(intro music) >> "theCUBE's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) >> We're not going to- >> Hey everybody, welcome back to the Fira in Barcelona. My name is Dave Vellante, I'm here with Dave Nicholson, day four of MWC23. I mean, it's Dave, it's, it's still really busy. And you walking the floors, you got to stop and start. >> It's surprising. >> People are cheering. They must be winding down, giving out the awards. Really excited. Pier, look at you and Elias here. He's the vice president of Engineering Technology for Edge Computing Offers Strategy and Execution at Dell Technologies, and he's joined by Dan Cummins, who's a fellow and vice president of, in the Edge Business Unit at Dell Technologies. Guys, welcome. >> Thank you. >> Thank you. >> I love when I see the term fellow. You know, you don't, they don't just give those away. What do you got to do to be a fellow at Dell? >> Well, you know, fellows are senior technical leaders within Dell. And they're usually tasked to help Dell solve you know, a very large business challenge to get to a fellow. There's only, I think, 17 of them inside of Dell. So it is a small crowd. You know, previously, really what got me to fellow, is my continued contribution to transform Dell's mid-range business, you know, VNX two, and then Unity, and then Power Store, you know, and then before, and then after that, you know, they asked me to come and, and help, you know, drive the technology vision for how Dell wins at the Edge. >> Nice. Congratulations. Now, Pierluca, I'm looking at this kind of cool chart here which is Edge, Edge platform by Dell Technologies, kind of this cube, like cubes course, you know. >> AK project from here. >> Yeah. So, so tell us about the Edge platform. What, what's your point of view on all that at Dell? >> Yeah, absolutely. So basically in a, when we create the Edge, and before even then was bringing aboard, to create this vision of the platform, and now building the platform when we announced project from here, was to create solution for the Edge. Dell has been at the edge for 30 years. We sold a lot of compute. But the reality was people want outcome. And so, and the Edge is a new market, very exciting, but very siloed. And so people at the Edge have different personas. So quickly realize that we need to bring in Dell, people with expertise, quickly realize as well that doing all these solution was not enough. There was a lot of problem to solve because the Edge is outside of the data center. So you are outside of the wall of the data center. And what is going to happen is obviously you are in the land of no one. And so you have million of device, thousand of million of device. All of us at home, we have all connected thing. And so we understand that the, the capability of Dell was to bring in technology to secure, manage, deploy, with zero touch, zero trust, the Edge. And all the edge the we're speaking about right now, we are focused on everything that is outside of a normal data center. So, how we married the computer that we have for many years, the new gateways that we create, so having the best portfolio, number one, having the best solution, but now, transforming the way that people deploy the Edge, and secure the Edge through a software platform that we create. >> You mentioned Project Frontier. I like that Dell started to do these sort of project, Project Alpine was sort of the multi-cloud storage. I call it "The Super Cloud." The Project Frontier. It's almost like you develop, it's like mission based. Like, "Okay, that's our North Star." People hear Project Frontier, they know, you know, internally what you're talking about. Maybe use it for external communications too, but what have you learned since launching Project Frontier? What's different about the Edge? I mean you're talking about harsh environments, you're talking about new models of connectivity. So, what have you learned from Project Frontier? What, I'd love to hear the fellow perspective as well, and what you guys are are learning so far. >> Yeah, I mean start and then I left to them, but we learn a lot. The first thing we learn that we are on the right path. So that's good, because every conversation we have, there is nobody say to us, you know, "You are crazy. "This is not needed." Any conversation we have this week, start with the telco thing. But after five minutes it goes to, okay, how I can solve the Edge, how I can bring the compute near where the data are created, and how I can do that secure at scale, and with the right price. And then can speak about how we're doing that. >> Yeah, yeah. But before that, we have to really back up and understand what Dell is doing with Project Frontier, which is an Edge operations platform, to simplify your Edge use cases. Now, Pierluca and his team have a number of verticalized applications. You want to be able to securely deploy those, you know, at the Edge. But you need a software platform that's going to simplify both the life cycle management, and the security at the Edge, with the ability to be able to construct and deploy distributed applications. Customers are looking to derive value near the point of generation of data. We see a massive explosion of data. But in particular, what's different about the Edge, is the different computing locations, and the constraints that are on those locations. You know, for example, you know, in a far Edge environment, the people that service that equipment are not trained in the IT, or train, trained in it. And they're also trained in the safety and security protocols of that environment. So you necessarily can't apply the same IT techniques when you're managing infrastructure and deploying applications, or servicing in those locations. So Frontier was designed to solve for those constraints. You know, often we see competitors that are doing similar things, that are starting from an IT mindset, and trying to shift down to cover Edge use cases. What we've done with Frontier, is actually first understood the constraints that they have at the Edge. Both the operational constraints and technology constraints, the service constraints, and then came up with a, an architecture and technology platform that allows them to start from the Edge, and bleed into the- >> So I'm laughing because you guys made the same mistake. And you, I think you learned from that mistake, right? You used to take X86 boxes and throw 'em over the fence. Now, you're building purpose-built systems, right? Project Frontier I think is an example of the learnings. You know, you guys an IT company, right? Come on. But you're learning fast, and that's what I'm impressed about. >> Well Glenn, of course we're here at MWC, so it's all telecom, telecom, telecom, but really, that's a subset of Edge. >> Yes. >> Fair to say? >> Yes. >> Can you give us an example of something that is, that is, orthogonal to, to telecom, you know, maybe off to the side, that maybe overlaps a little bit, but give us an, give us an example of Edge, that isn't specifically telecom focused. >> Well, you got the, the Edge verticals. and Pierluca could probably speak very well to this. You know, you got manufacturing, you got retail, you got automotive, you got oil and gas. Every single one of them are going to make different choices in the software that they're going to use, the hyperscaler investments that they're going to use, and then write some sort of automation, you know, to deploy that, right? And the Edge is highly fragmented across all of these. So we certainly could deploy a private wireless 5G solution, orchestrate that deployment through Frontier. We can also orchestrate other use cases like connected worker, or overall equipment effectiveness in manufacturing. But Pierluca you have a, you have a number. >> Well, but from your, so, but just to be clear, from your perspective, the whole idea of, for example, private 5g, it's a feature- >> Yes. >> That might be included. It happened, it's a network topology, a network function that might be a feature of an Edge environment. >> Yes. But it's not the center of the discussion. >> So, it enables the outcome. >> Yeah. >> Okay. >> So this, this week is a clear example where we confirm and establish this. The use case, as I said, right? They, you say correctly, we learned very fast, right? We brought people in that they came from industry that was not IT industry. We brought people in with the things, and we, we are Dell. So we have the luxury to be able to interview hundreds of customers, that just now they try to connect the OT with the IT together. And so what we learn, is really, at the Edge is different personas. They person that decide what to do at the Edge, is not the normal IT administrator, is not the normal telco. >> Who is it? Is it an engineer, or is it... >> It's, for example, the store manager. >> Yeah. >> It's, for example, the, the person that is responsible for the manufacturing process. Those people are not technology people by any means. But they have a business goal in mind. Their goal is, "I want to raise my productivity by 30%," hence, I need to have a preventive maintenance solution. How we prescribe this preventive maintenance solution? He doesn't prescribe the preventive maintenance solution. He goes out, he has to, a consult or himself, to deploy that solution, and he choose different fee. Now, the example that I was doing from the houses, all of us, we have connected device. The fact that in my house, I have a solar system that produce energy, the only things I care that I can read, how much energy I produce on my phone, and how much energy I send to get paid back. That's the only thing. The fact that inside there is a compute that is called Dell or other things is not important to me. Same persona. Now, if I can solve the security challenge that the SI, or the user need to implement this technology because it goes everywhere. And I can manage this in extensively, and I can put the supply chain of Dell on top of that. And I can go every part in the world, no matter if I have in Papua New Guinea, or I have an oil ring in Texas, that's the winning strategy. That's why people, they are very interested to the, including Telco, the B2B business in telco is looking very, very hard to how they recoup the investment in 5g. One of the way, is to reach out with solution. And if I can control and deploy things, more than just SD one or other things, or private mobility, that's the key. >> So, so you have, so you said manufacturing, retail, automotive, oil and gas, you have solutions for each of those, or you're building those, or... >> Right now we have solution for manufacturing, with for example, PTC. That is the biggest company. It's actually based in Boston. >> Yeah. Yeah, it is. There's a company that the market's just coming right to them. >> We have a, very interesting. Another solution with Litmus, that is a startup that, that also does manufacturing aggregation. We have retail with Deep North. So we can do detecting in the store, how many people they pass, how many people they doing, all of that. And all theses solution that will be, when we will have Frontier in the market, will be also in Frontier. We are also expanding to energy, and we going vertical by vertical. But what is they really learn, right? You said, you know you are an IT company. What, to me, the Edge is a pre virtualization area. It's like when we had, you know, I'm, I've been in the company for 24 years coming from EMC. The reality was before there was virtualization, everybody was starting his silo. Nobody thought about, "Okay, I can run this thing together "with security and everything, "but I need to do it." Because otherwise in a manufacturing, or in a shop, I can end up with thousand of devices, just because someone tell to me, I'm a, I'm a store manager, I don't know better. I take this video surveillance application, I take these things, I take a, you know, smart building solution, suddenly I have five, six, seven different infrastructure to run this thing because someone say so. So we are here to democratize the Edge, to secure the Edge, and to expand. That's the idea. >> So, the Frontier platform is really the horizontal platform. And you'll build specific solutions for verticals. On top of that, you'll, then I, then the beauty is ISV's come in. >> Yes. >> 'Cause it's open, and the developers. >> We have a self certification program already for our solution, as well, for the current solution, but also for Frontier. >> What does that involve? Self-certification. You go through you, you go through some- >> It's basically a, a ISV can come. We have a access to a lab, they can test the thing. If they pass the first screen, then they can become part of our ecosystem very easily. >> Ah. >> So they don't need to spend days or months with us to try to architect the thing. >> So they get the premature of being certified. >> They get the Dell brand associated with it. Maybe there's some go-to-market benefits- >> Yes. >> As well. Cool. What else do we need to know? >> So, one thing I, well one thing I just want to stress, you know, when we say horizontal platform, really, the Edge is really a, a distributed edge computing problem, right? And you need to almost create a mesh of different computing locations. So for example, even though Dell has Edge optimized infrastructure, that we're going to deploy and lifecycle manage, customers may also have compute solutions, existing compute solutions in their data center, or at a co-location facility that are compute destinations. Project Frontier will connect to those private cloud stacks. They'll also collect to, connect to multiple public cloud stacks. And then, what they can do, is the solutions that we talked about, they construct that using an open based, you know, protocol, template, that describes that distributed application that produces that outcome. And then through orchestration, we can then orchestrate across all of these locations to produce that outcome. That's what the platform's doing. >> So it's a compute mesh, is what you just described? >> Yeah, it's, it's a, it's a software orchestration mesh. >> Okay. >> Right. And allows customers to take advantage of their existing investments. Also allows them to, to construct solutions based on the ISV of their choice. We're offering solutions like Pierluca had talked about, you know, in manufacturing with Litmus and PTC, but they could put another use case that's together based on another ISV. >> Is there a data mesh analog here? >> The data mesh analog would run on top of that. We don't offer that as part of Frontier today, but we do have teams working inside of Dell that are working on this technology. But again, if there's other data mesh technology or packages, that they want to deploy as a solution, if you will, on top of Frontier, Frontier's extensible in that way as well. >> The open nature of Frontier is there's a, doesn't, doesn't care. It's just a note on the mesh. >> Yeah. >> Right. Now, of course you'd rather, you'd ideally want it to be Dell technology, and you'll make the business case as to why it should be. >> They get additional benefits if it's Dell. Pierluca talked a lot about, you know, deploying infrastructure outside the walls of an IT data center. You know, this stuff can be tampered with. Somebody can move it to another room, somebody can open up. In the supply chain with, you know, resellers that are adding additional people, can open these devices up. We're actually deploying using an Edge technology called Secure Device Onboarding. And it solves a number of things for us. We, as a manufacturer can initialize the roots of trust in the Dell hardware, such that we can validate, you know, tamper detection throughout the supply chain, and securely transfer ownership. And that's different. That is not an IT technique. That's an edge technique. And that's just one example. >> That's interesting. I've talked to other people in IT about how they're using that technique. So it's, it's trickling over to that side of the business. >> I'm almost curious about the friction that you, that you encounter because the, you know, you paint a picture of a, of a brave new world, a brave new future. Ideally, in a healthy organization, they have, there's a CTO, or at least maybe a CIO, with a CTO mindset. They're seeking to leverage technology in the service of whatever the mission of the organization is. But they've got responsibilities to keep the lights on, as well as innovate. In that mix, what are you seeing as the inhibitors? What's, what's the push back against Frontier that you're seeing in most cases? Is it, what, what is it? >> Inside of Dell? >> No, not, I'm saying out, I'm saying with- >> Market friction. >> Market, market, market friction. What is the push back? >> I think, you know, as I explained, do yourself is one of the things that probably is the most inhibitor, because some people, they think that they are better already. They invest a lot in this, and they have the content. But those are again, silo solutions. So, if you go into some of the huge things that they already established, thousand of store and stuff like that, there is an opportunity there, because also they want to have a refresh cycle. So when we speak about softer, softer, softer, when you are at the Edge, the software needs to run on something that is there. So the combination that we offer about controlling the security of the hardware, plus the operating system, and provide an end-to-end platform, allow them to solve a lot of problems that today they doing by themselves. Now, I met a lot of customers, some of them, one actually here in Spain, I will not make the name, but it's a large automotive. They have the same challenge. They try to build, but the problem is this is just for them. And they want to use something that is a backup and provide with the Dell service, Dell capability of supply chain in all the world, and the diversity of the portfolio we have. These guys right now, they need to go out and find different types of compute, or try to adjust thing, or they need to have 20 people there to just prepare the device. We will take out all of this. So I think the, the majority of the pushback is about people that they already established infrastructure, and they want to use that. But really, there is an opportunity here. Because the, as I said, the IT/OT came together now, it's a reality. Three years ago when we had our initiative, they've pointed out, sarcastically. We, we- >> Just trying to be honest. (laughing) >> I can't let you get away with that. >> And we, we failed because it was too early. And we were too focused on, on the fact to going. Push ourself to the boundary of the IOT. This platform is open. You want to run EdgeX, you run EdgeX, you want OpenVINO, you want Microsoft IOT, you run Microsoft IOT. We not prescribe the top. We are locking down the bottom. >> What you described is the inertia of, of sunk dollars, or sunk euro into an infrastructure, and now they're hanging onto that. >> Yeah. >> But, I mean, you know, I, when we say horizontal, we think scale, we think low cost, at volume. That will, that will win every time. >> There is a simplicity at scale, right? There is a, all the thing. >> And the, and the economics just overwhelm that siloed solution. >> And >> That's inevitable. >> You know, if you want to apply security across the entire thing, if you don't have a best practice, and a click that you can do that, or bring down an application that you need, you need to touch each one of these silos. So, they don't know yet, but we going to be there helping them. So there is no pushback. Actually, this particular example I did, this guy said you know, there are a lot of people that come here. Nobody really described the things we went through. So we are on the right track. >> Guys, great conversation. We really appreciate you coming on "theCUBE." >> Thank you. >> Pleasure to have you both. >> Okay. >> Thank you. >> All right. And thank you for watching Dave Vellante for Dave Nicholson. We're live at the Fira. We're winding up day four. Keep it right there. Go to siliconangle.com. John Furrier's got all the news on "theCUBE.net." We'll be right back right after this break. "theCUBE," at MWC 23. (outro music)

>>Welcome back to the Cube's exclusive coverage of the Dell Technology Summit. I'm Dave Ante. We're going inside with Dell Execs to extract the signal from the noise. And right now we're gonna dig into customer requirements in a data intensive world and how cross cloud complexities get resolved from a product development perspective and how the ecosystem fits in to that mosaic to close the gaps and accelerate innovation. And with me now as friend of the cube, Jeff Boudreau, he's the president of the Infrastructure Solutions Group, ISG at Dell Technologies. Jeff, always good to see you. Welcome. >>You too. Thank you for having me. It's great to see you. And thanks for having me back on the cube. I'm thrilled to be here. Yeah, >>It's our pleasure. Okay, so let's talk about what you're observing from customers today. You know, we talk all the time about operating in a data driven multi-cloud world, blah, blah, blah, blah. But what does that all mean to you when you have to translate that noise into products that solve specific customer problems, Jeff? >>Sure. Hey, great question. And everything always starts with our customers. They're our motivation, They're top of mind, everything we do. My leadership team and I spend a lot of time with our customers. We're listening, we're learning, we're really understanding their pain points, and we want to get their feedback in regards to our solutions, both turn and future offerings, really ensure that we're aligned to meeting their business objectives. I would say from these conversations, I'd say customers are telling us several things. First, it's all about data. So no surprise going back to your opening. And second, it's about the multi-cloud world. And I'd say the big thing coming from all of this is that both of those are driving a ton of complexity for our customers. And I'll unpack that just a bit, which is first the data. As we all know, data is growing at unprecedented rates with more than 90% of the world's data being produced in the last two years alone. >>And you can just think of that in its everywhere, right? And so as it is, the IT world shifts towards distributed compute to support that data growth and that data gravity to really extract more value from that data in real time environments become inherently more and more hybrid and more and more multi-cloud. Which leads me to the second key point that I've been hearing from our customers, which it's a multi-cloud world, not new news. Customers by default have multiple clouds running across multiple locations. That's OnPrem and off, it's running at the edge and it's serving a variety of different needs. Unfortunately, for most of our CU customers, multicloud actually added to their complexity. As we've discussed, it's been a lot more of multi-cloud by default versus multicloud by design. If you really think about our customers, I mean, I, I, I'm talking to 'EM all the time. >>You think about the data complexity, that's the growth and the gravity. You think about their infrastructure complexity shifting from central to decentralized it, you think about multi-cloud complexity. So you have these walled gardens, if you will. So you have multiple vendors and you have these multiple contracts that all creates operational complexity for their teams around their processes of their tools. And then you think about the security complexity that that drives with the, just the increased tax service and the list goes on. So what are we seeing for our customers? They, what they really want from, also what they're asking us for is simplicity, not complexity. The mediacy, not latency. They're asking for open and align versus I'd say siloed and closed. And they're looking for a lot more agility and not rigidity in what we do. So they really wanna simplify everything. They're looking for a simpler IT in a more agile it, and they want more control of their data, right? >>And so, and they want to extract more of the value to enrich their business or their customer engagements, which all sounds pretty obvious and we've probably all heard it a bunch, but it's really hard to achieve. And that's where I believe, and we believe as Dell, that we, it creates a big opportunity for us to really help our customers as that great simplifier of it. We're already doing this today. Just a couple quick examples. First is Salesforce. We've supported recently, we've supported their global expansion with a multi-cloud solution to help them drive their business growth. Our solution delivered a reliable and consistent IT experience will go back to that complexity. And it was across a very distributed environment, including more than 60 data centers, 230 countries in hundreds of thousands of customers. It really provided Salesforce with the flexibility of placing workloads and data in an environment based on the right service level. >>Objective things like cost complexity or even security compliance considerations. The second customer A is a big new knowing little Patriot fan. And Dan, Dave, I know you are as well. Oh yeah, this one's near, near and data to my heart, it's the craft group. We just created a platform to span all their businesses that created more, I'd say data driven, immersive, secure experience, which is allowing them to capture data at the edge and use it for real time insights for things like cyber resiliency, but also like safety of the facilities. And as being a PA patron fan like I am, did they truly are meeting us where we are in our seats on their mobile devices and also in the parking lot. So just keep that in mind next time you're there. The bottom line, everything we're doing is really to make it simpler for our customers and to help them get the most of their data. I'd say we're gonna do this, is it through a multi-cloud by design approach, which we've talked a lot about with you and and others at Dell Tech world earlier this >>Year, right? And we had Salesforce on, actually at Dell Tech Group. The craft group is interesting because, you know, when you get to the stadium, you know, everybody's trying to get, get, get out to the internet and, and, but then the experience is so much better if you can actually, you know, deal with that edge. So I wanna talk about complexity though. You got data, you got, you know, the, the edge, you got multiple clouds, you got a different operating model across security model, different. So a lot of times in this industry we solve complexity with more complexity and it's like a bandaid. So I wanna, I wanna talk to, to how you're innovating around simplicity in ISG to address this complexity and what this means for Dell's long term strategy. >>Sure, I'd love to. So first I, I'd like to state the obvious, which are our investments in our innovations really focused on advancing, you know, our, our our customers needs, right? So we are really, our investments are gonna be targeted. We, we believe customers can have the most value. And some of that's gonna be around how we create strategic partnerships as well. Connecting to what we just spoke about. Much of the complexity of customers have or experiencing is the orchestration and management of all the data in all these different places. And customers, you know, they must be able to quickly deploy and operate across cloud environments. They need to increase their developer productivity, really enabling those developers that do what they do best, which is creating more value for their customers than for their businesses. Our innovation efforts are really focused on addressing this by delivering an open and modern IT architecture that allows customers to run and manage any workload in any cloud anywhere. >>Data lives we're focused on, also focused on consumption based solutions, which allow for a greater degree of simplicity and flexibility, which they're really asking for as well. The foundation for this is our software defined common storage layer. That common storage layer, You can think about this, Dave, as our ias if you will. It underpins our data access in mobility across all data types of locations. So you can think private, public, telecom, colo, edge, and it's delivered in a secure, holistic, and consistent cloud experience through Apex. We are making a ton of progress to let you, just to be, just to be clear, we made headway in things like Project Alpine, which you're very well aware of. This is our storage as a service. We announced us back in in January, which brings our unique software IP from our flagship storage platform to all the major public clouds. >>Really delivering the best of both worlds, allowing our customers to take advantage of Dell's enterprise class data services and storage software, such as performance at scale, resiliency, efficiency and security. But in addition to that, we're leveraging the breadth of the public cloud services, right? They're on demand scaling capabilities and access to analytical services. So in addition, we're really, we're, we're on our way to win at the edge as well with Project Frontier, which reduces complexity at the edge by creating an open and secure software platform to help our customers simplify their edge operations, optimize their edge environments and investments, secure that edge environment as well. I believe you're gonna be discussing Project Frontier here with Sam Broco in the very near future. So I won't give up more, too many more details there. And lastly, we're also scaling Apex, which, you know, well shifting from our vision, really shifting from vision to reality and introducing several new Apex service offerings, which are coming to market over the next month or so. And the intent is really supporting our customers on there as a service transitions by modernize the con consumption experience and providing that flexible as a service model. Ultimately, we're trying to help our customers achieve that multi-cloud by design to really simplify it in a, unlock the power of their data. >>So some good examples there. I I like to talk about the super Cloud as you, you know, you're building on top of the, you know, hyperscale infrastructure and you got Apex is your cloud, the common storage layer, you call it your ISAs. And that's, that's a ingredient in what we call the super cloud out to the edge. You have to have a common platform there and one of the hallmarks of a cloud company. And as you become a cloud company, everybody's a cloud company ecosystem becomes really, really important in terms of product development and, and innovation. Matt Baker always loves to stress it's not a zero sum game. And, and I think Super Cloud recognizes that, that there's value to be built on top of other clouds and, and, and of course on top of your infrastructure so that your ecosystem can add value. So what role does the ecosystem play there? >>For me, it's, it's pretty clear. It's, it's, it's critical. I can't say that enough above the having an open ecosystem. Think about everything we just discussed, and I agree with your super cloud analogy. I agree with what Matt Baker had said to you, I would assert no one company can actually address all the pain points and all the issues and challenges that customers are having on their own, not one. I think customers really want and deserve an open technology ecosystem, one that works together. So not these close stacks that discourage this interoperability or stifles innovation and productivity of our, of each of our teams. We Dell, I guess, have a long history of supporting open ecosystems that really put customers first. And to be clear, we're gonna be at the center of the multi-cloud ecosystem and we're working with partners today to make that a reality. >>I mean, just think of what we're doing with VMware. We continue to build on our first investment alliances with them in August at their VMware explorer, which I know you were at. We announced several joint engineering initiatives to really help customers more easily manage and gain value from their data in their infrastructure. For multi-cloud specifically, we strength our relationship with VMware and know with Tansu as part of that. In addition, just a few weeks ago we announced our partnership with Red Hat to simplify our multicloud deployments for managing containerized workloads. I'd say, and using your analogy, I could think of that as our multicloud platform. So that's kind of our PAs layer, if you will. And as you're aware, we have a very longstanding and strategic partnership with Microsoft and I'd say stay tuned. There's a lot more to come with them and also others in this multi-cloud space. >>Shifting a bit to some of the growth engines that my team's responsible for the edge, right? As you think about data being everywhere, we've established partnerships for the Edge as well with folks like PTC and Litmus for the manufacturing edge, but also folks like Deep North for the retail edge analytics in data management, using your Supercloud analogy data, the sa right? This is our SAS layer. We've announced that we're collaborating, partnering with folks like Snowflake and, and there's other data management companies as well to really simplify data access and accelerate those data insights. And then given customers choice of where they'd like to have their IT and their infrastructure, we've we're expanding our colo partnerships as well with folks like Equinox and, and they're allowing us to broaden our availability of Apex, providing customers the flexibility to take advantage of those as a service offerings wherever it's delivered and where they can get the most value. So those are just some you can hear from me. I think it's critical not only for, for us, I think it's critical for our customers. I think it's been critical, critical for the entire, you know, industry as a whole to really have that open technology ecosystem as we work with our customers on our multi-cloud solutions really to meet their needs. We'll continue to collaborate with whoever customers choose and you know, and who they want us to do business with. So I'd say a lot more coming in that space. >>So it's been an interesting three years for you, just, just over three years now since you've been made the president of the IS isg. And so you had to dig in and it was obviously strange time around the world, but, but you really had to look at, okay, how do we modernize the platform? How do we make it, you know, cloud first? You've mentioned the Edge, we're expanding. So what are the big takeaways? What do you want customers and our audience to understand? Just some closing thoughts and if you could summarize. >>Sure. So I'd say first, you know, we discuss, we're working in a very fast paced, ever changing market with massive amounts of data that needs to be managed. It's very complex and our customers need help with that complexity. I believe that Dell Technologies is uniquely positioned to help as their multi-cloud champion. No one else can solve the breadth and depth of the challenges like we can. And we're gonna help our customers move forward when they basically moving from a multi-cloud by default, as we've discussed before, to multicloud by design. And I'm really excited for the opportunity to work with our customers to help them expand that ecosystem as they truly realize the future of it and, and what they're trying to accomplish. >>Jeff, thanks so much. Really appreciate your time. Always a pleasure. Go pats and we'll see you on the blog. >>Thanks Dave. >>All right, you're watching Exclusive Inside Insights from Dell Technology Summit on the cube, your leader in enterprise and emerging tech coverage.

>>As we said in our analysis of Dell's future, the transformation of Dell into Dell emc and now Dell Technologies has been one of the most remarkable stories in the history of the technology industry. After years of successfully integrated EMC and becoming VMware's number one distribution channel, the metamorphosis of Dell com culminated in the spin out of VMware from Dell and a massive wealth creation milestone pending, of course the Broadcom acquisition of VMware. So where's that leave Dell and what does the future look like for this technology powerhouse? Hello and welcome to the Cube's exclusive coverage of Dell Technology Summit 2022. My name is Dave Ante and I'll be hosting the program today In conjunction with the Dell Tech Summit. We'll hear from four of Dell's senior executives. Tom Sweet is the CFO of Dell Technologies. He's gonna share his views of the company's position and opportunities and answer the question, why is Dell good long term investment? >>Then we'll hear from Jeff Boudreau was the president of Dell's ISG business unit. He's gonna talk about the product angle and specifically how Dell is thinking about solving the multi-cloud challenge. And then Sam Grow Cot is the senior vice president of marketing's gonna come in the program and give us the update on Apex, which is Dell's as a service offering and a new edge platform called Project Frontier. By the way, it's also Cybersecurity Awareness Month, and we're gonna see if Sam has any stories there. And finally, for a company that's nearly 40 years old, Dell has some pretty forward thinking philosophies when it comes to its culture and workforce. And we're gonna speak with Jen Savira, who's Dell's chief Human Resource officer about hybrid work and how Dell is thinking about the future of work. We're gonna geek out all day and talk multi-cloud and edge and latency, but first, let's talk wallet. Tom Sweet cfo, and one of Dell's key business architects. Welcome back to the cube, >>Dave, it's good to see you and good to be back with you. So thanks for having me, Jay. >>Yeah, you bet. Tom. It's been a pretty incredible past 18 months. Not only the pandemic and all that craziness, but the VMware spin, you had to give up your gross margin binky as kidding, and, and of course the macro environment. I'm so sick of talking about the macro, but putting that aside for a moment, what's really remarkable is that for a company at your size, you've had some success at the top line, which I think surprised a lot of people. What are your reflections on the last 18 to 24 months? >>Well, Dave, it's been an incredible, not only last 18 months, but the whole transformation journey. If you think all the way back maybe to the LBO and forward from there, but, you know, stepping into the last 18 months, it's, you know, I, I think I remember talking with you and saying, Hey, you know, this scenario planning we did at the beginning of this pandemic journey was, you know, 30 different scenarios roughly, and none of which sort of panned out the way it actually did, which was a pretty incredible growth story as we think about how we helped customers, you know, drive workforce productivity, enabled their business model during the all remote work environment. That was the pandemic created. And couple that with the, you know, the, the rise then and the infrastructure spin as we got towards the tail end of the, of the pandemic coupled with, you know, the spin out of VMware, which culminated last November, as you know, as we completed that, which unlocked a pathway back to investment grade within unlocked, quite frankly shareholder value, capital allocation frameworks. It's really been a remarkable, you know, 18, 24 months. It's, it's never dull at Dell Technologies. Lemme put it that way. >>Well, well, I was impressed with you, Tom, before the leverage buyout and then what I've seen you guys navigate through is, is, is truly amazing. Well, let's talk about the challenging macro. I mean, I've been through a lot of downturns, but I've never seen anything quite like this with fed tightening and you're combating inflation, you got this recession looming, there's a bear market you got, but you got zero unemployment, you're rising wages, strong dollar, and it's very confusing. But it spending is, you know, it's somewhat softer, but it's still not bad. How are you seeing customers behave? How is Dell responding? >>Yeah, look, if you think about the markets we play in Dave, and we should start there as a grounding, you know, the, the total market, the core market that we think about is roughly 700 and, you know, 50 billion or so. If you think about our core IT services capability, you couple that with some of the, the growth initiatives that we're driving and the adjacent markets that that, that brings in, you're roughly talking a 1.4 to $1.5 trillion market opportunity, total addressable market. And so from from that perspective, we're extraordinarily bullish on where are we in the journey as we continue to grow and expand. You know, we have, we're number one share in just about every category that we plan, but yet when you look at that, you know, number one share in some of these, you know, our highest share position may be, you know, low thirties and maybe in the high end of storage you're at the upper end of thirties or 40%. >>But the opportunity there to continue to expand the core and, and continue to take share and outperform the market is truly extraordinary. So, so you step back and think about that, then you say, okay, what have we seen over the last number of months and quarters? It's been, you know, really great performance through the pandemic as, as you highlighted, we actually had a really strong first half of the year of our fiscal year 23 with revenue up 12% operating income up 12% for the first half. You know, what we talked about as you, if you might recall in our second quarter earnings, was the fact that we were starting to see softness. We had seen it in the consumer PC space, which is not a big area of focus for us in the sense of our, our total revenue stream, but we started to see commercial PC soften and we were starting to see server demand soften a bit and storage demand was, was holding quite frankly. >>And so we gave a a framework around guidance for the rest of the year as a, of what we were seeing. You know, the macro environment as you highlight it continues to be challenging. You know, if you look at inflation rates and the efforts by central banks across the globe to with through interest rate rise to press down and, and constrain growth and push down inflation, you couple that with supply chain challenges that continue principle, particularly in the ISG space. And then you couple that with the Ukraine war and the, and the energy crisis that that's created. And particularly in Europe, it's a pretty dynamic environment. And, but I'm confident, you know, I'm confident in the long term, but I do think that there is, you know, that there's navigation that we're going to have to do over the coming number of quarters, who knows quite how long, you know, to, to make sure the business is properly positioned and, you know, we've got a great portfolio and you're gonna talk to some of the team LA later on as you think your way through some of the solution capabilities we're driving what we're seeing around technology trends. >>So the opportunities there, there's some short term navigation that we're gonna need to do just to make sure that we address some of the, you know, some of the environmental things that we're seeing right >>Now. Yeah. And as a global company, of course you're converting local currencies back to appreciated dollars. That's, that's, that's another headwind. But as you say, I mean, that's math and you're navigating it. And again, I've seen a lot of downturns, but you know, the best companies not only weather the storm, but they invest in ways they that allow them to cut out, come out the other side stronger. So I wanna talk about that longer term opportunity, the relationship between the core, the the business growth. You mentioned the tam, I mean, even as a lower margin business, if, if you can penetrate that big of a tam, you could still throw off a lot of cash and you've got other levers to turn in potentially acquisitions and software. And, but so ultimately what gives you confidence in Dell's future? How should we think about Dell's future? >>Yeah, look, I, I think it comes down to we are extraordinarily excited about the opportunity over the long term digital transformation continues. I I am on numerous customer and CIO calls every week. Customers are continuing to invest in digital transformation and infrastructure to enable their business model. Yes, maybe it's gonna slow or, or pause or maybe they're not gonna invest quite at the same rate over the next number of quarters, but over the long term the needs are there. You look at what we're doing around the, the growth opportunities that we see, not only in our core space where we continue to invest, but also in the, what we call the strategic adjacencies. Things like 5G and modern telecom infrastructure as our, the telecom providers across the globe open up their, what a cl previous been closed ecosystems, you know, to open architecture. You think about, you know, what we're doing around the edge and the distribution now that we're seeing of compute and storage back to the edge given data gravity and latency matters. >>And so we're pretty bullish on the opportunity in front of us, you know, yes, we will and we're continuing to invest and you know, Jeff Boudreau talk about that I think later on in the program. So I'm excited about the opportunities and you look at our cash flow generation capability, you know, we are in, in, in normal times a, a cash flow generation machine and we'll continue to do so, You know, we've got a negative, you know, CCC in terms of, you know, how do we think about efficiency of working capital? And we look at our, you know, our capital allocation strategy, which has now returned, you know, somewhere in near 60% of our free cash flow back to shareholders. And so, you know, there's lots to, lots of reasons to think about why this, you know, we are a great sort of, I think value creation opportunity and a over the long term that the long term trends are with us, and I expect them to continue to be so, >>Yeah, and you guys, you, you, you do what you say you're gonna do. I mean, I said in my, in my other piece that I did recently, I think you guys put 46 billion on the, on the, on the balance sheet in terms of debt. That's down to I think 16 billion in the core, which that's quite remarking and that gives you some other opportunities. Give us your, your closing thoughts. I mean, you kind of just addressed why Dell is a good long term play, but I'll give you an opportunity to bring us home. >>Hey, Dave. Yeah, look, I, I just think if you look at the good, the market opportunity, the size and scale of Dell and how we think about the competitive advantages that we have, we com you know, if you look at, say we're a hundred billion revenue company, which we were a year, you know, last year, that as we reported roughly 60, 65 billion of that in the client, in in PC space, roughly, you know, 35 to 40 billion in the ISG or infrastructure space, those markets are gonna continue the opportunity to grow, share, grow at a premium to the market, drive, cash flow, drive, share gain is clearly there. You couple that with, you know, what we think the opportunity is in these adjacent markets, whether it's telecom, the edge, what we're thinking around data services, data management, you know, we, and you cut, you put that together with the long term trends around, you know, data creation and digital transformation. We are extraordinarily well positioned. We have the largest direct selling organization in in the technology space. We have the largest supply chain, our services footprint, you know, well positioned in my mind to take advantage of the opportunities as we move forward. >>Well Tom, really appreciate you taking the time to speak with us. Good to see you again. >>Nice seeing you. Thanks Dave. >>All right. You're watching the Cubes exclusive behind the scenes coverage of Dell Technology Summit 2022. In a moment, I'll be back with Jeff Boudreau. He's the president of Dell's ISG Infrastructure Solutions Group. He's responsible for all the important enterprise business at Dell, and we're excited to get his thoughts, keep it right there. >>Welcome back to the cube's exclusive coverage of the Dell Technology Summit. I'm Dave Ante and we're going inside with Dell execs to extract the signal from the noise. And right now we're gonna dig into customer requirements in a data intensive world and how cross cloud complexities get resolved from a product development perspective and how the ecosystem fits in to that mosaic to close the gaps and accelerate innovation. And with me now as friend of the cube, Jeff Boudreau, he's the president of the Infrastructure Solutions Group, ISG at Dell Technologies. Jeff, always good to see you. Welcome. >>You too. Thank you for having me. It's great to see you and thanks for having me back on the cube. I'm thrilled to be here. >>Yeah, it's our pleasure. Okay, so let's talk about what you're observing from customers today. You know, we talk all the time about operating in a data driven multi-cloud world, blah, blah, blah, blah. But what does that all mean to you when you have to translate that noise into products that solve specific customer problems, Jeff? >>Sure. Hey, great question. And everything always starts with our customers. There are motivation, they're top of mind, everything we do, my leadership team and I spend a lot of time with our customers. We're listening, we're learning, we're really understanding their pain points, and we wanna get their feedback in regards to our solutions, both turn and future offerings, really ensure that we're aligned to meeting their business objectives. I would say from these conversations, I'd say customers are telling us several things. First, it's all about data for no surprise going back to your opening. And second, it's about the multi-cloud world. And I'd say the big thing coming from all of this is that both of those are driving a ton of complexity for our customers. And I'll unpack that just a bit, which is first the data. As we all know, data is growing at unprecedented rates with more than 90% of the world's data being produced in the last two years alone. >>And you can just think of that in it's everywhere, right? And so as it as the IT world shifts towards distributed compute to support that data growth and that data gravity to really extract more value from that data in real time environments become inherently more and more hybrid and more and more multi-cloud. Which leads me to the second key point that I've been hearing from our customers, which it's a multi-cloud world, not new news. Customers by default have multiple clouds running across multiple locations that's on-prem and off-prem, it's running at the edge and it's serving a variety of different needs. Unfortunately, for most of our CU customers, multi-cloud is actually added to their complexity. As we've discussed. It's been a lot more of multi-cloud by default versus multi-cloud by design. And if you really think about our customers, I mean, I, I, I've talking to 'EM all the time, you think about the data complexity, that's the growth and the gravity. >>You think about their infrastructure complexity shifting from central to decentralized it, you think about multi-cloud complexity. So you have these walled gardens, if you will. So you have multiple vendors and you have these multiple contracts that all creates operational complexity for their teams around their processes of their tools. And then you think about security complexity that that dries with the, just the increased tax service and the list goes on. So what are we seeing for our customers? They, what they really want from us, and what they're asking us for is simplicity, not complexity. The immediacy, not latency. They're asking for open and aligned versus I'd say siloed and closed. And they're looking for a lot more agility and not rigidity in what we do. So they really wanna simplify everything. They're looking for a simpler IT and a more agile it. And they want more control of their data, right? >>And so, and they want to extract more of the value to enrich their business or their customer engagements, which all sounds pretty obvious and we've probably all heard it a bunch, but it's really hard to achieve. And that's where I believe, and we believe as Dell that we, it creates a big opportunity for us to really help our customers as that great simplifier of it. We're already doing this today on just a couple quick examples. First is Salesforce. We've supported recently, we've supported their global expansion with a multi-cloud solution to help them drive their business growth. Our solution delivered a reliable and consistent IT experience. We go back to that complexity and it was across a very distributed environment, including more than 60 data centers, 230 countries and hundreds of thousands of customers. It really provided Salesforce with the flexibility of placing workloads and data in an environment based on the right service level. >>Objective things like cost complexity or even security compliance considerations. The second customer A is a big New England Patriot fan. And Dan, Dave, I know you are as well. Oh yeah, this one's near, near data to my heart, it's the craft group. We just created a platform to span all the businesses that create more, I'd say data driven, immersive, secure experience, which is allowing them to capture data at the edge and use it for real time insights for things like cyber resiliency, but also like safety of the facilities. And as being a PA fan like I am, did they truly are meeting us where we are in our seats on their mobile devices and also in the parking lot. So just keep that in mind next time you're there. The bottom line, everything we're doing is really to make it simpler for our customers and to help them get the most of their data. I'd say we're gonna do this, is it through a multi-cloud by design approach, which we talked a lot about with you and and others at Dell Tech world earlier this year, >>Right? And we had Salesforce on, actually at Dell Tech group. The craft group is interesting because, you know, when you get to the stadium, you know, everybody's trying to get, get, get out to the internet and, and, but then the experience is so much better if you can actually, you know, deal with that edge. So I wanna talk about complexity though. You got data, you got, you know, the, the edge, you got multiple clouds, you got a different operating model across security model, different. So a lot of times in this industry we solve complexity with more complexity and it's like a bandaid. So I wanna, I wanna talk to, to how you're innovating around simplicity in ISG to address this complexity and what this means for Dell's long term strategy. >>Sure, I'd love to. So first I, I'd like to state the obvious, which are our investments in our innovations really focused on advancing, you know, our, our our customers needs, right? So we are really, our investments are gonna be targeted. We, we believe customers can have the most value. And some of that's gonna be around how we create strategic partnerships as well connected to what we just spoke about. Much of the complexity of customers have or experiencing is in the orchestration and management of all the data in all these different places and customers, you know, they must be able to quickly deploy and operate across cloud environments. They need to increase their developer productivity, really enabling those developers that do what they do best, which is creating more value for their customers than for their businesses. Our innovation efforts are really focused on addressing this by delivering an open and modern IT architecture that allows customers to run and manage any workload in any cloud anywhere. >>Data lives we're focused on, also focused on consumption based solutions, which allow for a greater degree of simplicity and flexibility, which they're really asking for as well. The foundation for this is our software to define common storage layer, that common storage layer. You can think about this Dave, as our ias if you will. It underpins our data access in mobility across all data types and locations. So you can think private, public, telecom, colo, edge, and it's delivered in a secure, holistic, and consistent cloud experience through Apex. We are making a ton of progress to let you just to be, just to be clear, we've made headway in things like Project Alpine, which you're very well aware of. This is our storage as a service. We announce this back in in January, which brings our unique software IP from our flagship storage platform to all the major public clouds. >>Really delivering the best of both worlds, allowing our customers to take advantage of Dell's enterprise class data services and storage software, such as performance at scale, resiliency, efficiency and security. But in addition to that, we're leveraging the breadth of the public cloud services, right? They're on demand scaling capabilities and access to analytical services. So in addition, we're really, we're, we're on our way to win at the edge as well with Project Frontier, which reduces complexity at the edge by creating an open and secure software platform to help our customers simplify their edge operations, optimize their edge environments and investments, secure that edge environment as well. I believe you're gonna be discussing Project Frontier here with Sam Gro Crop, the very near future. So I won't give up too many more details there. And lastly, we're also scaling Apex, which, oh, well, shifting from our vision, really shifting from vision to reality and introducing several new Apex service offerings, which are coming to market over the next month or so. And the intent is really supporting our customers on their as a service transitions by modernize the consumption experience and providing that flexible as a service model. Ultimately, we're trying to help our customers achieve that multi-cloud by design to really simplify it and unlock the power of their data. >>So some good examples there. I I like to talk about the super Cloud as you, you know, you're building on top of the, you know, hyperscale infrastructure and you got Apex is your cloud, the common storage layer, you call it your is. And that's, that's a ingredient in what we call the super cloud out to the edge. You have to have a common platform there and one of the hallmarks of a cloud company. And as you become a cloud company, everybody's a cloud company ecosystem becomes really, really important in terms of product development and, and innovation. Matt Baker always loves to stress it's not a zero zero sum game. And, and I think Super Cloud recognizes that, that there's value to be built on top of other clouds and, and, and of course on top of your infrastructure so that your ecosystem can add value. So what role does the ecosystem play there? >>For me, it's, it's pretty clear. It's, it's, it's critical. I can't say that enough above the having an open ecosystem. Think about everything we just discussed, and I agree with your super cloud analogy. I agree with what Matt Baker had said to you, I would certain no one company can actually address all the pain points and all the issues and challenges our customers are having on their own, not one. I think customers really want and deserve an open technology ecosystem, one that works together. So not these close stacks that discourages interoperability or stifles innovation and productivity of our, of each of our teams. We del I guess have a long history of supporting open ecosystems that really put customers first. And to be clear, we're gonna be at the center of the multi-cloud ecosystem and we're working with partners today to make that a reality. >>I mean, just think of what we're doing with VMware. We continue to build on our first and best alliances with them in August at their VMware explorer, which I know you were at, we announced several joint engineering initiatives to really help customers more easily manage and gain value from their data and their infrastructure. For multi-cloud specifically, we strength our relationship with VMware and with Tansu as part of that. In addition, just a few weeks ago we announced our partnership with Red Hat to simplify our multi-cloud deployments for managing containerized workloads. I'd say, and using your analogy, I could think of that as our multicloud platform. So that's kind of our PAs layer, if you will. And as you're aware, we have a very long standing and strategic partnership with Microsoft and I'd say stay tuned. There's a lot more to come with them and also others in this multicloud space. >>Shifting a bit to some of the growth engines that my team's responsible for the edge, right? As you think about data being everywhere, we've established partnerships for the Edge as well with folks like PTC and Litmus for the manufacturing edge, but also folks like Deep North for the retail edge analytics and data management. Using your Supercloud analogy, Dave the sa, right? This is our Sasa, we've announced that we're collaborating, partnering with folks like Snowflake and, and there's other data management companies as well to really simplify data access and accelerate those data insights. And then given customers choice of where they'd like to have their IT and their infrastructure, we've we're expanding our colo partnerships as well with folks like eex and, and they're allowing us to broaden our availability of Apex, providing customers the flexibility to take advantage of those as a service offerings wherever it's delivered and where they can get the most value. So those are just some you can hear from me. I think it's critical not only for, for us, I think it's critical for our customers. I think it's been critical, critical for the entire, you know, industry as a whole to really have that open technology ecosystem as we work with our customers on our multi-cloud solutions really to meet their needs. We'll continue to collaborate with whoever customers choose and you know, and who they want us to do business with. So I'd say a lot more coming in that space. >>So it's been an interesting three years for you, just, just over three years now since you've been made the president of the IS isg. And so you had to dig in and, and it was obviously a strange time around the world, but, but you really had to look at, okay, how do we modernize the platform? How do we make it, you know, cloud first, You've mentioned the edge, we're expanding. So what are the big takeaways? What do you want customers and our audience to understand? Just some closing thoughts and if you could summarize. >>Sure. So I'd say first, you know, we discussed we're working in a very fast paced, ever-changing market with massive amounts of data that needs to be managed. It's very complex and our customers need help with that complexity. I believe that Dell Technologies is uniquely positioned to help as their multicloud champion. No one else can solve the breadth and depth of the challenges like we can. And we're gonna help our customers move forward when they basically moving from a multi-cloud by default, as we've discussed before, to multicloud by design. And I'm really excited for the opportunity to work with our customers to help them expand that ecosystem as they truly realize the future of it and, and what they're trying to accomplish. >>Jeff, thanks so much. Really appreciate your time. Always a pleasure. Go pats and we'll see you on the blog. >>Thanks Dave. >>All right, you're watching exclusive insight insights from Dell Technology Summit on the cube, your leader in enterprise and emerging tech coverage. >>Hello everyone, this is Dave Lanta and you're watching the Cubes coverage of the Dell Technology Summit 2022 with exclusive behind the scenes interviews featuring Dell executive perspectives. And right now we're gonna explore Apex, which is Dell's as a service offering Dell's multi-cloud and edge strategies and the momentum around those. And we have news around Project Frontier, which is Dell's vision for its edge platform. And there's so much happening here. And don't forget it's cyber security Awareness month. Sam Grot is here, he's the senior vice president of marketing at Dell Technologies. Sam, always great to see you. How you doing? >>Always great to be here, Dave. >>All right, let's look at cloud. Everybody's talking about cloud Apex, multi-cloud, what's the update? How's it going? Where's the innovation and focal points of the strategy? >>Yeah, yeah. Look Dave, if you think back over the course of this year, you've really heard, heard us pivot as a company and discussing more and more about how multi-cloud is becoming a reality for our customers today. And when we listen and talk with our customers, they really describe multi-cloud challenges and a few key threads. One, the complexity is growing very, very quickly. Two, they're having a harder time controlling how their users are accessing the various different clouds. And then of course, finally the cloud costs are growing unchecked as well. So we, we like to describe this phenomenon as multi-cloud by design. We're essentially, organizations are waking up and seeing cloud sprawl around their organization every day. And this is creating more and more of those challenges. So of course at Dell we've got a strong point of view that you don't need to build multicloud by by default, rather it's multicloud by design where you're very intentional in how you do multicloud. >>And how we deliver multicloud by design is through apex. Apex is our modern cloud and our modern consumption experience. So when you think about the innovation as well, Dave, like we've been on a pretty quick track record here in that, you know, the beginning of this year we introduced brand new Apex backup services that provides that SAS based backup service. We've introduced or announced project outline, which is bringing our storage software, intellectual property from on-prem and putting it and running it natively in the public cloud. We've also introduced new Apex cyber recovery services that is simplifying how customers protect against cyber attacks. They can run an Amazon Azure, aw, I'm sorry, Amazon, aws, Azure or Google. And then, you know, we are really focused on this multi-cloud ecosystem. We announce key partnerships with SaaS providers such as Snowflake, where you can now access our information or our data from on-prem through the Snow Snowflake cloud. >>Or if needed, we can actually move the data to the Snowflake cloud if required. So we're continuing to build out that ecosystem SaaS providers. And then finally I would say, you know, we made a big strategic announcement just recently with Red Hat, where we're not only delivering new Apex container services, but we announce the strategic partnership to build jointly engineered solutions to address hybrid and multi-cloud solutions going forward. You know, VMware is gonna always continue to be a key partner of ours at the la at the recent VMware explorer we announced new Tansu integration. So, So Dave, I, I think in a nutshell we've been innovating at a very, very fast pace. We think there is a better way to do multi-cloud and that's multi-cloud by design. >>Yeah, we heard that at Dell Technologies world. First time I had heard that multi-cloud by design versus sort of default, which is great Alpine, which is sort of our, what we called super cloud in the making. And then of course the ecosystem is critical for any cloud company. VMware of course, you know, top partner, but the Snowflake announcement was very interesting Red Hat. So seeing that expand, now let's go out to the edge. How's it going with the edge expansion? There's gotta be new speaking of ecosystem, the edge is like a whole different, you know, OT type, that's right, ecosystem, that's telcos what and what's this new frontier platform all about? >>Yeah, yeah. So we've talked a lot about cloud and multi clouds, we've talked about private and hybrid cloud, we've talked about public clouds, clouds and cos, telcos, et cetera. There's really been one key piece of our multi-cloud and technology strategy that we haven't spent a lot of time on. And that's the edge. And we do see that as that next frontier for our customers to really gain that competitive advantage that is created from their data and get closer to the point of creation where the data lives. And that's at the edge. We see the edge infrastructure space growing very, very quickly. We see upwards of 300% year of year growth in terms of amount of data being created at the edge. That's almost 3000 exabytes of data by 2026. So just incredible growth. And the edge is not really new for Dell. We've been at it for over 20 years of delivering edge solutions. >>81% of the Fortune 100 companies in the US use Dell solutions today at the Edge. And we are the number one OEM provider of Edge solutions with over 44,000 customers across over 40 industries and things like manufacturing, retail, edge healthcare, and more. So Dave, while we've been at it for a long time, we have such a, a deep understanding of how our customers are using Edge solutions. Say the bottom line is the game has gotta change. With that growth that we talked about, the new use cases that are emerging, we've got to un unlock this new frontier for customers to take advantage of the edge. And that's why we are announcing and revealing Project Frontier. And Project Frontier in its most simplest form, is a software platform that's gonna help customers and organizations really radically simplify their edge deployments by automating their edge operations. You know, with Project Frontier organizations are really gonna be able to manage, OP, and operate their edge infrastructure and applications securely, efficiently and at scale. >>Okay, so it is, first of all, I like the name, it is software, it's a software architecture. So presumably a lot of API capabilities. That's right. Integration's. Is there hardware involved? >>Yeah, so of course you'll run it on Dell infrastructure. We'll be able to do both infrastructure orchestration, orchestration through the platform, but as well as application orchestration. And you know, really there's, there's a handful of key drivers that have been really pushing our customers to take on and look at building a better way to do the edge with Project Frontier. And I think I would just highlight a handful of 'em, you know, freedom of choice. We definitely see this as an open ecosystem out there, even more so at the Edge than any other part of the IT stack. You know, being able to provide that freedom of choice for software applications or I O T frameworks, operational technology or OT for any of their edge use cases, that's really, really important. Another key area that we're helping to solve with Project Frontier is, you know, being able to expect zero trust security across all their edge applications from design to deployment, you know, and of course backed by an end and secure supply chain is really, really important to customers. >>And then getting that greater efficiency and reliability of operations with the centralized management through Project Frontier and Zero Touch deployments. You know, one of the biggest challenges, especially when you get out to the far, far reach of the frontier is really IT resources and being able to have the IT expertise and we built in an enormous amount of automation helps streamline the edge deployments where you might be deploying a single edge solution, which is highly unlikely or hundreds or thousands, which is becoming more and more likely. So Dave, we do think Project Frontier is the right edge platform for customers to build their edge applications on now and certain, excuse me, certainly, and into the future. >>Yeah. Sam, no truck rolls. I like it. And you, you mentioned, you mentioned Zero trust. So we have Mother's Day, we have Father's Day. The kids always ask When's kids' day? And we of course we say every day is kids' day and every day should be cybersecurity awareness day. So, but we have cybersecurity awareness month. What does it mean for Dell? What are you hearing from customers and, and how are you responding? >>Yeah, yeah. No, there isn't a more prevalent pop of mind conversation, whether it's the boardroom or the IT departments or every company is really have been forced to reckon with the cybersecurity and ransom secure issues out there. You know, every decision in IT department makes impacts your security profile. Those decisions can certainly, positively, hopefully impact it, but also can negatively impact it as well. So data security is, is really not a new area of focus for Dell. It's been an area that we've been focused on for a long time, but there are really three core elements to cyber security and data security as we go forward. The first is really setting the foundation of trust is really, really important across any IT system. And having the right supply chain and the right partner to partner with to deliver that is kind of the foundation in step one. >>Second, you need to of course go with technology that is trustworthy. It doesn't mean you are putting it together correctly. It means that you're essentially assembling the right piece parts together. That, that coexist together in the right way. You know, to truly change that landscape of the attackers out there that are gonna potentially create risk for your environment. We are definitely pushing and helping to embrace the zero trust principles and architectures that are out there. So finally, while when you think about security, it certainly is not absolute all correct. Security architectures assume that, you know, there are going to be challenges, there are going to be pain points, but you've gotta be able to plan for recovery. And I think that's the holistic approach that we're taking with Dell. >>Well, and I think too, it's obviously security is a complicated situation now with cloud you've got, you know, shared responsibility models, you've got that a multi-cloud, you've got that across clouds, you're asking developers to do more. So I think the, the key takeaway is as a security pro, I'm looking for my technology partner through their r and d and their, you mentioned supply chain processes to take that off my plate so I can go plug holes elsewhere. Okay, Sam, put a bow on Dell Technology Summit for us and give us your closing thoughts. >>Yeah, look, I I think we're at a transformative point in it. You know, customers are moving more and more quickly to multi-cloud environments. They're looking to consume it in different ways, such as as a service, a lot of customers edge is new and an untapped opportunity for them to get closer to their customers and to their data. And of course there's more and more cyber threats out there every day. You know, our customers when we talk with them, they really want simple, consistent infrastructure options that are built on an open ecosystem that allows them to accomplish their goals quickly and successfully. And look, I think at Dell we've got the right strategy, we've got the right portfolio, we are the trusted partner of choice, help them lead, lead their, their future transformations into the future. So Dave, look, I think it's, it's absolutely one of the most exciting times in it and I can't wait to see where it goes from here. >>Sam, always fun catching up with you. Appreciate your time. >>Thanks Dave. >>All right. A Dell tech world in Vegas this past year, one of the most interesting conversations I personally had was around hybrid work and the future of work and the protocols associated with that and the mindset of, you know, the younger generation. And that conversation was with Jen Savira and we're gonna speak to Jen about this and other people and culture topics. Keep it right there. You're watching the cube's exclusive coverage of Dell Technology Summit 2022. Okay, we're back with Jen Vera, who's the chief human resource officer of Dell, and we're gonna discuss people, culture and hybrid work and leadership in the post isolation economy. Jen, the conversations that we had at Dell Tech World this past May around the new work environment were some of the most interesting and engaging that I had personally. So I'm really eager to, to get the update. It's great to see you again. Thanks for coming on the cube. >>Thanks for having me Dave. There's been a lot of change in just a short amount of time, so I'm excited to, to share some of our learnings >>With you. I, I mean, I bet there has, I mean, post pandemic companies, they're trying, everybody's trying to figure out the return to work and, and what it looks like. You know, last May there was really a theme of flexibility, but depending, we talked about, well, millennial or not young old, and it's just really was mixed, but, so how have you approached the topic? What, what are your policies? What's changed since we last talked? You know, what's working, you know, what's still being worked? What would you recommend to other companies to over to you? >>Yeah, well, you know, this isn't a topic that's necessarily new to Dell technology. So we've been doing hybrid before. Hybrid was a thing. So for over a decade we've been doing what we called connected workplace. So we have kind of a, a history and we have some great learnings from that. Although things did change for the entire world. You know, March of 2020, we went from kind of this hybrid to everybody being remote for a while. But what we wanted to do is, we're such a data driven company, there's so many headlines out there, you know, about all these things that people think could happen will happen, but there wasn't a lot of data behind it. So we took a step back and we asked our team members, How do you think we're doing? And we asked very kind of strong language because we've been doing this for a while. >>We asked them, Do you think we're leading in the world of hybrid in 86% of our team members said that we were, which is great, but we always know there's nuance right behind that macro level. So we, we asked 'em a lot of different questions and we just went on this kind of myth busting journey and we decided to test some of those things. We're hearing about Culture Willow Road or new team members will have trouble being connected or millennials will be different. And we really just collected a lot of data, asked our team members what their experience is. And what we have found is really, you don't have to be together in the office all the time to have a strong culture, a sense of connection, to be productive and to have it really healthy business. >>Well, I like that you were data driven around it in the data business here. So, but, but there is a lot of debate around your culture and how it suffers in a hybrid environment, how remote workers won't get, you know, promoted. And so I'm curious, you know, and I've, and I've seen some like-minded companies like Dell say, Hey, we, we want you guys to work the way you wanna work. But then they've, I've seen them adjust and say, Well yeah, but we also want you to know in the office be so we can collaborate a little bit more. So what are you seeing at Dell and, and, and how do you maintain that cultural advantage that you're alluding to in this kind of strange, new ever changing world? >>Yeah, well I think, look, one approach doesn't fit all. So I don't think that the approach that works for Dell Technologies isn't necessarily the approach that works for every company. It works with our strategy and culture. It is really important that we listen to our team members and that we support them through this journey. You know, they tell us time and time again, one of the most special things about our culture is that we provide flexibility and choice. So we're not a mandate culture. We really want to make sure that our team members know that we want them to be their best and do their best. And not every individual role has the same requirements. Not every individual person has the same needs. And so we really wanna meet them where they are so that they can be productive. They feel connected to the team and to the company and engaged and inspired. >>So, you know, for, for us, it really does make sense to go forward with this. And so we haven't, we haven't taken a step back. We've been doing hybrid, we'll continue to do hybrid, but just like if you, you know, we talk about not being a mandate. I think the companies that say nobody will come in or you have to come in three days a week, all of that feels more limiting. And so what we really say is, work out with your team, work out with your role, workout with your leader, what really makes the most sense to drive things forward. >>I >>You were, so >>That's what we, you were talking before about myths and you know, I wanna talk about team member performance cuz there's a lot of people believe that if, if you're not in the office, you have disadvantages, people in the office have the advantage cuz they get FaceTime. Is is that a myth? You know, is there some truth to that? What, what do you think about that? >>Well, for us, you know, we look, again, we just looked at the data. So we said we don't wanna create a have and have not culture that you're talking about. We really wanna have an inclusive culture. We wanna be outcome driven, we're meritocracy. But we went and we looked at the data. So pre pandemic, we looked at things like performance, we looked at rewards and recognition, we looked at attrition rates, we looked at sentiment, Do you feel like your leader is inspiring? And we found no meaningful differences in any of that or in engagement between those who worked fully remote, fully in the office or some combination between. So our data would bust that myth and say, it doesn't, you don't have to be in an office and be seen to get ahead. We have equitable opportunity. Now, having said that, you always have to be watching that data. And that's something that we'll continue to do and make sure that we are creating equal opportunity regardless of where you work. >>And it's personal too, I think, I think some people can be really productive at home. I happen to be one that I'm way more productive in the office cause the dogs aren't barking. I have less distractions. And so I think we think, and, and I think the takeaway that in just in talking to, to, to you Jen and, and folks at Dell is, you know, whatever works for you, we're we're gonna, we're gonna support. So I I wanted to switch gears a little bit, talk about leadership and, and very specifically empathic leadership has been said to be, have a big impact on attracting talent, retaining talent, but, but it's hard to have empathy sometimes. And I know I saw some stats in a recent Dell study. It was like two thirds the people felt like their organization underestimates the people requirements. And I, I ask myself, I'm like, what am I missing? I hope, you know, with our folks, so especially as it relates to, to transformation programs. So how can human resource practitioners support business leaders generally, specifically as it relates to leading with empathy? >>I think empathy's always been important. You have to develop trust. You can have the best strategy in the world, right? But if you don't feel like your leader understands who you are, appreciates the the value that you bring to the company, then you're not gonna get very far. So I think empathetic leadership has always been part of the foundation of a trusting, strong relationship between a leader and a team member. But if I think we look back on the last two years, and I imagine it'll be even more so as we go forward, empathetic leadership will be even more important. There's so much going on in the world, politically, socially, economically, that taking that time to say you want your team members to see you as credible, that you and confident that you can take us forward, but also that, you know, and understand me as a human being. >>And that to me is really what it's about. And I think with regard to transformation that you brought up, I think one of the things we forget about is leaders. We've probably been thinking about a decision or transformation for months or weeks and we're ready to go execute, we're ready to go operationalize that thing. And so sometimes when we get to that point, because we've been talking about it for so long, we send out the email, we have the all hands and we just say we're ready to go. But our team members haven't always been on that journey for those months that we have. And so I think that empathetic moment to say, Okay, not everybody is on a change curve where I am. Let's take a pause, let me put myself in their shoes and really think about how we bring everybody along. >>You know, Jen, in the spirit of myth busting, I mean I'm one of those people who felt like that a business is gonna have a hard time, harder time fostering this culture of collaboration and innovation post isolation economy as they, they could pre covid. But you know, I noticed there's a, there's an announcement today that came across my desk, I think it's from Newsweek. Yes. And, and it's the list of top hundred companies recognized for employee motivation satisfaction. And it was really interesting because you, you always see, oh, we're the top 10 or the top hundred, But this says as a survey of 1.4 million employees from companies ranging from 50 to 10,000 employees. And it recognizes the companies that put respect, caring, and appreciation for their employees at the center of their business model. And they doing so have earned the loyalty and respect of the people who work for them. >>Number one on the list is Dell sap. So congratulations SAP was number two. I mean, there really isn't any other tech company on there, certainly no large tech companies on there. So I always see these lists, they go, Yeah, okay, that's cool, top a hundred, whatever. But top one in, in, in an industry where there's only two in the top is, is pretty impressive. And how does that relate to fostering my earlier skepticism of a culture of collaboration? So first of all, congratulations, you know, how'd you do it? And how are you succeeding in, in this new world? >>Well thanks. It does feel great to be number one, but you know, it doesn't happen by accident. And I think while most companies have a, a culture and a spouse values, we have ours called the culture code. But it's really been very important to us that it's not just a poster on the wall or or words on paper. And so we embed our culture code into all of our HR practices, that whole ecosystem from recognition of rewards to performance evaluation, to interviewing, to development. We build it into everything. So it really reflects who we are and you experience it every day. And then to make sure that we're not, you know, fooling ourselves, we ask all of our employees, do you feel like the behaviors you see and the experience you have every day reflects the culture code? And 94% of our team members say that, in fact it does. So I think that that's really been kind of the secret to our success. If you, if you listen to Michael Dell, he'll always say, you know, the most special thing about Dell is our culture and our people. And that comes through being very thoughtful and deliberate to preserve and protect and continue to focus on our culture. >>Don't you think too that repetition and, well first of all, belief in that cultural philosophy is, is important. And then kind of repeating, like you said, Yeah, it's not just a poster in the wall, but I remember like, you know, when we're kids, your parents tell you, okay, power positive thinking, do one to others as others, you know, you have others do it to you. Don't make the say you're gonna do some dumb things but don't do the same dumb things twice and you sort of fluff it up. But then as you mature you say, Wow, actually those were, >>They might have had a >>Were instilled in me and now I'm bringing them forward and, you know, paying it forward. But, but so i, it, it, my, I guess my, my point is, and it's kind of a point observation, but I'll turn it into a question, is isn't isn't consistency and belief in your values really, really important? >>I couldn't agree with you more, right? I think that's one of those things that we talk about it all the time and as an HR professional, you know, it's not the HR people just talking about our culture, it's our business leaders, it's our ceo, it's our COOs ev, it's our partners. We share our culture code with our partners and our vendors and our suppliers and, and everybody, this is important. We say when you interact with anybody at Dell Technologies, you should expect that this is the experience that you're gonna get. And so it is something that we talk about that we embed in, into everything that we do. And I think it's, it's really important that you don't just think it's a one and done cuz that's not how things really, really work >>Well. And it's a culture of respect, you know, high performance, high expectations, accountability at having followed the company and worked with the company for many, many years. You always respect the dignity of your partners and your people. So really appreciate your time Jen. Again, congratulations on being number one. >>Thank you so much. >>You're very welcome. Okay. You've been watching a special presentation of the cube inside Dell Technology Summit 2022. Remember, these episodes are all available on demand@thecube.net and you can check out s silicon angle.com for all the news and analysis. And don't forget to check out wikibon.com each week for a new episode of breaking analysis. This is Dave Valante, thanks for watching and we'll see you next time.

>>Welcome back to the Cube's exclusive coverage of the Dell Technology Summit. I'm Dave Ante. We're going inside with Dell Execs to extract the signal from the noise. And right now we're gonna dig into customer requirements in a data intensive world and how cross cloud complexities get resolved from a product development perspective and how the ecosystem fits in to that mosaic to close the gaps and accelerate innovation. And with me now is friend of the Cube, Jeff Boudreau. He's the president of the Infrastructure Solutions Group, ISG at Dell Technologies. Jeff, always good to see you. Welcome. >>You too. Thank you for having me. It's great to see you and thanks for having me back on the key. I'm thrilled to be here. >>Yeah, it's our pleasure. Okay, so let's talk about what you're observing from customers today. You know, we talk all the time about operating in a data driven multi-cloud world, blah, blah, blah, blah. But what does that all mean to you when you have to translate that noise into products that solve specific customer problems, Jeff? >>Sure. Hey, great question. And everything always starts with our customers. They're our motivation. They're top of mind in everything we do. My leadership team and I spend a lot of time with our customers. We're listening, we're learning, we're really understanding their pain points, and we wanna get their feedback in regards to our solutions. Both turn and future offerings really ensured that we're aligned to meeting their business objectives. I would say from these conversations, I'd say customers are telling us several things. First, it's all about data for no surprise going back to your opening. And second, it's about the multi-cloud world. And I'd say the big thing coming from all of this is that both of those is driving a ton of complexity for our customers. And I'll unpack that just a bit, which is first the data. As we all know, data is growing at unprecedented rates with more than 90% of the world's data being produced in the last two years alone. >>And you can just think of that in its everywhere, right? And so as it is, the IT world shifts towards distributed compute to support that data growth and that data gravity to really extract more value from that data in real time environments become inherently more and more hybrid and more and more multi-cloud. Which leads me to the second key point that I've been hearing from our customers, which it's a multi-cloud world, not new news. Customers by default have multiple clouds running across multiple locations. That's on-prem and off, it's running at the edge and it's serving a variety of different needs. Unfortunately, for most of our CU customers, multicloud actually added to their complexity. As we've discussed, it's been a lot more of multicloud by default versus multicloud by design. Really think about customers, I I, I'm talking to 'EM all the time. You think about the data complexity, that's the growth in the graph. >>You think about their infrastructure complexity, shifting from central to decentralized it, you think of a multi-cloud complexity. So you have these walled gardens, if you will. So you have multiple vendors and you have these multiple contracts that all creates operational complexity for their teams around their processes of their tools. And then you think about the security complexity that that drives with the, just the increased tax service and the list goes on. So what are we seeing for our customers? They, what they really want from, also what they're asking us for is simplicity, not complexity. The immediacy, not latency. They're asking for open and align versus I'd say siloed and closed. And they're looking for a lot more agility and not rigidity in what we do. So they really wanna simplify everything. They're looking for a simpler IT in a more agile it, and they want more control of their data, right? >>And so, and they want to extract more of the value to enrich their business or their customer engagements, which all sounds pretty obvious and we've probably all heard it a bunch, but it's really hard to achieve. And that's where I believe, and we believe as Dell that we, it creates a big opportunity for us to really help our customers as that great simplifier of it. We're already doing this today on just a couple quick examples. First is Salesforce. We've supported recently, we've supported their global expansion with a multi-cloud solution to help them drive their business growth. Our solution delivered a reliable and consistent IT experience. We go back to that complexity and it was across a very distributed environment, including more than 60 data centers, 230 countries in hundreds of thousands of customers. It really provided Salesforce with the flexibility of placing workloads and data in an environment based on the right service level. >>Objective things like cost complexity or even security compliance considerations. The second customer A is a big New England Patriot fan. And Dan, Dave, I know you are as well. Oh yeah, this one's near, near data, my heart, it's the craft group. We just created a platform to span all their businesses that created more, I'd say data driven, immersive, secure experience, which is allowing them to capture data at the edge and use it for realtime insights for things like cyber resiliency, but also like safety of the facilities. And as being a pare fan like I am Dave, they truly are meeting us where we are in, in our seats on their mobile devices and also in the parking lot. So just keep that in mind next time you're there. But bottom line, everything we're doing is really to make it simpler for our customers and to help them get the most of their data. I'd say we're gonna do this, is it through a multi-cloud by design approach, which we talked a lot about with you and and others at Dell Tech world earlier this year, >>Right? And we had Salesforce on, actually at Dell Tech Group. The craft group is interesting because, you know, when you get to the stadium, you know, everybody's trying to get, get, get out to the internet and, and, but then the experience is so much better if you can actually, you know, deal with that edge. So I wanna talk about complexity though. You got data, you got, you know, the, the edge, you got multiple clouds, you got a different operating model across security models, different. So a lot of times in this industry we solve complexity with more complexity and it's like a bandaid. So I wanna, I wanna talk to, to how you're innovating around simplicity in ISG to address this complexity and what this means for Dell's long term strategy. >>Sure, I'd love to. So first I, I'd like to state the obvious, which are our investments in our innovations really focused on advancing, you know, our, our our customers needs, right? So we are really, our investments are gonna be targeted. We, we believe customers can have the most value. And some of that's gonna be around how we create strategic partnerships as well connected to what we just spoke about. Much of the complexity of customers have or experiencing is in the orchestration and management of all the data in all these different places and customers, you know, they must be able to quickly deploy and operate across cloud environments. They need to increase their developer productivity, really enabling those developers that do what they do best, which is creating more value for their customers than for their businesses. Our innovation efforts are really focused on addressing this by delivering an open and modern IT architecture that allows customers to run and manage any workload in any cloud anywhere. >>Data lives we're focused on, also focused on consumption based solutions, which allow for a greater degree of simplicity and flexibility, which they're really asking for as well. The foundation for this is our software defined common storage layer. That common storage layer, You can think about this, Dave, as our ias if you will. It underpins our data access in mobility across all data types of locations. So you can think private, public, telecom, colo, edge, and it's delivered in a secure, holistic, and consistent cloud experience through Apex. We are making a ton of progress to let you, just to be, just to be clear, we made headway in things like Project Alpine, which you're very well aware of. This is our storage as a service. We announce us back in, in January, which brings our unique software IP from our flagship storage platform to all the major public clouds, really delivering the best of both world, allowing our customers to take advantage of Dell's enterprise class data services and storage software, such as performance at scale, resiliency, efficiency and security. >>But in addition to that, we're leveraging the breadth of the public cloud services, right? They're on demand scaling capabilities and access to analytical services. So in addition, we're really, we're on our way to win at the edge as well with Project Frontier, which reduces complexity at the edge by creating an open and secure software platform to help our customers simplify their edge operations, optimize their edge environments and investments, secure that edge environment as well. I believe you're gonna be discussing Cru in Frontier here with Sam Broco in the very near future. So I won't give up more, too many more details there. And lastly, we're also scaling Apex, which, you know, well shifting from our vision, really shifting from vision to reality and introducing several new Apex service offerings, which are coming to market over the next month or so. And the intent is really supporting our customers on their as a service transitions by modernize the consumption experience and providing that flexible as a service model. Ultimately, we're trying to help our customers achieve that multicloud by design to really simplify it and unlock the power of their data. >>So some good examples there. I I like to talk about the super Cloud as you, you know, you're building on top of the, you know, hyperscale infrastructure and you got Apex is your cloud, the common storage layer, you call it your ISAs. And that's, that's a ingredient in what we call the super cloud out to the edge. You have to have a common platform there and one of the hallmarks of a cloud company. And as you become a cloud company, everybody's a cloud company ecosystem becomes really, really important in terms of product development and, and innovation. Matt Baker always loves to stress it's not a zero sum game. And, and I think Super Cloud recognizes that, that there's value to be built on top of other clouds and, and, and of course on top of your infrastructure so that your ecosystem can add value. So what role does the ecosystem play there? >>For me, it's, it's pretty clear. It's, it's, it's critical. I can't say that enough above the having an open ecosystem. Think about everything we just discussed, and I agree with your super cloud analogy. I agree with what Matt Baker had said to you, I would certain no one company can actually address all the pain points and all the issues and challenges our customers are having on their own. Not one. I think customers really want and deserve an open technology ecosystem, one that works together. So not these close stacks that discourages interoperability or stifles innovation and productivity of each of our teams. We del I guess, have a long history of supporting open ecosystems that really put customers first. And to be clear, we're gonna be at the center of the multi-cloud ecosystem and we're working with partners today to make that a reality. I mean, just think of what we're doing with VMware. >>We continue to build on our first and best alliances with them in August at their VMware explorer, which I know you were at. We announced several joint engineering initiatives to really help customers more easily manage and gain value from their data and their infrastructure. For multi-cloud. Specifically, we strength our relationship with VMware and know with Tansu as part of that. In addition, just a few weeks ago we announced our partnership with Red Hat to simplify our multicloud deployments for managing containerized workloads. I'd say, and using your analogy, I could think of that as our multicloud platform. So that's kind of our PAs layer, if you will. And as you're aware, we have a very long standing and strategic partnership with Microsoft and I'd say stay tuned. There's a lot more to come with them and also others in this multi-cloud space. Shifting a bit to some of the growth engines that my team's responsible for the edge, right? >>As you think about data being everywhere, we've established partnerships for the Edge as well with folks like PTC and Litmus for the manufacturing edge, but also folks like Deep North for the retail edge analytics in data management, using your Supercloud analogy, Dave the sa, right? This is our SAS layer. We've announced that we're collaborating, partnering with folks like Snowflake and, and there's other data management companies as well to really simplify data access and accelerate those data insights. And then given customers choice of where they'd like to have their IT and their infrastructure, we've we're expanding our colo partnerships as well with folks like Equinox and, and they're allowing us to broaden our availability of Apex, providing customers the flexibility, take advantage of those as a service offerings wherever it's delivered and where they can get the most value. So those are just some you can hear from me. I think it's critical not only for, for us, I think it's critical for our customers. I think it's been critical, critical for the entire, you know, industry as a whole to really have that open technology ecosystem as we work with our customers on our multi-cloud solutions really to meet their needs. We'll continue to collaborate with whoever customers choose and you know, and who they want us to do business with. So I'd say a lot more coming in that space. >>So it's been an interesting three years for you, just, just over three years now since you've been made the president of the I isg. And so you had to dig in and it was obviously strange time around the world, but, but you really had to look at, okay, how do we mo modernize the platform? How do we make it, you know, cloud first? You've mentioned the edge, we're expanding. So what are the big takeaways? What do you want customers and our audience to understand? Just some closing thoughts and if you could summarize. >>Sure. So I'd say first, you know, we've discussed, we're working in a very fast paced, ever changing market with massive amounts of data that needs to be managed. It's very complex and our customers need help with that complexity. I believe that Dell Technologies is uniquely positioned to help as their multi-cloud champion. No one else can solve the breadth and depth of the challenges like we can. And we're gonna help our customers move forward when they basically moving from a multicloud by default, as we've discussed before, to multicloud by design. And I'm really excited for the opportunity to work with our customers to help them expand that ecosystem as they truly realize the future of it and, and what they're trying to accomplish. >>Jeff, thanks so much. Really appreciate your time. Always a pleasure. Go pats and we'll see you on the blog. >>Thanks Dave. >>All right, you're watching exclusive insights from Dell Technology Summit on the cube, your leader in enterprise and emerging tech coverage.

>>Hey, we're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime and hook. When you do the math, it's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces lost opportunities. Michael. Great >>To see you take it away. >>All right, guys. Thank you so much. So I'd say we're going to talk a little bit about connected manufacturing, right? And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing and flute and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, mass assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution, things got interesting, right? You started to see automation, but that automation was done essentially programmed your robot to do something and did the same thing over and over and over irrespective about of how your outside operations, your outside conditions change fourth industrial revolution, very different, right? >>Cause now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adapted right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue, there we'll issue that, but it's important. Not for technology's sake, right? It's important because it actually drives very important business outcomes. First of all, quality, right? If you look at the cost of quality, even despite decades of, of, of, uh, companies and manufacturers moving to improve while its quality prompts still accounts for 20% of sales, right? So every fifth of what you meant are manufactured from a revenue perspective, do back quality issues that are costing you a lot planned downtime, cost companies, $50 billion a year. >>So when we're talking about using data and these industry 4.0 types of use cases, connected data types of new spaces, we're not doing it just merely to implement technology. We're doing it to move these from members, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life with what like, right, but this is actually the business. The cloud area is, is in. Let's talk a little bit about that. So we call this manufacturing edge to AI. This is analytics life cycle, and it starts with having your plants, right? Those plants are increasingly connected. As I say, sensor prices have come down two thirds over the last decade, right? And those sensors are connected over the internet. So suddenly we can collect all this data from your, um, manufacturing plants, and what do we want to be able to do? You know, we want to be able to collect it. >>We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things are taking about time, but this, the ability to take these real-time actions or, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into an enterprise data lake, right in that data lake enterprise data lake can be either within your data center or it could be in the cloud. You're going to, you're going to ingest that data. You're going to store it. >>You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you could start to think about, you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you, you bring these data sets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we can put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. But as I mentioned to you, and what's really important here is the fact that once you've stored one history sets data, you can build out those machine learning models. >>I talked to you about earlier. So like I said, you can start to say, which sensor values drove the need of correlated to the need for equipment maintenance for my maintenance management systems, right? And you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for maintenance. Once you understand that you can actually then build out the smiles, you could deploy the models after the edge where they will then work in that inference mode, that photographer, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that, that predicted the need for maintenance? If so, let's take real-time action, but schedule a work order and equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that piece of equipment fails and allows us to be very, very proactive. >>So, >>You know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connected, connected manufacturing. And we're working with many different manufacturers around the world. I want to just highlight. One of them is I thought it's really interesting. This company is for SIA for ECA is the, um, is the, was, is the, um, the, uh, a supplier associated with Pooja central line out of France. They are huge, right? This is a multinational automotive, um, parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, um, they connected 2000 machines, right. Um, and they once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? To be able to just monitor the data firms coming in, you know, monitor the process. >>That was the first step, right. Uh, and you know, 2000 machines, 300 different variables, things like, um, fibrations pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things to start to build out things like equipment, um, predictive maintenance models, or compute. What they really focused on is computer vision, wilding inspection. So let's take pictures of parts as they go through a process and then classify what that was this picture associated with the good or bad quality outcome. Then you teach the machine to make that decision on its own. So now, now the machine, the camera is doing the inspections beer. And so they both have those machine learning models. So they took that data. All this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. >>Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case, a great example of how you can start with monitoring, move to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go there and you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing and a lot more detail and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you wanted to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the costs, you know, 20% of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turn in the morning sessions and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of, of where the data is. You've gotta be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're gonna, they're gonna hit? >>You know, there's, there's there, there's a few of the, but I think, you know, one of the, uh, one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES systems, right? Those are your transactional systems that run on relational databases and your it departments are brilliant at running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are, um, all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietary pro protocols. That information can be very, very difficult to get to. Right. So, and it's unsafe, it's a much more unstructured than from your OT. So the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. >>Right? So that is one of the, if I had to boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own world for a long time, the silos, um, uh, you know, the silos, uh, bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge opportunity. >>Well, and again, this is a hybrid theme and you've kind of got this world, that's going toward an equilibrium. You've got the OT side, you know, pretty hardcore engineers. And we know, we know it. Uh, a lot of that data historically has been analog data. Now it's getting, you know, instrumented and captured. Uh, so you've got that, that cultural challenge. And, you know, you got to blend those two worlds. That's critical. Okay. So Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space. And when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a good, that's a great question. And you're right. I did allude to it earlier, but there really is. I want people to think about, there's a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right? And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, we're just talking about simple monitoring next level down. >>And we're seeing is something we would call quality event forensic announces. And now on this one, you say, imagine I've got warranty plans in the, in the field, right? So I'm starting to see warranty claims, kick kickoff. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots by about warranty issues. What were the manufacturing conditions of the day that caused it? Then you could also say which other tech, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of the car. So, and that, again, also not machine learning where simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day, so that you could take corrective actions, but then you get into a whole of machine learning use case, you know, and, and that ranges from things like quality or say yield optimization, where you start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. >>And you're certain start to say, which, um, you know, which map a sensor values or factors drove good or bad yield outcomes. And you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start something with monitoring, get a lot of value, start, then bring together more diverse data sets to do things like connect the.analytics then and all the way then to, to, to the more advanced machine learning use cases there's value to be had throughout. I >>Remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was the, the old days of football field, we were grass and, and a new player would come in and he'd be perfectly white uniform and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so my question relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question that it kind of, um, goes back to one of the things I alluded earlier, we've got some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they've built some adapters to be able to catch it practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Patera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to, to implement those types of industry 4.0, our analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, a barrier that we've always had and bring together those data sets that we can really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to lead this discussion on the technology advances. I'd love to talk tech here, uh, are the key technology enablers, and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space, sorry, manufacturing in >>A factory space. Yeah. I knew what you meant in know in the manufacturing space. There's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and have become ubiquitous that number one, we can w we're finally being able to get to the OT data, right? That's that's number one, number, number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got back way capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, uh, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, you know, super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed the book to build a GP, you know, GPU level machine learning, build out those models and then deployed by over the air updates to your equipment. All of those things are making this, um, there's, you know, there's the advanced analytics machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processor getting much smarter, very much more quickly. Yep. We got >>A lot of data and we have way lower cost, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, for everybody who joined us. Thanks. Thanks for joining.

>>Welcome to our industry. Drill-downs from manufacturing. I'm here with Michael Gerber, who is the managing director for automotive and manufacturing solutions at cloud era. And in this first session, we're going to discuss how to drive transportation efficiencies and improve sustainability with data connected trucks are fundamental to optimizing fleet performance costs and delivering new services to fleet operators. And what's going to happen here is Michael's going to present some data and information, and we're gonna come back and have a little conversation about what we just heard. Michael, great to see you over to you. >>Oh, thank you, Dave. And I appreciate having this conversation today. Hey, um, you know, this is actually an area connected trucks. You know, this is an area that we have seen a lot of action here at Cloudera. And I think the reason is kind of important, right? Because, you know, first of all, you can see that, you know, this change is happening very, very quickly, right? 150% growth is forecast by 2022. Um, and the reasons, and I think this is why we're seeing a lot of action and a lot of growth is that there are a lot of benefits, right? We're talking about a B2B type of situation here. So this is truck made truck makers providing benefits to fleet operators. And if you look at the F the top fleet operator, uh, the top benefits that fleet operators expect, you see this in the graph over here. >>Now almost 80% of them expect improved productivity, things like improved routing rates. So route efficiencies and improve customer service decrease in fuel consumption, but better technology. This isn't technology for technology sake, these connected trucks are coming onto the marketplace because Hey, it can provide for Mendez value to the business. And in this case, we're talking about fleet operators and fleet efficiencies. So, you know, one of the things that's really important to be able to enable this right, um, trucks are becoming connected because at the end of the day, um, we want to be able to provide fleet deficiencies through connected truck, um, analytics and machine learning. Let me explain to you a little bit about what we mean by that, because what, you know, how this happens is by creating a connected vehicle analytics machine learning life cycle, and to do that, you need to do a few different things, right? >>You start off of course, with connected trucks in the field. And, you know, you can have many of these trucks cause typically you're dealing at a truck level and at a fleet level, right? You want to be able to do analytics and machine learning to improve performance. So you start off with these trucks. And the first you need to be able to do is connect to those products, right? You have to have an intelligent edge where you can collect that information from the trucks. And by the way, once you conducted the, um, this information from the trucks, you want to be able to analyze that data in real-time and take real-time actions. Now what I'm going to show you the ability to take this real-time action is actually the result of your machine learning license. Let me explain to you what I mean by that. >>So we have this trucks, we start to collect data from it right at the end of the day. Well we'd like to be able to do is pull that data into either your data center or into the cloud where we can start to do more advanced analytics. And we start with being able to ingest that data into the cloud, into that enterprise data lake. We store that data. We want to enrich it with other data sources. So for example, if you're doing truck predictive maintenance, you want to take that sensor data that you've connected collected from those trucks. And you want to augment that with your dealership, say service information. Now you have, you know, you have sensor data and there was salting repair orders. You're now equipped to do things like predict one day maintenance will work correctly for all the data sets that you need to be able to do that. >>So what do you do here? Like I said, you adjusted your storage, you're enriching it with data, right? You're processing that data. You're aligning say the sensor data to that transactional system data from your, uh, from your, your pair maintenance systems, you know, you're bringing it together so that you can do two things you can do. First of all, you could do self-service BI on that date, right? You can do things like fleet analytics, but more importantly, what I was talking to you about before is you now have the data sets to be able to do create machine learning models. So if you have the sensor right values and the need, for example, for, for a dealership repair, or as you could start to correlate, which sensor values predicted the need for maintenance, and you could build out those machine learning models. And then as I mentioned to you, you could push those machine learning models back out to the edge, which is how you would then take those real-time action. >>I mentioned earlier as that data that then comes through in real-time, you're running it against that model, and you can take some real time actions. This is what we are, this, this, this, this analytics and machine learning model, um, machine learning life cycle is exactly what Cloudera enables this end-to-end ability to ingest, um, stroke, you know, store it, um, put a query, lay over it, um, machine learning models, and then run those machine learning models. Real-time now that's what we, that's what we do as a business. Now when such customer, and I just wanted to give you one example, um, a customer that we have worked with to provide these types of results is Navistar and Navistar was kind of an early, early adopter of connected truck analytics. And they provided these capabilities to their fleet operators, right? And they started off, uh, by, um, by, you know, connecting 475,000 trucks to up to well over a million now. >>And you know, the point here is with that, they were centralizing data from their telematics service providers, from their trucks, from telematics service providers. They're bringing in things like weather data and all those types of things. Um, and what they started to do was to build out machine learning models, aimed at predictive maintenance. And what's really interesting is that you see that Navistar, um, made tremendous strides in reducing the need or the expense associated with maintenance, right? So rather than waiting for a truck to break and then fixing it, they would predict when that truck needs service, condition-based monitoring and service it before it broke down so that you could do that in a much more cost-effective manner. And if you see the benefits, right, they, they reduced maintenance costs 3 cents a mile, um, from the, you know, down from the industry average of 15 cents a mile down to 12 cents cents a mile. >>So this was a tremendous success for Navistar. And we're seeing this across many of our, um, um, you know, um, uh, truck manufacturers. We were working with many of the truck OEMs and they are all working to achieve, um, you know, very, very similar types of, um, benefits to their customers. So just a little bit about Navistar. Um, now we're gonna turn to Q and a, Dave's got some questions for me in a second, but before we do that, if you want to learn more about our, how we work with connected vehicles and autonomous vehicles, please go to our lives or to our website, what you see up, uh, up on the screen, there's the URLs cloudera.com for slash solutions for slash manufacturing. And you'll see a whole slew of, um, um, lateral and information, uh, in much more detail in terms of how we connect, um, trucks to fleet operators who provide analytics, use cases that drive dramatically improved performance. So with that being said, I'm going to turn it over to Dave for questions. >>Thank you. Uh, Michael, that's a great example. You've got, I love the life cycle. You can visualize that very well. You've got an edge use case you do in both real time inference, really at the edge. And then you're blending that sensor data with other data sources to enrich your models. And you can push that back to the edge. That's that lifecycle. So really appreciate that, that info. Let me ask you, what are you seeing as the most common connected vehicle when you think about analytics and machine learning, the use cases that you see customers really leaning into. >>Yeah, that's really, that's a great question. They, you know, cause you know, everybody always thinks about machine learning. Like this is the first thing you go, well, actually it's not right for the first thing you really want to be able to go around. Many of our customers are doing slow. Let's simply connect our trucks or our vehicles or whatever our IOT asset is. And then you can do very simple things like just performance monitoring of the, of the piece of equipment in the truck industry, a lot of performance monitoring of the truck, but also performance monitoring of the driver. So how has the, how has the driver performing? Is there a lot of idle time spent, um, you know, what's, what's route efficiencies looking like, you know, by connecting the vehicles, right? You get insights, as I said into the truck and into the driver and that's not machine learning. >>Right. But that, that, that monitoring piece is really, really important. The first thing that we see is monitoring types of use cases. Then you start to see companies move towards more of the, uh, what I call the machine learning and AI models, where you're using inference on the edge. And then you start to see things like, uh, predictive maintenance happening, um, kind of route real-time, route optimization and things like that. And you start to see that evolution again, to those smarter, more intelligent dynamic types of decision-making, but let's not, let's not minimize the value of good old fashioned monitoring that site to give you that kind of visibility first, then moving to smarter use cases as you, as you go forward. >>You know, it's interesting. I'm, I'm envisioning when you talked about the monitoring, I'm envisioning a, you see the bumper sticker, you know, how am I driving this all the time? If somebody ever probably causes when they get cut off it's snow and you know, many people might think, oh, it's about big brother, but it's not. I mean, that's yeah. Okay, fine. But it's really about improvement and training and continuous improvement. And then of course the, the route optimization, I mean, that's, that's bottom line business value. So, so that's, I love those, uh, those examples. Um, I wonder, I mean, one of the big hurdles that people should think about when they want to jump into those use cases that you just talked about, what are they going to run into, uh, you know, the blind spots they're, they're going to, they're going to get hit with, >>There's a few different things, right? So first of all, a lot of times your it folks aren't familiar with the kind of the more operational IOT types of data. So just connecting to that type of data can be a new skill set, right? That's very specialized hardware in the car and things like that. And protocols that's number one, that that's the classic, it OT kind of conundrum that, um, you know, uh, many of our customers struggle with, but then more fundamentally is, you know, if you look at the way these types of connected truck or IOT solutions started, you know, oftentimes they were, the first generation were very custom built, right? So they were brittle, right? They were kind of hardwired. And as you move towards, um, more commercial solutions, you had what I call the silo, right? You had fragmentation in terms of this capability from this vendor, this capability from another vendor, you get the idea, you know, one of the things that we really think that we need with that, that needs to be brought to the table is first of all, having an end to end data management platform, that's kind of integrated, it's all tested together. >>You have the data lineage across the entire stack, but then also importantly, to be realistic, we have to be able to integrate to, um, industry kind of best practices as well in terms of, um, solution components in the car, how the hardware and all those types things. So I think there's, you know, it's just stepping back for a second. I think that there is, has been fragmentation and complexity in the past. We're moving towards more standards and more standard types of art, um, offerings. Um, our job as a software maker is to make that easier and connect those dots. So customers don't have to do it all on all on their own. >>And you mentioned specialized hardware. One of the things we heard earlier in the main stage was your partnership with Nvidia. We're talking about, you know, new types of hardware coming in, you guys are optimizing for that. We see the it and the OT worlds blending together, no question. And then that end to end management piece, you know, this is different from your right, from it, normally everything's controlled or the data center, and this is a metadata, you know, rethinking kind of how you manage metadata. Um, so in the spirit of, of what we talked about earlier today, uh, uh, other technology partners, are you working with other partners to sort of accelerate these solutions, move them forward faster? >>Yeah, I'm really glad you're asking that because we actually embarked on a product on a project called project fusion, which really was about integrating with, you know, when you look at that connected vehicle life cycle, there are some core vendors out there that are providing some very important capabilities. So what we did is we joined forces with them to build an end-to-end demonstration and reference architecture to enable the complete data management life cycle. Cloudera is Peter piece of this was ingesting data and all the things I talked about being storing and the machine learning, right? And so we provide that end to end. But what we wanted to do is we wanted to partner with some key partners and the partners that we did with, um, integrate with or NXP NXP provides the service oriented gateways in the car. So that's a hardware in the car when river provides an in-car operating system, that's Linux, right? >>That's hardened and tested. We then ran ours, our, uh, Apache magnify, which is part of flood era data flow in the vehicle, right on that operating system. On that hardware, we pump the data over into the cloud where we did them, all the data analytics and machine learning and, and builds out these very specialized models. And then we used a company called Arabic equity. Once we both those models to do, you know, they specialize in automotive over the air updates, right? So they can then take those models and update those models back to the vehicle very rapidly. So what we said is, look, there's, there's an established, um, you know, uh, ecosystem, if you will, of leaders in this space, what we wanted to do is make sure that our, there was part and parcel of this ecosystem. And by the way, you mentioned Nvidia as well. We're working closely with Nvidia now. So when we're doing the machine learning, we can leverage some of their hardware to get some further acceleration in the machine learning side of things. So, uh, yeah, you know, one of the things I always say about this types of use cases, it does take a village. And what we've really tried to do is build out that, that, uh, an ecosystem that provides that village so that we can speed that analytics and machine learning, um, lifecycle just as fast as it can be. This >>Is again another great example of, of data intensive workloads. It's not your, it's not your grandfather's ERP. That's running on, you know, traditional, you know, systems it's, these are really purpose-built, maybe they're customizable for certain edge use cases. They're low cost, low, low power. They can't be bloated, uh, ended you're right. It does take an ecosystem. You've got to have, you know, API APIs that connect and, and that's that, that takes a lot of work and a lot of thoughts. So that, that leads me to the technologies that are sort of underpinning this we've talked we've we talked a lot in the cube about semiconductor technology, and now that's changing and the advancements we're seeing there, what do you see as the, some of the key technical technology areas that are advancing this connected vehicle machine learning? >>You know, it's interesting, I'm seeing it in a few places, just a few notable ones. I think, first of all, you know, we see that the vehicle itself is getting smarter, right? So when you look at, we look at that NXP type of gateway that we talked about that used to be kind of a, a dumb gateway. That was really all it was doing was pushing data up and down and provided isolation, um, as a gateway down to the, uh, down from the lower level subsistence. So it was really security and just basic, um, you know, basic communication that gateway now is becoming what they call a service oriented gate. So it can run. It's not that it's bad desk. It's got memories that always, so now you could run serious compute in the car, right? So now all of these things like running machine learning, inference models, you have a lot more power in the corner at the same time. >>5g is making it so that you can push data fast enough, making low latency computing available, even on the cloud. So now you now you've got credible compute both at the edge in the vehicle and on the cloud. Right. And, um, you know, and then on the, you know, on the cloud, you've got partners like Nvidia who are accelerating, it's still further through better GPU based compute. So I mean the whole stack, if you look at it, that that machine learning life cycle we talked about, no, David seems like there's improvements and EV every step along the way, we're starting to see technology, um, optimum optimization, um, just pervasive throughout the cycle. >>And then real quick, it's not a quick topic, but you mentioned security. If it was seeing a whole new security model emerge, there is no perimeter anymore in this use case like this is there. >>No there isn't. And one of the things that we're, you know, remember where the data management platform platform and the thing we have to provide is provide end-to-end link, you know, end end-to-end lineage of where that data came from, who can see it, you know, how it changed, right? And that's something that we have integrated into from the beginning of when that data is ingested through, when it's stored through, when it's kind of processed and people are doing machine learning, we provide, we will provide that lineage so that, um, you know, that security and governance is a short throughout the, throughout the data learning life cycle, it >>Federated across in this example, across the fleet. So, all right, Michael, that's all the time we have right now. Thank you so much for that great information. Really appreciate it, >>Dave. Thank you. And thank you. Thanks for the audience for listening in today. Yes. Thank you for watching. >>Okay. We're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime. And look, when you do the math, that's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces loss opportunities. Michael. Great to see you >>Take it away. All right. Thank you so much. So I'd say we're going to talk a little bit about connected manufacturing, right. And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing improve and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, massive assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution. Things got interesting, right? You started to see automation, but that automation was done, essentially programmed a robot to do something. It did the same thing over and over and over irrespective about it, of how your outside operations, your outside conditions change fourth industrial revolution, very different breakfast. >>Now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adaptive right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue. There we'll issue that, but it's important, not for technology sake, right? It's important because it actually drives and very important business outcomes. First of all, quality, right? If you look at the cost of quality, even despite decades of, of, of, of, uh, companies, um, and manufacturers moving to improve while its quality promise still accounted to 20% of sales, right? So every fifth of what you meant or manufactured from a revenue perspective, you've got quality issues that are costing you a lot. >>Plant downtime, cost companies, $50 billion a year. So when we're talking about using data and these industry 4.0 types of use cases, connected data types of use cases, we're not doing it just merely to implement technology. We're doing it to move these from drivers, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life cycle, what like, right, because this is actually the business that cloud era is, is in. Let's talk a little bit about that. So we call this manufacturing edge to AI, this, this analytics life cycle, and it starts with having your plants, right? Those plants are increasingly connected. As I said, sensor prices have come down two thirds over the last decade, right? And those sensors have connected over the internet. So suddenly we can collect all this data from your, um, ma manufacturing plants. What do we want to be able to do? >>You know, we want to be able to collect it. We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent real-time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things, right? Taking the time. But this, the ability to take these real-time actions, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into our enterprise data lake, right in that data lake enterprise data lake can be either within your data center or it could be in the cloud. You've got, you're going to ingest that data. >>You're going to store it. You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you can start to think about do you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you, you bring these data sets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we could put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. >>But as I mentioned, you, and what's really important here is the fact that once you've stored long histories that say that you can build out those machine learning models I talked to you about earlier. So like I said, you can start to say, which sensor values drove the need, a correlated to the need for equipment maintenance for my maintenance management systems, right? And you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for Maples. Once you understand that you can actually then build out those models for deploy the models out the edge, where they will then work in that inference mode that we talked about, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that PR that predicted the need for maintenance? If so, let's take real-time action, right? >>Let's schedule a work order or an equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that piece of equipment fails and allows us to be very, very proactive. So, you know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connecting connected manufacturing. And we're working with many different manufacturers around the world. I want to just highlight. One of them is I thought it's really interesting. This company is bought for Russia, for SIA, for ACA is the, um, is the, was, is the, um, the, uh, a supplier associated with Peugeot central line out of France. They are huge, right? This is a multi-national automotive parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, they connected 2000 machines, right. >>Um, and then once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? To be able to just monitor data firms coming in, you know, monitor the process. That was the first step, right. Uh, and, you know, 2000 machines, 300 different variables, things like, um, vibration pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things to start to build out things like equipment, um, predictive maintenance models or compute. And what they really focused on is computer vision while the inspection. So let's take pictures of, um, parts as they go through a process and then classify what that was this picture associated with the good or bad Bali outcome. Then you teach the machine to make that decision on its own. >>So now, now the machine, the camera is doing the inspections. And so they both had those machine learning models. They took that data, all this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case. Um, great example of how you can start with monitoring, moved to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go there and you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing, a lot more detail, and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you want to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the cost, you know, 20% of, of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turned in the morning sessions, and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of, of where the data is. You've gotta be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're going to, they're going to hit, >>You know, there's, there's, there, there's a few of the, but I think, you know, one of the ones, uh, w one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES systems, right? Those are your transactional systems that run on relational databases and your it departments are brilliant, are running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are, um, all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietorial pro protocols. That information can be very, very difficult to get to. Right. So, and it's, it's a much more unstructured than from your OT. So th the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. Right? So that is one of the, if I boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own world. And for a long time, the silos, um, uh, the silos a, uh, bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge, >>Well, and again, this is a hybrid team and you, you've kind of got this world, that's going toward an equilibrium. You've got the OT side and, you know, pretty hardcore engineers. And we know, we know it. A lot of that data historically has been analog data. Now it's getting, you know, instrumented and captured. Uh, so you've got that, that cultural challenge. And, you know, you got to blend those two worlds. That's critical. Okay. So, Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space, when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a good, that's a great question. And you're right. I did allude to a little bit earlier, but there really is. I want people to think about, there's a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right. And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, but just talking about simple monitoring next level down, and we're seeing is something we would call quality event forensic analysis. >>And now on this one, you say, imagine I've got warranty plans in the, in the field, right? So I'm starting to see warranty claims kick up. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots. What about warranty issues? What were the manufacturing conditions of the day that caused it? Then you could also say which other tech, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of the car. So, and that, again, also not machine learning, we're simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day, so that you could take corrective actions, but then you get into a whole slew of machine learning, use dates, you know, and that ranges from things like Wally or say yield optimization. >>We start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. And you're certain start to say, which, um, you know, which on a sensor values or factors drove good or bad yield outcomes, and you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start simply with, with monitoring, get a lot of value, start then bringing together more diverse data sets to do things like connect the.analytics then and all the way then to, to, to the more advanced machine learning use cases, there's this value to be had throughout. >>I remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was, uh, the, the old days of football field, we were grass and, and the new player would come in and he'd be perfectly white uniform, and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so I question it relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question. And it kind of goes back to one of the things I alluded to alluded upon earlier. We've had some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they built some adapters to be able to catch it practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Idera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to, to implement those types of, um, industry for porno, our analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, a barrier that we've always had and, and bring together those data sets that we can really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to li lead this discussion on the technology advances. I'd love to talk tech here. Uh, what are the key technology enablers and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space. Sorry, manufacturing. Yeah. >>Yeah. I know in the manufacturing space, there's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and have become ubiquitous that number one, we can, we've finally been able to get to the OT data, right? That's that's number one, you know, numb number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got, we've got great capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, uh, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, the super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed a book to build a GP, you know, GPU level machine learning, build out those models and then deployed by over the air updates to, to your equipment. All of those things are making this, um, there's, you know, the advanced analytics and machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processor getting much smarter, uh, very much more quickly. Yeah, we got >>A lot of data and we have way lower cost, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, uh, for everybody who joined us. Thanks. Thanks for joining today. Yes. Thank you for watching. Keep it right there.

>>Okay. We're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime and hook. When you do the math, that's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces lost opportunities. Michael. Great to see you, >>Dave. All right, guys. Thank you so much. So I'll tell you, we're going to talk a little bit about connected manufacturing, right? And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing improve and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, mass assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution. Things got interesting, right? You started to see automation, but that automation was done essentially programmed a robot to do something. It did the same thing over and over and over irrespective about of how your outside operations, your outside conditions change fourth industrial revolution, very different breakfasts. >>Now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adapted right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue. There we'll issue that, but it's important, not for technology sake, right? It's important because it actually drives very important business outcomes. First of all, falling, right? If you look at the cost of quality, even despite decades of, of, uh, companies and manufacturers moving to improve while its quality prompts still account to 20% of sales, right? So every fifth of what you meant or manufactured from a revenue perspective, you've got quality issues that are costing you a lot. Plant downtime, cost companies, $50 billion a year. >>So when we're talking about using data and these industry 4.0 types of use cases, connected data types of use cases, we're not doing it just narrowly to implement technology. We're doing it to move these from adverse, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life cycle with what like, right. But so this is actually the business that cloud areas is in. Let's talk a little bit about that. So we call this manufacturing edge to AI. This is analytics, life something, and it starts with having your plants, right? Those plants are increasingly connected. As I said, sensor prices have come down two thirds over the last decade, right? And those sensors are connected over the internet. So suddenly we can collect all this data from your, um, manufacturing plants, and what do we want to be able to do? You know, we want to be able to collect it. >>We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent real-time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things, right? Taking that time. But this, the ability to take these real-time actions, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into our enterprise data lake, right? And that data lake enterprise data lake can be either within your data center or it could be in the cloud. You're going to, you're going to ingest that data. You're going to store it. >>You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you can start to think about do you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you bring these datasets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we could put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. But as I mentioned to you, and what's really important here is the fact that once you've stored one histories that say that you can build out those machine learning models I talked to you about earlier. >>So like I said, you can start to say, which sensor values drove the need of correlated to the need for equipment maintenance for my maintenance management systems, right? And then you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for maintenance. And once you understand that you can actually then build out those models, you deploy the models out to the edge where they will then work in that inference mode, that photographer, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that, that predicted the need for maintenance? If so, let's take real-time action, right? Let's schedule a work order and equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that a piece of equipment fails and allows us to be very, very proactive. >>So, you know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connected, connected manufacturing. And we're working with many different, um, manufacturers around the world. I want to just highlight one of them. Cause I thought it's really interesting. This company is bought for Russia. And for SIA for ACA is the, um, is the, is the, um, the, uh, a supplier associated with out of France. They are huge, right? This is a multi-national automotive, um, parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, they connected 2000 machines, right. Um, I mean at once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? >>To be able to just monitor the data from coming in, you know, monitor the process. That was the first step, right. Uh, and you know, 2000 machines, 300 different variables, things like, um, vibration pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things, just start to build out things like equipment, um, predictive maintenance models, or compute. What they really focused on is computer vision while the inspection. So let's take pictures of, um, parts as they go through a process and then classify what that was this picture associated with the good or bad quality outcome. Then you teach the machine to make that decision on its own. So now, now the machine, the camera is doing the inspections for you. And so they both had those machine learning models. They took that data, all this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. >>Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case. Um, great example of how you start with monitoring, move to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go, then you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing and a lot more detail and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you want to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the costs, you know, 20% of, of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turned in the morning sessions, and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of where the data is, you've got to be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're going to, they're going to hit? >>No, there's, there's there, there's a few of the, but I think, you know, one of the, uh, one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES system, Freightos your transactional systems that run on relational databases and your it departments are brilliant at running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietary pro protocols. That information can be very, very difficult to get to. Right? So, and it's uncertain, it's a much more unstructured than from your OT. So the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. Right? So that is one of the, if I had to boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own. And for a long time, the silos, the silos, a bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge opportunity. >>Well, and again, this is a hybrid team and you, you've kind of got this world, that's going toward an equilibrium. You've got the OT side and, you know, pretty hardcore engineers. And we know, we know it. A lot of that data historically has been analog data. This is Chris now is getting, you know, instrumented and captured. Uh, and so you've got that, that cultural challenge and, you know, you got to blend those two worlds. That's critical. Okay. So Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space, when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a great, that's a great question. And you're right. I did allude to a little bit earlier, but there really is. I want people to think about this, a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right? And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards the internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, we're just talking about simple monitoring next level down. >>And we're seeing is something we would call quality event forensic announces. And now on this one, you say, imagine I'm got warranty plans in the, in the field, right? So I'm starting to see warranty claims kicked off on them. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots I've got, I've got warranty issues. What were the manufacturing conditions of the day that caused it? Then you could also say which other, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of a car. So, and that, again, also not machine learning is simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day so that you could take corrective actions, but then you get into a whole slew of machine learning use case, you know, and, and that ranges from things like quality or say yield optimization, where you start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. >>And you're certain start to say, which, um, you know, which map a sensor values or factors drove good or bad yield outcomes. And you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start simply with monitoring, get a lot of value, start, then bring together more diverse datasets to do things like connect the.analytics then all and all the way then to, to, to the more advanced machine learning use cases this value to be had throughout. >>I remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was, uh, the, the old days of football field, we were grass and, and a new player would come in and he'd be perfectly white uniform and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so my question relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question. I kind of, um, goes back to one of the things I alluded a little bit about earlier. We've got some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they built some adapters to be able to get to practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Idera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to implement those types of, um, industry 4.0, uh, analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, uh, barrier that we've always had and, and bring together those data sets that really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to Lee lead this discussion on the technology advances. I'd love to talk tech here. Uh, what are the key technology enablers and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space. Sorry. Manufacturing in >>Factor space. Yeah, I know in the manufacturing space, there's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and it had become ubiquitous that number one, we can w we're finally been able to get to the OT data, right? That's that's number one, number, number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got, we've got great capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, um, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, you know, super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed the book, bullet, uh, GP, you know, GPU level, machine learning, all that, those models, and then deployed by over the air updates to your equipment. All of those things are making this, um, there's, you know, there's the advanced analytics and machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processes are getting much smarter, uh, very much more quickly. >>Yep. We've got a lot of data and we have way lower costs, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, uh, for everybody who joined. Uh, thanks. Thanks for joining today. Yes. Thank you for watching. Keep it right there.

>> Sue: Hi everybody, thank you for joining us today for the virtual Vertica BDC 2020. Today's breakout session is entitled "Autonomous Monitoring Using Machine Learning". My name is Sue LeClaire, director of marketing at Vertica, and I'll be your host for this session. Joining me is Larry Lancaster, founder and CTO at Zebrium. Before we begin, I encourage you to submit questions or comments during the virtual session. You don't have to wait, just type your question or comment in the question box below the slide and click submit. There will be a Q&A session at the end of the presentation and we'll answer as many questions as we're able to during that time. Any questions that we don't address, we'll do our best to answer them offline. Alternatively, you can also go and visit Vertica forums to post your questions after the session. Our engineering team is planning to join the forums to keep the conversation going. Also, just a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slides. And yes, this virtual session is being recorded and will be available for you to view on demand later this week. We'll send you a notification as soon as it's ready. So, let's get started. Larry, over to you. >> Larry: Hey, thanks so much. So hi, my name's Larry Lancaster and I'm here to talk to you today about something that I think who's time has come and that's autonomous monitoring. So, with that, let's get into it. So, machine data is my life. I know that's a sad life, but it's true. So I've spent most of my career kind of taking telemetry data from products, either in the field, we used to call it in the field or nowadays, that's been deployed, and bringing that data back, like log file stats, and then building stuff on top of it. So, tools to run the business or services to sell back to users and customers. And so, after doing that a few times, it kind of got to the point where I was really sort of sick of building the same kind of thing from scratch every time, so I figured, why not go start a company and do it so that we don't have to do it manually ever again. So, it's interesting to note, I've put a little sentence here saying, "companies where I got to use Vertica" So I've been actually kind of working with Vertica for a long time now, pretty much since they came out of alpha. And I've really been enjoying their technology ever since. So, our vision is basically that I want a system that will characterize incidents before I notice. So an incident is, you know, we used to call it a support case or a ticket in IT, or a support case in support. Nowadays, you may have a DevOps team, or a set of SREs who are monitoring a production sort of deployment. And so they'll call it an incident. So I'm looking for something that will notice and characterize an incident before I notice and have to go digging into log files and stats to figure out what happened. And so that's a pretty heady goal. And so I'm going to talk a little bit today about how we do that. So, if we look at logs in particular. Logs today, if you look at log monitoring. So monitoring is kind of that whole umbrella term that we use to talk about how we monitor systems in the field that we've shipped, or how we monitor production deployments in a more modern stack. And so basically there are log monitoring tools. But they have a number of drawbacks. For one thing, they're kind of slow in the sense that if something breaks and I need to go to a log file, actually chances are really good that if you have a new issue, if it's an unknown unknown problem, you're going to end up in a log file. So the problem then becomes basically you're searching around looking for what's the root cause of the incident, right? And so that's kind of time-consuming. So, they're also fragile and this is largely because log data is completely unstructured, right? So there's no formal grammar for a log file. So you have this situation where, if I write a parser today, and that parser is going to do something, it's going to execute some automation, it's going to open or update a ticket, it's going to maybe restart a service, or whatever it is that I want to happen. What'll happen is later upstream, someone who's writing the code that produces that log message, they might do something really useful for me, or for users. And they might go fix a spelling mistake in that log message. And then the next thing you know, all the automation breaks. So it's a very fragile source for automation. And finally, because of that, people will set alerts on, "Oh, well tell me how many thousands of errors are happening every hour." Or some horrible metric like that. And then that becomes the only visibility you have in the data. So because of all this, it's a very human-driven, slow, fragile process. So basically, we've set out to kind of up-level that a bit. So I touched on this already, right? The truth is if you do have an incident, you're going to end up in log files to do root cause. It's almost always the case. And so you have to wonder, if that's the case, why do most people use metrics only for monitoring? And the reason is related to the problems I just described. They're already structured, right? So for logs, you've got this mess of stuff, so you only want to dig in there when you absolutely have to. But ironically, it's where a lot of the information that you need actually is. So we have a model today, and this model used to work pretty well. And that model is called "index and search". And it basically means you treat log files like they're text documents. And so you index them and when there's some issue you have to drill into, then you go searching, right? So let's look at that model. So 20 years ago, we had sort of a shrink-wrap software delivery model. You had an incident. With that incident, maybe you had one customer and you had a monolithic application and a handful of log files. So it's perfectly natural, in fact, usually you could just v-item the log file, and search that way. Or if there's a lot of them, you could index them and search them that way. And that all worked very well because the developer or the support engineer had to be an expert in those few things, in those few log files, and understand what they meant. But today, everything has changed completely. So we live in a software as a service world. What that means is, for a given incident, first of all you're going to be affecting thousands of users. You're going to have, potentially, 100 services that are deployed in your environment. You're going to have 1,000 log streams to sift through. And yet, you're still kind of stuck in the situation where to go find out what's the matter, you're going to have to search through the log files. So this is kind of the unacceptable sort of position we're in today. So for us, the future will not be index and search. And that's simply because it cannot scale. And the reason I say that it can't scale is because it all kind of is bottlenecked by a person and their eyeball. So, you continue to drive up the amount of data that has to be sifted through, the complexity of the stack that has to be understood, and you still, at the end of the day, for MTTR purposes, you still have the same bottleneck, which is the eyeball. So this model, I believe, is fundamentally broken. And that's why, I believe in five years you're going to be in a situation where most monitoring of unknown unknown problems is going to be done autonomously. And those issues will be characterized autonomously because there's no other way it can happen. So now I'm going to talk a little bit about autonomous monitoring itself. So, autonomous monitoring basically means, if you can imagine in a monitoring platform and you watch the monitoring platform, maybe you watch the alerts coming from it or more importantly, you kind of watch the dashboards and try to see if something looks weird. So autonomous monitoring is the notion that the platform should do the watching for you and only let you know when something is going wrong and should kind of give you a window into what happened. So if you look at this example I have on screen, just to take it really slow and absorb the concept of autonomous monitoring. So here in this example, we've stopped the database. And as a result, down below you can see there were a bunch of fallout. This is an Atlassian Stack, so you can imagine you've got a Postgres database. And then you've got sort of Bitbucket, and Confluence, and Jira, and these various other components that need the database operating in order to function. So what this is doing is it's calling out, "Hey, the root cause is the database stopped and here's the symptoms." Now, you might be wondering, so what. I mean I could go write a script to do this sort of thing. Here's what's interesting about this very particular example, and I'll show a couple more examples that are a little more involved. But here's the interesting thing. So, in the software that came up with this incident and opened this incident and put this root cause and symptoms in there, there's no code that knows anything about timestamp formats, severities, Atlassian, Postgres, databases, Bitbucket, Confluence, there's no regexes that talk about starting, stopped, RDBMS, swallowed exception, and so on and so forth. So you might wonder how it's possible then, that something which is completely ignorant of the stack, could come up with this description, which is exactly what a human would have had to do, to figure out what happened. And I'm going to get into how we do that. But that's what autonomous monitoring is about. It's about getting into a set of telemetry from a stack with no prior information, and understanding when something breaks. And I could give you the punchline right now, which is there are fundamental ways that software behaves when it's breaking. And by looking at hundreds of data sets that people have generously allowed us to use containing incidents, we've been able to characterize that and now generalize it to apply it to any new data set and stack. So here's an interesting one right here. So there's a fella, David Gill, he's just a genius in the monitoring space. He's been working with us for the last couple of months. So he said, "You know what I'm going to do, is I'm going to run some chaos experiments." So for those of you who don't know what chaos engineering is, here's the idea. So basically, let's say I'm running a Kubernetes cluster and what I'll do is I'll use sort of a chaos injection test, something like litmus. And basically it will inject issues, it'll break things in my application randomly to see if my monitoring picks it up. And so this is what chaos engineering is built around. It's built around sort of generating lots of random problems and seeing how the stack responds. So in this particular case, David went in and he deleted, basically one of the tests that was presented through litmus did a delete of a pod delete. And so that's going to basically take out some containers that are part of the service layer. And so then you'll see all kinds of things break. And so what you're seeing here, which is interesting, this is why I like to use this example. Because it's actually kind of eye-opening. So the chaos tool itself generates logs. And of course, through Kubernetes, all the log files locations that are on the host, and the container logs are known. And those are all pulled back to us automatically. So one of the log files we have is actually the chaos tool that's doing the breaking, right? And so what the tool said here, when it went to determine what the root cause was, was it noticed that there was this process that had these messages happen, initializing deletion lists, selection a pod to kill, blah blah blah. It's saying that the root cause is the chaos test. And it's absolutely right, that is the root cause. But usually chaos tests don't get picked up themselves. You're supposed to be just kind of picking up the symptoms. But this is what happens when you're able to kind of tease out root cause from symptoms autonomously, is you end up getting a much more meaningful answer, right? So here's another example. So essentially, we collect the log files, but we also have a Prometheus scraper. So if you export Prometheus metrics, we'll scrape those and we'll collect those as well. And so we'll use those for our autonomous monitoring as well. So what you're seeing here is an issue where, I believe this is where we ran something out of disk space. So it opened an incident, but what's also interesting here is, you see that it pulled that metric to say that the spike in this metric was a symptom of this running out of space. So again, there's nothing that knows anything about file system usage, memory, CPU, any of that stuff. There's no actual hard-coded logic anywhere to explain any of this. And so the concept of autonomous monitoring is looking at a stack the way a human being would. If you can imagine how you would walk in and monitor something, how you would think about it. You'd go looking around for rare things. Things that are not normal. And you would look for indicators of breakage, and you would see, do those seem to be correlated in some dimension? That is how the system works. So as I mentioned a moment ago, metrics really do kind of complete the picture for us. We end up in a situation where we have a one-stop shop for incident root cause. So, how does that work? Well, we ingest and we structure the log files. So if we're getting the logs, we'll ingest them and we'll structure them, and I'm going to show a little bit what that structure looks like and how that goes into the database in a moment. And then of course we ingest and structure the Prometheus metrics. But here, structure really should have an asterisk next to it, because metrics are mostly structured already. They have names. If you have your own scraper, as opposed to going into the time series Prometheus database and pulling metrics from there, you can keep a lot more information about metadata about those metrics from the exporter's perspective. So we keep all of that too. Then we do our anomaly detection on both of those sets of data. And then we cross-correlate metrics and log anomalies. And then we create incidents. So this is at a high level, kind of what's happening without any sort of stack-specific logic built in. So we had some exciting recent validation. So Mayadata's a pretty big player in the Kubernetes space. Essentially, they do Kubernetes as a managed service. They have tens of thousands of customers that they manage their Kubernetes clusters for them. And then they're also involved, both in the OpenEBS project, as well as in the Litmius project I mentioned a moment ago. That's their tool for chaos engineering. So they're a pretty big player in the Kubernetes space. So essentially, they said, "Oh okay, let's see if this is real." So what they did was they set up our collectors, which took three minutes in Kubernetes. And then they went and they, using Litmus, they reproduced eight incidents that their actual, real-world customers had hit. And they were trying to remember the ones that were the hardest to figure out the root cause at the time. And we picked up and put a root cause indicator that was correct in 100% of these incidents with no training configuration or metadata required. So this is kind of what autonomous monitoring is all about. So now I'm going to talk a little bit about how it works. So, like I said, there's no information included or required about, so if you imagine a log file for example. Now, commonly, over to the left-hand side of every line, there will be some sort of a prefix. And what I mean by that is you'll see like a timestamp, or a severity, and maybe there's a PID, and maybe there's function name, and maybe there's some other stuff there. So basically that's kind of, it's common data elements for a large portion of the lines in a given log file. But you know, of course, the contents change. So basically today, like if you look at a typical log manager, they'll talk about connectors. And what connectors means is, for an application it'll generate a certain prefix format in a log. And that means what's the format of the timestamp, and what else is in the prefix. And this lets the tool pick it up. And so if you have an app that doesn't have a connector, you're out of luck. Well, what we do is we learn those prefixes dynamically with machine learning. You do not have to have a connector, right? And what that means is that if you come in with your own application, the system will just work for it from day one. You don't have to have connectors, you don't have to describe the prefix format. That's so yesterday, right? So really what we want to be doing is up-leveling what the system is doing to the point where it's kind of working like a human would. You look at a log line, you know what's a timestamp. You know what's a PID. You know what's a function name. You know where the prefix ends and where the variable parts begin. You know what's a parameter over there in the variable parts. And sometimes you may need to see a couple examples to know what was a variable, but you'll figure it out as quickly as possible, and that's exactly how the system goes about it. As a result, we kind of embrace free-text logs, right? So if you look at a typical stack, most of the logs generated in a typical stack are usually free-text. Even structured logging typically will have a message attribute, which then inside of it has the free-text message. For us, that's not a bad thing. That's okay. The purpose of a log is to inform people. And so there's no need to go rewrite the whole logging stack just because you want a machine to handle it. They'll figure it out for themselves, right? So, you give us the logs and we'll figure out the grammar, not only for the prefix but also for the variable message part. So I already went into this, but there's more that's usually required for configuring a log manager with alerts. You have to give it keywords. You have to give it application behaviors. You have to tell it some prior knowledge. And of course the problem with all of that is that the most important events that you'll ever see in a log file are the rarest. Those are the ones that are one out of a billion. And so you may not know what's going to be the right keyword in advance to pick up the next breakage, right? So we don't want that information from you. We'll figure that out for ourselves. As the data comes in, essentially we parse it and we categorize it, as I've mentioned. And when I say categorize, what I mean is, if you look at a certain given log file, you'll notice that some of the lines are kind of the same thing. So this one will say "X happened five times" and then maybe a few lines below it'll say "X happened six times" but that's basically the same event type. It's just a different instance of that event type. And it has a different value for one of the parameters, right? So when I say categorization, what I mean is figuring out those unique types and I'll show an example of that next. Anomaly detection, we do on top of that. So anomaly detection on metrics in a very sort of time series by time series manner with lots of tunables is a well-understood problem. So we also do this on the event types occurrences. So you can think of each event type occurring in time as sort of a point process. And then you can develop statistics and distributions on that, and you can do anomaly detection on those. Once we have all of that, we have extracted features, essentially, from metrics and from logs. We do pattern recognition on the correlations across different channels of information, so different event types, different log types, different hoses, different containers, and then of course across to the metrics. Based on all of this cross-correlation, we end up with a root cause identification. So that's essentially, at a high level, how it works. What's interesting, from the perspective of this call particularly, is that incident detection needs relationally structured data. It really does. You need to have all the instances of a certain event type that you've ever seen easily accessible. You need to have the values for a given sort of parameter easily, quickly available so you can figure out what's the distribution of this over time, how often does this event type happen. You can run analytical queries against that information so that you can quickly, in real-time, do anomaly detection against new data. So here's an example of that this looks like. And this kind of part of the work that we've done. At the top you see some examples of log lines, right? So that's kind of a snippet, it's three lines out of a log file. And you see one in the middle there that's kind of highlighted with colors, right? I mean, it's a little messy, but it's not atypical of the log file that you'll see pretty much anywhere. So there, you've got a timestamp, and a severity, and a function name. And then you've got some other information. And then finally, you have the variable part. And that's going to have sort of this checkpoint for memory scrubbers, probably something that's written in English, just so that the person who's reading the log file can understand. And then there's some parameters that are put in, right? So now, if you look at how we structure that, the way it looks is there's going to be three tables that correspond to the three event types that we see above. And so we're going to look at the one that corresponds to the one in the middle. So if we look at that table, there you'll see a table with columns, one for severity, for function name, for time zone, and so on. And date, and PID. And then you see over to the right with the colored columns there's the parameters that were pulled out from the variable part of that message. And so they're put in, they're typed and they're in integer columns. So this is the way structuring needs to work with logs to be able to do efficient and effective anomaly detection. And as far as I know, we're the first people to do this inline. All right, so let's talk now about Vertica and why we take those tables and put them in Vertica. So Vertica really is an MPP column store, but it's more than that, because nowadays when you say "column store", people sort of think, like, for example Cassandra's a column store, whatever, but it's not. Cassandra's not a column store in the sense that Vertica is. So Vertica was kind of built from the ground up to be... So it's the original column store. So back in the cStor project at Berkeley that Stonebraker was involved in, he said let's explore what kind of efficiencies we can get out of a real columnar database. And what he found was that, he and his grad students that started Vertica. What they found was that what they can do is they could build a database that gives orders of magnitude better query performance for the kinds of analytics I'm talking about here today. With orders of magnitude less data storage underneath. So building on top of machine data, as I mentioned, is hard, because it doesn't have any defined schemas. But we can use an RDBMS like Vertica once we've structured the data to do the analytics that we need to do. So I talked a little bit about this, but if you think about machine data in general, it's perfectly suited for a columnar store. Because, if you imagine laying out sort of all the attributes of an event type, right? So you can imagine that each occurrence is going to have- So there may be, say, three or four function names that are going to occur for all the instances of a given event type. And so if you were to sort all of those event instances by function name, what you would find is that you have sort of long, million long runs of the same function name over and over. So what you have, in general, in machine data, is lots and lots of slowly varying attributes, lots of low-cardinality data that it's almost completely compressed out when you use a real column store. So you end up with a massive footprint reduction on disk. And it also, that propagates through the analytical pipeline. Because Vertica does late materialization, which means it tries to carry that data through memory with that same efficiency, right? So the scale-out architecture, of course, is really suitable for petascale workloads. Also, I should point out, I was going to mention it in another slide or two, but we use the Vertica Eon architecture, and we have had no problems scaling that in the cloud. It's a beautiful sort of rewrite of the entire data layer of Vertica. The performance and flexibility of Eon is just unbelievable. And so I've really been enjoying using it. I was skeptical, you could get a real column store to run in the cloud effectively, but I was completely wrong. So finally, I should mention that if you look at column stores, to me, Vertica is the one that has the full SQL support, it has the ODBC drivers, it has the ACID compliance. Which means I don't need to worry about these things as an application developer. So I'm laying out the reasons that I like to use Vertica. So I touched on this already, but essentially what's amazing is that Vertica Eon is basically using S3 as an object store. And of course, there are other offerings, like the one that Vertica does with pure storage that doesn't use S3. But what I find amazing is how well the system performs using S3 as an object store, and how they manage to keep an actual consistent database. And they do. We've had issues where we've gone and shut down hosts, or hosts have been shut down on us, and we have to restart the database and we don't have any consistency issues. It's unbelievable, the work that they've done. Essentially, another thing that's great about the way it works is you can use the S3 as a shared object store. You can have query nodes kind of querying from that set of files largely independently of the nodes that are writing to them. So you avoid this sort of bottleneck issue where you've got contention over who's writing what, and who's reading what, and so on. So I've found the performance using separate subclusters for our UI and for the ingest has been amazing. Another couple of things that they have is they have a lot of in-database machine learning libraries. There's actually some cool stuff on their GitHub that we've used. One thing that we make a lot of use of is the sequence and time series analytics. For example, in our product, even though we do all of this stuff autonomously, you can also go create alerts for yourself. And one of the kinds of alerts you can do, you can say, "Okay, if this kind of event happens within so much time, and then this kind of an event happens, but not this one," Then you can be alerted. So you can have these kind of sequences that you define of events that would indicate a problem. And we use their sequence analytics for that. So it kind of gives you really good performance on some of these queries where you're wanting to pull out sequences of events from a fact table. And timeseries analytics is really useful if you want to do analytics on the metrics and you want to do gap filling interpolation on that. It's actually really fast in performance. And it's easy to use through SQL. So those are a couple of Vertica extensions that we use. So finally, I would like to encourage everybody, hey, come try us out. Should be up and running in a few minutes if you're using Kubernetes. If not, it's however long it takes you to run an installer. So you can just come to our website, pick it up and try out autonomous monitoring. And I want to thank everybody for your time. And we can open it up for Q and A.

>> Announcer: Live, from Las Vegas, it's The Cube! Covering Veritas Vision 2017. Brought to you by Veritas. >> Welcome back to the Aria Hotel in Las Vegas, everybody. We are covering Veritas Vision 2017, and this is The Cube, the leader in live tech coverage. My name is Dave Vellante, with Stu Miniman and Mike Palmer is here, he's the Executive Vice President and Chief Product Officer at Veritas. Mike, thanks for coming to The Cube. >> Thank you for having me here. >> Great keynote, yesterday. We see hundreds, if not thousands of these discussions, and talking head presentations, and yours was hilarious. Let's set up for the people who didn't see it, yesterday. Mike gets up there, and he's talking about the, there's a video that's playing about the end of the world. And the basic theme is that you didn't take care of your data, and now the world's coming to an end. Las Vegas was in shambles, and there were waterfalls running through the hotels, drones attacking people, and then you picked it up from there and then took it into, just a really funny soliloquy. But, where'd you come up with that idea? And, how do you think, I thought it went great, but how did you feel afterwards? >> Well, I can take only partial credit. I have an amazing creative team, and when you work at a company that's been doing, you know, large-scale enterprise data-center stuff, we know that part of our obligation for our audience is kind of making it more palpable for them, making it feel a little bit more, bringing the emotion to it. So we want to have a little bit of excitement in there. But at the same time, we have a real message, you know, and hopefully that came across, too. >> It did, and then, you know, but again, a lot of good humor, the megabytes, gigabytes, you know, up to zetabytes, yadabytes, Mike Tyson-bytes, on and on and on. (laughing) Very clever, so congratulations on that... >> Mike: Oh, thanks! >> We really enjoyed it. Mixed things up a little bit. So, and again, very transparent. We talked about the UX, not the best. You're not happy with it. >> Mike: Right. >> And again, very transparent about that, I think that's a theme of many successful companies, today. But, so, let's start with, sort of, what does it take as the Chief Product Officer, to transform a company from somebody who's been around since 1983, into a modern, you know, cloud-like, hyper-scale, you know, set of service and software offerings. >> That's a big question, but I can tell you the first thing that it takes, the most important thing that it takes, is the best engineering team in the world. You can do a lot of things around the outside, we need to fix our UX, we know that, often considered that to be the kitchens and bathrooms of our house remodel. But if your foundation's broken, if your framing isn't there, you really don't have much of an asset to put on the market. We have a great engineering team, we are releasing products at a velocity that is incomparable in the enterprise ISV space, and we're super proud of that. So I think that's the number one thing. I think number two is the other thing, that we're the envy of the industry, for, and that is, an amazing install-base of customers. Very hard to name a fortune 2000 company that isn't a significant customer of Veritas, so we have a great basis to collaborate and innovate. You know, the rest, we know we have some work to do as we bring it into the modern age. You know, we talked a lot about the fact that workloads are changing in data centers, architectures are changing, we're establishing new partnerships with some of the sponsors that you see here today, like Microsoft, like Google, like IBM, and Oracle, and others. And, you know, it takes a village and they're helping us move into the next 10 years. >> Stu: You know, Mike, talk a little bit about the transition from, you know, software that lived on servers, to now, well, cloud is just isn't somebody else's servers, I think, is the word for that. >> Mike: Right. >> You know, it definitely, we've talked many times this week, you know, Veritas was software-defined before there was such a thing, used to be the FUD from the traditional players, that it was like, oh, you can't trust stuff like that, and now, of course, they're all software-defined and, you know, talking about that, too, so, what does that mean, going to kind of, being completely agnostic, for where things lived, and some of the intricacies of trying to work with, you know, some of the big and small cloud-players? >> A lot of questions in there, and I think David Noy, who I know you guys are going to talk to later, is going to talk a bit more specifically about this, but one of the first things you have to keep in mind, is if you're building software to be software-defined, then you have to build it without considering the hardware platform that you may deliver it on. And I think that's where some of our competitors get it wrong, they can say that they're software-defined, but the litmus test is, can I really pick up this software without modification, and go run it in one of those hyper-scalers? Or put it on one of the white boxes that I went into the market and procured and integrated myself? Veritas has been doing that for a long time. In fact, if you really look at Veritas's core, we're an integrator. We've been an integrator of applications, through the protection space, in our file-system and our info-scale technologies, we are integrators of operating systems, when you look at hyper-scalers, they're just the next operating system. Someone else's hardware, as you said. So we look to protect our customers in terms of their choices, make flexibility a real part of the multi-cloud architecture they're putting together, still doing the things we do well with protection, and ultimately layer on that last little bit when we're talking software-defined and that is not just focus on the infrastructure, but really aspire to this, how do I better manage data and get value from data? >> You know, Mike, I want to dig one level deeper on that. So, the cloud providers, it's all well and good to say, yeah, I'm agnostic, but each of them have their own little nuances. It's, at least today, it's not like, oh, I choose today to wake up and this one has cheaper prices, and it's not a commodity, it's not a utility, and each one of them have services that they want you to integrate with, have to have deeper, how do you balance that, you know, integration, how much work's done, where the customers are pulling you, how does that product portfolio get put together? >> That's an excellent question and I will be fully honest, that a year ago we thought about the answer to that question very differently than we do today. You know, a year ago, I think we were somewhat naive, and thought, hey, we're going to throw a thin layer of capability on top of the clouds, and in effect commoditize them ourselves, and hope our customers just move around as if there were no underlying services. And obviously if you're a cloud-provider, that is not an approach that you're a big fan of. (laughing) And frankly, it's a disservice to the customers, because they are building some really valuable services, and they are differentiating themselves. Our approach has changed, our approach today is a very deep-level integration with each cloud provider, and the specialization they're bringing to the market, without sacrificing the portability, without sacrificing the built-in protections that the cloud providers aren't putting on their platforms and don't want to put on their platforms. And again going back to this idea of data, ultimately, if it's someone else's hardware, in effect, in some cases, someone else's application, it's always your data, and how we are servicing that data is really the key. >> So, that's really hard work. In a lot of cases, you have to interface with very low-level, primitive APIs from the cloud service providers. How do you, sort of, balance your resources, or a portion of your resources, between doing that, because you guys, I call it the compatibility matrix, all kinds of data stores, all kinds of clouds, every one of those is engineering resources. And it seems that's a key part of your strategy, but you got to be sacrificing something, which is maybe, you know, the next widget on your existing products. How do you think about proportioning those? >> You know, at Veritas, in a way, the emergence of the cloud ecosystem actually improves that situation for us. We're carrying 30 years of operating systems that have come and gone, that have incremented versions, and our customers often strand or isolate single examples of those boxes, from 20 years ago that they expect us to test all of our software against, on their behalf. (laughing) For example, right, and so when you look at where we are today, there are five or 10 cloud providers, versus hundreds of operating system versions, and application, we have no problem supporting the proliferation in cloud, we actually welcome the ability to support those... >> Stu: You're much happier with the one version of AJUR, as opposed to the old Patch Monday. >> Exactly right, and you know, they upgrade the whole thing at once... >> Yeah. >> They issue a couple new services, and we adapt 'em, no problem. >> Am I thinking about it the wrong way? Because, while that's true, and I understand that, but within an individual cloud, you could have 15 data services. I think about AWS data services, their data pipeline is increasingly complex, so. Doesn't that complexity scale in a different direction? >> Mike: It scales differently for sure, but I would give a lot of credit to the cloud providers, because they're taking a lot of the regression testing that we used to have to do, for example, with application providers and operating system providers who didn't think about us when they were building their products. The cloud providers take accountability for regression testing all of the things that they release to their customers. So when we adopt an API, we're fully confident that that API works in the context of that cloud environment. So that's off our plate. It really isolates the need for us to simply test that API against our environment. >> Dave: OK, so much more stable and predictable environment for you. I want to ask you, I've heard the term modern data protection a lot, what is modern data protection? Everyone wants to be next-gen, how do you define modern data protection? >> Mike: And this is something we're super passionate about, because our industry has been around for quite a long time, and you get terms thrown out there, like legacy or modern, and everyone's fighting for brand recognition, and kind of, end of the growth spaces in the market. For us, it actually is very simple. We recognize that there are a lot of different techniques to protect data, we think of these protection schemes like lots of different insurance policies, and lots of different tools in your toolbox. Where Veritas is going to win, and continues to win, is that we can offer our customers all of those techniques. We're not trying to convince them that one technique is so much more special than another one, that they need to diversify and create complexity in their environment, so we talk about modern data protection as the ability to choose snapshots, or back-ups, or copy data management, or workload migration, in the future there will be other ways to do this: continuous data protection, or scale-out platforms for cloud providers. These are just techniques inside of a Veritas portfolio, as opposed to stand-alone companies that create complexity for our customers. So, modernization is choice. >> Dave: OK, so you have this awesome install-base. Bill Coleman said to us yesterday, in response to a similar but related question, that it's ours to lose. And the question that we have is, as you look at that install base, you got to get them onto this modern data platform. How do you do that? Do you write some abstraction layer? You talked about that thin layer in the cloud, you must have thought about doing that. Is that what you're doing? How is that going? What does that journey look like? >> Mike: You know, that is one of the most fundamental strategy questions for Veritas. And one of the things we recognized early on, is that while we do have an amazing install base of customers, and those customers are hyper-scaled themselves, you're talking about customers with tens of thousands of servers running our software, both on the storage and the protection site, so the thing that we cannot ask them to do is continuously upgrade their environment to take advantage of new features. We will put out one-to-two major releases of our software, particularly on a protection side, annually. But we're innovating at a far greater pace than that, so we've made some conscious choices to create new architectures for our customer that are workload-specific, so Cloud Point, being a great example, coming out in July, our Object Store announcements, underpinning our next generation protection solutions. So they have modern storage capabilities, our second example. But pulling them together is where only Veritas can offer a customer a complete catalog of that data. So, combining your net back-up catalog with Cloud Point, for example, with your storage, with what you've put into cloud, provides a customer, for the first time, kind of a complete view of the secondary estate. And so, as long as we get that right, we don't have to upgrade, we don't have to seed, what we have to do is enable our customers, through simple adoption of new tools, provide that visibility over the top, and I think that they'll be good to go. >> So that's kind of like a, I think of a term, backward compatibility, is essentially what you're providing for your install base, is that right? >> Mike: That's exactly right. Providing, and this is where API-based infrastructure and service-driven architectures help us a lot. We don't have to fully instantiate a code-base every time that we want to offer a service to a customer. >> Dave: There aren't many independent, in fact there aren't any independent, is one, two-and-a-half billion dollar software companies in your space, but there are many emerging guys, who are getting a lot of attention, well-funded, some, you know, hitting that kind of, 100 million dollar revenue mark, at least it appears that way. How do you look at those guys? What do you learn from them? You know, Branson said today, you know, you learn by listening and watching, in this case. You're watching the market, obviously, what are you seeing there, it's the hottest space in the infrastructure market right now, is your space and security. Are the two, you know, smoking hot spaces. What are you observing, and what are you learning? >> And I think the direct answer to your question is probably the user. You know, and I think that's the lesson of the industry even over the last 15 years, is that when a new workload arises, it's creating a new user inside the enterprise IT department. And that user often gets to determine all of the services that they need to make themselves successful. If that is a cloud workload, and they need availability services, or they need protection services, they want that to integrate in the same place that they buy in provision their cloud workload. If it's a container workload it's the same. We saw the rise of some of our competitors that got to multi-hundred million dollar revenue streams, by focusing on a single user, and a single type of transaction, with a single type of interface. And Veritas kind of lost its way, I think, a little bit, back in that time. So what we are watching today, is who are our users? What workloads are emerging? What sort of interfaces do we need to develop for those users? Which is why we made our UX statements as strongly as we did. We're committed to those. That is going to be the future of Veritas, it's serving the broadening user-base inside of enterprise. >> Dave: You're seeing a lot of discussion in the industry around design thinking, I know we're out of time, here, but, you know, you see companies, like, for instance, Charles Phillips's company, Infor, bought a company called Hook and Loop, and they're all about design, and, how is design thinking fitting into your, sort of, UX/UI plans? >> I mean, the parlance that we use internally is jobs to be done. Right, we clearly want to create a very consistent user experience, and look and feel, we want our customers to be proud to be Veritas customers. But we have to be super cognizant of, what is the job they're trying to get accomplished? And allow the system to be designed around accommodating that. If that is, I want three workflows in three steps or less, can I do that? It could be, I have a very complicated job and I want the ability to control very granular things, do I have an interface to do that? So, if we know the user and the job to be done, we can create a consistent look and feel, I think that we are, we're going to not only ride the wave, of change inside of our particular industry, but I think we're going to wind up in a consolidation space where we're a big winner. >> All right, last question, the bumper sticker on Vision 2017, as the trucks are pulling away from the area, what's the bumper sticker? >> Mike: Secondary data is your most under-utilized asset, and a platform provider is what you need to take advantage of it. >> Dave: All right, Mike, thanks very much for coming to The Cube. Congratulations, and good luck. >> Thank you for having me. >> All right, you're welcome, keep right there, buddy, Stu and I will be back with our next guest. The Cube, live we're live from Veritas Vision 2017. Be right back.