>>The cube presents HPE discover 2022 brought to you by HPE. >>Welcome back to the Venetian convention center. You're watching the Cube's coverage of HPE discover 2022. The first discover live discover in three years, 2019 was the last one. The cube we were just talking about. This has been at H HP discover. Now HPE since 2011, my co-host John furrier. We're pleased to welcome Phil Maru. Who's the executive vice president and general manager of HPE Aruba. And he's joined by David Hughes, the chief product and technology officer at HPE Aruba gentleman. Welcome to the cube. Good to see you. Thank you. Thank >>You. >>Okay, so you guys talk a lot, Phil, about the intelligent edge. Yep. Okay. What do you, what do you mean by that? >>Yeah, so we, well, we're kind of focused on, is providing technology to customers that sits out at the edge and typically the edge would be, uh, any location out of the data center or out of the cloud. So for the most part, our customers would deploy our technology either in their office premises or maybe retail premises shops, uh, maybe deploying out of the home where their employees are on a factory floor. And we're really talking about technology to connect both people and devices back to, um, systems and technology throughout an organization. So, but >>I, I, you know, sometimes I call it the near edge and the far edge yeah. Near, near edge. Maybe as we saw home Depot up on the stage yesterday far, Edge's like space. Right. You're including all of that. Right. That's >>Edge. >>Yeah. And actually we, we, we, you know, we've got a broad range of technology that actually works within the data center as well. So, you know, what we are focused on is providing, uh, network technology, software and services. And, you know, for the most part, our heritage is at the edge, but it's more pervasive than that. So >>If you have the edge, you got connectivity and power, that's an edge. How much, um, is the physical world being connected now you're seeing robotics automation. Yeah. Ex and with machine learning specifically in compute, really driving a new acceleration at the edge. What you, how do you guys view that? What's your reaction? Yeah. >>I think, look, it, I think as connectivity is improving and that's both in terms of wifi connectivity, so, you know, wifi technology continues to, uh, advance and also you've got this new kind of private 5g area, just generally connectivity is becoming more pervasive and that's helping some industries that haven't previously embraced it. And I think industrial is, is one of the big ones. So, you know, historically it was difficult for kind of car manufacturers to really enable a factory floor. But now the connectivity is connectivity is better. That gives them the opportunity to be able to really change how they do things. So >>David, if you do take an outside in view, mm-hmm <affirmative>, uh, and, and, and when you talk to customers, what are they telling you and how is that informing your product strategy? >>Yeah, well, you >>Know, I think there's, there's several themes we hear. One is, you know, it's really important, better work from anywhere they wanna enable their employees, um, to get the same experience, whether they're at home or on the road or in their branch office or at headquarters. Um, you know, people are also concerned that as they deploy, deploy all of this IOT and pursuit of digital transformation, they don't want those devices to be a weak point where someone breaks into one device and moves naturally, um, across the network. So they want to have this great experience for their customers and their users, but they wanna make sure that they're not compromising security, um, in any way. And so it's about getting that balance between ease of use and, and security. That's one of the primary things we hear, >>You know, Dave, one of the things we talked about many, many years ago was when hybrid and was starting to come out multi-cloud was on the, on the table early on. Uh, we were, we were saying, Hey, the data center is just a big edge, right? I mean, if you have cloud operations and you see what's going on with GreenLake here now, the momentum hybrid cloud is cloud operations, right? An edge off data centers to a big edge on premises. And you got the edge as you have cloud operations, like say GreenLake, plugging in partners and diverse environments. You're connecting, not just branch offices that are per perimeter based. You have no perimeter and you have now other companies connecting mm-hmm <affirmative> so you got data and you got network. How do you guys see that transition as GreenLake has a very big ecosystem part of it, partners and whatnot. >>Yeah. So, you know, I think for us, um, the ecosystem of partners that we have is critical in terms of delivering what our customers need. And, you know, I think one of the really important areas is around verticals. So, um, you know, when you think about different verticals, they have similar problems, but you need to tailor the solutions. Um, to each of those, you know, we are talking a bit about devices and people. When you look at say a healthcare environment, there can be 30 devices there for each patient. And, um, so there's connecting all those devices securely, but we have partners that will help pull all of that together that may be focused on, um, you know, medical environment that may focused on stadiums. They may be focused on industrial. Um, so having partners that understand those verticals and working closely with them to deliver solutions is important in our go to market. >>So another kind of product question and related to what you just said, David, I got connectivity, speed, reliability, cost security, or maybe a missing something. But you, you said earlier, you gonna gotta balance those. How do you do that? And do you do that for the specific use cases? Like for instance, you just mentioned stadiums and 81 and how do you balance those and, and do you tailor those for the use cases? >>Yeah, well, I think it depends on the customer and different people have different views about where they need to be. So some people are, are so afraid about security. They wanna be air gapped and completely separate than the internet. That would be one extreme mm-hmm <affirmative> other people, you know, look at it and see what's happening with COVID with everyone working from home with people being able to work from Starbucks or the airport. And they're beginning to think, well, why is the branch that much different? And so what I think we are seeing is, you know, a reevaluation of how people connect to, um, the apps they're using and, uh, you know, you, you, you've probably for sure heard people talking about zero trust, talking about micro segmentation. You know, I think what we we see is that people wanna be able to build a network in a way where rather than any device being able to talk to any device or any person, which is where the internet started, we wanna build to build networks where people or devices can only talk to the destinations that are necessary for them to do their job. >>And so a lot of the technology that we are building into the network is really about making security intrinsic by limiting what can talk to what that's >>Actually micro, micro segmentations, zero trust, um, these all point to a modern, the modern network, as you say, Antonio Neri was just on the cube, talking about programmability, substrate, the words like that come to mind, what is the modern network look like? I mean, you have to be agile. You have to be programmable. You have to have security. Can you describe in your words, what does the modern network these days need to look like? How should customers think about architecting them? What are some of the table stakes and what are some of the differentiators that customers need to do to have a modern network? >>Yeah, well, you covered off a coup a few quarter, one there with clarity and so on. So let me pick one that you didn't mention. And, and I, you know, I think we are seeing, you know, a lot of interest around network as a service. And, you know, when we think about network as a service, we think about it broadly, um, you know, for consumers, we're getting more and more used to buying things as a service versus buying a thing. When you, when you get Alexa, you care about how well she answers your questions, you don't care about what CPU is or how much Ram Alexa has. And likewise with networking, people are caring about the outcomes of keeping their employees connected, keeping their, their devices and systems running. And so what for us, what NASA is all about is that shift of thinking about a network as being a collection of devices that get managed to being a framework for connectivity and running it from the point of view of those outcomes. >>And so whether, you know, it's about CapEx versus OPEX or about do it yourself, managing the network yourself versus outsourcing that, um, or it's about the, you know, Greenfield versus brownfield, each of our customers has got a different starting point, but they're all getting heading towards this destination of being able to treat their network as a service. And so that is, you know, a key area of innovation for us and whether it's big customers like home Depot that you heard about yesterday, um, where we kind of manage everything for them on a, as on a store basis, um, for connectivity, um, or, you know, the recent, um, skew based nest that we launched, which is a really scalable foundation for our partners to build nest offerings around. Um, we see this as a key part of network modernization. Yeah. >>And one of the things, again, that's great stuff. Uh, infrastructure is code, which was really kind of pioneer the DevOps movement in cloud kind of as platform level. And you got data ops now and AI at the top of the stack, we were always wondering when network as code was gonna come, uh, and where you actually have it, where it's programmable. I mean, we all know what policies do do. They're good. That's all great network as code. >>Yeah. >>And that's the concept that's like DevOps, it's like, make it work just seamlessly, just be always on. And >>Yeah. And smart, you know, people are always looking for the, for the easy button. Um, and so they want, they want things to operate easily. They want it to be easy to manage. And, you know, I actually think there's a little bit of a, um, a conflict between networkers code and the easy button, right? So it depends on the class of customers. Some customers like financials, for instance, have a huge software development organizations that are extremely capable that could, that can go with program ability that want things as code. But the majority of the, of, of the verticals that we deal with, um, don't have those big captive software organizations. And so they're really looking for automation and simplicity and they wanna outsource that problem. So in Aruba central, we have invested a lot to make it really easy for our customers to, um, get what they need, you know, is that movement of zero code. It's more like zero code. They want, they want something packaged now >>The headless networks. Yeah. Low code, no code >>Kind of thing. Yeah, that's right. And, you know, obviously for people that have the sophistication that want to, um, do the most advanced things, we have APIs. And so we support that kind of programmable way of doing things. But I'd say that that's that's, those are more specialized customers. So >>Phil, yeah. Uh, is that the strategy? I mean, David listed off a number of, of factors here is that Aruba's strategy to modernize networks to actually create the easy button through network as a service is as simple as dial tone. Is that how we >>Should think? I mean, the way I think about the strategy is I think about it as a triangle, really, along the bottom, we've got the products and services that we offer and we continue to add more products and services. We either buy companies such as silver peak a couple of years ago, or we build, uh, additional products and by, and by the way, that's in response to customers who are frustrated with some other suppliers and wanna move on mass over to, uh, companies like ourselves. So at the bottom layer of the product and services, and then the other side of the triangle one would be NAS, which we talked about, which is kind of move to buying network and as a service. And then the other side of the triangle is the platform, which for us is river central, which is part of HP GreenLake. And that's really all about, you know, kind of making it easy for customers to manage networks and Aruba central right now has got about 120,000 live customers on it. It connects to about 2 million devices and it's collecting a lot of data as well. So we anonymously collect data from all of our customers. We've got one and a half billion data points in the platform. And what we do is we let that data kind of look for anomalies and spot problems on the network before they happen for customers. >>So Aruba central predated, uh, uh, GreenLake GreenLake. Yeah. And, and so did you write to GreenLake through GreenLake APIs? How, what was the engineering work to accomplish that? >>Yeah, so really, um, Aruba central is kind of the Genesis of the GreenLake platform. So we took Aruba central and made it more generic okay. To build the GreenLake cloud platform. And you know, what we've done very recently is bring, bring Aruba into that unified infrastructure, along with storage and compute. So the same sign-on applies across all of HP's, um, products, the same way of managing licenses, managing devices. And so it provides us, uh, great foundation going forwards to, um, solve more comprehensively. Our customers automation requires. >>So, so just a quick follow. So Aruba actually was the main spring of GreenLake from the standpoint of okay. Sing, like you said, single sign on a platform that could evolve and become more, more generic. Yes. So, okay. So that was a nice little, um, bonus of the acquisition, you know, it's now the whole company >><laugh> Aruba taking over. >>Yeah. There's been a lot of work to, to, uh, you know, make it generic and, and widely applicable. Right. Yeah. Um, so, but >>You were purpose >>Built for yeah. Well it's foundational. Yes. So foundational for GreenLake, they built on top of it. Yeah. So you mentioned the data points, billions of data points. So I gotta ask you, cuz we're seeing this, um, copy more and more with machine learning, driving a lot of acceleration, cuz you can do simulations with machine learning and compute. We had Neil McDonal done earlier. He's a compute guy, you got networking. So with all this, um, these services and devices being put on and off the network humans, can't actually figure this out. You can discover what's on the network. How are you guys viewing the discovery and monitoring because there's no perimeter okay. On the network anymore. So I want to know what's out there. Um, how do you get through it? How does machine learning and AI play into this? >>Yeah. I mean, what we are trying to do is obviously flag trends for customers and say, Hey look, you know, we can either see something happening with your network. So there's a particular issue over here and we need to, I dunno, free up more capacity to solve that. Or we're looking at how their network is running and then comparing that with anonymized data from all of our other customers as well. So we're just helping find those problems. But yeah, you're right. I mean, I think it is becoming more of an issue for organizations, you know, how do you manage the network, >>But you see machine learning and AI playing a big part. >>Yeah, yeah. Yeah. I think, uh, AI massively and, and other technology advances as well that we make. So recently we, uh, also announced the availability of location awareness within our access points. And that might sound like a simple thing. But when network, when companies build out their networks, they often lose or they potentially could lose the records as to, well, where were the access points that we laid out and actually where are they not within, you know, 20 feet, but where actually are they? So we introduced kind of location, finding technology as well into our, uh, access points to make it easy for >>Customers. So Aruba one of the best, if not the best acquisition. I think that HP E has made, um, it's made by three par was, you know, good. It saved the storage business. Okay. That was more of a defensive play. Uh, but to see Aruba, it's a growth business. You guys report on it every quarter. Yeah. It's obviously a key ingredient to enable uh, uh, GreenLake and, and a that's another example, nimble was similar. We're much smaller sort of more narrow, but taking the AI ops piece and bringing it over. So it's, it was great to see HPE executing on some of its M and a as opposed to just leaving them alone and not really leveraging 'em. So guys, yeah. Congratulations really appreciate you guys coming on and explaining that. Congratulations on all the, all the great work and thanks for coming on the cube. Okay. >>Thank you guys. Yeah. Thanks for having us. >>All right, John, and I'll be back right after this short break. You're watching the cube, the leader in enterprise tech coverage from HPE Las Vegas, 2022. We'll be right back.

>>I'm john free with the Q we are here. It's exciting news around the next evolution switching, Sony jean Donny, co founder and chief business officer Pensando and David Hughes chief product and technology officer Aruba HP. Welcome back. We just heard from Antonio neary and john Chambers about the HPV Ruba partnership with Pensando and the new switching platform. Tell me more about the exciting news you're announcing? >>Yeah, I'm really excited today to be introducing the CX 10,000 distributed services switch. It's a brand new class of switch way bringing together the best of Aruba switching technology adding to R C X portfolio combining with Pence Sandoz technology that technology embedded in the platform. The problem we're solving is that in a traditional data center, all of those services like fire walling and low balancing provided by centralized appliances. And while that might be okay for north south traffic traffic that's going in and out of the data center. It's not scalable and it's not cost effective to apply to every service in every port to every flow traversing their data center As we all know with microservices more and more of the traffickers east west over 70% today and growing and so what we're doing here with the C X 10,000 is giving enterprises away to take the smart nick technology that's been proven out by hyper scholars and introduce it into their data centers in a very cost effective and easy to deploy way we're embedding that capability in the top of rack switch so that we can apply Fireable services, low balancing services to every port To every flow, delivering 100 times a scale in terms of a CLS 10 times of performance, in terms of encryption at a third of the cost of those traditional network architectures. So it's a super exciting time, >>love the speed, love the energy there. But I gotta ask what makes this a new category of switch. >>Well if you take a look at the journey we have been on as we have evolved our data centers and the applications have evolved for our customers. Uh and the world is now a bold new world of multi cloud. Uh the architecture is in the data center which are leaves spine architectures have become the new norm. Software defined, networking is pervasively deployed by our customers but as this journey began five or seven or even about 10 years ago uh and has culminated into a much more mature set of building blocks. We have taken the problem from one space of automating networks in the data center to then introducing lots and lots of expensive appliances to bring about security for example, or the state full services, whether it's load balancing or whether it's encryption and visibility and telemetry types of services. Now the customers had to try, you know, trombone all the traffic in and out of these appliances driving up the cost uh and the complexity and when time comes to troubleshoot these environments, it's extremely complex because you're trying to rationalize fabrics coming from one place appliances coming from four or five different vendors, maintaining all the software elements that need to be kept track off. Uh and as more and more customers want to aspire towards zero trust security model. Uh we need to start to embrace a lot of the principles that have been implemented by the hyper scholars and the cloud vendors, which is doing away with the appliances doing away with agent technology on servers, but instead to bring that technology for east west uh into play as well as to ensure that if there are bad actors that are landing inside of the data centers that they do not have the ability to, you know, create attack surfaces with complete lateral movement. Today, that is possible. Uh if you look at 70% of all the attacks that have been happening here in the past few years, it's as a result of having a attack surface which is pretty large in the data centers. And that gets further complicated when you move towards a multi cloud environment where the perimeter of the data center is now moving into the edge. Whether that edges, whether fleet resides for our customers or whether that edge happens to be a co location edge where you're building your own rampant off ramps. So I think the compelling event essentially is driven by the whole notion of distribution of services and having them available from a security and from a services point of view and these are state full services as close to the workload as you possibly can get them. >>So you guys really hit on some key points, their cloud, native microservices East west, north south, um no perimeter edge. These are topics that we would talk about kind of individually over the years, it's happening now all at the same time, this is causing a lot of complexities and then the security challenges you just laid out are everywhere. This brings up a big conversation around solving this. How does this new architecture, this solution solve the complexity and the security challenges in the data center. >>If you look at the use cases that our customers are talking about. The first, the initial use case really is to bring about security and state full security for east west traffic right into the fabric of their data centers. So having the ability to deliver that while eliminating the complex appliances only to do the job which they do very well, which is not South protection of services. Uh that also allows us the ability then to start to deliver visibility and telemetry at the same time that we're delivering state full security firewall and micro segmentation services because what I cannot see, I cannot secure. Uh so those two elements are initial use cases out of the box for our customers as we deliver this platform to them and then as more and more use cases that are becoming evident to us through customer interactions come into play. For example, the co location edge that I would like. David to walk you through a bit more in terms of how we help solve for that use case. >>So for the cooler use case, I think we're moving from a world where people talk about data centers to now talking about centers of data and those centers of data. Yes, they can be in a core private data center, they could be in the cloud but more and more they're going to be distributed around the edge in co location environments. And what we need to be able to do is extend those services that were provided in the data center to be provided in those Kahlo's at the edge And again we want to do that without having to deploy a whole rack of appliances that may be cost more than a computer itself and so with the CX- 10,000 we can have that as a top of rack switch for that polo. And from that switch deploy all of the encryption and firewall ng services that that polo requires. And what's important is that we're doing it with the same policy framework under the same management system across the whole enterprise in the data center as well as in these co location environments and out into the cloud. >>So you guys mentioned visibility and a quick follow up on this question because you mentioned visibility can't see it, you can't protect it. But also there's a lot of workloads that people are trying to automate. These are two factors. Can you guys just double down on that? I want to just get that out there because I think this becomes a big thing. >>I think policy having the ability to have an intent based policy that is a foundational technology building block that we are brought together is a very important element. And then when you map it back to tools that Aruba is extending support for including this platform, become very valuable. So David, why don't you walk us >>through? You know, I think one of the advantages that we bring is that this is an extension of the Aruba C X switching portfolio. So yeah, it's a cloud native microservices, very modern switch architecture and we have a comprehensive management platform, the Aruba fabric controller. And so what we are doing is making sure that everything fits together nicely, that we're delivering a complete solution to our customers. But one important thing to mention here is that we are thinking about how customers can do this step by step. So no, we're not requiring them to rebuild their entire data center, They can do this one rack at a time. We can work with their existing spine and deploy one leaf at a time in a very measured way. And so we think it's a great way for enterprises to be able to consume this modern distributed platform. >>That's a great segment. The next question. I mean I totally see this as you guys are talking about the cloud native trend, driving a cloud operational model to every edge. The data center is just another edge. It's a center of data. Love that. I love that line. So I have to kind of ask the operational side of the question, how would an enterprise customers manage all this take us through the nuts and bolts of deploying and managing of his gum? A customer >>That's a very good question. If you take a look at the customer's deployment models and let's let's take the example of they want to now bring in this technology and build a part or highly secure part with it for east west and to make sure that they're protecting 100% of that east west traffic. I think that leveraging all the building blocks that we have innovated between us and Aruba. We want to make sure that the ecosystem that the customer has built, they want whether they have built it with companies like Splunk and service now or Guardianco, they want integration points will be made available to them. If you take a look at, take a step back and say for these environments as you aspire to go toward zero trade security. The issues of inserting security appliances into network flows and having the ability to map it to the knowledge of applications and their dependencies for policy becomes an important function to tackle. So once you accept that, Okay, I have state full security functions built into this top of rack device available for my applications and all workloads, whether they're container workloads, bare metal workload, virtualized workloads uh and I have complete visibility into those workloads without compromising on connectivity and I can control through enforcement of policy where I need it because now security is part of the fabric, it's not a bolt on. Then comes the job of integration with an ecosystem. So whether you're looking at seem and sold companies where we are delivering in close collaboration with Splunk, A Pensando app for Splunk there's also going to be the availability of an elastic module, A plug in module. Uh then turn attention to what's more automation and devops and civil playbooks for the C X 10-K will be made available day one so that where you do not have the ability to deploy the A. F. C. You can use your existing answerable toolkit and they're making those playbooks available to our customers. Uh They want integration with application discovery mapping companies like Guardianco, allowing them to discover who's talking to whom and push and enforce that policy through the C X 10-K will allow for more automated deployments of those policies and finally, compliance integration with vendors like too thin for continuous security compliance monitoring becomes extremely important as the screen depicts a lot of lot of visualization capabilities with companies like Elk which are in beta today and answerable and Splunk and Elk will all be targeted at first customer shipment. So again, telemetry visibility with the integration of the ecosystem. Uh, it becomes a very powerful combination for the customers as they look to operationalize this for day to day three and they, you know, day one, day two, day three automation. >>That's awesome. David, I'd like to let you weigh in on this whole question of operations because you're hitting all the marks here that are relevant cloud, native microservices, apps, explosion and data volume and velocity, hyper scale operational cloud operations, performance, price point security all in this one solution. This is big. Um, it's not like you mentioned earlier, it's not a rip and replace but you can roll it out how how do you see a customer best operational izing this new, >>You know, I think the answer is a little bit different for each customer but you are very careful at the beginning, we introduced this. It's an evolution of switching. It's not a revolution where we have to replace everything and I think that's really exciting is that it builds on the foundational architecture of leaf and spine. And what we're able to do is let that customer introduced these new capabilities one leaf at a time. So maybe when they're upgrading from 10 gigs to 25 gigs, it's a great time for them to introduce this capability into their data center um and then depending on their application, you know, it may be, as Sony said that they've got one particular application, a crown jewel application and so they want to build out that in one rack and provide, you know, very, very robust East west as well as north south um security around that application, but there's so many different ways that customers can deploy this technology and what's really exciting is now is we're beginning to work with our customers, learning about these new use cases and then feeding that back into our roadmap and we all >>know, as you get down lower in the network layer, security is distributed architecture. So everything is paramount like security, super relevant, great conversation, I gotta ask what's next with this technology. Yeah, >>well, you know the teams, the two engineering teams are working together and this is step one on, on a really exciting new path, I don't know, Sony, what would you say? >>I think there's a lot more to come here. This is just a starting point. We have an incredibly strong partnership and go to market partnership here with Uber team with this platform. It is just the beginning uh and it will lead our customers onto the multi cloud journey. Uh and last but not least, I would like to say that you know, in closing uh that are seldom opportunities where you look at disrupting the way things are happening while fitting into customers existing models. So this is, as I said with everything being software defined, you will continue to see as delivering at great velocity more and more software defined services, whether it's encryption, Lord balancing and other state full services over time. Making this technology easier to deploy by fitting into the existing ecosystem and continuing to provide them with the 100 extra scale, 10 X. The performance as well as the ability to do it at a third of the same, you know, at the third of the cost of what they would need to if they had to build this uh today with disparate devices, >>exciting news in the industry. You guys are the pros you've seen all the waves of innovation over the years. I guess my final final question would be, how would you summarize this point in time right now? This is pretty um exciting all this is all happening At the same time, customers are having opportunity to innovate the pandemic has shown a lot of scale and and the need for stability and security. This is a special moment. How would you guys weigh in on that? >>Yeah, I think about it every decade, there's a change in how data centers a belt. And so this is the change that's happening this decade. Moving to a distributed services, switch. The other big mega trend that I see is this move, as I said from data centers to stand as a data and the opportunity for customers to use this technology as they move out to the edge. Have distributed compute and tell us, what do you think Sony? >>I think I couldn't agree more. I think there are so many various technology transitions occurring now. The cloud being the biggest one. Uh the explosion of data and uh, you know, the customers making decisions of having a distributed model And if indeed two thirds, if not 75% of all data will be processed at the edge over the next few years. This architecture is prime for the enterprise to go leverage their best practices of today while they can gradually move that architecture is for the future, which is a multi cloud future >>centers of data, large scale cloud operations automation. The speed of innovation has never seen this before. Uh It's exciting time. Sunny, thank you for coming on. And David, thanks for chatting about this exciting new announcement. Thank you very much. >>Thank you. Thank you. >>This is the power of and hp. Ruba and Pensando partnership. I'm john forward the cube. Thanks for watching. Mhm

>>LA Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Good morning from Las Vegas. Lisa Martin with Summa and Amanda, we are coming to you live from AWS reinvent 19. This is the QSA second full day of coverage and Stu and I are pleased to welcome one of our cube alum back to the program. We have Greg Hughes, the CEO of bear toss. Greg, welcome back. Good morning. >>Be here. Thank you. Yeah, >>this, this is 10, 10, 15 in the morning and this is already jam pack. Lots of buzz. Lots of, lots of news yesterday. I think that's kind of an understatement. Give us a little, a bit of an overview of their and AWS, what you guys got going on. >>It's, it's an amazing show, first of all. And uh, I was, the keynote yesterday was pretty incredible. Three hours long. I mean that, that stamina involved in a three hour keynote. I got to get hats off to Andy Jassy for doing that. Uh, one of the big announcements that was in that, uh, keynote was that outpost has been, is now generally available. And, uh, Amazon outpost is basically the Amazon web services that you can put within your data center. Okay. So we talk a lot about this hybrid cloud model on-prem in your data center and private cloud all the way to the public cloud. And so that is the outpost announcement and we're really excited to say that we are a partner with AWS on Amazon, uh, outpost and we have a designed and tested and validated solution on AWS outpost. So if you move your applications of customer moves, their applications on to outpost, they have the peace of mind knowing that their data is protected by Veritas. >>So we're really excited. Yeah. So, so Greg, everybody absolutely is very interested in outposts. Uh, I've just spent a couple of days in meetings trying to dig in. Uh, it is the building block, Amazon juicing for things like AWS, local zones, uh, AWS wavelength for 5g. One of the questions I really have for the ecosystem, cause I've seen a lot of announcement is this is yes it's the nitro chip and hardware and a subset of services that you would get from AWS. And from a management standpoint it looks like you've just put in a Z in your data center. But talk to us a little bit about what does it mean to actually integrate there. Cause Veritas has been an AWS partner for a number of years. I understand what it means to use Veritas in the public cloud. Walk us through some of the nuance and detail of what new we, well we have a very, very close partnership with AWS. >>Our engineers work very closely together and we did proceeding this announcement. And basically in this specific case, it means if you have an application or data on Alpos, it will be automatically backed up to the cloud, to a S3 through Veritas NetBackup. And so you can manage your outposts through Amazon, you're Veritas to state through our NetBackup console. And though things work seamlessly together. Yeah. So, so just one, when I looked at it, it's a, there, there's things like a, you know, ECS and EMR and RDS are in there. Yes. Three is not yet a service available on outpost, not available on outposts, but we can button connect it as three in the back. So that's what I'm trying to understand is where does my data live and how do I protect it without posts? Well you can, you can manage that through a essentially. So that's primarily the use case is for backing up to make sure your data is protected when it's on outpost. We see customers that want to experiment with outposts, they want to try cloud services. They have certain applications and certain workloads that are low latency and need low latency. And so they're going to run those in their data centers and those applications, those workloads can be protected through Veritas just like everything else is protected by Veritas. That's the idea. >>So for customers who have been with Veritas for a long time and they've got a cloud strategy that they're working on, walk us through maybe a, I don't want to use the word migration, but maybe an evolution. If they're saying, all right, there's workloads that we want to move to public cloud, what would that process be like for an existing customer? We spend >>a lot of time working with AWS on what that journey looks like and, uh, we developed a set of what we call well architected reviewed solutions that, that Amazon reviews and that we've invested in so that, uh, our customers can depend on this. These solutions working well together usually starts with backing up to the cloud. Okay. Instead of using secondary storage or tape backing up to the cloud. And so, uh, we have a customer put to furrow grope. It's a financial services firm that was able to leverage our appliances for on-prem rapid restore, but tear off the data to S three. So that's usually the start. Uh, the second step is using the cloud as a dr site. A lot of companies as you know, invest in data center capacity just for disaster recovery. It's not used all that often. And so that's an obvious thing. >>You can move to the cloud and have a data center on demand, so to speak. So we have a customer China Marine that is using our, our product Veritas resiliency platform to do that with AWS. And then finally it's moving your whole application stack to the cloud in migrating your data to the cloud. It still needs to be protected, right? Uh, it's still a customer's responsibility to protect that data in the cloud. And in that case, the Veritas products work really well in AWS. We can protect the workloads in the cloud. We have a environmental services firm, they, uh, that has moved their applications cloud still using Veritas for data protection. So that's really how we think about it. So Greg Veritas, what one of your strengths do you have? A very large install base and therefore I expect you to have a good visibility into what your customers are doing. >>Bring us on site a little bit when we talk about, you know, leveraging the cloud, it's agility and a modernizing my applications. We know, you know, changing my application stack is a longterm challenging thing to do. But do you have any kind of business outcomes, any proof points as to, you know, what your customers, you know, what do they get as they're uh, maturing their, their, their cloud journey. And evolution has a Veritas, so we work with, you know, our customers are 86% of the fortune 500. We work with the largest institutions on the planet the most. So I like to say the largest, most complicated, most highly regulated enterprises on the, on in the world, 10 of the top 10 financial services, 10 of the top 10 telco healthcare. Those are all our customers and they're all moving towards a hybrid world to leverage the cloud, but also have on prem data centers in a hybrid environment. >>And one of these they really want to leverage is that cheap and deep storage in the cloud. So, and we're seeing a very easy thing for our customers to do is to migrate from backing up to disk and tape for long term retention to backing up to the cloud, leveraging S3 glacier deep archive. Andy talked about it yesterday. It's lower than the price to tape our cus. Many of our customers still using tape for longterm retention. And it's just very simple step to data protection modernization, leveraging the cloud to replace though that secondary storage and tape with, with the cloud and the storage in the cloud. So for an organization that has thousands of endpoints, servers, virtual environments, SAS applications, can they manage all of that in the cloud through like a, a single bear toss dashboard? How do you allow that, like comprehensive data protection? >>That's our journey essentially. And you know, weight and, and our value proposition is that end to end data management. Uh, we, we kind of think of ourselves as the Switzerland of the storage and data protection world. We work with everything. Uh, we work with a 500 different workload types. We back up to 150 different, uh, storage targets, including many of the cloud service providers. So that's really our whole value proposition is that ability to give you that abstraction across all the complex storage silos you have. We can take care of availability, protection and insights. That's what we do. So a few years ago, uh, we, we saw a little bit of a bit flip when we talk about security and the cloud. It used to be, you know, for, for the early days of the cloud it was, I can't do cloud because I'm worried about the security. And now many thought security is an opportunity, uh, when I go to the cloud the last year or two, it's a very nuanced discussion. >>Uh, and the relationship between my data, my cloud providers, my information and data protection, uh, is something that we've been digging into a lot. Uh, at the 80 most reinforced show inaugural show this year. Uh, we had a lot of great conversations. CSOs, um, have a challenge. It's a board level discussion. Uh, what are you hearing from your customers and what's their tosses role? And a straight question. Uh, look, uh, security is a board level conversation and when I go talk, I spend about 30 to 50% of my time talking to customers and the cyber threat is real in particular ransomware. What are, what the enterprise is worried about is uh, that their data gets completely encrypted through a ransomware attack and they're dead in the water. You know, they can't ship product, they can't bill, they can't run payroll. And the challenge is that the malware is going to get in spearfishing works. >>When you get those emails that say click on this link, you know, in many cases people still click on the link and the bad stuff gets in. So what you need is you need a resilient infrastructure and at the foundation of that is to make sure you've got a protected copy of your data that you can depend on and roll back to that, you know, is good. And that's really driven a lot of our business in the last few years is making sure we have, we can help our customers have that resilient data. It's true, whether in the public cloud or on prem, same, same challenge, right? From a ransomware perspective to help reduce spread is data protection in the cloud and an enabler of mitigating the rest that ransomware provides in terms of data not traversing through a customer's network. If it's protected in the cloud, ransomware attack can occur too in your cloud or on prem. >>It really doesn't matter. It's a. They don't really care. The bad guys don't care. Uh, but what you need ultimately is your copy of your data that you can go back to and you can restore everything else you can. Uh, you know, you, you can create, but that data, that state of your business, that's really the crown jewels and if they've corrupted that, you know, you're in trouble. So related to the security governance of course, has been a big discussion. Uh, last year, uh, every single show I went to was talking about GDPR. This year we're all waiting for the California law to roll out and we expect more of this to happen. Uh, you know, what are the discussions you're having with your customers there? And, uh, what, what, what's the, the regulatory environment is really moving fast. A lot more regulations around data. I was taught, it's funny, I was talking to a customer in st Louis and they said the California data privacy act was going to be the death of them. >>Yeah. You don't think about that for a customer in st Louis, a big customer. Uh, I was talking to a customer in Australia and she said she has to deal with 27 different regulatory regimes. So how do you do that when the dirty secret is you don't know where all your data is, you don't know what's important, what's not. Uh, you know, most of our customers have very difficult time assessing that. So where Veritas comes in is we have some solutions that provide insight into that to allow you to understand where is your data, what can be deleted, what's really important, what's pie, what's protected, what's not protected. To really give you some insight in that across all the different silos that Andy was talking about. Yes. >>Showing them really where some of the vulnerabilities are that they might be completely blind to >>say the risks and vulnerabilities that they have that they don't know and that's now becoming a board level topic. So we're getting pulled in. I was actually in Europe a couple of weeks ago and one of our customers said, look, I need to present to the board they insights that Veritas is providing me on my infrastructure is safe so that they're aware, >>let's push them for you. Yesterday when everything kicked off and you're right, it was a marathon. A three hour keynote is very impressive. There's a lot of news packed in there. One of the biggest themes is transformation. It's a word that we talk about transformation, right? We talk about security, transformation, digital transformation, workforce transformation. It's used commonly, but yesterday was really sort of like this, this sort of reinvention of of AWS, but this transformation that Andy was saying, and it sounds like something that you're hearing that this has to come from senior, the senior level of really understanding transformation. Not just do we go to the cloud, how, when, what? But also ultimately it's about data and if you can't access the data, if you can't restore data quickly, if there is, whether it's a human error or some sort of catastrophic event, you can't get to what you need, the business suffers. Right. And there's going to be another competitor whose objects are close to the Napier in the mirror. Right. Who are ready to come in and take over that business. Give me a little bit of a, of a kind of an overview as we wrap up here about their tosses transformation as customers are having to pivot really quickly to use data as a competitive advantage. Yes. >>Well look, we think about, uh, you know, our, our role in digital transformation is in the data transformation part of that. Uh, you can't have digital transformation without transforming your data. Uh, we've talk a lot here about data has gravity. It can distance to stick where it is. We're trying to make that data mobile flexible, protected, available and visible. And that's really our role is in, in digital transformation to give you the freedom to use your data wherever you want to. >>That's what we do. One last question actually on the data has gravity. We talk about that a lot. Your last thoughts on Amazon and moving towards where that gravity is with post for an example, this is another example of I think yesterday and John furrier even uncovered this and his exclusive with Andy Jassy. It's AWS everywhere, which is just like Amazon. >>I can actually just to to build on that, uh, John furrier and I to Andy Jassy like three years ago said the next flywheel for Amazon is data. You stayed, absolutely. Data's come up over and over again. I think, you know, we're working very closely together with AWS to help make this journey easy for our customers. We're both very customer obsessed, you know, that came up a lot. We are customer obsessed as well. We're innovating step-by-step. We were the first launch launch partner with a glacier deep archive. So we attend to be at the forefront of allowing our customers to leverage AWS and know that they're protected by their AtoZ. >>Yeah, those demanding customers. Right. Well, Greg, thank you for joining Stu and me on the program sharing what's new with their toss and AWS and the transformation that you're both undergoing. We appreciate it. Take care of our student, man. I am Lisa Martin. You're watching the cube live from day two of our coverage of AWS reinvent 19 thanks for watching.

>> live from San Francisco celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019 brought to you by IBM wear and its ecosystem partners. >> Well, good afternoon. And welcome back to San Francisco. Where Mosconi north along with David Dante, John Wall's You're watching our coverage here. Live on the Cuba Veum world. 2019 days. I've been over on the other set. I know you've been busy on this side as well. Show going. All right for you >> so far. Yeah, A lot of action going on over here. We had a pact Hellsing on this morning, Michael Dell, with this VM wear hat, we get Sanjay Putin downtown later. >> Yeah, yeah. Good light up. And that lineup continues. Great. Use the CEO Veritas. >> Great to be here. Very John, >> actually, just outside the Veritas Meadow here. Sponsored the this area. This is the meadow set. That >> nice to be here? Yeah, I didn't know >> that. All right, just first off, just give me your your idea of the vibe here. What you are. You're feeling >> what? I think there's a tremendous amount of energy. It's been a lot of fun to be here Obviously VM was talking about this hybrid multi cloud world, and Veritas is 100% supportive of that vision. We work with all the major cloud service providers, you know, eight of us. Google. Microsoft is or we share thousands of customers with the M, where some of the biggest customers, the most complicated customers in the world, where we provide availability and protection and insights for those customers has always >> been the ethos of veritas. When you go back to the early days of Veritas, essentially, it was the storage management, you know, the no hardware agenda, the sort of independent storage company, but pure software. >> That sounds. You >> know, years ago there was no cloud, but there were different platforms, and so that that that that culture has really migrated now into this multi cloud work world. Your thoughts on that >> absolutely look, you know, I'll give an example of a customer that we worked with closely with VM wear on, and that is Renault. America's Renault is Ah, big joint venture. They've got a huge ASAP installation 8000 users 40 terabytes, Big Net backup customer. They also use their products in for a scale and V. R P for availability and D r. And they work with us because we are hardware agnostic. They looked at us against the other competitors, and we're hardware agnostic. And because of that where we came in its 60% lower TCO than those other providers. So we that hardware agnostic approach works really well. You were >> Just touch it on this great little bit when you said, You know whether Tiger, whether it's multi, whether it's private, whatever it is, you know we're here to provide solutions. The fact that this stuff is hard to figure out and really kind of boggle the mind a bit, it's very complex. Um, how much of an inhibitor is that? In terms of what you're hearing from clients and in terms of their progress and and their decision making >> well, let me explain where we sit. And we are the leader in enterprise data protection, availability and insights. We work with the largest, most complex, most high route, highly regulated and most demanding customers on the planet. 99 of the Fortune 100 are customers of Veritas. 10 of the top 10 tell coast 10 of the top 10 healthcare companies and 10 of the top 10 financial institutions. I spend about 50% of my time talking to these customers, so we learn a lot. And here the four big challenges they're facing first is the explosion of data. Data is just growing so fast, Gardner estimates will be 175 Zita bytes of data in 2025. If you cram that in, iPhones will take 2.6 trillion iPhones and go to the sun and back, right? It's an enormous amount of data. Second, they're worried about Ransomware. It's not a question off if you'll be attacked. It's when you'll be attacked. Look at what's happening in Texas right now with the 22 municipalities dealing with that. What you want in that case is a resilient infrastructure. You wanna be terrible to restore from a really good backup copy of data. Third, they want the hybrid multi cloud world, just like Pak Gil Singer has been talking about. That's what customers want, but they want to be able to protect their data wherever it is, make it highly available and get insights in the data wherever it's located. And then finally, they're dealing with this massive growth in government regulations around the world because of this concern about privacy. I was in Australia a few weeks ago and one of our customers she was telling me that she deals with 27 different regulatory environments. Another customer was saying the California Privacy Act will be the death of him. And he's based in St Louis, right? So our strategy is focused on taking away the complexity and helping the largest companies in the world deal with these challenges. And that's why we introduce the enterprise. Data Service is platform, and that's why we're here. VM world Talking >> about Greg. Let's unpack some of those, Asai said. Veritas kind of created a market way back when and now you see come full circle, you got multi cloud. You have a lot of new entrance talking about data management. That's it's always been your play, but you came to the king of the Hell's. Everybody wants a piece of your hide, so that's kind of interesting, But but data growth. So let's let's start there. So it used to be data was, ah, liability. Now it's becoming an asset. So what? What your customers saying about sort of data is something that needs to be managed, needs to be done cost effectively and efficiently versus getting more value on data. And what's Veritas is sort of perspective. >> They're really trying to get insights in their data. Okay. And, uh, that's why we acquired a company called Apt Are. So when I This is my second time of Veritas. I was here from 2003 to 2010 rejoined the company of 2018. I talked to a lot of customers. I've found that their infrastructure was so complex that storage infrastructure so complex the companies were having a hard time figuring out anything about their data. So they're having the hardest time just answering some fundamental questions that boards were asking. Boards are saying because of the ransomware threat. Is all our data protected? Is it backed up? Are all our applications backed up and protected and customers could not answer that question. On the other hand, they also were backing up some data 678 times wasting storage. What apt are does, and it's really amazing. I recommend seeing a demo of that. If you get a chance, it pulls information from Santa raise network file systems, virtual machines, uh, san networking and all data protection applications to get a complete picture of what's happening with your data. And that is one example off what customers really want. >> Okay, so then that kind of leads to the second point, which is ransomware now. Part of part of that is analytics and understanding what's going on in the system as well. So but it's a relatively new concept, right? And ransom. Where is the last couple of years? We've really started to see it escalate. How does Veritas help address that problem? And does apt our play a role there? >> Well, Veritas, it just helps it. Cos address that problem because veritas helps create a resilient infrastructure. Okay, the bad guys are going to get in spear. Phishing works. You know, you you are going to find some employees were gonna click on a link, and the malware is going to get in so all you can do to protect you ultimately have tohave a good backup copies so you can restore at scale and quickly. And so there's been a lot of focus from these large enterprises on restoring at scale very quickly after ransom or attack, it's you're not beholden. You can't be extorted by the ransom or >> the third piece was hybrid. And of course, that leads to a kind of hybrid multi cloud. Let's let's put that category out there now. I've been kind of skeptical on hybrid multi cloud from an application perspective in other words, the vision that you can run any app anywhere in the world without having a retest Rica pile. I've been skeptical that, but the one area that I'm not skeptical and the courage with is data protection because I think actually, you can have a consistent data protection model across your on Prem different on prams, different clouds, because you know you're partnering with all the different cloud cos you obviously have expertise in on premise. So so talk about your approach, their philosophy and maybe any offering. >> Well, this is really what sets us apart. We have been around for 25 years, 2000 patents. We protect everything. 500 different sources of data 150 different targets, 60 different cloud service providers, you know, we compete with two categories of players. We compete with the newcomers, and they only they will only protect your most current technology. They don't go back. We've been around for 25 years. We protect everything, right? We also can't compete with the conglomerates, Okay? In their case, they're not focused. They're trying to do everything. All we do is availability, protection and insights. And that's why we've been in Gardner M Q 13 times and where the market share leader also absolutely >> touch me. Someone Dave was saying about the application side of this. I mean, just your thoughts about, you know, the kinds of concerns the day raises. I mean, it is not alone in that respect. I mean, there are general concerns here, right about whether that that'll fly. What do you think? In terms, >> I think the vision is spot on and like, oh, visions, it takes a while to get to. But I think what VM wears done recently in the acquisition, there've been basically trying to make the control plane for compute okay, and their acquisition of carbon, black and pivotal add to that control plane we're gonna be We are the control plane for data protection. I mean, that's that's the way our customers rely on, >> but that makes sense to me. So I think I feel like the multi cloud vision is very aspirational today, and I think it's gonna be really hard to get there without homogeneous infrastructure. And that's why you see things like Outpost to see the Oracle has clouded customer. You've got Azure Stack. So and I think it's gonna be a multi vendor world. However I do think is it relates the data protection you can set a standard and safe. We were going to standardize on Veritas. So one of us So I think that it's it's achievable. So that was my point there. The last one was was regulations. Do you think GDP are will be a sort of a framework globally body of customers seeing there? >> Well, they're dealing with more than GDP are like I talked about that one customer, 27 different regulatory environments and the challenge there is. How do you deal with that when you don't know what you have in terms of data, the 50% of data is what we call dark data. You don't know anything about right, so you need help classifying it, understanding and getting insight into that data, and that's what we can help >> our customers. But howdy, howdy, dildo. In that environment, I mean, I mean, a day raises the point. This is obvious. A swell that mean you cite California right, which is somewhat infamous for its own regulatory mindset. I mean, how do you exist? What? The United States has privacy concerns and Congress can address it, and various federal agencies could do the same Europe. Obviously we talked about now Australia. Now here. Now there you get this Balkan I system that has no consistency, no framework. And so how do you operate on a global scale? >> A. Mentally. It relies on classifying that data right. Understanding what's where and what do you have is a P I. I personally identifiable information. Is it information that's intellectual property? What kind of data you have once you have that insight, which is what we provide, you can layer on top of the regulatory Is that compliance? >> Star I P. Is that Veritas i p. A blender? >> It's a blend of avatar and veritas I p. We have a product called Info Studio that helps toe provide that now Remember one of the things that are net backup product has is a catalogue of data. So we know where the data is primary to secondary storage, and we have all the versions of that data. And then we can run analytics against the secondary storage and not hit the primary systems. Right? So we're out of band to the primary systems, and that turns out to be very valuable in the state's a >> question. The catalog. I can't do this without a catalogue in the enough to geek out here a little bit, but but you've got a little bit when you bring in multi clouds. Other clouds. How do you incorporate you know that knowledge into your catalog? >> Yeah. Art, art, technology work Idol of works across multiple clouds. So we work with 60 different Cloud service providers. There's three big ones represented here today. Microsoft, AWS and Google. We work very closely with all three, and >> that's because you do the engineering at the A P. I level. Our engineering teams work very, very closely together. Okay, um, so let's talk about competition a little bit. The markets heated up. It's great. It's good to see all this VC money floating in. Everybody I said wants a piece of your hide. Why Veritas? >> Well, I explained that, you know, we are the leader in enterprise, data protection, availability and insights. There are some newcomers. They just will support you on your current technology. They don't support the infrastructure you've had for many years. If your large complicated enterprise you have layers of technology, we support all that with VIN amount for 25 years against, the big conglomerates were completely focused. And that's why we're the leader, according to Gartner, in the Leader's Quadrant 13 years >> now. And just as we close up you talked about, you brought up the case in Texas, about 22 municipalities. You do a lot of public sector work states, federal government ever. It's just what is the difference of different animal between public and private and and what you need to do in terms of providing that >> we're struggling with the same challenge. In fact, we work with some of the largest government agencies in the world, and they're struggling with exactly the same challenge. They also want leverage the public cloud. They're worried about ransom where you know they're dealing with data growth. All of these are challenges to them. And that's the, uh So these are common challenges we're addressing. Our strategy is to help our customers with these challenges so they can focus on the value of data >> 18 months in. You seem pumped up. Does having a great time team fired up >> way. Get that right. Great. But you're okay with big geeking out to write a very good thanks for the time You've run out of time. 40 Niners next time. All right. Greg Hughes joining us from Veritas. Back with more Veum, World 2019 right here on the Cube. >> Thank you.

>> Announcer: Live from Miami Beach, Florida, it's theCUBE. Covering VeeamON 2019. Brought to you by Veeam. >> Welcome back to the Fontainebleau, Miami, everybody. My name is Dave Vellante, I'm here with my co-host for this segment, Justin Warren. Justin it's great to see you. This is theCUBE, the leader in live tech coverage, day two of our coverage of VeeamON 2019 here in Miami. Jason Buffington, @Jbuff is here, he's the vice president of solution strategy congratulations on the promotion and great to see you again, my friend. >> Thank you very much. >> Dave: And Nathan Hughes who is the IT director at Flex-N-Gate. Great to see you, thanks for coming on. We love to get the customer's perspective, so welcome. >> Great to be here. >> Okay, so, Jason let me start with you. Former analyst, you've been at Veeam now for long enough to A, get promoted, but also, get the Kool-Aid injection, you're wearing the green, and, what are the big trends that you're seeing in the market that are really driving this next era, what do guys call it? Act two of data protection? >> Sure. So, I preached on this even before I joined Veeam that every 10 years or so, when the industry shifts the platform of choice, the data protection vendors almost always reset, right? The people that lead in NetWare don't lead in Windows. The people that lead in Windows didn't lead in Vert. The next wave is we're moving from servers to services. Right, we're going from on prem into cloud and so, and every time the problem is the secret sauce doesn't line up, right? So you got to reinvent yourself each time. And what we saw in the past generations, what we learned from, is, you can't be so busy taking care of your install base that you forget to keep innovating on what that next platform is and so for us, act two is all about cloud. We're going to take everything we know about reliability but we're moving into cloud. The difference is, that in virtualization there was one hero scenario. VMs, right? This time around it's IaaS, it's SaaS, it's PaaS, it's using cloud storage, it's BaaS and DRaaS, there's not a single hero scenario which means we have a lot more innovation to do. That's round two. >> And you made that point today, you used the Archimedes quote, give me a lever and a fulcrum and I'll change the world. You used the analogy of backup as now becoming much more than just backup, it's data protection, it's data management, we're going to get into that. And test some of that with Nathan. So, Nathan, tell us about Flex-N-Gate what does the company do and what does your role as IT director entail? >> Okay, so Flex-N-Gate is a tier one automotive supplier. Which means that we provide parts, most of the things that go into a car besides electronics and glass, to the final automotive makers. So most of the companies that you're familiar with when you go to buy one. >> Okay, so you guys are global, I think you've got what, 24,000 associates worldwide, 64 locations. So what're some of the things that are, fundamental drivers of your business, that are rippling through to your IT strategy? >> Well, our business is varied in the sense that we do a lot of different things in house so, we do, obviously, manufacturing, that's a big part of what we do. And then, even that is broken down into different kinds and then beyond manufacturing we have advanced product development and engineering so we do a lot of that in house. >> Dave: You support it all? >> Yes. >> So you've got diverse lines of business, you've got different roles and personas, you know, engineers versus business people versus finance people. And you got to make 'em all happy. >> We've got to make 'em all happy. >> So, one of the things I love about manufacturing examples, is if you think about it it's the two extremes of high tech and low tech, right? On the low tech side of things you've got this manufacturing floor and it's just producing real stuff, not the zeros and ones that we live with, but real things come off this line. And then you have the engineering and R and D side. Where they're absolutely focused on stuff that comes out of some engineer's head into a computer, which is truly unique data, so, one of the things I love about the story is, talk about the downtime challenges you have around the manufacturing floor. Because I learned some things when we first met, that I think is phenomenal when it comes to manufacturing things that I didn't realize. >> Sure. So, we have a lot of different kinds of manufacturing environments. Some of them are more passive and some of them are more active. The most active environments are, a form of manufacturing known as sequencing. And it's sort of where you bring final assembly of parts together right before they go to the customer. The way that customers order up parts these days, it's not like they used to back in the 70s and 80s. Where they would warehouse huge volumes of everything on their site and then just draw it down if they needed it. And you just kept the queue full. Now they want everything just in time delivery. So they basically want parts to come to the line right when they're needed and actually in the order they're needed. So, a final car maker, they're not necessarily making, 300 of the same thing in a row, they're going to make one of this in blue and one of that in red and they're all going to be sequenced behind each other, one right after the other on the assembly line. And they want the parts from the suppliers to come in the exact right order for that environment. So, the challenge with that from our perspective is that we have trucking windows that are between 30, maybe 60 minutes on the high end, and if anything goes badly, you can put the customer down. And now you're talking about stopping production at Ford, Chrysler, GM, whatever. And that's a lot of money and a lot of other suppliers impacted. >> Dave: So this is a data problem isn't it? >> Yeah, it definitely is. And it's an interesting point, 'cause, you talk about sequencing. Veeam has their own sequence about how customers use the product and they start with backup, everything starts with backup, and then they move further to the right so that you get, ideally, to fully automated data protection. So, what are you actually using Veeam for today? And where do you see yourself going with Veeam? >> So, right now, we're using Veeam primarily as backup and recovery. It's how we started with it. We came from another product that was, great conceptually, but in the real world it had terrible reliability and its performance was very poor as time went on and so, when Veeam came on the scene it was a breath of fresh air because we got to the place where we knew that what we had was dependable, it was reliable. We got to understand how the product worked and to improve the way that we'd implemented it. And so, one of the key features in Veeam that really actually excited us, especially in those sequencing environments are these instant recovery options, right? So, we were used to the idea of having to write down a VM out of snapshot storage. And then being put in a position where it might take an hour, two hours, three hours before you could get that thing back online now, or again, to be able to launch that right out of snapshot storage was a blessing in the industry we're in. >> Yeah, did you see the tech demo yesterday where they were showing off how you could do an instant recovery directly from cloud storage? >> Yes, yeah. >> Did that get you excited? >> Yes. That is exciting. >> Are you using cloud at the moment or is this something that you're looking to move towards? >> Cloud is something we're sort of investigating but it's not something that we're actively utilizing right now. >> So this instance recovery, you guys obviously make a big deal out of that, I was talking to Danny Allan yesterday offline about it. He claims it's unique in the industry. And I asked him a question, I said specifically, if you lose the catalog, can I actually get the data back? And he said yes. And I'm like, that sounds like magic. So, so I guess my question to maybe both of you is, instant, how instant? And how does it actually work? (he laughs) >> It just works, isn't that? >> It just works! >> It's just magic, new tagline? >> I guess we don't have to get into the weeds but when you say, when I hear instant recovery, we're talking like, (fingers clicking) instantaneous recovery with, very short RTOs? >> To us what that means is that in practice, we can expect to have a VM from snapshot data back into production in about a five minute window. >> Dave: Five minutes? Okay. >> And that is sufficient for our needs in any environment. >> Okay, so now we're talking RTO, right? And then, what about, so we said 64 sites across the world, 24,000 associates, is Veeam your enterprise wide data protection strategy or are you rolling it out now? Where are you at? >> Yes, no. Veeam, we started with it in a handful of key sites. And we were using it to specifically back up SharePoint and a few other platforms. But once we understood what the product was capable of, and we were sort of reaching the end of our rope with this former product, yeah, we began an active roll out and we've now had Veeam in our facilities for five, six years. >> So you swept the floor of that previous product. And how complicated was it for you to move from the legacy product to Veeam? >> It was a challenge just rethinking the way that we do things, the previous product, one thing that it really had going for it, if this could be considered a positive, I guess, is that it was very very simple to set up. So, you could take an entry level IT administrator and they just next, next, next, next, next. And it would do all the things that they needed it to do. But the problem was that in the real world, that was sort of the Achilles' Heel, because, it meant that it wasn't very well customized and it meant also that, the way that they've developed that product, it became performance, it had poor performance. >> So the reason I ask that question is because, so many times customers are stuck. And it's like they don't want to move, because it's a pain. But the longer they go, the more costly it is, down the road. So I'm always looking to IT practitioners like, advice that you would give in terms of others, things that you might do differently if you had a mulligan, I don't know, maybe you would've started sooner, or maybe there were some things that you'd do differently. What would you advise? >> Yeah, I mean, if we'd understood, the whole context of what was happening with that other product, we would've moved sooner. And the one thing that I will say about Veeam is, it's not click and point. It does involve a little more setup. But the Veeam team is excellent when it comes to support. So there's nothing to fear in that category because they stand behind their product and it's very easy to get qualified technicians to help you out. >> Is that by design? >> I don't know if it's. Well, the being great to work with, yes, that's by design. >> Yeah, but I mean. >> I was talking to Danny yesterday and asked about the interface thing. Because there is always that tension between making it really really simple to use but then it doesn't have any knobs to change when you need to. >> That's what I'm asking. >> But it can't be too complex either. >> Our gap actually comes a little bit later in the process, right? So, you asked earlier about, in what ways do you use Veeam? And we think about Veeam as a progression, right? So, everybody if they're using Veeam at all, they're using it for Veeam backup and replication and because foundationally, until you can protect your stuff, right? Until you can reliably do that, all the other stuff that you'd like to do around data management is aspirational and unattainable at best, right? So, we think the journey comes in at yeah, it is pretty easy, to go next, next, next, finish. Just a few tweaks, right? To get backup going. But then when you go beyond that, now there's a whole range of other things you can do, right? So Danny, I'm sure, talked about DataLabs yesterday. The orchestration engine, those are not, next, next, next, finish. But anything that's worthwhile takes a little bit of effort, right? So as we pivot from, now that you've solved backup, then you can do those other things and that's where we really start going back into something which is really more expertise driven. >> Well, and it's early days too and as you get more data and more experience you can begin to automate things. >> Yeah, absolutely. So Justin was asking, Nathan, where the direction is. Today it's really backup. You've seen the stages where, talking about full automation. Is that something that, is on the horizon, it is sort of near term, midterm, longterm? >> I mean, coming to the conference, our experience with backup, or Veeam, is primarily backup and recovery operations but, I've seen a lot of things in the last few days that have piqued my interest. Particularly when it comes to the cloud integration. That's being actively baked into the product now. And, some of the automated, API stuff, that's being built into the product. Any place where I can get to where we simplify our procedures for recovery, that's a plus. So I'm really excited about the idea of the virtual labs, being able to actively test backup on a regular basis without human intervention and have reporting out of that. Those are things that I don't see in any other product that's out there. >> You know, there's another piece of the innovation that we should think through, and, so we've talked about the sequencing side which is where we focus on RTO, how fast can you get back and running again? And when you and I talked earlier, the example that we worked on was think of a zipper, right? You've got the bumpers coming in to a line of cars and if either side slows down, everything breaks, and at the end, by the way, is the truck, right? And everything has to come at the same time at the same rate, if there's downtime on either side of the source, you're done. But that's an RTO problem. The engineering side, for high tech, is an RPO problem, right? You have unique stuff coming out of somebody's brain into a PC and it'll never come out that way again. And so, when we look at backup and replication, that should be the next pieces to go on. And then as you mentioned, DataLabs becomes really interesting and orchestration, so. >> Well speaking of human brains, and you kind of touched on it, Nathan, that you came here to learn some things and you've learned things from different sessions. So, what is it about coming to VeeamON that is worth the time for IT practitioners like yourself? >> I think it's all those, I mean we were talking about Veeam, doing backup and recovery operations, fairly straightforwardly, in terms of getting in, but once you see some of this stuff here at a conference like this, you get a better sense of all the more, elaborate aspects of the product. And, you wouldn't get that >> See the possibilities. >> I think, if you were just sitting in front of it using it conventionally, this is a good place to really learn the depth and the level that you can go with it. >> And you're like most of your peers here, is that right, highly virtualized, is that right? Lot of Microsoft apps. And, they say, mid-sized global organization, actually kind of bumping up into big. >> Nathan: Sure. >> Yeah, cool. I asked about the data problem before, it sounds like the zipper's coming together, that's some funky math that you got to figure out to make sure everything's there. So, talk about the data angle. How important data is to your organization, we know much data's growing, data's the new oil, all those promides but, what about your organization specifically as it relates to a digital strategy? It's a buzzword that we hear a lot but, does it have meaning for you, and what does it mean? >> Data is vital in any organization. I mean, we were referencing earlier, how you've got low tech in manufacturing, or at least people think of it as lower tech. And then high tech in R and D, and how those things merge together in a single company. But the reality is all of that is data driven, right? Even when you go to the shop floor, all your scheduling, all your automation equipment, all this stuff is talking and it's all laying down data. You're putting rivets in the parts, you're probably taking pictures of that now with imagers when you're in manufacturing. And you do that so that if you get 300 bad ones you can see exactly when that started and what happened at the machine level, right? So, >> That's a good one. >> We're just constantly collecting massive volumes of new data, and being able to store that reliably is everything. >> Well, and the reason I'm asking is you guys have been around for a while and your a highly distributed organization so, in the old days, even still today, you'd build, you'd get a server for an application, you'd harden that application, you'd secure that box and the application running on it, you'd lock the data inside and, my question is, can, the backup approach, the data protection approach, the data management, or whatever we want to call it, can it help solve that data silo problem? Is that part of the strategy or is it just too early for that? >> I'm, sorry, I'm going to get you to repeat that question in a slightly different way. >> Yeah so, am I correct that you've got data in silos from all the years and years and years of building up applications and-- >> I mean, we have-- >> And can you use something like Veeam to help unify that data model? >> Draw that all together? Yeah. I think a lot of that has, it's more on the hosting side, right? So it depends on how those systems were rolled out originally and all that kind of thing. But yeah, as we've moved towards Veeam, we've necessarily rebuilt some of those systems in such a way that they are more aggregated and that Veeam can pick them up in an integrated kind of way. >> You see that as a common theme? Veeam as one of the levers of the fulcrum to new data architecture? >> We're getting there, so here's the trick. So, first you got to solve for basic protection, right? But the next thing along the way to really get towards data management is you got to know what you got, right? You got to know what's actually in those zeros and ones. And so, some of the things that you've already seen from us are around what we do around GDPR compliance, some of the things we do around sanitization of data for DevOps scenarios and reuse scenarios. All of that opens up a box of, okay, now that the data is curated. Now that it's ingested into our system, what else can you do with it? You know, when I talk to C-level execs, what I tell them is, data protection, no matter who it comes from, including Veeam, is really expensive if the only thing you do is put that data in a box and wait for bad things to happen, right? Now the good news is, bad things are going to happen, so you're going to get ROI. But better is don't just leave your data in a box, right? Do other stuff with that data, unlock the value of it and some of that value comes in, now that I'm more aware of it, let's reduce some of the copies, let's reduce some of the compliance mandates. Let's only put data that has sovereignty requirements where it goes, but to do all of that, you got to know what you got. >> Go ahead, please. >> There was some impressive demo yesterday about exactly that, so, we have the data. You can use the API to script it and you can do all kinds of, basically, you're limited by your imagination. So it's going to be fascinating to see what customers do with it once they've put it in place, they've got their data protected. And then they start playing with things, come to a conference like this and learn, ooh, I might just give that a try on my data when I get back home. >> That's right. >> We'll give the customer the last word, Nathan. Impressions of VeeamON 2019? >> It's been great. And like I say, if you're a company that's been using Veeam even for a while, and you have your entry level setup for backup and recovery and I think there's a lot of, probably, companies out there that use Veeam in that kind of way, this is a great place to have a better understanding of all that's available to you in that product. And there's a lot more than just meets the eye. >> And it's fun, good food, fun people. Thanks you guys for coming on, really appreciate it. >> Yeah, thank you. >> Alright, keep it right there, buddy, we'll be back with our next guest, you're watching theCUBE, Dave Vellante, Justin Warren, and Peter Burris is also here. VeeamON 2019, we'll be right back. (electronic music)

(upbeat music) >> From Washington, D.C., it's theCUBE, covering ScienceLogic Symposium 2019. Brought to you by ScienceLogic. >> Hi, I'm Stu Miniman, and you're watching theCUBE's exclusive coverage of ScienceLogic Symposium 2019 here at the Ritz Carlton in Washington, D.C. Happy to welcome to the program first-time guest but a long-time customer of ScienceLogic, Karl Fosburg, who's the senior director of systems integration at Hughes. Thanks so much for joining us. >> Thanks for having me. >> Alright, so we're here in D.C., and that's important 'cause first of all, you're based down here, and ScienceLogic is based down here. >> Yup. Bring us back a little bit. You said you'd been a customer a long time as to... maybe give us a little bit of the before picture, if you could. >> Sure, so yeah, we've been a customer for 12 years now, and we picked ScienceLogic for a big list of reasons, actually wrote the RFI itself, and probably 20 pages long. Lots of people came back and gave us responses. ScienceLogic was one of the short-listed candidates that we picked out. We did a bake-off with a couple other vendors, and ScienceLogic was the clear winner. >> All right. So Karl, lets zoom out for a second here >> Okay. and just give us a level set on Hughes, what Hughes is today. You know, I'm familiar with what Hughes was back in the day and there's certain pieces that are no longer there so give us a level set on the company in the business. >> Yeah, sure. So, Hughes is formally known as Hughes Network Systems, were owned by EchoStar Corporation and we're a managed service provider. We have a consumer business where we provide broadband internet to folks that live really out in the countryside and can't get cable, or DSL, or FIOS, things like that. We have about 1.4 million subscribers in our consumer business. We've also launched consumer services in South America, Brazil, Ecuador, Columbia, places like that. Really serving under-served areas for giving them broadband. We also have an enterprise business where we sell to credit card processing, gas and oil, pipelines, fast foods, places like that. >> Okay. So Karl, is it safe to say you use satellites but no longer put them into space? >> We use satellites, that's correct. We contract that out now. Yeah, we are the last remaining Hughes company. >> Yeah. So, service providers are always fascinating to me because we talk about enterprise IT and how fast things are changing. At least for my entire career, when I talk to service providers, change and growth is really just baked into the DNA. >> Yep. I need to move fast. When you talk about scale, it means something very different and living in that complex world, and just give us a little bit about what things are like in 2019 for you. >> Sure, yeah. The scale is always our challenge. Like I like to say, we have sales people too and they're out there selling new products and services constantly. So we needed to be able to grow with those sales. We started out with a couple thousand devices that needed monitor in applications. Now we're up to almost 30 thousand Nox systems that we monitor. Also, we're keeping track of nearly 2 million terminals and the status of them and things like that. So, yeah, scale is super important to us. >> Okay. So, bring us inside, where ScienceLogic fits into your equation. >> Sure. So when we put out our FI to industry years ago, we were trying to replace a whole bunch of different tools. We had other vendor products and things like that. We really wanted to consolidate tools as much as possible into a single platform. Traditional ICNP, SNMP monitoring is how we originally started. Now we have lots and lots of integration with other tools, APM products, different streaming media products. We're integrating more and more with streaming services now in terms of getting data into the platform. So, yeah ... >> Yeah. Karl, I'd love to get your viewpoint. Something that came through to me in the keynote is on the one hand the years like, oh, well AIOps is going to replace things like some of the traditional players here, but then you see onto the stage it's like, oh okay, we're actually going to have integrations with a number of these tools. So yes, there's overlap but it needs to be integrated. How do you look at that as, is this the primary product? Is this a piece of the product? How do data collection between all these various tools go together? Well, that's a great question 'cause that's exactly what we and lots of other folks are grappling with right now. We've got data producers all over the place now, and we're really focused on the data production and high quality data back at the source into a real pub-sub type of architecture of which we believe that ScienceLogic will be both a producer and consumer of that pub-sub architecture, and whether it's the one tool to rule them all or not? Probably not, no ones going to be that, and we've got lots of vendors that purport to be the one tool to rule them all. But really, we're focused on ScienceLogic at this point to be really the focus, especially for our operations folks. We've got 24/7 staff. They use ScienceLogic as their main tool that they go to. So that's really where we want the data to end. That's where we want as much intelligence to end as possible. >> So, I'd be curious... You've been using the tool for a dozen years now. 12 years ago the discussion of data wasn't no where near what it was today. >> Correct. Can you bring us through a little bit of that journey, and you mentioned data a bunch, but how important is that? Where are you in your journey for... There was that maturity model that was put up there, the role of data today, and where do you see it going? >> Well, data is everything today. 12 years ago we were grappling with things like naming conventions and simple firewall rules and whatnot. Those days are long, long past. Now, the data quality and the pipeline is what we're focused on right now 'cause like Dave said in the keynote, "Garbage in, garbage out". We're really really focused on trying to get good quality data by focusing on the source of the data. As opposed to fixing it after it's been moved into whatever platform it ends up in. So we're using proper scheme of management and trying to bake-day the governance into the actual engineered products, and if it's not governed data then you don't get to look at it. And that's really our focus. We're an engineering company at heart so we actually write most of our own software. So we're kind of in control of our own destiny there, and we're really focused on pushing that back because we think the benefits in the long run are going to be worth that investment to get clean data all the way back to the source. >> Yeah. So Karl, one of the big shifts I've seen in the last few years... When you talked about managing and monitoring, I used to as the administrator or controller, used to be able to go and touch all of those pieces. Today, there's more and more some of those pieces I need to manage not just the stuff that's in my environment or my hosted environment, but outside of my environment and doing public clouds. >> Yep. >> Bring us up to speed as to where does Cloud fit? What's your Cloud strategy? >> Sure. We're actually launching some of our first applications in GCP right now. So we're working with our Google partners in this particular case to integrate the data that they can collect natively in their systems, bring it back in as actionable events into ScienceLogic platform, while keeping the vast majority of the data native to their platform. No need to bring back application specific data unless we're actually going to do something with it, or if we need to cross-correlate it with other information. The data sources live in our data centers, not in GCP. So we need to combine it with information we know about, our on-prem equipment, plus the applications running there. So that's the data we'll bring back to cross correlate. >> How do you decide what lives where, and where does ScienceLogic fit in the whole discussion? >> Yeah, that's a good question. What lives where... We kind of go back to license models and cost models. We're pretty good sticklers about focus on doing proper upfront analysis to make sure we don't end up with some six or seven figure bill at the end of the year from a Cloud provider. We also tend to do a lot of stuff on-prem because a lot of our systems have to run in one of our data centers. If you've ever driven past our building you'll see these large large dish's antennas outside. A lot of our equipment has to be within milliseconds or microseconds even of those dishes. So we actually have a large data center presence kind of scattered around the country and around the world. So, we have the compute resources to do it ourselves. >> Yeah, and even I would think edge computing something that plays into what you're doing. What do you see as some of the main challenges as the kind of footprint for what you're doing and things to spread out more? >> Yeah. Keeping, let's say pet projects, and shadow IP projects, keeping them in check is a really big focus right now, and also with DevOps sort of the "I'll do everything, I'm going to be my own IP department" philosophy is a new challenge that we're facing. So integrating with what the DevOps guys are building into our overall monitoring strategy, that's when a new challenge has really creeped up or it last, lets say six months or a year. >> Okay. Is there an intersection between your use of the ScienceLogic in the DevOps team yet? >> Not a big one yet. I think we're still learning DevOps at this point. I consider it a lifestyle change, not really a thing that you go get. So, I think we're still kind of early adoption for DevOps, and really only greenfield projects at this point in time. >> Okay. How about the term of the show is AIOps, so what's your act in the AIOps? Where do things like machine learning and automation fit into your environment? >> Yeah. We actually have quite a few used cases where we really think that machine learning is going to help us a lot. Cross-correlation is a big area for us. We have lots of information, but figuring it out, feeding like the APMs and Cisco ACI software defined networking, and those bits of information all into one product, we've been challenging ScienceLogic on this for quite a while. It's like, okay, you guys know about everything now. Tell us something that we didn't know before, and that's kind of where we're at, and seeing the announcements from this morning was really encouraging that we're finally see the horizon at this point. >> Yeah. If you can, (mumble), but how has ScienceLogic been doing on the roadmap? What helps between ScienceLogic and your vendor ecosystem out there? What more could they be doing to make your life easier? >> Yeah, that's a good question. So, if you would ask me that a year ago I probably wouldn't have been as encouraged as I am today. It was a challenge and they're engineering company, we're an engineering company. Sometimes you have to focus on foundation, and it's not cool, it's not sexy, it's not shiny, but you have to do it. And I think they've been focused a lot on their foundational aspects of the product which will actually enable doing things like machine learning. There's no point in doing machine learning if you have bad data or if you have a platform that doesn't support very very fast queries, and the graph QL database. We think that we're going to use that extensively and through the API, not even through the UIs. So, I think foundation is important. I think they focused on it for the last couple of years. I think we're finally going to start to see the benefits of it. Both single factor sort of machine learning, anomaly detection, but we really want to see it on a cross domain. I want to be able to see in ScienceLogic impacted by in our full stack environment. >> Yeah. I'd expect you probably had some visibility into what was coming up in the Big Ben release. Is there anything that jumped out at you, or that you're ready to use day one? >> The automations, for sure. We'll use that definitely day one. The way they've gone through and really made it a lot easier to use. You don't have to be a python developer anymore to actually get a lot of benefits out of the product. So I can turn that over to some of our junior engineers to actually handle those things, and we get a lot more sophisticated with them now. Primarily we used to focus on, "oh, let's send an email" type of thing. Now we can actually execute back-end actions without having to have a programmer to do it. So that right away we're going to use out of box. >> Okay. And in that forward looking piece, without breaking any visibility you have into their roadmap, what would you like to see more? >> I'd like to see more getting performance data into their real scalable, laterally scalable back end. And that's certainly an area that I'd love to see as much progress as fast as possible on. Also the Pub-Sub subscribing to streams coming out of our Kafka cluster. We want that to be in the product as soon as possible 'cause we really believe that that's where the majority of our data of the future is going to come from. Also, new applications, they come and go. Docker containers spin up, spin down. So the state of something is no longer fixed and we need to be able to integrate with Kubernetes and our open shift platform to be able to know, "Well what should be running right now?" So, those are the things that are on our roadmap that we need out of the product as soon as possible. >> Yeah. So it definitely came to me that ScienceLogic's listening. Are they moving fast enough for you? >> No. No ones ever moved fast enough. So, yeah, they're moving so that's good, but yeah, I could use it today if they had it. >> All right. Karl, last thing, you've been to a few of the ScienceLogic events in the past. You've been to other industry shows, what's special about the show? What brings you and your team to ScienceLogic symposiums? >> One of the things that ScienceLogic does a really good job is they bring a lot of resources here, and actual resources that actually know stuff. It's not just telling me, "Oh, that shiny new object is going to be in the platform at some indeterminate time in the future." It's the actual engineers, people writing code, product managers, things like that. So having access directly to the people who actually do the platform updates and changes is super valuable. The new sensor where we can touch and feel, take attires on new things has been excellent this year. So I think that's probably the thing, just quick access to all the resources. We have a bit of an advantage, we're only 45 minutes up the road. We can come down here as need be to visit their headquarters but having everyone here at one time is great. >> All right. Well Karl Forsberb, really appreciate you sharing your history and experience in future direction as to where things are going on your end. >> All right. >> I'm Stu Miniman. We'll be back with lots more coverage here from ScienceLogic 2019. Thanks for watching theCube. (upbeat music)

>> From Tavern on the Green in Central Park, New York, it's theCUBE, covering Veritas Vision Solution Day. Brought to you by Veritas. (robotic music) >> We're back in the heart of Central Park. We're here at Tavern on the Green. Beautiful location for the Veritas Vision Day. You're watching theCUBE, my name is Dave Vellante. We go out to the events, we extract the signal from the noise, we got the CEO of Veritas here, Greg Hughes, newly minted, nine months in. Greg, thanks for coming on theCUBE. >> It's great to be here Dave, thank you. >> So let's talk about your nine. What was your agenda your first nine months? You know they talk about the 100 day plan. What was your nine month plan? >> Yeah, well look, I've been here for nine months, but I'm a boomerang. So I was here from 2003 to 2010. I ran all of global services, during that time and became the chief strategy officer after that. Was here during the merger by Semantic. And then ran the Enterprise Product Group. So I had all the products and all the engineering teams for all the Enterprise products. And really my starting point is the customer. I really like to hear directly from the customer. So I've spent probably 50% of my time out and about, meeting with customers. And at this point, I've met with a 100 different accounts all around the world. And what I'm hearing, makes me even more excited to be here. Digital transformation is real. These customers are investing a lot in digitizing their companies. And that's driving an explosion of data. That data all needs to be available and recoverable and that's where we step in. We're the best at that. >> Okay, so that was sort of alluring to you. You're right, everybody's trying to get digital transformation right. It changes the whole data protection equation. It kind of reminds me, in a much bigger scale, of virtualization. You remember, everybody had to rethink their backup strategies because you now have less physical resources. This is a whole different set of pressures, isn't it? It's like you can't go down, you have to always have access to data. Data is-- >> 24 by seven. >> Increasingly valuable. >> Yup. >> So talk a little bit more about the importance of data, the role of data, and where Veritas fits in. >> Well, our customers are using new, they're driving new applications throughout the enterprise. So machine learning, AI, big data, internet of things. And that's all driving the use of new data management technologies. Cassandra, Hadoop, Open Sequel, MongoDB. You've heard all of these, right? And then that's driving the use of new platforms. Hyper-converged, virtual machines, the cloud. So all this data is popping up in all these different areas. And without Veritas, it can exist, it'll just be in silos. And that becomes very hard to manage and protect it. All that data needs to be protected. We're there to protect everything. And that's really how we think about it. >> The big message we heard today was you got a lot of different clouds, you don't want to have a different data protection strategy for each cloud. So you've got to simplify that for people. Sounds easy, but from an R&D perspective, you've got a large install base, you've been around for a long, long time. So you've got to put investments to actually see that through. Talk about your R&D and investment strategy. >> Well, our investment strategy's very simple. We are the market share leader in data protection and software-defined storage. And that scale, gives us a tremendous advantage. We can use that scale to invest more aggressively than anybody else, in those areas. So we can cover all the workloads, we can cover wherever our customers are putting their data, and we can help them standardize on one provider of data protection, and that's us. So they don't have to have the complexity of point products in their infrastructure. >> So I wonder if we could talk, just a little veer here, and talk about the private equity play. You guys are the private equity exit. And you're seeing a lot of high profile PE companies. It used to be where companies would go to die, and now it's becoming a way for the PE guys to actually get step-ups, and make a lot of money by investing in companies, and building communities, investing in R&D. Some of the stuff we've covered. We've followed Syncsort, BMC, Infor, a really interesting company, what's kind of an exit from PE, right? Dell, the biggest one of all. Riverbed, and of course Veritas. So, there's like a new private equity playbook. It's something you know well from your Silver Lake days. Describe what that dynamic is like, and how it's changed. >> Oh look, private equity's been involved in software for 10 or 15 years. It's been a very important area of investment in private equity. I've worked for private equity firms, worked for software companies, so I know it very well. And the basic idea is, continue the investment. Continue in the investment in the core products and the core customers, to make sure that there is continued enhancement and innovation, of the core products. With that, there'll be continuity in customer relationships, and those customer relationships are very valuable. That's really the secret, if you will, of the private equity playbook. >> Well and public markets are very fickle. I mean, they want growth now. They don't care about profits. I see you've got a very nice cash flow, you and some of the brethren that I mentioned. So that could be very attractive, particularly when, you know, public markets they ebb and flow. The key is value for customers, and that's going to drive value for shareholders. >> That's absolutely right. >> So talk about the TAM. Part of a CEOs job, is to continually find new ways, you're a strategy guy, so TAM expansion is part of the role. How do you look at the market? Where are the growth opportunities? >> We see our TAM, or our total addressable market, at being around $17 billion, cutting across all of our areas. Probably growing into high single digits, 8%. That's kind of a big picture view of it. When I like to think about it, I like to think about it from the themes I'm hearing from customers. What are our customers doing? They're trying to leverage the cloud. Most of our customers, which are large enterprises. We work with the blue-chip enterprises on the planet. They're going to move to a hybrid approach. They're going to on-premise infrastructure and multiple cloud providers. So that's really what they're doing. The second thing our customers are worried about is ransomware, and ransomware attacks. Spearfishing works, the bad guys are going to get in. They're going to put some bad malware in your environment. The key is to be resilient and to be able to restore at scale. That's another area of significant investment. The third, they're trying to automate. They're trying to make investments in automation, to take out manual labor, to reduce error rate. In this whole world, tape should go away. So one of the things our customers are doing, is trying to get rid of tape backup in their environment. Tape is a long-term retention strategy. And then finally, if you get rid of tape, and you have all your secondary data on disc or in the cloud, what becomes really cool, is you can analyze all that data. Out of bound, from the primary storage. That's one of the bigger changes I've seen since I've returned back to Veritas. >> So $17 billion, obviously, that transcends backup. Frankly, we go back to the early days of Veritas, I always thought of it as a data management company and sort of returned to those roots. >> Backup, software defined storage, compliance, all those areas are key to what we do. >> You mentioned automation. When you think about cloud and digital transformation, automation is fundamental, we had NBCUniversal on earlier, and the customer was talking about scripts and how scripts are fragile and they need to be maintained and it doesn't scale. So he wants to drive automation into his processes as much as possible, using a platform, a sort of API based, modern, microservices, containers. Kind of using all those terms. What does that mean for you guys in terms of your R&D roadmap, in terms of the investments that you're making in those types of software innovations? >> Well actually one of the things we're talking about today is our latest release of NetBackup 812, which had a significant investment in APIs and that allow our customers to use the product and automate processes, tie it together with their infrastructure, like ServiceNow, or whatever they have. And we're going to continue full throttle on APIs. Just having lunch with some customers just today, they want us to go even further in our APIs. So that's really core to what we're doing. >> So you guys are a little bit like the New England Patriots. You're the leader, and everybody wants to take you down. So you always start-- >> Nobody's confused me for Tom Brady. Although my wife looks... I'll stack her up against Giselle anytime, but I'm no Tom Brady. >> So okay, how do you maintain your leadership and your relevance for customers? A lot of VC money coming into the marketplace. Like I said, everybody wants to take the leader down. How do you maintain your leadership? >> We've been around for 25 years. We're very honored to have 95% of the Fortune 100, are our customers. If you go to any large country in the world it's very much like that. We work with the bluest of blue-chips, the biggest companies, the most complex, the most demanding (chuckling), the most highly regulated. Those are our customers. We steer the ship based on their input, and that's why we're relevant. We're listening to them. Our customer's extremely relevant. We're going to help them protect, classify, archive their data, wherever it is. >> So the first nine months was all about hearing from customers. So what's the next 12 to 18 months about for you? >> We're continuing to invest, delighted to talk about partnerships, and where those are going, as well. I think that's going to be a major emphasis of us to continue to drive our partnerships. We can't do this alone. Our customers use products from a variety of other players. Today we had Henry Axelrod, from Amazon Web Services, here talking about how we're working closely with Amazon. We announced a really cool partnership with Pure Storage. Our customers that use Pure Storage's all-flash arrays, they know their data's backed up and protected with Veritas and with NetBackup. It's continually make sure that across this ecosystem of partners, we are the one player that can help our large customers. >> Great, thank you for mentioning that ecosystem is a key part of it. The channel, that's how you continue to grow. You get a lot of leverage out of that. Well Greg, thanks very much for coming on theCUBE. Congratulations on your-- >> Dave, thank you. >> On the new role. We are super excited for you guys, and we'll be watching. >> I enjoyed it, thank you. >> All right. Keep it right there everybody we'll be back with our next guest. This is Dave Vellante, we're here in Central Park. Be right back, Veritas Vision, be right back. (robotic music)

>>We're here live in Las Vegas with a cubes coverage of Amazon re Mars. It's a reinvent re Mars reinforced. The big three shows called the res. This is Mars machine learning, automation, robotic and space. It's a program about the future it and the future innovation around industrial cloud scale climate change the moon, a lot of great topics, really connecting all the dots together here in Las Vegas with Amazon re Mars I'm John ER, host of the cube. Our first guest is Howard Hughes program manager, necess Ryan program. Howard is involved with all the action and space and the moon project, which we'll get into Howard. Thanks for coming on the cube. >>Well, Hey, thanks for having me here this morning. Appreciate you guys inviting me here. >>So this show is not obvious to the normal tech observer, the insiders in, in the industry. It's the confluence of a lot of things coming together. It's gonna be obvious very soon because the stuff they're showing here is pretty impressive. It's motivating, it's positive and it's a force for change in good. All of it coming together, space, machine learning, robotics, industrial, you have one of the coolest areas, the space what's going on with your Orion program. You guys got the big moon project statement to >>Explain. Well, let me tell you, I'll start with Orion. Orion is our next human space craft. That's gonna take humans beyond low earth orbit and we're part of the broader Artis campaign. So Artis is our plan, our NASA plan to return the first person of color, first woman, back to the moon. And we're very excited to do that. We have several missions that I could talk to you about starting with in a very few months, Artis one. So Artis one is going to fly on the space launch system, which is gonna be the biggest rocket we call the mega rocket has been built since the Saturn five on top of the SLS is the Ryan spacecraft and that Ryan spacecraft houses four crew members for up to 21 days in deep space. And we'll have an unru test in a few months launch on the S SLS. And Orion's gonna go around the moon for up to 40 days on Aus two, we will have the first test of the humans on board Orion. So four people will fly on Aus two. We will also circle the moon for about 10 to 12 days. And then our third mission will be our landing. >>So the moon is back in play, obviously it's close to the earth. So it's a short flight, relatively speaking the Mars a little bit further out. I'll see everyone as know what's going on in Mars. A lot of people are interested in Mars. Moon's closer. Yes, but there's also new things going on around discovery. Can you share the big story around why the moon what's? Why is the moon so important and why is everyone so excited about it? >>Yeah. You, you know, you know, coming to this conference and talking about sustainability, you know, I mean it is exploration is I think ingrained in our DNA, but it's more than just exploration is about, you know, projecting human presence beyond our earth. And these are the stepping stones. You know, we talk about Amazon talked about day one, and I think about, we are on those very early days where we're building the infrastructure Ryans of transportation infrastructure, and we're gonna build infrastructure on the moon to learn how to live on a surface and how to utilize the assets. And then that's very important because you know, it's very expensive to carry fuel, to carry water and all the necessities that you need to survive as a human being and outer space. If you can generate that on the surface or on the planet you go to, and this is a perfect way to do it because it's very in your backyard, as I told you earlier. So for future mission, when you want to go to Mars, you're nine months out, you really wanna make sure you have the technologies and you're able to utilize those technologies robustly and in a sustainable way. >>Yeah, we were talking before you came on, came camera camping in your backyard is a good practice round. Before you go out into the, to the wilderness, this is kind of what's going on here, but there's also the discovery angle. I mean, I just see so much science going on there. So if you can get to the moon, get a base camp there, get set up, then things could come out of that. What are some of the things that you guys are talking about that you see as possible exploration upside? >>Yeah. Well, several things. One is power generation recently. We just released some contracts that from vision power, so long, sustainable power capability is very, very important. You know, the other technologies that you need utilize is regenerative, you know, air, water, things that are, you need for that, but then there's a science aspect of it, which is, you know, we're going to the south pole where we think there's a lot of water potentially, or, or available water that we can extract and utilize that to generate fuel. So liquid hydrogen liquid oxygen is one of the areas that are very interesting. And of course, lunar minerals are very exciting, very interesting to bring and, and, and be able to mine potentially in the future, depending on what is there. >>Well, a lot of cool stuff happening. What's your take on this show here, obviously NASA's reputation as innovators and deep technologists, you know, big moonshot missions, pun intended here. You got a lot of other explorations. What's this show bring together, share your perspective because I think the story here to me is you got walkout retail, like the Amazon technology, you got Watson dynamics, the dog, everyone loves that's walking on. Then you got supply chain, robotics, machine learning, and space. It all points to one thing, innovation around industrial. I think what, what, what's your, what's your, what's your take? >>You know, I think one of the things is, is, you know, normally we are innovating in a, in our aerospace industry. You know, I think there's so much to learn from innovation across all these areas you described and trying to pull some of that into the spacecraft. You know, when, when you're a human being sitting in spacecraft is more than just flying the spacecraft. You know, you have interaction with displays, you have a lot of technologies that you normally would want to interact with on the ground that you could apply in space to help you and make your tasks easier. And I think those are things that are really important as we look across, you know, the whole entire innovative infrastructure that I see here in this show, how can we extract some that and apply it in the space program? I think there is a very significant leveraging that you could do off of that. >>What are some of the look at what's going on in donors? What are some of the cool people who aren't following the day to day? Anything? >>Well, well, certainly, you know, the Artman's mission Artis campaign is one of the, the, the coolest things I could think of. That's why I came into, you know, I think wrapping around that where we are not only just going to a destination, but we're exploring, and we're trying to establish a very clear, long term presence that will allow us to engage. What I think is the next step, which is science, you know, and science and the, and the things that can, can come out of that in terms of scientific discoveries. And I think the cool, coolest thing would be, Hey, could we take the things that we are in the labs and the innovation relative to power generation, relative to energy development of energy technologies, robotics, to utilize, to help explore the surface. And of course the science that comes out of just naturally, when you go somewhere, you don't know what to expect. And I think that's what the exciting thing. And for NASA, we're putting a program, an infrastructure around that. I think that's really exciting. Of course, the other parts of NASA is science. Yeah. And so the partnering those two pieces together to accomplish a very important mission for everybody on planet earth is, is really important. >>And also it's a curiosity. People are being curious about what's going on now in space, cuz the costs are down and you got universities here and you got the, of robotics and industrial. This is gonna provide a, a new ground for education, younger, younger generation coming up. What would you share to teachers and potential students, people who wanna learn what's different about now than the old generation and what's the same, what what's the same and what's new. What's how does someone get their arms around this, their mind around it? Where can they jump in? This is gonna open up the aperture for, for, for talent. I mean with all the technology, it's not one dimensional. >>Yeah. I think what is still true is core sciences, math, you know, engineering, the hard science, chemistry, biology. I mean, I think those are really also very important, but what we're we're getting today is the amount of collaboration we're able to do against organically. And I think the innovation that's driven by a lot of this collaboration where you have these tools and your ability to engage and then you're able to, to get, I would say the best out of people in lots of different areas. And that's what I think one of the things we're learning at NASA is, you know, we have a broad spectrum of people that come to work for us and we're pulling that. And now we're coming to these kinds of things where we're kind getting even more innovation ideas and partnerships so that we are not just off on our own thinking about the problem we're branching out and allowing a lot of other people to help us solve the problems that >>We need. You know, I've noticed with space force too. I had the same kind of conversations around those with those guys as well. Collaboration and public private partnerships are huge. You've seen a lot more kind of cross pollination of funding, col technology software. I mean, how do you do break, fix and space at software, right? So you gotta have, I mean, it's gotta work. So you got security challenges. Yeah. This is a new frontier. It is the cybersecurity, the usability, the operationalizing for humans, not just, you know, put atypical, you know, scientists and, and, and astronauts who are, you know, in peak shape, we're talking about humans. Yeah. What's the big problem to solve? Is it security? Is it, what, what would you say the big challenges >>Are? Yeah. You know, I think information and access to information and how we interact with information is probably our biggest challenge because we have very limited space in terms of not only mass, but just volume. Yeah. You know, you want to reserve the space for the people and they, they need to, you know, you want maximize your space that you're having in spacecraft. And so I think having access to information, being able to, to utilize information and quickly access systems so you can solve problems cuz you don't know when you're in deep space, you're several months out to Mars, what problems you might encounter and what kind of systems and access to information you need to help you solve the problems. You know, both, both, both from a just unplanned kind of contingencies or even planned contingencies where you wanna make sure you have that information to do it. So information is gonna be very vital as we go out into deep >>Space and the infrastructure's changed. How has the infrastructure changed in terms of support services? I mean see, in the United States, just the growth of a aerospace you mentioned earlier is, is just phenomenal. You've got smaller, faster, cheaper equipment density, it solved the technology. Where's there gonna be the, the big game changing move movement. Where do you see it go? Is it AIST three? It kind of kicks in AIST ones, obviously the first one unmanned one. But where do in your mind, do you see key milestones that are gonna be super important to >>Watch? I think, I think, I think, you know, we've already, you know, pushed the boundaries of what we, we are, you know, in terms of applying our aerospace technologies for AIST one and certainly two, we've got those in, in work already. And so we've got that those vehicles already in work and built yeah. One already at the, at the Kennedy space center ready for launch, but starting with three because you have a lot more interaction, you gotta take the crew down with a Lander, a human landing system. You gotta build rovers. You've gotta build a, a capability which they could explore. So starting with three and then four we're building the gateway gateways orbiting platform around the moon. So for all future missions after Rist three, we're gonna take Aion to the gateway. The crew gets into the orbiting platform. They get on a human landing system and they go down. >>So all that interaction, all that infrastructure and all the support equipment you need, not only in the orbit of the moon, but also down the ground is gonna drive a lot of innovation. You're gonna have to realize, oh, Hey, I needed this. Now I need to figure out how to get something there. You know? And, and how much of the robotics and how much AI you need will be very interesting because you'll need these assistance to help you do your daily routine or lessen your daily routine. So you can focus on the science and you can focus on doing the advancing those technologies that you're gonna >>Need. And you gotta have the infrastructure. It's like a road. Yeah. You know, you wanna go pop down to the moon, you just pop down, it's already built. It's ready for you. Yep. Come back up. So just ease of use from a deployment standpoint is, >>And, and the infrastructure, the things that you're gonna need, you know, what is a have gonna look like? What are you gonna need in a habitat? You know, are, are you gonna be able to have the power that you're gonna have? How many station power stations are you gonna need? Right. So all these things are gonna be really, things are gonna be driven by what you need to do the mission. And that drives, I think a lot of innovation, you know, it's very much like the end goal. What are you trying to solve? And then you go, okay, here's what I need to solve to build things, to solve that >>Problem. There's so many things involved in the mission. I can imagine. Safety's huge. Number one, gotta be up safe. Yep. Space is dangerous game. Yes. Yeah. It's not pleasant there. Not for the faint of heart. As you say, >>It's not for the faint >>Heart. That's correct. What's the big safety concerns obviously besides blowing up and oxygen and water and the basic needs. >>I think, I think, you know, I think you, you said it very well, you know, it is not for the faint of heart. We try to minimize risk. You know, asset is one of the big, you're sitting under 8.8 million pounds of thrust on the launch vehicle. So it is going very fast and you're flying and you, and, and it's it's light cuz we got solid rocket motors too as well. Once they're lit. They're lit. Yeah. So we have a escape system on Orion that allows a crew to be safe. And of course we build in redundancy. That's the other thing I think that will drive innovation. You know, you build redundancy in the system, but you also think about the kind of issues that you would run into potentially from a safety perspective, you know, how you gonna get outta situation if you get hit by a meteor, right? Right. You, you, you are going through the band, Ellen belt, you have radiation. So you know, some of these things that are harsh on your vehicle and on, on the human side of this shop too. And so when you have to do these things, you have to think about what are you gonna protect for and how do you go protect for that? And we have to find innovations for >>That. Yeah. And it's also gonna be a really exciting air for engineering work. And you mentioned the data, data's huge simulations, running scenarios. This is where the AI comes in. And that seems to me where the dots connect from me when you start thinking about how to have, how to run those simulations, to identify what's possible. >>I think that's a great point, you know, because we have all this computing capability and because we can run simulations and because we can collect data, we have terabytes of data, but it's very challenging for humans to analyze at that level. So AI is one of the things we're looking at, which is trying to systematically have a process by which data is called through so that the engineering mind is only looking at the things and focus on things that are problematic. So we repeat tests, every flight, you don't have to look at all the terabytes of data of each test. You have a computer AI do that. And you allow yourself to look at just the pieces that don't look right, have anomalies in the data. Then you're going to do that digging, right. That's where the power of those kinds of technologies can really help us because we have that capability to do a lot of computing. >>And I think that's why this show to me is important because it, it, it shows for the first time, at least from my coverage of the industry where technology's not the bottleneck anymore, it's human mind. And we wanna live in a peaceful world with climate. We wanna have the earth around for a while. So climate change was a huge topic yesterday and how the force for good, what could come outta the moon shots is to, is to help for earth. >>Yeah. >>Yeah. Better understanding there all good. What's your take on the show. If you had to summarize this show, re Mars from the NASA perspective. So you, the essence space, what's the what's going on here? What's the big, big story. >>Yeah. For, for me, I think it's eyeopening in terms of how much innovation is happening across a spectrum of areas. And I look at various things like bossy, scientific robots that the dog that's walking around. I mean to think, you know, people are applying it in different ways and then those applications in a lot of ways are very similar to what we need for exploration going forward. And how do you apply some of these technologies to the space program and how do we leverage that? How do we leverage that innovation and how we take the innovations already happening organically for other reasons and how would those help us solve those problems that we're gonna encounter going forward as we try to live on another planet? >>Well, congratulations on a great assignment. You got a great job. I do super fun. I love being an observer and I love space. Love how at the innovations there. And plus space space is cool. I mean, how many millions of live views do you see? Everyone's stopping work to watch SpaceX land and NASA do their work. It's just, it's bringing back the tech vibe. You know what I'm saying? It's just, it's just, things are going you a good tailwind. Yeah. >>Congratulations. Thank you very much. >>Appreciate it on the, okay. This cube coverage. I'm John fur. You're here for the cube here. Live in Las Vegas back at reinvent reinforce re Mars, the reser coverage here at re Mars. We'll be back with more coverage after this short break.

>>Hello. I'm Professor Toru Suzuki Cherif cardiovascular medicine on associate dean of the College of Life Sciences at the University of Leicester in the United Kingdom, where I'm also director of the Lester Life Sciences accelerator. I'm also honorary consultant cardiologist within our university hospitals. It's part of the national health system NHS Trust. Today, I'd like to talk to you about our Lester Clinical Data Science Initiative. Now brief background on Lester. It's university in hospitals. Lester is in the center of England. The national health system is divided depending on the countries. The United Kingdom, which is comprised of, uh, England, Scotland to the north, whales to the west and Northern Ireland is another part in a different island. But national health system of England is what will be predominantly be discussed. Today has a history of about 70 years now, owing to the fact that we're basically in the center of England. Although this is only about one hour north of London, we have a catchment of about 100 miles, which takes us from the eastern coast of England, bordering with Birmingham to the west north just south of Liverpool, Manchester and just south to the tip of London. We have one of the busiest national health system trust in the United Kingdom, with a catchment about 100 miles and one million patients a year. Our main hospital, the General Hospital, which is actually called the Royal Infirmary, which can has an accident and emergency, which means Emergency Department is that has one of the busiest emergency departments in the nation. I work at Glen Field Hospital, which is one of the main cardiovascular hospitals of the United Kingdom and Europe. Academically, the Medical School of the University of Leicester is ranked 20th in the world on Lee, behind Cambridge, Oxford Imperial College and University College London. For the UK, this is very research. Waited, uh, ranking is Therefore we are very research focused universities as well for the cardiovascular research groups, with it mainly within Glenn Field Hospital, we are ranked as the 29th Independent research institution in the world which places us. A Suffield waited within our group. As you can see those their top ranked this is regardless of cardiology, include institutes like the Broad Institute and Whitehead Institute. Mitt Welcome Trust Sanger, Howard Hughes Medical Institute, Kemble, Cold Spring Harbor and as a hospital we rank within ah in this field in a relatively competitive manner as well. Therefore, we're very research focused. Hospital is well now to give you the unique selling points of Leicester. We're we're the largest and busiest national health system trust in the United Kingdom, but we also have a very large and stable as well as ethnically diverse population. The population ranges often into three generations, which allows us to do a lot of cohort based studies which allows us for the primary and secondary care cohorts, lot of which are well characterized and focused on genomics. In the past. We also have a biomedical research center focusing on chronic diseases, which is funded by the National Institutes of Health Research, which funds clinical research the hospitals of United Kingdom on we also have a very rich regional life science cluster, including med techs and small and medium sized enterprises. Now for this, the bottom line is that I am the director of the letter site left Sciences accelerator, >>which is tasked with industrial engagement in the local national sectors but not excluding the international sectors as well. Broadly, we have academics and clinicians with interest in health care, which includes science and engineering as well as non clinical researchers. And prior to the cove it outbreak, the government announced the £450 million investment into our university hospitals, which I hope will be going forward now to give you a brief background on where the scientific strategy the United Kingdom lies. Three industrial strategy was brought out a za part of the process which involved exiting the European Union, and part of that was the life science sector deal. And among this, as you will see, there were four grand challenges that were put in place a I and data economy, future of mobility, clean growth and aging society and as a medical research institute. A lot of the focus that we have been transitioning with within my group are projects are focused on using data and analytics using artificial intelligence, but also understanding how chronic diseases evolved as part of the aging society, and therefore we will be able to address these grand challenges for the country. Additionally, the national health system also has its long term plans, which we align to. One of those is digitally enabled care and that this hope you're going mainstream over the next 10 years. And to do this, what is envision will be The clinicians will be able to access and interact with patient records and care plants wherever they are with ready access to decision support and artificial intelligence, and that this will enable predictive techniques, which include linking with clinical genomic as well as other data supports, such as image ing a new medical breakthroughs. There has been what's called the Topol Review that discusses the future of health care in the United Kingdom and preparing the health care workforce for the delivery of the digital future, which clearly discusses in the end that we would be using automated image interpretation. Is using artificial intelligence predictive analytics using artificial intelligence as mentioned in the long term plans. That is part of that. We will also be engaging natural language processing speech recognition. I'm reading the genome amusing. Genomic announced this as well. We are in what is called the Midland's. As I mentioned previously, the Midland's comprised the East Midlands, where we are as Lester, other places such as Nottingham. We're here. The West Midland involves Birmingham, and here is ah collective. We are the Midlands. Here we comprise what is called the Midlands engine on the Midland's engine focuses on transport, accelerating innovation, trading with the world as well as the ultra connected region. And therefore our work will also involve connectivity moving forward. And it's part of that. It's part of our health care plans. We hope to also enable total digital connectivity moving forward and that will allow us to embrace digital data as well as collectivity. These three key words will ah Linkous our health care systems for the future. Now, to give you a vision for the future of medicine vision that there will be a very complex data set that we will need to work on, which will involve genomics Phanom ICS image ing which will called, uh oh mix analysis. But this is just meaning that is, uh complex data sets that we need to work on. This will integrate with our clinical data Platforms are bioinformatics, and we'll also get real time information of physiology through interfaces and wearables. Important for this is that we have computing, uh, processes that will now allow this kind of complex data analysis in real time using artificial intelligence and machine learning based applications to allow visualization Analytics, which could be out, put it through various user interfaces to the clinician and others. One of the characteristics of the United Kingdom is that the NHS is that we embrace data and captured data from when most citizens have been born from the cradle toe when they die to the grave. And it's important that we were able to link this data up to understand the journey of that patient. Over time. When they come to hospital, which is secondary care data, we will get disease data when they go to their primary care general practitioner, we will be able to get early check up data is Paula's follow monitoring monitoring, but also social care data. If this could be linked, allow us to understand how aging and deterioration as well as frailty, uh, encompasses thes patients. And to do this, we have many, many numerous data sets available, including clinical letters, blood tests, more advanced tests, which is genetics and imaging, which we can possibly, um, integrate into a patient journey which will allow us to understand the digital journey of that patient. I have called this the digital twin patient cohort to do a digital simulation of patient health journeys using data integration and analytics. This is a technique that has often been used in industrial manufacturing to understand the maintenance and service points for hardware and instruments. But we would be using this to stratify predict diseases. This'll would also be monitored and refined, using wearables and other types of complex data analysis to allow for, in the end, preemptive intervention to allow paradigm shifting. How we undertake medicine at this time, which is more reactive rather than proactive as infrastructure we are presently working on putting together what's it called the Data Safe haven or trusted research environment? One which with in the clinical environment, the university hospitals and curated and data manner, which allows us to enable data mining off the databases or, I should say, the trusted research environment within the clinical environment. Hopefully, we will then be able to anonymous that to allow ah used by academics and possibly also, uh, partnering industry to do further data mining and tool development, which we could then further field test again using our real world data base of patients that will be continually, uh, updating in our system. In the cardiovascular group, we have what's called the bricks cohort, which means biomedical research. Informatics Center for Cardiovascular Science, which was done, started long time even before I joined, uh, in 2010 which has today almost captured about 10,000 patients arm or who come through to Glenn Field Hospital for various treatments or and even those who have not on. We asked for their consent to their blood for genetics, but also for blood tests, uh, genomics testing, but also image ing as well as other consent. Hable medical information s so far there about 10,000 patients and we've been trying to extract and curate their data accordingly. Again, a za reminder of what the strengths of Leicester are. We have one of the largest and busiest trust with the very large, uh, patient cohort Ah, focused dr at the university, which allows for chronic diseases such as heart disease. I just mentioned our efforts on heart disease, uh which are about 10,000 patients ongoing right now. But we would wish thio include further chronic diseases such as diabetes, respiratory diseases, renal disease and further to understand the multi modality between these diseases so that we can understand how they >>interact as well. Finally, I like to talk about the lesser life science accelerator as well. This is a new project that was funded by >>the U started this January for three years. I'm the director for this and all the groups within the College of Life Sciences that are involved with healthcare but also clinical work are involved. And through this we hope to support innovative industrial partnerships and collaborations in the region, a swells nationally and further on into internationally as well. I realized that today is a talked to um, or business and commercial oriented audience. And we would welcome interest from your companies and partners to come to Leicester toe work with us on, uh, clinical health care data and to drive our agenda forward for this so that we can enable innovative research but also product development in partnership with you moving forward. Thank you for your time.

(upbeat music) >> Announcer: Live from Las Vegas, it's theCUBE! Covering UIPath FORWARD America's 2019. Brought to you by UIPath. >> Welcome back, everyone, to theCUBE's live coverage of UIPath FORWARD, here in Sin City, Las Vegas Nevada. I'm your host, Rebecca Knight, co-hosting alongside Dave Velante. We have two guests for this segment. We have Kurt Carlson, Associate Dean for faculty and academic affairs of the Mason School of Business at the college of William and Mary. Thanks for coming on the show. >> Thanks you for having me. >> Rebecca: And we have Tom Clancy, the SVP of learning at UIPath, thank you so much. >> Great to be here. >> You're a Cube alum, so thank you for coming back. >> I've been here a few times. >> A Cube veteran, I should say. >> I think 10 years or so >> So we're talking today about a robot for every student, this was just announced in August, William and Mary is the first university in the US to provide automation software to every undergraduate student, thanks to a four million dollar investment from UIPath. Tell us a little bit about this program, Kurt, how it works and what you're trying to do here. >> Yeah, so first of all, to Tom and the people at UIPath for making this happen. This is a bold and incredible initiative, one that, frankly, when we had it initially, we thought that maybe we could get a robot for every student, we weren't sure that other people would be willing to go along with that, but UIPath was, they see the vision, and so it was really a meeting of the minds on a common purpose. The idea was pretty simple, this technology is transforming the world in a way that students, we think it's going to transform the way that students actually are students. But it's certainly transforming the world that our students are going into. And so, we want to give them exposure to it. We wanted to try and be the first business school on the planet that actually prepares students not just for the way RPA's being used today, but the way that it's going to be used when AI starts to take hold, when it becomes the gateway to AI three, four, five years down the road. So, we talked to UIPath, they thought it was a really good idea, we went all in on it. Yeah, all of our starting juniors in the business school have robots right now, they've all been trained through the academy live session putting together a course, it's very exciting. >> So, Tom, you've always been an innovator when it comes to learning, here's my question. How come we didn't learn this school stuff when we were in college? We learned Fortran. >> I don't know, I only learned BASIC, so I can't speak to that. >> So you know last year we talked about how you're scaling, learning some of the open, sort of philosophy that you have. So, give us the update on how you're pushing learning FORWARD, and why the College of William and Mary. >> Okay, so if you buy into a bot for every worker, or a bot for every desktop, that's a lot of bots, that's a lot of desktops, right? There's studies out there from the research companies that say that there's somewhere a hundred and 200 million people that need to be educated on RPA, RPA/AI. So if you buy into that, which we do, then traditional learning isn't going to do it. We're going to miss the boat. So we have a multi-pronged approach. The first thing is to democratize RPA learning. Two and a half years ago we made, we created RPA Academy, UIPath academy, and 100% free. After two and a half years, we have 451,000 people go through the academy courses, that's huge. But we think there's a lot more. Over the next next three years we think we'll train at least two million people. But the challenge still is, if we train five million people, there's still a hundred million that need to know about it. So, the second biggest thing we're doing is, we went out, last year at this event, we announced our academic alliance program. We had one university, now we're approaching 400 universities. But what we're doing with William and Mary is a lot more than just providing a course, and I'll let Kurt talk to that, but there is so much more that we could be doing to educate our students, our youth, upscaling, rescaling the existing workforce. When you break down that hundred million people, they come from a lot of different backgrounds, and we're trying to touch as many people as we can. >> You guys are really out ahead of the curve. Oftentimes, I mean, you saw this a little bit with data science, saw some colleges leaning in. So what lead you guys to the decision to actually invest and prioritize RPA? >> Yeah, I think what we're trying to accomplish requires incredibly smart students. It requires students that can sit at the interface between what we would think of today as sort of an RPA developer and a decision maker who would be stroking the check or signing the contract. There's got to be somebody that sits in that space that understands enough about how you would actually execute this implementation. What's the right buildout of that, how we're going to build a portfolio of bots, how we're going to prioritize the different processes that we might automate, How we're going to balance some processes that might have a nice ROI but be harder for the individual who's process is being automated to absorb against processes that the individual would love to have automated, but might not have as great of an ROI. How do you balance that whole set of things? So what we've done is worked with UIPath to bring together the ideas of automation with the ideas of being a strategic thinker in process automation, and we're designing a course in collaboration to help train our students to hit the ground running. >> Rebecca, it's really visionary, isn't it? I mean it's not just about using the tooling, it's about how to apply the tooling to create competitive advantage or change lives. >> I used to cover business education for the Financial Times, so I completely agree that this really is a game changer for the students to have this kind of access to technology and ability to explore this leading edge of software robotics and really be, and graduate from college. This isn't even graduate school, they're graduating from college already having these skills. So tell me, Kurt, what are they doing? What is the course, what does it look like, how are they using this in the classroom? >> The course is called a one credit. It's 14 hours but it actually turns into about 42 when you add this stuff that's going on outside of class. They're learning about these large conceptual issues around how do you prioritize which processes, what's the process you should go through to make sure that you measure in advance of implementation so that you can do an audit on the backend to have proof points on the effectiveness, so you got to measure in advance, creating a portfolio of perspective processes and then scoring them, how do you do that, so they're learning all that sort of conceptual straight business slash strategy implementation stuff, so that's on the first half, and to keep them engaged with this software, we're giving them small skills, we're calling them skillets. Small skills in every one of those sessions that add up to having a fully automated and programmed robot. Then they're going to go into a series of days where every one of those days they're going to learn a big skill. And the big skills are ones that are going to be useful for the students in their lives as people, useful in lives as students, and useful in their lives as entrepreneurs using RPA to create new ventures, or in the organizations they go to. We've worked with UIPath and with our alums who've implement this, folks at EY, Booz. In fact, we went up to DC, we had a three hour meeting with these folks. So what are the skills students need to learn, and they told us, and so we build these three big classes, each around each one of those skills so that our students are going to come out with the ability to be business translators, not necessarily the hardcore programmers. We're not going to prevent them from doing that, but to be these business translators that sit between the programming and the decision makers. >> That's huge because, you know, like, my son's a senior in college. He and his friends, they all either want to work for Amazon, Google, an investment bank, or one of the big SIs, right? So this is a perfect role for a consultant to go in and advise. Tom, I wanted to ask you, and you and I have known each other for a long time, but one of the reasons I think you were successful at your previous company is because you weren't just focused on a narrow vendor, how to make metrics work, for instance. I presume you're taking the same philosophy here. It transcends UIPath and is really more about, you know, the category if you will, the potential. Can you talk about that? >> So we listen to our customers and now we listen to the universities too, and they're going to help guide us to where we need to go. Most companies in tech, you work with marketing, and you work with engineering, and you build product courses. And you also try to sell those courses, because it's a really good PNL when you sell training. We don't think that's right for the industry, for UIPath, or for our customers, or our partners. So when we democratize learning, everything else falls into place. So, as we go forward, we have a bunch of ideas. You know, as we get more into AI, you'll see more AI type courses. We'll team with 400 universities now, by end of next year, we'll probably have a thousand universities signed up. And so, there's a lot of subject matter expertise, and if they come to us with ideas, you mentioned a 14 hour course, we have a four hour course, and we also have a 60 hour course. So we want to be as flexible as possible, because different universities want to apply it in different ways. So we also heard about Lean Six Sigma. I mean, sorry, Lean RPA, so we might build a course on Lean RPA, because that's really important. Solution architect is one of the biggest gaps in the industry right now so, so we look to where these gaps are, we listen to everybody, and then we just execute. >> Well, it's interesting you said Six Sigma, we have Jean Younger coming on, she's a Six Sigma expert. I don't know if she's a black belt, but she's pretty sure. She talks about how to apply RPA to make business processes in Six Sigma, but you would never spend the time and money, I mean, if it's an airplane engine, for sure, but now, so that's kind of transformative. Kurt, I'm curious as to how you, as a college, market this. You know, you're very competitive industry, if you will. So how do you see this attracting students and separating you guys from the pack? >> Well, it's a two separate things. How do we actively try to take advantage of this, and what effects is it having already? Enrollments to the business school, well. Students at William and Mary get admitted to William and Mary, and they're fantastic, amazingly good undergraduate students. The best students at William and Mary come to the Raymond A. Mason school of business. If you take our undergraduate GPA of students in the business school, they're top five in the country. So what we've seen since we've announced this is that our applications to the business school are up. I don't know that it's a one to one correlation. >> Tom: I think it is. >> I believe it's a strong predictor, right? And part because it's such an easy sell. And so, when we talk to those alums and friends in DC and said, tell us why this is, why our students should do this, they said, well, if for no other reason, we are hiring students that have these skills into data science lines in the mid 90s. When I said that to my students, they fell out of their chairs. So there's incredible opportunity here for them, that's the easy way to market it internally, it aligns with things that are happening at William and Mary, trying to be innovative, nimble, and entrepreneurial. We've been talking about being innovative, nimble, and entrepreneurial for longer than we've been doing it, we believe we're getting there, we believe this is the type of activity that would fit for that. As far as promoting it, we're telling everybody that will listen that this is interesting, and people are listening. You know, the standard sort of marketing strategy that goes around, and we are coordinating with UIPath on that. But internally, this sells actually pretty easy. This is something people are looking for, we're going to make it ready for the world the way that it's going to be now and in the future. >> Well, I imagine the big consultants are hovering as well. You know, you mentioned DC, Booz Allen, Hughes and DC, and Excensior, EY, Deloitte, PWC, IBM itself. I mean it's just, they all want the best and the brightest, and now you're going to have this skill set that is a sweet spot for their businesses. >> Kurt: That's the plan. >> I'm just thinking back to remembering who these people are, these are 19 and 20 year olds. They've never experienced the dreariness of work and the drudge tasks that we all know well. So, what are you, in terms of this whole business translator idea, that they're going to be the be people that sit in the middle and can sort of be these people who can speak both languages. What kind of skills are you trying to impart to them, because it is a whole different skill set. >> Our vision is that in two or three years, the nodes and the processes that are currently... That currently make implementing RPA complex and require significant programmer skills, these places where, right now, there's a human making a relatively mundane decision, but it's sill a model. There's a decision node there. We think AI is going to take over that. The simple, AI's going to simply put models into those decision nodes. We also think a lot of the programming that takes place, you're seeing it now with studio X, a lot of the programming is going to go away. And what that's going to do is it's going to elevate the business process from the mundane to the more human intelligent, what would currently be considered human intelligence process. When we get into that space, people skills are going to be really important, prioritizing is going to be really important, identifying organizations that are ripe for this, at this moment in time, which processes to automate. Those are the kind of skills we're trying to get students to develop, and what we're selling it partly as, this is going to make you ready of the world the way we think it's going to be, a bit of a guess. But we're also saying if you don't want to automate mundane processes, then come with us on a different magic carpet ride. And that magic carpet ride is, imagine all the processes that don't exist right now because nobody would ever conceive of them because they couldn't possibly be sustained, or they would be too mundane. Now think about those processes through a business lens, so take a business student and think about all the potential when you look at it that way. So this course that we're building has that, everything in the course is wrapped in that, and so, at the end of the course, they're going to be doing a project, and the project is to bring a new process to the world that doesn't currently exist. Don't program it, don't worry about whether or not you have a team that could actually execute it. Just conceive of a process that doesn't currently exist and let's imagine, with the potential of RPA, how we would make that happen. That's going to be, we think we're going to be able to bring a lot of students along through that innovative lens even though they are 19 and 20, because 19 and 20 year olds love innovation, while they've never submitted a procurement report. >> Exactly! >> A innovation presentation. >> We'll need to do a Cube follow up with that. >> What Kurt just said, is the reason why, Tom, I think this market is being way undercounted. I think it's hard for the IDCs and the forces, because they look back they say how big was it last year, how fast are these companies growing, but, to your point, there's so much unknown processes that could be attacked. The TAM on this could be enormous. >> We agree. >> Yeah, I know you do, but I think that it's a point worth mentioning because it touches so many different parts of every organization that I think people perhaps don't realize the impact that it could have. >> You know, when listening to you, Kurt, when you look at these young kids, at least compared to me, all the coding and setting up a robot, that's the easy part, they'll pick that up right away. It's really the thought process that goes into identifying new opportunities, and that's, I think, you're challenging them to do that. But learning how to do robots, I think, is going to be pretty easy for this new digital generation. >> Piece of cake. Tom and Kurt, thank you so much for coming on theCUBE with a really fascinating conversation. >> Thank you. >> Thanks, you guys >> I'm Rebecca Knight, for Dave Velante, stay tuned for more of theCUBEs live coverage of UIPath FORWARD. (upbeat music)

>> From Washington DC, it's theCUBE. Covering ScienceLogic Symposium 2019. Brought to you by ScienceLogic. >> I'm Stu Miniman and this is theCUBE's coverage of ScienceLogic Symposium 2019 here at The Ritz-Carlton in Washington DC. Really excited to welcome back to the program. It's the co-founder CEO and the Headmaster of Wizarding school, >> Wizarding school, yes. >> Dave Link, thank you so much for joining us. Great to be here Steve. >> All right, so Dave first of all congratulations, really been enjoying the event you know you you kicked it off in the keynote this morning great energy, really I think capturing you know where we are in you know IT in business today. We understand how things are changing so much and it's a complex world and ScienceLogic is trying to do Its part to help simplify and make it easier for IT to you know run at the speed of business and machines. >> That's exactly right. What's happening in the world right now is you've got a confluence of cloud apps, traditional legacy apps and they're colliding together and as they collide together you need new tools to manage that in a way that's different than what we've seen in the past. You're looking at lots of sources coming together to contextualize, not just seeing what's happening, understanding how systems relate to one another but acting upon them. Machine at machine speed means that automation is king and the wizard hat actually relates to a storyline we had earlier today when we think about how to educate the marketplace and the customers we realized that we needed a very new way of communicating. So videos E-Learning The Wizard of learning has been a theme of the show to help our customers to get up to speed and actually take full advantage of the application that we provide to help them deliver great service quality. >> Yeah well and we appreciate you bringing theCUBE to help with that video education of the community overall. >> That's right >> Yeah so you know look Dave you know wanted... let's step back for a second and you know we want to going to get to the business update but first you know the company is founded in 2003. You know cloud wasn't a term, some of the underlying foundations of what became cloud, you know existed back there. Those of us in the industry understand some of the waves that have happened there but you know to talk about cloud and micro services and all of these changes that have... So give us a little bit about that evolution about the original premise of the company, as we move now to you know the world of today and how you manage to keep the company moving and relevant >> I love telling this story Stu because it never gets old >> Yeah >> A lot of the original feces that we had about where service business service analysis was going, the application analysis connected to the infrastructure. Our belief was we were going to move to a world where it wasn't based on devices or nodes or systems. It was really based on this service and what we're seeing with cloud has accentuated that tenfold because services now are made up of compound things, technologies, service delivery mechanisms as a service platforms and they all have to work with one another The platform we built had an architecture that was very open that could take data streams from lots of different sources, create a common information model contextualize that and then act upon it. So now more than ever before, we really built the right platform with multi-tenancy, with role based access control with all the things that were really hard problems to solve code day one and now the thesis that we had that it was more about the service view is as important as it as it's ever been with ephemeral systems that are coming and going, with really containerized systems on top of virtual machines on top of their metal. All these abstraction layers require a different mindset but an open architecture is really at the heart of pulling lots of data streams together contextualizing it and then acting upon it >> Yeah, so I'm a sucker for Venn diagrams. so you heard that the analyst in the keynote this morning talked about AI ops and he said to the inner structure intersection of IT operations data science and machine learning >> Yes >> Data at the center of everything, it's something we've had a couple of waves of trying on intelligence and automation, are things we've been talking about for decades in IT. Give us a little bit as why some of those waves are coming together so that now and what you're doing is the right moment to really help accelerate. You've been having great growth for a number of years and project out some really strong growth for the next few. >> We have over the last five years the company has grown over five hundred and forty percent from a revenue perspective and I think that's the underpinnings of that relates to do we have the right market fit. Are we solving a problem that's material to customers that it's hard for them to solve without our product. But I really envision a future we've been working on this for a couple decades, right? The future is one I hope where from a artificial intelligence at machine speed where we're getting so predictive and understanding, through really smart scalable algorithms the future faults that may occur for you know we've both been at this for a long time. we've been talking about event correlation for many years. I envision a world where you're not doing event correlation when you've had an event, it's actually too late. Usually that's caused by a system telling you that there is a problem. So what we're really working on what we've talked a lot about here at the show is not just predictive analytics but really understanding what's abnormal and getting in front of a problem before there is a problem with the system with really super smart algorithms that help customers understand, many different data sets converge together and what they really mean so that you can get ahead of a service outage rather than have the fault that you're then working on correlating to infrastructure to application layers. >> You know the other thing that's been interesting for me to watch is, the core of where you started was really working with the service Fighters. I've had a chance to talk to a number of your service fighters >> Yes and Hughes has been with you since the early days up to you know one that just bought a couple of weeks ago and you know they're happy very. Talked about kind of the compare/contrast of the service riders and the enterprise because you know cloud is impacting you know the big hybrid hyper scale clouds are impacting both of those and the rate of change is affecting both of those in a lot of ways. So I'm curious as you see you know what what what's similar and what's different between going into those markets. >> When we thought about the problem for service providers there were two axes that we were looking at. Number one was from one instance of our platform you had to serve many customers that all had their own tenancy. But on top of that, you had to layer in a role based access control who could see what the customer had their view, the internal ops teams had their view. So building out a really complicated foundational model and an architecture that would support tenancy on steroids with one instance of our product was a really important linchpin of what's now, incredibly important to enterprises, because enterprises are getting into a moment where they're having to really act as service bureau's, service brokers and that means that all the different teams that support different technology silos, really have to work together as one and... but yet they still need their own views.So a lot of the foundational highly differentiated capabilities we built for service providers, for large scale globally distributed enterprises, actually meets a need profile that is very hard to find solutions that fit that profile and can give them that consolidated view but yet the deep dive view for the practitioner and we're finding that more and more enterprises, have follow-the-sun operations, follow-the-sun architecture teams, follow-the-sun engineering teams that need different views that is really hard to get most products that were built in this space were built for a single tenant enterprise view and that never gives you the granularity for each consumer and each persona to get the view that they need. So it's interesting that although we kind of over engineered those capabilities for the service provider needs it's becoming involved with the enterprises as they're looking at how do they need to do things as really a converged team, working as one team across many silo disciplines and that requires a very different way of thinking, a different tool space a different solution to the problem that we built kind of from the ground up. It's now really appropriate for the DevOps teams the teams that are really having to break down the silos and work as one team. >> Yeah the, the the the term that often gets misused and misunderstood is scale. But if you truly can build something that's distributed architecture for scale, It really opens up a lot of opportunities. One of the things you highlight it also is that, ScienceLogic puts a lot of investment into you know R&D and keep working on things big announcement of Big Ben, seemed I've had a chance to hear what everybody likes and the best. Talk a little bit about you know how you keep the development efforts going how you put that strong and effort on it and you know boy you know you said you worked on the UI for three years and now it sounds you know it's a bold statement to be like okay and everybody you're using this, you know you can't have the safety blanket of old away in new way for a while, >> You're constantly reinventing and refactoring code base to get to new outcomes for customers. we're spending between 35 and 40 percent of revenues on R&D. That's generally almost twice as much as many of our competitors and we're doing that because there is so much still to do. At times we have really thought carefully, could we scale back should we scale back our R&D spend but fortunately we've had a very supportive board of directors that believes in our vision. Believes in the vision that this is a unique moment in time the whole market is transitioning to a new tool set, because of all of the crosswinds of public cloud refactoring of applications containerization abstraction of the network, a storage, compute. All of these things combining together require a very different way of solving this problem We've, we've actually seen this play out in the past which again is why we're over investing in engineering. When you look at the mainframes and the compute architecture of mainframes and then we went to client-server, the tools that managed the mainframe really didn't manage the client-server. we've now gone from client-server to cloud the same things happening again. Because the needs are so different and we're going to see a very different generation of tools rule this next gen of requirements the customers have when they have a multitude of clouds that all work together to deliver an outcome to an application that you as a user are benefiting from. >> Alright so talked about the growth, talked about the investment, it's a strong industry validation today also. Gartner up on stage talked about the definition of AI ops they might not be fully in sync as to how mature the market is but it's still important that they are you know this is a trend and something to watch and it's on their hype cycle and Forrester released the wave which had congratulations ScienceLogic as the the top scorer up in the leaders category. So congratulations on that and what does that mean. >> Well we're thrilled about that because that external validation is what customers look at. It helps them with their analysis and that the talk tracks that everybody's on in our industry sometimes it's hard to discern who does what and how well each company does it to some degree from a marketing perspective many people use the same words so the good words are already used up. So sometimes it's hard to understand how each product is differentiated in the marketplace the Forrester wave report was so thorough so comprehensive, put us through over 30 use case scenarios where we had to demonstrate to get the qualifications for that ranking. So it wasn't just us responding in writing and waving our arms and throwing out a few powerpoints to get to that result we had to prove it and it feels the satisfaction of actually proving it for our team for our engineering team for everybody here at the company I'm so proud of everybody because that's really from a product perspective. We love those product recognition awards are actually sometimes more enjoyable than the growth recognition awards because that means you're really delivering a value to the customer where they're going to when they deploy the product they're going to have a good outcome. So that's what we're focused on and having Forrester put us at the top of the wave report is a special moment in the history of the company. >> Alright so Dave this is your user conference, so what I want to end on... Let's talk about the customers and here's here's my observation as you know, my first time coming to your event and I've talked to a number of seen some of the interactions there. There are certain products that customers love the relationship is an interesting and I would say a really good one the customers are really engaged and enjoying and liking it and it's almost like that friend that you can be like I really like you and your friends in their car I can be like this is how I want you to get better in ScienceLogic this is what you've done and I'm excited once on the roadmap and this is where I want you to go even more. So it's it's like you know that that friend that you can kind of hang out with and joke with and I've seen some of those relationships it's a good robust relationship and strong partnerships. It seems that you build with your customers am I getting the right vibe how do you look at your relationships with your customers. >> From a simple business perspective, I look at a couple things this is just as a run the business metric. On average our customers buy about twenty four, twenty five percent more capacity each year. On average our customers stay with us for 7-10 years. On average our customers pay us within 59 days. So I look at are we getting paid on time, do our customers buy more capacity each and every year and do we retain our customers. We retain about ninety five percent of our customers. So those metrics are really best-in-class, net subscription retention, DSO. All of those things are really good foundational indicators of we're doing a great job for our customers but what I love is this interaction that we have with them where they're they're never ending pressure on us to do better to strive for something that makes a day in their life a better day. I love that pressure it's uncomfortable many days of the week as I mentioned in my opening presentation but it makes us a better company and everybody in the company embodies this sense of how do we capture that synthesize it and then deliver against their needs and wants as quick as we can. So our innovation rates now are as high as they've ever been the throughput our of our development team this last quarter was the best we've ever seen in the history of the company, not just because we have more people but we're getting more done in the same amount of time. So all the KPIs that I look at are pointing in a really positive direction of great momentum for the business and really good alignment with customer needs and wants. We have probably the best market fit I've ever seen with the needs and wants of a net new customer and how our product fits against that. The Forrester wave report was yet another independent validation of how good our market fit in our strategy is right now to solve real problems that are very painful for customers to solve without our product >> Alright, Dave I can't let the head wizard gone without looking a little bit into the future. So as you look down the road what should we be looking as industry watchers to seem from ScienceLogic, seen from the industry you know I asked customers if they had a magic wand you know what would they do to make things better. You had a magic wand up on stage what will you be doing to make the industry better for all of us. >> There's so many things that when we think about making the industry better, it's a community and that means that among the key things that everybody's focused on right now for AI OPS is automation. So sharing those lessons learned cauterizing, validating the automation opportunities whether it's with provisioning systems, with end devices for capacity planning. All the things that we're doing we're starting to work with our customers to publish that broadly so that they can benefit from one another as quick as possible to take those best practices and throughout our community put them into production. If we do that each and every day and really focus on delivering that value across the customer base even for competitive customers. They compete with one another what we've seen is the spirit of cooperation and that to me is among the most satisfying parts of our customer and user community that it's a community that wants to help each other get better every day of the week and that's really hard mission as well. So from a trend line for the entire industry, I think we're all moving towards a moment in time where we have this autonomic capability where we know the applications are infrastructure, we're the tools that help us keep those applications running are getting smarter and smarter by the day and basically move us away from a fault and event correlation storyline to a predictive automation storyline >> Alright well Dave actually I said it on theCUBE a couple of years ago data holds the potential be that flywheel of growth for many years to come. Really appreciate you sharing the story and thanks again for having theCUBE at the event. >> Thanks too great to be here with you. Alright we'll be back with more coverage here from ScienceLogic Symposium 2019, I'm Stu Miniman and thank you for watching theCUBE.

>> fly from San Francisco. It's the Cube covering Google Cloud. Next nineteen, right Tio by Google Cloud and its ecosystem partners. >> Welcome back to the cues live coverage Here in San Francisco, this is day two of Google Cloud. Next twenty nineteen cubes. Exclusive coverage. We're in the middle of the show floor. All the action Aquino's are still going on a little bit over. I'm John for David Law student and kicking off, breaking down the keynote analysis. Also breaking down Post Day one. All the action in the evening, where all the parties are all the action on alway conversations. Dave's to picking off day to day one was setting the table. New CEO on stage Date date. You gets into the into the products really about data data. I machine learning's all aboutthe data cloud data, and we're seeing a machine learning data management. Smart analytics say Aye and machine learning and collaborations. The four themes of Today Google. Clearly using data has a key value proposition. Big table, Big Queary machine learning the G A support for auto ml for tables, big announcements, your thoughts >> Yes. Oh, John, I think answering some of the things that we brought up yesterday is when When Google puts out their vision of why they should be your partner of choice, like customers choose way thought that data and I and M l would be let read upfront. So they kind of buried the lead a little bit. And, you know, question we had coming this week is and they reclaim that really thought leadership that, you know, a couple years ago, You know, data. You know, they really that G technical science stuff is what Google was really good at. So I thought they laid out some really good things. I think everybody was, you know, impressed. To see there was good diversity of customers as well as all the Google me. There were a lot of the women of Google that you've written about John here showing their sewing their chops here. So a lot of pieces to go through and everything from the G sweetened the chromebooks and sick security and privacy is something I like to talk a little bit about when we get into it here. But quite quite a lot of use that day. Today I at the center of it >> and one of the power Women dipped to use the big table you see and think we're all that stuff, Dave with >> big steam Us on the Kino also was B I with a II B. I think we've covered that do space going back to our ten years of doing the tube. It's the promise of Do Remember those days. Do came from Google about Eric. The emergent Borden works and do this kind of small little sliver of the ecosystem into Google's now showing what was once the promise. Big data. They're giving demos democratizing. Bring in for the masses. Wait stories on silicon engels dot com outlining this, But the reality is there. Now remember hitting the road with promise of big data? Now, with Cloud really changed the game? Your bosses, you've been covering this from Day one? >> Well, I think that there's no question that this is a date, a game, WeII said early on John on the Cube. That big data war was going to be one in the cloud. Data was going to reside in the cloud. And having now machine intelligence applied >> to that data is what's giving companies competitive >> advantage at scale and economics I was struck by the stats that Google gave >> at the beginning of the Kino today. Google in the last three years has spent forty seven billion dollars >> capital expenditures. This year to date alone, they've spent thirteen billion dollars in Cap Xidan Data Centers. Thirteen billion. It would take IBM three and a half years to spend that much in cap back there would take Oracle six years. So from an economic standpoint, in the scale standpoint, Google, Microsoft, Amazon are gonna win that game. There's no question in my mind. So, John, you know it is a game of scale and data and I What do you think? First >> of all, Google, they got the Cuban aunties two of the white paper. They wrote that they did commercialized communities in a way that I thought was really excellent, well executed. I like a Jew where they left out on the side of the road. You got picked up by a Cloudera Michaels and memorable Jeff. I'm a Wagner. We saw what happened do communities. It is true that up. They basically put it out there in the open source system, the way they get behind Ciencia really positive there. On the data front, Google's got so much in the tool shed all across Google from day one. Their legacy is data data driven, large scale. They built software and systems to manage data at scale at a hole on president. Well, I think that they have their well ahead of the marketplace on the technology that our inside Google proper Google Cloud will be proper alphabet, whatever you wanna call it. Self driving cars question for Google is, Can they bring it to get there? They >> need to hire a team of people, just >> go out and just get it all >> together, pull the jewels together and put it into a coherent platform. That's kind of the tea leaves that I see that we're reading here. Is that Curry and pointed down the keynote. We got tons of technology. The question is, can they pull it together in a package and make a consumable addressable programmable programing, FBI's? We've seen that movie that's happening right now. The next level of innovation for Google is, can they make data programmable? This is going to be a ten year opportunity. If they get that right, they will win. Big move the ball down the field to see Amazon going big on stage maker. It's all about data data, analytics at scale, auto machine learning. These are the tell signs do data program ability. They got all the things. Can >> they bring it to bear? >> Yeah, Well, John, one of the things I saw it got a lot of people excited is if I have, You know, I'm a G sweet. Customers were geese sweet customers, and I'm using spreadsheets. Now I can use Big Query with that. So the power of analytics and big data be able to plug that right in, make it really easy. And what's interesting is trying to squint through. You know what was kind of the Google consumer side of the house that many of us know. And if used for for lots of years versus the Enterprise G sweet chromebooks and mobile? Well, you know, under Diane Green, it was Google Enterprise, and now it's all part of Google Cloud. Just when we talk about Microsoft, it's like, Well, is it azure or is it au three sixty five? Well, it was a G sweet words. Is it Google and one that I want to, you know, get get your guys comment on is they talk about privacy way. No, Google as a whole alphabet is You know what, ninety five percent plus ad revenue and they were very strong out here is that we do not own your data. We will not sell it to a third party. Privacy, privacy, privacy. And it's great to hear them say that. But way all interacted work with Google. We know all the cloud providers. The data is an important thing. When I do Aye aye and ml type activities. I need to be able to anonymous isat and leverage it train on it. So data privacy issue is still something that, you know, I heard what they said, but you know, there's got to be some concerns. >> There is another angle here that I'd like to talk about, and that's the database. Google, Amazon, Microsoft, Oracle, IBM, Mike Attention, Alibaba. All the big cloud guys. They want your data. That's why Amazon spending so much effort on the database market. That's why you don't see Oracle having such a dominant position in database. You like Google's announcement yesterday they were basically doing a backhanded slap but Amazon, saying, We're more open. They didn't deal with Mongo. There's a lot of discussion in the community of software community about how how Amazon, obviously Bogart's open source. But But if you if you look, it's something that's true if you look at Amazon, they basically taken a lot of open source products. It built their own databases. But if you look at Google, Google's got relational databases. They got non relational databases. They got operational databases. So I wonder out loud, Is this a Trojan horse strategy? Because they need to own your data that databases so important now that I think that is I talked to one noise that yesterday was a executive VP at Oracle, and he said to me that the cloud providers basically looked at the data base as another application to run on top of servers in virtual machines, >> he said, Were Oracle we integrate, you know, they do all the exit data stuff, etcetera. So my point is, database is the war to be won. That's where it starts. And if you're going to go away, I you want to have the data proximate to the application. Well, >> I mean there's two ways to look at that day. I would say that what might take on >> the database war or a position in the stack is you look out from the old way the new way the old way would be an oracle. Well, we got to preserve the database. We license that we have the license agreements. The new way is to change the game with automation. Like what? Google showing where all this stuff is gonna be done on behalf of the customer. So the business model of how database and the impact of data is being used well dictated my opinion, the monetization. And that's the question that everyone that I've talked to on the show floor offline on email, on direct messages, how we're gonna make money with containers, how we're gonna make money with Cooper Netease. How am I going to make money with data? This is the fundamental question. Now, if you look at the success pattern of the partner ecosystem, moneymaking is about new economics, new price points and new services. So if you're Deloitte or you're a censure, you're saying wow of goo could automate all the stuff that used to be really hard to do, like data migration, moving application were close around. That was once a high profit yield activity for this system integrators or selling databases like Oracle. That's the old way. The smart partners are essential, saying, OK, I'LL take the new economics where all that cost is distracted away by the automation. And I'll lower my price point but still capture the margin margin. Opportunity for cloud is significant, and this is where the smart money is going. The smart monetization schemes are around leveraging what Google and Amazon are doing at scale and shifting their business model. Take advantage of the lower cost but then lowering the price not as much, so they still capture the margin. So this's the immigration, and these are things that were like months and months project going. Data migrations to Melrose projects are like could be months. So smart money is saying Okay, how dowe I make money on this. It's not the old way. So this classic you know what side his treaty on old way or new way that's going to define who wins and who loses >> weight. By the way, I mean it. Sue Ellen >> license selling database license, for instance, is an old way. Well, essentially, it was Ramadan. Amazon does databases of service. What is the license by as you go? But you don't have, You >> know, the Oracle sells a zit buys you go to mean they play that same game. To me, it's more about when it comes to database. It's more about workloads. How much of the world needs acid property databases? Because that's oracles game versus how much of the world needs you no less database data store for for Lex structure data. And that's really I think, what Google and to a certain extent, Amazon are betting on. Although both companies, especially Amazon, is making a bet on both transactional data bases and non relationship, I >> mean in the ideal world database would be free from the margin get shifted to another spot. That's not clear yet, but still it can make money on database but lower caught in lower price. So Google makes money at scale, so with clouds scale, they can lower the price of the database like this, whether it's it's a service or some fee. But it's the people implementing, like the integrators and the people that are building applications as they build that agility. And how are they going to monetize? How does a company out in this floor make money? >> I just remember data stacks and probably like twenty twelve. I was talking to Billy Bob's worth the CEO about the merits of being in the US marketplace, and he said, You know, I'm a little nervous about that. What do you think, Dave? Do you think? Do you think they're gonna like, own me at some point in time and compete with me? So And that's what Google's announcement yesterday said is, You know, you're our friends, we're not going. They don't really come out and say, We're not going to compete with you They just basically said We are more open than aided us without mentioning a W S >> s. So it's interesting, you know, I've only had a little bit of a chance to walk around, but it's a different ecosystem, then Amazon. I remember six years ago, when we first went to Amazon. It was like game developers and all these weird start ups that I couldn't understand what they do. And now it's like, you know, like VM world, but bigger with just that. A broad ecosystem here, you know, there's a big section on collaboration. I went toe Enterprise connect a couple of weeks ago, talking about contact centers and see a lot of the same companies here heard five nines mentioned on stage zooms. Here, you know howto they plug into Google Cloud hurt sales force talking very devout Contact center. So it's a diverse ecosystem, but it's different than than Amazon, and there's not and Amazon. There's always that underlying, you know thing. Oh, is Amazon going to take over this business here? You know, I haven't heard that concern at this show. Well, >> I mean, the bottom line is that there's a shift in the economics and his model technology back in the database. Question. The fact that Mongo D. B. Was once forecast to go out of business. Oh, Amazon's going kill Mongo Devi that dynamo d B. Google's got databases. The fact the matter is, there's no one database anymore. Every application at some level has a database. So if you think about that, then you're gonna have a a new model where everything's has a database and the database is going be characterises on the workload in application. So I do agree with that point. Question is, it's not mutually exclusive one database license for all versus databases everywhere. So if databases air everywhere, then the connective tissue becomes the opportunity. That's where I think you see somebody's data playing technologies with Cloud very compelling, because I can move data very quickly around, and that's where the machine learning really shines. That's going to be a latent see question that's going to be a data integrity question. This is the new model. This is what horizontal scale ability means in the cloud, not by Oracle database. And we're good. This is It's kind of that game is that game is slowly moving into the oblivion. >> Well, I think you know, I think Amazon would say, Hey, if you're a database vendor, you gotta innovate or because we're not going to stop innovating. Whereas I think Google's message to the database vendors is somewhat different is, you know we want to partner with you, and maybe that's because they're not coming from a position of enterprise strength. But that ice I'm sensing, too, apparently different strategies. I just don't know what the end game is. And I believe the endgame is on the data. >> The tell sign on the databases of the developer, right? If I want to run a document store because that's best for my Jason or my my feeds from using Sage, eh, John? A lot of drama script. I'LL use document store. I want to use a relational database. I'll use a relational David So the ideal world does not have to develop are forced into a tooling and database decision that data >> mongo changed its licensing policy as a direct result of what Amazon was doing. So they made their community edition Ah, licence terms more restrictive if you follow that. So what? They said anybody, any cloud service provider that distributes the our community edition has to open source their entire software stack associated with distributing that, or they got to pay us. So basically saying you have to pay an open source tax or you gonna pay us we'LL be looking very interesting change in their database. One of >> the one the announcements here on the day two was the data fusion thing, which essentially means tell sign as well that fusion data moving data integrating Data's a critical thing. Pray ay, ay, ay and machine machine learning in a eyes only as good as the data that it's working with. So the data is, if his missing data saying a retail transaction, you potentially missing out on an opportunity to better user experience. So address ability of data. Having that accessible is a critical feature for machine learning, an a I and again, it's garbage in garbage out relatives of the data equation. High quality data gets high quality machine learning. High quality machine learning is high quality. I. So let's do that's that's kind of cloud offers with large compute large horizontal scale ability. >> Well, I said yes, and I said yesterday was kind of disappointed. It wasn't of talk about a I will. Google certainly made up for that today, didn't they? Still, >> Yeah, sorry was their questions >> were what was your favorite keynote moment today? >> Look, it was it was good when they actually let a couple of customers go up there and talk was that was a little bit disappointed that, you know, some of the sessions field a little bit too scripted for my take, but they laid out a lot of pieces there It takes a little wild, uh, you know, squint through all of the adjustment, you know, and all the changes that they have their I'm still digging through, like on the Antos. We talked about it quite a bit yesterday, but, you know, had some good conversations afterwards. They've got the cloud run announcement that's coming out this afternoon. But But, you know, digging into that open source discussion that you were just talking about from the database is something that I have a lot of interested. I'm glad we're actually right had on today will get their opinion as to, you know, they know a thing or two about open source and communities. And how does something like open shift fit with aunt those? They can work together, but it's not a owe it. Everything works back and forth If I'm p k s if I'm open shift or from you know, the geek based Antos, it's not seamless, and it sure ain't free you >> for not customers so weird from UPS. Scotiabank Baker Hughes McCasland heard from Cole's yesterday. So it's pretty high level senior people from the customer side speaking on stage, which is progress in the C e >> o of ups. I thought was great. He really laid out, You know, the scale of their business and how they grow. >> All right, guys, we got dates. You were kicking off here on the show floor here in San Francisco for Google Cloud next twenty nineteen. They never got it all day. And every day, two of three days, a live coverage. Stay with us as we kick off a full day of great interviews. Executives, entrepreneurs and ecosystem parties here at Google next stay with us for more after this short break.

(upbeat inspirational music) >> Hello everyone, I'm John Furrier in the Palo Alto Studios for Cube Conversation. I'm here with David Link who's the CEO of ScienceLogic. David, thanks for coming in. Good to see you. >> Great to be here, John. >> So, thanks for coming in. You came in from D.C., that's where your headquarters and ScienceLogic, you guys are having good business run right now. You're self-funded early on, now you get to venture back. Take a minute to explain how you guys got started, what does the company do? >> So, this is the classic story of entrepreneurship. We started in the garage. Myself and a couple of co-founders believed that IT management operations was broken and it was broken because a lot of the industry had really focused on having silos of data, the silos of data, the network, the application, the security, the storage, now cloud, containers and every technology had its own data silo of manageability. We believe that that was intrinsically wrong to understand how the service that combined all these different applications and technologies was behaving. We wanted a service view, so we brought it all together, kicked off, really the first seven years we boot strapped the company, the first year and a half we coded, got the product to market, it grew very quickly, got to the Inc. 500 a couple times, and then we attracted a lot of financing options. We had about 250 companies approach us. We never made one outbound call and fortunately, we had some really great and strong investors in EA, then Intel Capital, and three and a half years ago, our last round of financing was with Goldman Sachs and they've really been a great catalyst to help us continue our growth over the last five years. I think we've grown about 540% on the revenue side, so it's been an exciting time. >> Well congratulations. It's always a good success story to be a hot deal when you don't have to make any calls, they come to you. >> Yes. >> And that's good, that's part of growth, but I got to ask you what year did you start the company up? >> 2003. >> So, it's not obvious then, it's obvious to you as a visionary, but now people now know IT operations is broken. Cloud highlights it in a big way. The lights get turned on, the cockroaches are running around, but web services were still booming at that time. You start to see the beginning of the whole web services movement, you guys saw this early. Now, it's well recognized that IT operations can be automated away and Cloud certainly has an automations vibe to it. AI has been a big part of the AI operations. Is this kind of where you guys started with that vision? Was the original vision kind of where it is today? Take us through kind of what you saw and what's happening today. >> So, thematically we have this next wave of the computer architecture, Cloud computer architecture, edge computing where the way you manage that kind of infrastructure is different than the classic client server. There are different needs, different requirements, and that thematically has led with the change of infrastructure. Applications are changing and applications are now more infrastructure-aware. When we started the company, usually applications sat on one system or a cluster of systems and they weren't widely distributed. So now that the applications profile is changing, the architects are changing to microservices, that really puts huge strain on our industry. The industry, the total adjustable market, is about 25 billion dollars a year annual spent on tools. John, if you can imagine that. 25 billion a year is spent. It's going through an amazing, I would say, tectonic shift because why? Infrastructure's shifting and as more people move workloads to the Cloud into what I would call ephemeral workloads where they're moving around, that causes all kinds of pressure on the systems and record to manage that so that you understand what is happening at this moment in time. Where is it? What Cloud is it running on? How's the application performing? And you really need to tie the application to the infrastructure real-time. >> I want to get your thoughts on this. I interviewed a CIO this past week for a big company. I won't say the name 'cause we haven't published the video yet, but he told me candidly, he said that, look it, we outsourced everything and we outsourced our way into oblivion and what he meant by that was is that the core competency of IT, and he reference the book, Nick Carr, IT Doesn't Matter, which kind of was true, but wasn't true. Now, IT has a competitive advantage and essentially, they had this anemic IT department that was outsourced and they lost their competitive advantage, so he's like the reinvestment in IT is more than ever now because of Cloud, because of these new environments. So, I kind of believe that to be true. I'm sure you do too, but the reaction really is is you've got a lot of Legacy vendors that were dictating how to do things. >> Yes. >> I'm IBM, I'm Oracle, you got to do it this way and you were kind of constrained, IT was constrained by that. Now, you got to be much more agile, you have workloads that are dynamic, provisioning, orchestration, this is a whole new dynamic. What's the impact to the IT buyer, the IT environment with this new model, this new modern dynamic, new modern era? >> When you think about CIOs and CEOs, the pressure that they have to be Cloud first. Cloud first is such a strong... At the Board level, there's pressure. The adoption of Cloud now is happening faster and more rapidly than the adoption of virtualization, maybe it's doubling in the speed in the time warp, but what that means is that most CIOs are dealing with as many as nine to 11 Clouds, not one. You have a federation of Clouds: Private Clouds, public Clouds, software as a service Clouds, and that's your IT landscape, so it's changing so quickly that you have to think of it in a more federated approach. That means that the way you used to manage your private systems, and now your public systems, are really different and you've got to look at them more holistically because often they're communicating with one another in hybrid architectures. So, that's really the heart at our mission, to provide the context of how all the services you're trying to deliver as a CIO are behaving. What's their availability? What's the risk of the service having a problem? And knowing that real-time is ultimately what you want to do with your Cloud first strategy, but you need the right tooling operationally to affect that kind of outcome for your team. >> So, what's the core problem that you guys are solving? 'Cause obviously, there's a lot of complexity now, it's a new environment, so I still got the baggage of some Legacy environments. Is it monitoring you're solving? I guess, what's the core problem is my question that you guys are solving? If you had to kind of finish that, the core problem is blank. >> The core problem is visibility. The Holy Grail is application to infrastructure and the problem is that's becoming so complicated because everything is moving around. The more abstraction layers where it's a container, which is abstracted on top of a virtual machine, which is on top of bare-metal server. SD-WAN is an abstraction on top of an MPLS network. So, you have all of these layers that get from a software-defined perspective, they get abstracted away from the actual equipment that it's running on. Well, when that happens, where is the problem? Because it's moving around. The problem isn't in one place. So, that application to infrastructure awareness, it's almost like one of the things that we've looked at in the world of Facebook. You've got a lot of relationships, you've got videos, you've got friends, you've got all these different connections that are constantly moving around with data streams. What we do as a company is pull all these different data streams from the technologies themselves, from the Cloud providers, from the application layer, pull it together in a data hub that we can then understand how they all relate to one another so you can really, truly understand service impact and that is the crux of the problem most companies are dealing with now. You've got to fight with your Legacy, 'cause you still have that and it's not going away tomorrow, so you've got to make sure you're good at that, you've also got Cloud, the Cloud first initiative, and then you've got in between systems that are using both. That's really where we play. We're really good at the Legacy, we're good at Cloud, and connecting the two together and that is a really tough space because most Legacy providers really didn't get good with managing hyperactive ephemeral Cloud estates. The guys who started over the last five years building tools to manage the Cloud are really good at Cloud, but they don't cover Legacy. They're not going to cover a net app or hyper-converge, typically. So, we combine the both, Legacy and Cloud together in one management system, monitoring management paradigm, and then there's an automation engine where we actually proactively remediate problems real-time. So, the three together is where algorithmic operations, AI Ops, comes together. >> David, I want to dig into the offering, but before we get there, I want to get your thoughts on two trends: one is multi-Cloud. Recently, we've seen a lot of hybrid Cloud discussion, but now the big hubbub is multi-Cloud and the other one is AI Operations. So, I've been saying on The Cube, everyone who's in IT Operations is screwed, going to get automated away by AI. It's kind of tongue in cheek, but it's kind of a reality is that those old business models that were based upon certain service levels are going to be done in software. Now, you've got multi-Cloud. So, first question is what is multi-Cloud definition that you have for that? What does it mean? What is multi-Cloud? >> In our world, multi-Cloud is... Most large organizations use more than one Cloud and half of that is driven by what Cloud is best to operate a particular application profile? Amazon's really good at a lot of application profiles, but Azure might be better at certain Microsoft profiles, and then Google has profiles, and IBM Watson has profiles. Depending upon what you're trying to do with the application, where it was born, how it's living, how it's been re-factored, you're going to use one Cloud or the other, but most customers that we see have many Clouds. There really isn't one Cloud management scape when you're using... Vendors are still reasonably proprietary in the public hyper-scales. >> Some are better than others. >> And some are better. It depends on the use case. So, we try to bring all that together so that you're not looking at four panels, you're looking at one. >> So, you make it easy with one dash port. Okay, AI Operations. This is a hot trend, a lot of venture capitals are funding companies that have AI Ops in it, machine-learning obviously booming, no doubt software automation is coming. I'm seeing it everywhere. What does that mean? What is the definition of AI Operations? I mean, I'm bombastic at saying the industry sectors is going to crumble. I kind of think it will, but it will shift, but what is the impact to IT Operations with AI and what is AI Ops? >> We like to think of it as a life cycle. So, when you look at the life cycle of operations you have at the beginning of the life cycle, provisioning, so when we think about algorithmic, there's many different layers of automation: machine learning, cognitive learning, and you're going to use different parts of algorithmic operations for different parts of the life cycle. So at the very beginning, you're going to connect generally to a provisioning system so you know what's been provisioned or de-provisioned so we can automatically align a manageability template because nobody can be on a keyboard now, John. This has to be all machine to machine. So, once then it gets provisioned, then there's the run operate part and how do you learn from the normal operating conditions that you're looking for? The anomalies that you would look for to detect things aren't behaving appropriately? And then, once you understand those anomalies and the patterns, you can remediate them proactively, adding resources, decreasing resources, changing configurations, those are the things that kind of that last tier, and then that final tier, when there is a problem, if there is a problem, you've got to then raise a ticket, you've got to then work through the incident management of that ticket so there's another multi-step layers of automation to the incident management orchestration layer of solving problems, closing out a ticket. So, we have so many different layers across that life cycle that we plug into, most of which are native to our core platform. >> And your secret sauce is managing all the workloads that are moving around really fast, so to complicate that even further, you've got a lot of stuff moving around to track it all. I love what you said about not typing on the keyboard anymore, but essentially I'll translate that from what I heard was command line interface of CLIs has been the primary mechanism for dealing with either network and or storage, which is moving packets from here to there and moving storage from now to then, storing stuff. So, CLI is moving to a programmable model? This is the big takeaway. So, I totally think this is the mega trend. The command line interface mode of operation is moving to programmable, which hits your run and operate. >> Correct. >> This is the mega trend. Your thoughts? >> It is and that's one of the layers of complication because instead of a CLI, it's an API, and it's usually a restful API or a graph API. Those APIs are very different in construct and instead of talking to one device, that one device is virtualized into a hundred or a thousand and so with one API call, you actually create a thousand devices versus one device and understanding how one system is behaving, like a CLI would be to one system, right? So, that is a layer of complication where when we make an API call, we break it up into hundreds of things that then we track and understand the tenancy of what is a multi-tenant nature of that? What is the organization? What is the service view for all these little components that are part of one API call? And that abstraction layer makes it really difficult for the enterprise because the one thing about our API economy right now, there is no standard. Every vendor chooses their own formats for their products and in some cases, many formats for products in a product family. So, that layer of complexity, John, is what we're really solving for. The customer doesn't have to worry about that. We take care of that for them, but you're right, the API has become the CLI and it's just a level of complexity beyond what most enterprises are wanting to deal with themselves. That's why they bring us in to help. >> That is so important too that the data's in the API. >> That's right. >> That's key and Cloud's got orchestration challenges, state and state-less applications. All right, let's get into ScienceLogic's offering. So, what do you guys provide to customers? Talk about the product. How do you guys deliver it? Is it software, is it Cloud, is it service, is it appliance? Take us through the offering. What's the key secret sauce? How do people buy and use your product? >> So, our product's delivered as a service. You can use it in the Cloud. We deliver it as a service in our Cloud, but we also provide it if customers are using Amazon or IBM or Google or Microsoft. They can put our product, same code-base, same product, they subscribe to it, it's a subscription license model, so it's a pay-as-you-go and you pay for the number of devices that are under management. Typically, there are some customers, whether it's in the government, financial services, or international locations where they might want to deploy our product on premise, so we offer the same mode, either in the Cloud or on premise, but most customers now are choosing to deploy the product in the Cloud and that is a really easy... It's easy to get >> That's good for you guys. >> It's great for us because there's consistency of operations, we can keep everything up to date, and most customers want technology delivered as a service. They just want it to work. They want it to solve the business problem and do it easily, efficiently, even better, solve complex problems in an easy format. >> Give some customer examples or benefits or anecdotal stories around customers that have used your service that extracted benefits and value out of it, and second part of that question is when does someone know they need your product? What are the smoke signals? Is something breaking or is it just pain? When do they know to call you guys? So first one is customer examples or stories and then how does someone know who's watching this, hey I might need these guys? >> There are four segments that we cover. We have customers all over the world. There's enterprise customers. This is really a product for large enterprise, Fortune 1000 companies, so Clorox would be a customer, Hughes Satellite would be a customer, Cisco Systems out here in the valley is a customer, Dell, EMC, so it depends on what problem we're trying to solve for the customer. >> So large IT deployments basically? >> Very large, multinational, big networks, hundreds of thousands of devices, tens of thousands of devices is where those companies have immense complexity, lots of heterogeneous technology that comes together to deliver a service. They need a really robust solution to manage that proactively. So, enterprise customers, service providers, so a lot of managed service providers, infrastructure service providers, Telcos, they all use it, so I think we have about 60% of the infrastructure as a service providers use our product to deliver managed services to their customers and then the federal government all over the world, we have government customers around the world. I think right now about 70,000 organizations use our product every day and it's fairly evenly split, AMIA and AsiaPac, and then the US is our biggest market. >> You know, it's interesting you mention heterogeneous. I always kind of smile because you mentioned client server earlier. Every wave has their reflection point and I think what's going on with Cloud and I'd love to get your reaction is that Cloud, where it's winning, is it's a scale out, large scale, pool of resources. We look at what's going on with Amazon, all this, is that you don't need to know what service they have, just get more servers, so you're scaling out. >> Yes. >> But now, you need to have heterogeneous components. It's not just X-86. You could have a GPU, you have other stuff, AI going on, so heterogeneous is different now, but it's still the same came, it's still complex, it needs to be abstracted away. Is this kind of the key area that you're riding on? Is that right? What's your thoughts about that concept? >> Well to a large degree, John, the Cloud providers have really provided a layer for you to not have to worry about that, but we've seen customers actually with hyper-converged environments that they build in-house and or systems that they built because of geo-fencing in different countries that need the data kept in the country. There are requirements that drive people to build their own system, so the real thing that we're seeing a tremendous struggle with right now is that context, understanding what connects to what. All the different technologies that come together, all the heterogeneity that comes together to deliver a service, and whether you buy best in class technologies to solve one part of the stack, the landscape of whether it's your load balancer or a caching server or the database or the server, the network, all those different components, the security layer, those components that come together, often people have chosen specific technologies to solve those problems. The Cloud kind of abstracts that away with they hyper-scalers, but often you're putting infrastructure that you have on prem combined with infrastructure in the Cloud to deliver an aggregate solution so that multi-tiered architecture, just like back in the day, a three-tiered architecture, we're seeing those emerging again with public Cloud because you might want the data that actually generates the information on the web client's side to be in your data center, but you still have to understand how the service is behaving. So, we really look at all layers of the stack to solve the problem and that's really hard to do. >> Well David, great to have this conversation. Before we end, I want you to get a quick plug in for the company. How many employees, offices? What's the revenue like? What's your goals? You don't have to share the revenue if you don't want to, but if you want to, you can. Give a plug for the company. What's happening? >> Well, I'm really proud of what the team's done. We've got a great team of employees, about 370 employees today, full-time, they're spread all over the world, probably 80% are here in the Americas and the vision for the company, we think that this is a big opportunity. We are far from done. We really started the company to disrupt the industry 'cause the industry, as I said, was a silo industry and it really is, 20 years later, it's still that way. It's not really converged into a unified solution. We have great aspirations. Every year we've been growing the business 40, 50% a year for the last several years, and this year, we'll round over 100 million within the next 12 months of our run rate, so it's an exciting time for the company. >> Well, you've got a great model, SAS, in a massively growing and changing market, complex market, heterogeneous networks, apps are all being abstracted away and automation's driving this, so I think it's a perfect storm of innovation. Congratulations and thanks for chatting on The Cube here in Palo Alto. >> Love to be here, John. Thanks for having me. >> John Ferrier here, Cube Conversation, and we're here with David Link, CEO of ScienceLogic, and also the founder. Self-funded, big venture rounds, growing like a weed, based in D.C. This is the Cube Conversation. I'm John Furrier. Thanks for watching. (dramatic inspirational music)

>> wait. >> Okay, We're back. We are in Cambridge, Massachusetts. This is Dave Volante. I'm here with Jeff Kelly. Where with Wicked Bond. This is the Cube Silicon Angles production. We're here at the Mighty Information Quality Symposium in the heart of database design and development. We've had some great guests on Scott Hauser is here. He's the head of marketing at Adapt Company that we've introduced to our community. You know, quite some time ago, Um, really bringing multiple channels into the Duke Duke ecosystem and helping make sense out of all this data bringing insights to this data. Scott, welcome back to the Cube. >> Thanks for having me. It's good to be here. >> So this this notion of data quality, the reason why we asked you to be on here today is because first of all, you're a practitioner. Umm, you've been in the data warehousing world for a long, long time. So you've struggled with this issue? Um, people here today, uh, really from the world of Hey, we've been doing big data for a long time. This whole big data theme is nothing new to us. Sure, but there's a lot knew. Um, and so take us back to your days as a zoo. A data practitioner. Uh, data warehousing, business intelligence. What were some of the data quality issues that you faced and how did you deal with him? So >> I think a couple of points to raise in that area are no. One of things that we like to do is try and triangulate on user to engage them. And every channel we wanted to go and bring into the fold, creating unique dimension of how do we validate that this is the same person, right? Because each channel that you engage with has potentially different requirements of, um, user accreditation or, ah, guarantee of, you know, single user fuel. That's why I think the Holy Grail used to be in a lot of ways, like single sign on our way to triangulate across the spirit systems, one common identity or person to make that world simple. I don't think that's a reality in the in the sense that when you look at, um, a product provider or solution provider and a customer that's external, write those those two worlds Avery spirit and there was a lot of channels and pitch it potentially even third party means that I might want to engage this individual by. And every time I want to bring another one of those channels online, it further complicates. Validating who? That person eighty. >> Okay, so So when you were doing your data warehouse thing again as an I t practitioner, Um, you have you You try to expand the channels, but every time he did that and complex if I hide the data source So how did you deal with that problem? So just create another database and stole five Everything well, >> unfortunately, absolutely creates us this notion of islands of information throughout the enterprise. Because, as you mentioned, you know, we define a schema effectively a new place, Um, data elements into that schema of how you identified how you engage in and how you rate that person's behaviors or engagement, etcetera. And I think what you'd see is, as you'd bring on new sources that timeto actually emerge those things together wasn't in the order of days or weeks. It's on months and years. And so, with every new channel that became interesting, you further complicate the problem and effectively, What you do is, you know, creating these pools of information on you. Take extracts and you try and do something to munch the data and put in a place where you give access to an analyst to say, Okay, here's it. Another, um, Sample said a day to try and figure out of these things. Align and you try and create effectively a new schema that includes all the additional day that we just added. >> So it's interesting because again, one of the themes that we've been hearing a lot of this conference and hear it a lot in many conferences, not the technology. It's the people in process around the technology. That's certainly any person person would agree with that. But at the same time, the technology historically has been problematic, particularly data. Warehouse technology has been challenging you. So you've had toe keep databases relatively small and despair, and you had to build business processes around those that's right a basis. So you've not only got, you know, deficient technology, if you will, no offense, toe data, warehousing friends, but you've got ah, process creep that's actually fair. That's occurred, and >> I think you know what is happening is it's one of the things that's led to sort of the the revolution it's occurring in the market right now about you know, whether it's the new ecosystem or all the tangential technologies around that. Because what what's bound not some technology issues in the past has been the schema right. As important as that is because it gives people a very easy way to interact with the data. It also creates significant challenges when you want to bring on these unique sources of information. Because, you know, as you look at things that have happened over the last decade, the engagement process for either a consumer, a prospect or customer have changed pretty dramatically, and they don't all have the same stringent requirements about providing information to become engaged that way. So I think where the schema has, you know, has value you obviously, in the enterprise, it also has a lot of, um, historical challenges that brings along with >> us. So this jump movement is very disruptive to the traditional market spaces. Many folks say it isn't traditional guy, say, say it isn't but clearly is, particularly as you go Omni Channel. I threw that word out earlier on the channels of discussion that we had a dupe summit myself. John Ferrier, Hobby lobby meta and as your and this is something that you guys are doing that bringing in data to allow your customers to go Omni Channel. As you do that, you start again. Increase the complexity of the corpus of data at the same time. A lot of a lot of times into do you hear about scheme alight ski, but less so how do you reconcile the Omni Channel? The scheme of less It's their scheme alight. And the data quality >> problems, Yes, I think for, you know, particular speaking about adapt one of things that we do is we give customers the ability to take and effectively dump all that data into one common repository that is HD if s and do and leverage some of those open source tools and even their own, you know, inventions, if you will, you know, with m R code pig, whatever, and allow them to effectively normalized data through it orations and to do and then push that into tables effectively that now we can give access to the sequel interface. Right? So I think for us the abilities you're absolutely right. The more channels. You, Khun, give access to write. So this concept of anomie channel where Irrespective of what way we engaged with a customer what way? They touch us in some way. Being able to provide those dimensions of data in one common repository gives the marketeer, if you will, an incredible flexibility and insights that were previous, Who'd be discoverable >> assuming that data qualities this scene >> right of all these So so that that was gonna be my question. So what did the data quality implications of using something like HD FSB. You're essentially scheme unless you're just dumping data and essentially have a raw format and and it's raw format. So now you've gotto reconcile all these different types of data from different sources on build out that kind of single view of a customer of a product, Whatever, whatever is yours. You're right. >> So how do you go >> about doing that in that kind of scenario? So I think the repository in Hindu breach defense himself gives you that one common ground toa workin because you've got, you know, no implications of schema or any other preconceived notions about how you're going toe to toe massage weight if you will, And it's about applying logic and looking for those universal ides. There are a bunch of tools around that are focused on this, but applying those tools and it means that doesn't, um, handy captain from the start by predisposing them to some structure. And you want them to decipher or call out that through whether it's began homegrown type scripts, tools that might be upstairs here and then effectively normalizing the data and moving it into some structure where you can interact with it on in a meaningful way. So that really the kind the old way of trying to bring, you know, snippets of the data from different sources into ah, yet another database where you've got a play structure that takes time, months and years in some cases. And so Duke really allows you to speed up that process significantly by basically eliminating that that part of the equation. Yeah, I think there's and there's a bunch of dimensions we could talk about things like even like pricing exercises, right quality of triangulating on what that pricing should be per product for geography, for engagement, etcetera. I think you see that a lot of those types of work. Let's have transitioned from, you know, mainframe type environments, environments of legacy to the Duke ecosystem. And we've seen cases where people talk about they're going from eight month, you know, exercises to a week. And I think that's where the value of this ecosystem in you know, the commodity scalability really provides you with flexibility. That was just previously you unachievable. >> So could you provide some examples either >> you know, your own from your own career or from some customers you're seeing in terms of the data quality implications of the type of work they're doing. So one of our kind of *** is that you know the data quality measures required for any given, uh, use case various, in some cases, depending on the type of case. You know, in depending on the speed that you need, the analysis done, uh, the type of data quality or the level data qualities going is going to marry. Are you seeing that? And if >> so, can you give some examples of the different >> types of way data quality Gonna manifest itself in a big data were close. Sure. So I think that's absolutely fair. And you know. Obviously there's there's gonna be some trade off between accuracy and performance, right? And so you have to create some sort of confidence coefficient part, if you will, that you know, within some degree of probability this is good enough, right? And there's got to be some sort of balance between that actor Jerseyan time Um, some of the things that you know I've seen a lot of customers being interested in is it is a sort of market emerging around providing tools for authenticity of engagement. So it's an example. You know, I may be a large brand, and I have very, um, open channels that I engage somebody with my B e mail might be some Web portal, etcetera, and there's a lot of fishing that goes on out there, right? And so people fishing for whether it's brands and misrepresenting themselves etcetera. And there's a lot of, you know, desire to try and triangulate on data quality of who is effectively positioned themselves as me, who's really not me and being able to sort of, you know, take a cybersecurity spin and started to block those things down and alleviate those sort of nefarious activities. So We've seen a lot of people using our tool to effectively understand and be able to pinpoint those activities based upon behavior's based upon, um, out liars and looking at examples of where the engagement's coming from that aren't authentic if that >> makes you feel any somewhat nebulous but right. So using >> analytics essentially to determine the authenticity of a person of intensity, of an engagement rather than taking more rather than kind of looking at the data itself using pattern detection to determine. But it also taking, you know, there's a bunch of, um, there's a bunch of raw data that exists out there that needs you when you put it together again. Back to this notion of this sort of, you know, landing zone, if you will, or Data Lake or whatever you wanna call it. You know, putting all of this this data into one repository where now I can start to do you know, analytics against it without any sort of pre determined schema. And start to understand, you know, are these people who are purporting to be, you know, firm X y Z are there really from X y Z? And if they're not, where these things originating and how, when we start to put filters or things in place to alleviate those sort of and that could apply, it sounds like to certainly private industry. But, I mean, >> it sounds like >> something you know, government would be very interested in terms ofthe, you know, in the news about different foreign countries potentially being the source of attacks on U. S. Corporations are part of the, uh, part of our infrastructure and trying to determine where that's coming from and who these people are. And >> of course, people were trying to get >> complicated because they're trying to cover up their tracks, right? Certainly. But I think that the most important thing in this context is it's not necessarily about being able to look at it after the fact, but it's being able to look at a set of conditions that occur before these things happen and identify those conditions and put controls in place to alleviate the action from taking place. I think that's where when you look at what is happening from now an acceleration of these models and from an acceleration of the quality of the data gathering being able to put those things into place and put effective controls in place beforehand is changing. You know the loss prevention side of the business and in this one example. But you're absolutely right. From from what I see and from what our customers were doing, it is, you know, it's multi dimensional in that you know this cyber security. That's one example. There's pricing that could be another example. There's engagements from, ah, final analysis or conversion ratio that could be yet another example. So I think you're right in it and that it is ubiquitous. >> So when you think about the historical role of the well historical we had Stewart on earlier, he was saying, the first known chief data officer we could find was two thousand three. So I guess that gives us a decade of history. But if you look back at the hole, I mean data quality. We've been talking about that for many, many decades. So if you think about the traditional or role of an organization, trying tio achieved data quality, single version of the truth, information, quality, information value and you inject it with this destruction of a dupe that to me anyway, that whole notion of data quality is changing because in certain use, cases inference just fine. Um, in false positives are great. Who cares? That's right. Now analyzing Twitter data from some cases and others like healthcare and financial services. It's it's critical. But so how do you see the notion of data quality evolving and adapting to this >> new world? Well, I think one of these you mentioned about this, you know, this single version of the truth was something that was, you know, when I was on the other side of the table, >> they were beating you over the head waken Do this, We >> can do this, and it's It's something that it sounds great on paper. But when you look at the practical implications of trying to do it in a very finite or stringent controlled way, it's not practical for the business >> because you're saying that the portions of your data that you can give a single version of the truth on our so small because of the elapsed time That's right. I think there's that >> dimension. But there's also this element of time, right and the time that it takes to define something that could be that rigid and the structure months. It's months, and by that time a lot of the innovations that business is trying to >> accomplish. The eyes have changed. The initiatives has changed. Yeah, you lost the sale. Hey, but we got the data. It would look here. Yeah, I think that's your >> right. And I think that's what's evolving. I think there's this idea that you know what Let's fail fast and let's do a lot of it. Orations and the flexibility it's being provided out in that ecosystem today gives people an opportunity. Teo iterated failed fast, and you write that you set some sort of, you know confidence in that for this particular application. We're happy with you in a percent confidence. Go fish. You are something a little >> bit, but it's good enough. So having said that now, what can we learn from the traditional date? A quality, you know, chief data officer, practitioners, those who've been very dogmatic, particularly in certain it is what can we learn from them and take into this >> new war? I think from my point of view on what my experience has always been is that those individuals have an unparalleled command of the business and have an appreciation for the end goal that the business is trying to accomplish. And it's taking that instinct that knowledge and applying that to the emergence of what's happening in the technology world and bringing those two things together. I think it's It's not so much as you know, there's a practical application in that sense of Okay, here's the technology options that we have to do these, you know, these desired you engaged father again. It's the pricing engagement, the cyber security or whatever. It's more. How could we accelerate what the business is trying to accomplish and applying this? You know, this technology that's out there to the business problem. I think in a lot of ways, you know, in the past it's always been here. But this really need technology. How can I make it that somewhere? And now I think those folks bring a lot of relevance to the technology to say Hey, here's a problem. Trying to solve legacy methodologies haven't been effective. Haven't been timely. Haven't been, uh, scaleable. Whatever hock me. Apply what's happening. The market today to these problems. >> Um, you guys adapt in particular to me any way a good signal of the maturity model and with the maturity of a dupe, it's It's starting to grow up pretty rapidly, you know, See, due to two auto. And so where are we had? What do you see is the progression, Um, and where we're going. >> So, you know, I mentioned it it on the cue for the last time it So it and I said, I believe that you know who do busy operating system of big data. And I believe that, you know, there's a huge transition taking place that was there were some interesting response to that on Twitter and all the other channels, but I stand behind that. I think that's really what's happening. Lookit. You know what people are engaging us to do is really start to transition away from the legacy methodologies and they're looking at. He's not just lower cost alternatives, but also more flexibility. And we talked about, you know, its summit. The notion of that revenue curve right and cost takeouts great on one side of the coin, and I are one side of the defense here. But I think equally and even more importantly, is the change in the revenue curve and the insights that people they're finding because of these unique channels of the Omni Challenge you describe being able to. So look at all these dimensions have dated one. Unified place is really changing the way that they could go to market. They could engage consumers on DH that they could provide access to the analyst. Yeah. I mean, ultimately, that's the most >> we had. Stewart Madness con who's maybe got written textbooks on operating systems. We probably use them. I know I did. Maybe they were gone by the time you got there, but young, but the point being, you know, a dupe azan operating system. The notion of a platform is really it's changing dramatically. So, um, I think you're right on that. Okay. So what's what's next for you guys? Uh, we talked about, you know, customer attraction and proof points. You're working. All right on that. I know. Um, you guys got a great tech, amazing team. Um, what's next for >> you? So I think it's it's continuing toe. Look at the market in being flexible with the market around as the Hughes case is developed. So, you know, obviously is a startup We're focused in a couple of key areas where we see a lot of early adoption and a lot of pain around the problem that we can solve. But I think it's really about continuing to develop those use cases, um, and expanded the market to become more of a, you know, a holistic provider of Angelique Solutions on top of a >> house. Uh, how's Cambridge working out for you, right? I mean, the company moved up from the founders, moved up from New Haven and chose shows the East Coast shows cameras were obviously really happy about. That is East Coast people. You don't live there full time, but I might as well. So how's that working out talent pool? You know, the vibrancy of the community, the the you know, the young people that you're able to tap. So >> I see there's a bunch of dimensions around that one. It's hot. It's really, really hot >> in human, Yes, but it's been actually >> fantastic. And if you look it not just a town inside the team, but I think around the team. So if you look at our board right Jet Saxena. Chris Lynch, I've been very successful. The database community over decades of experience, you know, and getting folks like that onto the board fell. The Hardiman has been, you know, in this space as well for a long time. Having folks like that is, you know, advisors and providing guidance to the team. Absolutely incredible. Hack Reduce is a great facility where we do things like hackathons meet ups get the community together. So I think there's been a lot of positive inertia around the company just being here in Cambridge. But, you know, from AA development of resource or recruiting one of you. It's also been great because you've got some really exceptional database companies in this area, and history will show you like there's been a lot of success here, not only an incubating technology, but building real database companies. And, you know, we're on start up on the block that people are very interested in, and I think we show a lot of, you know, dynamics that are changing in the market and the way the markets moving. So the ability for us to recruit talent is exceptional, right? We've got a lot of great people to pick from. We've had a lot of people joined from no other previously very successful database companies. The team's growing, you know, significantly in the engineering space right now. Um, but I just you know, I can't say enough good things about the community. Hack, reduce and all the resource is that we get access to because we're here in Cambridge. >> Is the hacker deuces cool? So you guys are obviously leveraging that you do how to bring people into the Sohag produces essentially this. It's not an incubator. It's really more of a an idea cloud. It's a resource cloud really started by Fred Lan and Chris Lynch on DH. Essentially, people come in, they share ideas. You guys I know have hosted a number of how twos and and it's basically open. You know, we've done some stuff there. It's it's very cool. >> Yeah, you know, I think you know, it's even for us. It's also a great place to recruit, right. We made a lot of talented people there, and you know what? The university participation as well We get a lot of talent coming in, participate in these activities, and we do things that aren't just adapt related, that we've had people teach had obsessions and just sort of evangelize what's happening in the ecosystem around us. And like I said, it's just it's been a great resource pool to engage with. And, uh, I think it's been is beneficial to the community, as it has been to us. So very grateful for that. >> All right. Scott has always awesome. See, I knew you were going to have some good practitioner perspectives on data. Qualities really appreciate you stopping by. My pleasure. Thanks for having to see you. Take care. I keep right to everybody right back with our next guest. This is Dave a lot. They would. Jeff Kelly, this is the Cube. We're live here at the MIT Information Quality Symposium. We'LL be right back.