(upbeat music) >> Hey everyone, Lisa Martin here with you on theCUBE Live from Mobile World Congress '23 in Barcelona. We're having a great day at the show. We hope you are too. I've got two guests here with me next. We're going to be talking about telco's 5G, all that exciting stuff. Please welcome Jason Inskeep, the AVP 5G, and Private 5G Center of Excellence at AT&T Business. And Doug Lieberman is here as well. Senior Director, Global Solutions Co-Creation Services at Dell Technologies. Guys, it's great to have you on the show, live from the show floor, talk to me about what's going on, how are you? >> Hey, thanks for having us on. It's a great show, I'm happy to be back here this year and really looking forward to the conversations that are going on and really continuing these partnerships that Dell has with companies like AT&T to truly drive the realities and the benefits of 5G. >> Absolutely, Doug, talk to me a little bit. You have an interesting title, Director of Global Solutions Co-Creation Services at Dell. Tell me a little bit about your role, what you're responsible for, and then Jason we'll have you do the same. >> Yeah, thanks for bringing that up. So, I have a very interesting role and a very exciting role at Dell because we have a unique organization that I run globally whose job it is, is to work with telcos to co-create services for enterprise and jointly go to market with those. So that basically take the combined power of AT&T and Dell and bring that to enterprise customers and other telcos so that enterprises can realize the value of, and truly leverage and harness the capabilities of 5G for private mobility and Mac and IOT and connected devices. >> Jason, let's bring you into the conversation now. You have an interesting title as well. You're with the 5G Center of Excellence at AT&T. Talk a little bit about your role and that COE. >> Yeah, thanks for having me again as well. The role with my team at AT&T is we're on the cutting edge. We're sitting in between our customers and our product houses that are working with folks at Dell, really helping putting our products together in the space of 5G. A lot of open opportunities here, a lot of things changing really fast. So my teams are off as well as putting this stuff in customers production sites it's also taking and capturing that information, working with my internal partners, both on the technology side, on the product side, and partners like Dell who are coming in helping us enabling those products and services that we can take and scale out through the different opportunities that we're seeing in this space. >> Let's double click on that partner angle, Jason, will stay with you. The 5G revolution, it's here, we are all excited about it. There's so much potential that will come from that. Let's talk about the AT&T/Dell partnership. How are you guys working together to deliver 5G globally? Jason, we'll start with you and then Doug will go to you. >> Yeah, at the core of it, when we started looking at 5G and seeing the changes that were happening, one of the biggest changes is it became software defined. So, the way we could deploy the hardware with the software becomes a whole new conversation. And what we saw coming out of that is it's not going to be a single winner and loser to really execute the way it's necessary for the experiences of tomorrow. It has to be an ecosystem that comes together. Dell creates a great opportunity for us from the hardware perspective to move those services around, to scale those services ultimately to all kinds of site types up to cities right down to small offices. And those different form factors that they bring with the software and the network pieces that we're adding on top of it help to streamline the flows and processes and really gets to that next generation that we see happening, which is this converged architecture. This meeting of network and application, creating a whole new skillset along with products. So we're at the very top we've got Dell/AT&T, at a partner level, it gets at a granular level too. The users and the developers underneath are starting to change as well, so very interesting dichotomy happening right now. >> Right, Doug, what's through Dell's lens? Tell us a little bit about the partnership and how you're working together to deliver 5G and really unlock its potential globally. >> Yeah, thank you, I'd love to bonus off of what Jason was saying, for Dell, what we look at is through the lens of an enterprise. An enterprise needs to execute their business function, their outcome, their mission that they need to operate. And so therefore they have workloads that they need to run. And 5G is an enabler for that technology, and there's lots of other enablers but the key piece is how can they get their business work done better, faster, cheaper, more efficiently, more securely? And the combination of AT&T and Dell truly is a combination that brings in a partnership that brings together a full breadth of those capabilities, with understanding what those enterprise workloads are and how they work and how an enterprise would leverage these capabilities. And then bonus on top of that and merge together with that the capabilities of AT&T. And when you look at 5G, there's a lot of people that talk about 5G being the enterprise G. And a lot of that is because of things that Jason mentioned. As we move to a disaggregated stack where you have software-defined aspects of it, and the ability in the underlying definition of what 5G in the specifications to allow much more customization. It means that enterprises now cannot just take connectivity as it is and use it however it comes but actually work with a telco and work with Dell to customize that connectivity in a way that better meets their requirements. Whether that be with slicing or private mobility or roaming between private and the public network and things like profiles and being able to have different views of how different users and devices connect to that network are all key in truly harnessing the power of that connectivity to have always on, always connected, always integrated systems from the edge, the core, to the cloud. >> Always on, always connected. That's what we all expect these days. Wherever we are in the world, whatever we're trying to do. But to be able to take advantage of all that 5G offers for all of us, telcos have to create infrastructures that can support it, let's double click guys on the infrastructure that Dell and AT&T have put in place to enable this. Jason, I want to get your perspective first and then Doug will go to you. >> Yeah, I mean, it's foundational, the things that we're trying to do and build out here and there's a lot of complexity in it now that we didn't have before because of the flexibility in it. It's one of those things like the good news in software is you can do whatever you want. The bad news in software is you can do whatever you want. Once you have that foundation there though in terms of infrastructure, which for us is really air to glass. Fiber through the spectrum on top. But underneath of that, we have the servers, we have that infrastructure where those fibers come together where that air meets the radios and so forth. And we've got to have that great foundation. So working with Dell and getting those spaces built together preps the area as needed so that there's additional place now for compute and scale at that new converged meet-me point. And that's going to be the opportunity that we're trying to think about really to get that foundation in a way that maximizes capacity and maximizes control for the customers and enterprises in particular. And more importantly, maximizes the time horizon. Because if you're putting in a foundation like this, you're not looking at two to three years. You're trying to skate beyond, skate where the puck is going, look at five, 10 years out, set that foundation. And that's what we're trying to look at with Dell. What can our network do already? Where can we push it? In return, they're going to build their solutions to help maximize that potential. >> From an infrastructure perspective, Doug, I want to understand really where Dell is really shining there but also what are some of the differentiators that Dell brings to this foundational infrastructure that to your point, is built for scale? >> Yeah, so it really all comes down to as we start to see this transition that's been happening for years, but it's accelerating because of always-connected devices and everything connected and the great proliferation of data at the edge. As we move assets from the data center and out to the edge we introduce new challenges that have to be overcome. You have things like security, automation, infrastructure cost, maintenance, day-one operations, day-two operations, all of these things are new complexities which enterprises want to enable their workloads, enable the outcomes that they want to generate. But they need to make sure that they're not taking a step backwards when it comes to things like regulations. In Europe, you've got GDPR, and in the US you've got different security regulations. No one wants to be the next front page headline about their company being attacked and having a ransomware attack. And so as we spread out these assets, what Dell is here to do is to work with AT&T and enable enterprises to effectively build their virtual enterprise around the world where those assets, whether they're at the edge or the core or in the cloud, are all managed with the same profiles and the same security features and the same automation that they have in a core data center. So my ability to deploy an edge cloud so that I can leverage AT&T's network and have end-user devices do things like gaming or connect to video services or get directed retail advertising to you are not basically introducing new vectors for security vulnerabilities into that network. And so Dell has worked really hard and is a leader in the industry in providing automation and lower cost of ownership and security for those solutions. So it's not just about putting a server out there but it's about putting an infrastructure and a cloud that is connected by AT&T's backbone and to a central core of automation management and orchestration capabilities so that I can leverage those assets securely and efficiently. >> That security element that you bring up, Doug, is so incredibly critical. We talk about it at every event, we talk about it every day. We've seen such dramatic changes in the threat landscape in the last couple of years with covid and things like that. So that security element isn't trivial, it's essential for every type of enterprise regardless of where they are. I want to talk a little bit now about best practices. And Doug, go back to you, looking at what AT&T is achieving, the 5G COE, what you're doing with Dell. From your lens and your experiences, what are some of the best practices for telcos deploying secured network and connectivity at the edge? >> Yeah, well, I think the first one is that automation and that orchestration, right? The answer is that you cannot have snowflakes at every single ag point. You need to make sure that those infrastructures are consistent and compliant with the integrations and with the policies that have been set across the network. The second thing is that you want to make sure that the connectivity is monitored and metered and managed so that we know whether, for example that endpoint is there and it's not there, if it goes offline. And ensuring the end-user experience is consistent throughout. And so what we are seeing is that it's really important that we provide an implementation where the enterprise can get a consistent and a predictable outcome for what they're trying to accomplish. What they don't want to do, what enterprises hate and is really bad for them is when they provide an inconsistent or inappropriate results to their users, to their customer base. So if your website goes offline or you're a gaming platform, if people can't get to your game you're going to lose customers, you're going to lose business you're going to have people lose faith in your network. And so our partnership with AT&T and with other telcos is about ensuring that we have all those aspects covered, day zero, day one, and day two, as well as the security aspects. And that back haul is an essential piece of that because as we get more and more devices and more and more edge devices set up, there's more sprawl. And so the complexity goes up substantially, but what really wins is when you can take that complexity and use it to your advantage and be able to manage and deploy those systems as though they were all within your virtual enterprise. >> Using complexity to your advantage. That's an interesting one, Doug, that you're bringing up. Jason, I want to know, what does that mean for you and how is AT&T leveraging complexity to its advantage for its customers all over the globe? >> You know, first thing is if you're thinking about, we're a network company we're not just a 5G company, so we're wireline, we're wireless, we're global in terms of the amount of fiber we have in the ground, the amount of in the US, domestic sailor deployments, our investments in FirstNet, is our first responder network here in the US. So we have a big portfolio inclusive of IOT. That is a global brand as well. That, if you look at it through the outside lens, that's super complex, all over the planet. So when we're talking to our customers now in this new world, which is very much, "Hey, you can do these things on your own." We go back and the bigger, obviously have the products, and the network and the tech but now that customers can take advantage of it and take things that Dell have rolled out, they need that new new age expertise. You also got the Dell expertise of building these platforms from maybe a software level, from an orchestration level, those kind of things. And at the edge that's creating a new type of person and a new type of workflow, a new type of way to respond and work. So that combination of those two is going to be that new skillset. It's in small pockets now, it's growing in how that looks because it's a little combination of both the app developer and the network developer, that's coming together. Our footprint and in terms of what we provide in there is not just 5G, it's 5G, it's fiber, it's all of those pieces together. And that's what's going to super enable that experience that Doug talked about when you're thinking about gaming or transportation, it's not just the network performance, it's the roundtrip, so we're really trying to focus on that and educate our customers in that way with the expertise that we bring over years and years of building these things. >> And if I could just jump in there. I'd like to just emphasize something Jason just said. When we look at workloads at the edge, very rarely are those workloads uniquely just an edge workload, there are components. The example I like to use is video surveillance. If you are a big box store and you have video surveillance inside your store, there is a set of workloads and outcomes you need for immediate response at that edge. You want to know if there's a safety hazard, if there is a theft or those things. Those things need to be processed real time in the store before the thief leaves the store. But then there's a set of connectivity as well where you want to process that data up in the cloud to get long-term analytics and data off of that information. What's my average store density on a Thursday afternoon in November when it's 20 degrees out. Because that would drive how many employees I have, how much inventory I carry, et cetera. And that combination is a factor that drives all the different aspects of AT&T's network. We need the connectivity in the store for the practicability and the spectrum for the cameras that talk to a central server. We need the high-speed backup and throughput in order to provide cyber recovery as well as point-of-sales services so that they can do credit card transactions flawlessly, which is using a lot of wireline services for AT&T. And together with their cloud and their other capabilities, an enterprise needs all those different aspects to work, both the edge, the core and the cloud coming together to form an outcome from one piece of input. So that one piece of input, that video stream is used in multiple different ways and because of that, that network that AT&T brings can support the end-to-end outcome and use cases for that implementation, as an example. >> That end-to-end roundtrip that you guys talked about is essential for every type of enterprise. A lot of great work that Dell and AT&T are doing together to really enable enterprises to really capitalize on all that the new technology that 5G has the potential to deliver. So I got to wrap things up, Jason, with you. From a business-customer perspective, what's next for AT&T? What can those business customers expect? >> Just continued to scaling because you're looking at a space that's evolving rapidly. It's evolving rapidly, there's a lot of opportunity. You look at the private wireless space in particular, it's nascent, but growing rapidly with the customers having their ability to do this on their own. So for us, and really where we're starting to think now is we're seeing the things move from POC, starting to move to production, customers are starting to think about what's next. For us, we're trying to skate ahead of that knowing how we built our own networks, knowing how we engaged in our own partnerships like with Dell and trying to bring that expertise back to the customer, because it isn't cookie cutter anymore. There's a lot of flexibility and each input creates a different output. So it's up to us to at least help them balance that. Define what I like to affectionately call, "Find their Goldilocks." What is that just right for them? >> Great point, Jason, it is no longer a cookie cutter. Cookie cutter isn't going to cut it. Jason, Doug, thank you so much for joining me on theCUBE today from Mobile World Congress in Barcelona. We appreciate thank you all of your insights. Sounds like some great work that AT&T and Dell are doing together. Enterprises have a lot to look forward to. Thank you again for your time. >> Thank you very much, >> Thank you. >> Looking forward to seeing you at the show. >> I'm Lisa Martin from theCUBE at Mobile World Congress '23 in Barcelona. Thanks for watching. (upbeat music)

(bright upbeat music) >> Well, hi everybody, John Wallace here and welcome to theCUBE, the leader in high-tech coverage. Glad to have you aboard here as we continue our coverage here at re:Invent 2022. We're out at The Venetian in Las Vegas. A lot of energy down on that exhibit floor, I promise you. We're a little bit away from the maddening crowd, but we're here with the Executive Summit sponsored by Accenture. I've got two guests I want to introduce you to. Jason Beyer who is the vice president of Data and Analytics at Bridgestone Americas. Jason, good to see you, sir. >> Hello, John. >> And Josh von Schaumburg, who is the managing director and North America lead for AWS Security at Accenture. Josh, good to see you. >> Thanks for having us. >> Yeah, first off, just quick take on the show. I know you've only been here about a day or so, but just your thoughts about what you're seeing on the floor in terms of energy, enthusiasm and, I think, turnout, right? I'm really impressed by it. We've got a lot of people down there. >> Yeah, I've been certainly impressed, John, with the turnout. But just as you say, the energy of the crowd, the excitement for the new things coming, it seems like it's a really pivotal moment for many organizations, including my own, and really excited to see what's coming over the next couple days. >> Let's jump into Bridgestone then. I kind of kidded you before we started the interview saying, all right, tires and golf balls, that's what I relate to, but you have a full array of consumer products and solution you're offering and your responsibility is managing the data and the analytics and making sure those business lines are as efficient as possible. >> Absolutely, John. So in my role, I have the privilege of being in an enterprise position. So I get to see the vast array of Bridgestone, which it is a large, highly vertically integrated company all the way from raw material sourcing of natural rubber to retail services in the automotive industry. We're at scale across those areas. The exciting thing about the company right now is we're going through this business transformation of becoming, you know, building on that heritage and that great legacy of having high quality high performance, highly focused on safety products to becoming a product and solutions company, and particular a sustainable solutions company. So what that means is we're bringing not only those great products to market, tires, golf balls, hoses, all kinds of rubber, air springs products to market, but thinking about how do we service those after they're in the market, how do we bring solutions to help fleets, vehicle owners, vehicle operators operate those in a sustainable way, in a cost effective way? So those solutions, of course, bring all new sets of data and analytics that come with it, and technology and moving to the cloud to be cloud native. So this new phase for the organization that we refer to as Bridgestone 3.0, and that business strategy is driving our cloud strategy, our technology strategy, and our data strategy and AWS and Accenture are important partners in that. >> Yeah, so we hear a lot about that these days about this transformation, this journey that people are on now. And Josh, when Bridgestone or other clients come to you and they talk about their migrations and what's their footprint going to look like and how do they get there, in the case of Bridgestone when they came to you and said, "All right, this is where we want to go with this. We're going to embark on a significant upgrade of our systems here," how do you lead 'em? How do you get 'em there? >> Yeah, I think there are a couple key cloud transformation value drivers that we've emphasized and that I've seen at Bridgestone in my time there. I mean, number one, just the rapid increase in the pace of innovation that we've seen over the last couple years. And a lot of that is also led by the scalability of all of the cloud native AWS services that we're leveraging, and in particular with the CDP platform. It really started off as a single-use case and really a single-tenant data lake. And then through the strategic vision of Jason and the leadership team, we've been able to expand that to 10 plus tenants and use cases. And a big reason behind that is the scalability of all these AWS services, right? So as we add more and more tenants, all the infrastructure just scales without any manual provisioning any tuning that we need to do. And that allows us to go really from idea, to POC, to production in really a matter of months when traditionally it might take years. >> So- >> If I can build upon that. >> Please do, yeah. >> The CDP, or central data platform, is part of a broader reference architecture that reflects that business strategy. So we looked at it and said, we could have taken a couple of different approaches to recognize the business challenges we're facing. We needed to modernize our core, our ERP, our manufacturing solutions move to smart factory and green factories, our PLM solutions. But at the same time, we're moving quickly. We have a startup mindset in our mobility solutions businesses where we're going to market on our customer and commerce solutions, and we needed to move at a different pace. And so to decouple those, we, in partnership with Accenture and AWS, built out a reference architecture that has a decoupling layer that's built around a data fabric, a data connected layer, integrated data services as well. A key part of that architecture is our central data platform built on AWS. This is a comprehensive data lake architecture using all the modern techniques within AWS to bring data together, to coalesce data, as well as recognize the multiple different modes of consumption, whether that's classic reporting, business intelligence, analytics, machine learning data science, as well as API consumption. And so we're building that out. A year ago it was a concept on a PowerPoint and just show and kind of reflect the innovation and speed. As Josh mentioned, we're up to 10 tenants, we're growing exponentially. There's high demand from the organization to leverage data at scale because of the business transformation that I mentioned and that modernization of the core ecosystem. >> That's crazy fast, right? And all of a sudden, whoa! >> Faster than I expected. >> Almost snap overnight. And you raise an interesting point too. I think when you talk about how there was a segment of your business that you wanted to get in the startup mode, whereas I don't think Bridgestone, I don't think about startup, right? I think in a much more, I wouldn't say traditional, but you've got big systems, right? And so how did you kind of inject your teams with that kind of mindset, right? That, hey, you're going to have to hit the pedal here, right? And I want you to experiment. I want you to innovate. And that might be a little bit against the grain from what they were used to. >> So just over two years ago, we built and started the organization that I have the privilege of leading, our data and analytics organization. And it's a COE. It's a center of expertise in the organization. We partner with specialized teams in product development, marketing, other places to enable data and analytics everywhere. We wanted to be pervasive, it's a team sport. But we really embraced at that moment what we refer to as a dual speed mindset. Speed one, we've got to move at the speed of the business. And that's variable. Based on the different business units and lines of lines of business and functional areas, the core modernization efforts, those are multi-year transformation programs that have multiple phases to them, and we're embedded there building the fundamentals of data governance and data management and reporting operational things. But at the same time, we needed to recognize that speed of those startup businesses where we're taking solutions and service offerings to market, doing quick minimum viable product, put it in a market, try it, learn from it adapt. Sometimes shut it down and take those learnings into the next area as well as joint ventures. We've been much more aggressive in terms of the partnerships in the marketplace, the joint ventures, the minority investments, et cetera, really to give us that edge in how we corner the market on the fleet and mobility solutions of the future. So having that dual speed approach of operating at the speed of the business, we also needed to balance that with speed two, which is building those long term capabilities and fundamentals. And that's where we've been building out those practical examples of having data governance and data management across these areas, building robust governance of how we're thinking about data science and the evolution of data science and that maturity towards machine learning. And so having that dual speed approach, it's a difficult balancing act, but it's served us well, really partnering with our key business stakeholders of where we can engage, what services they need, and where do we need to make smart choices between those two different speeds. >> Yeah, you just hit on something I want to ask Josh about, about how you said sometimes you have to shut things down, right? It's one thing to embark on I guess a new opportunity or explore, right? New avenues. And then to tell your client, "Well, might be some bumps along the way." >> Yeah. >> A lot of times people in Jason's position don't want to hear that. (laughs) It's like, I don't want to hear about bumps. >> Yeah. >> We want this to be, again, working with clients in that respect and understanding that there's going to be a learning curve and that some things might not function the way you want them to, we might have to take a right instead of a left. >> Yeah, and I think the value of AWS is you really can fail fast and try to innovate and try different use cases out. You don't have any enormous upfront capital expenditure to start building all these servers in your data center for all of your use cases. You can spin something up easily based in idea and then fail fast and move on to the next idea. And I also wanted to emphasize I think how critical top-down executive buy-in is for any cloud transformation. And you could hear it, the excitement in Jason's voice. And anytime we've seen a failed cloud transformation, the common theme is typically lack of executive buy-in and leadership and vision. And I think from day one, Bridgestone has had that buy-in from Jason throughout the whole executive team, and I think that's really evident in the success of the CDP platform. >> Absolutely. >> And what's been your experience in that regard then? Because I think that's a great point Josh raised that you might be really excited in your position, but you've got to convince the C-suite. >> Yeah. >> And there are a lot of variables there that have to be considered, that are kind of out of your sandbox, right? So for somebody else to make decisions based on a holistic approach, right? >> I could tell you, John, talking with with peers of mine, I recognize that I've probably had a little bit of privilege in that regard because the leadership at Bridgestone has recognized to move to this product and solutions organization and have sustainable solutions for the future we needed to move to the cloud. We needed to shift that technology forward. We needed to have a more data-driven approach to things. And so the selling of that was not a huge uphill a battle to be honest. It was almost more of a pull from the top, from our global group CEO, from our CEOs in our different regions, including in Bridgestone Americas. They've been pushing that forward, they've been driving it. And as Josh mentioned, that's been a really huge key to our success, is that executive alignment to move at this new pace, at this new frame of innovation, because that's what the market is demanding in the changing landscape of mobility and the movement of vehicles and things on the road. >> So how do you two work together going forward, Ben? Because you're in a great position now. You've had this tremendous acceleration in the past year, right? Talking about this tenfold increase and what the platform's enabled you to do, but as you know, you can't stand still. Right? (laughs) >> Yeah. There's so much excitement, so many use cases in the backlog now, and it's really been a snowball effect. I think one of the use cases I'm most excited about is starting to apply ML, you know, machine learning to the data sets. And I think there's an amazing IoT predictive maintenance use case there for all of the the censored data collected across all of the tires that are sold. There's an immense amount of data and ultimately we can use that data to predict failures and make our roads safer and help save lives >> Right. >> It's hard to not take a long time to explain all the things because there is a lot ahead of us. The demand curve for capabilities and the enabling things that AWS is going to support is just tremendous. As Josh mentioned, the, the AI ML use cases ahead of us, incredibly exciting. The way we're building and co-innovating things around how we make data more accessible in our data marketplace and more advanced data governance and data quality techniques. The use of, you know, creating data hubs and moving our API landscape into this environment as well is going to be incredibly empowering in terms of accessibility of data across our enterprise globally, as well as both for our internal stakeholders and our external stakeholders. So, I'll stop there because there's a lot of things in there. >> We could be here a long time. >> Yes, we could. >> But it is an exciting time and I appreciate that you're both sharing your perspectives on this because you've got a winning formula going and look forward to what's happening. And we'll see you next year right back here on the Executive Summit. >> Absolutely. >> To measure the success in 2023. How about that? >> Sounds good, thank you, Jim. >> Is that a deal? >> Awesome. >> Sounds good. >> Excellent, good deal. You've been watching AWS here at Coverage of Reinvent '22. We are the Executive Summit sponsored by Accenture and you are watching theCUBE, the leader in high tech coverage. 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>>Hey everyone, and welcome back to Las Vegas. Viva Las Vegas, baby. This is the Cube live at AWS Reinvent 2022 with tens of thousands of people. Lisa Martin here with Dave Valante. Dave, we've had some great conversations. This is day one of four days of wall to wall coverage on the cube. We've been talking data. Every company is a data company. Data protection, data resiliency, absolutely table stakes for organizations to, >>And I think ecosystem is the other big theme. And that really came to life last year. You know, we came out of the pandemic and it was like, wow, we are entering a new era. People no longer was the ecosystem worried about it, AWS competing with them. They were more worried about innovating and building on top of AWS and building their own value. And that's really, I think, the theme of the 2020s within the ecosystem. >>And we're gonna be talking about building on top of aws. Two guests join us, two alumni join us. Stephen Manley is here, the CTO of Druva. Welcome back. Jason crat as well is here. CIO and CTO of Summit Carbon Solutions. Guys, great to have you back on the program. >>Thank you. >>Let's start with you giving the audience an understanding of the company. What do you guys do? What do you deliver value for customers? All that good >>Stuff. Yeah, no, for sure. So Summit Carbon is the world's largest carbon capture and sequestration company capturing close to 15 million tons of carbon every year. So it doesn't go into the atmosphere. >>Wow, fantastic. Steven, the, the risk landscape today is crazy, right? There's, there's been massive changes. We've talked about this many times. What are some of the things, you know, ransomware is a, is, I know as you say, this is a, it's not a, if it's gonna happen, it's when it's how frequent, it's what's gonna be the damage. What are some of the challenges and concerns that you're hearing from customers out there today? >>Yeah, you know, it really comes down to three things. And, and everybody is, is terrified of ransomware and justifiably so. So, so the first thing that comes up is, how do I keep up? Because I have so much data in so many places, and the threats are evolving so quickly. I don't have enough money, I don't have enough people, I don't have enough skilled resources to be able to keep up. The second thing, and this ties in with what Dave said, is, is ecosystem. You know, it used to be that your, your backup was siloed, right? They'd sit in the basement and, and you wouldn't see, see them. But now they're saying, I've gotta work with my security team. So rather than hoping the security team stays away from me, how do I integrate with them? How do I tie together? And then the third one, which is on everybody's mind, is when that attack happens, and like you said, it's win and, and the bell rings and they come to me and they say, all right, it's time for you to recover. It's time for, for all this investment we've put in. Am I gonna be ready? Am I going to be able to execute? Because a ransom or recovery is so different than any other recovery they've ever done. So it's those three things that really are top of mind for >>How, so what is the, what are the key differences, if you could summarize? I mean, I >>Know it's so, so the first one is you can't trust the environment you're restoring into. Even with a disaster, it would finish and you'd say, okay, I'm gonna get my data center set up again and I'm gonna get things working. You know, when I try to recover, I don't know if everything's clean yet. I'm trying to recover while I'm still going through incident response. So that's one big difference. A second big difference is I'm not sure if the thing I'm recovering is good, I've gotta scan it. I've gotta make sure what's inside it is, is, is alright. And then the third thing is what we're seeing is the targets are usually not necessarily the crown jewels because those tend to be more protected. And so they're running into this, I need to recover a massive amount of what we might call tier two, tier three apps that I wasn't ready for because I've always been prepared for that tier one disaster. And so, so those three things they go, it's stuff I'm not prepared or covering. It's a flow. I'm not used to having to check things and I'm not sure where I'm gonna recover too when the, when the time comes. >>Yeah, just go ahead. Yeah, that's right. I mean, I think for me, the biggest concern is the blind spots of where did I actually back it up or not. You know, what did I get it? Cuz you, we always protect our e r p, we always protect these sort of classes of tiers of systems, but then it's like, oh, that user's email box didn't get it. Oh, that, you know, that one drive didn't get it. You know, or, or, or whatever it is. You know, the infrastructure behind it all. I forgot to back that up. That to me the blind spots are the scariest part of a ransomware attack. >>And, and if you think about it, some of the most high profile attacks, you know, on the, on the colonial pipeline, they didn't go after the core assets. They went after billing. That's right. But billing brought everything down so they're smart enough to say, right, I'm not gonna take the, the castle head on. Is there is they're that. Exactly. >>And so how do you, I get, I mean you can air gap and do things like that in terms of protecting the, the, the data, the corrupt data. How do you protect the corrupt environment? Like that's, that's a really challenging issue. Is >>It? I don't know. I mean, I'll, I'll you can go second here. I think that what's interesting to me about is that's what cloud's for. You can build as many environments as you want. You only pay for what you use, right? And so you have an opportunity to just reconstruct it. That's why things, everything is code matters. That's why having a cloud partner like Druva matters. So you can just go restore wherever you need to in a totally clean environment. >>So the answer is you gotta do it in the cloud. Yeah. What if it's on prem? >>So if it's on prem, what we see people do is, and, and, and this is where testing and, and where cloud can still be an asset, is you can look and say a lot of those assets I'm running in the data center, I could still recover in the cloud. And so you can go through DR testing and you can start to define what's in your on-prem so that you could make it, you know, so you can make it cloud recoverable. Now, a lot of the people that do that then say, well actually why am I even running this on prem anymore in the first place? I should just move this to the cloud now. But, but, but there are people in that interim step. But, but, but it's really important because you, you're gonna need a clean environment to play in. And it's so hard to have a clean environment set up in a data center cuz it basically means I'm not touching this, I'm just paying for something to sit idle. Whereas cloud, I can spin that up, right? Get a, a cloud foundation suite and, and just again, infrastructures code, spin things up, test it, spin it down. It doesn't cost me money on a daily basis. >>Jason, talk a little bit about how you are using Druva. Why Druva and give us a kind of a landscape of your IT environment with Druva. >>Yeah. You know, so when we first started, you know, we did have a competitor solution and, and, and it was only backing up, you know, we were a startup. It was only backing up our email. And so as you pointed out, the ecosystem really matters because we grew out of email pretty quick as a startup. And we had to have real use cases to protect and the legacy product just wouldn't support us. And so our whole direction, or my direction to my team is back it up wherever it is, you know, go get it. And so we needed somebody in the field, literally in the middle of Nebraska or Iowa to have their laptop backed up. We needed our infrastructure, our data center backed up and we needed our, our SaaS solutions backed up. We needed it all. And so we needed a partner like Druva to help us go get it wherever it's at. >>Talk about the value in, with Druva being cloud native. >>Yeah. To us it's a big deal, right? There's all sorts of products you could go by to go just do endpoint laptop protection or just do SAS backups. For us, the value is in learning one tool and mastering it and then taking it to wherever the data is. To me, we see a lot of value for that because we can have one team focus on one product, get good at it, and drive the value. >>That consolidation theme is big right now, you know, the economic headwinds and so forth. What was the catalyst for you? Was it, is that something you started, you know, years ago? Just it's good practice to do that? What's, >>Well, no, I mean luckily I'm in a very good position as a startup to do define it, you know, but I've been in those legacy organizations where we've got a lot of tech debt and then how do you consolidate your portfolio so that you can gain more value, right? Cause you only get one budget a year, right? And so I'm lucky in, in the learnings I've had in other enterprises to deal with this head on right now as we grow, don't add tech debt, put it in right. Today. >>Talk to us a little bit about the SaaS applications that you're backing up. You know, we, we talk a lot with customers, the shared, the shared responsibility model that a lot of customers aren't aware of. Where are you using that competing solution to protect SaaS applications before driven and talk about Yeah. The, the value in that going, the data protection is our responsibility and not the SA vendor. >>No, absolutely. I mean, and it is funny to go to, you know, it's like Office 365 applications and go to our, our CFO and a leadership and be like, no, we really gotta back it up to a third party. And they're like, but why? >>It's >>In the cloud, right? And so there's a lot of instruction I have to provide to my peers and, and, and my users to help them understand why these things matter. And, and, and it works out really well because we can show value really quick when anything happens. And now we get, I mean, even in SharePoint, people will come to us to restore things when they're fully empowered to do it. But my team's faster. And so we can just get it done for them. And so it's an extra from me, it's an extra SLA or never service level I can provide to my internal customers that, that gives them more faith and trust in my organization. >>How, how are the SEC op teams and the data protection teams, the backup teams, how are they coming together? Is is, is data protection backup just morphing into security? Is it more of an adjacency? What's that dynamic like? >>So I'd say right now, and, and I'll be curious to hear Jason's organization, but certainly what we see broadly is, you know, the, the teams are starting to work together, but I wouldn't say they're merging, right? Because, you know, you think of it in a couple of ways. The first is you've got a production environment and that needs to be secured. And then you've got a protection environment. And that protection environment also has to be secured. So the first conversation for a lot of backup teams is, alright, I need to actually work with the security team to make sure that, that my, my my backup environment, it's air gapped, it's encrypted, it's secured. Then I think the, the then I think you start to see people come together, especially as they go through, say, tabletop exercises for ransomware recovery, where it's, alright, where, where can the backup team add value here? >>Because certainly recovery, that's the basics. But as there log information you can provide, are there detection pieces that you can offer? So, so I think, you know, you start to see a partnership, but, but the reality is, you know, the, the two are still separate, right? Because, you know, my job as a a protection resiliency company is I wanna make sure that when you need your data, it's gonna be there for you. And I certainly want to, to to follow best secure practices and I wanna offer value to the security team, but there's a whole lot of the security ecosystem that I want to plug into. I'm not trying to replace them again. I want to be part of that broader ecosystem. >>So how, how do you guys approach it? Yeah, >>That's interesting. Yeah. So in my organization, we, we are one team and, and not to be too cheesy or you know, whatever, but as Amazon would say, security is job one. And so we treat it as if this is it. And so we never push something into production until we are ready. And ready to us means it's got a security package on it, it's backed up, the users have tested it, we are ready to go. It's not that we're ready just be to provide the service or the thing. It's that we are actually ready to productionize this. And so it's ready for production data and that slows us down in some cases. But that's where DevOps and this idea of just merging everything together into a central, how do we get this done together, has worked out really well for us. So, >>So it's really the DevOps team's responsibility. It's not a separate data protection function. >>Nope. Nope. We have specialists of course, right? Yeah, yeah. Because you need the extra level, the CISSPs and those people Yeah, yeah. To really know what they're doing, but they're just part of the team. Yeah. >>Talk about some of the business outcomes that you're achieving with Druva so far. >>Yeah. The business outcomes for me are, you know, I meet my SLAs that's promising. I can communicate that I feel more secure in the cloud and, and all of my workloads because I can restore it. And, and that to me helps everybody in my organization sleep well, sleep better. We are, we transport a lot of the carbon in a pipeline like Colonial. And so to us, we are, we are potential victims of, of a pipe, a non pipeline group, right? Attacking us, but it's carbon, you know, we're trying to get it outta atmosphere. And so by protecting it, no matter where it is, as long as we've got internet access, we can back it up. That provides tons of value to my team because we have hundreds of people in the field working for us every day who collect data and generate it. >>What would you say to a customer who's maybe on the fence looking at different technologies, why dva? >>You know, I think, you know, do the research in my mind, it'll win if you just do the research, right? I mean, there might be vendors that'll buy you nice dinners or whatever, and those are, those are nice things, but the, the reality is you have to protect your data no matter where it is. If it's in a SaaS application, if it's in a cloud provider, if it's infrastructure, wherever it is, you need it. And if you just go look at the facts, there it is, right? And so I, I'd say be objective. Look at the facts, it'll prove itself. >>Look at the data. There you go. Steven Druva recently announced a data resiliency guarantee with a big whopping financial sum. Talk to us a little bit about that, the value in it for your customers and for prospects, >>Right? So, so basically there's, there's really two parts to this guarantee. The first is, you know, across five different SLAs, and I'll talk about those, you know, if we violate those, the customers can get a payout of up to 10 million, right? So again, putting, putting our money where our mouth is in a pretty large amount. But, but for me, the exciting part, and this is, this is where Jason went, is it's about the SLAs, right? You know, one of Drew's goals is to say, look, we do the job for you, we do the service for you so you can offer that service to your company. And so the SLAs aren't just about ransomware, some of them certainly are, you know, that, that you're going to be able to recover your data in the event of a ransomware attack, that your data won't get exfiltrated as part of a ransomware attack. >>But also things like backup success rates, because as much as recovery matters a lot more than backup, you do need a backup if you're gonna be able to get that recovery done. There's also an SLA to say that, you know, if 10 years down the road you need to recover your data, it's still recoverable, right? So, so that kind of durability piece. And then of course the availability of the service because what's the point of a service if it's not there for you when you need it? And so, so having that breadth of coverage, I think really reflects who Druva is, which is we're doing this job for you, right? We want to make this this service available so you can focus on offering other value inside your business. And >>The insurance underwriters, if they threw holy water on >>That, they, they, they were okay with it. The legal people blessed it, you know, it, you know, the CEO signed off on it, the board of directors. So, you know, it, and it, it's all there in print, it's all there on the web. If you wanna look, you know, make sure, one of the things we wanted to be very clear on is that this isn't just a marketing gimmick that we're, we're putting, that we're putting substance behind it because a lot of these were already in our contracts anyway, because as a SAS vendor, you're signing up for service level agreements anyway. >>Yeah. But most of the service level agreements and SaaS vendors are crap. They're like, you know, hey, you know, if something bad happens, you know, we'll, we'll give you a credit, >>Right? >>For, you know, for when you were down. I mean, it's not, you never get into business impact. I mean, even aws, sorry, I mean, it's true. We're a customer. I read define print, I know what I'm signing up for. But, so that's, >>We read it a lot and we will not, we don't really care about the credits at all. We care about is it their force? Is it a partner? We trust, we fight that every day in our SLAs with our vendors >>In the end, right? I mean this, we are the last line of defense. We are the thing that keeps the business up and running. So if your business, you know, can't get to his data and can't operate, me coming to you and saying, Dave, I've got some credits for you after you, you know, after you declare bankruptcy, it'll be great. Yeah, that's not a win. >>It's no value, >>Not helpful. The goal's gotta be, your business is up and running cuz that's when we're both successful. So, so, so, you know, we view this as we're in it together, right? We wanna make sure your business succeeds. Again, it's not about slight of hand, it's not about, you know, just, just putting fine print in the contract. It's about standing up and delivering. Because if you can't do that, why are we here? Right? The number one thing we hear from our customers is Dr. Just works. And that's the thing I think I'm most proud of is Druva just works. >>So, speaking of Juva, just working, if there's a billboard in Santa Clara near the new offices about Druva, what's, what's the bumper sticker? What's the tagline? >>I, I, I think, I think that's it. I think Druva just works. Keeps your data safe. Simple as that. Safe and secure. Druva works to keep your data safe and secure. >>Saved me. >>Yeah. >>Truva just works. Guys, thanks so much for joining. David, me on the program. Great to have you back on the cube. Thank you. Talking about how you're working together, what Druva is doing to really putting, its its best foot forward. We appreciate your insights and your time. Thank >>You. Thanks guys. It's great to see you guys. Likewise >>The show for our guests and Dave Ante. I'm Lisa Martin, you're watching the Cube, the leader in enterprise and emerging tech coverage.

(upbeat music) >> Welcome back to Fal.Con 2022. My name is Dave Vallante. We're here with my co-host Dave Nicholson. On the last earnings call George Kurts made a really big emphasis on the relationship with managed service providers. CrowdStrike has announced a new service provider capability. The powered service provider program. Jason Cook is here. He is the president of cyber defense labs. He's joined by Mike Riolo. Who's the vice president of global system integrators and service providers at CrowdStrike gents. Welcome to TheCube. Good to see you. >> Thank you very much. >> Thank you >> Jason, tell us about cyber defense labs. What do you guys do? Give us the bumper sticker, please. >> Cyber defense labs uses the best technology in the world to put together services that help protect our clients >> Simple. Like it. What's XDR? (people laughing) >> I've not heard of that before, sorry. >> So Mike, we've seen the rise of service providers. I saw a stat, I don't know, six, seven months ago that 50% of us companies don't even have a SOC. We're talking about mid to large companies. So service providers are crucial. What's the CrowdStrike powered service provider program all about? >> Well, it's an evolution for us. We've been dealing with this market for some time. And the idea is, is like how do we expand the opportunity to stop reaches? I mean, that's what it's all about. Like how more routes to market, more partners like cyber defense labs that can really go in and bring our technology coupled with their services to power their offerings to their customers and just help us reach every end user out there, to stop reaches. >> So Jason, how do you guys differentiate? Cause I see, you know, as an analyst, I'll look back, I'll read the press releases and they'll see, okay. They just look so similar. So how do you differentiate from the competition? What do you tell customers? >> So when it comes to our selection of technology we test it, we work it, we literally put it into real world situations with our clients. And then we differentiate ourselves with expert services. It's a white glove service from us. We embed ourselves right in with our clients. That's why we call 'em our client partners. And they see us as part of their team and extension of their team. They don't have the time to play with technology and work out what's best. They don't know the time to select it or even then the expertise to use it effectively in the environment. So that's where the trust comes in with us. And then for us, likewise, we are the technology provider such as CrowdStrick, we need to know the technology works and it does what it says. >> I always ask CISOs; What's your number one challenge? And they'll say lack of talent. The only time I didn't get that answer was at... The Mongo DB CISO at reinforced. I'm like yeah, it's cause you're Mongo, I guess reinforced or AWS doesn't have the same problem, but do you... Obviously you see that problem. And you compliment that, is that a fair? >> Yeah, absolutely. Many, many companies mid-market enterprises are really struggling to find talent and then retain the talent. So for us where that's all we are about and then we are there to enable your business to do what your business does. It is just working and I think more and more so you're going to see an industry clearly CrowdStrike's going in that direction. That it's the service provider that becomes a critical element of that trusted circle. >> Does that translate into a market segment by size of organization typically or? You mentioned the ever never ending quest for talent which is critical regardless of size but what does your target market look like? >> So I, I think the biggest gap in the market frankly, is still the mid-market. Many smaller companies still are really just struggling with 'what is the problem.' At least in the mid-market, in the enterprises they really beginning to understand the problem and want to invest and lean in. And here's the irony. They now want to partner to solve the problem cause they recognize they can't do it on their own. >> So Mike, what are the critical aspects of this program? I mean, got the press release out there, but put some meat on the bone for us. >> So if you look at what we were doing to enable managed service providers to go in and, and be powered by CrowdStrike before it was in a corporate market segment it was a specific set of product from us to really enable MDR, you know, sort of that, that generation of services that a lot of customers looked at MSPs for. And what the big message about this is is we are now expanding that. We're taking it out of corporate, we're going upmarket, we're going enterprise. We can leverage partners like cyber defense labs to package our software into their offering and help them power them more than just endpoint. Right? We've had a lot of exciting announcements and probably more to come around identity, you know XDR, the new buzz, right? Like what does it mean? And in, if you look at our approach, it's a very platform centric approach and that's something that partners can monetize. That's something that partners can really help clients grow with is that it's not just about endpoint. It's more about how do I make sure that I'm in a position with a partner that allows me to grow as a market decides it's necessary. So things like identity, cloud on and on and on, that we're investing in and continuing to grow. We are making that available to the CrowdStrike powered service about our marketplace. >> So Jason, service providers historically outsourcing, okay. And it used to be a lot of; 'okay, you know, I'll take over your mess for less kind of thing.' Right? And so the pattern was you would have one of everything and then, that limited your scale. The bigger you got, you had this economies of scale. So am I hearing that, like how do you partner with CrowdStrike? Are you kind of standardizing on that platform or not necessarily cause you have to be agnostic. What's your posture on that? >> So there's a level of, you have to be technology agnostic. We pride ourselves in just using the best technology that's out there. But at the same time, very much with the Fal.Con platform they're building out and maturing in a way that's making significant risk mitigation abilities for a solution provider like us to say we'll take one of those, one of those and put our service around it because that's the best fit service to reduce the risk of this particular client. And having that flexibility for us to do that really allows us then to stay within the same sort of product suite rather than going outside when integration is still one of the biggest challenges that you have. >> So you're one of those organizations that's consolidating a bevy of point tools. Is that right? I mean, you're going through that transformation now. Have you already gone through that? What's your journey look like there? >> Oh, we help companies do that. That's how they mitigate and reduce their risk. >> Okay. But you're using tools as, as well. Are you not? So I mean, you've got to also I mean you're like an extension of those clients. >> Absolutely. So it comes down to a lot of the time do you have the right team? We have a team of experts that deliver expert services. You get to a level of skillset and experience, which goes what's just the best tool out there. And it becomes that's our insight. So one of the reasons why we like the Fal.Con product is because regardless of what the mess is, that's happening you can rapidly deploy stuff to make a difference. And then you then work out how to fix the mess which is quite a change from how traditionally things are done, which is let's analyze the problem. Let's look at options around it. And by the time you've done that time has passed and you can't afford to just allow time to pass these days. So having the right technology allows you to rapidly deploy. Of course, we use what we sell. So we are proud to say that we use a number of the Fal.Con products to protect ourselves and consolidate onto that technology as we then offer that out as a service to our clients. >> So Mike, I'm thinking about the program in general and specifically how you are implementing this program thinking about the path to bringing the customer on board. There are a finite number of strategic seats at any customer's table. So who is at the customer's table? Is it CDL saying; 'Hey, I'm going to bring in my folks from CrowdStrike to have a conversation with you.' Is it CrowdStrike saying; 'Hey, it looks like a service provider might be the best solution for you. Let's go talk to CDL.' How does that work? >> It's a great question. And I think we talk a lot about how there's a gap in people to support cyber efforts inside of companies. But we don't talk about the gap in like experts that can go in and actually sit down with CISOs, with CIOs, with CFOs. And so for us, like it's all about the flexibility. It's it's what do you need in the moment? Because at the end of the day, it comes down to the people. If Jason has a great trusted relationship, he's like; 'Hey I just need some content.' 'Help me push why we're powered by CrowdStrike in this moment.' Great, go run. If we have an opportunity where we know that cyber defense labs has a presence then we go in together, right? Like that flexibility is there. We've done a lot. When you build a program like this, like it's easy to tell the market what they need. It's easy to tell everybody, but it's also you're looking at a cultural shift and how CrowdStrike goes to market, right? Like this is all about how do we get every possible route to market to stop reaches for customers of all size. >> I would echo that. there's three ways that that's working for our two companies at the moment. Many times a lot of the relationships that we have are trusted advisor at the owner or board level of these mid-market and enterprise companies. They're looking to ask for a number of things. And one of the things that we then say is, Hey for your technology roadmap, hey we want to bring in co-present coded us, co-discuss co-strategize with you what your roadmap is. And so we often bring CrowdStrike into the conversations that cyber defense lab is having at the board level. Then on the other side, CrowdStrike obviously has a significant sales force and trusted advisors. They go in with the product and then it's apparent that the you know, the client wants way more than just the product. They say, this is great. I love it. I've made my decision, but I can't operate it effectively. And so we then get pulled in from that perspective >> You get to all the time from product companies, right? It's like, okay, now what? How do I do this? And you go, oh, I'll call somebody. So this is going to accelerate. You go to market. >> Well, and everybody looks at it like, you know how does your sales play with their sales, right? Everyone's going after the same thing. And I'm, you know, that's important, but you have to look at CrowdStrike as more than sales, right? We have an amazing threat intel group that are helping clients understand the risk factors and what bad people are trying to do to them. We can bring so many experts to the side of a cyber defense labs in, in that realm. You know, we've been doing this a long time. >> This is what's interesting to me when I think about your threat hunting, because you guys are experts and you guys are experts. But the... Correct me if I'm wrong. But the advantage I see at the CrowdStrike has is your cloud platform allows you to have such a huge observation space. You got a ton of data and you bring that to the relationship as well and then you benefit from that? >> It's two way. It's absolutely two way. CrowdStrike has a whole bunch of experts and expertise in this space. So do cyber defense labs. We call it for us because we're providing a service to multiple clients. Many of them have a global presence. We call it our global threat view. And absolutely we are exchanging real time threat telemetry data with, with our friends at CrowdStrike Which is impacting the value that we have and the ability to respond extremely quickly when something's happening to one of our clients. >> Well, I just add to that, you know if you look at all of our alliances, right? We've got solution providers, tech reliant, everything. The one thing that's really interesting about the CrowdStrike powered service provider program; it lives in alliances, It's a partnership program, but they're our customer. They have chosen to standardize on our platform, right. To help drive the best results for their customers. And so we treat them like a partner because it's not for internal use. There's unlimited aspect to it. And so as that treating like partnership we have to enable them with more than just product. Right? We want to bring the right experts. We want to bring the right, you know, vision of where the market's going the threats out there, things of that nature. And that's something that we do every day with you guys. >> And it was even expressed earlier with the keynote speech that George gave. Look there's an ecosystem of very good technologies, very good providers. And there there's that sort of friend-of-me view here. You put the best thing together for the client at the end of the day. And if we all acknowledge, which I think is the maturity of our partnership, that one plus one equals, I always say at 51 now, if you play it right, then the partner sees... That the client sees the value of the partnership. And so they want more of that. >> So it sounds like... We got to wrap, but I wonder if we could close on this. It sounds like this was happening just organically in the field. Now you've codified it. So my question to each of you is; What's your vision for the future? Where do you guys want to take this thing? >> What a wrap question right there. I love it. Honestly, like we look at it in... Look at what does it mean to be a CrowdStrike powered service provider. It is more than just the platform. It's the program in general, offering them tools to go in and do early assessments. One thing about service providers, they're in there before vendors, right? We're still a vendor at the end of the day. And so they have that relationship, like how do we enable them to leverage our platform leverage our tools, leverage our programs in order to help a client understand, like, what is your risk factor Could a breach come, things of that nature. And so it's really building in really enabling a partner like cyber defense labs to take on the full suite of programs, services, platform that we can provide to them as a customer, treated them like a partner. >> And Jason, from your perspective, bring us on if you would. >> So our partnership with CrowdStrike is really enabling cyber defense labs to increase our share of wallet, our presence in very specific market segments; The mid-market to enterprise especially around banking, financial services auto dealerships, healthcare, manufacturing, where last year we saw a significant progress there. And we think we're going to double it between this year and next year. >> Jason Cook, Mike Riolo. thanks for coming in TheCube. Great story. >> Thank you for having us >> Alright, thank you for watching. Keep it right there. Dave Vallante and Dave Nicholson will be back right after this short break from Fal.Con 22. You're watching TheCube. (soft electronic music)

>> It's no surprise that 73% of organizations indicate analytics spend will outpace other software investments in the next 12 to 18 months. After all, as we know, data is changing the world, and the world is changing with it. But is everyone's spending resulting in the same ROI? This is Lisa Martin. Welcome to the Cube's presentation of "Democratizing Analytics Across the Enterprise," made possible by Alteryx. An Alteryx-commissioned IDC InfoBrief entitled, Four Ways to Unlock Transformative Business Outcomes From Analytics Investments, found that 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. On this special Cube presentation, Jason Klein, Product Marketing Director of Alteryx, will join me to share key findings from the new Alteryx-commissioned IDC Brief, and uncover how enterprises can derive more value from their data. In our second segment, we'll hear from Alan Jacobson, Chief Data and Analytics Officer at Alteryx. He's going to discuss how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. And then, in our final segment, Paula Hansen, who is the President and Chief Revenue Officer of Alteryx, and Jacqui Van der Leij-Greyling, who is the Global Head of Tax Technology at eBay, they'll join me. They're going to share how Alteryx is helping the global eCommerce company innovate with analytics. Let's get the show started. (upbeat music) Jason Klein joins me next, Product Marketing Director at Alteryx. Jason, welcome to the program. >> Hello, nice to be here. >> Excited to talk with you. What can you tell me about the new Alteryx IDC research which spoke with about 1500 leaders? What nuggets were in there? >> Well, as the business landscape changes over the next 12 to 18 months, we're going to see that analytics is going to be a key component to navigating this change. 73% of the orgs indicated that analytics spend will outpace other software investments. But just putting more money towards technology, it isn't going to solve everything. And this is why everyone's spending is resulting in different ROIs. And one of the reasons for this gap is because 93% of organizations, they're still not fully using the analytics skills of their employees. And this widening analytics gap, it's threatening operational progress by wasting workers' time, harming business productivity, and introducing costly errors. So in this research, we developed a framework of enterprise analytics proficiency that helps organizations reap greater benefits from their investments. And we based this framework on the behaviors of organizations that saw big improvements across financial, customer, and employee metrics. And we're able to focus on the behaviors driving higher ROI. >> So the InfoBrief also revealed that nearly all organizations are planning to increase their analytics spend. And it looks like from the InfoBrief that nearly three quarters plan on spending more on analytics than any other software. And can you unpack what's driving this demand, this need for analytics across organizations? >> Sure, well, first, there's more data than ever before. The data's changing the world, and the world is changing data. Enterprises across the world, they're accelerating digital transformation to capitalize on new opportunities, to grow revenue, to increase margins, and to improve customer experiences. And analytics, along with automation and AI, is what's making digital transformation possible. They're providing the fuel to new digitally enabled lines of business. >> Yet not all analytics spending is resulting in the same ROI. So, what are some of the discrepancies that the InfoBrief uncovered with respect to ROI? >> Well, our research with IDC revealed significant roadblocks across people, processes and technologies, all preventing companies from reaping greater benefits from their investments. So on the people side, for example, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% in our survey, are still not using the full breadth of data types available. Data has never been this prolific. It's going to continue to grow, and orgs should be using it to their advantage. And lastly, organizations, they need to provide the right analytic tools to help everyone unlock the power of data, yet instead, they're relying on outdated spreadsheet technology. Nine out of 10 survey respondents said that less than half of their knowledge workers are active users of analytics software. True analytics transformation can't happen for an organization in a few select pockets or silos. We believe everyone, regardless of skill level, should be able to participate in the data and analytics process and drive value. >> So if I look at this holistically then, what would you say organizations need to do to make sure that they're really deriving value from their investments in analytics? >> Yeah, sure. So overall, the enterprises that derive more value >> from their data and analytics and achieved more ROI, they invested more aggressively in the four dimensions of enterprise analytics proficiency. So they've invested in the comprehensiveness of analytics, across all data sources and data types, meaning they're applying analytics to everything. They've invested in the flexibility of analytics across deployment scenarios and departments, meaning they're putting analytics everywhere. They've invested in the ubiquity of analytics and insights for every skill level, meaning they're making analytics for everyone. And they've invested in the usability of analytics software, meaning they're prioritizing easy technology to accelerate analytics democratization. >> So are there any specific areas that the survey uncovered where most companies are falling short? Like any black holes organizations need to be aware of from the outset? >> It did. You need to build a data-centric culture, and this begins with people. But we found that the people aspect of analytics is most heavily skewed towards low proficiency. In order to maximize ROI, organizations need to make sure everyone has access to the data and analytics technology they need. Organizations that align their analytics investments with upskilling enjoy higher ROI than orgs that are less aligned. For example, among the high ROI achievers in our survey, 78% had good or great alignment between analytics investments and workforce upskilling, compared to only 64% among those without positive ROI. And as more enterprises adopt cloud data warehouses or cloud data lakes to manage increasingly massive data sets, analytics needs to exist everywhere, especially for those cloud environments. And what we found is organizations that use more data types and more data sources generate higher ROI from their analytics investments. Among those with improved customer metrics, 90% were good or great at utilizing all data sources compared to only 67% among the ROI laggards. >> So interesting that you mentioned people. I'm glad that you mentioned people. Data scientists, everybody talks about data scientists. They're in high demand. We know that, but there aren't enough to meet the needs of all enterprises. So given that discrepancy, how can organizations fill the gap and really maximize the investments that they're making in analytics? >> Right. So analytics democratization, it's no longer optional, but it doesn't have to be complex. So we at Alteryx, we're democratizing analytics by empowering every organization to upskill every worker into a data worker. And the data from this survey shows this is the optimal approach. Organizations with a higher percentage of knowledge workers who are actively using analytics software enjoy higher returns from their analytics investment than orgs still stuck on spreadsheets. Among those with improved financial metrics, AKA the high ROI achievers, nearly 70% say that at least a quarter of their knowledge workers are using analytics software other than spreadsheets compared to only 56% in the low ROI group. Also, among the high ROI performers, 63% said data and analytic workers collaborate well or extremely well, compared to only 51% in the low ROI group. The data from the survey shows that supporting more business domains with analytics and providing cross-functional analytics correlates with higher ROI. So to maximize ROI, orgs should be transitioning workers from spreadsheets to analytics software. They should be letting them collaborate effectively, and letting them do so cross-functionally >> Yeah, that cross-functional collaboration is essential for anyone in any organization and in any discipline. Another key thing that jumped out from the survey was around shadow IT. The business side is using more data science tools than the IT side, and is expected to spend more on analytics than other IT. What risks does this present to the overall organization? If IT and the lines of business guys and gals aren't really aligned? >> Well, there needs to be better collaboration and alignment between IT and the line of business. The data from the survey, however, shows that business managers, they're expected to spend more on analytics and use more analytics tools than IT is aware of. And this is because the lines of business have recognized the value of analytics and plan to invest accordingly. But a lack of alignment between IT and business, this will negatively impact governance, which ultimately impedes democratization and hence, ROI. >> So Jason, where can organizations that are maybe at the outset of their analytics journey, or maybe they're in environments where there's multiple analytics tools across shadow IT, where can they go to Alteryx to learn more about how they can really simplify, streamline, and dial up the value on their investment? >> Well, they can learn more, you know, on our website. I also encourage them to explore the Alteryx community, which has lots of best practices, not just in terms of how you do the analytics, but how you stand up an Alteryx environment. But also to take a look at your analytics stack, and prioritize technologies that can snap to and enhance your organization's governance posture. It doesn't have to change it, but it should be able to align to and enhance it. >> And of course, as you mentioned, it's about people, process and technologies. Jason, thank you so much for joining me today, unpacking the IDC InfoBrief and the great nuggets in there. Lots that organizations can learn, and really become empowered to maximize their analytics investments. We appreciate your time. >> Thank you. It's been a pleasure. >> In a moment, Alan Jacobson, who's the Chief Data and Analytics Officer at Alteryx, is going to join me. He's going to be here to talk about how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. You're watching the Cube, the leader in tech enterprise coverage. (gentle music)

>> It's no surprise that 73% of organizations indicate analytics spend will outpace other software investments in the next 12 to 18 months. After all, as we know, data is changing the world, and the world is changing with it. But is everyone's spending resulting in the same ROI? This is Lisa Martin. Welcome to the Cube's presentation of "Democratizing Analytics Across the Enterprise," made possible by Alteryx. An Alteryx-commissioned IDC InfoBrief entitled, Four Ways to Unlock Transformative Business Outcomes From Analytics Investments, found that 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. On this special Cube presentation, Jason Klein, Product Marketing Director of Alteryx, will join me to share key findings from the new Alteryx-commissioned IDC Brief, and uncover how enterprises can derive more value from their data. In our second segment, we'll hear from Alan Jacobson, Chief Data and Analytics Officer at Alteryx. He's going to discuss how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. And then, in our final segment, Paula Hansen, who is the President and Chief Revenue Officer of Alteryx, and Jacqui Van der Leij-Greyling, who is the Global Head of Tax Technology at eBay, they'll join me. They're going to share how Alteryx is helping the global eCommerce company innovate with analytics. Let's get the show started. (upbeat music) Jason Klein joins me next, Product Marketing Director at Alteryx. Jason, welcome to the program. >> Hello, nice to be here. >> Excited to talk with you. What can you tell me about the new Alteryx IDC research which spoke with about 1500 leaders? What nuggets were in there? >> Well, as the business landscape changes over the next 12 to 18 months, we're going to see that analytics is going to be a key component to navigating this change. 73% of the orgs indicated that analytics spend will outpace other software investments. But just putting more money towards technology, it isn't going to solve everything. And this is why everyone's spending is resulting in different ROIs. And one of the reasons for this gap is because 93% of organizations, they're still not fully using the analytics skills of their employees. And this widening analytics gap, it's threatening operational progress by wasting workers' time, harming business productivity, and introducing costly errors. So in this research, we developed a framework of enterprise analytics proficiency that helps organizations reap greater benefits from their investments. And we based this framework on the behaviors of organizations that saw big improvements across financial, customer, and employee metrics. And we're able to focus on the behaviors driving higher ROI. >> So the InfoBrief also revealed that nearly all organizations are planning to increase their analytics spend. And it looks like from the InfoBrief that nearly three quarters plan on spending more on analytics than any other software. And can you unpack what's driving this demand, this need for analytics across organizations? >> Sure, well, first, there's more data than ever before. The data's changing the world, and the world is changing data. Enterprises across the world, they're accelerating digital transformation to capitalize on new opportunities, to grow revenue, to increase margins, and to improve customer experiences. And analytics, along with automation and AI, is what's making digital transformation possible. They're providing the fuel to new digitally enabled lines of business. >> One of the things that the study also showed was that not all analytics spending is resulting in the same ROI. What are some of the discrepancies that the InfoBrief uncovered with respect to the the changes in ROI that organizations are achieving? >> Our research with IDC revealed significant roadblocks across people, processes, and technologies. They're preventing companies from reaping greater benefits from their investments. So for example, on the people side, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy, as compared to the technology itself. And next, while data is everywhere, most organizations, 63%, from our survey, are still not using the full breadth of data types available. Yet, data's never been this prolific. It's going to continue to grow, and orgs should be using it to their advantage. And lastly, organizations, they need to provide the right analytics tools to help everyone unlock the power of data. They instead rely on outdated spreadsheet technology. In our survey, 9 out of 10 respondents said less than half of their knowledge workers are active users of analytics software beyond spreadsheets. But true analytic transformation can't happen for an organization in a few select pockets or silos. We believe everyone, regardless of skill level, should be able to participate in the data and analytics process and be driving value. >> Should we retake that, since I started talking over Jason accidentally? >> Yep, absolutely, you can do so. Yep, we'll go back to Lisa's question. Let's retake the question and the answer. >> That'll be not all analog spending results in the same ROI. What are some of the discrepancies? >> Yes, Lisa, so we'll go from your ISO, just so we can get that clean question and answer. >> Okay. >> Thank you for that. on your ISO, we're still speeding, Lisa. So give it a beat in your head, and then on you. >> Yet not all analytics spending is resulting in the same ROI. So, what are some of the discrepancies that the InfoBrief uncovered with respect to ROI? >> Well, our research with IDC revealed significant roadblocks across people, processes and technologies, all preventing companies from reaping greater benefits from their investments. So on the people side, for example, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% in our survey, are still not using the full breadth of data types available. Data has never been this prolific. It's going to continue to grow, and orgs should be using it to their advantage. And lastly, organizations, they need to provide the right analytic tools to help everyone unlock the power of data, yet instead, they're relying on outdated spreadsheet technology. Nine out of 10 survey respondents said that less than half of their knowledge workers are active users of analytics software. True analytics transformation can't happen for an organization in a few select pockets or silos. We believe everyone, regardless of skill level, should be able to participate in the data and analytics process and drive value. >> So if I look at this holistically then, what would you say organizations need to do to make sure that they're really deriving value from their investments in analytics? >> Yeah, sure. So overall, the enterprises that derive more value from their data and analytics and achieved more ROI, they invested more aggressively in the four dimensions of enterprise analytics proficiency. So they've invested in the comprehensiveness of analytics, across all data sources and data types, meaning they're applying analytics to everything. They've invested in the flexibility of analytics across deployment scenarios and departments, meaning they're putting analytics everywhere. They've invested in the ubiquity of analytics and insights for every skill level, meaning they're making analytics for everyone. And they've invested in the usability of analytics software, meaning they're prioritizing easy technology to accelerate analytics democratization. >> So very strategic investments. Did the survey uncover any specific areas where most companies are falling short, like any black holes that organizations need to be aware of at the outset? >> It did. It did. So organizations, they need to build a data-centric culture. And this begins with people. But what the survey told us is that the people aspect of analytics is the most heavily skewed towards low proficiency. In order to maximize ROI, organizations need to make sure everyone in the organization has access to the data and analytics technology they need. And then the organizations also have to align their investments with upskilling in data literacy to enjoy that higher ROI. Companies who did so experience higher ROI than companies who underinvested in analytics literacy. So among the high ROI achievers, 78% have a good or great alignment between analytics investment and workforce upskilling compared to only 64% among those without positive ROI. And as more orgs adopt cloud data warehouses or cloud data lakes, in order to manage the massively increasing workloads. Can I start that one over? Can I redo this one? >> Sure. >> Yeah >> Of course. Stand by. >> Tongue tied. >> Yep. No worries. >> One second. >> If we could get, if we could do the same, Lisa, just have a clean break. We'll go to your question. Yep. >> Yeah. >> On you Lisa. Just give that a count and whenever you're ready, here, I'm going to give us a little break. On you Lisa. >> So are there any specific areas that the survey uncovered where most companies are falling short? Like any black holes organizations need to be aware of from the outset? >> It did. You need to build a data-centric culture, and this begins with people. But we found that the people aspect of analytics is most heavily skewed towards low proficiency. In order to maximize ROI, organizations need to make sure everyone has access to the data and analytics technology they need. Organizations that align their analytics investments with upskilling enjoy higher ROI than orgs that are less aligned. For example, among the high ROI achievers in our survey, 78% had good or great alignment between analytics investments and workforce upskilling, compared to only 64% among those without positive ROI. And as more enterprises adopt cloud data warehouses or cloud data lakes to manage increasingly massive data sets, analytics needs to exist everywhere, especially for those cloud environments. And what we found is organizations that use more data types and more data sources generate higher ROI from their analytics investments. Among those with improved customer metrics, 90% were good or great at utilizing all data sources compared to only 67% among the ROI laggards. >> So interesting that you mentioned people. I'm glad that you mentioned people. Data scientists, everybody talks about data scientists. They're in high demand. We know that, but there aren't enough to meet the needs of all enterprises. So given that discrepancy, how can organizations fill the gap and really maximize the investments that they're making in analytics? >> Right. So analytics democratization, it's no longer optional, but it doesn't have to be complex. So we at Alteryx, we're democratizing analytics by empowering every organization to upskill every worker into a data worker. And the data from this survey shows this is the optimal approach. Organizations with a higher percentage of knowledge workers who are actively using analytics software enjoy higher returns from their analytics investment than orgs still stuck on spreadsheets. Among those with improved financial metrics, AKA the high ROI achievers, nearly 70% say that at least a quarter of their knowledge workers are using analytics software other than spreadsheets compared to only 56% in the low ROI group. Also, among the high ROI performers, 63% said data and analytic workers collaborate well or extremely well, compared to only 51% in the low ROI group. The data from the survey shows that supporting more business domains with analytics and providing cross-functional analytics correlates with higher ROI. So to maximize ROI, orgs should be transitioning workers from spreadsheets to analytics software. They should be letting them collaborate effectively, and letting them do so cross-functionally >> Yeah, that cross-functional collaboration is essential for anyone in any organization and in any discipline. Another key thing that jumped out from the survey was around shadow IT. The business side is using more data science tools than the IT side, and is expected to spend more on analytics than other IT. What risks does this present to the overall organization? If IT and the lines of business guys and gals aren't really aligned? >> Well, there needs to be better collaboration and alignment between IT and the line of business. The data from the survey, however, shows that business managers, they're expected to spend more on analytics and use more analytics tools than IT is aware of. And this is because the lines of business have recognized the value of analytics and plan to invest accordingly. But a lack of alignment between IT and business, this will negatively impact governance, which ultimately impedes democratization and hence, ROI. >> So Jason, where can organizations that are maybe at the outset of their analytics journey, or maybe they're in environments where there's multiple analytics tools across shadow IT, where can they go to Alteryx to learn more about how they can really simplify, streamline, and dial up the value on their investment? >> Well, they can learn more, you know, on our website. I also encourage them to explore the Alteryx community, which has lots of best practices, not just in terms of how you do the analytics, but how you stand up an Alteryx environment. But also to take a look at your analytics stack, and prioritize technologies that can snap to and enhance your organization's governance posture. It doesn't have to change it, but it should be able to align to and enhance it. >> And of course, as you mentioned, it's about people, process and technologies. Jason, thank you so much for joining me today, unpacking the IDC InfoBrief and the great nuggets in there. Lots that organizations can learn, and really become empowered to maximize their analytics investments. We appreciate your time. >> Thank you. It's been a pleasure. >> In a moment, Alan Jacobson, who's the Chief Data and Analytics Officer at Alteryx, is going to join me. He's going to be here to talk about how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. You're watching the Cube, the leader in tech enterprise coverage. (gentle music)

>>Welcome back everyone to the cubes coverage of VM wear Explorer, 2022 formerly VM world. The Cube's 12th year covering the annual conference. I'm Jennifer Daveon. We got Jason Bloomberg here. Who's a Silicon angle contributor guest author, president of inte analyst firm. Great to see you, Jason. Thanks for coming on the queue. >>Yeah, it's great to be here. Thanks a lot. >>And thanks for contributing to Silicon angle. We really appreciate your articles and, and so does the audience, so thanks for that. >>Very good. We're happy >>To help. All right. So I gotta ask you, okay. We've been here on the desk. We haven't had a chance to really scour the landscape here at Moscone. What's going, what's your take on what's going on with VMware Explorer, not world. Yeah. Gotta see the name change. You got the overhang of the, the cloud Broadcom, which from us, it seems like it's energized people like, like shocked to the system something's gonna happen. What's your take. >>Yeah, something's definitely going to happen. Well, I've been struggling with VMware's messaging, you know, how they're messaging to the market. They seem to be downplaying cloud native computing in favor of multi-cloud, which is really quite different from the Tansu centric messaging from a year or two ago. So Tansu is still obviously part of the story, but it's really, they're relegating the cloud native story to an architectural pattern, which it is, but I believe it's much more than that. It's really more of a paradigm shift in how organizations implement it. Broadly speaking, where virtualization is part of the cloud native story, but VMware is making cloud native part of the virtualization story. Do so >>Do you think that's the, the mischaracterization of cloud native or a bad strategy or both? >>Well, I think they're missing an opportunity, right? I think they're missing an opportunity to be a cloud native leader. They're well positioned to do that with Tansu and where the technology was going and the technology is still there. Right? It's not that that >>They're just downplaying it. >>They're just downplaying it. Right. So >>As, as they were security too, they didn't really pump up security at >>All. Yeah. And you know, vSphere is still gonna be based on Kubernetes. So it's, they're going to be cloud native in terms of Kubernetes support across their product line. Anyway. So, but they're, they're really focusing on multi-cloud and betting the farm on multi-cloud and that ties to the change of the name of the conference. Although it's hard to see really how they're connecting the dots. Right. >>It's a bridge you can't cross, you can't see that bridge crossing what you're saying. Yeah. I mean, I thought that was a clever way of saying, oh, we're exploring new frontiers, which is kinda like, we don't really know what it is >>Yet. Yeah. Yeah. I think the, the term Explorer was probably concocted by a committee where, you know, they eliminated all the more interesting names and that was the one that was left. But, you know, Raghu explained that that Explorer is supposed to expand the audience for the conference beyond the VMware customer to this broader multi-cloud audience. But it's hard to say whether you >>Think it worked. Was there people that you recognize here or identified as a new audience? >>I don't think so. Not, not at this show, but over time, they're hoping to have this broader audience now where it's a multi-cloud audience where it's more than just VMware. It's more than just individual clouds, you know, we'll see if that works. >>You heard the cl the cloud chaos. Right. Do you, do you think they're, multi-cloud cross cloud services is a solution looking for a problem or is the problem real? Is there a market there? >>Oh, oh, the cloud chaos. That's a real problem. Right? Multi-cloud is, is a reality. Many organizations are leveraging different clouds for different reasons. And as a result, you have management security, other issues, which lead to this chaos challenge. So the, the problem is real aria. If they can get it up and running and, you know, straightened out, it's gonna be a great solution, but there are other products on the market that are more mature and more well integrated than aria. So they're going to, you know, have to compete, but VMware is very good at that. So, you know, I don't, I don't count the outing. Who >>Do you see as the competition lay out the horses on the track from your perspective? >>Well, you know, there's, there's a lot of different companies. I, I don't wanna mention any particular ones cuz, cuz I don't want to, you know, favor certain ones over others cuz then I get into trouble. But there's a, a lot of companies that >>Okay, I will. So you got a red hat with, you got obvious ones, Cisco, Cisco, I guess is Ashi Corp plays a role? Well, >>Cisco's been talking about this, >>Anybody we missed. >>Well, there's a number of smaller players, including some of the exhibitors at the, at the show that are putting together this, you know, I guess cloud native control plane that covers more than just a single cloud or cover on premises of virtualization as well as multiple clouds. And that's sort of the big challenge, right? This control plane. How do we come up with a way of managing all of this, heterogeneous it in a unified way that meets the business need and allows the technology organization, both it and the application development folks to move quickly and to do what they need to do to meet business needs. Right? So difficult for large organizations to get out of their own way and achieve that, you know, level of speed and scalability that, that, that technology promises. But they're organizationally challenged to, >>To accomplish. I think I've always looked at multi-cloud as a reality. I do see that as a situational analysis on the landscape. Yeah, I got Azure because I got Microsoft in my enterprise and they converted everything to the cloud. And so I didn't really change that. I got Amazon cause that's from almost my action is, and I gotta use Google cloud for some AI stuff. Right. All good. Right. I mean that's not really spanning anything. There's no ring. It's not really, it's like point solutions within the ecosystem, but it's interesting to see how people are globbing onto multi-cloud because to me it feels like a broken strategy trying to get straightened out. Right. Like, you know, multi-cloud groping from multi-cloud it feels that way. And, and that makes a lot of sense cuz if you're not on the right side of this historic shift right now, you're gonna be dead. >>So which side of the street do you wanna be on? I think it's becoming clear. I think the good news is this year. It's like, if you're on this side of the street, you're gonna be, be alive. Yeah. And this side of the street, not so much. So, you know, that's cloud native obviously hybrid steady state mul how multi-cloud shakes out. I don't think the market's ready personally in terms of true multi-cloud I think it's, it's an opportunity to have the conversation. That's why we're having the super cloud narrative. Cause it's a lit more attention getting, but it focuses on, it has to do something specific. Right? It can't be vaporware. The market won't tolerate vaporware and the new cloud architecture, at least that's my opinion. What's your reaction? Yeah. >>Well the, well you're quite right that a lot of the multiple cloud scenarios involve, you know, picking and choosing the various capabilities each of the cloud provider pro offers. Right? So you want TensorFlow, you have a little bit of Google and you want Amazon for something, but then Amazon's too expensive for something else. So you go with a Azure for that or you have Microsoft 365 as well as Amazon. Right? So you're, that's sort of a multi-cloud right there. But I think the more strategic question is organizations who are combining clouds for more architectural reasons. So for example, you know, back backup or failover or data sovereignty issues, right, where you, you can go into a single cloud and say, well, I want, you know, different data and different regions, but they may a, a particular cloud might not have all the answers for you. So you may say, okay, well I want, I may one of the big clouds or there's specialty cloud providers that focus on data sovereignty solutions for particular markets. And, and that might be part of the mix, right? Isn't necessarily all the big clouds. >>I think that's an interesting observation. Cause when you look at, you know, hybrid, right. When you really dig into a lot of the hybrid was Dr. Right? Yeah. Well, we got, we're gonna use the cloud for backup. And that, and that, what you're saying is multi-cloud could be sort of a similar dynamic, >>The low-head fruit, >>Which is fine, which is not that interesting. >>It's the low hanging fruit though. It's the easy, it's that risk free? I won't say risk free, but it's the easiest way not to get killed, >>But there's a translate into just sort of more interesting and lucrative and monetizable opportunities. You know, it's kind of a big leap to go from Dr. To actually building new applications that cross clouds and delivering new monetization value on top of data and you know, this nerve. >>Yeah. Whether that would be the best way to build such applications, the jury's still out. Why would you actually want to do well? >>I was gonna ask you, is there an advantage? We talked to Mariana, Tess, who's, you know, she's CTO of into it now of course, into it's a, you know, different kind of application, but she's like, yeah, we kinda looked hard at that multiple cloud thing. We found it too complex. And so we just picked one cloud, you know, in, for kind of the same thing. So, you know, is there an advantage now, the one advantage John, you pointed this out is if I run on Microsoft, I'll make more money. If I run on Amazon and you know, they'll, they'll help me sell. So, so that's a business justification, but is there a technical reason to do it? You know, global presence, there >>Could be technical reason not to do it either too. So >>There's more because of complexity. >>You mean? Well, and or technical debt on some services might not be there at this point. I mean the puzzle pieces gotta be there, assume that all clouds have have the pieces. Right. Then it's a matter of composability. I think E AJ who came on AJ Patel who runs modern applications development would agree with your assessment of cloud native being probably the driving front car on this messaging, because that's the issue like once you have the, everything there, then you're composing, it's the orchestra model, Dave. It's like, okay, we got everything here. How do I stitch it together? Not so much coding, writing code, cuz you got everything in building blocks and patterns and, and recipes. >>Yeah. And that's really what VMware has in mind when they talk about multi-cloud right? From VMware's perspective, you can put their virtual machine technology in any cloud. So if you, if you do that and you put it in multiple clouds, then you have, you know, this common, familiar environment, right. It's VMware everywhere. Doesn't really matter which cloud it's in because you get all the goodness that VMware has and you have the expertise on staff. And so now you have, you know, the workload portability across clouds, which can give you added benefits. But one of the straw men of this argument is that price arbitrage, right. I'm gonna, you know, put workloads in Amazon if it's cheaper. But if then if Amazon, you know, Azure has a different pricing structure for something I'm doing, then maybe I'll, I'll move a workload over there to get better pricing. That's difficult to implement in practice. Right. That's so that's that while people like to talk about that, yeah. I'm gonna optimize my cost by moving workloads across clouds, the practicalities at this point, make it difficult. Yeah. But with, if you have VMware, any your clouds, it may be more straightforward, but you still might not do it in order to save money on a particular cloud bill. >>It still, people don't want data. They really, really don't want to move >>Data. This audience does not want do it. I mean, if you look at the evolution, this customer base, even their, their affinity towards cloud native that's years in the making just to good put it perspective. Yeah. So I like how VMware's reality is on crawl, walk, run their clients, no matter what they want 'em to do, you can't make 'em run. And when they're still in diapers right. Or instill in the crib. Right. So you gotta get the customers in a mode of saying, I can see how VMware could operate that. I know and know how to run in an environment because the people who come through this show, they're like teams, it's like an offsite meeting, meets a conference and it's institutionalized for 15 plus years of main enterprise workload management. So I like, that's just not going away. So okay. Given that, how do you connect to the next thing? >>Well, I think the, the missing piece of the puzzle is, is the edge, right? Because it's not just about connecting one hyperscaler to another hyperscaler or even to on-premises or a private cloud, it's also the edge, the edge computing and the edge computing data center requirements. Right. Because you have, you could have an edge data center in a, a phone tower or a point of presence, a telco point of presence, which are those nondescript buildings, every town has. Right? Yeah, yeah. Yeah. And you know, we have that >>Little colo that no one knows about, >>Right, exactly. That, you know, used to be your DSL end point. And now it's just a mini data center for the cloud, or it could be the, you know, the factory computer room or computer room in a retailer. You know, every retailer has that computer room in the modern retails target home Depot. They will have thousands of these little mini cloud data centers they're handling their, their point of sale systems, their, you know, local wifi and all these other local systems. That's, that's where the interesting part of this cloud story is going because that is inherently heterogeneous inherently mixed in terms of the hardware requirements, the software requirements and how you're going to build applications to support that, including AI based applications, which are sort of the, one of the areas of major innovation today is how are we going to do AI on the edge and why would we do it? And there's huge, huge opportunity to >>Well, real time referencing at the edge. Exactly. Absolutely. With all the data. My, my question is, is, is, is the cloud gonna be part of that? Or is the edge gonna actually bring new architectures and new economics that completely disrupt the, the economics that we've known in the cloud and in the data center? >>Well, this for hardware matters. If form factor matters, you can put a data center, the size of four, you know, four U boxes and then you're done >>Nice. I, >>I think it's a semantic question. It's something for the marketers to come up with the right jargon for is yeah. Is the edge part of the cloud, is the cloud part of the edge? Are we gonna come up with a new term, super cloud HyperCloud? >>Yeah. >>Wonder woman cloud, who knows? Yeah. But what, what >>Covers everything, but what might not be semantic is the, I, I come back to the Silicon that inside the, you know, apple max, the M one M two M two ultras, the, what Tesla's doing with NPUs, what you're seeing, you know, in, in, in arm based innovations could completely change the economics of computing, the security model. >>As we say, with the AJ >>Power consumption, >>Cloud's the hardware middleware. And then you got the application is the business everything's completely technology. The business is the app. I >>Mean we're 15 years into the cloud. You know, it's like every 15 years something gets blown up. >>We have two minutes left Jason. So I want to get into what you're working on for when your firm, you had a great, great traction, great practice over there. But before that, what's the, what's your scorecard on the event? How would you, what, what would be your constructive analysis? Positive, good, bad, ugly for VMwares team around this event. What'd they get right? What'd they need to work on >>Well as a smaller event, right? So about one third, the size of previous worlds. I mean, it's, it's, it's been a reasonably well run event for a smaller event. I, you know, in terms of the logistics and everything everything's handled well, I think their market messaging, they need to sort of revisit, but in terms of the ecosystem, you know, I think the ecosystem is, is, is, is doing well. You know, met with a number of the exhibitors over the last few days. And I think there's a lot of, a lot of positive things going on there. >>They see a wave coming and that's cloud native in your mind. >>Well, some of them are talking about cloud native. Some of them aren't, it's a variety of different >>Potentially you're talking where they are in this dag are on the hardware. Okay, cool. What's going on with your research? Tell us what you're focused on right now. What are you digging into? What's going on? Well, >>Cloud native, obviously a big part of what we do, but cybersecurity as well, mainframe modernization, believe it or not. It's a hot topic. DevOps continues to be a hot topic. So a variety of different things. And I'll be writing an article for Silicon angle on this conference. So highlights from the show. Great. Focusing on not just the VMware story, but some of the hot spots among the exhibitors. >>And what's your take on the whole crypto defi world. That's emerging. >>It's all a scam hundred >>Percent. All right. We're now back to enterprise. >>Wait a minute. Hold on. >>We're out of time. >>Gotta go. >>We'll make that a virtual, there are >>A lot of scams. >>I'll admit that you gotta, it's a lot of cool stuff. You gotta get through the underbelly that grows the old bolt. >>You hear kit earlier. He's like, yeah. Well, forget about crypto. Let's talk blockchain, but I'm like, no, let's talk crypto. >>Yeah. All good stuff, Jason. Thanks for coming on the cube. Thanks for spending time. I know you've been busy in meetings and thanks for coming back. Yeah. Happy to help. All right. We're wrapping up day two. I'm Jeff David ante cube coverage. Two sets three days live coverage, 12th year covering VMware's user conference called explore now was formerly VM world onto the next level. That's what it's all about. Just the cube signing off for day two. Thanks for watching.

(upbeat music) >> Welcome back to San Francisco, "theCUBE" is live, our day two coverage of VMware Explore 2022 continues. Lisa Martin with Dave Nicholson. Dave and I are pleased to welcome Jason Collier, principal member of technical staff at AMD to the program. Jason, it's great to have you. >> Thank you, it's great to be here. >> So what's going on at AMD? I hear you have some juicy stuff to talk about. >> Oh, we've got a ton of juicy stuff to talk about. Clearly the Project Monterey announcement was big for us, so we've got that to talk about. Another thing that I really wanted to talk about was a tool that we created and we call it, it's the VMware Architecture Migration Tool, call it VAMT for short. It's a tool that we created and we worked together with VMware and some of their professional services crew to actually develop this tool. And it is also an open source based tool. And really the primary purpose is to easily enable you to move from one CPU architecture to another CPU architecture, and do that in a cold migration fashion. >> So we're probably not talking about CPUs from Tandy, Radio Shack systems, likely this would be what we might refer to as other X86 systems. >> Other X86 systems is a good way to refer to it. >> So it's interesting timing for the development and the release of a tool like this, because in this sort of X86 universe, there are players who have been delayed in terms of delivering their next gen stuff. My understanding is AMD has been public with the idea that they're on track for by the end of the year, Genoa, next gen architecture. So can you imagine a situation where someone has an existing set of infrastructure and they're like, hey, you know what I want to get on board, the AMD train, is this something they can use from the VMware environment? >> Absolutely, and when you think about- >> Tell us exactly what that would look like, walk us through 100 servers, VMware, 1000 VMs, just to make the math easy. What do you do? How does it work? >> So one, there's several things that the tool can do, we actually went through, the design process was quite extensive on this. And we went through all of the planning phases that you need to go through to do these VM migrations. Now this has to be a cold migration, it's not a live migration. You can't do that between the CPU architectures. But what we do is you create a list of all of the virtual machines that you want to migrate. So we take this CSV file, we import this CSV file, and we ask for things like, okay, what's the name? Where do you want to migrate it to? So from one cluster to another, what do you want to migrate it to? What are the networks that you want to move it to? And then the storage platform. So we can move storage, it could either be shared storage, or we could move say from VSAN to VSAN, however you want to set it up. So it will do those storage migrations as well. And then what happens is it's actually going to go through, it's going to shut down the VM, it's going to take a snapshot, it is going to then basically move the compute and/or storage resources over. And once it does that, it's going to power 'em back up. And it's going to check, we've got some validation tools, where it's going to make sure VM Tools comes back up where everything is copacetic, it didn't blue screen or anything like that. And once it comes back up, then everything's good, it moves onto the next one. Now a couple of things that we've got feature wise, we built into it. You can parallelize these tasks. So you can say, how many of these machines do you want to do at any given time? So it could be, say 10 machines, 50 machines, 100 machines at a time, that you want to go through and do this move. Now, if it did blue screen, it will actually roll it back to that snapshot on the origin cluster. So that there is some protection on that. A couple other things that are actually in there are things like audit tracking. So we do full audit logging on this stuff, we take a snapshot, there's basically kind of an audit trail of what happens. There's also full logging, SYS logging, and then also we'll do email reporting. So you can say, run this and then shoot me a report when this is over. Now, one other cool thing is you can also actually define a change window. So I don't want to do this in the middle of the afternoon on a Tuesday. So I want to do this later at night, over the weekend, you can actually just queue this up, set it, schedule it, it'll run. You can also define how long you want that change window to be. And what it'll do, it'll do as many as it can, then it'll effectively stop, finish up, clean up the tasks and then send you a report on what all was successfully moved. >> Okay, I'm going to go down the rabbit hole a little bit on this, 'cause I think it's important. And if I say something incorrect, you correct me. >> No problem. >> In terms of my technical understanding. >> I got you. >> So you've got a VM, essentially a virtual machine typically will consist of an entire operating system within that virtual machine. So there's a construct that containerizes, if you will, the operating system, what is the difference, where is the difference in the instruction set? Where does it lie? Is it in the OS' interaction with the CPU or is it between the construct that is the sort of wrapper around the VM that is the difference? >> It's really primarily the OS, right? And we've not really had too many issues doing this and most of the time, what is going to happen, that OS is going to boot up, it's going to recognize the architecture that it's on, it's going to see the underlying architecture, and boot up. All the major operating systems that we test worked fine. I mean, typically they're going to work on all the X86 platforms. But there might be instruction sets that are kind of enabled in one architecture that may not be in another architecture. >> And you're looking for that during this process. >> Well usually the OS itself is going to kind of detect that. So if it pops up, the one thing that is kind of a caution that you need to look for. If you've got an application that's explicitly using an instruction set that's on one CPU vendor and not the other CPU vendor. That's the one thing where you're probably going to see some application differences. That said, it'll probably be compatible, but you may not get that instruction set advantage in it. >> But this tool remediates against that. >> Yeah, and what we do, we're actually using VM Tools itself to go through and validate a lot of those components. So we'll look and make sure VM Tools is enabled in the first place, on the source system. And then when it gets to the destination system, we also look at VM Tools to see what is and what is not enabled. >> Okay, I'm going to put you on the spot here. What's the zinger, where doesn't it work? You already said cold, we understand, you can schedule for cold migrations, that's not a zinger. What's the zinger, where doesn't it work? >> It doesn't work like, live migrations just don't work. >> No live, okay, okay, no live. What about something else? What's the oh, you've got that version, you've got that version of X86 architecture, it-won't work, anything? >> A majority of those cases work, where it would fail, where it's going to kick back and say, hey, VM Tools is not installed. So where you would see this is if you're running a virtual appliance from some vendor, like insert vendor here that say, got a firewall, or got something like that, and they don't have VM Tools enabled. It's going to fail it out of the gate, and say, hey, VM Tools is not on this, you might want to manually do it. >> But you can figure out how to fix that? >> You can figure out how to do that. You can also, and there's a flag in there, so in kind of the options that you give it, you say, ignore VM Tools, don't care, move it anyway. So if you've got less, some VMs that are in there, but they're not a priority VM, then it's going to migrate just fine. >> Got It. >> Can you elaborate a little bit on the joint development work that AMD and VMware are doing together and the value in it for customers? >> Yeah, so it's one of those things we worked with VMware to basically produce this open source tool. So we did a lot of the core component and design and we actually engaged VMware Professional Services. And a big shout out to Austin Browder. He helped us a ton in this project specifically. And we basically worked, we created this, kind of co-designed, what it was going to look like. And then jointly worked together on the coding, of pulling this thing together. And then after that, and this is actually posted up on VMware's public repos now in GitHub. So you can go to GitHub, you can go to the VMware samples code, and you can download this thing that we've created. And it's really built to help ease migrations from one architecture to another. So if you're looking for a big data center move and you got a bunch of VMs to move. I mean, even if it's same architecture to same architecture, it's definitely going to ease the pain of going through and doing a migration of, it's one thing when you're doing 10 machines, but when you're doing 10,000 virtual machines, that's a different story. It gets to be quite operationally inefficient. >> I lose track after three. >> Yeah. >> So I'm good for three, not four. >> I was going to ask you what your target market segment is here. Expand on that a little bit and talk to me about who you're working with and those organizations. >> So really this is targeted toward organizations that have large deployments in enterprise, but also I think this is a big play with channel partners as well. So folks out there in the channel that are doing these migrations and they do a lot of these, when you're thinking about the small and mid-size organizations, it's a great fit for that. Especially if they're kind of doing that upgrade, the lift and shift upgrade, from here's where you've been five to seven years on an architecture and you want to move to a new architecture. This is really going to help. And this is not a point and click GUI kind of thing. It's command line driven, it's using PowerShell, we're using PowerCLI to do the majority of this work. And for channel partners, this is an excellent opportunity to put the value and the value add and VAR, And there's a lot of opportunity for, I think, channel partners to really go and take this. And once again, being open source. We expect this to be extensible, we want the community to contribute and put back into this to basically help grow it and make it a more useful tool for doing these cold migrations between CPU architectures. >> Have you seen any in the last couple of years of dynamics, obviously across the world, any industries in particular that are really leading edge for what you guys are doing? >> Yeah, that's really, really interesting. I mean, we've seen it, it's honestly been a very horizontal problem, pretty much across all vertical markets. I mean, we've seen it in financial services, we've seen it in, honestly, pretty much across the board. Manufacturing, financial services, healthcare, we have seen kind of a strong interest in that. And then also we we've actually taken this and presented this to some of our channel partners as well. And there's been a lot of interest in it. I think we presented it to about 30 different channel partners, a couple of weeks back about this. And I got contact from 30 different channel partners that said they're interested in basically helping us work on it. >> Tagging on to Lisa's question, do you have visibility into the AMD thought process around the timing of your next gen release versus others that are competitors in the marketplace? How you might leverage that in terms of programs where partners are going out and saying, hey, perfect time, you need a refresh, perfect time to look at AMD, if you haven't looked at them recently. Do you have any insight into that in what's going on? I know you're focused on this area. But what are your thoughts on, well, what's the buzz? What's the buzz inside AMD on that? >> Well, when you look overall, if you look at the Gartner Hype Cycle, when VMware was being broadly adopted, when VMware was being broadly adopted, I'm going to be blunt, and I'm going to be honest right here, AMD didn't have a horse in the race. And the majority of those VMware deployments we see are not running on AMD. Now that said, there's an extreme interest in the fact that we've got these very cored in systems that are now coming up on, now you're at that five to seven year refresh window of pulling in new hardware. And we have extremely attractive hardware when it comes to running virtualized workloads. The test cluster that I'm running at home, I've got that five to seven year old gear, and I've got some of the, even just the Milan systems that we've got. And I've got three nodes of another architecture going onto AMD. And when I got these three nodes completely maxed to the number of VMs that I can run on 'em, I'm at a quarter of the capacity of what I'm putting on the new stuff. So what you get is, I mean, we worked the numbers, and it's definitely, it's like a 30% decrease in the amount of resources that you need. >> That's a compelling number. >> It's a compelling number. >> 5%, 10%, nobody's going to do anything for that. You talk 30%. >> 30%. It's meaningful, it's meaningful. Now you you're out of Austin, right? >> Yes. >> So first thing I thought of when you talk about running clusters in your home is the cost of electricity, but you're okay. >> I'm okay. >> You don't live here, you don't live here, you don't need to worry about that. >> I'm okay. >> Do you have a favorite customer example that you think really articulates the value of AMD when you're in customer conversations and they go, why AMD and you hit back with this? >> Yeah. Actually it's funny because I had a conversation like that last night, kind of random person I met later on in the evening. We were going through this discussion and they were facing exactly this problem. They had that five to seven year infrastructure. It's funny, because the guy was a gamer too, and he's like, man, I've always been a big AMD fan, I love the CPUs all the way since back in basically the Opterons and Athlons right. He's like, I've always loved the AMD systems, loved the graphics cards. And now with what we're doing with Ryzen and all that stuff. He's always been a big AMD fan. He's like, and I'm going through doing my infrastructure refresh. And I told him, I'm just like, well, hey, talk to your VAR and have 'em plug some AMD SKUs in there from the Dells, HPs and Lenovos. And then we've got this tool to basically help make that migration easier on you. And so once we had that discussion and it was great, then he swung by the booth today and I was able to just go over, hey, this is the tool, this is how you use it, here's all the info. Call me if you need any help. >> Yeah, when we were talking earlier, we learned that you were at Scale. So what are you liking about AMD? How does that relate? >> The funny thing is this is actually the first time in my career that I've actually had a job where I didn't work for myself. I've been doing venture backed startups the last 25 years and we've raised couple hundred million dollars worth of investment over the years. And so one, I figured, here I am going to AMD, a larger corporation. I'm just like, am I going to be able to make it a year? And I have been here longer than a year and I absolutely love it. The culture at AMD is amazing. We still have that really, I mean, almost it's like that underdog mentality within the organization. And the team that I'm working with is a phenomenal team. And it's actually, our EVP and our Corp VP, were actually my executive sponsors, we were at a prior company. They were one of my executive sponsors when I was at Scale. And so my now VP boss calls me up and says, hey, I'm putting a band together, are you interested? And I was kind of enjoying a semi-retirement lifestyle. And then I'm just like, man, because it's you, yes, I am interested. And the group that we're in, the work that we're doing, the way that we're really focusing on forward looking things that are affecting the data center, what's going to be the data center like three to five years from now. It's exciting, and I am having a blast, I'm having the time of my life. I absolutely love it. >> Well, that relationship and the trust that you will have with each other, that bleeds into the customer conversations, the partner conversations, the employee conversations, it's all inextricably linked. >> Yes it is. >> And we want to know, you said three to five years out, like what? Like what? Just general futurist stuff, where do you think this is going. >> Well, it's interesting. >> So moon collides with the earth in 2025, we already know that. >> So we dialed this back to the Pensando acquisition. When you look at the Pensando acquisition and you look at basically where data centers are today, but then you look at where basically the big hyperscalers are. You look at an AWS, you look at their architecture, you specifically wrap Nitro around that, that's a very different architecture than what's being run in the data center. And when you look at what Pensando does, that's a lot of starting to bring what these real clouds out there, what these big hyperscalers are running into the grasps of the data center. And so I think you're going to see a fundamental shift. The next 10 years are going to be exciting because the way you look at a data center now, when you think of what CPUs do, what shared storage, how the networking is all set up, it ain't going to look the same. >> Okay, so the competing vision with that, to play devil's advocate, would be DPUs are kind of expensive. Why don't we just use NICs, give 'em some more bandwidth, and use the cheapest stuff. That's the competing vision. >> That could be. >> Or the alternative vision, and I imagine everything else we've experienced in our careers, they will run in parallel paths, fit for function. >> Well, parallel paths always exist, right? Otherwise, 'cause you know how many times you've heard mainframe's dead, tape's dead, spinning disk is dead. None of 'em dead, right? The reality is you get to a point within an industry where it basically goes from instead of a growth curve like that, it goes to a growth curve of like that, it's pretty flat. So from a revenue growth perspective, I don't think you're going to see the revenue growth there. I think you're going to see the revenue growth in DPUs. And when you actually take, they may be expensive now, but you look at what Monterey's doing and you look at the way that those DPUs are getting integrated in at the OEM level. It's going to be a part of it. You're going to order your VxRail and VSAN style boxes, they're going to come with them. It's going to be an integrated component. Because when you start to offload things off the CPU, you've driven your overall utilization up. When you don't have to process NSX on basically the X86, you've just freed up cores and a considerable amount of them. And you've also moved that to where there's a more intelligent place for that pack to be processed right, out here on this edge. 'Cause you know what, that might not need to go into the host bus at all. So you have just alleviated any transfers over a PCI bus, over the PCI lanes, into DRAM, all of these components, when you're like, but all to come with, oh, that bit needs to be on this other machine. So now it's coming in and it's making that decision there. And then you take and integrate that into things like the Aruba Smart Switch, that's running the Pensando technology. So now you got top of rack that is already making those intelligent routing decisions on where packets really need to go. >> Jason, thank you so much for joining us. I know you guys could keep talking. >> No, I was going to say, you're going to have to come back. You're going to have to come back. >> We've just started to peel the layers of the onion, but we really appreciate you coming by the show, talking about what AMD and VMware are doing, what you're enabling customers to achieve. Sounds like there's a lot of tailwind behind you. That's awesome. >> Yeah. >> Great stuff, thank you. >> It's a great time to be at AMD, I can tell you that. >> Oh, that's good to hear, we like it. Well, thank you again for joining us, we appreciate it. For our guest and Dave Nicholson, I'm Lisa Martin. You're watching "theCUBE Live" from San Francisco, VMware Explore 2022. We'll be back with our next guest in just a minute. (upbeat music)

(upbeat music) >> Hey everyone. Welcome back to San Francisco, VMware Explorer 2022, Lisa Martin and Dave Nicholson here. We've been having some great conversations today. Lots of news coming out about VMware and its partner ecosystem. We're going to have another conversation about that next. Please welcome two guests to the program, Chance Bingen, technical marketing engineer at NetApp and Jason Massae, staff technical marketing architect, storage and vVols at VMware. Guys, welcome to the program. >> Thanks. >> Glad to be here. >> It's nice to be back in person. >> It is. It's very nice. Oh my gosh. >> And we're hearing there about 7,000 to 10,000 people here, when I was in the Keynote, this morning it was definitely standing room only. >> Yeah, yeah. You've definitely seen the numbers ticked up at the last minute. It was good to see that. It's good, I think a lot of people have really wanted to get back, get that one on one that face to face. There's nothing like being able to, you know, talk to, the experts, talk to the vendors, you know, see your comrades. I mean, that's the thing. I mean, we've seen people that I haven't seen for years, even on my own team, so really good to be back into it. >> It is and it was lots of news coming out this morning during the Keynote. My goodness. But Jason, talk to me, the NetApp and VMware folks had been in tight partnership for a long time. Talk to me about, get both of your perspective from a technical perspective about the depth of the partnership. >> Yeah, so actually NetApp was one of the original design partners for vVols. And with that, now with some of the stuff we're doing with more current stuff with virtual volumes is, NetApp is back and we've got some pretty neat stuff that we've been working on with vVols. And NetApp's got some pretty neat stuff that they've been working on to enable the customers with more features, more functionality with the virtual volume functionality. >> Yeah, absolutely. >> Give us a quick primer on what is a vVol? What is a virtual volume? How does it fit into the, into this stack of stuff that we do in IT? >> Yeah. So the easiest way to kind of think of what a vVol is or a virtual volume is you can think of it kind of like an RDM, those row device map, which is kind of a four letter word. We don't really like those, but the idea is that object, that virtual volume is native on the array and presented directly to the VM. But now what we do is we're presenting all of the storage array features up to vSphere and we're managing those storage features via policy based management. But instead of applying storage capabilities at a data store level, we're now applying them at a VM or an application level. So you can have one data store and multiple VMs, and every VM can have a different storage capability managed by a policy that the VI admin gets to manage now. So he doesn't have to go to the storage admin to say, I need a new line, or I need a new volume. He can just go in and create a policy or change a policy. And now that storage capability is applied to the VM or the application. >> Yeah. One thing I'd like to add to that is you can mentioned the word capabilities. >> So we look at the actual data protocols, whether they're file based or block based, you know, I-scuzzy, fiber channel, whatever the case might be. Those protocols have defined sets of capabilities and attributes and things they can expose. What vVols along with the VASA protocol brings to the table is the ability to expose things that are just impossible to expose via the data protocols themselves. So that the, actual nature of the array, what kind of array is it? What's it capable of doing? What is the nature of, you know, encryption? You know, is this going to be a secure, encrypted data store? Is it going to be something else? It just allows you to do so much more with the advanced capabilities that modern storage arrays have than you could ever do if you were just using the data protocols by themselves. >> Right? Yeah. Kind of under that same context. If you think about before with traditional storage, the vSphere or the array really doesn't understand what's going on underlying storage, but with vVols the array and vSphere completely understand at a disc level even, how that VM should be treated. So that helps the storage admin. Storage admin can now go in and see a specific disc of a VM and see the performance on the array. They can go in the array and see, oh, this disc on this VM has got performance issues or needs to be encrypted, or here's the size of that disc. And you couldn't easily see that with your traditional storage. So there's really a lot of benefits and it frees up a lot of time for the storage administrator and enables the VI admin to be able to do a lot of the storage management. >> So there have been, there been a lot of movements over the last decade in the realm of software defined storage. Where essentially all of the things that you are talking about are completely abstracted from the underlying hardware. In this case, you're leveraging the horsepower, if you will and the intelligence of a storage array that has a lot of horsepower and intelligence, and you're accessing those features. You mentioned encryption, whether if you're doing a snapshot or something like that, what's interesting here is it kind of maps to what we're looking at now, which is the trend in the direction of things like DPUs. >> If you go back in history long enough, we had the, you know, the TOE, NIC, TCP offload, you know, the idea of, hey, you know what, what if we had a smart device with its own brain power and we leveraged it. Well, you guys have been doing that from a vVols all perspective with NetApp filers, for lack of better term. For how long now, when did, when were they originally? >> 6.0 it was so it's been what? 11, 12 years. Something like that. >> It's been a while. So yeah, but it's been a decade or so. >> Mm-hmm >> So what's on the frontier. What's the latest there in terms of, in terms of cool stuff that's coming out. >> So actually today, in one of the things that we worked with NetApp that was part of the design partnership was, you know, the NVMe over Fabric protocol has become very popular to extend that functionality of all flash to the, an external array. And now we announce today, in including with that NVMe over Fabrics, you can now do vVols with NVMe over Fabrics. And again, that was something that we worked with NetApp to be a design partner for them. >> That's right. We're very excited about it. We've always been, you know, NVMe been something we've been very proud of for a while. Delivering the first end to end NVMe stack from inside the host, through the fabric, to the array, with the arrays front ports, all the way to the disc on the backend. So we're very excited about that. >> So target market joint NetApp, VMware customers, I presume. >> Really it's, the key here that I like to make sure customers understand is to see that vVols are on the leading edge of VMware's storage design. Some tend to think that maybe vVols wasn't the primary focus, but actually now it is the primary focus. Now I always like to give the caveat that VMFS and NFS are not going away. Those are still very much stuff that we work on. It's just that most of the engineering focus is on virtual volumes or vVols. >> Yeah. Similarly, when you talk about and you're sort of alluding to vSAN when we start talking about VMFS and things like that. >> Yeah. >> Architecturally, we've been talking to folks about the recent announcements with capabilities within AWS. You know, NetApp in AWS for VMware environments. Breaking out of the stranglehold that the, oh, you want more storage, you must buy more CPU and memory, building block process entails. The reality is no matter what you do with vSAN, you're going to have certain constraints that go away when now you have the option to leverage storage from the NetApp filers. >> Yeah, absolutely. >> So how does, how do vVols play in the cloud strategy moving forward? >> Well, so one of the things that we do with, vVols currently is mostly On-prem. But when you have the storage architecture, that vVols gives you as far as individual objects, it makes it much easier to migrate up into the cloud because you're not trying to migrate individual VMs that are on another type of system, whatever it might be, those objects are already their own entity. Right, so cloud, Tanzu, those type of things, those vVol objects are already their own entity. So it makes it very easy to migrate them on and off prem. >> So Chance talk to us a little bit about this from NetApp's perspective. You're in customer conversations, who are you talking to? Is this primarily an engineering conversation? Has this gone up the stack in terms of customers are finding themselves in this default multi-cloud environment? >> Yeah, so interestingly, when I talk to customers these days they are almost all either on a journey to a hybrid multi-cloud or they're in some kind of phase of transforming themselves into their own hyperscaler, right? They're be adopting a cloud service provider model and vVols is a perfect fit for that kind of model, because you have the ability to offer different tiers of service, different qualities of service with VM granular controls or VMDK granular controls, even. And even if you look at First Class Disc, right? Which is something that came out largely to support Tanzu, I think which fantastic use case for vVols as well there, but that gives you the ability to offer something like Amazon EBS, right? You can offer Amazon EBS in a native VMware stack using First Class Discs and vVols. And you're able to apply things like quality of service with that granular control that allows you to guarantee that customer the disc that they bought and paid for. They're going to get the IOPS that they're paying for because you're applying those QoS policies directly to that object on the array. And instead of having to worry about is the array going to be able to handle it? Are you going to have one VM that consumes all your IO, you know? You don't have to worry about that with vVols because you've got that integration with the array's native quality controls. >> And Chance what's in this for me as a customer? I'm hearing productivity, I'm hearing cost savings, control efficiency. Talk to me about the benefits in it for the folks that you're talking to. >> Yeah, absolutely. A lot of times it comes down to, you know I mentioned like the cloud service provider model, right? When you're looking to build a robust service catalog and you're able, you want to be able to meet all these like, we mentioned Tanzu, right? Containers as a service, you're able to provide the persistent volumes for your Kubernetes containers that are again, these native objects on the array and you have these fine grain controls, but it's handled at massive scale because it's all handled by storage policies, Kubernetes storage classes, which are natively mapped to VM storage policies through Tanzu. So it just, it gives you the ability to offer all of these services in a, again a rich and robust contents catalog. >> So what are you doing? So you mentioned a couple of things in terms of using array based quality of service. So give me an example of how you're avoiding issues of contention and over subscription in an environment where I'm an administrator and I've got this virtual volume that's servicing this VM or this app on this VM. What kind of visibility do I have down into the actual resources because look at the end of that chain there's a physical resource. And that physical resource represents, what? IOPS and bandwidth and latency and throughput and all of this bundle of things. So how do you avoid colliding with others who are trying to carve vVols out of this world? >> You mean like a noisy neighbor type of thing? >> Yeah. Yeah. >> So that's actually one of the big benefits that you get with vVols is that because those vial objects are native on the array, they're not sharing a loan or a volume. They're not sharing a resource. The only resource they're actually sharing is the array itself. So you don't get that typical noisy neighbor where this one's using all the resources of that volume because really you're looking out at the all encompassing array. And so a storage administrator and the VI admin have a lot more insight. The VI admin can now go to the storage admin if there's say a debugging issue, they want to find a problem. The storage admin now can see those individual objects and say, oh, well this VM, it's not really this, it's not all the discs. It's just disc number two or disc number three or they can actually see at a single disc level on the array, the performance, the latency, you know, the QS, all that stuff. >> Oh, absolutely. >> And that really is what, it frees up at the storage admin's time because the debugging is so much more simple. And it also allows the storage admin a lot more insight. Right? They know those, what's the problem. If you were typically looking at a loaner volume, they don't really know what's going on inside that and neither does the array. But with vVols, the array knows what each disc and how it's supposed to be treated based on the policies that the customer defines. So if one VM is supposed to have a certain QS and another VM isn't. The array knows that that VM, if it goes above it, it's going to be like, nope, you can't have those resources. You weren't granted those resources, but this one was. So you have much more control. And again, it's at an application or a VM level. >> And it's still, it's fairly dynamically configurable. I spoke to a customer just the other day. They are a cloud service provider. And what they do is their customers are able to go in and change their quality of service. So they go into that service portal and they say, okay, I'm paying for gold and I want platinum and they'll go in. They know that they've got a certain time where they need more IO capacity. So they'll go in, they'll pay the fee, increase that capability. And then when they don't need it anymore, they'll downgrade again. >> Okay, so that assumes some ability at the array level to do some sort of resource sharing and balancing to be able to go out and get, say more IO. Because again, fundamentally, if you have a virtual volume, that's drawing its resources from five storage devices, whether those are SSD based or NVMe or spinning disc that represents a finite it amount of resource. The assumption is if you're saying that the array is the pool that you need to worry about, that assumes the array has the ability to go beyond here, based on a policy. >> So that's how it works. It does... >> Well, essentially. I mean, you can't outrun physics. So if the array can't go faster, but the idea is that you understand the performance profile of your array and then you create your service tiers appropriately. >> Okay. >> Yeah. And one of the big benefits is like Chance was saying, if you want to change a profile that used to be a Storage vMotion to a different data store. Now it's just a policy change. The storage admin doesn't have to do anything. The VI admin just changes the policy. And then the array understands, oh, I now need to treat that different. And that's exactly what Chance was talking about in that cloud provider situation, where today I'm using a 100,000 IOPS. I need to use 200,000 tomorrow for special, whatever it is, but I only need to use it for tomorrow. So they don't have to move anything. They just change the policy for that time. And then they change it back. They don't have to do anything on the array itself. They don't have to change anything physically on the VM. It's just a policy change. And that's really where you get that dynamic control of the storage capability. >> So as business dynamics are changing and I'm thinking of like black Friday or Prime day, being able to dial things up and dial it down, they have the ability to do that with a policy. >> Yes. >> Exactly. >> So huge time savings there. >> Oh, it's huge. Yeah. >> Yeah. >> And it simplifies because now, I don't have to have multiple data stores. You can have one data store, all your VMs in there. You can limit test and dev and you can maximize business critical applications. Again, all via policy. So you've simplified your infrastructure. You've gone to more of a programmatic approach of managing your storage capabilities. But you're now managing at the VM level. >> So we mentioned that the cloud chaos (indistinct) that was mentioned this morning during the Keynote and we're saying a lot of customers are still in this cloud chaos phase. They want to get to Cloud Smart. How is this going to be one of those tools that helps customers pull the levers, dial the knobs, to be able to get to eventually, Cloud Smart. >> I could go on for this for hours. (Lisa Laughs) (Chance chuckles) This is really what simplifies storage. Because typically when you use traditional storage, you're going to have to figure out that this data store has this capability or another example, as you mentioned was Tanzu. If you're managing persistent volumes and you're not using something like vVols, if you want to get a certain storage capability, you have to either tag it or you have to create that data store with that capability. All of that goes away when you use vVols. So now that chaos of multiple data stores, multiple lines or multiple volumes, all that stuff goes away. So now you're simplifying your infrastructure, you have a programmatic approach to managing your storage and you can use it for all of your different types of workloads. So cloud, Kubernetes, persistent volumes, all that type of stuff. And again, all being managed via a simple and again, programmatic approach. So you could automate this. You know today, like you said, black Friday. Okay, Black, Friday's coming up. I want to change the policy. You could automate that. So you don't even have to go in and physically make the change of the policy now. You just say on Fridays, change it to this policy on Sunday night, change it back. >> Yep. >> Again, that's not something you can do with traditional storage. >> Okay. >> And I think from a simplification standpoint as well, you know, I was telling you about that other customer a couple days ago, they were running into the inability to grow beyond the bounds of VMFS file systems for very, very large VMs. And so what I talked to them about was look, if you go to vVols, you're not bound by file systems anymore. You have the capacity of the array and you can have VM discs up to 62 terabytes, you know, as many as you want. And it doesn't matter what they fit in because we can fit them all. So it's, to be able to, and that's some of our largest customers, the reason they go with vVols is to be able to grow beyond the bounds of traditional storage, anything like path limits, you know. That's something you have to contend with. >> Path limits, line limits, all that stuff. Typically just disappears with vVols. >> All those limits go away. Guys- >> They go away. >> Amazing. Congratulations on the work that you guys have done. Thank you so much for joining us on theCUBE talking about the value in it for customers and obviously the technical depths of the NetApp, VMware relationship. Guys, we appreciate your time. >> Yeah, thanks for having us on. >> Our pleasure. For my guests and Dave Nicholson. I'm Lisa Martin. You're watching theCUBE live from VMware Explorer 2022, Dave and I will be right back with our next guest. So stick around. (upbeat music)

(introductory riff) >> Hey everyone. Welcome to theCUBE's presentation of the "AWS Startup Showcase." This is Season 2, Episode 3 of our ongoing series that features great partners in the massive AWS partner ecosystem. This series is focused on, "MarTech, Emerging Cloud-Scale Customer Experiences." I'm Lisa Martin, and I've got two guests here with me to talk about this. Please welcome Daisy Urfer, Cloud Alliance Sales Director at Algolia, and Jason Lang, the Head of Product for Apply Digital. These folks are here to talk with us today about how Algolia's Search and Discovery enables customers to create dynamic realtime user experiences for those oh so demanding customers. Daisy and Jason, it's great to have you on the program. >> Great to be here. >> Thanks for having us. >> Daisy, we're going to go ahead and start with you. Give the audience an overview of Algolia, what you guys do, when you were founded, what some of the gaps were in the market that your founders saw and fixed? >> Sure. It's actually a really fun story. We were founded in 2012. We are an API first SaaS solution for Search and Discovery, but our founders actually started off with a search tool for mobile platforms, so just for your phone and it quickly expanded, we recognize the need across the market. It's been a really fun place to grow the business. And we have 11,000 customers today and growing every day, with 30 billion searches a week. So we do a lot of business, it's fun. >> Lisa: 30 billion searches a week and I saw some great customer brands, Locost, NBC Universal, you mentioned over 11,000. Talk to me a little bit about some of the technologies, I see that you have a search product, you have a recommendation product. What are some of those key capabilities that the products deliver? 'Cause as we know, as users, when we're searching for something, we expect it to be incredibly fast. >> Sure. Yeah. What's fun about Algolia is we are actually the second largest search engine on the internet today to Google. So we are right below the guy who's made search of their verb. So we really provide an overall search strategy. We provide a dashboard for our end users so they can provide the best results to their customers and what their customers see. Customers want to see everything from Recommend, which is our recommended engine. So when you search for that dress, it shows you the frequently bought together shoes that match, things like that, to things like promoted items and what's missing in the search results. So we do that with a different algorithm today. Most in the industry rank and they'll stack what you would want to see. We do kind of a pair for pair ranking system. So we really compare what you're looking for and it gives a much better result. >> And that's incredibly critical for users these days who want results in milliseconds. Jason, you, Apply Digital as a partner of Algolia, talk to us about Apply Digital, what it is that you guys do, and then give us a little bit of insight on that partnership. >> Sure. So Apply Digital was originally founded in 2016 in Vancouver, Canada. And we have offices in Vancouver, Toronto, New York, LA, San Francisco, Mexico city, Sao Paulo and Amsterdam. And we are a digital experiences agency. So brands and companies, and startups, and all the way from startups to major global conglomerates who have this desire to truly create these amazing digital experiences, it could be a website, it could be an app, it could be a full blown marketing platform, just whatever it is. And they lack either the experience or the internal resources, or what have you, then they come to us. And and we are end-to-end, we strategy, design, product, development, all the way through the execution side. And to help us out, we partner with organizations like Algolia to offer certain solutions, like an Algolia's case, like search recommendation, things like that, to our various clients and customers who are like, "Hey, I want to create this experience and it's going to require search, or it's going to require some sort of recommendation." And we're like, "Well, we highly recommend that you use Algolia. They're a partner of ours, they've been absolutely amazing over the time that we've had the partnership. And that's what we do." And honestly, for digital experiences, search is the essence of the internet, it just is. So, I cannot think of a single digital experience that doesn't require some sort of search or recommendation engine attached to it. So, and Algolia has just knocked it out of the park with their experience, not only from a customer experience, but also from a development experience. So that's why they're just an amazing, amazing partner to have. >> Sounds like a great partnership. Daisy, let's point it back over to you. Talk about some of those main challenges, Jason alluded to them, that businesses are facing, whether it's e-commerce, SaaS, a startup or whatnot, where search and recommendations are concerned. 'Cause we all, I think I've had that experience, where we're searching for something, and Daisy, you were describing how the recommendation engine works. And when we are searching for something, if I've already bought a tent, don't show me more tent, show me things that would go with it. What are some of those main challenges that Algolia solution just eliminates? >> Sure. So I think, one of the main challenges we have to focus on is, most of our customers are fighting against the big guides out there that have hundreds of engineers on staff, custom building a search solution. And our consumers expect that response. You expect the same search response that you get when you're streaming video content looking for a movie, from your big retailer shopping experiences. So what we want to provide is the ability to deliver that result with much less work and hassle and have it all show up. And we do that by really focusing on the results that the customers need and what that view needs to look like. We see a lot of our customers just experiencing a huge loss in revenue by only providing basic search. And because as Jason put it, search is so fundamental to the internet, we all think it's easy, we all think it's just basic. And when you provide basic, you don't get the shoes with the dress, you get just the text response results back. And so we want to make sure that we're providing that back to our customers. What we see average is even, and everybody's going mobile. A lot of times I know I do all my shopping on my phone a lot of the time, and 40%-50% better relevancy results for our customers for mobile users. That's a huge impact to their use case. >> That is huge. And when we talked about patients wearing quite thin the last couple of years. But we have this expectation in our consumer lives and in our business lives if we're looking for SaaS or software, or whatnot, that we're going to be able to find what we want that's relevant to what we're looking for. And you mentioned revenue impact, customer churn, brand reputation, those are all things that if search isn't done well, to your point, Daisy, if it's done in a basic fashion, those are some of the things that customers are going to experience. Jason, talk to us about why Algolia, what was it specifically about that technology that really led Apply Digital to say, "This is the right partner to help eliminate some of those challenges that our customers could face?" >> Sure. So I'm in the product world. So I have the wonderful advantage of not worrying about how something's built, that is left, unfortunately, to the poor, poor engineers that have to work with us, mad scientist, product people, who are like, "I want, make it do this. I don't know how, but make it do this." And one of the big things is, with Algolia is the lift to implement is really, really light. Working closely with our engineering team, and even with our customers/users and everything like that, you kind of alluded to it a little earlier, it's like, at the end of the day, if it's bad search, it's bad search. It just is. It's terrible. And people's attention span can now be measured in nanoseconds, but they don't care how it works, they just want it to work. I push a button, I want something to happen, period. There's an entire universe that is behind that button, and that's what Algolia has really focused on, that universe behind that button. So there's two ways that we use them, on a web experience, there's the embedded Search widget, which is really, really easy to implement, documentation, and I cannot speak high enough about documentation, is amazing. And then from the web aspect, I'm sorry, from the mobile aspect, it's very API fort. And any type of API implementation where you can customize the UI, which obviously you can imagine our clients are like, "No we want to have our own front end. We want to have our own custom experience." We use Algolia as that engine. Again, the documentation and the light lift of implementation is huge. That is a massive, massive bonus for why we partnered with them. Before product, I was an engineer a very long time ago. I've seen bad documentation. And it's like, (Lisa laughing) "I don't know how to imple-- I don't know what this is. I don't know how to implement this, I don't even know what I'm looking at." But with Algolia and everything, it's so simple. And I know I can just hear the Apply Digital technology team, just grinding sometimes, "Why is a product guy saying that (mumbles)? He should do it." But it is, it just the lift, it's the documentation, it's the support. And it's a full blown partnership. And that's why we went with it, and that's what we tell our clients. It's like, listen, this is why we chose Algolia, because eventually this experience we're creating for them is theirs, ultimately it's theirs. And then they are going to have to pick it up after a certain amount of time once it's theirs. And having that transition of, "Look this is how easy it is to implement, here is all the documentation, here's all the support that you get." It just makes that transition from us to them beautifully seamless. >> And that's huge. We often talk about hard metrics, but ease of use, ease of implementation, the documentation, the support, those are all absolutely business critical for the organization who's implementing the software, the fastest time to value they can get, can be table stakes, and it can be on also a massive competitive differentiator. Daisy, I want to go back to you in terms of hard numbers. Algolia has a recent force or Total Economic Impact, or TEI study that really has some compelling stats. Can you share some of those insights with us? >> Yeah. Absolutely. I think that this is the one of the most fun numbers to share. We have a recent report that came out, it shared that there's a 382% Return on Investment across three years by implementing Algolia. So that's increase to revenue, increased conversion rate, increased time on your site, 382% Return on Investment for the purchase. So we know our pricing's right, we know we're providing for our customers. We know that we're giving them the results that we need. I've been in the search industry for long enough to know that those are some amazing stats, and I'm really proud to work for them and be behind them. >> That can be transformative for a business. I think we've all had that experience of trying to search on a website and not finding anything of relevance. And sometimes I scratch my head, "Why is this experience still like this? If I could churn, I would." So having that ability to easily implement, have the documentation that makes sense, and get such high ROI in a short time period is hugely differentiated for businesses. And I think we all know, as Jason said, we measure response time in nanoseconds, that's how much patience and tolerance we all have on the business side, on the consumer side. So having that, not just this fast search, but the contextual search is table stakes for organizations these days. I'd love for you guys, and on either one of you can take this, to share a customer example or two, that really shows the value of the Algolia product, and then also maybe the partnership. >> So I'll go. We have a couple of partners in two vastly different industries, but both use Algolia as a solution for search. One of them is a, best way to put this, multinational biotech health company that has this-- We built for them this internal portal for all of their healthcare practitioners, their HCPs, so that they could access information, data, reports, wikis, the whole thing. And it's basically, almost their version of Wikipedia, but it's all internal, and you can imagine the level of of data security that it has to be, because this is biotech and healthcare. So we implemented Algolia as an internal search engine for them. And the three main reasons why we recommended Algolia, and we implemented Algolia was one, HIPAA compliance. That's the first one, it's like, if that's a no, we're not playing. So HIPAA compliance, again, the ease of search, the whole contextual search, and then the recommendations and things like that. It was a true, it didn't-- It wasn't just like a a halfhearted implementation of an internal search engine to look for files thing, it is a full blown search engine, specifically for the data that they want. And I think we're averaging, if I remember the numbers correctly, it's north of 200,000 searches a month, just on this internal portal specifically for their employees in their company. And it's amazing, it's absolutely amazing. And then conversely, we work with a pretty high level adventure clothing brand, standard, traditional e-commerce, stable mobile application, Lisa, what you were saying earlier. It's like, "I buy everything on my phone," thing. And so that's what we did. We built and we support their mobile application. And they wanted to use for search, they wanted to do a couple of things which was really interesting. They wanted do traditional search, search catalog, search skews, recommendations, so forth and so on, but they also wanted to do a store finder, which was kind of interesting. So, we'd said, all right, we're going to be implementing Algolia because the lift is going to be so much easier than trying to do everything like that. And we did, and they're using it, and massively successful. They are so happy with it, where it's like, they've got this really contextual experience where it's like, I'm looking for a store near me. "Hey, I've been looking for these items. You know, I've been looking for this puffy vest, and I'm looking for a store near me." It's like, "Well, there's a store near me but it doesn't have it, but there's a store closer to me and it does have it." And all of that wraps around what it is. And all of it was, again, using Algolia, because like I said earlier, it's like, if I'm searching for something, I want it to be correct. And I don't just want it to be correct, I want it to be relevant. >> Lisa: Yes. >> And I want it to feel personalized. >> Yes. >> I'm asking to find something, give me something that I am looking for. So yeah. >> Yeah. That personalization and that relevance is critical. I keep saying that word "critical," I'm overusing it, but it is, we have that expectation that whether it's an internal portal, as you talked about Jason, or it's an adventure clothing brand, or a grocery store, or an e-commerce site, that what they're going to be showing me is exactly what I'm looking for, that magic behind there that's almost border lines on creepy, but we want it. We want it to be able to make our lives easier whether we are on the consumer side, whether we on the business side. And I do wonder what the Go To Market is. Daisy, can you talk a little bit about, where do customers go that are saying, "Oh, I need to Algolia, and I want to be able to do that." Now, what's the GTM between both of these companies? >> So where to find us, you can find us on AWS Marketplace which another favorite place. You can quickly click through and find, but you can connect us through Apply Digital as well. I think, we try to be pretty available and meet our customers where they are. So we're open to any options, and we love exploring with them. I think, what is fun and I'd love to talk about as well, in the customer cases, is not just the e-commerce space, but also the content space. We have a lot of content customers, things about news, organizations, things like that. And since that's a struggle to deliver results on, it's really a challenge. And also you want it to be relevant, so up-to-date content. So it's not just about e-commerce, it's about all of your solution overall, but we hope that you'll find us on AWS Marketplace or anywhere else. >> Got it. And that's a great point, that it's not just e-commerce, it's content. And that's really critical for some industry, businesses across industries. Jason and Daisy, thank you so much for joining me talking about Algolia, Apply Digital, what you guys are doing together, and the huge impact that you're making to the customer user experience that we all appreciate and know, and come to expect these days is going to be awesome. We appreciate your insights. >> Thank you. >> Thank you >> For Daisy and Jason, I'm Lisa Martin. You're watching "theCUBE," our "AWS Startup Showcase, MarTech Emerging Cloud-Scale Customer Experiences." Keep it right here on "theCUBE" for more great content. We're the leader in live tech coverage. (ending riff)

>>Okay, welcome back. Everyone's Cube's coverage here in Las Vegas for Amazon re Mars machine learning, automation, robotics, and space out. John fir host of the queue. Got a great set of guests here talking about AI, Jason Montgomery CTO and co-founder man and Ryans CEO, founder guys. Thanks for coming on. We're just chatting, lost my train of thought. Cuz we were chatting about something else, your history with DataRobot and, and your backgrounds entrepreneurs. Welcome to the queue. Thanks >>Tur. Thanks for having >>Us. So first, before we get into the conversation, tell me about the company. You guys have a history together, multiple startups, multiple exits. What are you guys working on? Obviously AI is hot here as part of the show. M is Mars machine learning, which we all know is the basis for AI. What's the story. >>Yeah, really. We're we're here for two of the letters and Mars. We're here for the machine learning and the automation part. So at the high level, man is a no code AI application development platform. And basically anybody could log in and start making AI applications. It could be anything from just texting it with the Twilio integration to tell you that you're doing great or that you need to exercise more to integrating with zenes to get support tickets classified. >>So Jason, we were talking too about before he came on camera about the cloud and how you can spin up resources. The data world is coming together and I, and I like to see two flash points. The, I call it the 2010 big data era that began and then failed Hadoop crashed and burned. Yeah. Then out of the, out of the woodwork came data robots and the data stacks and the snowflakes >>Data break snowflake. >>And now you have that world coming back at scale. So we're now seeing a huge era of, I need to stand up infrastructure and platform to do all this heavy lifting. I don't have time to do. Right. That sounds like what you guys are doing. Is that kind of the case? >>That's absolutely correct. Yeah. Typically you would have to hire a whole team. It would take you months to sort of get the infrastructure automation in place, the dev ops DevOps pipelines together. And to do the automation to spin up, spin down, scale up scale down requires a lot of special expertise with, you know, Kubernetes. Yeah. And a lot of the other data pipelines and a lot of the AWS technologies. So we automate a lot of that. So >>If, if DevOps did what they did, infrastructure has code. Yeah. Data has code. This is kind of like that. It's not data ops per se. Is there a category? How do you see this? Cuz it's you could say data ops, but that's also it's DevOps dev. It's a lot going on. Oh yeah. It's not just seeing AI ops, right? There's a lot more, what, what would you call this? >>It's a good question. I don't know if we've quite come up with the name. I know >>It's not data ops. It's not >>Like we call it AI process automation >>SSPA instead of RPA, >>What RPA promised to be. Yes, >>Exactly. But what's the challenge. The number one problem is it's I would say not, not so much all on ever on undifferent heavy lifting. It's a lot of heavy lifting that for sure. Yes. What's involved. What's the consequences of not going this way. If I want to do it myself, can you take me through the, the pros and cons of what the scale scope, the scale of without you guys? >>Yeah. Historically you needed to curate all your data, bring it together and have some sort of data lake or something like that. And then you had to do really a lot of feature engineering and a lot of other sort of data science on the back end and automate the whole thing and deploy it and get it out there. It's a, it's a pretty rigorous and, and challenging problem that, you know, we there's a lot of automation platforms for, but they typically focus on data scientists with these large language models we're using they're pre-trained. So you've sort of taken out that whole first step of all that data collection to start out and you can basically start prototyping almost instantly because they've already got like 6 billion parameters, 10 billion parameters in them. They understand the human language really well. And a lot of other problems. I dunno if you have anything you wanna add to that, Ryan, but >>Yeah, I think the other part is we deal with a lot of organizations that don't have big it teams. Yeah. And it would be impossible quite frankly, for them to ever do something like deploy text, track as an example. Yeah. They're just not gonna do it, but now they can come to us. They know the problem they want solved. They know that they have all these invoices as an example and they wanna run it through a text track. And now with us they can just drag and drop and say, yeah, we want tech extract. Then we wanted to go through this. This is what we >>Want. Expertise is a huge problem. And the fact that it's changing too, right? Yeah. Put that out there. You guys say, you know, cybersecurity challenges. We guys do have a background on that. So you know, all the cutting edge. So this just seems to be this it, I hate to say transformation. Cause I not the word I'm looking for, I'd say stuck in the mud kind of scenario where they can't, they have to get bigger, faster. Yeah. And the scale is bigger and they don't have the people to do it. So you're seeing the rise of managed service. You mentioned Kubernetes, right? I know this young 21 year old kid, he's got a great business. He runs a managed service. Yep. Just for Kubernetes. Why? Because no, one's there to stand up the clusters. >>Yeah. >>It's a big gap. >>So this, you have these sets of services coming in now, where, where do you guys fit into that conversation? If I'm the customer? My problem is what, what is my, what is my problem that I need you guys for? What does it look like to describe my problem? >>Typically you actually, you, you kind of know that your employees are spending a lot of time, a lot of hours. So I'll just give you a real example. We have a customer that they were spending 60 hours a week just reviewing these accounts, payable, invoices, 60 hours a week on that. And they knew there had to be a better way. So manual review manual, like when we got their data, they were showing us these invoices and they had to have their people circle the total on the invoice, highlight the customer name, the >>Person who quit the next day. Right? >>No like they, they, Hey, you know, they had four people doing this, I think. And the point is, is they come to us and we say, well, you know, AI can, can just basically using something like text track can just do this. And then we can enrich those outputs from text track with the AI. So that's where the transformers come in. And when we showed them that and got them up and running in about 30 minutes, they were mind blown. Yeah. And now this is a company that doesn't have a big it department. So the >>Kind, and they had the ability to quantify the problem >>They knew. And, and in this case it was actually a business user. It was not a technical >>In is our she consequence technical it's hours. She consequences that's wasted. Manual, labor wasted. >>Exactly. Yeah. And, and to their point, it was look, we have way more high, valuable tasks that our people could be doing yeah. Than doing this AP thing. It takes 60 hours. And I think that's really important to remember about AI. What're I don't think it's gonna automate away people's jobs. Yeah. What it's going to do is it's going to free us up to focus on what really matters and focus on the high value stuff. And that's what people should >>Be doing. I know it's a cliche. I'm gonna say it again. Cause I keep saying, cause I keep saying for people to listen, the bank teller argument always was the big thing. Oh yeah. They're gonna get killed by the ATM machine. No, they're opening up more branches. That's right. That's right. So it's like, come on. People let's get, get over that. So I, I definitely agree with that. Then the question, next question is what's your secret sauce? I'm the customer I'm gonna like that value proposition. You make something go away. It's a pain relief. Then there's the growth side. Okay. You can solve from problems. Now I want this, the, the vitamin you got aspirin. And I want the vitamin. What's the growth angle for you guys with your customers. What's the big learnings. Once they get the beach head with problem solving. >>I think it, it, it it's the big one is let's say that we start with the account payable thing because it's so our platform's so approachable. They go in and then they start tinkering with the initial, we'll call it a template. So they might say, Hey, you know what, actually, in this edge case, I'm gonna play with this. And not only do I want it to go to our accounting system, but if it's this edge case, I want it to email me. So they'll just drag and drop an email block into our canvas. And now they're making it >>Their own. There is the no code, low code's situation. They're essentially building a notification engine under the covers. They have no idea what they're doing. That's >>Right. They get the, they just know that, Hey, you know what? When, when like the amount's over $10,000, I want an email. They know that's what they want. They don't, they don't know that's the notification engine. Of >>Course that's value email. Exactly. I get what I wanted. All right. So tell me about the secret sauce. What's under the covers. What's the big, big, big scale, valuable, valuable, secret sauce. >>I would say part of it. And, and honestly, the reason that we're able to do this now is transformer architecture. When the transformer papers came out and then of course the attention is all you need paper, those kind of unlocked it and made this all possible. Beyond that. I think the other secret sauce we've been doing this a long time. >>So we kind of, we know we're in the paid points. We went to those band points. Cause we weren't data scientists or ML people. >>Yeah. >>Yeah. You, you walked the snow and no shoes on in the winter. That's right. These kids now got boots on. They're all happy. You've installed machines. You've loaded OSS on, on top of rack switches. Yeah. I mean, it's unbelievable how awesome it's right now to be a developer and now a business user's doing the low code. Yep. If you have the system architecture set up, so back to the data engineering side, you guys had the experience got you here. This is a big discussion right now. We're having in, in, on the cube and many conversations like the server market, you had that go away through Amazon and Google was one of the first, obviously the board, but the idea that servers could be everywhere. So the SRE role came out the site reliability engineer, right. Which was one guy or gal and zillions of servers. Now you're seeing the same kind of role with data engineering. And then there's not a lot of people that fit the requirement of being a data engineer. It's like, yeah, it's very unique. Cause you're dealing with a system architecture, not data science. So start to see the role of this, this, this new persona, because they're taking on all the manual challenges of doing that. You guys are kind of replaced that I think. Well, do you agree with it about the data engineer? First of all? >>I think, yeah. Well and it's different cuz there's the older data engineer and then there's sort of the newer cloud aware one who knows how to use all the cloud technologies. And so when you're trying, we've tried to hire some of those and it's like, okay, you're really familiar with old database technology, but can you orchestrate that in a serverless environment with a lot of AWS technology for instance. And it's, and that's hard though. They don't, they don't, there's not a lot of people who know that space, >>So there's no real curriculum out there. That's gonna teach you how to handle, you know, ETL. And also like I got I'm on stream data from this source. Right. I'm using sequel I'm I got put all together. >>Yeah. So it's yeah, it's a lot of just not >>Data science. It's >>Figure that out. So its a large language models too. We don't have to worry about some of the data there too. It's it's already, you know, codified in the model. And then as we collect data, as people use our platform, they can then curate data. They want to annotate or enrich the model with so that it works better as it goes. So we're kind of curating, collecting the data as it's used. So as it evolves, it just gets better. >>Well, you guys obviously have a lot of experience together and congratulations on the venture. Thank you. What's going on here at re Mars. Why are you here? What's the pitch. What's the story. Where's your, you got two letters. You got the, you got the M for the machine learning and AI and you got the, a for automation. What's the ecosystem here for you? What are you doing? >>Well, I mean, I think you, you kind of said it right. We're here because the machine learning and the automation part, >>But >>More, more widely than that. I mean we work very, very closely with Amazon on a number of front things like text track, transcribe Alexa, basically all these AWS services are just integrations within our system. So you might want to hook up your AI to an Alexa so that you could say, Hey Alexa, tell me updates about my LinkedIn feed. I don't know, whatever, whatever your hearts content >>Is. Well what about this cube transcription? >>Yeah, exactly. A hundred percent. >>Yeah. We could do that. You know, feed all this in there and then we could do summarization of everything >>Here, >>Q and a extraction >>And say, Hey, these guys are >>Technicals. Yeah, >>There you go. No, they mentioned Kubernetes. We didn't say serverless chef puppet. Those are words straight, you know, and no linguistics matters right into that's a service that no one's ever gonna build. >>Well, and actually on that point, really interesting. We work with some healthcare companies and when you're basically, when people call in and they call into the insurance, they have a question about their, what like is this gonna be covered? And what they want to key in on are things like I just went to my doctor and got a cancer diagnosis. So the, the, the relevant thing here is they just got this diagnosis. And why is that important? Well, because if you just got a diagnosis, they want to start a certain triage to make you successful with your treatments. Because obviously there's an >>Incentive to do time. That time series matters and, and data exactly. And machine learning reacts to it. But also it could be fed back old data. It used to be time series to store it. Yeah. But now you could reuse it to see how to make the machine learning better. Are you guys doing anything, anything around that, how to make that machine learning smarter, look doing look backs or maybe not the right word, but because you have data, I might as well look back at it's happened. >>So part of, part of our platform and part of what we do is as people use these applications, to your point, there's lots of data that's getting generated, but we capture all that. And that becomes now a labeled data set within our platform. And you can take that label data set and do something called fine tuning, which just makes the underlying model more and more yours. It's proprietary. The more you do it. And it's more accurate. Usually the more you do it. >>So yeah, we keep all that. I wanna ask your reaction on this is a good point. The competitive advantage in the intellectual property is gonna be the workflows. And so the data is the IP. If this refinement happens, that becomes intellectual property. Yeah. That's kind of not software. It's the data modeling. It's the data itself is worth something. Are you guys seeing that? >>Yeah. And actually how we position the company is man team is a control plane and you retain ownership of the data plane. So it is your intellectual property. Yeah. It's in your system, it's in your AWS environment. >>That's not what everyone else is doing. Everyone wants to be the control plane and the data plan. We >>Don't wanna own your data. We don't, it's a compliance and security nightmare. Yeah. >>Let's be, Real's the question. What do you optimize for? Great. And I think that's a fair, a fair bet. Given the fact that clients want to be more agile with their data anyway, and the more restrictions you put on them, why would that this only gets you in trouble? Yeah. I could see that being a and plus lock. In's gonna be a huge factor. Yeah. I think this is coming fast and no one's talking about it in the press, but everyone's like run to silos, be a silo and that's not how data works. No. So the question is how do you create siloing of data for say domain specific applications while maintaining a horizontally scalable data plan or control plan that seems to be kind of disconnected everyone to lock in their data. What do you guys think about that? This industry transition we're in now because it seems people are reverting back to fourth grade, right. And to, you know, back to silos. >>Yeah. I think, well, I think the companies probably want their silo of data, their IP. And so as they refine their models and, and we give them the ability to deploy it in their own stage maker and their own VPC, they, they retain and own it. They can actually get rid of us and they still have that model. Now they may have to build, you know, a lot of pipelines and other technology to support it. But well, >>Your lock in is usability. Exactly. And value. Yeah. Value proposition is the lock in bingo. That's not counterintuitive. Exactly. Yeah. You say, Hey, more value. How do I wanna get rid of it? Valuable. I'll pay for it. Right. As long as you have multiple value, step up. And that's what cloud does. I mean, think that's the thing about cloud. That's gonna make all this work. In my opinion, the value enablement is much higher. Yeah. So good business model. Anything else here at the show that you observed that you like, that you think people would be interested in? What's the most important story coming out of the, the holistic, if you zoom up and look at re Mars, what's, what's coming out of the vibe. >>You know, one thing that I think about a lot is we're, you know, we have Artis here, humanity hopefully soon gonna be going to Mars. And I think that's really, really exciting. And I also think when we go to Mars, we're probably not gonna send a bunch of software engineers up there. >>Right. So like robots will do break fix now. So, you know, we're good. It's gone. So services are gonna be easy. >>Yeah. But I, oh, >>I left that device back at earth. I just think that's not gonna be good. Just >>Replicated it in one. I think there's like an eight >>Minute, the first monopoly on next day delivery in space. >>They'll just have a spaceship that sends out drones to Barss. Yeah. But I think that when we start going back to the moon and we go to Mars, people are gonna think, Hey, I need this application now to solve this problem that I didn't anticipate having. And in science fiction, we kind of saw this with like how, right? Like you had this AI on this computer or this, on this spaceship that could do all this stuff. We need that. And I haven't seen that here yet. >>No, it's not >>Here yet. And >>It's right now I think getting the hardware right first. Yep. But we did a lot of reporting on this with the D O D and the tactile edge, you know, military applications. It's a fundamental, I won't say it's a tech, religious argument. Like, do you believe in agile realtime data or do you believe in democratizing multi-vendor, you know, capability? I think, I think the interesting needs to sort itself out because sometimes multi vendor multi-cloud might not work for an application that needs this database or this application at the edge. >>Right. >>You know, so if you're in space, the back haul, it matters. >>It really does. Yeah. >>Yeah. Not a good time to go back and get that highly available data. You mean highly, is it highly available or there's two terms highly available, which means real time and available. Yeah. Available means it's on a dis, right? >>Yeah. >>So that's a big challenge. Well guys, thanks for coming on. Plug for the company. What are you guys up to? How much funding do you have? How old are you staff hiring? What's some of the details. >>We're about 45 people right now. We are a globally distributed team. So we hire every like from every country, pretty much we are fully remote. So if you're looking for that, hit us up, definitely always look for engineers, looking for more data scientists. We're very, very well funded as well. And yeah. So >>You guys headquarters out, you guys headquartered. >>So a lot of us live in Columbus, Ohio that's technically HQ, but like I said, we we're in pretty much every continent except in Antarctica. So >>You're for all virtual. >>Yeah. A hundred percent virtual, a hundred percent. >>Got it. Well, congratulations and love to hear that Datadog story at another time >>Or DataBot >>Yeah. I mean data, DataBot sorry. Let's get, get all confused >>Data dog data company. >>Well, thanks for coming on and congratulations for your success and thanks for sharing. Yeah. >>Thanks for having us for having >>Pleasure to be here. It's a cube here at rebars. I'm John furier host. Thanks for watching more coming back after this short break.

(upbeat music) >> Welcome back to theCUBE's coverage of VEEMON 2022. We're here at the Aria in Las Vegas. Dave Vellante with David Nicholson, my co-host for the week, two days at wall to wall coverage. Jason Buffington is here, JBuff, who does some amazing work for VEEAM, former Analyst from the Enterprise Strategy Group. So he's got a real appreciation for independence data, and we're going to dig into some data. You guys, I got to say, Jason, first of all, welcome back to theCUBE. It's great to see you again. >> Yeah, two and a half years, thanks for having me back. >> Yeah, that's right. (Jason laughs) Seems like a blur. >> No doubt. >> But so here's the thing as analysts, you can appreciate this, the trend is your friend, right? and everybody just inundates you with now, ransomware. It's the trend. So you get everybody's talking about the ransomware, cyber resiliency, immutability, air gaps, et cetera. Okay, great. Technology's there, it's kind of like the NFL, everybody kind of does the same thing. >> There's a lot of wonderful buzzwords in that sentence. >> Absolutely, but what you guys have done that's different is you brought in some big time thought leadership, with data and survey work which of course as an analyst we love, but you drive strategies off of this. So you got to, I'll set it up. You got a new study out that's pivoted off of February study of 3,600 organizations, and then you follow that up with a thousand organizations that actually got hit with ransomware. So tell us more about the study and the work that you've done there. >> Yeah, I got to say I have the best job ever. So I spent seven years as an analyst. And when I decided I didn't want to be an analyst anymore, I called VEEAM and said, I'd like to get in the fight and they let me in. But they let me do independent research on their behalf. So it's kind of like being an in-house counsel. I'm an in-house analyst. And for the beginning of this year, in February, we published a report called the Data Protection Trends Report. And it was over 3000 responses, right? 28 countries around the world looking at digital transformation, the effects of COVID, where are they are on BAS and DRS. But one of the new areas we wanted to look at was how pervasive is ransomware? How does that align with BCDR overall? So some of those just big thought questions that everyone's trying to solve for. And out of that, we said, "Wow, this is really worth double clicking." And so today, actually about an hour ago we published the Ransomware Trends Report and it's a thousand organizations all of which have all been survived. They all had a ransomware attack. One of the things I think I'm most proud of for VEEAM in this particular project, we use an independent research firm. So no one knows it's VEEAM that's asking the questions. We don't have any access to the respondents along the way. I wish we did, right? >> Yeah, I bet >> Go sell 'em back up software. But of the thousands 200 were CISOs, 400 were security professionals which we don't normally interact with, 200 backup admins, 200 IT ops, and the idea was, "Okay, you've all been through a really bad day. Tell us from your four different views, how did that go? What did you solve for? What did you learn? What are you moving forward with?" And so, yeah, some great learnings all around helping us understand how do we deliver solutions that meet their needs? >> I mean, there's just not enough time here to cover all this data. And I think I like about it is, like you said, it's a blind survey. You used an independent third party whom I know they're really good. And you guys are really honest about it. It's like, it was funny that the analyst called today for the analyst meeting when Danny was saying if 54% and Dave Russell was like, it's 52%, actually ended up being 53%. (Jason laughs) So, whereas many companies would say 75%. So anyway, what were some of the more striking findings of that study? Let's get into it a little bit. >> So a couple of the ones that were really startling for me, on average about one in four organizations say they have not been hit. But since we know that ransomware has a gestation for around 200 days from first intrusions, so when you have that attack, 25% may be wrong. That's 25% in best case. Another 16% said they only got hit once in the last year. And that means 60%, right on the money got hit more than once per year. And so when you think about it's like that school bully Once they take your lunch money once and they want lunch money, again, they just come right back again. Did you fix this hole? Did you fix that hole? Cool, payday. And so that was really, really scary. Once they get in, on average organizations said 47% of their production data was encrypted. Think about that. So, and we tested for, hey, was it in the, maybe it's just in the ROBO. So on the edge where the tech isn't as good, or maybe it's in the cloud because it's in a broad attack surface. Whatever it is, turns out, doesn't matter. >> So this isn't just nibbling around the edges. >> No. >> This is going straight to the heart of the enterprise. >> 47% of production data, regardless of where it's stored, data center ROBO or cloud, on average was encrypted. But what I thought was really interesting was when you look at the four personas, the security professional and the backup admin. The person responsible for prevention or mediation, they saw a much higher rate of infection than the CSOs and the IT pros, which I think the meta point there is the closer you are to the problem. the worst this is. 47% is bad. it's worse than that. As you get closer to it. >> The other thing that struck me is that a large proportion of, I think it was a third of the companies that paid ransom. >> Oh yeah. >> Weren't able to recover it. Maybe got the keys and it didn't work or maybe they never got the keys. >> That's crazy too. And I think one thing that a lot of folks, you watch the movies and stuff and you think, "Oh, I'm going to pay the Bitcoin. I'm going to get this magic incantation key and all of a sudden it's like it never happened. That is not how this works. And so yeah. So the question actually was did you pay and did it work right? And so 52%, just at half of organization said, yes. I paid and I was able to recover it. A third of folks, 27%. So a third of those that paid, they paid they cut the check, they did the ransom, whatever, and they still couldn't get back. Almost even money by the way. So 24% paid, but could not get back. 19% did not pay, but recovered from backup. VEEAM's whole job for all of 2022 and 23 needs to be invert that number and help the other 81% say, "No, I didn't pay I just recovered." >> Well, in just a huge number of cases they attacked the backup Corpus. >> Yes. >> I mean, that's was... >> 94% >> 94%? >> 94% of the time, one of the first intrusions is to attempt to get rid of the backup repository. And in two thirds of all cases the back repository is impacted. And so when I describe this, I talk about it this way. The ransomware thief, they're selling a product. They're selling your survivability as a product. And how do you increase the likelihood that you will buy what they're selling? Get rid of the life preserver. Get rid of their only other option 'cause then they got nothing left. So yeah, two thirds, the backup password goes away. That's why VEEAM is so important around cloud and disk and tape, immutable at every level. How we do what we do. >> So what's the answer here. We hear things like immutability. We hear terms like air gap. We heard, which we don't hear often, is orchestrated recovery and automated recovery. I wonder if you could get, I want to come back to... So, okay. So you're differentiating with some thought leadership, that's nice. >> Yep. >> Okay, good. Thank you. The industry thanks you for that free service. But how about product and practices? How does VEEAM differentiate in that regard? >> Sure. Now full disclosure. So when you download that report, for every five or six pages of research, the marketing department is allowed to put in one paragraph. It says, this is our answer. They call the VEEAM perspective. That's their rebuttal. To five pages of research, they get one paragraph, 250 word count and you're done. And so there is actually a commercial... >> We're here to buy here in. (chuckles) >> To the back of that. It's how we pay for the research. >> Everybody sells an onset. (laughs) >> All right. So let's talk about the tech that actually matters though, because there actually are some good insights there. Certainly the first one is immutability. So if you don't have a survivable repository you have no options. And so we provide air gaping, whether you are cloud based. So your favorite hyper-scale or one of the tens of thousands of cloud service providers that offer VEEAM products. So you can have, immutability at the cloud layer. You can certainly have immutability at the object layer on-prem or disk. We're happy to use all your favorite DDoS and then tape. It is hard to get more air-gaped and take the tape out drive, stick it on a shelf or stick it in a white van and have it shipped down the street. So, and the fact that we aren't dependent on any architecture, means choose your favorite cloud, choose your favorite disc, choose your favorite tape and we'll make all of 'em usable and defendable. So that's super key Number one. Super key number two there's three. >> So Platform agnostic essentially. >> Yeah. >> Cloud platform agenda, >> Any cloud, any physical, we work happily with everybody. Just here for your data. So, now you know you have at least a repository, which is not affectable. The next thing is you need to know, do you actually have recoverable data? And that's two different questions. >> How do you know? Right, I mean... >> You don't. So one of my colleagues, Chris Hoff, talks about how you can have this Nalgene bottle that makes sure that no water spills. Do you know that that's water? Is it vodka? Is it poison? You don't know. You just know that nothing's spilling out of it. That's an immutable repository. Then you got to know, can you actually restore the data? And so automating test restores every night, not just did the backup log work. Only 16% actually test their backups. That breaks my heart. That means 84% got it wrong. >> And that's because it just don't have the resource or sometimes testing is dangerous. >> It can be dangerous. It can also just be hard. I mean, how do you spend something up without breaking what's already live. So several years ago, VEEAM created the sandbox is what we call a data lab. And so we create a whole framework for you with a proxy that goes in you can stand up whatever you want. You can, if file exists, you can ping it, you can ODBC SQL, you can map the exchange. I mean, you can, did it actually come up. >> You can actually run water through the recovery pipes. >> Yes. >> And tweak it so that it actually works. >> Exactly. So that's the second thing. And only 16% of organizations do. >> Wow. >> And then the third thing is orchestration. So there's a lot of complexity that happens when you recover one workload. There is a stupid amount of complexity happens when you try cover a whole site or old system, or I don't know, 47% of your infrastructure. And so what can you do to orchestrate that to remediate that time? Those are the three things we found. >> So, and that orchestration piece, a number of customers that were in the survey were trying to recover manually. Which is a formula for failure. A number of, I think the largest percentage were scripts which I want you to explain why scripts are problematic. And then there was a portion that was actually doing it right. Maybe it was bigger, maybe it was a quarter that was doing orchestrated recovery. But talk about why scripts are not the right approach. >> So there were two numbers in there. So there was 16% test the ability to recover, 25% use orchestration as part of the recovery process. And so the problem where it is, is that okay, if I'm doing it manually, think about, okay, I've stood back up these databases. Now I have to reconnect the apps. Now I have to re IP. I mean, there's lots of stuff to stand up any given application. Scripts says, "Hey, I'm going to write those steps down." But we all know that, that IT and infrastructure is a living breathing thing. And so those scripts are good for about the day after you put the application in, and after that they start to gather dust pretty quick. The thing about orchestration is, if you only have a script, it's as frequently as you run the script that's all you know. But if you do a workflow, have it run the workflow every night, every week, every month. Test it the same way. That's why that's such a key to success. And for us that's VEEAM disaster recovery orchestra tour. That's a product that orchestrates all the stuff that VEEAM users know and love about our backend recovery engine. >> So imagine you're, you are an Excel user, you're using macros. And I got to go in here, click on that, doing this, sort of watching you and it repeats that, but then something changes. New data or new compliance issue, whatever... >> That got renamed directly. >> So you're going to have to go in and manually change that. How do you, what's the technology behind automated orchestration? What's the magic there? >> The magic is a product that we call orchestrator. And so it actually takes all of those steps and you actually define each step along the way. You define the IP addresses. You define the paths. You define where it's going to go. And then it runs the job in test mode every night, every week, whatever. And so if there's a problem with any step along the way, it gives you the report. Fix those things before you need it. That's the power of orchestrator. >> So what are you guys doing with this study? What can we expect? >> So the report came out today. In a couple weeks, we'll release regional versions of the same data. The reason that we survey at scale is because we want to know what's different in a PJ versus the Americas versus Europe and all those different personas. So we'll be releasing regional versions of the data along the way. And then we'll enable road shows and events and all the other stuff that happens and our partners get it so they can use it for consulting, et cetera. >> So you saw differences in persona. In terms of their perception, the closer you were to the problem, the more obvious it was, did you have enough end to discern its pearly? I know that's why you're due the drill downs but did you sense any preliminary data you can share on regions as West getting hit harder or? >> So attack rate's actually pretty consistent. Especially because so many criminals now use ransomware as a service. I mean, you're standing it up and you're spreading wide and you're seeing what hits. Where we actually saw pretty distinct geographic problems is the cloud is not of as available in all segments. Expertise around preventative measures and remediation is not available in all segments, in all regions. And so really geographic split and segment split and the lack of expertise in some of the more advanced technologies you want to use, that's really where things break down. Common attack plane, uncommon disadvantage in recovery. >> Great stuff. I want to dig in more. I probably have a few more questions if you don't mind, I can email you or give you a call. It's Jason Buffington. Thanks so much for coming on theCUBE. >> Thanks for having me. >> All right, keep it right there. You're watching theCUBE's live coverage of VEEAMON 2022. We're here in person in Las Vegas, huge hybrid audience. Keep it right there, be right back. (upbeat music)

(bright music) >> Hey, welcome to this "CUBE Conversation." I'm your host, Lisa Martin. I've got two guests from NETSCOUT here with me today. Eileen Haggerty joins us, the AVP of Product and Solutions Marketing and Jason Chaffee, Senior Product Manager. We're going to be talking about the importance of quality end user experience with UC&C, Unified Communications and Collaborations services for something that will be near and dear to all of our hearts, employee productivity. Eileen, let's go ahead and start with you and the impact of COVID on UC&C, what has it been? >> Oh, Lisa, great question, because we really have seen an evolution in the importance and reliance on UCC. COVID would not have allowed us to go to work, do business continuity, any of those things had it not been for strong communications platforms to help us do that. And in fact, really the hero of all of this has been what's called Unified Communications as a Service or UCaaS. Enterprise businesses really depended entirely on the communications between the home office and the employees remotely. This is also known to be the way we all went to work. It was no longer a car. We picked up the phone basically or the computer. So Zoom, WebEx, Teams, Google Meets, they've all become household names really over the last two years. That's kind of exciting for them. And businesses during that period of time expanded their tools to keep business running and employees in communication using these very platforms, and we'll refer to this a couple of times during this conversation too, Lisa. We did a survey at the end of 2021, IT leaders, about their use of UCaaS and UCC during this period of time. We found that almost of them had used collaboration tools, and in fact, added to their arsenal of tools during this period of time to such an extent that they're now ranging, the majority of them, between three and nine different platforms that their corporate employees use. This became unwieldy, of course, during that time, and so their strategy going forward is going to be to reduce some of that number, but pretty interesting details. >> Yeah, between three and nine is a lot, and certainly, UC&C became a lifeline for all of us, professionally and personally. Even my mom learned how to use Zoom during this time. I was pretty proud of helping her with that. But, Jason, talk to us about all these new communication services. We're completely dependent on them, but overall what have you found out in terms of how they worked out? >> Well, to be honest with you, I think from the IT organization, it's been a challenge. It's been difficult. I think every IT organization is really motivated to ensure the quality of the services throughout the whole company, but as you can imagine, the increase of these communications Eileen just talked about during the pandemic is just significantly increasing the number of IT help desk tickets that have come through. And in that survey that Eileen just talked about, in fact, a third of those that responded said that 50 to 75% of their help desk tickets right now are related to UC&C or UCaaS services, and in fact, they say that over half of them have said that they get those tickets at least once a day, if not multiple times a day. And I think another big aspect of this that's been a challenge is everybody working from home now and the whole hybrid environment, and IT teams are really trying to understand and make sure that they get the same delivery of services if they were in the corporate headquarters, and I think they felt a loss of control and visibility in the services that are being delivered. I think the other thing that came out of this survey was about 25% of those said that they could get these issues if and when they happen, resolved in just a matter of minutes, but most said that it can take hours or even days to get through those, and that's obviously a really bad look for the company and really hinders productivity. So overall, I'd say it's been a challenge. I think as this onslaught of services that have come through and hampered and that everyone's trying to manage and get through, along with the lack of visibility when everybody's working from home. Of course, it's been fantastic for those of us that are working from home and made everything easier, but I think it's just made it that much more difficult for the IT teams that are trying to manage this new environment. >> Right, definitely difficulty behind the scenes there. You talked about the 25% of IT organizations being able to resolve quickly, but that leaves 75% of organizations where it takes more than a few minutes, and I can imagine individually, that might not be a big impact, but, Eileen, overall if it's taking more than a few minutes to resolve UC&C IT help desk issues, what's the overall impact to a business? >> It can be significant, and we hear a lot of little stories sadly sometimes on Lester Holt's evening news, but really what you are looking at here are longer periods of time where employees can't talk to each other. We've got email. We can probably compensate a different manner, but when it happens to be your customers not being able to talk to customer service reps in the contact center, couple of hours, that can be a big issue. Partners and suppliers who might be trying to get you important information very quickly. Maybe it's a supply chain issue item that they want to alert you to that you need to act on. That's a long period of time. And I think it's kind of important here to call out one special group, and that would be corporate executives. I think we've all heard about these big town hall meetings that corporate executives may be holding with employees or investors and all of a sudden their UCaaS support freezes, or it doesn't connect the voice in the video, and all of a sudden, you've got a very embarrassing situation. It really gets the attention of the public. Losing communication for a couple of hours, bottom line, it is going to impact productivity, customer service, and it could impact reputation, especially with those social media influencers that we all both favor and fear. So, when we were talking about our survey results, that is actually a top concern of IT executives, that productivity will get hit if communications problems do exist. So I think really ultimately for all of us in the business, disruptions and communication, it's going to be bad for business, any length. >> It is bad for business at any length, and that's a huge risk for businesses in any industry. I've been on those executive town halls where video wouldn't connect, and you just think, as much as we wanted that human connection during this time, and you couldn't get it, it made the the interaction not as ideal and obviously a risk for the organization. So, Jason, how can IT then jump in and resolve these disruptions faster, because time is of the essence here? >> Well, yeah, exactly. As we've discussed and Eileen just talked about, I think resolving issues quickly is really the key. I think we all know issues are going to happen, they just will, but it's really the IT team that can solve those the fastest is the team that's going to win, and so I think that's really the key to all of that. And one of the things that comes out of that is, again, from this survey is that only about 54% of the respondents said that they felt confident that they could understand root cause and be able to get to those issues quickly, which leaves about 43%, almost as many, that said they were less than confident or somewhat confident in finding that root cause, and so I think that's really the key there is really having the confidence to be able to find that, and to get that confidence, you need to be able to understand root cause quickly. And in order to get that, I think you need a combination of two things, which is passive, packet-based monitoring as well as continuous active testing or monitoring of those solutions. So, what I mean by that is being able to automatically and continuously test these services, even if nobody's on the system and nobody's on trying to make a phone call. So you have somebody who's trying to host, an active agent that's trying to host a meeting and others that are trying to join the meeting and sending an audio and sending and receiving video and looking at the measurements and trying to take all of that data in to really proactively understand what's going on and doing this every 15 minutes or every once an hour to really, again, get ahead of things before they become a problem. But I think beyond that, it's really about being able to take that data and the packets from those transactions that you were just testing and be able to trend that data and define problems and diagnose issues proactively. Again, as Eileen just said, before the CEO gets on there and tries to make his town hall call, so that that's really important to be able to solve those things more quickly. I think it's really a combination of a passive, scalable monitoring solution along with scheduled automatic testing of those, and along with the packets that go with that, that's really a combination of both. It's kind of a best of both worlds in order to get those things solved quickly. >> To get them solved quickly, I want to go back to something that Eileen said. You mentioned the word 'confidence,' and that I think it's important to point out that you're not saying that trivially, that IT needs to have the confidence that it has the right solutions in place to discover these faster. Eileen, from your perspective, talk to me about what that confidence means to IT and how it can shift up the stack to the C-suite. >> You know, honestly, processes and policies in these organizations are critical. They need to be able to notice when the trouble ticket comes in, and there's a lot of 'em, let's face it, and they're coming from all kinds of locations. Now, it's some of the remote offices. Some of 'em are still people at home. You've got to be able to know where to turn, what screen to use, what tool to adjust, what workflow to process, and that does come with practice, but it also comes with a solid set of tools and visibility strategies, and then you follow that process through, you work together. Maybe the voice, people in the network, people have to work together, maybe the cloud people, 'cause it's a contract with UCaaS, work together, gather the evidence and pinpoint the solution that's going to fix the problem with those locations. And it is, it becomes then a confidence builder, proof points. >> Right, proof points are critical. So, the solution that you both talked about, Jason, you elaborated on this, I'd love to get some real world examples. Tell me how you've seen this in practice. Jason, we'll start with you and then, Eileen, we'll go to you. >> Okay, yeah, great, I was just thinking of one that we had that really was one of the largest insurance companies in the country, if not the world, and when the pandemic hit, they suddenly had to send everybody home, and this is the lifeblood of their company, the contact centers that are answering these calls and the ones that were processing these claims. And as everybody went home, their strategy really was to actually go buy new laptops for everyone and implement VPNs that had a little bit of, but not fully and then implement SD-WAN, and so they had all of this traffic going over VPNs and through SD-WANs and new UCaaS solutions and all of this and what they quickly learned and found out was they just didn't have the visibility to be able to fuel, again, that word confidence that they were serving their their customers very well. So, they actually implemented one of our solutions and put these agents out at all their different desktops and started watching and doing these proactive calls and making going through the meeting life cycle and actually testing the bandwidths of their SD-WAN and ensuring all of those services. And what they found was they were able to solve some of the solutions that are even harder to solve normally, because it was affecting some users, but not all of them, and that's often harder to try and get their arms around. And so as they continued to do this, and just got their arms more around it and got more visibility, they really feel like everything's under control. And as of now, they're actually planning on leaving all those users working from home now, because they can actually ensure the same type of experience for both the users and for their customers as if those people were working from their corporate headquarters. >> Jason, that sort of sounds like a bit of a COVID silver lining. >> (laughs) Yeah, I think so. I think a lot of us actually started working from home and so there was kind of the silver lining of flexibility for the employee, but for the customer and the company itself, they learned this new visibility and this new way to ensure that across everywhere, wherever they may be, and I don't know that that would've come out without the COVID silver lining, as you just said. So I think it was something that really came out of it that might've been a good thing. >> And there are a few of those, which is nice. Eileen, talk to me about some of the experiences that you've had. What have you seen out in the field? >> Yeah, we have one really terrific energy company that was talking with us the other day, and their employees use Microsoft Office 365 which has the teams collaboration and communications system with it. And, what they've been doing for those at-home employees was configuring tests on their works stations, much like Jason explained, but it mimics exactly how an employee might be making their call and joining the sessions from video to audio, to going through login and log out. What's interesting is, and this is a compelling differentiator, a lot of tools may just watch traffic as it's happening, and certainly that's a value, but these tests even run when our agents are asleep. And what that does is these are all 24 hour a day businesses, and so maybe they have followed-the-sun contact centers or whatnot and something's happening in one part of the world, but then it's rolling to others, and we have all heard those disaster stories online when we wake up and we're hearing it on the morning news. So, if an organization can find the problem and detect it early enough and then get it when it's a few people that are involved, they can actually resolve it with our tools, find the root cause, implement a corrective action before the majority of their agents are even logging in in the morning. Nobody even knew that there was a problem overnight, because they were able to get to it and resolve it faster, and when you can do that, you're being proactive. And this, again, builds on the confidence that you get doing this kind of activity over and over and over again. But at the same time, it's also enormously beneficial from a business productivity perspective for the employees and certainly reputationally in revenue-based customer service, making sure that things are available whenever they're necessary. So, making sure they can perform their jobs, I know it sounds trite, but it's really the most critical thing we can help 'em with. >> Absolutely, 'cause I think, Eileen, one of the things that I've always thought for years is that employee productivity and employee satisfaction is directly tied to customer satisfaction, customer delight, and as you talked about, there's plenty of social media influencers who are happy to share news, good or bad, so that employee productivity is a direct relation on the customer satisfaction, the brand reputation. Jason, what are your thoughts there? >> Well, I think that's exactly right. I think it's, again, being able to continuously have your arms around that and make sure, because if you can't make phone calls or customers can't call in or things aren't working then it is, it's really a revenue impact, but it's also reputation impact, and you're going to remember that company that just didn't have their act together if you will, so I think it's important to, again, invest in this and make sure that no matter what, wherever your end users are or wherever your employees are, you're providing that experience just as if they were in the corporate office, and even when they're in the corporate office, being able to, as Eileen talked about, know ahead of time and proactively when issues happen in these very complex UCaaS and UC&C solutions that are out there now. >> And last question, Eileen for you, I imagine that these solutions are horizontal across every industry, every type of business, every size of business? >> Yeah, it's one of those phenomenon that's really critical is the ability to be ubiquitous in any environment, not being vendor-specific or dependent because now look at it, we shot that stat, three to nine different platforms in one company. If you had to buy three or nine different platforms to resolve problems, that reduces your ability to build workflows, consistent ones and know what you're doing every single time. You'd have to learn nine different platforms. That's not productive and that's certainly not realistic. So yeah, I think that this is really key. You have to be able to look at all of the traffic and be able to resolve the problems, regardless of what they happen to be running on. >> And the great thing is hearing the tools and the capabilities and solutions that NETSCOUT has to help businesses in any industry, at any size be able to identify these issues, resolve them faster and then create some silver linings. Guys, thank you so much for joining me today. Always a pleasure talking to you. This was really interesting to talk about the importance of quality end user experience with communication services for the employee productivity and of course, ultimately consumer customer satisfaction. We appreciate your insights. >> Thank you so much. >> Thank you. >> For Eileen Haggerty and Jason Chaffee, I'm Lisa Martin, you're watching a "CUBE Conversation." (bright music)

(upbeat ambient music) >> Hey everyone, welcome to this CUBE Conversation. I'm your host, Lisa Martin. I've got NETSCOUT guests here with me today. Eileen Haggerty joins us, the AVP of product and solutions marketing and Jason Chaffee as well, senior product manager. We're going to be talking about gaps in edge visibility. Guys, great to have you on theCube. >> Yeah, thanks Lisa, I really appreciate it. I appreciate the time to be able to talk with everyone. >> Good to be here. >> Yeah. All right, Eileen, we're going to start with you. Oh, what a last two years we have had, COVID, digital transformation massively accelerated, it also both changed networking dynamics over the last couple of years. How has that changed? >> Yeah, and that's the absolute truth. I think we've really seen it in the huge swings where people are performing their jobs right now. You know, I think when people went home two years ago to do their jobs, no one ever expected we where we would be two years later, right? There's really been a variety of different stages from totally working at home to now where you see an awful lot of hybrid work. People splitting their time between the offices and home. That's really where we're at right now. And in fact, some of the studies that we've been reading up on and seeing, majority of workers actually really prefer a hybrid work model, I can understand that. (chuckles) As well the managers believe they are going to have some of their employees that work from a remote location on a regular basis going forward too. So that becomes one of the biggest issues that we have to support them from an IT perspective and a corporate of going forward. One aspect was interesting, two thirds of high revenue growth companies are really embracing hybrid work. This is going to require a couple of things though. Business continuity has depended on this model now, remote workers have been a part of it and we need applications in the network to support that as well as it used to when they were all based in one set of buildings, right? So one of the things that we find IT executives lamenting, some think that they have a lot of confidence in being able to troubleshoot problems when employees are having them remotely, others are actually not quite so confident. So we're going to have to look at this as an industry and help them assure IT infrastructure and services are performing flawlessly so that the hybrid workforce can be successful. >> Yeah, that hybrid environment, it's kind of like must embrace at this point. But Jason, there brings some network complexity to this. The digital transformation was absolutely essential, right, the last couple of years for businesses to first survive and then to thrive, but network complexity has increased. Jason, walk us through what the communications path looks like in today's hybrid environment. >> Yeah, Lisa, it really has. It's just become more complex, you know, in today's hybrid environment and the whole digital transformation, there's really three areas of visibility or three location types if you will that they really need to have to focus on. And one is sort of that data center, cloud services edge and then there's the network edge. And now the ever expanding and growing client edge and really what's common about all of these different edges is this is where the traffic gets altered and crosses across those two domains or those multiple domains really. And so what happens there is those are areas where problems can occur and these are possibly, if not probably blind spots for IT. So when you think about these different edges, like the service edge, just as your private data center or the cloud where applications that are actually hosted or the network edge are going through colocations and WANs and through the internet and your typical kind of network throughout the whole organization. And then lastly is the client edge. And the client edge is again, just continues to grow. And this, people now working from home as Eileen was just talking about and remote offices or their home one day and a remote office the next day and whatever that might be and heck, they might be working in coffee shops or in the corporate headquarters. So it's just really added complexity. And as we get further away from the data center, we start to lose all of that infrastructure that we own and can control. And so it makes it difficult to really manage and understand that. In fact, I think that's probably one of the biggest consequences or challenges of this whole digital transformation. You know, it's made it real easy for those of us to work from home and all these new systems, and it's real simple to work from literally anywhere, but it's just made it that much more complex and challenging for the IT organization to really be able to manage and kind of provide that ubiquitous end user experience regardless of where the users are. You know, kind of last point, I was just thinking about this is, when you think about an organization that maybe has 30,000 employees and if 80,000 of those sudden, or 80% of those suddenly got sent home and had to work during COVID and the whole pandemic, and maybe they're hybrid now, well, that's 24 to 25,000 employees that are really key to your revenue and customer satisfaction. So IT organizations just simply can't afford not to invest in all of this and really try and to understand these complexities and make sure that everything's working the way it should for productivity for the company. >> That's a great point, Jason, as consumers, we are so demanding. That's I think one of the things in COVID that went away was patients and maybe it's slowly coming back but the customer satisfaction, the brand reputation, those are all things that are dependent on solid networking. And of course IT challenges, IT is challenged to to really smooth out those challenges. But Eileen, as employees and end users, we've faced a lot of challenges. I know I have in the last couple of years. Walk us through some of those main pain points that the employees and the end users have been through in the last two years. >> You couldn't be more on spot there. So we really haven't. The irony is all of us on this call have been amongst the ones who have probably suffered from some of these issues as well. You know, one of the things that we found during this period is all of us were coming in over VPN or on video desktop interfaces like Citrix and others. And we were connecting to data center applications and software as service apps for everything. You know, it could be customer order processing, customer records, emails, and in any time or place that we were accessing, we could have slow responsiveness, we could have log issues, we had timeouts. I think one of the interesting ones was communications apps that became huge overnight, Teams, Zoom, WebEx, and those have had issues for us too. They had connection issues, poor quality voice, terrible video. So it became a very frustrating period of time in many cases for employees, they're there to deliver customer care, protect the corporate revenue. But networks and application disruptions counter that. So leading to productivity loss, that's a big issue and a concern. And certainly one of the bigger worries is customer dissatisfaction. But I'd say, you know what's fascinating here too is IT staffs were equally frustrated but for different reasons during this period. They were combating network and application issues for these employees. And usually, they were used to everybody being in one location or to a few buildings and they had total control if you will. Now they're managing 100s and sometimes 1000s of locations known as homes and those staff members we've heard them say things like, they felt like they were losing sight of the remote workforce or simply losing control. And hybrid work models, as they become the norm for many of these organizations, groups of IT professionals still have to ensure the quality of service performance for those employees wherever they do their jobs, home, remote offices or headquarters, and for any application wherever it happens to be hosted. So this is a big challenge. >> Big challenge for IT. You talked about that customer satisfaction. Reminded me of one time I was on the phone with a contact center and I heard a dog barking in the background and I first I thought was that my dog and I thought, no, that customer contact center person is also stuck working from home. Talk about losing control of your environment. But Jason, next question is actually for both of you and Jason, I'll start with you, how does IT assure performance? I mean, it's hard to manage, talk to us about that. >> Well, yeah, Lisa, really kind of as Eileen just said, we used to all sort of be in the same common area in the headquarters. And so people, you know, the IT organization was looking at things like the data center or server farms or internet links just going into and out of the headquarters or off to the data center itself. And so, but that's just not really the case anymore. And as people continue again, as I said before, work from literally anywhere, it's just made the ecosystem that much more challenging and much more complex and bigger frankly to try to manage. And so I think what you really need to do is you see something where it's challenging because the old adage is you just can't manage what you can't see. And so that's become that challenge. And I think what you need is visibility at every edge including up to the client edge that we've been talking about so that you can do things like track and trend volumes and bandwidth and capacity and application utilization and all the different things that make up that end user experience. You need that visibility from all of those locations to really understand and see what's going on from there. >> Eileen, what's your take? >> Well, Jason makes a bunch of valid points. I think what triggers in my mind as he was speaking was that there are other enterprises that have very specific remote locations which present themselves a very different kind of importance for value of the performance assurance. There's banks. They have financial transactions that are going on there, which are instantaneous in nature or need to be for a variety of different reasons, right? Factory plants they've been now wirelessly or WiFi driven production lines. And as a result, any disruption in that production line could have it turned off for 20 minutes an hour. And that has a deep impact to the business as well. Retail stores and distribution centers. This one rings probably for everybody, 'cause during this period of time, we were all online and we were ordering things, right? And so any issues with disruptions, slow downs from those types of remote locations for touchless pickup for the ability to go to a store or get something picked up that day, this was critical. And we all know those services broke down for one reason or another and it created a bit of a problem if you didn't have the right snack for the four year old today. >> Right, definitely the four year old losing control or losing patients, the poor parent. Jason, you talked about visibility and you said something really poignant, you can't manage what you can't see, what is needed for IT to have that visibility as this environment just scattered, what's needed to effectively manage it? >> Yeah. Lisa, you know I think as you just said, and as I said earlier, I think the real key is visibility at every edge and all of the different edges that we've talked about including the home user and it's really been impractical and expensive to instrument at every one of those, especially at all the home locations which are now everybody's home work office. And you just really can't get a view into it. And of course there's some solutions out there that sort of gives you a view of what's going on, what's that end user experience. And, but it doesn't really tell you why. And I think that's one of the key components of that. So as I think about this large new hybrid ecosystem, I think there's really three main things that you need in order to do that. And the first of all, again, is that complete visibility at every edge. And to me that means a combination of both passive and active synthetic based monitoring. So you're passive monitoring where you're actually looking at the real user traffic, and detecting trends and being able to understand what's going on there. But then you also do need, there's a time and a place for that synthetic active testing where we're getting an understanding and a baseline where we're testing continuously and automatically to understand what that end user experience is and understand what performance should look like. But as I mentioned, I think that's just not necessarily enough to do just do synthetics from those synthetic locations. I think it's really key to be able to get deep down into the wire day or the packets of that to really understand why something is happening. You know, for instance, today there might be an issue and you can say, well, I see it's a DNS issue. Hey, DNS guy, go off and try to fix this and figure out what's going on. Well, again, I think you need that visibility, you need a solution that can pull the packets back and give you that combination of both of those things. The next thing I think. >> So. >> Oh, sorry, go ahead. >> Go ahead. >> Well, the next thing I was going to say that I think that's really critical and key is tools that can manage the complexity as you go across all of these. I mean, there's just so many tools out there that are, SaaS and Ucast, your WebEx, Zoom teams, all of those. And it's just frankly, almost impossible to be an expert on all of those different solutions that you might have. And of course they have their own management platform forms that make it, so you can kind of see what's going on but being able to understand and see how to fix WebEx for instance, doesn't really help you when one of your users happens to be having a problem with teams. And so I think again, you need a solution that can go across all of those different applications to be able to see what's going on. And then thirdly, I think is the key, one of the key components is service and application collaboration. And what I mean by this is you need a tool that can give you the ability to have proof and evidence that you can share with your service providers, your application providers because they're very complex, right? They all have dozens of servers in different parts of the world, in different parts of the country. And frankly, they're going to probably blame you first, they're going to say, no, this is something on your end. And it really adds to the time of troubleshooting and get resolution. But if you can actually have that data to say, hey, I can see exactly what's going on. And it's this server at this data center and it's causing high retransmissions or latency. That just makes again more of a collaborative effort but allows you to sort of put all of those things together and stop the finger pointing and blame game and be able to solve those solutions much more quickly. So again, I think it's really a combination of active and passive together all in one console or one solution that really gives you that visibility across all of the different environments that we might have today. >> All those different environments, and there still are so many. Eileen, how have you seen this? How has NETSCOUT seen this actually in practice and how have the tool that you and talked about, how is it actually helping to give the visibility, to reduce the complexity and to ensure that ultimately the end users and the employees are productive so that the customers can be happy? >> Yeah, and you know, it's interesting as he was talking, I was thinking one of the accounts that I've talked to, an organization that's based in software development and they literally have offices all over the world. The hybrid workforce model is their choice going forward. And most of those worldwide employees are part of the sales and support organization for local prospects and customers. So one of the biggest things that's critical to them is communications. And that has to be top of their concerns for quality. The other business related applications are equally important because as you're talking to them, you're pulling those services up and they are in fact using solutions for looking at those edges, the WAN links are key for visibility. They're able to look at the inbound and outbound traffic at those locations. And then what they're able to do is to do two things, one, like Jason was talking about, collaborate, work with those local WAN providers. They're all different over the world. So you really need to build a relationship with them, you have to have some evidence and they're able to do that now. It helps them cost effectively and proactively plan bandwidth changes, and that's really important too. They're also using the synthetic testing that Jason referred to. These locations have the buildings able to test for different applications that are going to be important to the business that's at that location. They do it over ethernet, so for the people with hardwired desks, they can see what the traffic experience is. And then for those that are in open spaces or conference rooms on using WiFi, they're measuring there as well. They want to make sure that no matter where you sit in the office and how you connect, the quality is the same and that's important. Bottom line, they're being able to reduce dramatically the time it's taken to troubleshoot and resolve issues as they emerge. It's great. >> That is great. And of course, as we all have that patience limitation, that speed is key there. Guys, thank you so much for joining me talking about not just the gaps in edge visibility but the ways that they can be remedied and fixed so that ultimately customer satisfaction, employee productivity, all things are harmonious. We appreciate your insights. >> Absolutely, thank you. >> Thank you. >> For Eileen Haggerty and Jason Chaffee I'm Lisa Martin, you're watching a CUBE Conversation. 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>>From the Bellagio hotel in Las Vegas. It's the cube covering UI path forward for brought to you by UI path. >>Hey, welcome back to Las Vegas. Lisa Martin, with Dave Volante, the cube is here, live at UI path forward for very excited to be here in person. Next topic, the smart factory, a couple of guests here to unpack that for us, Jason Brixton joins us the smart factory lead at Deloitte and Thomas Hanson, the CRO of UI path gentlemen, and welcome to the program. Thank you. Thank you for having us great to have you great to be in person. Let's talk about smart track factory factory Ford auto. What is it from Deloitte perspective and then UI path. >>So if you think about smart factory, it's really that transition from the old kind of analog manufacturing environment to the digital, digital operating type environment that we see today. So technology has really changed in the last three or four years. And as a result of that elevation of technology, we're able to do a lot more on the manufacturing floor than we ever could. So what used to be more analog or hybrid with a little bit of technology is now starting to shift really to end to end integrated manufacturing operations that are based on digital platforms and we're loving it. It's a great place to be >>Great. Tell us what's your perspective? >>Well, first of all, it's great to be here. Thank you for the invite. It's so nice to be away from soon calls or, or other type of, uh, of calls, right. And be in person. Uh, look, we have an amazing partnership with the lights. Um, we have worked together for years. We've done more than 400 joint engagements with the large companies across the world. And in that process, we've really gone deeper from a vertical and industry perspective and smart factory is really the starting point of going super specific and figuring out what does automation or how does automation rather play into, um, to a, to a smart factory, like a beautiful trombone, that music from a beautiful trombone. >>So years ago, we wrote a piece talking about the cloud as an opportunity and how to take advantage of it. And one of the, the premise of the piece was you've got to build ecosystems and maybe it's within an industry or within a practice and build data in different disciplines because the power of many versus the capabilities of one, this smart factory initiative that you guys have going, it feels like an ecosystem play. Can you describe that ecosystem? Who's involved? I know SAP in for AWS, but, but tell us more about the ECOS. >>Yeah, sure. So your, your hunch, there is a great one, right? We, we learned early on that trying to do this as Deloitte or Deloitte plus one just, wasn't going to get it done, right? You really needed to harness the power of the many. And so at the, at the core of what we're doing at the smart factory at Wichita, that you alluded to is about bringing an ecosystem to life. So we have 21 partners that are going to be participating out of the gate with the smart factory. Wichitan the intent is to show a seamless solution and actual end-to-end production facility that showcases 21 amazing technologies and partners. And we're just really thrilled about what we're able to show our clients. So, >>Yep. So Koch industries owns Inforce. So obviously that's the Wichita connection, is that right? So they got to be involved in this. I mean, they were amazing company, but what can you tell us about, uh, their, their involvement? >>Yep. So Coke, obviously the in for connection, uh, Dragos, which is another in four company as a founder within, uh, within the ecosystem, which is fantastic. There they play at the core. They're also an incredibly important client, right? So the Coke business on the whole is critical to how we think about manufacturing across a whole range of industries from discreet production to scale process. Um, they're fantastic partners and we've had a great time working with them. And you guys are just, >>It's about to launch through soft launch. Can you tell us more where you are in the progression? >>Sure. So soft launch started two days ago. Oh wow. So the building, we have the keys, uh, we are doing some visits with a handful of friends and family, that ecosystem partners that you mentioned, there'll be coming out, uh, to see it and to provide some feedback. And then we go live in earnest in January >>At Thomas where's UI path fit. >>Well, we fit in essay as a key part in this initiative. Um, look, we, as a company, we are part the preferred partner. First, we do all our business together with partners and we have right about almost 5,000 partners now, globally. And then there's a few, then there's a few in that 5,000 that are unique that really stand out. And Deloitte of course is one of those very, very special partners that we work with globally, but also locally here in the us, across all the states across all the industries. So we're thrilled to be part of this automation plays a key key part of smart factory. When you think about it, the evolution of work there's so much boring, mundane work on there. Humankind is better served, spending their time and effort on the non mundane on the innovative on the creative. And that's what we try to ensure that the humans in the loop so to speak are focused on the innovative work, the graded work, and we have software robots, RPA automation handle all that boring and mundane work, >>Right? Letting the folks focus on the value, add to themselves a value add to the organization, more strategic investments. Thomas question for you is in terms of you talked about this being horizontal across industries, but I'm curious about what some of the feedback is from some of your customers, 8,000 customers. Now you've got a very large what, 726 million ARR, huge lot of customers over a hundred million ARR. What's been the feedback from some of those guys. >>Well, so first of all, uh, personally, I I've been in enterprise software for more than 20 years. And what I've experienced over the years are most large scale enterprise software projects tends to be multi-year in nature, be rather complex. And the failure rate can be rather high. Then in comes RPA and automation, which is a complete different kettle of fish in the sense that from conceptualization of identifying a process, to getting it built, getting it tested, getting it into production, you're talking days and weeks only. So the path to seeing value is so fast. What I've learned yesterday and today from the 1516 customer meetings I've had so far is the same unique trend or learning across all industries and also from various parts of the world. And that is very fast realization of value, perhaps starting initially with 5, 10 20 processes and then scaling super fast because the find that return on investment incredibly quickly with our solution. So that's what unifies it across geographies and across industries. >>What'd you think about the smart factory? And one of the things we've learned during COVID is there's so much unknown. So sometimes these processes aren't linear like a trombone, you know, going back and forth in and out, but is there unknown in the smart factory processes or is it pretty well known? And you can do the process mining on that known base. What's the dynamic >>Back there. So there's a few different dimensions to it. So yes, it is well known because it's a controlled environment, but one of the things that we're doing is we're actually actively introducing a lot of unknown factors to try to let the bots and the process mining kick into effect. Right? So we're artificially, let's just say injecting opportunity for us to do that. The other thing that we're doing is, and what's really unique about the smart factory at Wichita is it's one of four across the globe for Deloitte. And so we're bringing data in from the other three sites, which is data, that'll be less controlled. We're going to do process mining on that. Just try to take advantage of some of the, some of the capabilities associated with the solutions. >>Okay. So, so w when you think about process mining, do you start there, or do you start with, I sometimes call it paving the cow path, you know, taking what you've known, that linear process that, that hit that as the quick win, and then worry about the process money, or do you step back and say, wait a minute, we have to rethink the entire factory experience. Where do you start? >>I think it depends in the case of the smart factory with that, we've got a few different places, so we're using it to do ingestion of orders. So that's obviously a very controlled environment. We're then using it to do a lot of work around inventory management and optimization as well as month end close plays, which will be a lot more we're learning as we go. Right. So I think on the spectrum, it could be on either end my personal belief. If you look at it more long-term or actually out in the real world is that this is all about learning new things. It's about generating insights from data that frankly, you don't want human beings to have to go do that. And so having the ability to take advantage of an intelligent automation solution, as powerful as UI path is really a great advantage. >>One of the things that's misunderstood, I think about UI path is they look at what happened post let's say 2015, 2016, and say, oh, just like, just like every other Silicon valley company, double, double, triple, triple. And that's not how you guys started. You sort of let things bake for the better part of a decade and then got product market fit and then exploded. Um, and so that's, that to me was a key to your success in scaling this. I feel like you guys are building a new offering here. This is not just doing a one-off the product market fit. It's not like a point product. It's a, it's a big thing. So can you talk about the go to market, your product market fit? You're testing it out now, your goals, are you trying to scale this up? What, what are some of the things that you can share about your aspirations? So >>The partnership from a UI path perspective to Deloitte is a critical partnership. One of the select few on a global level, uh, we have enjoyed tremendous, uh, amount of engagements together. I mentioned early on 400, and I believe we, we now have together right about 1000 developers trained within your organization on your iPod, right? That's right. Yep. So we have a strong base that, of course we want to build full and hopefully put a syrup behind the thousand to 10,000. And over time, we want to make sure that it's globally inclusive, that we can serve all the marketers across the world where we have giant presence. And there's a select number of verticals and industries where we really have had success together that we of course want to go and specifically shoe in on what would have caused now be manufacturing together. And of course, a classic vertical we've been very strong in together as BFSI bank and financial services industry. So those are good areas. >>Well, Jason, you're building a business out of this, right? I mean, you've got a business plan around it and you're going to scale this thing. >>Oh, absolutely. Yeah. That's 100% the case. So we have smart factory at Wichita. That is part of our positioning in the marketplace. What we found is that telling people about tech and about solutions is one thing, showing it to them in a production environment is altogether different, right? Giving clients the opportunity to explore the art of the possible in a real setting like that is incredibly impactful. And so you talked about go to market, we see this relationship with the ecosystem and what we're trying to do in Wichita, that's sort of the epicenter of building an entire business, which ultimately will have huge global potential. >>We talk about speed for a minute. And the growth trajectory that UI path Thomas has been on for the last five years or so. I think I was reading, I think it was analysis that Dave wrote that in 2016 revenue was 1,000,020, 20, 15, 20, 20 600 million. So massive growth very quickly. My question, Jason is for you in terms of the speed. Ha how quickly are you looking to see the smart factory for Dato really impacting organizations around the globe because these guys are on a fast bulleted. >>Yeah. So I wish we had those growth rates. I will say though, selling and delivering these solutions holistically to manufacturers takes more time. So we think of our cycle as be measured, certainly in many months, certainly not years. We are starting to see an acceleration of that entire sales cycle and delivery cycle, just because of things like the pandemic driving organizations to just need to move faster. Frankly, if you're not moving towards digital manufacturing operations right now, you're probably behind. And so we're seeing that urgency from the market start to pick up, but we don't have that kind of growth rate, unfortunately. >>Well, what's it. What's interesting about Deloitte to me is you guys here, I think of you as a virtual company. I mean, I know you got a lot of bodies out there, but it's not like you've got a lot of physical locations. Right. And so now, but now you're just, you're investing in a physical plant essentially, >>Which is extremely exciting. We, we keep telling ourselves when we talk to folks, they own lots of buildings. So just because we're excited about our building doesn't mean they are, but you're exactly right, right. We're obviously a global services and products company. So this is one of a handful of buildings that are going to start to represent us as an organization. And we're really excited about what should we watch? >>It's kind of milestones for progress success. What are the markers that we should be paying attention to is independence. >>I think specifically on this, um, rapid experiment together, I think one of the key learnings we can take away that we can apply to other companies in the manufacturing industry specifically look from a UI perspective. We work with many large scale manufacturers around the world, but we've seen amazing fast progress with Bridgestone. For example, we implemented a smaller set of, uh, uh, bots that help them reduce their paperwork by 85% onto their branches with a Turkish e-commerce retailer called Archer. Lik I think I get the pronunciation correctly. They put 85 processes in place with our bots and are now to date transacting or running. I think it's 3 million e-commerce transactions with our processes. So the impact we can have in manufacturing together with the learnings from this, my factory, I think is just so exciting. Really? >>Yeah. The impact, the potential there is, is unlimited. Guys. Thank you for joining David, me talking to us about smart factory Ford auto, what it means for both businesses, how the partnership is evolving. It sounds like music from a beautiful trombone. Thank you so much for joining Dave and me today. Thank you For Dave Volante. I'm Lisa Martin. The Cubas live in Las Vegas at the Bellagio at UI path forward for we'll be right back.

(upbeat music) >> Hello and welcome to today's session, theCUBE's presentation of the AWS Startup Showcase, New Breakthroughs in DevOps, Data Analytics, Cloud Management Tools, featuring Ascend.io for the data and analytics track. I'm your host, John Furrier with theCUBE. Today, we're proud joined by Sean Knapp, CEO and founder of Ascend.io and Jason Robinson who's the VP of Data Science and Engineering at Steady. Guys, thanks for coming on and congratulations, Sean, for the continued success, loves our cube conversation and Jason, nice to meet you. >> Great to meet you. >> Thanks for having us. >> So, the session today is really kind of looking at automating analytics workloads, right? So, and Steady as a customer. Sean, talk about the relationship with the customer Steady. What's the main product, what's the core relationship? >> Yeah, it's a really great question. when we work with a lot of companies like Steady we're working hand in hand with their data engineering teams, to help them onboard onto the Ascend platform, build these really powerful data pipelines, fueling their analytics and other workloads, and really helping to ensure that they can be successful at getting more leverage and building faster than ever before. So we tend to partner really closely with each other's teams and really think of them even as extensions of each other's own teams. I watch in slack oftentimes and our teams just go back and forth. And it's like, as if we were all just part of the same company. >> It's a really exciting time, Jason, great to have you on as a person cutting your teeth into this kind of what I call next gen data as intellectual property. Sean and I chat on theCUBE conversation previous to this event where every company is a data company, right? And we've heard that cliche. >> Right. >> But it's true, right? It's going to, it's getting more powerful with the edge. You seeing more diverse data, faster data, small, big, large, medium, all kinds of different aspects and patterns. And it's becoming a workflow kind of intellectual property paradigm for companies, not so much. >> That's right. >> Just the tech it's the database is you can, it's the data itself, data in flight, it's moving around, it's got value. What's your take-- >> Absolutely. >> On this trend? >> Basically, Steady helps our members and we have a community of members earn more income. So we want to help them steady their financial lives. And that's all based on data, so we have a web app, you could go to the iOS Store, you could go to the Google Play Store, you can download the app. And we have a large number of members, 3 million plus, who are actively using this. And we also have a very exciting new product called income passport. And this helps 1099 and mixed wage earners verify their income, which is very important for different government benefits. And then third, we help people with emergency cash grants as well as awards. So all of that is built on a bedrock of data, so if you're using our apps, it's all data powered. So what you were mentioning earlier from pipelines that are running it real time to yeah, anything, that's a kind of a small data aggregation, we do everything from small to real-time and large. >> You guys are like a multiple sided marketplace here, you've got it, you're a FinTech app, as well as the future of work and with virtual space-- >> That's right. >> Happening now, this is becoming, actually encapsulates kind of the critical problems that people trying to solve right now, you've got multiple stakeholders. >> That's right. >> In the data. >> Yes, we absolutely do. So we have our members, but we also, within the company, we have product, we have strategy, we have a growth team, we have operations. So data engineering and data science also work with a data analytics organization. So at Steady we're very much a data company. And we have a data organization led by our chief data officer and we have data engineering and data science, which are my teams, but also that business insights and analytics. So a lot of what we're building on the data engineering side is powering those insights and analytics that the business stakeholders use every day to run the organization. >> Sean, I want to get your thoughts on this because we heard from Emily Freeman in the keynote about how this revolution in DevOps or for premiering her talk around how, it's not just one persona anymore, I'm a release engineer, I'm this kind of engineer, you're seeing now all engineering, all developers are developers. You have some specialty, but for the most part, the team makeups are changing. We touched on this in our cube conversation. The journey of data is not just the data people, the data folks. It's like there's, they're developers too. So the confluence of data science, data management, developing, is changing the team and cultural makeup of companies. Could you share your thoughts on this dynamic and how it impacts customers? >> Absolutely, I think the, we're finding a similar trend to what we saw a number of years ago, when we talked about how software was eating the world and every company was now becoming a software company. And as a result, we saw this proliferation and expansion of what the software roles look like and thought of a company pulled through this entire new era of DevOps. We were finding that same pattern now emerging around data as not only is every company a software company, every company is a data company and data really is that field, that oil that fuels the business and in doing so, we're finding that as Jason describes it's pervasive across the team, it is no longer just one team that is creating some insights and reports around operational analytics, or maybe a team over here doing data science or machine learning. It is expensive. And I think the really interesting challenges that start to come with this too, are so many data teams are so over capacity. We did a recent study that highlighted that 96% of data teams are at, or over capacity, only 4% had spare capacity. But as a result, the net is being cast even wider to pull in people from even broader and more adjacent domains to all participate in the data future of their organization. >> Yeah, and I think I'd love to get your guys react to this conversation with Andy Jassy, who's now the CEO of Amazon, but when he was the CEO of AWS last year, I talked with him about how the old guard and new guard are thinking around team formations. Obviously team capacity is growing and challenged when you've got the right formula. So that's one thing, right? But what if you don't have the right formula? If you're in the skills gap, problem, or team formation side of it, where you maybe there was two years ago where the mandate came down? Well, we got to build a data team even in two years, if you're not inquisitive. And this is what Andy and I were talking about is the thinking and the mindset of that mission and being open to discovering and understanding the changes, because if you were deciding what your team was two, three years ago, that might have changed a lot. So team capacity, Sean, to your point, if you got it right, and that's a challenge in and of itself, but what if you don't have it, right? What do you guys think about this? >> Yeah, I think that's exactly right. Basically trying to see, look and gaze into the crystal ball and see what's going to happen in a year or two years, even six months is quite difficult. And if you don't have it right, you do spend a lot of time because of the technical debt that you've amassed. And we certainly spend quite a bit of time with technical debt for things we wanted to build. So, deconvolving that, getting those ETLs to a runnable state, getting performance there, that's what we spend a bit of time on. And yeah, it's something that it's really part of the package. >> What do you guys see as the big challenge on teams? The scaling challenge okay. Formation is one thing, Sean, but like, okay, getting it right, getting it formed properly and then scaling it, what are the big things you're seeing? >> One of the, I think the overarching management themes in general, it is the highest out by the highest performing teams are those where the individual with the context and the idea is able to execute as far and as fast and as efficiently as possible, and removing a lot of those encumbrances and put it a slightly different way. If DevOps was all basically boiled down to, how do we help more people write more software faster and safely data ops would be very similarly, how do we enable more people to do more things with data faster and safely? And to do that, I think the era of these massive multi-year efforts around data are gone and hopefully in the not too distant future, even these multi-quarter efforts around data are gone and we get into a much more agile, nimble methodology where smaller initiatives and smaller efforts are possible by more diverse skillsets across the business. And really what we should be doing is leveraging technology and automation to ensure that people are able to be productive and efficient and that we can trust our data and that systems are automated. And these are problems that technology is good at. And so in many ways, how in the early days Amazon would described as getting people out of the muck of DevOps. I think we're going to do the same thing around getting people out of the muck of the data and get them really focused on the higher level aspects. >> Yeah, we're going to get into that complexity, heavy lifting side muck, and then the heavy lifting taking away from the customers. But I want to go back to real quick with Jason while we're on this topic. Jason, I was just curious, how much has your team grown in the recent year and how much could've, should've grown, what's the status and how has Ascend helped you guys? What's the dynamic there? ' Cause that's their value proposition. So, take us through that. >> Absolutely, so, since the beginning of the year data engineering has doubled. So, we're a lean team, we certainly use the agile mindset and methodologies, but we have gone from, yeah, we've essentially doubled. So a lot of that is there's just so much to do and the capacity problem is certainly there. So we also spend a lot of time figuring out exactly what the right tooling is. And I was mentioning the technical debt. So you have those, there's the big O notation of whatever that involves technical debt. And when you're building new things, you're fixing old things. And then you're trying to maintain everything. That scaling starts to hit hard. So even if we continue to double, I mean, we could easily add more data engineers. And a lot of that is, I mean, you know about the hiring cycles, like, a lot of of great talent, but it's difficult to make all of those hires. So, we do spend quite a bit of time thinking about exactly what tools data engineering is using day-to-day. And what I mentioned, were technologies on the streaming side all the way to like the small batch things, but, like something that starts as a small batch getting grow and grow and grow and take, say 15 hours, it's possible, I've seen it. But, and getting that back down and managing that complexity while not overburdening people who probably don't want to spend all their waking hours building ETLs, maintaining ETL, putting in monitoring, putting in alerting, that I think is quite a challenge. >> It's so funny because you mentioned 18 hours, you got to kind of being, you didn't roll your eyes, but you almost did, but this is, but people want it yesterday, they want real time, so there's a lot of demand-- >> Yes. >> On the minds of the business outcome side of it. So, I got to ask you, because this comes up a lot with technical debt, and now we're starting to see that come into the data conversation. And so I always curious, is there a different kind of technical debt with data? Because again, data is like software, but it's a little bit of more elusive in the sense it's always changing. So is there, what kind of technical debt do you see in the data side that's different than say software side? >> Absolutely, now that's a great question. So a lot of thinking about your data and structuring your data and how you want to use that data going into a particular project might be different from what happens after stakeholders have a new considerations and new products and new items that need to be built. So thinking about how that, let's say you have a document store, or you have something that you thought was going to be nice and structured, how that can evolve and support those particular products can essentially, unless you take the time and go through and say, well, let's architect it perfectly so that we can handle that. You're going to make trade-offs and choices, and essentially that debt builds up. So you start cutting corners, you start changing your normalization. You start essentially taking those implicit schema that then tend to build into big things, big implicit schema. And then of course, with implicit schema, you're going to have a lot of null values, you're going to have a lot of items to deal with. So, how do you deal with that? And then you also have the opportunity to create keys and values and oops, do we take out those keys that were slightly misspelled? So, I could go on for hours, but basically the technical debt certainly is there with on data. I see a lot of this as just a spectrum of technical debt, because it's all trade-offs that you made to build a product, and the efficiency has start to hit you. So, the 15 hour ETL, I was mentioning, basically you start with something and you were building things for stakeholders and essentially you have so much complex logic within that. So for the transforms that you're doing from if you're thinking of the bronze, silver, gold, kind of a framework, going from that bronze to a silver, you may have a massive number of transformations or just a few, just to lightly dust it. But you could also go to gold with many more transformations and managing that, managing the complexity, managing what you're spending for servers day after day after day. That's another real challenge of that technical debt stuff. >> That's a great lead into my next question, for Sean, this is the disparate system complexity, technical debt and software was always kind of the belief was, oh yeah, I'll take some technical debt on and work it off once I get visibility and say, unit economics or some sort of platform or tool feature, and then you work it off as fast as possible. I was, this becomes the art and science of technical debt. Jason, what you're saying is that this can be unwieldy pretty quickly. You got state and you got a lot of different inter moving parts. This is a huge issue, Sean, this is where it's, technical debt in the data world is much different architecturally. If you don't get it right, this is a huge, huge issue. Could you aluminate why that is and what you guys are doing to help unify and change some of those conditions? >> Yeah, absolutely. When we think about technical debt and I'll keep drawing some parallels between DevOps and data ops, 'cause I think there's a tremendous number of similarities in these worlds. We used to always have the saying that "Your tech debt grows manually across microservices, "but exponentially within services." And so you want that right level of architecture and composibility if you will, of your systems where you can deploy changes, you can test, you can have high degrees of competence and the roll-outs. And I think the interesting part in the data side, as Jason highlighted, the big O-notation for tech debt in the data ecosystem, is still fairly exponential or polynomial in nature. As right now, we don't have great decomposition of the components. We have different systems. We have a streaming system, we have a databases, we have documents, doors and so on, but how the whole data pipeline data engineering part works generally tends to be pretty monolithic in nature. You take your whole data pipeline and you deploy the whole thing and you basically just cross your fingers, and hopefully it's not 15 hours, but if it is 15 hours, you go to sleep, you wake up the next morning, grab a coffee and then maybe it worked. And that iteration cycle is really slow. And so when we think about how we can improve these things, right? This is combinations of intelligent systems that do instantaneous schema detection, and validation, excuse me, it's combinations of things that do instantaneous schema detection and validation. It's things like automated lineage and dependency tracking. So you know that when you deploy code, what piece of data it affects, it's things like automated testing on individual core parts of your data pipelines to validate that you're getting the expected output that you need. So it's pulling a lot of these same DevOps style principles into the data world, which is really designed to going back to how do you help more people build more things faster and safely really rapid iterations for rapid feedback. So you know if there's breaks in the system much earlier on. >> Well, I think Sean, you're onto something really big there. And I think this is something that's emerging pretty quickly in the cloud scale that I called, 2.0, whatever, what version we're in, is the systems thinking mindset. 'Cause you mentioned the model that that was essentially a silo or subsystem. It was cohesive in it's own way, but now it's been monolithic. Now you have a broken down set of decomposed sets of data pieces that have to work together. So Jason, this is the big challenge that everyone, not really people are talking about, I think most these guys are, and you're using them. What are you unifying? Because this is a systems operating systems thinking, this is not like a database problem. It's a systems problem applied to data where databases are just pieces of it, what's your thoughts? >> That's absolutely right. And I would, so Sean touched on composibility of ETL and thinking about reusable components, thinking about pieces that all fit together, because as you're building something as complex as some of these ETS are, we do think about the platform itself and how that lends to the overarching output. So one thing, being able to actually see the different components of an ETL and blend those in and you as the dry principal, don't repeat yourself. So you essentially are able to take pieces that one person built, maybe John builds a couple of our connectors coming in, Sean also has a bunch of transforms and I just want this stuff out, so I can use a lot of what you guys have already built. I think that's key, because a lot of engineering and data engineering is about managing complexity. So taking that complexity and essentially getting it out fast and getting out error free, is where we're going with all of the data products we're building. >> What are some of the complexity that you guys have that you're dealing with? Can you be specific and share what these guys are doing to solve that problem for you? That's, this is a big problem everyone's having, I'm seeing that all over the place. >> Absolutely, so I could start at a couple of places. So I don't know if you guys are on the three Vs, four Vs or five Vs, but we have all of those. And if you go to that five, four or five V model, there is the veracity piece, which you have to ask yourself, is it true? Is it accurate when? So change happens throughout the pipeline, change can come from web hooks, change can come from users. You have to make sure that you're managing that complexity and what we we're building, I mentioned that we are paying down a lot of tech debt, but we're also building new products. And one pretty challenging, quite challenging ETL that we're building is something going from a document store to an analytical application. So in that document store, we talked about flexible schema. Basically, you don't really know exactly what you're going to get day to day, and you need to be able to manage that change through the whole process in a way that the ultimate business users find value. So, that's one of the key applications that we're using right now. And that's one that the team at Ascend and my team are working hand in hand going through a lot of those challenges. And it's, I also watch the slack just as Sean does, and it's a very active discussion board. So it is essentially like they're just partnering together. It's fabulous, but yeah-- >> And you're seeing kind of a value on this too, I mean, in terms of output what's the business results? >> Yes, absolutely. So essentially this is all, so yes, the fifth V value. So, getting to that value is essentially, there were a few pieces of the, to the value. So there's some data products that we're building within that product and their data science, data analytics based products that essentially do things with the data that help the user. There's also the question of exactly the usage and those kinds of metrics that people in ops want to understand as well as our growth team. So we have internal and external stakeholders for that. >> Jason, this is a great use case, a great customer, Sean, you guys are automating. For the folks watching, who were seeing their peer living the dream here and the data journey, as we say, things are happening. What's the message to customers that you guys want to send because you guys are really cutting your teeth into a whole another level of data engineering, data platform. That's really about the systems view and about cloud. What's the pitch, Sean? What should people know about the company? >> Absolutely, yeah, well, so one, I'd say even before the pitch, I would encourage people to not accept the status quo. And in particular, in data engineering today, the status quo is an incredibly high degree of pain and discomfort. And I think the important part of why Ascend exists and why we're so helpful for our customers, there is a much more automated future of how we build data products, how we optimize those and how we can get a larger cohort of builders into the data ecosystem. And that helps us get out of the muck as we talked about before and put really advanced technology to work for more people inside of our companies to build these data products, leveraging the latest and greatest technologies to drive increased business value faster. >> Jason, what's your assessment of these guys, as people are watching might say, hey, you know what, I'm going to contact them, I need this. How would you talk about Ascend into your peers? >> Absolutely, so I think just thinking about the whole process has been a great partnership. We started with a POC, I think Ascend likes to start with three use cases, I think we came out with four and we went through the ones that we really cared about and really wanted to bring value to the company with. So we have roadmaps for some, as we're paying down technical debt and transitioning, others we can go directly to. And I think that thinking about just like you're saying, John, that systems view of everything you're building, where that makes sense, you can actually take a lot of that complexity and encapsulate it in a way that you can essentially manage it all in that platform. So the Ascend platform has the composibility piece that we touched on. It also, not only can you compose it, but you can drill into it. And my team is super talented and is going to drill into it. So basically loves to open up each of those data flows each of the components therein and has the control there with the combination of Spark Sequel, PI Spark SQL Scala and so on. And I think that the variety of connections is also quite helpful. So thinking about the dry principle from a systems perspective is extremely useful because it's dry, you often get that in a code review, right? I think you can be a little bit more dry here. >> Yeah. >> But you can really do that in the way that you're composing your systems as well. >> That's a great, great point. One quick thing for the folks that they're watching that are trying to figure this out, and a lot of architecture is going on. A lot of people are looking at different solutions. What things have you learned that you could give them a tip like to avoid like maybe some scar tissue or tips of the trade, where you can say, hey, this way, be careful, what's some of the learnings? Could you give a few pointers to folks out there, if they're kicking tires on the direction, what's the wrong direction? What's the right direction look like? >> Absolutely, I think that, I think it through, and I don't know how much time we have that, that feels like a few days conversation as far as ways to go wrong. But absolutely, I think that thinking through exactly where want to be is the key. Otherwise it's kind of like when you're writing a ticket on Jarrah, if you don't have clear success criteria, if you don't know where you going to go, then you'll end up somewhere building something and it might work. But if you think through your exact destination that you want to be at, that will drive a lot of the decisions as you think backwards to where you started. And also I think that, so Sean also mentioned challenging the status quo. I think that you really have to be ready to challenge the status quo at every step of that journey. So if you start with some particular service that you had and its legacy, if it's not essentially performing what you need, then it's okay to just take a step back and say, well, maybe that's not the one. So I think that thinking through the system, just like you were saying, John, and also I think that having a visual representation of where you want to go is critical. So hopefully that encapsulates a lot of it, but yes, the destination is key. >> Yeah, and having an engineering platform that also unifies the multiple components and it's agile. >> That's right. >> It gets you out of the muck and on the last day and differentiate heavy lifting is a cloud plan. >> Absolutely. >> Sean, wrap it up for us here. What's the bumper sticker for your vision, share your founding principles of the company. >> Absolutely, for us, we started the company as a former in recovery and CTO. The last company I founded, we had nearly 60 people on our data team alone and had invested tremendous amounts of effort over the course of eight years. And one of the things that I've learned is that over time innovation comes just as much from deciding what you're no longer going to do as what you're going to do. And focusing heavily around, how do you get out of that muck? How do you continue to climb up that technology stack? Is incredibly important. And so really we are excited to be a part of it and taking the industry is continuing to climb higher and higher level. We're building more and more advanced levels of automation and what we call our data awareness into the automated engine of the Ascend platform that takes us across the entire data ecosystem, connecting and automating all data movement. And so we have a very exciting vision for this fabric that's emerging over time. >> Awesome, Sean, thank you so much for that insight, Jason, thanks for coming on customer of Ascend.io. >> Thank you. >> I appreciate it, gentlemen, thank you. This is the track on automating analytic workloads. We here at the end of us showcase, startup showcase, the hottest companies here at Ascend.io, I'm John Furrier, with theCUBE, thanks for watching. (upbeat music)