engineering business for software for 50 years, and Ansis and folks like that who do the simulation of industrial products or any kind of a product that gets built. Is there a natural starting point for digital twin coming out of that area? That would be the vice president of engineering would be the guy that would be a key target for this kind of thinking. >> George: Great point. This is, I think PTC is closely aligned with Terradata and they're attitude is, hey if it's not captured in the CAD tool, then you're just hand waving because you won't have a high fidelity twin. >> Dave: Yeah, it's a logical starting point for any mechanical kind of device. What's a thing built to do and what's it built like? >> George: Yeah, but if it's something that was designed in a CAD tool, yes, but if it's something that was not, then you start having to build it up in a different way. I think, I'm trying to remember, but IBM did not look like they had something that was definitely oriented around CAD. Theirs looked like it was more where the knowledge graph was the core glue that pulled all the structure and behavior together. Again, that was a reflection of their product line which doesn't have a CAD tool and the fact that they're doing these really, really, really bespoke twins. >> Dave: I'm thinking that it strikes me that from the industrial design in engineering area, it's really the individual product is really the focus. That's one part of the map. The dynamic you're pointing at, there's lots of other elements of the map in terms of an operational, a business process. That might be the fleet of wind turbines or the fleet of trucks. How they behave collectively. There's lots of different entry points. I'm just trying to grapple with, isn't the CAD area, the engineering area at least for hard products, have an obvious starting point for users to begin to look at this. The BP of Engineering needs to be on top of this stuff. >> George: That's a great point that I didn't bring up which is, a guy at Microsoft who was their CTO in their IT organization gave me an example which was, you have a pipeline that's 1,000 miles long. It's got 10,000 valves in it, but you're not capturing the CAD design of the valve, you just put a really simple model that measures pressure, temperature, and leakage or something. You string 10,000 of those together into an overall model of the pipeline. That is a low fidelity thing, but that's all they need to start with. Then they can see when they're doing maintenance or when the flow through is higher or what the impact is on each of the different valves or flanges or whatever. It doesn't always have to start with super high fidelity. It depends on which optimizing for. >> Dave: It's funny. I had a conversation years ago with a guy, the engineering McNeil Schwendler if you remember those folks. He was telling us about 30 to 40 years ago when they were doing computational fluid dynamics, they were doing one dimensional computational fluid dynamics if you can imagine that. Then they were able, because of the compute power or whatever, to get the two dimensional computational fluid dynamics and finally they got to three dimensional and they're looking also at four and five dimensional as well. It's serviceable, I guess what I'm saying in that pipeline example, the way that they build that thing or the way that they manage that pipeline is that they did the one dimensional model of a valve is good enough, but over time, maybe a two or three dimensional is going to be better. >> George: That's why I say that this is a journey that's got to take a decade or more. >> Dave: Yeah, definitely. >> Take the example of airplane. The old joke is it's six million parts flying in close formation. It's going to be a while before you fit that in one model. >> Dave: Got it. Yes. Right on. When you have that model, that's pretty cool. All right guys, we're about out of time. I need a little time to prep for my next meeting which is in 15 minutes, but final thoughts. Do you guys feel like this was useful in terms of guiding things that you might be able to write about? >> George: Hugely. This is hugely more valuable than anything we've done as a team. >> Jim: This is great, I learned a lot. >> Dave: Good. Thanks you guys. This has been recorded. It's up on the cloud and I'll figure out how to get it to Peter and we'll go from there. Thanks everybody. (closing thank you's)

(electronic music) >> Narrator: Live from Boston, Massachusetts it's The Cube covering OpenStack Summit 2017. Brought to you by the OpenStack Foundation, RedHat and additional ecosystem support. >> Welcome back, I'm Stu Miniman joined by my cohost John Troyer. We always want to give the community what they want. and I think from the early returns on day one, we brought back Mark Shuttleworth. So Mark, founder of Canonical, had you on yesterday. A lot of feedback from the communities, so welcome back. >> Thank you, great to be here and looking forward to questions from the community and you. >> Yeah, so let's start with, we love at the show you get some of these users up on stage and they get to talk about what they're doing. We were actually, John and I, were catching up with a friend of ours that talked about how a private cloud, the next revision is going to use OpenStack, so really, OpenStack's been a little under the covers in many ways. The composability of OpenStack now, we're going to see pieces of it show up a lot of places. We've heard a lot about the Telco places, maybe talk about some of the emerging areas, enterprise customers, that you find for Ubuntu and OpenStack specifically? >> Sure. Well it seems as if every industry has a different name for the same phenomenon, right. So, for some it's "digital", for other's it's essentially a transformation of some aspect of what they're doing. The Telcos call it NFV, in media you have OTT as a sort of emerging threat and the response, in every case, is really to empower developers. That's why it's such a fun time to be a software developer, because the established guys realize that if they aren't already competing with Silicon Valley, they're going to be competing with Silicon Valley. So in each industry there's a sort of challenges or labels that they give this process of kind of unleashing developers and it's fun for us, because we get to be part of that in many cases. I think the big drivers under the hood, other than the operational and economic dynamics of cloudification, I think the really big changes are going to be machine learning, which seems to be moving very quickly into every industry. Retailers are using it for predictive analytics on what to put in store or what to recommend online. It just has this huge effect on almost any business when you figure out how to use your data in that way. All of that is developer driven, all of that needs this kind of underlying infrastructure to power it and it's kind of relevant to every industry. For us media is a key prospect, you know that we've done very, very well in Telco. Media is now a sort-of critical focus. Companies like Bloomberg for example us Ubuntu as an elastic platform for agility for the developers. They're a pretty astonishing operation; media company, but very tech-centric, very tech-savvy. I don't know if you've had them on the show. In retail, Ebay, PayPal it's kind of a crossover finance. They're all using Ubuntu in that sort of way. They may now see the major financials who are looking at the intersection of machine learning and transactions systems effectively as the driver for that kind of change. >> Stu: So in our last interview we talked about are companies making money in OpenStack and your answer, resoundingly, was yes. >> Mark: For us, certainly, yeah. >> One of the things we always look at is kind of the open source model itself. I was at DockerCon a few weeks ago, it's like everybody's using Docker. How do they make money? The question I get from a number of people in the community is, everybody I talk to knows Ubuntu, uses Ubuntu, when do they transition to paying for some of the products? >> Well so one of our key tenants is that we want to put no friction in front of developers. So many of the people that you'll meet here or that you'll meet at other developer-centric summits, they're developer-oriented. They're creatives, effectively. So our products, our commercial products aren't really designed to tax developers effectively. What we want is developers to have the latest and greatest platforms, to have that absolutely free, to be able to have confidence in the fact that it can go into production. When applications get into production, a whole different set of people get involved. For example the security guys will say, does this comply with FIPS security? And that's a commercial capability that customers get from Canonical if they wanted so we're now getting a set of security certifications that enable people to take apps on Ubuntu into production inside defense industries or other high security industries. Similarly if you look at the support life cycle, our standard public free support maintenance window is five years, which is a long time, but for certain applications it turns out the app needs to be in production for 10 years and again that's a driver for a different set of people. Not the developers, but for compilers and system administration operation types to engage with Canonical commercially. Sometimes we would walk through the building and the developers love us as everything's free and then the ops guys love us because we will support them for longer than we would support the developers. >> Can we talk about Open Source as a component of business models in general maybe, and how you would like to see the ecosystem growing, and even Canonical's business model. In the course of the last decade in the industry itself, right, a lot of people sniping at each other; "Well, you know open core is the way to go, open source is not a business model" there's a lot of yelling. You've been around, you know what works. How do you a set of healthy companies that use open source develop in our ecosystem? >> So this is a really, really interesting topic and I'll start at the high end. If you think of the Googles, and the Facebooks, and the Amazons, and the Microsofts, and the Oracles, I think for them open source is now a weapon. It's a way to commoditize something that somebody else attaches value to and in the game of love and war, or Go, or chess, or however you want to think of it, between those giants open source very much has become a kind of root to market in order to establish standards for the next wave. Right now in machine learning for example we see all of these major guys pushing stuff out as open source. People wouldn't really ask "what's the business model" there 'cause they understand that this is these huge organizations essentially trying to establish standards for the next wave through open source. Okay, so that's one approach. On the startup side it's a lot more challenging and there I think we need to do two things. So right now I would say, if you're a single app startup it's very difficult with open source. If you've got a brilliant idea for a database, if you've got a brilliant idea for a messaging system, it's very, very difficult to do that with open source and I think you've seen the consequences of that over the years. That's actually not a great result for us in open source. At the end of the day, what drives brilliant folks to invest 20 hours a day for three years of their life to create something new, part of it is the sense they'll get a return on that and so, actually, we want that innovation. Not just from the Googles, and the Oracles, and the Microsofts, but we want innovation from real startups in open source. So one of the things I'd like to see is that I'd like to see the open source community being more generous of spirit to the startups who are doing that. That's not Canonical, particularly, but it is the Dockers of the world, it is the RethinkDBs, as a recent example. Those are great guys who had really good ideas and we should caution open source folks when they basically piss on the parade of the startup. It's a very short-sighted approach. The other thing that I do need to do is we need to figure out the monetization strategy. Selling software the old way is really terrible. There's a lot of friction associated with it. So one of the things that I'm passionate about is hacking Ubuntu to enable startups to innovate as open source if they want to, but then deliver their software to the enterprise market. Everywhere where you can find Ubuntu, and you know now that's everywhere right? Every Global 2000 company is running Ubuntu. Whether we can call them a customer or not is another question. But how can we enable all those innovators and startups to deliver their stuff to all of those companies and make money doing it? That's really good for those companies, and it's really good for the startups, and that's something I'm very passionate about. >> We've seen such a big transformation. I mean, the era of the shrink wrapped software is gone. An era that I want to get your long term perspective on is, when it comes to internet security. Back to your first company, we had Edward Snowden and the keynote this morning talking about security, and he bashed the public cloud guys and said "We need private cloud, and you need to control a lot more there" any comments on his stuff, the public/private era and internet security in general today? Are we safer today than we were back in '99? >> We certainly are safer in part because of Edward Snowden. Awareness is the only way to start the process of getting stuff better. I don't think it's simplistically that you can bash the public clouds. For example Google does incredible work around security and there's a huge amount of stuff in the Linux stack today around security specifically that we have Google to thank for. Amazon and others are also starting to invest in those areas. So I think the really interesting question is, how do we make security easy in the field and still make it meaningful? That's something we can have a big impact on because security when you touch it it can often feel like friction. So for example we use AppArmor. Now AppArmor is a more modern of the SC Linux ideas that is just super easy to use which means people don't even know that they're using it. Every copy of Ubuntu out there is actually effectively as secure as if you've turned on SC Linux, but administrators don't ever have to worry about that because the way AppArmor works is designed to be really, really easy to just integrate and that allows each piece of the ecosystem, the upstreams, the developers, the end users to essentially upgrade their security without really have to think about that as a budget item or a work ticket item, or something that's friction. >> Mark, any conversations on the show surprise you? Excite you? There's always such a great collection of some really smart and engaged people at this show. I'm curious what your experience has been so far. >> Sure. I think it's interesting. Open Stack moved so quickly from idea to superstar. I guess it's like a child prodigy, you know, a child TV star. The late teens can be a little rocky, right? (Mark laughs) I think it will emerge from all of that as quite a thoughtful community. There were a ton of people who came to these shows who were just stuffed, effectively, there by corporates who just wanted to do something in cloud. Now I think the conversation is much more measured. You've got folks here who really want these pieces to fit together and be useful. Our particular focus is the consumption of OpenStack in a way that is really economically impactful for enterprises. But the people who I see continuing to make meaningful contributions here are people who really want something to work. Whether that's networking, or storage, or compute, or operations as in our case but they're the folks who care about that infrastructure really working rather than the flash in the pan types and I think that's a good transition for the community to be making. >> Can you say a little more about the future of OpenStack and the direction you see the community going. I don't know. If you had a magic wand and you look forward a couple of years. We talked a lot about operability and maintainability, upgradeability, ease of use. That seems to be one of the places that you're trying to drive the ecosystem. >> One of the things that I think the community is starting to realize is that if you try to please everybody, you'll end up with something nobody can really relate to. I think if you take the mission of OpenStack as to say, look, open source is going to do lots of complicated things but if we can essentially just deliver virtualized infrastructure in a super automated way so that nobody has to think about it, the virtual machines, virtual disks, virtual networks on demand. That's an awesome contribution to the innovation stack. There are a ton of other super shiny things that could happen on any given culture and ODS but if we just get that piece right, we've made a huge contribution and I think for a while OpenStack was trying to do everything for everybody. Lots of reasons why that might be the case but now I think there's a stronger sense of "This is the mission" and it will deliver on that mission, I have great confidence. It was contrarian then to say we shouldn't be doing everything, it's contrarian now to say "actually, we're fine". We're learning what we need to be. >> The ebb and flows of this community have been really interesting. NASA helped start it. NASA went to Amazon, NASA went back to OpenStack. >> Think about the economics of cars, right. It's kind of incredible that I can sit outside the building and pull up the app, and I have a car. It's also quite nice to own a car. People do both. The economics of ownership and the economics of renting, they're pretty well understood and most institutions or most people can figure out that sometimes they'll do a bit of either. What we have to do is, at the moment we have a situation where if you want to own your infrastructure the operations are unpredictable. Whereas if you rent it it's super predictable. If we can just put predictability of price and performance into OpenStack, which is, for example what the manage services, what BootStack does. Also what JUJU and MAAS do. They allow you to say, I can do that. I can do that quickly, and I don't have to go and open a textbook to do that or hire 50 people to do it. That essentially allows people now to make the choice between owning and renting in a very natural way, and I think once people understand that that's what this is all about it'll give them a sense of confidence again. >> Curious your viewpoint on the future of jobs in tech. We talked a little bit before about autonomous vehicles. It has the opportunity to be a great boon from a technology standpoint but could hollow out this massive amount of jobs globally. Is technology an enabler of some of these things? Do we race with the machines? We interviewed Erik Brynjolfsson and Andy McAfee from the MIT Sloan School. Did you personally have some thoughts on that? In places where Canonical looks about our future workforce, do we end up with "coding becomes the new blue collar job"? >> I don't know if I can speak to a single career but I think the simple fact is there's nothing magical about the brain. The brain is a mesh network competing flows and it makes decisions, and I think we will simulate that pretty soon and we'll suddenly realize there's nothing magical about the brain but there is something magical about humans and so, what is a job? A job is kind of how we figure out what we want to do most of the day and how we want to define ourselves in some sense. That's never going to go away. I think it's highly likely that humans are obsolete as decision makers and surprisingly soon. Simply because there's nothing magic about the brain and we'll build bigger and better brains for any kind of decision you can imagine. But the art of being human? That's kind of magical, and humans will find a way to evolve into that time. I'm not too worried about it. >> Okay. Last thing I want to ask is, what's exciting you these days? We've talked about space exploration a few times. Happy to comment on it. I mean, the last 12 months has been amazing to watch for those of us. I grew up studying engineering. You always look up to the stars. What's exciting you these days? >> Well the commercialization of space, the commercial access to space is just fantastic to see, sure, really dawning and credit to the Bezoses and the Musks who are kind of shaking up the status quo in those industries. We will be amongst the stars. I have no doubt about it. It will be part of the human experience. For me personally, I expect I'll go back to space and do something interesting there. It'll get easier and easier and so I can pack my walking stick and go to the moon, maybe. But right now from a love of technology and business point of view, IoT is such rich pickings. You can't swing a cat but find something that can be improved in a very physical way. It's great to see that intersection of entrepreneurship and tinkering suddenly come alive again. You don't have to be a giant institution to go and compete with the giant institutions that are driving the giant clouds. You just have to be able to spot a business opportunity in real life around you and how the right piece of software in the right place with the right data can suddenly make things better and so it's just delicious the sort of things people are doing. Ubuntu again is a great platform for innovating around that. It's just great fun for me to see really smart people who three years ago would say, do I really want to go work at a giant organization in Silicon Valley? Or can I have fun with something for a while that's really mine and whether that's worth 12 bucks or 12 billion who knows? But it just feels fun and I'm enjoying that very much, seeing people find interesting things to do at the edge. >> Mark Shuttleworth, appreciate being able to dig into a lot more topics with you today and we'll be right back with lots more coverage here from OpenStack 2017 in Boston. You're watching the cube. (electronic music)