>>Welcome back to Falcon 2022. This is Dave LAN. We get a special presentation segment for you today. This is Walter Wall day one of day two's cube coverage, JC Herrera. Here's my designated cohost. Who's the chief human resource officer at CrowdStrike. Craig Neri is to my left. He's the beneficiary and the beneficiary trustee and ambassador of, of operation Motorsport and former us air force. Thank you for your service. Thank you. And Deel Lauder, who is CEO and co-founder of operation Motorsport. Jen, welcome to the cube. Thanks so much for coming on. Great to be JC set this up for us. Explain your role, explain the corporate giving the whole student connection and the veterans take us through that. >>Yeah, sure. Yeah. So as, as head of HR, one of the, one of the things that we do is, is help manage part of the corporate giving strategy. And, and one of those things that, that we love to do is to also invest in students and in our veterans, it's just a part of our giving program. So this partnership with operation Motorsport is really critical to that. And if you want to dive a little bit deeper into that, we just see that there's a gigantic skills gap in cyber security. And so when we, when there's over millions of open roles around the world and 700,000 of 'em in the us alone, we've gotta go close that gap. And so our next gen scholarships that come out of the, that are giving funds are, are awarded to students who are studying cyber security or AI. And the other side of that is that this partnership with operation motor sport, then we get the opportunity to do some internships with veterans through operation motor sport as well, the >>Number 700,000 now, but pre pandemic. I remember number 3 50, 300 50,000. It's it's doubled now just in the us. Amazing. All right, diesel, tell us about the mission of operation motor sport, like who are the beneficiaries let's get into it. >>So operation motor sport engages ill, injured, wounded service members, those that are medically retiring from the service or disabled veterans, these individuals be taken out of their units. They lose their team identity, their purpose. And, and what we do is those that apply to the program and have a desire to work around shiny objects and fast cars and all the great smells or just car guys or gals that we have some of those as well. They, we, we bring them onto the teams as beneficiaries. So embed them into a race team and give them opportunity to find something new. We're a recovery program. We're not about, you know, finding jobs for these folks. It's about networking and getting outta that, you know, outta the dark places where some of them end up going, because this is a, a huge change for them. And, and in doing so, we now expose them to crowd strike. You know, that's, that's one of the new relationships that, that we have where potentially if they want to, they can pursue new opportunities in areas like cyber security. >>And they're chosen through an application process. You're I'm, I'm inferring. >>Yeah. They just go online and say, you know, through word of mouth or through a friend or through the, the USO and other organizations, they go online and they click the apply here and they fill it out. And our beneficiary trustee, Craig, and calls 'em up and says, Hey, tell me about what you're looking for. And, and we, we pair them up with the race team and Craig, >>You're also a, a beneficiary in addition to being the beneficiary trustee. So explain that, what's your story? >>Right. So I started in this organization as a beneficiary. I was the one that hit the button on the website. And, and then a few minutes later, I got a phone call from then Tiffany Lader, diesel's wife, who's our executive director in the organization. And, and I had that same conversation that I now have with beneficiaries today. I did a, I did a full season with them last year in 2021 as a beneficiary. But at the end I realized how big of an impact that this has with folks. Transition can be very difficult, especially if they're ill injured or wounded. And so I asked if I could help if I could give back, cuz it meant such it had such a big impact on me. I'd like to, to help other veterans as well. Can I >>Ask you what made you hit that button? What made you apply? >>That's a great question. So I was one of the very fortunate ones that had a transition coach. I was in the military for 29 years and had a lot of great connections in the military and, and was connected to a coach, a transition coach and just exploring, you know, what that, what that would look like. And she was the one who said, Hey, why don't we, why don't we explore this passion of Motorsports that you have? My family had been going to, to Motorsports events for, you know, 50 years. And so, so I thought back, all right, this is, I like this idea. Let's, let's pursue this. So a quick Google search and operation Motorsport popped up and I hit the button and >>What programs are available in operation >>Motorsport? Yeah. So diesel kind of outline outlined it. We have basically three different programs. We have the, our immersion program, which is exactly what diesel described, where we take that veteran. And we actually immerse them in a race team. They're doing the, exactly what I was doing, doing tires and fuel and whatever the team needs them to do. We also have our emo sports program where folks who can't do the immersion program, immersion program is takes a pretty big time commitment sometimes. And so they just don't have the capacity or abilities to be able to do those. We could put 'em in our emo sports program where they can do it all virtually we're actually, we have a season going on right now where we, we have veterans racing in that emo sports program. And then we have a, a diversionary therapy program where we have a, a Patriot car corral set up at all these tracks. So they can go out with like-minded individuals and spend the day out there with those folks, other veterans. And we do pit pit tours and, and we get 'em out on the track for a little bit of a, you know, highway speeds, nothing ridiculous. But we, we did doing some highway speeds. So we have a, a few, few different ways for them to be >>Involved. So, so the number three is like a splash in the pond, whereas number ones, the, to like full immersion. Right? Correct. And so what are you doing in the full immersion? What is, what is that like? I mean, you're literally changing tires and, and, and you're >>Yeah. You name it. You're >>In the you're you're you're in that sort of sphere of battle, if you will. Right. >>The beauty of this is we could take somebody's capabilities and skill set and we can match it to whatever that looks like on a race team. Some people come in and have no experience whatsoever. And so we find a team that needs, you know, that has a development opportunities where they could come in, their, their initial job might be to fuel fuel cans or, you know, take tires off the car, wipe the car down, it's little things in the beginning. And then slowly as they start to grow and learn, then they take on bigger roles. But we also have different positions. They can be immersed in, in teams, but they can also be immersed in the series. So we have folks that are doing like tech inspections. We have folks that are doing race control up in the, up in the tower, directing race operations. So we have lots of opportunities, tons of potential. We, we foster those relationships and take the folks, whatever their capabilities and, and abilities are and find the right position for >>'em think, thinking about your personal experience, how, how did it, how would you say it affected you? >>Yeah. To understand that you really have to understand military transition. And I think that's where a lot of the folks that have never experienced this really struggle transition from the military is really difficult. And it's really difficult, even if you're, if you're not broken or you don't have some kind of illness or injury, but you add that factor into at the same time and it could be extremely difficult. And that's why we see like the 22, a day suicide rates with veterans, it's very, very high. Right? And so when you, when you come into this program, it, it is a little bit of a leap of faith, right? This is very new experience for somebody, right? For somebody like myself who had 29 years of experience in the military, very senior person in the military. And now you're at the bottom of the totem pole and trying to figure it all out again, it's, it's a, it's a big jump. But what you realize really quickly is a lot of the things that you experience in the military, you experience in that Pata, same exact things, lots of small team environment, lots of diversity, lots of challenges, lots of roadblocks ups downs, you, you deploy just like you would deploy in, in the military, you bring the cars to a track, you execute a mission, then you pack it up and bring it home. So it's, there's so many similarities in >>The process. I mean, yeah. Diesel hearing Craig explained that there are the similarities sound very clear, but, but, but how did how'd you come up with this idea? It makes sense now in retrospect, but somebody just said, Hey, you know, we have this and we have this and we can marry him or no, not >>Really. And it it's a funny story because I always said, I, I, I don't believe in reinventing the wheel, I believe in stealing the car. And so there's a sister organization that we have in the UK called mission Motorsport. And, and, and they invented this five years before we did. And, and they were successful. And I was, you know, through, through friendships and opportunities, I got to witness it in, in 2016. So went over to, to Wales in the UK and, and watched it in action. And we were there for one race weekend, race of remembrance, which is where we go back to, we'll be going back to November, taking 13 beneficiaries over to race in our own race team for a 12 hour race. And that's a whole other story, but that's where it all started. You know, we, we saw the opportunities and said, wow, they're changing lives through recovery, you know, through motor sport and the similarities and what they were achieving. >>Our initial goal was let's just come back and do this again next year, because we need to bring north American transitioning members over to, to witness this and take part. And then fast forward, we said, why stop there? And we stood up an organization. Now I'll tell you that the organization is not what it was, the, the initial vision. This is not where, I mean, I never imagine that we get to this point this day, especially with the announcement this morning, you know, with the partnership with CrowdStrike, it it's huge for us, but we've evolved into something that was very similar to the initial vision. And that was helping, helping medically transitioning service members with their own personal struggles and recovery. You know, the reason we call it operation Motorsport is because operations have no beginning and no end and our, and what we do makes us so different in that we're not a one and done, we take care of these guys. Even when they become alumni, they, they still come back. They, they come back to volunteer, they come back to check in their friends and, and all kinds. It's really, really neat. And, >>And JC of course, CrowdStrike has an affinity for Motorsports, right? You got the logo on the Mercedes. You you've got the safety car at, this is, I think it's called the safety car. Right. That's it? Yeah. So, okay. So that's an obvious connection, but, but where did the idea germinate for this partnership? >>There's so many things, but first and foremost, I think that the, the values of CrowdStrike and those of operation motors were very much aligned. If you think about it, we, we focus a lot on teamwork. There's no way we do these jobs without the teamwork part. We all love data. These guys are all in the data all the time, trying to figure out, you know, what your adversaries are doing. So there's that kind of component to it. And I'd say the last bit is critical thinking. So when we think about our organizations and how well aligned they are, that was a, that was a no brainer. And into the other side of it, we get the opportunity to do mentorship programs. I mean, I think both ways, hopefully I get invited to the Patriot corral. At some point I can go, go work on a car, but we'll do those both ways or mentorship opportunities. If folks from operation motor sport win a team up with a crowd striker. So >>Do you ever get to drive the car? Or is that just an awful question? No, that's >>A good question. Actually I do from the, from the track to the pits, very slow >>Speeds. They don't let you out in the train. That's right. No, I don't get to go out on the track. Diesel, you ever, you ever drive one >>Of these? I, I, I I've been on, on the track on, on different cars, not in the race cars that, that, that, that are on the team, but something that's unique in the Patriot corral, for instance, because JC brought that up is that when we do these Patriot corrals, part of that program at lunchtime is, is taking the individuals and doing parade laps. And now, you know, a parade lap. Well, what's the fun in that, but you drive highway speeds on a racetrack and your own personal car, following a pace car. That's a pretty cool experience. Cool. >>Yeah, that's very cool guys. Congratulations on this program and all your success and all the, the giving that you do for the community and, and your peers really appreciate you guys coming on the cube and telling me great story. Thanks >>For having, thanks for the opportunity. You're very >>Welcome. All right. Keep it right there. Everybody. Dave ante and Dave Nicholson, we'll be back from Falcon 2022 at the area in Las Vegas. You watching the cube.

>> Welcome back to FalCon 2022. This is Dave Vellante. We get a special presentation segment for you today. This is Walter Wall day one of day two's cube coverage. JC Herrera is here, he's my designated cohost. He's the chief human resource officer at CrowdStrike. Craig Neri is to my left. He's the beneficiary and the beneficiary trustee and ambassador of, of operation Motorsport and former US air force. Thank you for your service. >> Thank you. >> And Diezel Lodder, who is CEO and co-founder of operation Motorsport. Gents, welcome to the cube. Thanks so much for coming on. >> Thank you, Great to be here >> JC, set this up for us. Explain your role, explain the corporate giving, the whole student connection, and the veterans, take us through that. >> Yeah, sure. Yeah, so as, as head of HR, one of the one of the things that we do is, is help manage part of the corporate giving strategy. And, and one of those things that, that we love to do is to also invest in students and in our veterans, it's just a part of our giving program. So this partnership with operation Motorsport is really critical to that. And if you want to dive a little bit deeper into that we just see that there's a gigantic skills gap in cybersecurity. And so when we, when there's over millions of open roles around the world and 700,000 of them in the us alone, we've got to go close that gap. And so our next gen scholarships that come out of the, are giving funds are, are awarded to students who are studying cyber security or AI. And the other side of that, is that this partnership with operation Motorsport then, we get the opportunity to do some internships with veterans through operation Motorsport as well. >> The number is 700,000 now, but pre pandemic I remember number 350, 350,000. It's, it's doubled now just in the US, amazing. All right, diezel, tell us about the mission of operation Motorsport like who are the beneficiaries let's get into it. >> So operation Motorsport engages ill, injured wounded service members, those that are medically retiring from the service or disabled veterans these individuals will be taken out of their units. They lose their team identity, their purpose. And, and what we do is those that apply to the program and have a desire to work around shiny objects and fast cars and all the great smells or just car guys or gals that we have some of those as well. They, we, we bring them onto the teams as beneficiaries. So embed them into a race team and give them opportunity to find something new. We're a recovery program. We're not about, you know, finding jobs for these folks. It's about networking and getting out of that, you know out of the dark places where some of them end up going because this is a, a huge change for them. And, and in doing so, we now expose them to CrowdStrike. You know, that's, that's one of the new relationships that, that we have where potentially if they want to they can pursue new opportunities in areas like cybersecurity. >> And they're chosen through an application process you're, I, I'm inferring. >> Yep. They just go online and say, you know through word of mouth or through a friend or through the, the USO and other organizations, they go online and they click the apply here and they fill it out. And, our beneficiary trustee Craig, and calls them up and says, Hey, tell me about what you're looking for. And, and we, we pair them up with the race team. >> And Craig you're also a, a beneficiary in addition to being the beneficiary trustee. So explain that, what's your story? >> Right. So I started in this organization as a beneficiary. I was the one that hit the button on the website. And, and then a few minutes later, I got a phone call from then Tiffany Lodder, Diezel's wife, who's our executive director in the organization. And, and I had that same conversation that I now have with beneficiaries today. I did a, I did a full season with them last year in 2021 as a beneficiary. But at the end I realized how big of an impact that this has with folks. Transition can be very difficult, especially if they're ill injured or wounded. And so I asked if I could help if I could give back cause it meant such, it had such a big impact on me. I'd like to, to help other veterans as well. >> Can I ask you what made you hit that button? What made you apply? >> Oh, that's a great question. So I was one of the very fortunate ones that had a transition coach. I was in the military for 29 years and had a lot of great connections in the military and, and was connected to a coach, a transition coach and just exploring, you know what that, what that would look like and she was the one who say, why don't we, why don't we explore this passion of Motorsports that you have? My family had been going to, to Motorsports events for you know, 50 years. And so, so I thought back, all right, this is I like this idea. Let's, let's pursue this. So a quick Google search and operation Motorsport popped up and I hit the button. >> And what programs are available in operation Motorsport? >> And so, Diezel kind of outline, outlined it. We have basically three different programs. We have the, our immersion program, which is exactly what Diezel described, where we take that veteran and we actually immerse them in a race team they're doing the, exactly what I was doing, doing tires and fuel and whatever the team needs them to do. We also have our E-motor sports program where folks who can't do the immersion program, immersion program is takes a pretty big time commitment sometimes. And so, they just don't have the capacity or abilities to be able to do those. We could put them in our E-motor sports program where they can do it all virtually. we're actually, we have a season going on right now where we're, we have veterans racing in that E-motor sports program. And then we have a, the diversionary therapy program where we have a, a Patriot car corral set up at all these tracks so, they can go out with like-minded individuals and spend the day out there with those folks, other veterans. And we do pit, pit tours and, and we get 'em out on the track for a little bit of a, you know, highway speeds nothing ridiculous, but we, we been doing some highway speeds. So we have a, a few, few different ways for them to be involved. >> So, so the number three is like a splash in the pond whereas number one's the, like full immersion. >> Yeah, correct, yes. >> And so what are you doing in the full immersion? What is, what is that like? I mean you're literally changing tires and, and you're, >> Yeah. You name it. >> In the, you're, you're in that sort of sphere of battle, if you will. >> The beauty of this is we could take somebody's capabilities and skill set and we can match it to whatever that looks like on a race team. Some people come in and have no experience whatsoever. And so we find a team that needs, you know, that has a development opportunities where they could come in, their, their initial job might be to fuel fuel cans or, you know, take tires off the car or wipe the car down, it's little things in the beginning. And then slowly as they start to grow and learn then they take on bigger roles. But we also have different positions. They can be immersed in, in teams, but they can also be immersed in the series. So we have folks that are doing like tech inspections. We have folks that are doing race control up in the, up in the tower, directing race operations. So, we have lots of opportunities, tons of potential. We, we foster those relationships and take the folks and whatever their capabilities and, and abilities are and find the right position for them. >> Think, thinking about your personal experience, how, how did it, how would you say it affected you? >> Yeah, um, to understand that you really have to understand military transition. And I think that's where a lot of the folks that have never experienced this really struggle. transition from the military is really difficult. And it's really difficult, even if you're, if you're not broke and, or you don't have some kind of illness or injury but, you add that factor into it at the same time and it could be extremely difficult. And that's why we see like the 22 a day suicide rates with veterans, it's very, very high, Right? And so when you, when you come into this program, it's, it is a little bit of a leap of faith, right? This is very new experience for somebody, right? For somebody like myself who had 29 years of experience in the military, very senior person in the military. And now you're at the bottom of the totem pole and trying to figure it all out again, it's, it's a it's a big jump. But, what you realize really quickly is a lot of the things that you experience in the military you experience in that paddock, same exact things, lots of, small team environment, lots of diversity, lots of challenges, lots of roadblocks ups downs, you, you'd deploy just like you would deploy in, in the military you bring the cars to a track, you execute a mission then you pack it up and bring it home. So it's, there's so many similarities in the process. >> I mean, yeah. Diezel hear, hearing Craig explained that there are, the similarities sound very clear, but, but, but how did how'd you come up with this idea? (Diezel laughs) It makes sense now in retrospect, but, somebody just said Hey, you know, we have this and we have this and we can marry them or... >> No, not really. And it, it's a funny story because I always said, I, I, I don't believe in reinventing the wheel I believe in stealing the car. And so there's a sister organization that we have in the UK called mission Motorsport. And, and, and they invented this five years before we did. And, and they were successful. And I was, you know, through, through friendships and opportunities, I got to witness it in, in 2016. So went over to, to Wales in, in the UK and, and watched it in action. And we were there for one race weekend, race of remembrance which is where we go back to we'll be going back to November, taking 13 beneficiaries over to race in our own race team for a 12 hour race. And that's a whole other story but that's where it all started. You know, we, we saw the opportunities and said, wow they're changing lives through recovery, you know through Motorsport and the similarities and what they were achieving, our initial goal was let's just come back and do this again next year, because we need to bring north American transitioning members over to, to witness this and take part. And then fast forward, we said, why stop there? And we, stood up an organization. Now, I'll tell you that the organization is not what it was the initial vision, this not where, I mean I never imagine that we get to this point this day especially with the announcement this morning, you know with the partnership with CrowdStrike, it it's huge for us but, we've evolved into something that was very similar to the initial vision. And that was, helping, helping medically transitioning service members with their own personal struggles and recovery. You know, the reason we call it operation Motorsport is because operations have no beginning and no end and our, and what we do makes us so different in that we're not a one and done, we take care of these guys. Even when they become alumni, they, they still come back. They, they come back to volunteer they come back to check in their friends and, and all kinds, it's really, really neat. >> And, and JC of course CrowdStrike has an affinity for Motorsports, right? You got the logo on the Mercedes. You, you've got the safety car at this. I think it's called the safety car, right? >> That's it, yeah. >> So, okay. So that's an obvious connection, but, but where did the idea germinate for this partnership? >> There's so many things, but first and foremost, I think that the, the values of CrowdStrike and those of operation motors were very much aligned. If you think about it, we, we focus a lot on teamwork. There's no way we do these jobs without the teamwork part. We all love data. These guys are all in the data all the time trying to figure out, you know, what your adversaries are doing. So there's that kind of component to it. And I'd say the last bit is critical thinking. So when we think about our organizations and how well aligned they are, that was a, that was a no brainer. And into the other side of it, we get the opportunity to do mentorship programs. I mean, I think both ways, hopefully I get invited to the Patriot corral at some point I can go, go work on a car but, we'll do those both ways or mentorship opportunities. If folks from operation Motorsport win a team up with a CrowdStrikers. >> Do you ever get to drive the car? Or is that just an awful question? >> No, it's a good question. Actually I do from the from the track to the pits at, you know, very slow speeds. >> They don't let you out on the track? >> That's right, no, I don't get to go out the track. >> Diezel You ever, you ever drive one of these? >> I, I, I, I've been on, on the track on, on different cars not in the race cars that, that, that that are on the team, but something that's unique in the Patriot corral, for instance, because JC brought that up, is that when we do these Patriot corrals part of that program at lunchtime is, is taking the individuals and doing parade laps. And I'll, you know, a parade lap, well, what's the fun in that? but you drive highway speeds on a racetrack and your own personal car following a pace car, that's a pretty cool experience. >> Yeah, that's very cool. Guys, congratulations on this program and all your success and all the, the giving that you do for the community and, and your peers, really appreciate you guys coming on The Cube and telling your story. >> Thanks for having us. >> Thanks for the opportunity. >> You're very welcome. All right, keep it right there everybody. Dave Vellante and Dave Nicholson, we'll be back from FalCon 2022, at the ARIA in Las Vegas. You're watching the cube. (relaxing 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 "theCUBE"'s presentation of democratizing analytics across the enterprise, made possible by Alteryx. An Alteryx commissioned IDC info brief 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 info brief also revealed that nearly all organizations are planning to increase their analytics spend. And it looks like from the info brief 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 info brief uncovered with respect to 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. >> So they- >> They instead rely on outdated spreadsheet technology. In our survey, nine 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 we can do so. We'll just go, yep, we'll go back to Lisa's question. Let's just, let's do the, retake the question and the answer, that'll be able to. >> It'll be not all analytics spending results in the same ROI, what are some of the discrepancies? >> Yes, Lisa, so we'll go from your ISO, just so we 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 info brief 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 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 achieve 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. >> Sure. >> Can I redo this one? >> Yeah. >> Of course, stand by. >> Tongue tied. >> Yep, no worries. >> One second. >> If we could do the same, Lisa, just have a clean break, we'll go 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 it's 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 isn't 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 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 in 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 info brief 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 "theCUBE", the leader in tech enterprise coverage. >> Somehow many have come to believe that data analytics is for the few, for the scientists, the PhDs, the MBAs. Well, it is for them, but that's not all. You don't have to have an advanced degree to do amazing things with data. You don't even have to be a numbers person. You can be just about anything. A titan of industry or a future titan of industry. You could be working to change the world, your neighborhood, or the course of your business. You can be saving lives or just looking to save a little time. The power of data analytics shouldn't be limited to certain job titles or industries or organizations because when more people are doing more things with data, more incredible things happen. Analytics makes us smarter and faster and better at what we do. It's practically a superpower. That's why we believe analytics is for everyone, and everything, and should be everywhere. That's why we believe in analytics for all. (upbeat music) >> Hey, everyone. Welcome back to "Accelerating Analytics Maturity". I'm your host, Lisa Martin. Alan Jacobson joins me next. The chief of data and analytics officer at Alteryx. Alan, it's great to have you on the program. >> Thanks, Lisa. >> So Alan, as we know, everyone knows that being data driven is very important. It's a household term these days, but 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. What's your advice, your recommendations for organizations who are just starting out with analytics? >> You're spot on, many organizations really aren't leveraging the full capability of their knowledge workers. And really the first step is probably assessing where you are on the journey, whether that's you personally, or your organization as a whole. We just launched an assessment tool on our website that we built with the International Institute of Analytics, that in a very short period of time, in about 15 minutes, you can go on and answer some questions and understand where you sit versus your peer set versus competitors and kind of where you are on the journey. >> So when people talk about data analytics, they often think, ah, this is for data science experts like people like you. So why should people in the lines of business like the finance folks, the marketing folks, why should they learn analytics? >> So domain experts are really in the best position. They know where the gold is buried in their companies. They know where the inefficiencies are. And it is so much easier and faster to teach a domain expert a bit about how to automate a process or how to use analytics than it is to take a data scientist and try to teach them to have the knowledge of a 20 year accounting professional or a logistics expert of your company. Much harder to do that. And really, if you think about it, the world has changed dramatically in a very short period of time. If you were a marketing professional 30 years ago, you likely didn't need to know anything about the internet, but today, do you know what you would call that marketing professional if they didn't know anything about the internet, probably unemployed or retired. And so knowledge workers are having to learn more and more skills to really keep up with their professions. And analytics is really no exception. Pretty much in every profession, people are needing to learn analytics to stay current and be capable for their companies. And companies need people who can do that. >> Absolutely, it seems like it's table stakes these days. Let's look at different industries now. Are there differences in how you see analytics in automation being employed in different industries? I know Alteryx is being used across a lot of different types of organizations from government to retail. I also see you're now with some of the leading sports teams. Any differences in industries? >> Yeah, there's an incredible actually commonality between the domains industry to industry. So if you look at what an HR professional is doing, maybe attrition analysis, it's probably quite similar, whether they're in oil and gas or in a high tech software company. And so really the similarities are much larger than you might think. And even on the sports front, we see many of the analytics that sports teams perform are very similar. So McLaren is one of the great partners that we work with and they use Alteryx across many areas of their business from finance to production, extreme sports, logistics, wind tunnel engineering, the marketing team analyzes social media data, all using Alteryx, and if I take as an example, the finance team, the finance team is trying to optimize the budget to make sure that they can hit the very stringent targets that F1 Sports has, and I don't see a ton of difference between the optimization that they're doing to hit their budget numbers and what I see Fortune 500 finance departments doing to optimize their budget, and so really the commonality is very high, even across industries. >> I bet every Fortune 500 or even every company would love to be compared to the same department within McLaren F1. Just to know that wow, what they're doing is so incredibly important as is what we're doing. >> So talk- >> Absolutely. >> About lessons learned, what lessons can business leaders take from those organizations like McLaren, who are the most analytically mature? >> Probably first and foremost, is that the ROI with analytics and automation is incredibly high. Companies are having a ton of success. It's becoming an existential threat to some degree, if your company isn't going on this journey and your competition is, it can be a huge problem. IDC just did a recent study about how companies are unlocking the ROI using analytics. And the data was really clear, organizations that have a higher percentage of their workforce using analytics are enjoying a much higher return from their analytic investment, and so it's not about hiring two double PhD statisticians from Oxford. It really is how widely you can bring your workforce on this journey, can they all get 10% more capable? And that's having incredible results at businesses all over the world. An another key finding that they had is that the majority of them said that when they had many folks using analytics, they were going on the journey faster than companies that didn't. And so picking technologies that'll help everyone do this and do this fast and do it easily. Having an approachable piece of software that everyone can use is really a key. >> So faster, able to move faster, higher ROI. I also imagine analytics across the organization is a big competitive advantage for organizations in any industry. >> Absolutely the IDC, or not the IDC, the International Institute of Analytics showed huge correlation between companies that were more analytically mature versus ones that were not. They showed correlation to growth of the company, they showed correlation to revenue and they showed correlation to shareholder values. So across really all of the key measures of business, the more analytically mature companies simply outperformed their competition. >> And that's key these days, is to be able to outperform your competition. You know, one of the things that we hear so often, Alan, is people talking about democratizing data and analytics. You talked about the line of business workers, but I got to ask you, is it really that easy for the line of business workers who aren't trained in data science to be able to jump in, look at data, uncover and extract business insights to make decisions? >> So in many ways, it really is that easy. I have a 14 and 16 year old kid. Both of them have learned Alteryx, they're Alteryx certified and it was quite easy. It took 'em about 20 hours and they were off to the races, but there can be some hard parts. The hard parts have more to do with change management. I mean, if you're an accountant that's been doing the best accounting work in your company for the last 20 years, and all you happen to know is a spreadsheet for those 20 years, are you ready to learn some new skills? And I would suggest you probably need to, if you want to keep up with your profession. The big four accounting firms have trained over a hundred thousand people in Alteryx. Just one firm has trained over a hundred thousand. You can't be an accountant or an auditor at some of these places without knowing Alteryx. And so the hard part, really in the end, isn't the technology and learning analytics and data science, the harder part is this change management, change is hard. I should probably eat better and exercise more, but it's hard to always do that. And so companies are finding that that's the hard part. They need to help people go on the journey, help people with the change management to help them become the digitally enabled accountant of the future, the logistics professional that is E enabled, that's the challenge. >> That's a huge challenge. Cultural shift is a challenge, as you said, change management. How do you advise customers if you might be talking with someone who might be early in their analytics journey, but really need to get up to speed and mature to be competitive, how do you guide them or give them recommendations on being able to facilitate that change management? >> Yeah, that's a great question. So people entering into the workforce today, many of them are starting to have these skills. Alteryx is used in over 800 universities around the globe to teach finance and to teach marketing and to teach logistics. And so some of this is happening naturally as new workers are entering the workforce, but for all of those who are already in the workforce, have already started their careers, learning in place becomes really important. And so we work with companies to put on programmatic approaches to help their workers do this. And so it's, again, not simply putting a box of tools in the corner and saying free, take one. We put on hackathons and analytic days, and it can be great fun. We have a great time with many of the customers that we work with, helping them do this, helping them go on the journey, and the ROI, as I said, is fantastic. And not only does it sometimes affect the bottom line, it can really make societal changes. We've seen companies have breakthroughs that have really made great impact to society as a whole. >> Isn't that so fantastic, to see the difference that that can make. It sounds like you guys are doing a great job of democratizing access to Alteryx to everybody. We talked about the line of business folks and the incredible importance of enabling them and the ROI, the speed, the competitive advantage. Can you share some specific examples that you think of Alteryx customers that really show data breakthroughs by the lines of business using the technology? >> Yeah, absolutely, so many to choose from. I'll give you two examples quickly. One is Armor Express. They manufacture life saving equipment, defensive equipments, like armor plated vests, and they were needing to optimize their supply chain, like many companies through the pandemic. We see how important the supply chain is. And so adjusting supply to match demand is really vital. And so they've used Alteryx to model some of their supply and demand signals and built a predictive model to optimize the supply chain. And it certainly helped out from a dollar standpoint. They cut over a half a million dollars of inventory in the first year, but more importantly, by matching that demand and supply signal, you're able to better meet customer demand. And so when people have orders and are looking to pick up a vest, they don't want to wait. And it becomes really important to get that right. Another great example is British Telecom. They're a company that services the public sector. They have very strict reporting regulations that they have to meet and they had, and this is crazy to think about, over 140 legacy spreadsheet models that they had to run to comply with these regulatory processes and report, and obviously running 140 legacy models that had to be done in a certain order and length, incredibly challenging. It took them over four weeks each time that they had to go through that process. And so to save time and have more efficiency in doing that, they trained 50 employees over just a two week period to start using Alteryx and learn Alteryx. And they implemented an all new reporting process that saw a 75% reduction in the number of man hours it took to run in a 60% run time performance. And so, again, a huge improvement. I can imagine it probably had better quality as well, because now that it was automated, you don't have people copying and pasting data into a spreadsheet. And that was just one project that this group of folks were able to accomplish that had huge ROI, but now those people are moving on and automating other processes and performing analytics in other areas. So you can imagine the impact by the end of the year that they will have on their business, potentially millions upon millions of dollars. And this is what we see again and again, company after company, government agency after government agency, is how analytics are really transforming the way work is being done. >> That was the word that came to mind when you were describing the all three customer examples, transformation, this is transformative. The ability to leverage Alteryx, to truly democratize data and analytics, give access to the lines of business is transformative for every organization. And also the business outcome you mentioned, those are substantial metrics based business outcomes. So the ROI in leveraging a technology like Alteryx seems to be right there, sitting in front of you. >> That's right, and to be honest, it's not only important for these businesses. It's important for the knowledge workers themselves. I mean, we hear it from people that they discover Alteryx, they automate a process, they finally get to get home for dinner with their families, which is fantastic, but it leads to new career paths. And so knowledge workers that have these added skills have so much larger opportunity. And I think it's great when the needs of businesses to become more analytic and automate processes actually matches the needs of the employees, and they too want to learn these skills and become more advanced in their capabilities. >> Huge value there for the business, for the employees themselves to expand their skillset, to really open up so many opportunities for not only the business to meet the demands of the demanding customer, but the employees to be able to really have that breadth and depth in their field of service. Great opportunities there, Alan. Is there anywhere that you want to point the audience to go to learn more about how they can get started? >> Yeah, so one of the things that we're really excited about is how fast and easy it is to learn these tools. So any of the listeners who want to experience Alteryx, they can go to the website, there's a free download on the website. You can take our analytic maturity assessment, as we talked about at the beginning, and see where you are on the journey and just reach out. We'd love to work with you and your organization to see how we can help you accelerate your journey on analytics and automation. >> Alan, it was a pleasure talking to you about democratizing data and analytics, the power in it for organizations across every industry. We appreciate your insights and your time. >> Thank you so much. >> In a moment, Paula Hansen, who is the president and chief revenue officer of Alteryx, and Jacqui Van der Leij Greyling, who's the global head of tax technology at eBay, will join me. You're watching "theCUBE", the leader in high tech enterprise coverage. >> 1200 hours of wind tunnel testing, 30 million race simulations, 2.4 second pit stops. >> Make that 2.3. >> Sector times out the wazoo. >> Way too much of this. >> Velocities, pressures, temperatures, 80,000 components generating 11.8 billion data points and one analytics platform to make sense of it all. When McLaren needs to turn complex data into winning insights, they turn to Alteryx. Alteryx, analytics automation. (upbeat music) >> Hey, everyone, welcome back to the program. Lisa Martin here, I've got two guests joining me. Please welcome back to "theCUBE" Paula Hansen, the chief revenue officer and president at Alteryx, and Jacqui Van der Leij Greyling joins us as well, the global head of tax technology at eBay. They're going to share with you how Alteryx is helping eBay innovate with analytics. Ladies, welcome, it's great to have you both on the program. >> Thank you, Lisa, it's great to be here. >> Yeah, Paula, we're going to start with you. In this program, we've heard from Jason Klein, we've heard from Alan Jacobson. They talked about the need to democratize analytics across any organization to really drive innovation. With analytics, as they talked about, at the forefront of software investments, how's Alteryx helping its customers to develop roadmaps for success with analytics? >> Well, thank you, Lisa. It absolutely is about our customers' success. And we partner really closely with our customers to develop a holistic approach to their analytics success. And it starts of course with our innovative technology and platform, but ultimately we help our customers to create a culture of data literacy and analytics from the top of the organization, starting with the C-suite. And we partner with our customers to build their roadmaps for scaling that culture of analytics, through things like enablement programs, skills assessments, hackathons, setting up centers of excellence to help their organization scale and drive governance of this analytics capability across the enterprise. So at the end of the day, it's really about helping our customers to move up their analytics maturity curve with proven technologies and best practices, so they can make better business decisions and compete in their respective industries. >> Excellent, sounds like a very strategic program, we're going to unpack that. Jacqui, let's bring you into the conversation. Speaking of analytics maturity, one of the things that we talked about in this event is the IDC report that showed that 93% of organizations are not utilizing the analytics skills of their employees, but then there's eBay. How Jacqui did eBay become one of the 7% of organizations who's really maturing and how are you using analytics across the organization at eBay? >> So I think the main thing for us is when we started out was is that, our, especially in finance, they became spreadsheet professionals instead of the things that we really want our employees to add value to. And we realized we had to address that. And we also knew we couldn't wait for all our data to be centralized until we actually start using the data or start automating and being more effective. So ultimately we really started very, very actively embedding analytics in our people and our data and our processes. >> Starting with people is really critical. Jacqui, continuing with you, what were some of the roadblocks to analytics adoption that you faced and how did you overcome them? >> So I think eBay is a very data driven company. We have a lot of data. I think we are 27 years around this year, so we have the data, but it is everywhere. And how do you use that data? How do you use it efficiently? How do you get to the data? And I believe that that is definitely one of our biggest roadblocks when we started out and just finding those data sources and finding ways to connect to them to move forward. The other thing is that people were experiencing a lot of frustration. I mentioned before about the spreadsheet professionals. And there was no, we were not independent. You couldn't move forward, you would've put it on somebody else's roadmap to get the data and to get the information if you want it. So really finding something that everybody could access analytics or access data. And finally we have to realize is that this is uncharted territory. This is not exactly something that everybody is used to working with every day. So how do you find something that is easy, and that is not so daunting on somebody who's brand new to the field? And I would call those out as your major roadblocks, because you always have, not always, but most of the times you have support from the top, and in our case we have, but at the end of the day, it's our people that need to actually really embrace it, and making that accessible for them, I would say is definitely not per se, a roadblock, but basically a block you want to be able to move. >> It's really all about putting people first. Question for both of you, and Paula we'll start with you, and then Jacqui we'll go to you. I think the message in this program that the audience is watching with us is very clear. Analytics is for everyone, should be for everyone. Let's talk now about how both of your organizations are empowering people, those in the organization that may not have technical expertise to be able to leverage data, so that they can actually be data driven. Paula. >> Yes, well, we leverage our platform across all of our business functions here at Alteryx. And just like Jacqui explained, at eBay finance is probably one of the best examples of how we leverage our own platform to improve our business performance. So just like Jacqui mentioned, we have this huge amount of data flowing through our enterprise and the opportunity to leverage that into insights and analytics is really endless. So our CFO Kevin Rubin has been a key sponsor for using our own technology. We use Alteryx for forecasting all of our key performance metrics, for business planning, across our audit function, to help with compliance and regulatory requirements, tax, and even to close our books at the end of each quarter. So it's really going to remain across our business. And at the end of the day, it comes to how do you train users? How do you engage users to lean into this analytic opportunity to discover use cases? And so one of the other things that we've seen many companies do is to gamify that process, to build a game that brings users into the experience for training and to work with each other, to problem solve and along the way, maybe earn badges depending on the capabilities and trainings that they take. And just have a little healthy competition as an employee base around who can become more sophisticated in their analytic capability. So I think there's a lot of different ways to do it. And as Jacqui mentioned, it's really about ensuring that people feel comfortable, that they feel supported, that they have access to the training that they need, and ultimately that they are given both the skills and the confidence to be able to be a part of this great opportunity of analytics. >> That confidence is key. Jacqui, talk about some of the ways that you're empowering folks without that technical expertise to really be data driven. >> Yeah, I think it means to what Paula has said in terms of getting people excited about it, but it's also understanding that this is a journey and everybody is at a different place in their journey. You have folks that's already really advanced who has done this every day. And then you have really some folks that this is brand new or maybe somewhere in between. And it's about how you get everybody in their different phases to get to the initial destination. I say initial, because I believe a journey is never really complete. What we have done is that we decided to invest, and built a proof of concept, and we got our CFO to sponsor a hackathon. We opened it up to everybody in finance in the middle of the pandemic. So everybody was on Zoom and we told people, listen, we're going to teach you this tool, it's super easy, and let's just see what you can do. We ended up having 70 entries. We had only three weeks. So and these are people that do not have a background. They are not engineers, they're not data scientists. And we ended up with a 25,000 hour savings at the end of that hackathon from the 70 entries with people that have never, ever done anything like this before. And there you have the result. And then it just went from there. People had a proof of concept. They knew that it worked and they overcame the initial barrier of change. And that's where we are seeing things really, really picking up now. >> That's fantastic. And the business outcome that you mentioned there, the business impact is massive, helping folks get that confidence to be able to overcome sometimes the cultural barriers is key here. I think another thing that this program has really highlighted is there is a clear demand for data literacy in the job market, regardless of organization. Can each of you share more about how you're empowering the next generation of data workers? Paula, we'll start with you. >> Absolutely, and Jacqui says it so well, which is that it really is a journey that organizations are on and we as people in society are on in terms of upskilling our capabilities. So one of the things that we're doing here at Alteryx to help address this skillset gap on a global level is through a program that we call SparkED, which is essentially a no-cost analytics education program that we take to universities and colleges globally to help build the next generation of data workers. When we talk to our customers like eBay and many others, they say that it's difficult to find the skills that they want when they're hiring people into the job market. And so this program's really developed just to do just that, to close that gap and to work hand in hand with students and educators to improve data literacy for the next generation. So we're just getting started with SparkED. We started last May, but we currently have over 850 educational institutions globally engaged across 47 countries, and we're going to continue to invest here because there's so much opportunity for people, for society and for enterprises, when we close the gap and empower more people with the necessary analytics skills to solve all the problems that data can help solve. >> So SparkED has made a really big impact in such a short time period. It's going to be fun to watch the progress of that. Jacqui, let's go over to you now. Talk about some of the things that eBay is doing to empower the next generation of data workers. >> So we basically wanted to make sure that we kept that momentum from the hackathon, that we don't lose that excitement. So we just launched the program called eBay Masterminds. And what it basically is, is it's an inclusive innovation in each other, where we firmly believe that innovation is for upskilling for all analytics roles. So it doesn't matter your background, doesn't matter which function you are in, come and participate in in this where we really focus on innovation, introducing new technologies and upskilling our people. We are, apart from that, we also said, well, we shouldn't just keep it to inside eBay. We have to share this innovation with the community. So we are actually working on developing an analytics high school program, which we hope to pilot by the end of this year, where we will actually have high schoolers come in and teach them data essentials, the soft skills around analytics, but also how to use Alteryx. And we're working with, actually, we're working with SparkED and they're helping us develop that program. And we really hope that at, say, by the end of the year, we have a pilot and then also next year, we want to roll it out in multiple locations in multiple countries and really, really focus on that whole concept of analytics for all. >> Analytics for all, sounds like Alteryx and eBay have a great synergistic relationship there that is jointly aimed at especially going down the stuff and getting people when they're younger interested, and understanding how they can be empowered with data across any industry. Paula, let's go back to you, you were recently on "theCUBE"'s Supercloud event just a couple of weeks ago. And you talked about the challenges the companies are facing as they're navigating what is by default a multi-cloud world. How does the Alteryx Analytics Cloud platform enable CIOs to democratize analytics across their organization? >> Yes, business leaders and CIOs across all industries are realizing that there just aren't enough data scientists in the world to be able to make sense of the massive amounts of data that are flowing through organizations. Last I checked, there was 2 million data scientists in the world, so that's woefully underrepresented in terms of the opportunity for people to be a part of the analytics solution. So what we're seeing now with CIOs, with business leaders is that they're integrating data analysis and the skillset of data analysis into virtually every job function, and that is what we think of when we think of analytics for all. And so our mission with Alteryx Analytics Cloud is to empower all of those people in every job function, regardless of their skillset, as Jacqui pointed out from people that are just getting started all the way to the most sophisticated of technical users. Every worker across that spectrum can have a meaningful role in the opportunity to unlock the potential of the data for their company and their organizations. So that's our goal with Alteryx Analytics Cloud, and it operates in a multi cloud world and really helps across all sizes of data sets to blend, cleanse, shape, analyze, and report out so that we can break down data silos across the enterprise and help drive real business outcomes as a result of unlocking the potential of data. >> As well as really lessening that skill gap. As you were saying, there's only 2 million data scientists. You don't need to be a data scientist, that's the beauty of what Alteryx is enabling and eBay is a great example of that. Jacqui, let's go ahead and wrap things with you. You talked a great deal about the analytics maturity that you have fostered at eBay. It obviously has the right culture to adapt to that. Can you talk a little bit and take us out here in terms of where Alteryx fits in as that analytics maturity journey continues and what are some of the things that you are most excited about as analytics truly gets democratized across eBay? >> When we're starting up and getting excited about things when it comes to analytics, I can go on all day, but I'll keep it short and sweet for you. I do think we are on the top of the pool of data scientists. And I really feel that that is your next step, for us anyways, is that how do we get folks to not see data scientists as this big thing, like a rocket scientist, it's something completely different. And it's something that is in everybody in a certain extent. So again, partnering with Alteryx who just released the AI ML solution, allowing folks to not have a data scientist program, but actually build models and be able to solve problems that way. So we have engaged with Alteryx and we purchased the licenses, quite a few. And right now through our Masterminds program, we're actually running a four month program for all skill levels, teaching them AI ML and machine learning and how they can build their own models. We are really excited about that. We have over 50 participants without a background from all over the organization. We have members from our customer services. We have even some of our engineers are actually participating in the program. We just kicked it off. And I really believe that that is our next step. I want to give you a quick example of the beauty of this is where we actually just allow people to go out and think about ideas and come up with things. And one of the people in our team who doesn't have a data scientist background at all, was able to develop a solution where there is a checkout feedback functionality on the eBay side where sellers or buyers can verbatim add information. And she built a model to be able to determine what relates to tax specific, what is the type of problem, and even predict how that problem can be solved before we as a human even step in, and now instead of us or somebody going to verbatim and try to figure out what's going on there, we can focus on fixing the error versus actually just reading through things and not adding any value, and it's a beautiful tool and I was very impressed when I saw the demo and definitely developing that sort of thing. >> That sounds fantastic. And I think just the one word that keeps coming to mind, and we've said this a number of times in the program today is empowerment. What you're actually really doing to truly empower people across the organization with varying degrees of skill level, going down to the high school level, really exciting. We'll have to stay tuned to see what some of the great things are that come from this continued partnership. Ladies, I want to thank you so much for joining me on the program today and talking about how Alteryx and eBay are really partnering together to democratize analytics and to facilitate its maturity. It's been great talking to you. >> Thank you, Lisa. >> Thank you so much. (cheerful electronic music) >> As you heard over the course of our program, organizations where more people are using analytics who have deeper capabilities in each of the four Es, that's everyone, everything, everywhere, and easy analytics, those organizations achieve more ROI from their respective investments in analytics and automation than those who don't. We also heard a great story from eBay, great example of an enterprise that is truly democratizing analytics across its organization. It's enabling and empowering line of business users to use analytics, not only focused on key aspects of their job, but develop new skills rather than doing the same repetitive tasks. We want to thank you so much for watching the program today. Remember you can find all of the content on thecube.net. You can find all of the news from today on siliconangle.com and of course alteryx.com. We also want to thank Alteryx for making this program possible and for sponsoring "theCUBE". For all of my guests, I'm Lisa Martin. We want to thank you for watching and bye for now. (upbeat music)

>>Alteryx is a company with a long history that goes all the way back to the late 1990s. Now the one consistent theme over 20 plus years has been that Ultrix has always been a data company early in the big data and Hadoop cycle. It saw the need to combine and prep different data types so that organizations could analyze data and take action Altrix and similar companies played a critical role in helping companies become data-driven. The problem was the decade of big data, brought a lot of complexities and required immense skills just to get the technology to work as advertised this in turn limited, the pace of adoption and the number of companies that could really lean in and take advantage of the cloud began to change all that and set the foundation for today's theme to Zuora of digital transformation. We hear that phrase a ton digital transformation. >>People used to think it was a buzzword, but of course we learned from the pandemic that if you're not a digital business, you're out of business and a key tenant of digital transformation is democratizing data, meaning enabling, not just hypo hyper specialized experts, but anyone business users to put data to work. Now back to Ultrix, the company has embarked on a major transformation of its own. Over the past couple of years, brought in new management, they've changed the way in which it engaged with customers with the new subscription model and it's topgraded its talent pool. 2021 was even more significant because of two acquisitions that Altrix made hyper Ana and trifecta. Why are these acquisitions important? Well, traditionally Altryx sold to business analysts that were part of the data pipeline. These were fairly technical people who had certain skills and were trained in things like writing Python code with hyper Ana Altryx has added a new persona, the business user, anyone in the business who wanted to gain insights from data and, or let's say use AI without having to be a deep technical expert. >>And then Trifacta a company started in the early days of big data by cube alum, Joe Hellerstein and his colleagues at Berkeley. They knocked down the data engineering persona, and this gives Altryx a complimentary extension into it where things like governance and security are paramount. So as we enter 2022, the post isolation economy is here and we do so with a digital foundation built on the confluence of cloud native technologies, data democratization and machine intelligence or AI, if you prefer. And Altryx is entering that new era with an expanded portfolio, new go-to market vectors, a recurring revenue business model, and a brand new outlook on how to solve customer problems and scale a company. My name is Dave Vellante with the cube and I'll be your host today. And the next hour, we're going to explore the opportunities in this new data market. And we have three segments where we dig into these trends and themes. First we'll talk to Jay Henderson, vice president of product management at Ultrix about cloud acceleration and simplifying complex data operations. Then we'll bring in Suresh Vetol who's the chief product officer at Altrix and Adam Wilson, the CEO of Trifacta, which of course is now part of Altrix. And finally, we'll hear about how Altryx is partnering with snowflake and the ecosystem and how they're integrating with data platforms like snowflake and what this means for customers. And we may have a few surprises sprinkled in as well into the conversation let's get started. >>We're kicking off the program with our first segment. Jay Henderson is the vice president of product management Altryx and we're going to talk about the trends and data, where we came from, how we got here, where we're going. We get some launch news. Well, Jay, welcome to the cube. >>Great to be here, really excited to share some of the things we're working on. >>Yeah. Thank you. So look, you have a deep product background, product management, product marketing, you've done strategy work. You've been around software and data, your entire career, and we're seeing the collision of software data cloud machine intelligence. Let's start with the customer and maybe we can work back from there. So if you're an analytics or data executive in an organization, w J what's your north star, where are you trying to take your company from a data and analytics point of view? >>Yeah, I mean, you know, look, I think all organizations are really struggling to get insights out of their data. I think one of the things that we see is you've got digital exhaust, creating large volumes of data storage is really cheap, so it doesn't cost them much to keep it. And that results in a situation where the organization's, you know, drowning in data, but somehow still starving for insights. And so I think, uh, you know, when I talk to customers, they're really excited to figure out how they can put analytics in the hands of every single person in their organization, and really start to democratize the analytics, um, and, you know, let the, the business users and the whole organization get value out of all that data they have. >>And we're going to dig into that throughout this program data, I like to say is plentiful insights, not always so much. Tell us about your launch today, Jay, and thinking about the trends that you just highlighted, the direction that your customers want to go and the problems that you're solving, what role does the cloud play in? What is what you're launching? How does that fit in? >>Yeah, we're, we're really excited today. We're launching the Altryx analytics cloud. That's really a portfolio of cloud-based solutions that have all been built from the ground up to be cloud native, um, and to take advantage of things like based access. So that it's really easy to give anyone access, including folks on a Mac. Um, it, you know, it also lets you take advantage of elastic compute so that you can do, you know, in database processing and cloud native, um, solutions that are gonna scale to solve the most complex problems. So we've got a portfolio of solutions, things like designer cloud, which is our flagship designer product in a browser and on the cloud, but we've got ultra to machine learning, which helps up-skill regular old analysts with advanced machine learning capabilities. We've got auto insights, which brings a business users into the fold and automatically unearths insights using AI and machine learning. And we've got our latest edition, which is Trifacta that helps data engineers do data pipelining and really, um, you know, create a lot of the underlying data sets that are used in some of this, uh, downstream analytics. >>Let's dig into some of those roles if we could a little bit, I mean, you've traditionally Altryx has served the business analysts and that's what designer cloud is fit for, I believe. And you've explained, you know, kind of the scope, sorry, you've expanded that scope into the, to the business user with hyper Anna. And we're in a moment we're going to talk to Adam Wilson and Suresh, uh, about Trifacta and that recent acquisition takes you, as you said, into the data engineering space in it. But in thinking about the business analyst role, what's unique about designer cloud cloud, and how does it help these individuals? >>Yeah, I mean, you know, really, I go back to some of the feedback we've had from our customers, which is, um, you know, they oftentimes have dozens or hundreds of seats of our designer desktop product, you know, really, as they look to take the next step, they're trying to figure out how do I give access to that? Those types of analytics to thousands of people within the organization and designer cloud is, is really great for that. You've got the browser-based interface. So if folks are on a Mac, they can really easily just pop, open the browser and get access to all of those, uh, prep and blend capabilities to a lot of the analysis we're doing. Um, it's a great way to scale up access to the analytics and then start to put it in the hands of really anyone in the organization, not just those highly skilled power users. >>Okay, great. So now then you add in the hyper Anna acquisition. So now you're targeting the business user Trifacta comes into the mix that deeper it angle that we talked about, how does this all fit together? How should we be thinking about the new Altryx portfolio? >>Yeah, I mean, I think it's pretty exciting. Um, you know, when you think about democratizing analytics and providing access to all these different groups of people, um, you've not been able to do it through one platform before. Um, you know, it's not going to be one interface that meets the, of all these different groups within the organization. You really do need purpose built specialized capabilities for each group. And finally, today with the announcement of the alternates analytics cloud, we brought together all of those different capabilities, all of those different interfaces into a single in the end application. So really finally delivering on the promise of providing analytics to all, >>How much of this you've been able to share with your customers and maybe your partners. I mean, I know OD is fairly new, but if you've been able to get any feedback from them, what are they saying about it? >>Uh, I mean, it's, it's pretty amazing. Um, we ran a early access, limited availability program that led us put a lot of this technology in the hands of over 600 customers, um, over the last few months. So we have gotten a lot of feedback. I tell you, um, it's been overwhelmingly positive. I think organizations are really excited to unlock the insights that have been hidden in all this data. They've got, they're excited to be able to use analytics in every decision that they're making so that the decisions they have or more informed and produce better business outcomes. Um, and, and this idea that they're going to move from, you know, dozens to hundreds or thousands of people who have access to these kinds of capabilities, I think has been a really exciting thing that is going to accelerate the transformation that these customers are on. >>Yeah, those are good. Good, good numbers for, for preview mode. Let's, let's talk a little bit about vision. So it's democratizing data is the ultimate goal, which frankly has been elusive for most organizations over time. How's your cloud going to address the challenges of putting data to work across the entire enterprise? >>Yeah, I mean, I tend to think about the future and some of the investments we're making in our products and our roadmap across four big themes, you know, in the, and these are really kind of enduring themes that you're going to see us making investments in over the next few years, the first is having cloud centricity. You know, the data gravity has been moving to the cloud. We need to be able to provide access, to be able to ingest and manipulate that data, to be able to write back to it, to provide cloud solution. So the first one is really around cloud centricity. The second is around big data fluency. Once you have all of the data, you need to be able to manipulate it in a performant manner. So having the elastic cloud infrastructure and in database processing is so important, the third is around making AI a strategic advantage. >>So, uh, you know, getting everyone involved and accessing AI and machine learning to unlock those insights, getting it out of the hands of the small group of data scientists, putting it in the hands of analysts and business users. Um, and then the fourth thing is really providing access across the entire organization. You know, it and data engineers, uh, as well as business owners and analysts. So, um, cloud centricity, big data fluency, um, AI is a strategic advantage and, uh, personas across the organization are really the four big themes you're going to see us, uh, working on over the next few months and, uh, coming coming year. >>That's good. Thank you for that. So, so on a related question, how do you see the data organizations evolving? I mean, traditionally you've had, you know, monolithic organizations, uh, very specialized or I might even say hyper specialized roles and, and your, your mission of course is the customer. You, you, you, you and your customers, they want to democratize the data. And so it seems logical that domain leaders are going to take more responsibility for data, life cycles, data ownerships, low code becomes more important. And perhaps this kind of challenges, the historically highly centralized and really specialized roles that I just talked about. How do you see that evolving and, and, and what role will Altryx play? >>Yeah. Um, you know, I think we'll see sort of a more federated systems start to emerge. Those centralized groups are going to continue to exist. Um, but they're going to start to empower, you know, in a much more de-centralized way, the people who are closer to the business problems and have better business understanding. I think that's going to let the centralized highly skilled teams work on, uh, problems that are of higher value to the organization. The kinds of problems where one or 2% lift in the model results in millions of dollars a day for the business. And then by pushing some of the analytics out to, uh, closer to the edge and closer to the business, you'll be able to apply those analytics in every single decision. So I think you're going to see, you know, both the decentralized and centralized models start to work in harmony and a little bit more about almost a federated sort of a way. And I think, you know, the exciting thing for us at Altryx is, you know, we want to facilitate that. We want to give analytic capabilities and solutions to both groups and types of people. We want to help them collaborate better, um, and drive business outcomes with the analytics they're using. >>Yeah. I mean, I think my take on another one, if you could comment is to me, the technology should be an operational detail and it has been the, the, the dog that wags the tail, or maybe the other way around, you mentioned digital exhaust before. I mean, essentially it's digital exhaust coming out of operationals systems that then somehow, eventually end up in the hand of the domain users. And I wonder if increasingly we're going to see those domain users, users, those, those line of business experts get more access. That's your goal. And then even go beyond analytics, start to build data products that could be monetized, and that maybe it's going to take a decade to play out, but that is sort of a new era of data. Do you see it that way? >>Absolutely. We're actually making big investments in our products and capabilities to be able to create analytic applications and to enable somebody who's an analyst or business user to create an application on top of the data and analytics layers that they have, um, really to help democratize the analytics, to help prepackage some of the analytics that can drive more insights. So I think that's definitely a trend we're going to see more. >>Yeah. And to your point, if you can federate the governance and automate that, then that can happen. I mean, that's a key part of it, obviously. So, all right, Jay, we have to leave it there up next. We take a deep dive into the Altryx recent acquisition of Trifacta with Adam Wilson who led Trifacta for more than seven years. It's the recipe. Tyler is the chief product officer at Altryx to explain the rationale behind the acquisition and how it's going to impact customers. Keep it right there. You're watching the cube. You're a leader in enterprise tech coverage. >>It's go time, get ready to accelerate your data analytics journey with a unified cloud native platform. That's accessible for everyone on the go from home to office and everywhere in between effortless analytics to help you go from ideas to outcomes and no time. It's your time to shine. It's Altryx analytics cloud time. >>Okay. We're here with. Who's the chief product officer at Altryx and Adam Wilson, the CEO of Trifacta. Now of course, part of Altryx just closed this quarter. Gentlemen. Welcome. >>Great to be here. >>Okay. So let me start with you. In my opening remarks, I talked about Altrix is traditional position serving business analysts and how the hyper Anna acquisition brought you deeper into the business user space. What does Trifacta bring to your portfolio? Why'd you buy the company? >>Yeah. Thank you. Thank you for the question. Um, you know, we see, uh, we see a massive opportunity of helping, um, brands, um, democratize the use of analytics across their business. Um, every knowledge worker, every individual in the company should have access to analytics. It's no longer optional, um, as they navigate their businesses with that in mind, you know, we know designer and are the products that Altrix has been selling the past decade or so do a really great job, um, addressing the business analysts, uh, with, um, hyper Rana now kind of renamed, um, Altrix auto. We even speak with the business owner and the line of business owner. Who's looking for insights that aren't real in traditional dashboards and so on. Um, but we see this opportunity of really helping the data engineering teams and it organizations, um, to also make better use of analytics. Um, and that's where the drive factor comes in for us. Um, drive factor has the best data engineering cloud in the planet. Um, they have an established track record of working across multiple cloud platforms and helping data engineers, um, do better data pipelining and work better with, uh, this massive kind of cloud transformation that's happening in every business. Um, and so fact made so much sense for us. >>Yeah. Thank you for that. I mean, you, look, you could have built it yourself would have taken, you know, who knows how long, you know, but, uh, so definitely a great time to market move, Adam. I wonder if we could dig into Trifacta some more, I mean, I remember interviewing Joe Hellerstein in the early days. You've talked about this as well, uh, on the cube coming at the problem of taking data from raw refined to an experience point of view. And Joe in the early days, talked about flipping the model and starting with data visualization, something Jeff, her was expert at. So maybe explain how we got here. We used to have this cumbersome process of ETL and you may be in some others changed that model with ELL and then T explain how Trifacta really changed the data engineering game. >>Yeah, that's exactly right. Uh, David, it's been a really interesting journey for us because I think the original hypothesis coming out of the campus research, uh, at Berkeley and Stanford that really birth Trifacta was, you know, why is it that the people who know the data best can't do the work? You know, why is this become the exclusive purview of the highly technical? And, you know, can we rethink this and make this a user experience, problem powered by machine learning that will take some of the more complicated things that people want to do with data and really help to automate those. So, so a broader set of, of users can, um, can really see for themselves and help themselves. And, and I think that, um, there was a lot of pent up frustration out there because people have been told for, you know, for a decade now to be more data-driven and then the whole time they're saying, well, then give me the data, you know, in the shape that I could use it with the right level of quality and I'm happy to be, but don't tell me to be more data-driven and then, and, and not empower me, um, to, to get in there and to actually start to work with the data in meaningful ways. >>And so, um, that was really, you know, what, you know, the origin story of the company and I think is, as we, um, saw over the course of the last 5, 6, 7 years that, um, you know, uh, real, uh, excitement to embrace this idea of, of trying to think about data engineering differently, trying to democratize the, the ETL process and to also leverage all these exciting new, uh, engines and platforms that are out there that allow for processing, you know, ever more diverse data sets, ever larger data sets and new and interesting ways. And that's where a lot of the push-down or the ELT approaches that, you know, I think it could really won the day. Um, and that, and that for us was a hallmark of the solution from the very beginning. >>Yeah, this is a huge point that you're making is, is first of all, there's a large business, it's probably about a hundred billion dollar Tam. Uh, and the, the point you're making, because we've looked, we've contextualized most of our operational systems, but the big data pipeline is hasn't gotten there. But, and maybe we could talk about that a little bit because democratizing data is Nirvana, but it's been historically very difficult. You've got a number of companies it's very fragmented and they're all trying to attack their little piece of the problem to achieve an outcome, but it's been hard. And so what's going to be different about Altryx as you bring these puzzle pieces together, how is this going to impact your customers who would like to take that one? >>Yeah, maybe, maybe I'll take a crack at it. And Adam will, um, add on, um, you know, there hasn't been a single platform for analytics, automation in the enterprise, right? People have relied on, uh, different products, um, to solve kind of, uh, smaller problems, um, across this analytics, automation, data transformation domain. Um, and, um, I think uniquely Alcon's has that opportunity. Uh, we've got 7,000 plus customers who rely on analytics for, um, data management, for analytics, for AI and ML, uh, for transformations, uh, for reporting and visualization for automated insights and so on. Um, and so by bringing drive factor, we have the opportunity to scale this even further and solve for more use cases, expand the scenarios where it's applied and so multiple personas. Um, and we just talked about the data engineers. They are really a growing stakeholder in this transformation of data and analytics. >>Yeah, good. Maybe we can stay on this for a minute cause you, you you're right. You bring it together. Now at least three personas the business analyst, the end user slash business user. And now the data engineer, which is really out of an it role in a lot of companies, and you've used this term, the data engineering cloud, what is that? How is it going to integrate in with, or support these other personas? And, and how's it going to integrate into the broader ecosystem of clouds and cloud data warehouses or any other data stores? >>Yeah, no, that's great. Uh, yeah, I think for us, we really looked at this and said, you know, we want to build an open and interactive cloud platform for data engineers, you know, to collaboratively profile pipeline, um, and prepare data for analysis. And that really meant collaborating with the analysts that were in the line of business. And so this is why a big reason why this combination is so magic because ultimately if we can get the data engineers that are creating the data products together with the analysts that are in the line of business that are driving a lot of the decision making and allow for that, what I would describe as collaborative curation of the data together, so that you're starting to see, um, uh, you know, increasing returns to scale as this, uh, as this rolls out. I just think that is an incredibly powerful combination and, and frankly, something that the market is not crack the code on yet. And so, um, I think when we, when I sat down with Suresh and with mark and the team at Ultrix, that was really part of the, the, the big idea, the big vision that was painted and got us really energized about the acquisition and about the potential of the combination. >>And you're really, you're obviously writing the cloud and the cloud native wave. Um, and, but specifically we're seeing, you know, I almost don't even want to call it a data warehouse anyway, because when you look at what's, for instance, Snowflake's doing, of course their marketing is around the data cloud, but I actually think there's real justification for that because it's not like the traditional data warehouse, right. It's, it's simplified get there fast, don't necessarily have to go through the central organization to share data. Uh, and, and, and, but it's really all about simplification, right? Isn't that really what the democratization comes down to. >>Yeah. It's simplification and collaboration. Right. I don't want to, I want to kind of just what Adam said resonates with me deeply. Um, analytics is one of those, um, massive disciplines inside an enterprise that's really had the weakest of tools. Um, and we just have interfaces to collaborate with, and I think truly this was all drinks and a superpower was helping the analysts get more out of their data, get more out of the analytics, like imagine a world where these people are collaborating and sharing insights in real time and sharing workflows and getting access to new data sources, um, understanding data models better, I think, um, uh, curating those insights. I boring Adam's phrase again. Um, I think that creates a real value inside the organization because frankly in scaling analytics and democratizing analytics and data, we're still in such early phases of this journey. >>So how should we think about designer cloud, which is from Altrix it's really been the on-prem and the server desktop offering. And of course Trifacta is with cloud cloud data warehouses. Right. Uh, how, how should we think about those two products? Yeah, >>I think, I think you should think about them. And, uh, um, as, as very complimentary right designer cloud really shares a lot of DNA and heritage with, uh, designer desktop, um, the low code tooling and that interface, uh, the really appeals to the business analysts, um, and gets a lot of the things that they do well, we've also built it with interoperability in mind, right. So if you started building your workflows in designer desktop, you want to share that with design and cloud, we want to make it super easy for you to do that. Um, and I think over time now we're only a week into, um, this Alliance with, um, with, um, Trifacta, um, I think we have to get deeper inside to think about what does the data engineer really need? What's the business analysts really need and how to design a cloud, and Trifacta really support both of those requirements, uh, while kind of continue to build on the trifecta on the amazing Trifacta cloud platform. >>You know, >>I think we're just going to say, I think that's one of the things that, um, you know, creates a lot of, uh, opportunity as we go forward, because ultimately, you know, Trifacta took a platform, uh, first mentality to everything that we built. So thinking about openness and extensibility and, um, and how over time people could build things on top of factor that are a variety of analytic tool chain, or analytic applications. And so, uh, when you think about, um, Ultrix now starting to, uh, to move some of its capabilities or to provide additional capabilities, uh, in the cloud, um, you know, Trifacta becomes a platform that can accelerate, you know, all of that work and create, uh, uh, a cohesive set of, of cloud-based services that, um, share a common platform. And that maintains independence because both companies, um, have been, uh, you know, fiercely independent, uh, and, and really giving people choice. >>Um, so making sure that whether you're, uh, you know, picking one cloud platform and other, whether you're running things on the desktop, uh, whether you're running in hybrid environments, that, um, no matter what your decision, um, you're always in a position to be able to get out your data. You're always in a position to be able to cleanse transform shape structure, that data, and ultimately to deliver, uh, the analytics that you need. And so I think in that sense, um, uh, you know, this, this again is another reason why the combination, you know, fits so well together, giving people, um, the choice. Um, and as they, as they think about their analytics strategy and their platform strategy going forward, >>Yeah. I make a chuckle, but one of the reasons I always liked Altrix is cause you kinda did the little end run on it. It can be a blocker sometimes, but that created problems, right? Because the organization said, wow, this big data stuff has taken off, but we need security. We need governance. And it's interesting because you've got, you know, ETL has been complex, whereas the visualization tools, they really, you know, really weren't great at governance and security. It took some time there. So that's not, not their heritage. You're bringing those worlds together. And I'm interested, you guys just had your sales kickoff, you know, what was their reaction like? Uh, maybe Suresh, you could start off and maybe Adam, you could bring us home. >>Um, thanks for asking about our sales kickoff. So we met for the first time and you've got a two years, right. For, as, as it is for many of us, um, in person, uh, um, which I think was a, was a real breakthrough as Qualtrics has been on its transformation journey. Uh, we added a Trifacta to, um, the, the potty such as the tour, um, and getting all of our sales teams and product organizations, um, to meet in person in one location. I thought that was very powerful for other the company. Uh, but then I tell you, um, um, the reception for Trifacta was beyond anything I could have imagined. Uh, we were working out him and I will, when he's so hot on, on the deal and the core hypotheses and so on. And then you step back and you're going to share the vision with the field organization, and it blows you away, the energy that it creates among our sellers out of partners. >>And I'm sure Madam will and his team were mocked, um, every single day, uh, with questions and opportunities to bring them in. But Adam, maybe you should share. Yeah, no, it was, uh, it was through the roof. I mean, uh, uh, the, uh, the amount of energy, the, uh, certainly how welcoming everybody was, uh, uh, you know, just, I think the story makes so much sense together. I think culturally, the company is, are very aligned. Um, and, uh, it was a real, uh, real capstone moment, uh, to be able to complete the acquisition and to, and to close and announced, you know, at the kickoff event. And, um, I think, you know, for us, when we really thought about it, you know, when we ended, the story that we told was just, you have this opportunity to really cater to what the end users care about, which is a lot about interactivity and self-service, and at the same time. >>And that's, and that's a lot of the goodness that, um, that Altryx is, has brought, you know, through, you know, you know, years and years of, of building a very vibrant community of, you know, thousands, hundreds of thousands of users. And on the other side, you know, Trifacta bringing in this data engineering focus, that's really about, uh, the governance things that you mentioned and the openness, um, that, that it cares deeply about. And all of a sudden, now you have a chance to put that together into a complete story where the data engineering cloud and analytics, automation, you know, coming together. And, um, and I just think, you know, the lights went on, um, you know, for people instantaneously and, you know, this is a story that, um, that I think the market is really hungry for. And certainly the reception we got from, uh, from the broader team at kickoff was, uh, was a great indication. >>Well, I think the story hangs together really well, you know, one of the better ones I've seen in, in this space, um, and, and you guys coming off a really, really strong quarter. So congratulations on that jets. We have to leave it there. I really appreciate your time today. Yeah. Take a look at this short video. And when we come back, we're going to dig into the ecosystem and the integration into cloud data warehouses and how leading organizations are creating modern data teams and accelerating their digital businesses. You're watching the cube you're leader in enterprise tech coverage. >>This is your data housed neatly insecurely in the snowflake data cloud. And all of it has potential the potential to solve complex business problems, deliver personalized financial offerings, protect supply chains from disruption, cut costs, forecast, grow and innovate. All you need to do is put your data in the hands of the right people and give it an opportunity. Luckily for you. That's the easy part because snowflake works with Alteryx and Alteryx turns data into breakthroughs with just a click. Your organization can automate analytics with drag and drop building blocks, easily access snowflake data with both sequel and no SQL options, share insights, powered by Alteryx data science and push processing to snowflake for lightning, fast performance, you get answers you can put to work in your teams, get repeatable processes they can share in that's exciting because not only is your data no longer sitting around in silos, it's also mobilized for the next opportunity. Turn your data into a breakthrough Alteryx and snowflake >>Okay. We're back here in the queue, focusing on the business promise of the cloud democratizing data, making it accessible and enabling everyone to get value from analytics, insights, and data. We're now moving into the eco systems segment the power of many versus the resources of one. And we're pleased to welcome. Barb Hills camp was the senior vice president partners and alliances at Ultrix and a special guest Terek do week head of technology alliances at snowflake folks. Welcome. Good to see you. >>Thank you. Thanks for having me. Good to see >>Dave. Great to see you guys. So cloud migration, it's one of the hottest topics. It's the top one of the top initiatives of senior technology leaders. We have survey data with our partner ETR it's number two behind security, and just ahead of analytics. So we're hovering around all the hot topics here. Barb, what are you seeing with respect to customer, you know, cloud migration momentum, and how does the Ultrix partner strategy fit? >>Yeah, sure. Partners are central company's strategy. They always have been. We recognize that our partners have deep customer relationships. And when you connect that with their domain expertise, they're really helping customers on their cloud and business transformation journey. We've been helping customers achieve their desired outcomes with our partner community for quite some time. And our partner base has been growing an average of 30% year over year, that partner community and strategy now addresses several kinds of partners, spanning solution providers to global SIS and technology partners, such as snowflake and together, we help our customers realize the business promise of their journey to the cloud. Snowflake provides a scalable storage system altereds provides the business user friendly front end. So for example, it departments depend on snowflake to consolidate data across systems into one data cloud with Altryx business users can easily unlock that data in snowflake solving real business outcomes. Our GSI and solution provider partners are instrumental in providing that end to end benefit of a modern analytic stack in the cloud providing platform, guidance, deployment, support, and other professional services. >>Great. Let's get a little bit more into the relationship between Altrix and S in snowflake, the partnership, maybe a little bit about the history, you know, what are the critical aspects that we should really focus on? Barb? Maybe you could start an Interra kindly way in as well. >>Yeah, so the relationship started in 2020 and all shirts made a big bag deep with snowflake co-innovating and optimizing cloud use cases together. We are supporting customers who are looking for that modern analytic stack to replace an old one or to implement their first analytic strategy. And our joint customers want to self-serve with data-driven analytics, leveraging all the benefits of the cloud, scalability, accessibility, governance, and optimizing their costs. Um, Altrix proudly achieved. Snowflake's highest elite tier in their partner program last year. And to do that, we completed a rigorous third party testing process, which also helped us make some recommended improvements to our joint stack. We wanted customers to have confidence. They would benefit from high quality and performance in their investment with us then to help customers get the most value out of the destroyed solution. We developed two great assets. One is the officer starter kit for snowflake, and we coauthored a joint best practices guide. >>The starter kit contains documentation, business workflows, and videos, helping customers to get going more easily with an altered since snowflake solution. And the best practices guide is more of a technical document, bringing together experiences and guidance on how Altryx and snowflake can be deployed together. Internally. We also built a full enablement catalog resources, right? We wanted to provide our account executives more about the value of the snowflake relationship. How do we engage and some best practices. And now we have hundreds of joint customers such as Juniper and Sainsbury who are actively using our joint solution, solving big business problems much faster. >>Cool. Kara, can you give us your perspective on the partnership? >>Yeah, definitely. Dave, so as Barb mentioned, we've got this standing very successful partnership going back years with hundreds of happy joint customers. And when I look at the beginning, Altrix has helped pioneer the concept of self-service analytics, especially with use cases that we worked on with for, for data prep for BI users like Tableau and as Altryx has evolved to now becoming from data prep to now becoming a full end to end data science platform. It's really opened up a lot more opportunities for our partnership. Altryx has invested heavily over the last two years in areas of deep integration for customers to fully be able to expand their investment, both technologies. And those investments include things like in database pushed down, right? So customers can, can leverage that elastic platform, that being the snowflake data cloud, uh, with Alteryx orchestrating the end to end machine learning workflows Alteryx also invested heavily in snow park, a feature we released last year around this concept of data programmability. So all users were regardless of their business analysts, regardless of their data, scientists can use their tools of choice in order to consume and get at data. And now with Altryx cloud, we think it's going to open up even more opportunities. It's going to be a big year for the partnership. >>Yeah. So, you know, Terike, we we've covered snowflake pretty extensively and you initially solve what I used to call the, I still call the snake swallowing the basketball problem and cloud data warehouse changed all that because you had virtually infinite resources, but so that's obviously one of the problems that you guys solved early on, but what are some of the common challenges or patterns or trends that you see with snowflake customers and where does Altryx come in? >>Sure. Dave there's there's handful, um, that I can come up with today, the big challenges or trends for us, and Altrix really helps us across all of them. Um, there are three particular ones I'm going to talk about the first one being self-service analytics. If we think about it, every organization is trying to democratize data. Every organization wants to empower all their users, business users, um, you know, the, the technology users, but the business users, right? I think every organization has realized that if everyone has access to data and everyone can do something with data, it's going to make them competitively, give them a competitive advantage with Altrix is something we share that vision of putting that power in the hands of everyday users, regardless of the skillsets. So, um, with self-service analytics, with Ultrix designer they've they started out with self-service analytics as the forefront, and we're just scratching the surface. >>I think there was an analyst, um, report that shows that less than 20% of organizations are truly getting self-service analytics to their end users. Now, with Altryx going to Ultrix cloud, we think that's going to be a huge opportunity for us. Um, and then that opens up the second challenge, which is machine learning and AI, every organization is trying to get predictive analytics into every application that they have in order to be competitive in order to be competitive. Um, and with Altryx creating this platform so they can cater to both the everyday business user, the quote unquote, citizen data scientists, and making a code friendly for data scientists to be able to get at their notebooks and all the different tools that they want to use. Um, they fully integrated in our snow park platform, which I talked about before, so that now we get an end to end solution caring to all, all lines of business. >>And then finally this concept of data marketplaces, right? We, we created snowflake from the ground up to be able to solve the data sharing problem, the big data problem, the data sharing problem. And Altryx um, if we look at mobilizing your data, getting access to third-party datasets, to enrich with your own data sets, to enrich with, um, with your suppliers and with your partners, data sets, that's what all customers are trying to do in order to get a more comprehensive 360 view, um, within their, their data applications. And so with Altryx alterations, we're working on third-party data sets and marketplaces for quite some time. Now we're working on how do we integrate what Altrix is providing with the snowflake data marketplace so that we can enrich these workflows, these great, great workflows that Altrix writing provides. Now we can add third party data into that workflow. So that opens up a ton of opportunities, Dave. So those are three I see, uh, easily that we're going to be able to solve a lot of customer challenges with. >>So thank you for that. Terrick so let's stay on cloud a little bit. I mean, Altrix is undergoing a major transformation, big focus on the cloud. How does this cloud launch impact the partnership Terike from snowflakes perspective and then Barb, maybe, please add some color. >>Yeah, sure. Dave snowflake started as a cloud data platform. We saw our founders really saw the challenges that customers are having with becoming data-driven. And the biggest challenge was the complexity of having imagine infrastructure to even be able to do it, to get applications off the ground. And so we created something to be cloud-native. We created to be a SAS managed service. So now that that Altrix is moving to the same model, right? A cloud platform, a SAS managed service, we're just, we're just removing more of the friction. So we're going to be able to start to package these end to end solutions that are SAS based that are fully managed. So customers can, can go faster and they don't have to worry about all of the underlying complexities of, of, of stitching things together. Right? So, um, so that's, what's exciting from my viewpoint >>And I'll follow up. So as you said, we're investing heavily in the cloud a year ago, we had two pre desktop products, and today we have four cloud products with cloud. We can provide our users with more flexibility. We want to make it easier for the users to leverage their snowflake data in the Alteryx platform, whether they're using our beloved on-premise solution or the new cloud products were committed to that continued investment in the cloud, enabling our joint partner solutions to meet customer requirements, wherever they store their data. And we're working with snowflake, we're doing just that. So as customers look for a modern analytic stack, they expect that data to be easily accessible, right within a fast, secure and scalable platform. And the launch of our cloud strategy is a huge leap forward in making Altrix more widely accessible to all users in all types of roles, our GSI and our solution provider partners have asked for these cloud capabilities at scale, and they're excited to better support our customers, cloud and analytic >>Are. How about you go to market strategy? How would you describe your joint go to market strategy with snowflake? >>Sure. It's simple. We've got to work backwards from our customer's challenges, right? Driving transformation to solve problems, gain efficiencies, or help them save money. So whether it's with snowflake or other GSI, other partner types, we've outlined a joint journey together from recruit solution development, activation enablement, and then strengthening our go to market strategies to optimize our results together. We launched an updated partner program and within that framework, we've created new benefits for our partners around opportunity registration, new role based enablement and training, basically extending everything we do internally for our own go-to-market teams to our partners. We're offering partner, marketing resources and funding to reach new customers together. And as a matter of fact, we recently launched a fantastic video with snowflake. I love this video that very simply describes the path to insights starting with your snowflake data. Right? We do joint customer webinars. We're working on joint hands-on labs and have a wonderful landing page with a lot of assets for our customers. Once we have an interested customer, we engage our respective account managers, collaborating through discovery questions, proof of concepts really showcasing the desired outcome. And when you combine that with our partners technology or domain expertise, it's quite powerful, >>Dark. How do you see it? You'll go to market strategy. >>Yeah. Dave we've. Um, so we initially started selling, we initially sold snowflake as technology, right? Uh, looking at positioning the diff the architectural differentiators and the scale and concurrency. And we noticed as we got up into the larger enterprise customers, we're starting to see how do they solve their business problems using the technology, as well as them coming to us and saying, look, we want to also know how do you, how do you continue to map back to the specific prescriptive business problems we're having? And so we shifted to an industry focus last year, and this is an area where Altrix has been mature for probably since their inception selling to the line of business, right? Having prescriptive use cases that are particular to an industry like financial services, like retail, like healthcare and life sciences. And so, um, Barb talked about these, these starter kits where it's prescriptive, you've got a demo and, um, a way that customers can get off the ground and running, right? >>Cause we want to be able to shrink that time to market, the time to value that customers can watch these applications. And we want to be able to, to tell them specifically how we can map back to their business initiatives. So I see a huge opportunity to align on these industry solutions. As BARR mentioned, we're already doing that where we've released a few around financial services working in healthcare and retail as well. So that is going to be a way for us to allow customers to go even faster and start to map two lines of business with Alteryx. >>Great. Thanks Derek. Bob, what can we expect if we're observing this relationship? What should we look for in the coming year? >>A lot specifically with snowflake, we'll continue to invest in the partnership. Uh, we're co innovators in this journey, including snow park extensibility efforts, which Derek will tell you more about shortly. We're also launching these great news strategic solution blueprints, and extending that at no charge to our partners with snowflake, we're already collaborating with their retail and CPG team for industry blueprints. We're working with their data marketplace team to highlight solutions, working with that data in their marketplace. More broadly, as I mentioned, we're relaunching the ultra partner program designed to really better support the unique partner types in our global ecosystem, introducing new benefits so that with every partner, achievement or investment with ultra score, providing our partners with earlier access to benefits, um, I could talk about our program for 30 minutes. I know we don't have time. The key message here Alteryx is investing in our partner community across the business, recognizing the incredible value that they bring to our customers every day. >>Tarik will give you the last word. What should we be looking for from, >>Yeah, thanks. Thanks, Dave. As BARR mentioned, Altrix has been the forefront of innovating with us. They've been integrating into, uh, making sure again, that customers get the full investment out of snowflake things like in database push down that I talked about before that extensibility is really what we're excited about. Um, the ability for Ultrix to plug into this extensibility framework that we call snow park and to be able to extend out, um, ways that the end users can consume snowflake through, through sequel, which has traditionally been the way that you consume snowflake as well as Java and Scala, not Python. So we're excited about those, those capabilities. And then we're also excited about the ability to plug into the data marketplace to provide third party data sets, right there probably day sets in, in financial services, third party, data sets and retail. So now customers can build their data applications from end to end using ultrasound snowflake when the comprehensive 360 view of their customers, of their partners, of even their employees. Right? I think it's exciting to see what we're going to be able to do together with these upcoming innovations. Great >>Barb Tara, thanks so much for coming on the program, got to leave it right there in a moment, I'll be back with some closing thoughts in a summary, don't go away. >>1200 hours of wind tunnel testing, 30 million race simulations, 2.4 second pit stops make that 2.3. The sector times out the wazoo, whites are much of this velocity's pressures, temperatures, 80,000 components generating 11.8 billion data points and one analytics platform to make sense of it all. When McLaren needs to turn complex data into insights, they turn to Altryx Qualtrics analytics, automation, >>Okay, let's summarize and wrap up the session. We can pretty much agree the data is plentiful, but organizations continue to struggle to get maximum value out of their data investments. The ROI has been elusive. There are many reasons for that complexity data, trust silos, lack of talent and the like, but the opportunity to transform data operations and drive tangible value is immense collaboration across various roles. And disciplines is part of the answer as is democratizing data. This means putting data in the hands of those domain experts that are closest to the customer and really understand where the opportunity exists and how to best address them. We heard from Jay Henderson that we have all this data exhaust and cheap storage. It allows us to keep it for a long time. It's true, but as he pointed out that doesn't solve the fundamental problem. Data is spewing out from our operational systems, but much of it lacks business context for the data teams chartered with analyzing that data. >>So we heard about the trend toward low code development and federating data access. The reason this is important is because the business lines have the context and the more responsibility they take for data, the more quickly and effectively organizations are going to be able to put data to work. We also talked about the harmonization between centralized teams and enabling decentralized data flows. I mean, after all data by its very nature is distributed. And importantly, as we heard from Adam Wilson and Suresh Vittol to support this model, you have to have strong governance and service the needs of it and engineering teams. And that's where the trifecta acquisition fits into the equation. Finally, we heard about a key partnership between Altrix and snowflake and how the migration to cloud data warehouses is evolving into a global data cloud. This enables data sharing across teams and ecosystems and vertical markets at massive scale all while maintaining the governance required to protect the organizations and individuals alike. >>This is a new and emerging business model that is very exciting and points the way to the next generation of data innovation in the coming decade. We're decentralized domain teams get more facile access to data. Self-service take more responsibility for quality value and data innovation. While at the same time, the governance security and privacy edicts of an organization are centralized in programmatically enforced throughout an enterprise and an external ecosystem. This is Dave Volante. All these videos are available on demand@theqm.net altrix.com. Thanks for watching accelerating automated analytics in the cloud made possible by Altryx. And thanks for watching the queue, your leader in enterprise tech coverage. We'll see you next time.

>>Hi, everyone. Welcome to the cubes coverage of Avis reinvent 2021 we're onsite in person. It's a virtual event, also hybrid events. I'm Jennifer and my host, David Dante ninth year, Dave, we've been doing Avis reinvent the cube and it's 11th season. We've seen a lot. Yeah, I'll say. >>And the show is pretty packed, John. I mean, I think it's surprised some folks over 25,000 people here. I mean, obviously a lot of sponsors, but >>Customers to a bad event for AWS in terms of attendance is like record-breaking for any other company, people are standing in line for sessions. It's definitely happening. People are here to learn. They're not just all employees. So definitely a successful event in person as well in the live stream. But so much news to talk about. Andy Jassy is now the CEO of Amazon. That's the top story Adam's Lipsky's taking over as CEO of AWS time, Amazonian who left Amazon to take the CEO job of Tableau sold that company to Salesforce under mark Benioff. Now back to take the helm from Andy Jassy and quite the pressure cooker here as he takes the stage, a lot of people are asking, is will he do well? Will he fumble on stage? Will he do the right things? And does he have what it takes to take the cloud to the next generation with AWS as their number one clear far and away, then the second competitor in Microsoft and then a look distant third and Google. So Amazon's are under a ton of competitive pressure. At least from an industry standpoint, everyone's still trying to catch up. It's the same theme, Dave, every year Amazon is out front and the lead just gets extended and extended. And again, here, no exception. Well, the Uber >>Of course there's you mentioned is Andy Jassy is now taking over a CEO of Amazon. And you know, history would suggest that a lot of times that companies falter when there's a CEO transition, but it feels like it's different this time. Andy Jassy was here since the beginning launched AWS versus a profit engine of Amazon brought back Adam sill Lipski who has a deep understand. He's not as technical as Andy, but obviously as a deep understanding of the business, yeah, he was comfortable up in the keynote. It wasn't John, a typical firehose of announcements. Even those, a lot of announcements, they didn't shove them down our throat and they didn't in the analyst session as well. Usually in the analyst session, it's hours and hours and hours of firehose Kool-Aid injection, not this year. Why do you think that is, is that a COVID thing? Is that a change in now? >>I think Adam's Leschi wants to be his own guy. As, as leader here, a lot of things were eliminated from the keynote that Andy Jasmine did, for instance, Andy Jesse loves music. So we always had the music walk up music like you see in sports, uh, which is very cool. That's an Andy Jassy kind of tweak. Andy is all about announcements and he was just, uh, pushing the envelope. Adam was much more laid back. He sees, I think, more of a holistic picture being more of an app guy being more of a data guy, less of a, I would say under the covers nerd like Jassy was, Andy was very deep on, on a lot of the tech stuff as is Adam. But I think Andy a little bit more proactive on that. So Adam was very much more about the impact of 80 of us culturally, as a society, as a company and kind of brought in this kind of think different apple vibe, which is, you know, the people who are Pathfinders, um, as he takes that Jassy kind of, um, approach of leaders, but be a builder, be a change agent, be a game changer. >>Adam took it to another level by saying, Hey, it's okay to be a Pathfinder because it's net new disruption with the cloud. And I think that's the story that I see coming out of this where, uh, in talking to Adam one-on-one Amazon absolutely has a secret weapon in it's chips, custom Silicon. They're absolutely crushing it with how they're thinking about SAS and platforms and they have a huge ecosystem. And I think at the end of the day, and we talked about this in our story on Silicon angle, Amazon could actually wipe out Microsoft. And I think Microsoft's core competitive advantage has always been their ecosystem and their developers. I think right now in the next few years, if Microsoft doesn't match Amazon, they will be decimated anyway, you know? >>Yeah, hold on. Okay. Amazon's not going to wipe out Microsoft. Microsoft has too much of a cash cow. Look at the hanging on to windows. Couldn't, you know, the mistake and missing mobile event initially missing the cloud. Didn't wipe out Microsoft. So they've just got too much of a software cashflow. That's not gonna happen maybe a little bit over the top. >>I thought, but Microsoft has done a great job and it's not going to tell it to kind of stay in the game and do more. But if you look at the major inflection points, Dave where's digital equipment corporation, where's prime computer. Well, >>I think this is the point is again, history would show that those companies, when they handed the reigns over to a new CEO failed, they faltered, it was self-inflicted wounds. It almost happened. You thought it would happen with Microsoft, whether it became irrelevant under bomber, but when Nadella came in, he reinvigorated because specifically they had the cashflow to be able to do that. Now. So the big question is, okay, w what's going to happen. We ran a survey to our community to see what could disrupt Amazon. You know, that the us government wants to break them apart or wants to regulate them. But our survey respondents said there's a 60% plus probability that Amazon will be disrupted by other factors. And that's what I was self-inflicted wound that's Jesse's that's right. And that's, Jessie's big challenge is how to not make those disruptions, how to fight those disruptions. >>The number one, uh, reason why they could be disrupted was self-inflicted wounds, which again, history would show what happened. But one of the things we talked about is that normally happens when companies stop innovating when they rest on their laurels. Right. And you kind of saw that with those companies that you mentioned, but you mentioned their secret weapon. We wrote about that in our article, the chips. So we heard no secret. Everybody knew graviton three was coming, right? And so that is Amazon secret up. And you know, I've been thinking about this. John Amazon makes a lot of money on x86 instances that they've deployed years ago and they charge a lot for, I was wondering, you know, is the, or the old X 86 instances actually more profitable than graviton, maybe at this point in time, but long-term graviton. They control their own destiny because they control the hardware and software stack. And I bet you allows them to get better negotiating leverage with >>M D and it's of course, I mean, pat, Kelsey, we should talk about this all the time, but as bad as Jason Intel, you, if you're not out in the next wave, your driftwood, I think Intel and AMD and others, they have purpose-built general purpose chips. They're probably going to be for the lift and shift stuff when you, but if you're actually seriously writing software as an owner on the cloud, and you want specific advantages of speed and performance, you're going to want the custom Silicon that's purpose-built for your application and write code to that stack. So, so I think there's a whole nother level of platform as a service. Dave, that's kind of coming out of this re-invent that I think could be a multi generational trend, which is, Hey, the cloud is of super cloud or platform. Look at the riser, snowflake and Databricks. Those guys are on Amazon. Like they're super clouds in and of themselves they're platforms. They're not appoint SAS solution. I think Microsoft in my, my analysis is, yeah, they got office 365, okay. Word processing stuff. But what other SAS apps do they have besides SQL server and other things that are actually being built on there? And if, if I'm a developer you're going to want to go to the platform. That's the highest performance for office 365. It's a cash cow. But how long is that going to last >>A long time? I mean, major momentum. We argue about that later, but I wanna, I want to touch on graviton three because I think that was the big announcement of the day 25% faster than graviton to at least twice the floating point performance twice the crypto graphic performance in three times for machine learning, learning workloads, and very importantly, 60% less power. So at Amazon scale, uh, Adam said this in our meeting, he said, the economics really favor us because of our scale. And so, and they've also announced new training them instances and, and, and what, what having custom Silicon allows Amazon to do is release on a much, much faster cadence than traditional x86. And they could do, and they could do really cool things. Nitro is there, Nick they're smart NEC, which it says the basis, their new hypervisor, if you will. So it allows them to bring in x86, uh, Nvidia NPUs some of their own or Nvidia GPU, some of their own Silicon. So optionality is really the key there. You heard them announce, uh, an SAP instance. So that's a memory intensive instance. They can dial things up, dial things down. They've got full control of the stack. And by the way, copying them Google's copy of Microsoft is copying them. And who's leading this charge in custom Silicon, AWS, obviously Tesla, apple. I mean, these are leading companies that I don't think they all got it wrong. I think >>The Silicon angle is to have your own custom Silicon. And that's the, that is the clearly the advantage as it's vertically integrated. But the other thing that's coming out of this reinvents, the purpose built software concept where, you know, they're not copying Microsoft playbook as the wall street journal was saying, and some are saying Microsoft copying Amazon, Amazon has always been this horizontally scalable resource that's cloud, but with machine learning and AI, you now have this purpose-built kind of capability from software into the app itself where data has to be addressable. And I think the people in the data business kind of know this, but as the rest of the world comes out, architecturally having that horizontal observation space and data that's vertically tied to machine learning is a huge architectural shift. This is a complete rethinking of how software is built and that's going to be a game changer. I think Amazon's well out on front of that. And I think that's going to be a huge architectural shift. >>Well, let's quantify this a little bit because you know, you're, you're making the point that Amazon is the number one cloud, which I would agree with. We're talking here about IAS infrastructure as a service in the past layer that sits on top of that. Microsoft defines the cloud is we'll put in an office 365, Google we'll put in its Google apps, Amazon pure infrastructure as a service. And if you just look at that space, that's about $120 billion business. When you add up AWS, Azure, Alibaba and GCP, which I would contend are the only four hyperscalers out there. I don't include Oracle as a hyperscale. I don't include IBM. I get a lot of crap for that sometimes. Yeah, but we're talking big scaler, $120 billion. So actually relatively small compared to the trillion dollar opportunity that they have, but it's growing at 35% a year. Amazon will do more than 60 billion this year, 62 billion, just to quantify it in that ISS space. Microsoft will be about 38, 30 9 billion. Okay. So pretty substantial. Those two are far ahead of the others. Everybody else's, you know, Google is still in, you know, under 10 billion, Alibaba is right around there. So those two, it's really a two horse race. And I asked Microsoft using its software estate. Amazon's gotta be the innovator and has to have the best cloud to win. And it does well >>Also a platform. Let's go back to the little history lesson for the younger folks out there. When Microsoft was had a monopoly, they had windows operating system, which has had DAS under the covers, but windows was the operating system. And office was a suite of applications. They encourage software developers to build on top of windows and they had other servers off SQL server all came out of that small history. So their bread and butter was to have developers build on top of windows. Hence the monopoly, of course they had the application and the system software, hence the monopoly, hence the Microsoft breakup by the government in 1997. Now today cloud is essentially one big kind of PC concept. It's like windows, it's windows equivalent. So cloud is essentially an environment platform that has apps that run on top of it. Okay. In that world, Amazon by far is the number one windows model at Amazon's. >>I mean, Microsoft is used to is okay, I got Azure and I got office 365 that keeps them in business that keeps them from losing. So it's a placeholder. So that what I'm looking at is what is Amazon? I mean, Amazon versus Azure, doing relative to ISV and uptake for developers. And I'm suggesting that this trend of Amazon will go, if it goes uncontested by Azure, they'll wipe the table on ISV and suffer developers. If you're an owner of a software, you're not gonna write software, that's gonna be sub-optimized for a platform. That's not going to be before, >>Unless you're, unless you're a Microsoft developer, nearly all.net days. And there are a lot of those. And that's what, that's what Microsoft is doing. They're they're, they're, they've, they've shifted to cloud, they've gone everything into cloud. So Azure is their platform for innovation and acceleration. >>So those developers are going to build a sub application versus going over here on AWS. >>Well, that's the, that's the story with Microsoft. Good enough. I know >>Again, this is we're speculating, but we're going to watch that, but that is, to me, will be the battlefield of what will determine Azure versus AWS. And I think everything else is smoke and mirrors Amazon Webster way ahead of Azure, but the TeleSign is going to be does 80 bus attract those developers on their cloud with the custom Silicon, with the integrated stack and with the purpose-built software. I mean, it's looking really good. I think they've got a really compelling story. >>I think it's less about Azure versus AWS. I mean, that's an interesting storyline and I love to talk about it, but I think they'll go back to 120 billion out of 4 trillion. That's really the, the larger opportunity for, for both Microsoft and AWS to continue to grow. Because you look at, you look at Dell with apex, you look at HPE with GreenLake, Lenovo, Cisco, they've all got their own clouds. One of the things that didn't get into our article, but Adam Lipski when, when you asked him about hybrid is that hybrid cloud. When we were talking about some of the stuff they're doing, he S he said, look, that's not cloud what those guys are doing. That's not what we did. And he talked today about edge has to be AWS, not like AWS. That was the quote to use. Talk about, you know, private 5g, bringing out posts. And he gave some examples of that. The point is they, AWS is bringing its system, its architecture to the edge it's programming model infrastructure as code to the edge. Now, Kubernetes, Kubernetes does moderate that a little bit, but his point was, that's not AWS. That's not the cloud. >>Yeah. I think in summary, Dave had to wrap up what's the big trend this week is that Amazon web services is a, is a heaven environment for a developer, for the elite people who want to roll their own for the folks in it. In these other environments, you can have prefabricated purpose-built software platform to build on top of. And I think that isn't going to address the whole ease of ease of rollout. So if I'm a SAS developer, I don't, I want, I don't want to rebuild that over again. I don't want to roll my own. I'll take what you got and connects a good example. If you want to call shedder, you can take it and use it and then build on top of it and iterate on it. So I think it's more of here's a platform for you and take it. So I think that to me is the big story and that's not and think about it. How many people out there, a role in their own Amazon, you've got to be pretty strong at Amazon, uh, familiar ups to roll your own gut >>Of other quick points that he barely emphasized the primitives, the API APIs, that multiple databases, right tool for the right job, took a shot at Oracle without mentioning Oracle because they had sort of one database, but I will say this is mission critical. Oracle still owns that. Uh, they talked about a mainframe migration, tooling and runtime from mainframe compatible runtime. That's going to allow them to nip at the edges of those mainframe workloads and Oracle workloads. It, they're not going to get to the core anytime soon. They also talked about role level and cell level security. We think that's the squirrel acquisition from years ago. And then he made a statement. We have three X with Redshift price performance better than any cloud data warehouse sort of interesting shot at, at, at, at a snowflake and Databricks Databricks. So, um, anyway, yeah, >>I mean, I think, I think overall, I thought Adam did a good job. I think he didn't, uh, he didn't disappoint. Okay. But that's comfortable. I think his goal was to get through this and not have people go well, it's not Andy Jassy. I thought he did an awesome job and he did a good job. And he, he got, he got what he needed to do >>Comfortable. And he obviously leaned on some of his Pathfinder customers. NASDAQ, I thought was very impressive. United airlines dish. So, >>Okay. Cutie coverage, ninth year of the cube here at ADP reinvent, uh, 2021 is the cube. You're watching the leader in high-tech coverage. The cube.

>>Hello, and welcome back to the cubes coverage of splunk.com here in the virtual studios in Silicon valley broadcasting around the world's a virtual event. Um, John four-year host of the queue. We've got a great guest, Zach brown, chief executive officer of McLaren racing, really looking forward to this interview, Zach, welcome to the queue. Well, thanks for coming on. Thanks for having me. So we have a huge fan base in the tech community. A lot of geeks love the neurons. They love the tech behind the sport. Uh, and Netflix is driving to survive. Series has absolutely catapulted the popularity of F1 in the tech community. So congratulations on all the success in that program and on, and then on the >>Thank you very much, it's been a, it's been a good run. We've won our first race in a while, but we still have a ways to go to get in that, uh, world championship that, uh, >>So for the techies out there and the folks in our audience that aren't familiar with, the specifics of the racing team and the dynamics, take a minute to explain what you guys do. >>Uh, so McLaren racing, uh, which has a variety of, uh, racing teams, uh, a formula one team in indie car team and extremely team and an e-sports team. Uh, we're the second most successful form of the one team in the history of sport. Now 183 wins 182, uh, when I joined 20 world championships and, uh, we're, we're close to a thousand people to, to run a couple of racing cars and, uh, currently third in the championship, uh, with Lando Norris and, uh, Daniel, Ricardo. >>So talk about the, um, the, the dynamics of the spore. Obviously data is big part of it. Uh, we see the, a lot of the coverage. You can see anything can happen overnight. It's very quick. Um, technology has been being, uh, playing a big role in sport. What's your vision on how that's evolving? Are you happy with where things are, uh, and where do you see it going? >>Yeah, it does some interesting stats. So, um, the car that qualifies first at the beginning of the year, if you didn't touch, it would be last by the end of the year. So that's the pace of a development of a, of a formula one car. We change a, uh, and develop a new part on the car every 14 minutes, 365 days, days a year. Um, and technology plays a huge role. Uh, it's, it's probably the most technical, um, evolved sport in the world. Uh, both safety data, uh, the innovation it's it's awesome. And what a lot of people don't know is a lot of what we develop in a formula. One car ends up in other parts of the world, whether it was a ventilators that we helped develop for the UK government, uh, to working with our, uh, various partners or safety and innovation in the automotive industry. >>You know, I love it. I always loved the IOT internet of things, story around cars, because sensors or instrumentation is a big part of it. Um, and it all comes together. So it's pretty, it's not simple. No, give it feel, give it a taste a little bit about what's it. How complicated is it, how you guys pay attention to the details? What's important. Take us through some of the, some of the inside the ropes around the IOT of the sensors and all the data. >>Yeah. So we have over 300 sensors on our race car. We collect the one and a half terabytes of data. Every race weekend, we have a thousand people, um, and the strong majority of those are working around data and technology, as opposed to physically touching the car out of those thousand people, you probably only have about 60 or 70. They're actually touch the race card at a race weekend. We've been doing connected cars for about 25 years. So that's kind of a new thing here to, to most people, but we've been communicating back and forth with our race car for, for decades all around the world. And what a lot of people don't realize is it all starts in our mission control back in our factory in Woking, England. So wherever we are around the world, the racing team actually starts in England. >>So I want to ask you about the personalities on the team. How big is the staff? What's the makeup of the personnel has to get the drivers. They're critical. They're a very dynamic personalities. We'll come to the side question on that later, but what's the staff look like on when you guys put this together. So you get, you get race day and you got back office support. >>What's the team look like? Yeah. So you've got about a thousand people that, that make up the collective team. You'll have about a hundred in marketing. Uh, you'll have about a hundred in finance, HR, and then you kind of get to the, the racing team. If you'd like 800 people, you have about a hundred people traveling to each race, uh, about 50 people back at the factory, working with data and communications that are grand Prix weekend. And then everybody else is designing manufacturing, production laminating. So we run 24, 7 shifts, uh, three shifts, uh, in certain parts. Uh, we develop, uh, 85% of the car changes of what's allowed to be changed start of the year to the, the end of the year. So the development is, is unbelievable. >>I know you're here in the U S for the U S grand Prix in Austin. Um, coming up, I'm just curious how cars get transported. >>Uh, w when we're traveling around the world, uh, they, they travel on 7 47 and are flown around the world. And then when we're in Europe, we have about 18 trucks that were communing around when we're kind of in the European part of the circuit is usually in the middle of the year. But when we're going to Australia or Singapore, Bahrain, those are, those are on planes form of the one actually does that. They give us an allocation of, of space, and then we have to write a check if we need more space than where >>Yeah. We're allowed. Yeah. And that brings up the security question, because honestly, there's a lot of fans, a lot of people are into it. Also, this potentially security risks. Have you guys thought about that obviously like physical moving the supply chain around from event event, but also technology risk. Um, how do you guys think about security? >>Yeah, it's, it's critically important. We've had, uh, fortunately we've not had any breach of our technology. We have had a breach in the late nineties of our radio communications and, uh, it was in Australia, Mika Hakkinen and a fan, uh, who I think was probably having some fun and were able to break into our radio channel and actually asked Mika to pit. He pitted team wasn't ready. And fortunately, we will run in one, two, but we actually had to reverse the drivers. So security is >>Critically important, probably Katie Scrivener, and they all look, I just hack the radio, was talking to the driver. That is a funny story, but it could be serious. I mean, now you have all kinds of >>The stuff going on and, and, you know, there's a lot of money at stake, you know, so, you know, we're fortunate in this particular instance, it didn't hurt us cause we were running one, two, so we could reverse the drivers and the right guide one. Um, but you know, that could decide, uh, a world championship and you have, you know, tens of millions of dollars online, but even besides the economics, we want to win races. >>You know, what's funny is that you guys have a lot of serious on the line stakes with these races, but you're known for having a lot of fun, the team team dynamic. I have to ask you, when you finish on the podium one and two, there's a Shui with the drivers. How'd that go down. It was pretty, pretty a big spectacle online and >>Yeah, it was, it was good, fun. That's something, obviously Daniel Ricardo is kind of developed as his thing when he, uh, when he wins. And, uh, when we were, uh, before we went on the podium, he said to me, you're going to do the shoe. Yes, of course. In the car show you got to do, we have to like a bunch of 12 year old kids, uh, on the podium, but that's where we're just big kids going, motor racing and >>The end of the day. Well, I gotta say you guys come across really strong as a team, and I love the fun and, you know, competitive side. So congratulations on that, I think is good on the competitive side, take me through the advantage, driving the advantage with data, because that's really the theme here at.com, which is Splunk, which they're a big partner, as well as your other sponsors. Data's big, you know, and it's striving an advantage. Where do you see that coming from? Take us through where you guys see the advantages. Yes. >>So, you know, everything we do is, is precision and, you know, every second, every 10th counts and, um, you know, you can get all this data in, but what do you do with this data? And the humans can, uh, real, uh, react as quickly as is, you know, people like Splunk who can help us, uh, not only collect data, but help us understand data. And, um, you know, typically there's one pit stop, which can be the difference between winning and losing. Um, you have all these different scenarios playing out with weather with tire wear competition. And so, you know, we live by data. We didn't, uh, when, in, in Russia, when we, uh, could have, and it was because we got a bit emotionally caught up in the excitement of trying to win the race instead of staying disciplined and focused on, on data. And so it's a very data-driven sport when I'm on the pit wall, there's a thing called racer instinct, which is my 30 years in the sport. And, uh, your experience and your kind of your gut to make decisions. And every time our team makes a decision that I'm sitting there going, I'm not sure that was the right decision. They're staring at data. I'm not, I'm trusting my 30 years of experience. They'd beat me nine out of 10. >>Yeah. I mean, you know, this is a huge topic too, in the industry, explainable AI is one of the hottest trends in computer science where there's so much algorithms involved. The gut instinct is now coming back. What algorithms are available, knowing when to deploy what algorithms or what data to pay attention to is a huge new gut factor. Yep. Can you explain how the young drivers and the experience folks in the industry are dealing with this new instinct full data-driven? >>Yeah. That's, you know, that's what we have 50 people back at the factory doing, and they're looking at all sorts of information coming in, and then they're taking that information and they're feeding it to our head of strategy. Who's then feeding it to our racing director. Who's getting all these data points in from tire to performance, to reliability, and then the human data from both drivers coming through their engineers. And then he gets all that information in. He has to process it immediately and make decisions, but it's, it's a data-driven sport. >>I saw Lando walking around, got a selfie with them. It's great. Everyone's loving it on Twitter. My family, like get an autograph, the future of the sport. He's a young young driver. So that instincts coming in the future sport comes up all the time. The tires are a big discussion point, but also you've got a lot of presets going on, a lot of data, a lot of going on and you see the future where there's remote, you know, kind of video game you're in the pit wall and you can make decisions and deploy on behalf of the drivers. Is that something that >>Well, that technology is there and we used to do that, but now it's been outlawed because there's a real push to make sure the drivers are driving the car. So that technology is here. It has been deployed in the past. We could do it, but we're trying to find as a sport, the balance between, you know, letting the driver do it. So he, or she might make a mistake and a little bit of excitement to it. So, um, we now there are certain protocols on what we communicate. Um, we can't, um, everything has to be driver fed into the car. So we can now you'll hear all sorts of codes that we're talking through, which there are, um, about 300 different adjustments the driver can make on the steering wheel, which is unbelievable. And so that's us seeing information, getting data in coming to conclusions that we're giving him or her information that we think will help make the car >>A lot of new dimensions for drivers to think about when they're being successful with the gut, that the track data everything's kind of coming together. >>Yeah. It's amazing. Um, when you listen to these drivers on the radio, you forget that they're going 200 plus miles an hour. Cause they sound quite relaxed in this very, you know, open and easy communication of here's what I'm feeling with. Again, we're talking all these codes and then we all, because we can hear each other, there's a lot of trickery that goes on. So for a driver to be going to turn a miles an hour, taking this information and then know what code we're talking, are we kind of throwing a code out there to put the competition off is pretty amazing that they can take this all in. >>You know, I wish I was younger again, like we're old school and the younger generation, I was having a few conversations with a lot of the young audience. They wanted me to ask you, when are you guys going to metaverse the tracks? When can I get involved and participate and maybe even make the team, or how do I become more active, engaged with the McLaren racing team? >>And that technology is almost, we're actually, um, that's in development. So I, I think it won't be long before, you know, Sunday you can log on, uh, and, and race Lando around Monaco and be in the race. So that, that technology is around the corner. >>That's the shadow thing to developing. I see that. E-sports just quick. I know you've got to go on, but last minute we have here, e-sports, what's the future of e-sports with the team, >>But e-sports been great for the sport. You know, it's gone from, you know, when I was growing up, it was video games and now it's real simulation. And, uh, so we've held, I think we're going four years into it. Now we were the first team to really develop any sports platform and we've had competitors go on to help us with our simulation. So it's, it's real racially developed the race car before it goes on the racetrack it's in simulation. And that's where e-sports, >>And this is the new advantage. This is a new normal, this is where you guys see the data driving. The >>Definitely. And I think the other thing it is, you know, somewhat stick and ball sports, you can play in school. And motor racing has historically been partying, which can cost hundreds of thousands of dollars. Now with e-sports you have a less expensive platform to let young men and women around the world, but a steering wheel in their hand and go motor racing. So I think it's also going to kind of bring that younger generation of fan and >>There's so much collective intelligence, potentially competitive advantage data. Again, data coming up final word to end the segment, Splunk, big partner on the data side, obviously helping you guys financially, as well as you do need some sponsorship support to make the team run. Um, what's the relationship with Splunk? Take a minute to talk about the plug. >>It's been a, it's been great, you know, they're, they're two big contributors. We need a lot of money to run the racing team. So they're a great partner in that respect, but more importantly, they're helping us with our whole data journey, making smarter, quicker decisions. So their contribution to being part of the race team. And, uh, we used our technology. Um, it has been great. And I think, um, you know, if I look at our technology partners, uh, we have many that all contribute to making a >>Yeah. I mean, it really is nice. It's data inaction, it's teamwork, it's competitive, it's fun. That's kind of a good, good, >>I think fun is the center of everything that we do. It's the center of everything spunk does. Cause I think if you have fun, people enjoy going to working a little bit harder. We're seven days a week. And uh, you know, a lot of teammates you've got to work well together. So I think if you're having fun, you enjoy what you're doing and it doesn't feel like work. >>Congratulations on climbing up in the rankings and everything on your team. Two great drivers. Thanks for coming on the cube. We appreciate it. Thank you. All right. We're here. The key. We like to have fun here and get all the action on the tech side. Honestly, F1 is technology enabled data, driving the advantage and driving to is a great Netflix series. Check it out. McLaren's featured heavily in there and got a great team. Zach brown Siegel. Thanks for coming on. Appreciate it. I'm sure for your host. Thank you for watching.

>> Well, hello everybody, John Walls here on theCUBE and continuing our coverage. So splunk.com for 21, you know, we talk about big data these days, you realize the importance of speed, right? We all get that, but certainly Formula One Racing understands speed and big data, a really neat marriage there. And with us to talk about that is James Hodge, who was the global vice president and chief strategy officer international at Splunk. James, good to see it today. Thanks for joining us here on theCUBE. >> Thank you, John. Thank you for having me and yeah, the speed of McLaren. Like I'm, I'm all for it today. >> Absolutely. And I find it interesting too, that, that you were telling me before we started the interview that you've been in Splunk going on nine years now. And you remember being at splunk.com, you know, back in the past other years and watching theCUBE and here you are! you made it. >> I know, I think it's incredible. I love watching you guys every single year and kind of the talk that guests. And then more importantly, like it reminds me of conf for every time we see theCUBE, no matter where you are, it reminds me of like this magical week there's dot com for us. >> Well, excellent. I'm glad that we could be a part of it at once again and glad you're a part of it here on theCUBE. Let's talk about McLaren now and the partnership, obviously on the racing side and the e-sports side, which is certainly growing in popularity and in demand. So just first off characterize for our audience, that relationship between Splunk and McLaren. >> Well, so we started the relationship almost two years ago. And for us it was McLaren as a brand. If you think about where they were, they recently, I think it's September a Monza. They got a victory P1 and P2. It was over 3200 days since their last victory. So that's a long time to wait. I think of that. There's 3000 days of continual business transformation, trying to get them back up to the grid. And what we found was that ethos, the drive to digital the, the way they're completely changing things, bringing in kind of fluid dynamics, getting people behind the common purpose that really seem to fit the Splunk culture, what we're trying to do and putting data at the heart of things. So kind of Formula One and McLaren, it felt a really natural place to be. And we haven't really looked back since we started at that partnership. It's been a really exciting last kind of 18 months, two years. >> Well, talk a little bit about, about the application here a little bit in terms of data cars, the, the Formula One cars, the F1 cars, they've got hundreds of sensors on them. They're getting, you know, hundreds of thousands or a hundred thousand data points almost instantly, right? I mean, there's this constant processing. So what are those inputs basically? And then how has McLaren putting them to use, and then ultimately, how is Splunk delivering on that from McLaren? >> So I learned quite a lot, you know, I'm, I'm, I been a childhood Formula One fan, and I've learned so much more about F1 over the last kind of couple of years. So it actually starts with the car going out on the track, but anyone that works in the IT function, the car can not go out on track and less monitoring from the car actually is being received by the garage. It's seen as mission critical safety critical. So IT, when you see a car out and you see the race engineer, but that thumbs up the mechanical, the thumbs up IT, get their vote and get to put the thumbs up before the car goes out on track there around about 300 sensors on the car in practice. And there were two sites that run about 120 on race day that gets streamed on a two by two megabits per second, back to the FIA, the regulating body, and then gets streams to the, the garage where they have a 32 unit rack near two of them that have all of their it equipment take that data. They then stream it over the internet over the cloud, back to the technology center in working where 32 race engineers sit in calm conditions to be able to go and start to make decisions on when the car should pit what their strategy should be like to then relate that back to the track side. So you think about that data journey alone, that is way more complicated and what you see on TV, you know, the, the race energy on the pit wall and the driver going around at 300 kilometers an hour. When we look at what Splunk is doing is making sure that is resilient. You know, is the data coming off the car? Is it actually starting to hit the garage when it hits that rack into the garage, other than streaming that back with the right latency back to the working technology center, they're making sure that all of the support decision-making tools there are available, and that's just what we do for them on race weekend. And I'll give you one kind of the more facts about the car. So you start the beginning of the season, they launched the car. The 80% of that car will be different by the end of the season. And so they're in a continual state of development, like constantly developing to do that. So they're moving much more to things like computational fluid dynamics applications before the move to wind tunnel that relies on digital infrastructure to be able to go and accelerate that journey and be able to go make those assumptions. That's a Splunk is becoming the kind of underpinning of to making sure those mission critical applications and systems are online. And that's kind of just scratching the surface of kind of the journey with McLaren. >> Yeah. So, so what would be an example then maybe on race day, what's a stake race day of an input that comes in and then mission control, which I find fascinating, right? You've got 32 different individuals processing this input and then feeding their, their insights back. Right. And so adjustments are being made on the fly very much all data-driven what would be an example of, of an actual application of some information that came in that was quickly, you know, recorded, noted, and then acted upon that then resulted in an improved performance? >> Well, the most important one is pit stop strategy. It can be very difficult to overtake on track. So starting to look at when other teams go into the pit lane and when they come out of the, the pit lane is incredibly important because it gives you a choice. Do you stay also in your current set of tires and hope to kind of get through that team and kind of overtake them, or do you start to go into the pits and get your fresh sets of tires to try and take a different strategy? There are three people in mission control that have full authority to go and make a Pit lane call. And I think like the thing that really resonated for me from learning about McLaren, the technology is amazing, but it's the organizational constructs on how they turn data into an action is really important. People with the right knowledge and access to the data, have the authority to make a call. It's not the team principle, it's not the person on the pit wall is the person with the most amount of knowledge is authorized and kind of, it's an open kind of forum to go and make those decisions. If you see something wrong, you are just as likely to be able to put your hand up and say, something's wrong here. This is my, my decision than anyone else. And so when we think about all these organizations that are trying to transform the business, we can learn a lot from Formula One on how we delegate authority and just think of like technology and data as the beginning of that journey. It's the people in process that F1 is so well. >> We're talking a lot about racing, but of course, McLaren is also getting involved in e-sports. And so people like you like me, we can have that simulated experience to gaming. And I know that Splunk has, is migrating with McLaren in that regard. Right. You know, you're partnering up. So maybe if you could share a little bit more about that, about how you're teaming up with McLaren on the e-sports side, which I'm sure anybody watching this realizes there's a, quite a big market opportunity there right now. >> It's a huge market opportunity is we got McLaren racing has, you know, Formula One, IndyCar and now extreme E and then they have the other branch, which is e-sports so gaming. And one of the things that, you know, you look at gaming, you know, we were talking earlier about Ted Lasso and, you know, the go to the amazing game of football or soccer, depending on kind of what side of the Atlantic you're on. I can go and play something like FIFA, you know, the football game. I can be amazing at that. I have in reality, you know, in real life I have two left feet. I am never going to be good at football however, what we find with e-sports is it makes gaming and racing accessible. I can go and drive the same circuits as Lando Norris and Daniel Ricardo, and I can improve. And I can learn like use data to start to discover different ways. And it's an incredibly expanding exploding industry. And what McLaren have done is they've said, actually, we're going to make a professional racing team, an e-sports team called the McLaren Shadow team. They have this huge competition called the Logitech KeyShot challenge. And when we looked at that, we sort of lost the similarities in what we're trying to achieve. We are quite often starting to merge the physical world and the digital world with our customers. And this was an amazing opportunity to start to do that with the McLaren team. >> So you're creating this really dynamic racing experience, right? That, that, that gives people like me, or like our viewers, the opportunity to get even a better feel for, for the decision-making and the responsiveness of the cars and all that. So again, data, where does that come into play there? Now, What, what kind of inputs are you getting from me as a driver then as an amateur driver? And, and how has that then I guess, how does it express in the game or expressed in, in terms of what's ahead of me to come in a game? >> So actually there are more data points that come out of the F1 2021 Codemasters game than there are in Formula One car, you get a constant stream. So the, the game will actually stream out real telemetry. So I can actually tell your tire pressures from all of your tires. I can see the lateral G-Force longitudinal. G-Force more importantly for probably amateur drivers like you and I, we can see is the tire on asphalt, or is it maybe on graphs? We can actually look at your exact position on track, how much accelerator, you know, steering lock. So we can see everything about that. And that gets pumped out in real time, up to 60 Hertz. So a phenomenal amount of information, what we, when we started the relationship with McLaren, Formula One super excited or about to go racing. And then at Melbourne, there's that iconic moment where one of the McLaren team tested positive and they withdrew from the race. And what we found was, you know, COVID was starting and the Formula One season was put on hold. The FIA created this season and called i can't remember the exact name of it, but basically a replica e-sports gaming F1 series. We're using the game. Some of the real drivers like Lando, heavy gamer was playing in the game and they'd run that the same as race weekends. They brought celebrity drivers in there. And I think my most surreal zoom call I ever was on was with Lando Norris and Pierre Patrick Aubameyang, who was who's the arsenal football captain, who was the guest driver in the series to drive around Monaco and Randy, the head of race strategy as McLaren, trying to coach him on how to go drive the car, what we ended up with data telemetry coming from Splunk. And so Randy could look out here when he pressing the accelerator and the brake pedal. And what was really interesting was Lando was watching how he was entering corners on the video feed and intuitively kind of coming to the same conclusions as Randy. So kind of, you could see that race to intuition versus the real stats, and it was just incredible experience. And it really shows you, you know, racing, you've got that blurring of the physical and the virtual that it's going to be bigger and bigger and bigger. >> So to hear it here, as I understand what you were just saying now, the e-sports racing team actually has more data to adjust its performance and to modify its behaviors, then the real racing team does. Yep. >> Yeah, it completely does. So what we want to be able to do is turn that into action. So how do you do the right car setup? How do you go and do the right practice laps actually have really good practice driver selection. And I think we're just starting to scratch the surface of what really could be done. And the amazing part about this is now think of it more like a digital twin, what we learn on e-sports we can actually say we've learned something really interesting here, and then maybe a low, you know, if we get something wrong, it may be doesn't matter quite as much as maybe getting an analytics wrong on race weekend. >> Right. >> So we can actually start to look and improve through digital and then start to move that support. That's over to kind of race weekend analytics and supporting the team. >> If I could, you know, maybe pun intended here, shift gears a little bit before we run out of time. I mean, you're, you're involved on the business side, you know, you've got, you know, you're in the middle east Africa, right? You've got, you know, quite an international portfolio on your plate. Now let's talk about just some of the data trends there for our viewers here in the U S who maybe aren't as familiar with what's going on overseas, just in terms of, especially post COVID, you know, what, what concerns there are, or, or what direction you're trying to get your clients to, to be taking in terms of getting back to work in terms of, you know, looking at their workforce opportunities and strengths and all those kinds of things. >> I think we've seen a massive shift. I think we've seen that people it's not good enough just to be storing data its how do you go and utilize that data to go and drive your business forwards I think a couple of key terms we're going to see more and more over the next few years is operational resilience and business agility. And I'd make the assertion that operational resilience is the foundation for the business agility. And we can dive into that in a second, but what we're seeing take the Netherlands. For example, we run a survey last year and we found that 87% of the respondents had created new functions to do with data machine learning and AI, as all they're trying to do is go and get more timely data to front line staff to go. And next that the transformation, because what we've really seen through COVID is everything is possible to be digitized and we can experiment and get to market faster. And I think we've just seen in European markets, definitely in Asia Pacific is that the kind of brand loyalty is potentially waning, but what's the kind of loyalty is just to an experience, you know, take a ride hailing app. You know, I get to an airport, I try one ride hailing app. It tells me it's going to be 20 minutes before a taxi arrives. I'm going to go straight to the next app to go and stare. They can do it faster. I want the experience. I don't necessarily want the brand. And we're find that the digital experience by putting data, the forefront of that is really accelerating and actually really encouraging, you know, France, Germany are actually ahead of UK. Let's look, listen, their attitudes and adoption to data. And for our American audience and America, America is more likely, I think it's 72% more likely to have a chief innovation officer than the rest of the world. I think I'm about 64% in EMEA. So America, you are still slightly ahead of us in terms of kind of bringing some of that innovation that. >> I imagine that gap is going to be shrinking though I would think. >> It is massively shrinking. >> So before we, we, we, we are just a little tight on time, but I want to hear about operational resilience and, and just your, your thought that definition, you know, define that for me a little bit, you know, put a little more meat on that bone, if you would, and talk about why, you know, what that is in, in your thinking today and then why that is so important. >> So I think inputting in, in racing, you know, operational resilience is being able to send some response to what is happening around you with people processing technology, to be able to baseline what your processes are and the services you're providing, and be able to understand when something is not performing as it should be, what we're seeing. Things like European Union, in financial services, or at the digital operational resilience act is starting to mandate that businesses have to be operational in resilient service, monitoring fraud, cyber security, and customer experience. And what we see is really operational resilience is the amount of change that can be absorbed before opportunities become risk. So having a stable foundation of operational resilience allows me to become a more agile business because I know my foundation and people can then move and adjust quickly because I have the awareness of my environment and I have the ability to appropriately react to my environment because I've thought about becoming a resilient business with my digital infrastructure is a theme. I think we're going to see in supply chain coming very soon and across all other industries, as we realize digital is our business. Nowadays. >> What's an exciting world. Isn't it, James? That you're, that you're working in right now. >> Oh, I, I love it. You know, you said, you know, eight and an eight and a half years, nine years at Splunk, I'm still smiling. You know, it is like being at the forefront of this diesel wave and being able to help people make action from that. It's an incredible place to be. I, is liberating and yeah, I can't even begin to imagine what's, you know, the opportunities are over the next few years as the world continually evolves. >> Well, every day is a school day, right? >> It is my favorite phrase >> I knew that. >> And it is, James Hodge. Thanks for joining us on theCUBE. Glad to have you on finally, after being on the other side of the camera, it's great to have you on this side. So thanks for making that transition for us. >> Thank you, John. You bet James Hodge joining us here on the cube coverage of splunk.com 21, talking about McLaren racing team speed and Splunk.

(upbeat music) >> Hi, I'm Joe Rodriguez, Managing Director of Financial Services at Cloudera. Welcome to the Fight Fraud with Data session. At Cloudera we believe that fighting fraud begins with data. So financial services is Cloudera's largest industry vertical. We have approximately 425 global financial services customers, which consists of 82 out of a hundred of the largest global banks of which we have 27 that are globally systemic banks. Four out of the five top stock exchanges, eight out of the top 10 wealth management firms and all four of the top credit card networks. So as you can see, most financial services institutions utilize Cloudera for data analytics and machine learning. We also have over 20 central banks and a dozen or so financial regulators. So it's an incredible footprint which gives Cloudera lots of insight into the many innovations that our customers are coming up with. Criminals can steal thousands of dollars before a fraudulent transaction is detected. So the cost to purchase your account data is well worth the price to fraudsters. According to Experian, credit and a debit card account information sells on the dark web for a mere $5 with the CVV number and up to $110 if it comes with all the bank information, including your name, social security number, date of birth, complete account numbers, and other personal data. Our customers have several key data and analytics challenges when it comes to fighting financial crime. The volume of data that they need to deal with is huge and growing exponentially. All this data needs to be evaluated in real time. There are new sources of streaming data that need to be integrated with existing legacy data sources. This includes biometrics data and enhanced authentication video surveillance, call center data, and of course all that needs to be integrated with existing legacy data sources. There is an analytics Arms Race between the banks and the criminals, and the criminal networks never stop innovating. They also have to deal with disjointed security and governance. Security and governance policies are often set per data source or application requiring redundant work across workloads. And they have to deal with siloed environments. The specialized nature of platforms and people results in disparate data sources and data management processes. This duplicates efforts and divides the business risk and crime teams, limiting collaboration opportunities between them. CDP enhances financial crime solutions to be holistic by eliminating data gaps between siloed solutions, with an enterprise data approach, advanced data analytics and machine learning. By deploying an enterprise wide data platform, you reduce siloed divisions between business risk and crime teams and enable better collaboration through industrialized machine learning, you tighten up the loop between detection and new fraud patterns. Cloudera provides the data platform on which a best of breed applications can run and leverage integrated machine learning. Cloudera stands rather than replaces your existing fraud modeling applications. So Oracle, SAS, Actimize, to name a few, integrate with an enterprise data hub to scale the data, increase speed and flexibility and improve efficacy of your entire fraud system. It also centralizes the fraud workload on data that can be used for other use cases in applications like Enhanced KYC and Customer 360 for example. I just wanted to highlight a couple of our partners in financial crime prevention, Simudyne and Quantexa. So Simudyne provides fraud simulation using agent-based modeling machine learning techniques to generate synthetic transaction data. This data simulates potential fraud scenarios in a cost-effective GDPR-compliant virtual environment to significantly improve financial crime detection systems. Simudyne identifies future fraud topologies for millions of simulations that can be used to dynamically train new machine learning algorithms for enhanced identification. And Quantexa connects the dots within your data using dynamic entity resolution, and advanced network analytics to create context around your customers. This enables you to see the bigger picture and automatically assesses potential criminal behavior. Now let's go over some of our customers and how they're using Cloudera. First, we'll talk about United Overseas Bank or UOB. UOB is a leading full service bank in Asia with a network of more than 500 offices in 19 countries and territories, in Asia Pacific, Western Europe and North America. UOB built a modern data platform on Cloudera that gives it the flexibility and speed to develop new AI and machine learning solutions and to create a data-driven enterprise. UOB set up it's big data analytics center in 2017. It was Singapore's first centralized big data unit within a bank to deepen the bank's data analytic capabilities and to use data insights to enhance the bank's performance. Essential to this work was implementing a platform that could cost efficiently bring together data from dozens of separate systems and incorporate a range of unstructured data, including voice and text. Using Cloudera CDP and machine learning, UOB gained a richer understanding of its customer preferences to help make their banking experience simpler, safer, and more reliable. Working with Cloudera, UOB has a big data platform that gives business staff and data scientists, faster access to relevant and quality data for self-service analytics, machine learning and emerging artificial intelligence solutions. With new self-service analytics and machine learning driven insights, UOB has realized improvements in digital banking, asset management, compliance, AML, and more. Advanced AML detection capabilities, help analysts detect suspicious transactions either based on hidden relationships of shell companies and high risk individuals with Cloudera and machine learning technologies, UOB was able to enhance AML detection and reduce the time to identify new links from months to three weeks. Next, let's speak about MasterCard. So MasterCard's principle business is to process payments between banks and merchants and the credit issuing banks and credit unions of the purchasers who use the MasterCard brand debit and credit cards to make purchases. MasterCard chose Cloudera Enterprise for fraud detection and to optimize their DW infrastructure, delivering deep insights and best practices and big data security and compliance. Next, let's speak about Bank Rakyat in Indonesia or BRI. BRI is one of the largest and oldest banks in Indonesia and engages in the provision of general banking services. It's headquartered in Jakarta, Indonesia. BRI is well-known for its focus on microfinancing initiatives and serves over 75 million customers through its more than 11,000 offices and rural service outposts. BRI required better insight to understand customer activity and identify fraudulent transactions. The bank needed a solid foundation that allowed it to leverage the power of advanced analytics, artificial intelligence, and machine learning to gain better understanding of customers and the market. BRI used Cloudera Enterprise data platform to build an agile and reliable, predictive augmented intelligence solution to enhance its credit scoring system. And to address the rising concern around data security from regulators and customers, BRI developed a real-time fraud detection service powered by Cloudera and Kafka, BRI's data scientists developed a machine learning model for fraud detection by creating a behavioral scoring model based on customer savings, loan transactions, deposits, payroll and other financial real-time data. This led to improvements in its fraud detection and credit scoring capabilities, as well as the development of a new digital microfinancing product. With the enablement of real-time fraud detection, BRI was able to reduce the rate of fraud by 40%. It improved relationship manager productivity by two and a half fold. It improved the credit scoring system to cut down on micro-financing loan processing times from two weeks to two days to now two minutes. So fraud prevention is a good area to start with data focus if you haven't already. It offers a quick return on investment and it's a focused area that's not too entrenched across the company. To learn more about fraud prevention, go to www.cloudera.com, and you should schedule a meeting with Cloudera to learn even more. And with that, thank you for listening and thank you for your time. (upbeat music)

>>Edge computing is predict, projected to be a multi-trillion dollar business. You know, it's hard to really pinpoint the size of this market. Let alone fathom the potential of bringing software, compute, storage, AI, and automation to the edge and connecting all that to clouds and on-prem systems. But what, you know, what is the edge? Is it factories? Is it oil rigs, airplanes, windmills, shipping containers, buildings, homes, race cars. Well, yes and so much more. And what about the data for decades? We've talked about the data explosion. I mean, it's mind boggling, but guess what, we're gonna look back in 10 years and laugh. What we thought was a lot of data in 2020, perhaps the best way to think about edge is not as a place, but when is the most logical opportunity to process the data and maybe it's the first opportunity to do so where it can be decrypted and analyzed at very low latencies that that defines the edge. And so by locating compute as close as possible to the sources of data, to reduce latency and maximize your ability to get insights and return them to users quickly, maybe that's where the value lies. Hello everyone. And welcome to this cube conversation. My name is Dave Vellante and with me to noodle on these topics is Omar Assad, VP, and GM of primary storage and data management services at HPE. Hello, Omer. Welcome to the program. >>Hey Steve. Thank you so much. Pleasure to be here. >>Yeah. Great to see you again. So how do you see the edge in the broader market shaping up? >>Uh, David? I think that's a super important, important question. I think your ideas are quite aligned with how we think about it. Uh, I personally think, you know, as enterprises are accelerating their sort of digitization and asset collection and data collection, uh, they're typically, especially in a distributed enterprise, they're trying to get to their customers. They're trying to minimize the latency to their customers. So especially if you look across industries manufacturing, which is distributed factories all over the place, they are going through a lot of factory transformations where they're digitizing their factories. That means a lot more data is being now being generated within their factories. A lot of robot automation is going on that requires a lot of compute power to go out to those particular factories, which is going to generate their data out there. We've got insurance companies, banks that are creating and interviewing and gathering more customers out at the edge for that. >>They need a lot more distributed processing out at the edge. What this is requiring is what we've seen is across analysts. A common consensus is that more than 50% of an enterprise is data, especially if they operate globally around the world is going to be generated out at the edge. What does that mean? More data is new data is generated at the edge, but needs to be stored. It needs to be processed data. What is not required needs to be thrown away or classified as not important. And then it needs to be moved for Dr. Purposes either to a central data center or just to another site. So overall in order to give the best possible experience for manufacturing, retail, uh, you know, especially in distributed enterprises, people are generating more and more data centric assets out at the edge. And that's what we see in the industry. >>Yeah. We're definitely aligned on that. There's some great points. And so now, okay. You think about all this diversity, what's the right architecture for these deploying multi-site deployments, robo edge. How do you look at that? >>Oh, excellent question. So now it's sort of, you know, obviously you want every customer that we talk to wants SimpliVity, uh, in, in, and, and, and, and no pun intended because SimpliVity is reasoned with a simplistic edge centric architecture, right? So because let's, let's take a few examples. You've got large global retailers, uh, they have hundreds of global retail stores around the world that is generating data that is producing data. Then you've got insurance companies, then you've got banks. So when you look at a distributed enterprise, how do you deploy in a very simple and easy to deploy manner, easy to lifecycle, easy to mobilize and easy to lifecycle equipment out at the edge. What are some of the challenges that these customers deal with these customers? You don't want to send a lot of ID staff out there because that adds costs. You don't want to have islands of data and islands of storage and promote sites, because that adds a lot of States outside of the data center that needs to be protected. >>And then last but not the least, how do you push lifecycle based applications, new applications out at the edge in a very simple to deploy better. And how do you protect all this data at the edge? So the right architecture in my opinion, needs to be extremely simple to deploy. So storage, compute and networking, uh, out towards the edge in a hyperconverged environment. So that's, we agree upon that. It's a very simple to deploy model, but then comes, how do you deploy applications on top of that? How do you manage these applications on top of that? How do you back up these applications back towards the data center, all of this keeping in mind that it has to be as zero touch as possible. We at HBS believe that it needs to be extremely simple. Just give me two cables, a network cable, a power cable, tied it up, connected to the network, push it state from the data center and back up at state from the ed back into the data center. Extremely simple. >>It's gotta be simple because you've got so many challenges. You've got physics that you have to deal your latency to deal with. You got RPO and RTO. What happens if something goes wrong, you've gotta be able to recover quickly. So, so that's great. Thank you for that. Now you guys have hard news. W what is new from HPE in this space >>From a, from a, from a, from a deployment perspective, you know, HPE SimpliVity is just gaining like it's exploding, like crazy, especially as distributed enterprises adopt it as it's standardized edge architecture, right? It's an HCI box has got stories, computer networking, all in one. But now what we have done is not only you can deploy applications all from your standard V-Center interface, from a data center, what have you have now added is the ability to backup to the cloud, right? From the edge. You can also back up all the way back to your core data center. All of the backup policies are fully automated and implemented in the, in the distributed file system. That is the heart and soul of, of the SimpliVity installation. In addition to that, the customers now do not have to buy any third-party software into backup is fully integrated in the architecture and it's van efficient. >>In addition to that, now you can backup straight to the client. You can backup to a central, uh, high-end backup repository, which is in your data center. And last but not least, we have a lot of customers that are pushing the limit in their application transformation. So not only do we previously were, were one-on-one them leaving VMware deployments out at the edge sites. Now revolver also added both stateful and stateless container orchestration, as well as data protection capabilities for containerized applications out at the edge. So we have a lot, we have a lot of customers that are now deploying containers, rapid manufacturing containers to process data out at remote sites. And that allows us to not only protect those stateful applications, but back them up, back into the central data center. >>I saw in that chart, it was a light on no egress fees. That's a pain point for a lot of CEOs that I talked to. They grit their teeth at those entities. So, so you can't comment on that or >>Excellent, excellent question. I'm so glad you brought that up and sort of at that point, uh, uh, pick that up. So, uh, along with SimpliVity, you know, we have the whole green Lake as a service offering as well. Right? So what that means, Dave, is that we can literally provide our customers edge as a service. And when you compliment that with, with Aruba wired wireless infrastructure, that goes at the edge, the hyperconverged infrastructure, as part of SimpliVity, that goes at the edge, you know, one of the things that was missing with cloud backups is the every time you backup to the cloud, which is a great thing, by the way, anytime you restore from the cloud, there is that breastfeed, right? So as a result of that, as part of the GreenLake offering, we have cloud backup service natively now offered as part of HPE, which is included in your HPE SimpliVity edge as a service offering. So now not only can you backup into the cloud from your edge sites, but you can also restore back without any egress fees from HBS data protection service. Either you can restore it back onto your data center, you can restore it back towards the edge site and because the infrastructure is so easy to deploy centrally lifecycle manage, it's very mobile. So if you want to deploy and recover to a different site, you could also do that. >>Nice. Hey, uh, can you, Omar, can you double click a little bit on some of the use cases that customers are choosing SimpliVity for, particularly at the edge, and maybe talk about why they're choosing HPE? >>What are the major use cases that we see? Dave is obviously, uh, easy to deploy and easy to manage in a standardized form factor, right? A lot of these customers, like for example, we have large retailer across the us with hundreds of stores across us. Right now you cannot send service staff to each of these stores. These data centers are their data center is essentially just a closet for these guys, right? So now how do you have a standardized deployment? So standardized deployment from the data center, which you can literally push out and you can connect a network cable and a power cable, and you're up and running, and then automated backup elimination of backup and state and BR from the edge sites and into the data center. So that's one of the big use cases to rapidly deploy new stores, bring them up in a standardized configuration, both from a hardware and a software perspective, and the ability to backup and recover that instantly. >>That's one large use case. The second use case that we see actually refers to a comment that you made in your opener. Dave was where a lot of these customers are generating a lot of the data at the edge. This is robotics automation that is going to up in manufacturing sites. These is racing teams that are out at the edge of doing post-processing of their cars data. Uh, at the same time, there is disaster recovery use cases where you have, uh, you know, campsites and local, uh, you know, uh, agencies that go out there for humanity's benefit. And they move from one site to the other. It's a very, very mobile architecture that they need. So those, those are just a few cases where we were deployed. There was a lot of data collection, and there's a lot of mobility involved in these environments. So you need to be quick to set up quick, to up quick, to recover, and essentially you're up to your next, next move. >>You seem pretty pumped up about this, uh, this new innovation and why not. >>It is, it is, uh, you know, especially because, you know, it is, it has been taught through with edge in mind and edge has to be mobile. It has to be simple. And especially as, you know, we have lived through this pandemic, which, which I hope we see the tail end of it in at least 2021, or at least 2022. They, you know, one of the most common use cases that we saw, and this was an accidental discovery. A lot of the retail sites could not go out to service their stores because, you know, mobility is limited in these, in these strange times that we live in. So from a central center, you're able to deploy applications, you're able to recover applications. And, and a lot of our customers said, Hey, I don't have enough space in my data center to back up. Do you have another option? So then we rolled out this update release to SimpliVity verse from the edge site. You can now directly back up to our backup service, which is offered on a consumption basis to the customers, and they can recover that anywhere they want. >>Fantastic Omer, thanks so much for coming on the program today. >>It's a pleasure, Dave. Thank you. >>All right. Awesome to see you. Now, let's hear from red bull racing and HPE customer, that's actually using SimpliVity at the edge. Countdown really begins when the checkered flag drops on a Sunday. It's always about this race to manufacture >>The next designs to make it more adapt to the next circuit to run those. Of course, if we can't manufacture the next component in time, all that will be wasted. >>Okay. We're back with Matt kudu, who is the CIO of red bull racing? Matt, it's good to see you again. >>Great to say, >>Hey, we're going to dig into a real-world example of using data at the edge and in near real time to gain insights that really lead to competitive advantage. But, but first Matt, tell us a little bit about red bull racing and your role there. >>Sure. So I'm the CIO at red bull racing and that red bull race. And we're based in Milton Keynes in the UK. And the main job job for us is to design a race car, to manufacture the race car, and then to race it around the world. So as CIO, we need to develop the ITT group needs to develop the applications is the design, manufacturing racing. We also need to supply all the underlying infrastructure and also manage security. So it's really interesting environment. That's all about speed. So this season we have 23 races and we need to tear the car apart and rebuild it to a unique configuration for every individual race. And we're also designing and making components targeted for races. So 20 a movable deadlines, um, this big evolving prototype to manage with our car. Um, but we're also improving all of our tools and methods and software that we use to design and make and race the car. >>So we have a big can do attitude of the company around continuous improvement. And the expectations are that we continuously make the car faster. That we're, that we're winning races, that we improve our methods in the factory and our tools. And, um, so for, I take it's really unique and that we can be part of that journey and provide a better service. It's also a big challenge to provide that service and to give the business the agility, agility, and needs. So my job is, is really to make sure we have the right staff, the right partners, the right technical platforms. So we can live up to expectations >>That tear down and rebuild for 23 races. Is that because each track has its own unique signature that you have to tune to, or are there other factors involved there? >>Yeah, exactly. Every track has a different shape. Some have lots of strengths. Some have lots of curves and lots are in between. Um, the track surface is very different and the impact that has some tires, um, the temperature and the climate is very different. Some are hilly, some, a big curves that affect the dynamics of the power. So all that in order to win, you need to micromanage everything and optimize it for any given race track. >>Talk about some of the key drivers in your business and some of the key apps that give you a competitive advantage to help you win races. >>Yeah. So in our business, everything is all about speed. So the car obviously needs to be fast, but also all of our business operations needed to be fast. We need to be able to design a car and it's all done in the virtual world, but the, the virtual simulations and designs need to correlate to what happens in the real world. So all of that requires a lot of expertise to develop the simulation is the algorithms and have all the underlying infrastructure that runs it quickly and reliably. Um, in manufacturing, um, we have cost caps and financial controls by regulation. We need to be super efficient and control material and resources. So ERP and MES systems are running and helping us do that. And at the race track itself in speed, we have hundreds of decisions to make on a Friday and Saturday as we're fine tuning the final configuration of the car. And here again, we rely on simulations and analytics to help do that. And then during the race, we have split seconds, literally seconds to alter our race strategy if an event happens. So if there's an accident, um, and the safety car comes out, or the weather changes, we revise our tactics and we're running Monte Carlo for example. And he is an experienced engineers with simulations to make a data-driven decision and hopefully a better one and faster than our competitors, all of that needs it. Um, so work at a very high level. >>It's interesting. I mean, as a lay person, historically we know when I think about technology and car racing, of course, I think about the mechanical aspects of a self-propelled vehicle, the electronics and the light, but not necessarily the data, but the data's always been there. Hasn't it? I mean, maybe in the form of like tribal knowledge, if somebody who knows the track and where the Hills are and experience and gut feel, but today you're digitizing it and you're, you're processing it and close to real time. >>It's amazing. I think exactly right. Yeah. The car's instrumented with sensors, we post-process at Virgin, um, video, um, image analysis, and we're looking at our car, our competitor's car. So there's a huge amount of, um, very complicated models that we're using to optimize our performance and to continuously improve our car. Yeah. The data and the applications that can leverage it are really key. Um, and that's a critical success factor for us. >>So let's talk about your data center at the track, if you will. I mean, if I can call it that paint a picture for us, what does that look like? >>So we have to send, um, a lot of equipment to the track at the edge. Um, and even though we have really a great wide area network linked back to the factory and there's cloud resources, a lot of the trucks are very old. You don't have hardened infrastructure, don't have ducks that protect cabling, for example, and you could lose connectivity to remote locations. So the applications we need to operate the car and to make really critical decisions, all that needs to be at the edge where the car operates. So historically we had three racks of equipment, like a safe infrastructure, um, and it was really hard to manage, um, to make changes. It was too flexible. Um, there were multiple panes of glass, um, and, um, and it was too slow. It didn't run her applications quickly. Um, it was also too heavy and took up too much space when you're cramped into a garage with lots of environmental constraints. >>So we, um, we'd, we'd introduced hyperconvergence into the factory and seen a lot of great benefits. And when we came time to refresh our infrastructure at the track, we stepped back and said, there's a lot smarter way of operating. We can get rid of all the slow and flexible, expensive legacy and introduce hyperconvergence. And we saw really excellent benefits for doing that. Um, we saw a three X speed up for a lot of our applications. So I'm here where we're post-processing data, and we have to make decisions about race strategy. Time is of the essence in a three X reduction in processing time really matters. Um, we also, um, were able to go from three racks of equipment down to two racks of equipment and the storage efficiency of the HPE SimpliVity platform with 20 to one ratios allowed us to eliminate a rack. And that actually saved a hundred thousand dollars a year in freight costs by shipping less equipment, um, things like backup, um, mistakes happen. >>Sometimes the user makes a mistake. So for example, a race engineer could load the wrong data map into one of our simulations. And we could restore that VDI through SimpliVity backup at 90 seconds. And this makes sure it enables engineers to focus on the car to make better decisions without having downtime. And we sent them to, I take guys to every race they're managing 60 users, a really diverse environment, juggling a lot of balls and having a simple management platform like HPE SimpliVity gives us, allows them to be very effective and to work quickly. So all of those benefits were a huge step forward relative to the legacy infrastructure that we used to run at the edge. >>Yeah. So you had the nice Petri dish and the factory. So it sounds like your, your goals, obviously your number one KPI is speed to help shave seconds time, but also costs just the simplicity of setting up the infrastructure. >>Yeah. It's speed. Speed, speed. So we want applications absolutely fly, you know, get to actionable results quicker, um, get answers from our simulations quicker. The other area that speed's really critical is, um, our applications are also evolving prototypes, and we're always, the models are getting bigger. The simulations are getting bigger and they need more and more resource and being able to spin up resource and provision things without being a bottleneck is a big challenge in SimpliVity. It gives us the means of doing that. >>So did you consider any other options or was it because you had the factory knowledge? It was HCI was, you know, very clearly the option. What did you look at? >>Yeah, so, um, we have over five years of experience in the factory and we eliminated all of our legacy, um, um, infrastructure five years ago. And the benefits I've described, um, at the track, we saw that in the factory, um, at the track we have a three-year operational life cycle for our equipment. When into 2017 was the last year we had legacy as we were building for 2018. It was obvious that hyper-converged was the right technology to introduce. And we'd had years of experience in the factory already. And the benefits that we see with hyper-converged actually mattered even more at the edge because our operations are so much more pressurized time has even more of the essence. And so speeding everything up at the really pointy end of our business was really critical. It was an obvious choice. >>Why, why SimpliVity? What why'd you choose HPE SimpliVity? >>Yeah. So when we first heard about hyperconverged way back in the, in the factory, um, we had, um, a legacy infrastructure, overly complicated, too slow, too inflexible, too expensive. And we stepped back and said, there has to be a smarter way of operating. We went out and challenged our technology partners. We learned about hyperconvergence within enough, the hype, um, was real or not. So we underwent some PLCs and benchmarking and, and the, the PLCs were really impressive. And, and all these, you know, speed and agility benefits, we saw an HP for our use cases was the clear winner in the benchmarks. So based on that, we made an initial investment in the factory. Uh, we moved about 150 VMs in the 150 VDI into it. Um, and then as, as we've seen all the benefits we've successfully invested, and we now have, um, an estate to the factory of about 800 VMs and about 400 VDI. So it's been a great platform and it's allowed us to really push boundaries and, and give the business, um, the service that expects. >>So w was that with the time in which you were able to go from data to insight to recommendation or, or edict, uh, was that compressed, you kind of indicated that, but >>So we, we all telemetry from the car and we post-process it, and that reprocessing time really it's very time consuming. And, um, you know, we went from nine, eight minutes for some of the simulations down to just two minutes. So we saw big, big reductions in time and all, ultimately that meant an engineer could understand what the car was during a practice session, recommend a tweak to the configuration or setup of it, and just get more actionable insight quicker. And it ultimately helps get a better car quicker. >>Such a great example. How are you guys feeling about the season, Matt? What's the team's sentiment? >>Yeah, I think we're optimistic. Um, we w we, um, uh, we have a new driver >>Lineup. Uh, we have, um, max for stopping his carries on with the team and Sergio joins the team. So we're really excited about this year and, uh, we want to go and win races. Great, Matt, good luck this season and going forward and thanks so much for coming back in the cube. Really appreciate it. And it's my pleasure. Great talking to you again. Okay. Now we're going to bring back Omer for quick summary. So keep it real >>Without having solutions from HB, we can't drive those five senses, CFD aerodynamics that would undermine the simulations being software defined. We can bring new apps into play. If we can bring new them's storage, networking, all of that can be highly advises is a hugely beneficial partnership for us. We're able to be at the cutting edge of technology in a highly stressed environment. That is no bigger challenge than the formula. >>Okay. We're back with Omar. Hey, what did you think about that interview with Matt? >>Great. Uh, I have to tell you I'm a big formula one fan, and they are one of my favorite customers. Uh, so, you know, obviously, uh, one of the biggest use cases as you saw for red bull racing is Trackside deployments. There are now 22 races in a season. These guys are jumping from one city to the next, they've got to pack up, move to the next city, set up, set up the infrastructure very, very quickly and average formula. One car is running the thousand plus sensors on that is generating a ton of data on track side that needs to be collected very quickly. It needs to be processed very quickly, and then sometimes believe it or not, snapshots of this data needs to be sent to the red bull back factory back at the data center. What does this all need? It needs reliability. >>It needs compute power in a very short form factor. And it needs agility quick to set up quick, to go quick, to recover. And then in post processing, they need to have CPU density so they can pack more VMs out at the edge to be able to do that processing now. And we accomplished that for, for the red bull racing guys in basically two are you have two SimpliVity nodes that are running track side and moving with them from one, one race to the next race, to the next race. And every time those SimpliVity nodes connect up to the data center collector to a satellite, they're backing up back to their data center. They're sending snapshots of data back to the data center, essentially making their job a whole lot easier, where they can focus on racing and not on troubleshooting virtual machines, >>Red bull racing and HPE SimpliVity. Great example. It's agile, it's it's cost efficient, and it shows a real impact. Thank you very much. I really appreciate those summary comments. Thank you, Dave. Really appreciate it. All right. And thank you for watching. This is Dave Volante. >>You.

(upbeat music) >> Edge computing is projected to be a multi-trillion dollar business. It's hard to really pinpoint the size of this market let alone fathom the potential of bringing software, compute, storage, AI and automation to the edge and connecting all that to clouds and on-prem systems. But what is the edge? Is it factories? Is it oil rigs, airplanes, windmills, shipping containers, buildings, homes, race cars. Well, yes and so much more. And what about the data? For decades we've talked about the data explosion. I mean, it's a mind-boggling but guess what we're going to look back in 10 years and laugh what we thought was a lot of data in 2020. Perhaps the best way to think about Edge is not as a place but when is the most logical opportunity to process the data and maybe it's the first opportunity to do so where it can be decrypted and analyzed at very low latencies. That defines the edge. And so by locating compute as close as possible to the sources of data to reduce latency and maximize your ability to get insights and return them to users quickly, maybe that's where the value lies. Hello everyone and welcome to this CUBE conversation. My name is Dave Vellante and with me to noodle on these topics is Omer Asad, VP and GM of Primary Storage and Data Management Services at HPE. Hello Omer, welcome to the program. >> Thanks Dave. Thank you so much. Pleasure to be here. >> Yeah. Great to see you again. So how do you see the edge in the broader market shaping up? >> Dave, I think that's a super important question. I think your ideas are quite aligned with how we think about it. I personally think enterprises are accelerating their sort of digitization and asset collection and data collection, they're typically especially in a distributed enterprise, they're trying to get to their customers. They're trying to minimize the latency to their customers. So especially if you look across industries manufacturing which has distributed factories all over the place they are going through a lot of factory transformations where they're digitizing their factories. That means a lot more data is now being generated within their factories. A lot of robot automation is going on, that requires a lot of compute power to go out to those particular factories which is going to generate their data out there. We've got insurance companies, banks, that are creating and interviewing and gathering more customers out at the edge for that. They need a lot more distributed processing out at the edge. What this is requiring is what we've seen is across analysts. A common consensus is this that more than 50% of an enterprises data especially if they operate globally around the world is going to be generated out at the edge. What does that mean? New data is generated at the edge what needs to be stored. It needs to be processed data. Data which is not required needs to be thrown away or classified as not important. And then it needs to be moved for DR purposes either to a central data center or just to another site. So overall in order to give the best possible experience for manufacturing, retail, especially in distributed enterprises, people are generating more and more data centric assets out at the edge. And that's what we see in the industry. >> Yeah. We're definitely aligned on that. There's some great points and so now, okay. You think about all this diversity what's the right architecture for these multi-site deployments, ROBO, edge? How do you look at that? >> Oh, excellent question, Dave. Every customer that we talked to wants SimpliVity and no pun intended because SimpliVity is reasoned with a simplistic edge centric architecture, right? Let's take a few examples. You've got large global retailers, they have hundreds of global retail stores around the world that is generating data that is producing data. Then you've got insurance companies, then you've got banks. So when you look at a distributed enterprise how do you deploy in a very simple and easy to deploy manner, easy to lifecycle, easy to mobilize and easy to lifecycle equipment out at the edge. What are some of the challenges that these customers deal with? These customers, you don't want to send a lot of IT staff out there because that adds cost. You don't want to have islands of data and islands of storage and promote sites because that adds a lot of states outside of the data center that needs to be protected. And then last but not the least how do you push lifecycle based applications, new applications out at the edge in a very simple to deploy manner. And how do you protect all this data at the edge? So the right architecture in my opinion needs to be extremely simple to deploy so storage compute and networking out towards the edge in a hyper converged environment. So that's we agree upon that. It's a very simple to deploy model but then comes how do you deploy applications on top of that? How do you manage these applications on top of that? How do you back up these applications back towards the data center, all of this keeping in mind that it has to be as zero touch as possible. We at HPE believe that it needs to be extremely simple, just give me two cables, a network cable, a power cable, fire it up, connect it to the network, push it state from the data center and back up it state from the edge back into the data center, extremely simple. >> It's got to be simple 'cause you've got so many challenges. You've got physics that you have to deal, you have latency to deal with. You got RPO and RTO. What happens if something goes wrong you've got to be able to recover quickly. So that's great. Thank you for that. Now you guys have heard news. What is new from HPE in this space? >> Excellent question, great. So from a deployment perspective, HPE SimpliVity is just gaining like it's exploding like crazy especially as distributed enterprises adopted as it's standardized edge architecture, right? It's an HCI box has got storage computer networking all in one. But now what we have done is not only you can deploy applications all from your standard V-Center interface from a data center, what have you have now added is the ability to backup to the cloud right from the edge. You can also back up all the way back to your core data center. All of the backup policies are fully automated and implemented in the distributed file system that is the heart and soul of the SimpliVity installation. In addition to that, the customers now do not have to buy any third-party software. Backup is fully integrated in the architecture and it's then efficient. In addition to that now you can backup straight to the client. You can back up to a central high-end backup repository which is in your data center. And last but not least, we have a lot of customers that are pushing the limit in their application transformation. So not only, we previously were one-on-one leaving VMware deployments out at the edge site now evolved also added both stateful and stateless container orchestration as well as data protection capabilities for containerized applications out at the edge. So we have a lot of customers that are now deploying containers, rapid manufacture containers to process data out at remote sites. And that allows us to not only protect those stateful applications but back them up back into the central data center. >> I saw in that chart, it was a line no egress fees. That's a pain point for a lot of CIOs that I talked to. They grit their teeth at those cities. So you can't comment on that or? >> Excellent question. I'm so glad you brought that up and sort of at the point that pick that up. So along with SimpliVity, we have the whole Green Lake as a service offering as well, right? So what that means Dave is, that we can literally provide our customers edge as a service. And when you compliment that with with Aruba Wired Wireless Infrastructure that goes at the edge, the hyperconverged infrastructure as part of SimpliVity that goes at the edge. One of the things that was missing with cloud backups is that every time you back up to the cloud, which is a great thing by the way, anytime you restore from the cloud there is that egress fee, right? So as a result of that, as part of the GreenLake offering we have cloud backup service natively now offered as part of HPE, which is included in your HPE SimpliVity edge as a service offering. So now not only can you backup into the cloud from your edge sites, but you can also restore back without any egress fees from HPE's data protection service. Either you can restore it back onto your data center, you can restore it back towards the edge site and because the infrastructure is so easy to deploy centrally lifecycle manage, it's very mobile. So if you want to deploy and recover to a different site, you could also do that. >> Nice. Hey, can you, Omer, can you double click a little bit on some of the use cases that customers are choosing SimpliVity for particularly at the edge and maybe talk about why they're choosing HPE? >> Excellent question. So one of the major use cases that we see Dave is obviously easy to deploy and easy to manage in a standardized form factor, right? A lot of these customers, like for example, we have large retailer across the US with hundreds of stores across US, right? Now you cannot send service staff to each of these stores. Their data center is essentially just a closet for these guys, right? So now how do you have a standardized deployment? So standardized deployment from the data center which you can literally push out and you can connect a network cable and a power cable and you're up and running and then automated backup, elimination of backup and state and DR from the edge sites and into the data center. So that's one of the big use cases to rapidly deploy new stores, bring them up in a standardized configuration both from a hardware and a software perspective and the ability to backup and recover that instantly. That's one large use case. The second use case that we see actually refers to a comment that you made in your opener, Dave, was when a lot of these customers are generating a lot of the data at the edge. This is robotics automation that is going up in manufacturing sites. These is racing teams that are out at the edge of doing post-processing of their cars data. At the same time there is disaster recovery use cases where you have campsites and local agencies that go out there for humanity's benefit. And they move from one site to the other. It's a very, very mobile architecture that they need. So those are just a few cases where we were deployed. There was a lot of data collection and there was a lot of mobility involved in these environments, so you need to be quick to set up, quick to backup, quick to recover. And essentially you're up to your next move. >> You seem pretty pumped up about this new innovation and why not. >> It is, especially because it has been taught through with edge in mind and edge has to be mobile. It has to be simple. And especially as we have lived through this pandemic which I hope we see the tail end of it in at least 2021 or at least 2022. One of the most common use cases that we saw and this was an accidental discovery. A lot of the retail sites could not go out to service their stores because mobility is limited in these strange times that we live in. So from a central recenter you're able to deploy applications. You're able to recover applications. And a lot of our customers said, hey I don't have enough space in my data center to back up. Do you have another option? So then we rolled out this update release to SimpliVity verse from the edge site. You can now directly back up to our backup service which is offered on a consumption basis to the customers and they can recover that anywhere they want. >> Fantastic Omer, thanks so much for coming on the program today. >> It's a pleasure, Dave. Thank you. >> All right. Awesome to see you, now, let's hear from Red Bull Racing an HPE customer that's actually using SimpliVity at the edge. (engine revving) >> Narrator: Formula one is a constant race against time Chasing in tens of seconds. (upbeat music) >> Okay. We're back with Matt Cadieux who is the CIO Red Bull Racing. Matt, it's good to see you again. >> Great to see you Dave. >> Hey, we're going to dig in to a real world example of using data at the edge in near real time to gain insights that really lead to competitive advantage. But first Matt tell us a little bit about Red Bull Racing and your role there. >> Sure. So I'm the CIO at Red Bull Racing and at Red Bull Racing we're based in Milton Keynes in the UK. And the main job for us is to design a race car, to manufacture the race car and then to race it around the world. So as CIO, we need to develop, the IT group needs to develop the applications use the design, manufacturing racing. We also need to supply all the underlying infrastructure and also manage security. So it's really interesting environment that's all about speed. So this season we have 23 races and we need to tear the car apart and rebuild it to a unique configuration for every individual race. And we're also designing and making components targeted for races. So 23 and movable deadlines this big evolving prototype to manage with our car but we're also improving all of our tools and methods and software that we use to design make and race the car. So we have a big can-do attitude of the company around continuous improvement. And the expectations are that we continue to say, make the car faster. That we're winning races, that we improve our methods in the factory and our tools. And so for IT it's really unique and that we can be part of that journey and provide a better service. It's also a big challenge to provide that service and to give the business the agility of needs. So my job is really to make sure we have the right staff, the right partners, the right technical platforms. So we can live up to expectations. >> And Matt that tear down and rebuild for 23 races, is that because each track has its own unique signature that you have to tune to or are there other factors involved? >> Yeah, exactly. Every track has a different shape. Some have lots of straight, some have lots of curves and lots are in between. The track surface is very different and the impact that has on tires, the temperature and the climate is very different. Some are hilly, some have big curbs that affect the dynamics of the car. So all that in order to win you need to micromanage everything and optimize it for any given race track. >> COVID has of course been brutal for sports. What's the status of your season? >> So this season we knew that COVID was here and we're doing 23 races knowing we have COVID to manage. And as a premium sporting team with Pharma Bubbles we've put health and safety and social distancing into our environment. And we're able to able to operate by doing things in a safe manner. We have some special exceptions in the UK. So for example, when people returned from overseas that they did not have to quarantine for two weeks, but they get tested multiple times a week. And we know they're safe. So we're racing, we're dealing with all the hassle that COVID gives us. And we are really hoping for a return to normality sooner instead of later where we can get fans back at the track and really go racing and have the spectacle where everyone enjoys it. >> Yeah. That's awesome. So important for the fans but also all the employees around that ecosystem. Talk about some of the key drivers in your business and some of the key apps that give you competitive advantage to help you win races. >> Yeah. So in our business, everything is all about speed. So the car obviously needs to be fast but also all of our business operations need to be fast. We need to be able to design a car and it's all done in the virtual world, but the virtual simulations and designs needed to correlate to what happens in the real world. So all of that requires a lot of expertise to develop the simulations, the algorithms and have all the underlying infrastructure that runs it quickly and reliably. In manufacturing we have cost caps and financial controls by regulation. We need to be super efficient and control material and resources. So ERP and MES systems are running and helping us do that. And at the race track itself. And in speed, we have hundreds of decisions to make on a Friday and Saturday as we're fine tuning the final configuration of the car. And here again, we rely on simulations and analytics to help do that. And then during the race we have split seconds literally seconds to alter our race strategy if an event happens. So if there's an accident and the safety car comes out or the weather changes, we revise our tactics and we're running Monte-Carlo for example. And use an experienced engineers with simulations to make a data-driven decision and hopefully a better one and faster than our competitors. All of that needs IT to work at a very high level. >> Yeah, it's interesting. I mean, as a lay person, historically when I think about technology in car racing, of course I think about the mechanical aspects of a self-propelled vehicle, the electronics and the light but not necessarily the data but the data's always been there. Hasn't it? I mean, maybe in the form of like tribal knowledge if you are somebody who knows the track and where the hills are and experience and gut feel but today you're digitizing it and you're processing it and close to real time. Its amazing. >> I think exactly right. Yeah. The car's instrumented with sensors, we post process and we are doing video image analysis and we're looking at our car, competitor's car. So there's a huge amount of very complicated models that we're using to optimize our performance and to continuously improve our car. Yeah. The data and the applications that leverage it are really key and that's a critical success factor for us. >> So let's talk about your data center at the track, if you will. I mean, if I can call it that. Paint a picture for us what does that look like? >> So we have to send a lot of equipment to the track at the edge. And even though we have really a great wide area network link back to the factory and there's cloud resources a lot of the tracks are very old. You don't have hardened infrastructure, don't have ducks that protect cabling, for example and you can lose connectivity to remote locations. So the applications we need to operate the car and to make really critical decisions all that needs to be at the edge where the car operates. So historically we had three racks of equipment like I said infrastructure and it was really hard to manage, to make changes, it was too flexible. There were multiple panes of glass and it was too slow. It didn't run our applications quickly. It was also too heavy and took up too much space when you're cramped into a garage with lots of environmental constraints. So we'd introduced hyper convergence into the factory and seen a lot of great benefits. And when we came time to refresh our infrastructure at the track, we stepped back and said, there's a lot smarter way of operating. We can get rid of all the slow and flexible expensive legacy and introduce hyper convergence. And we saw really excellent benefits for doing that. We saw up three X speed up for a lot of our applications. So I'm here where we're post-processing data. And we have to make decisions about race strategy. Time is of the essence. The three X reduction in processing time really matters. We also were able to go from three racks of equipment down to two racks of equipment and the storage efficiency of the HPE SimpliVity platform with 20 to one ratios allowed us to eliminate a rack. And that actually saved a $100,000 a year in freight costs by shipping less equipment. Things like backup mistakes happen. Sometimes the user makes a mistake. So for example a race engineer could load the wrong data map into one of our simulations. And we could restore that DDI through SimpliVity backup at 90 seconds. And this enables engineers to focus on the car to make better decisions without having downtime. And we sent two IT guys to every race, they're managing 60 users a really diverse environment, juggling a lot of balls and having a simple management platform like HPE SimpliVity gives us, allows them to be very effective and to work quickly. So all of those benefits were a huge step forward relative to the legacy infrastructure that we used to run at the edge. >> Yeah. So you had the nice Petri dish in the factory so it sounds like your goals are obviously number one KPIs speed to help shave seconds, awesome time, but also cost just the simplicity of setting up the infrastructure is-- >> That's exactly right. It's speed, speed, speed. So we want applications absolutely fly, get to actionable results quicker, get answers from our simulations quicker. The other area that speed's really critical is our applications are also evolving prototypes and we're always, the models are getting bigger. The simulations are getting bigger and they need more and more resource and being able to spin up resource and provision things without being a bottleneck is a big challenge in SimpliVity. It gives us the means of doing that. >> So did you consider any other options or was it because you had the factory knowledge? It was HCI was very clearly the option. What did you look at? >> Yeah, so we have over five years of experience in the factory and we eliminated all of our legacy infrastructure five years ago. And the benefits I've described at the track we saw that in the factory. At the track we have a three-year operational life cycle for our equipment. When in 2017 was the last year we had legacy as we were building for 2018, it was obvious that hyper-converged was the right technology to introduce. And we'd had years of experience in the factory already. And the benefits that we see with hyper-converged actually mattered even more at the edge because our operations are so much more pressurized. Time is even more of the essence. And so speeding everything up at the really pointy end of our business was really critical. It was an obvious choice. >> Why SimpliVity, why'd you choose HPE SimpliVity? >> Yeah. So when we first heard about hyper-converged way back in the factory, we had a legacy infrastructure overly complicated, too slow, too inflexible, too expensive. And we stepped back and said there has to be a smarter way of operating. We went out and challenged our technology partners, we learned about hyperconvergence, would enough the hype was real or not. So we underwent some PLCs and benchmarking and the PLCs were really impressive. And all these speed and agility benefits we saw and HPE for our use cases was the clear winner in the benchmarks. So based on that we made an initial investment in the factory. We moved about 150 VMs and 150 VDIs into it. And then as we've seen all the benefits we've successfully invested and we now have an estate in the factory of about 800 VMs and about 400 VDIs. So it's been a great platform and it's allowed us to really push boundaries and give the business the service it expects. >> Awesome fun stories, just coming back to the metrics for a minute. So you're running Monte Carlo simulations in real time and sort of near real-time. And so essentially that's if I understand it, that's what ifs and it's the probability of the outcome. And then somebody got to make, then the human's got to say, okay, do this, right? Was the time in which you were able to go from data to insight to recommendation or edict was that compressed and you kind of indicated that. >> Yeah, that was accelerated. And so in that use case, what we're trying to do is predict the future and you're saying, well and before any event happens, you're doing what ifs and if it were to happen, what would you probabilistic do? So that simulation, we've been running for awhile but it gets better and better as we get more knowledge. And so that we were able to accelerate that with SimpliVity but there's other use cases too. So we also have telemetry from the car and we post-process it. And that reprocessing time really, is it's very time consuming. And we went from nine, eight minutes for some of the simulations down to just two minutes. So we saw big, big reductions in time. And ultimately that meant an engineer could understand what the car was doing in a practice session, recommend a tweak to the configuration or setup of it and just get more actionable insight quicker. And it ultimately helps get a better car quicker. >> Such a great example. How are you guys feeling about the season, Matt? What's the team's sentiment? >> I think we're optimistic. Thinking our simulations that we have a great car we have a new driver lineup. We have the Max Verstapenn who carries on with the team and Sergio Cross joins the team. So we're really excited about this year and we want to go and win races. And I think with COVID people are just itching also to get back to a little degree of normality and going racing again even though there's no fans, it gets us into a degree of normality. >> That's great, Matt, good luck this season and going forward and thanks so much for coming back in theCUBE. Really appreciate it. >> It's my pleasure. Great talking to you again. >> Okay. Now we're going to bring back Omer for quick summary. So keep it right there. >> Narrator: That's where the data comes face to face with the real world. >> Narrator: Working with Hewlett Packard Enterprise is a hugely beneficial partnership for us. We're able to be at the cutting edge of technology in a highly technical, highly stressed environment. There is no bigger challenge than Formula One. (upbeat music) >> Being in the car and driving in on the limit that is the best thing out there. >> Narrator: It's that innovation and creativity to ultimately achieves winning of this. >> Okay. We're back with Omer. Hey, what did you think about that interview with Matt? >> Great. I have to tell you, I'm a big formula One fan and they are one of my favorite customers. So obviously one of the biggest use cases as you saw for Red Bull Racing is track side deployments. There are now 22 races in a season. These guys are jumping from one city to the next they got to pack up, move to the next city, set up the infrastructure very very quickly. An average Formula One car is running the thousand plus sensors on, that is generating a ton of data on track side that needs to be collected very quickly. It needs to be processed very quickly and then sometimes believe it or not snapshots of this data needs to be sent to the Red Bull back factory back at the data center. What does this all need? It needs reliability. It needs compute power in a very short form factor. And it needs agility quick to set up, quick to go, quick to recover. And then in post processing they need to have CPU density so they can pack more VMs out at the edge to be able to do that processing. And we accomplished that for the Red Bull Racing guys in basically two of you have two SimpliVity nodes that are running track side and moving with them from one race to the next race to the next race. And every time those SimpliVity nodes connect up to the data center, collect up to a satellite they're backing up back to their data center. They're sending snapshots of data back to the data center essentially making their job a whole lot easier where they can focus on racing and not on troubleshooting virtual machines. >> Red bull Racing and HPE SimpliVity. Great example. It's agile, it's it's cost efficient and it shows a real impact. Thank you very much Omer. I really appreciate those summary comments. >> Thank you, Dave. Really appreciate it. >> All right. And thank you for watching. This is Dave Volante for theCUBE. (upbeat music)

>>Yeah, yeah. >>Welcome back. I'm so excited to introduce our next session empowerment through inclusion, reimagining society and technology. This is a topic that's personally very near and dear to my heart. Did you know that there's only 2% of Latinas in technology as a Latina? I know that there's so much more we could do collectively to improve these gaps and diversity. I thought spot diversity is considered a critical element across all levels of the organization. The data shows countless times. A diverse and inclusive workforce ultimately drives innovation better performance and keeps your employees happier. That's why we're passionate about contributing to this conversation and also partnering with organizations that share our mission of improving diversity across our communities. Last beyond, we hosted the session during a breakfast and we packed the whole room. This year, we're bringing the conversation to the forefront to emphasize the importance of diversity and data and share the positive ramifications that it has for your organization. Joining us for this session are thought spots Chief Data Strategy Officer Cindy Housing and Ruhollah Benjamin, associate professor of African American Studies at Princeton University. Thank you, Paola. So many >>of you have journeyed with me for years now on our efforts to improve diversity and inclusion in the data and analytic space. And >>I would say >>over time we cautiously started commiserating, eventually sharing best practices to make ourselves and our companies better. And I do consider it a milestone. Last year, as Paola mentioned that half the room was filled with our male allies. But I remember one of our Panelists, Natalie Longhurst from Vodafone, suggesting that we move it from a side hallway conversation, early morning breakfast to the main stage. And I >>think it was >>Bill Zang from a I G in Japan. Who said Yes, please. Everyone else agreed, but more than a main stage topic, I want to ask you to think about inclusion beyond your role beyond your company toe. How Data and analytics can be used to impact inclusion and equity for the society as a whole. Are we using data to reveal patterns or to perpetuate problems leading Tobias at scale? You are the experts, the change agents, the leaders that can prevent this. I am thrilled to introduce you to the leading authority on this topic, Rou Ha Benjamin, associate professor of African studies at Princeton University and author of Multiple Books. The Latest Race After Technology. Rou ha Welcome. >>Thank you. Thank you so much for having me. I'm thrilled to be in conversation with you today, and I thought I would just kick things off with some opening reflections on this really important session theme. And then we could jump into discussion. So I'd like us to as a starting point, um, wrestle with these buzzwords, empowerment and inclusion so that we can have them be more than kind of big platitudes and really have them reflected in our workplace cultures and the things that we design in the technologies that we put out into the world. And so to do that, I think we have to move beyond techno determinism, and I'll explain what that means in just a minute. Techno determinism comes in two forms. The first, on your left is the idea that technology automation, um, all of these emerging trends are going to harm us, are going to necessarily harm humanity. They're going to take all the jobs they're going to remove human agency. This is what we might call the techno dystopian version of the story and this is what Hollywood loves to sell us in the form of movies like The Matrix or Terminator. The other version on your right is the techno utopian story that technologies automation. The robots as a shorthand, are going to save humanity. They're gonna make everything more efficient, more equitable. And in this case, on the surface, he seemed like opposing narratives right there, telling us different stories. At least they have different endpoints. But when you pull back the screen and look a little bit more closely, you see that they share an underlying logic that technology is in the driver's seat and that human beings that social society can just respond to what's happening. But we don't really have a say in what technologies air designed and so to move beyond techno determinism the notion that technology is in the driver's seat. We have to put the human agents and agencies back into the story, the protagonists, and think carefully about what the human desires worldviews, values, assumptions are that animate the production of technology. And so we have to put the humans behind the screen back into view. And so that's a very first step and when we do that, we see, as was already mentioned, that it's a very homogeneous group right now in terms of who gets the power and the resource is to produce the digital and physical infrastructure that everyone else has to live with. And so, as a first step, we need to think about how to create more participation of those who are working behind the scenes to design technology now to dig a little more a deeper into this, I want to offer a kind of low tech example before we get to the more hi tech ones. So what you see in front of you here is a simple park bench public bench. It's located in Berkeley, California, which is where I went to graduate school and on this particular visit I was living in Boston, and so I was back in California. It was February. It was freezing where I was coming from, and so I wanted to take a few minutes in between meetings to just lay out in the sun and soak in some vitamin D, and I quickly realized, actually, I couldn't lay down on this bench because of the way it had been designed with these arm rests at intermittent intervals. And so here I thought. Okay, the the armrest have, ah functional reason why they're there. I mean, you could literally rest your elbows there or, um, you know, it can create a little bit of privacy of someone sitting there that you don't know. When I was nine months pregnant, it could help me get up and down or for the elderly, the same thing. So it has a lot of functional reasons, but I also thought about the fact that it prevents people who are homeless from sleeping on the bench. And this is the Bay area that we were talking about where, in fact, the tech boom has gone hand in hand with a housing crisis. Those things have grown in tandem. So innovation has grown within equity because we haven't thought carefully about how to address the social context in which technology grows and blossoms. And so I thought, Okay, this crisis is growing in this area, and so perhaps this is a deliberate attempt to make sure that people don't sleep on the benches by the way that they're designed and where the where they're implemented and So this is what we might call structural inequity. By the way something is designed. It has certain effects that exclude or harm different people. And so it may not necessarily be the intense, but that's the effect. And I did a little digging, and I found, in fact, it's a global phenomenon, this thing that architects called hostile architecture. Er, I found single occupancy benches in Helsinki, so only one booty at a time no laying down there. I found caged benches in France. And in this particular town. What's interesting here is that the mayor put these benches out in this little shopping plaza, and within 24 hours the people in the town rallied together and had them removed. So we see here that just because we have, uh, discriminatory design in our public space doesn't mean we have to live with it. We can actually work together to ensure that our public space reflects our better values. But I think my favorite example of all is the meter bench. In this case, this bench is designed with spikes in them, and to get the spikes to retreat into the bench, you have to feed the meter you have to put some coins in, and I think it buys you about 15 or 20 minutes. Then the spikes come back up. And so you'll be happy to know that in this case, this was designed by a German artists to get people to think critically about issues of design, not just the design of physical space but the design of all kinds of things, public policies. And so we can think about how our public life in general is metered, that it serves those that can pay the price and others are excluded or harm, whether we're talking about education or health care. And the meter bench also presents something interesting. For those of us who care about technology, it creates a technical fix for a social problem. In fact, it started out his art. But some municipalities in different parts of the world have actually adopted this in their public spaces in their parks in order to deter so called lawyers from using that space. And so, by a technical fix, we mean something that creates a short term effect, right. It gets people who may want to sleep on it out of sight. They're unable to use it, but it doesn't address the underlying problems that create that need to sleep outside in the first place. And so, in addition to techno determinism, we have to think critically about technical fixes that don't address the underlying issues that technology is meant to solve. And so this is part of a broader issue of discriminatory design, and we can apply the bench metaphor to all kinds of things that we work with or that we create. And the question we really have to continuously ask ourselves is, What values are we building in to the physical and digital infrastructures around us? What are the spikes that we may unwittingly put into place? Or perhaps we didn't create the spikes. Perhaps we started a new job or a new position, and someone hands us something. This is the way things have always been done. So we inherit the spike bench. What is our responsibility when we noticed that it's creating these kinds of harms or exclusions or technical fixes that are bypassing the underlying problem? What is our responsibility? All of this came to a head in the context of financial technologies. I don't know how many of you remember these high profile cases of tech insiders and CEOs who applied for Apple, the Apple card and, in one case, a husband and wife applied and the husband, the husband received a much higher limit almost 20 times the limit as his wife, even though they shared bank accounts, they lived in Common Law State. And so the question. There was not only the fact that the husband was receiving a much better interest rate and the limit, but also that there was no mechanism for the individuals involved to dispute what was happening. They didn't even know what the factors were that they were being judged that was creating this form of discrimination. So in terms of financial technologies, it's not simply the outcome that's the issue. Or that could be discriminatory, but the process that black boxes, all of the decision making that makes it so that consumers and the general public have no way to question it. No way to understand how they're being judged adversely, and so it's the process not only the product that we have to care a lot about. And so the case of the apple cart is part of a much broader phenomenon of, um, racist and sexist robots. This is how the headlines framed it a few years ago, and I was so interested in this framing because there was a first wave of stories that seemed to be shocked at the prospect that technology is not neutral. Then there was a second wave of stories that seemed less surprised. Well, of course, technology inherits its creator's biases. And now I think we've entered a phase of attempts to override and address the default settings of so called racist and sexist robots, for better or worse. And here robots is just a kind of shorthand, that the way people are talking about automation and emerging technologies more broadly. And so as I was encountering these headlines, I was thinking about how these air, not problems simply brought on by machine learning or AI. They're not all brand new, and so I wanted to contribute to the conversation, a kind of larger context and a longer history for us to think carefully about the social dimensions of technology. And so I developed a concept called the New Jim Code, which plays on the phrase Jim Crow, which is the way that the regime of white supremacy and inequality in this country was defined in a previous era, and I wanted us to think about how that legacy continues to haunt the present, how we might be coding bias into emerging technologies and the danger being that we imagine those technologies to be objective. And so this gives us a language to be able to name this phenomenon so that we can address it and change it under this larger umbrella of the new Jim Code are four distinct ways that this phenomenon takes shape from the more obvious engineered inequity. Those were the kinds of inequalities tech mediated inequalities that we can generally see coming. They're kind of obvious. But then we go down the line and we see it becomes harder to detect. It's happening in our own backyards. It's happening around us, and we don't really have a view into the black box, and so it becomes more insidious. And so in the remaining couple minutes, I'm just just going to give you a taste of the last three of these, and then a move towards conclusion that we can start chatting. So when it comes to default discrimination. This is the way that social inequalities become embedded in emerging technologies because designers of these technologies aren't thinking carefully about history and sociology. Ah, great example of this came Thio headlines last fall when it was found that widely used healthcare algorithm affecting millions of patients, um, was discriminating against black patients. And so what's especially important to note here is that this algorithm healthcare algorithm does not explicitly take note of race. That is to say, it is race neutral by using cost to predict healthcare needs. This digital triaging system unwittingly reproduces health disparities because, on average, black people have incurred fewer costs for a variety of reasons, including structural inequality. So in my review of this study by Obermeyer and colleagues, I want to draw attention to how indifference to social reality can be even more harmful than malicious intent. It doesn't have to be the intent of the designers to create this effect, and so we have to look carefully at how indifference is operating and how race neutrality can be a deadly force. When we move on to the next iteration of the new Jim code coded exposure, there's attention because on the one hand, you see this image where the darker skin individual is not being detected by the facial recognition system, right on the camera or on the computer. And so coated exposure names this tension between wanting to be seen and included and recognized, whether it's in facial recognition or in recommendation systems or in tailored advertising. But the opposite of that, the tension is with when you're over included. When you're surveiled when you're to centered. And so we should note that it's not simply in being left out, that's the problem. But it's in being included in harmful ways. And so I want us to think carefully about the rhetoric of inclusion and understand that inclusion is not simply an end point. It's a process, and it is possible to include people in harmful processes. And so we want to ensure that the process is not harmful for it to really be effective. The last iteration of the new Jim Code. That means the the most insidious, let's say, is technologies that are touted as helping US address bias, so they're not simply including people, but they're actively working to address bias. And so in this case, There are a lot of different companies that are using AI to hire, create hiring software and hiring algorithms, including this one higher view. And the idea is that there there's a lot that AI can keep track of that human beings might miss. And so so the software can make data driven talent decisions. After all, the problem of employment discrimination is widespread and well documented. So the logic goes, Wouldn't this be even more reason to outsource decisions to AI? Well, let's think about this carefully. And this is the look of the idea of techno benevolence trying to do good without fully reckoning with what? How technology can reproduce inequalities. So some colleagues of mine at Princeton, um, tested a natural learning processing algorithm and was looking to see whether it exhibited the same, um, tendencies that psychologists have documented among humans. E. And what they found was that in fact, the algorithm associating black names with negative words and white names with pleasant sounding words. And so this particular audit builds on a classic study done around 2003, before all of the emerging technologies were on the scene where two University of Chicago economists sent out thousands of resumes to employers in Boston and Chicago, and all they did was change the names on those resumes. All of the other work history education were the same, and then they waited to see who would get called back. And the applicants, the fictional applicants with white sounding names received 50% more callbacks than the black applicants. So if you're presented with that study, you might be tempted to say, Well, let's let technology handle it since humans are so biased. But my colleagues here in computer science found that this natural language processing algorithm actually reproduced those same associations with black and white names. So, too, with gender coded words and names Amazon learned a couple years ago when its own hiring algorithm was found discriminating against women. Nevertheless, it should be clear by now why technical fixes that claim to bypass human biases are so desirable. If Onley there was a way to slay centuries of racist and sexist demons with a social justice box beyond desirable, more like magical, magical for employers, perhaps looking to streamline the grueling work of recruitment but a curse from any jobseekers, as this headline puts it, your next interview could be with a racist spot, bringing us back to that problem space we started with just a few minutes ago. So it's worth noting that job seekers are already developing ways to subvert the system by trading answers to employers test and creating fake applications as informal audits of their own. In terms of a more collective response, there's a federation of European Trade unions call you and I Global that's developed a charter of digital rights for work, others that touches on automated and a I based decisions to be included in bargaining agreements. And so this is one of many efforts to change their ecosystem to change the context in which technology is being deployed to ensure more protections and more rights for everyday people in the US There's the algorithmic accountability bill that's been presented, and it's one effort to create some more protections around this ubiquity of automated decisions, and I think we should all be calling from more public accountability when it comes to the widespread use of automated decisions. Another development that keeps me somewhat hopeful is that tech workers themselves are increasingly speaking out against the most egregious forms of corporate collusion with state sanctioned racism. And to get a taste of that, I encourage you to check out the hashtag Tech won't build it. Among other statements that they have made and walking out and petitioning their companies. Who one group said, as the people who build the technologies that Microsoft profits from, we refuse to be complicit in terms of education, which is my own ground zero. Um, it's a place where we can we can grow a more historically and socially literate approach to tech design. And this is just one, um, resource that you all can download, Um, by developed by some wonderful colleagues at the Data and Society Research Institute in New York and the goal of this interventionist threefold to develop an intellectual understanding of how structural racism operates and algorithms, social media platforms and technologies, not yet developed and emotional intelligence concerning how to resolve racially stressful situations within organizations, and a commitment to take action to reduce harms to communities of color. And so as a final way to think about why these things are so important, I want to offer a couple last provocations. The first is for us to think a new about what actually is deep learning when it comes to computation. I want to suggest that computational depth when it comes to a I systems without historical or social depth, is actually superficial learning. And so we need to have a much more interdisciplinary, integrated approach to knowledge production and to observing and understanding patterns that don't simply rely on one discipline in order to map reality. The last provocation is this. If, as I suggested at the start, inequity is woven into the very fabric of our society, it's built into the design of our. Our policies are physical infrastructures and now even our digital infrastructures. That means that each twist, coil and code is a chance for us toe. We've new patterns, practices and politics. The vastness of the problems that we're up against will be their undoing. Once we accept that we're pattern makers. So what does that look like? It looks like refusing color blindness as an anecdote to tech media discrimination rather than refusing to see difference. Let's take stock of how the training data and the models that we're creating have these built in decisions from the past that have often been discriminatory. It means actually thinking about the underside of inclusion, which can be targeting. And how do we create a more participatory rather than predatory form of inclusion? And ultimately, it also means owning our own power in these systems so that we can change the patterns of the past. If we're if we inherit a spiked bench, that doesn't mean that we need to continue using it. We can work together to design more just and equitable technologies. So with that, I look forward to our conversation. >>Thank you, Ruth. Ha. That was I expected it to be amazing, as I have been devouring your book in the last few weeks. So I knew that would be impactful. I know we will never think about park benches again. How it's art. And you laid down the gauntlet. Oh, my goodness. That tech won't build it. Well, I would say if the thoughts about team has any saying that we absolutely will build it and will continue toe educate ourselves. So you made a few points that it doesn't matter if it was intentional or not. So unintentional has as big an impact. Um, how do we address that does it just start with awareness building or how do we address that? >>Yeah, so it's important. I mean, it's important. I have good intentions. And so, by saying that intentions are not the end, all be all. It doesn't mean that we're throwing intentions out. But it is saying that there's so many things that happened in the world, happened unwittingly without someone sitting down to to make it good or bad. And so this goes on both ends. The analogy that I often use is if I'm parked outside and I see someone, you know breaking into my car, I don't run out there and say Now, do you feel Do you feel in your heart that you're a thief? Do you intend to be a thief? I don't go and grill their identity or their intention. Thio harm me, but I look at the effect of their actions, and so in terms of art, the teams that we work on, I think one of the things that we can do again is to have a range of perspectives around the table that can think ahead like chess, about how things might play out, but also once we've sort of created something and it's, you know, it's entered into, you know, the world. We need to have, ah, regular audits and check ins to see when it's going off track just because we intended to do good and set it out when it goes sideways, we need mechanisms, formal mechanisms that actually are built into the process that can get it back on track or even remove it entirely if we find And we see that with different products, right that get re called. And so we need that to be formalized rather than putting the burden on the people that are using these things toe have to raise the awareness or have to come to us like with the apple card, Right? To say this thing is not fair. Why don't we have that built into the process to begin with? >>Yeah, so a couple things. So my dad used to say the road to hell is paved with good intentions, so that's >>yes on. In fact, in the book, I say the road to hell is paved with technical fixes. So they're me and your dad are on the same page, >>and I I love your point about bringing different perspectives. And I often say this is why diversity is not just about business benefits. It's your best recipe for for identifying the early biases in the data sets in the way we build things. And yet it's such a thorny problem to address bringing new people in from tech. So in the absence of that, what do we do? Is it the outside review boards? Or do you think regulation is the best bet as you mentioned a >>few? Yeah, yeah, we need really need a combination of things. I mean, we need So on the one hand, we need something like a do no harm, um, ethos. So with that we see in medicine so that it becomes part of the fabric and the culture of organizations that that those values, the social values, have equal or more weight than the other kinds of economic imperatives. Right. So we have toe have a reckoning in house, but we can't leave it to people who are designing and have a vested interest in getting things to market to regulate themselves. We also need independent accountability. So we need a combination of this and going back just to your point about just thinking about like, the diversity on teams. One really cautionary example comes to mind from last fall, when Google's New Pixel four phone was about to come out and it had a kind of facial recognition component to it that you could open the phone and they had been following this research that shows that facial recognition systems don't work as well on darker skin individuals, right? And so they wanted Thio get a head start. They wanted to prevent that, right? So they had good intentions. They didn't want their phone toe block out darker skin, you know, users from from using it. And so what they did was they were trying to diversify their training data so that the system would work better and they hired contract workers, and they told these contract workers to engage black people, tell them to use the phone play with, you know, some kind of app, take a selfie so that their faces would populate that the training set, But they didn't. They did not tell the people what their faces were gonna be used for, so they withheld some information. They didn't tell them. It was being used for the spatial recognition system, and the contract workers went to the media and said Something's not right. Why are we being told? Withhold information? And in fact, they told them, going back to the park bench example. To give people who are homeless $5 gift cards to play with the phone and get their images in this. And so this all came to light and Google withdrew this research and this process because it was so in line with a long history of using marginalized, most vulnerable people and populations to make technologies better when those technologies are likely going toe, harm them in terms of surveillance and other things. And so I think I bring this up here to go back to our question of how the composition of teams might help address this. I think often about who is in that room making that decision about sending, creating this process of the contract workers and who the selfies and so on. Perhaps it was a racially homogeneous group where people didn't want really sensitive to how this could be experienced or seen, but maybe it was a diverse, racially diverse group and perhaps the history of harm when it comes to science and technology. Maybe they didn't have that disciplinary knowledge. And so it could also be a function of what people knew in the room, how they could do that chest in their head and think how this is gonna play out. It's not gonna play out very well. And the last thing is that maybe there was disciplinary diversity. Maybe there was racial ethnic diversity, but maybe the workplace culture made it to those people. Didn't feel like they could speak up right so you could have all the diversity in the world. But if you don't create a context in which people who have those insights feel like they can speak up and be respected and heard, then you're basically sitting on a reservoir of resource is and you're not tapping into it to ensure T to do right by your company. And so it's one of those cautionary tales I think that we can all learn from to try to create an environment where we can elicit those insights from our team and our and our coworkers, >>your point about the culture. This is really inclusion very different from just diversity and thought. Eso I like to end on a hopeful note. A prescriptive note. You have some of the most influential data and analytics leaders and experts attending virtually here. So if you imagine the way we use data and housing is a great example, mortgage lending has not been equitable for African Americans in particular. But if you imagine the right way to use data, what is the future hold when we've gotten better at this? More aware >>of this? Thank you for that question on DSO. You know, there's a few things that come to mind for me one. And I think mortgage environment is really the perfect sort of context in which to think through the the both. The problem where the solutions may lie. One of the most powerful ways I see data being used by different organizations and groups is to shine a light on the past and ongoing inequities. And so oftentimes, when people see the bias, let's say when it came to like the the hiring algorithm or the language out, they see the names associated with negative or positive words that tends toe have, ah, bigger impact because they think well, Wow, The technology is reflecting these biases. It really must be true. Never mind that people might have been raising the issues in other ways before. But I think one of the most powerful ways we can use data and technology is as a mirror onto existing forms of inequality That then can motivate us to try to address those things. The caution is that we cannot just address those once we come to grips with the problem, the solution is not simply going to be a technical solution. And so we have to understand both the promise of data and the limits of data. So when it comes to, let's say, a software program, let's say Ah, hiring algorithm that now is trained toe look for diversity as opposed to homogeneity and say I get hired through one of those algorithms in a new workplace. I can get through the door and be hired. But if nothing else about that workplace has changed and on a day to day basis I'm still experiencing microaggressions. I'm still experiencing all kinds of issues. Then that technology just gave me access to ah harmful environment, you see, and so this is the idea that we can't simply expect the technology to solve all of our problems. We have to do the hard work. And so I would encourage everyone listening to both except the promise of these tools, but really crucially, um, Thio, understand that the rial kinds of changes that we need to make are gonna be messy. They're not gonna be quick fixes. If you think about how long it took our society to create the kinds of inequities that that we now it lived with, we should expect to do our part, do the work and pass the baton. We're not going to magically like Fairy does create a wonderful algorithm that's gonna help us bypass these issues. It can expose them. But then it's up to us to actually do the hard work of changing our social relations are changing the culture of not just our workplaces but our schools. Our healthcare systems are neighborhoods so that they reflect our better values. >>Yeah. Ha. So beautifully said I think all of us are willing to do the hard work. And I like your point about using it is a mirror and thought spot. We like to say a fact driven world is a better world. It can give us that transparency. So on behalf of everyone, thank you so much for your passion for your hard work and for talking to us. >>Thank you, Cindy. Thank you so much for inviting me. Hey, I live back to you. >>Thank you, Cindy and rou ha. For this fascinating exploration of our society and technology, we're just about ready to move on to our final session of the day. So make sure to tune in for this customer case study session with executives from Sienna and Accenture on driving digital transformation with certain AI.

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante as we watch an historic election unfold before our eyes we look back at the early days of the millennium with the memorable presidential race of 2000 that decade of course was defined by 911 which permanently reshaped our thinking and we exited that decade at the tail end of a massive financial crisis only to enter the 2010s with the hope and the momentum of fiscal stimulus a flat globe job growth and very importantly the ascendancy of the cloud cloud computing unquestionably powered the innovation engine over the last 10 years and the pandemic marks a new era where adoption of cloud data and ai have been accelerated by at least two to three years and that's what's going to shape the future of the technology industry and frankly all businesses and organizations hello everyone and welcome to this week's episode of thecube insights powered by etr in this breaking analysis we're going to update you on our latest cloud market share and dig in to some fresh october survey data from our partners over at etr let me start just with a brief summary of the latest action that's going on in cloud now quite interestingly each of the big three cloud players they showed nearly identical year-on-year growth rates in q3 as they did in q2 now we're going to dig into that in a moment but our data suggests that these three companies combined will account for more than 75 billion dollars in infrastructure as a service and platform as a service revenue in 2020 and they're potentially on track to hit 100 billion in 2021. customer survey data indicates that cio's top two infrastructure priorities remain security and cloud migration now that said as we previously reported the cloud it's not immune to the pandemic the remote worker pivot well it's a positive for cloud hasn't completely eradicated certain headwinds now what i mean here is that because the cloud vendors are now so large they're somewhat exposed to the softness in the overall i.t spending climate and also industries that have been hit hardest by the pandemic now would the cloud growth have been better if the pandemic didn't hit we'll never know for sure but our data suggests no covet has definitely been a benefactor to cloud in our view cloud will remain at the center of technological innovation for the foreseeable future the economics of cloud are becoming so compelling that we think the power of the big cloud companies will only increase this decade now importantly we're talking about the costs of running hyper-distributed systems we're not commenting here on what they charge customers that's a different story we believe the cost structure for the hyperscalers is superior to alternative approaches and we believe this advantage will only accelerate over the next several years we also believe that competition is going to continue to drive competitive pricing and innovation all right let's look at our latest market share numbers for the big three this chart shows our estimates of aws azure and the google cloud platform now viewers of this program know that these are is and pass figures and you also know that aws is the only company that provides clean numbers on that sector whereas azure and gcp are estimates that we make based on tidbits of guidance that the companies give us and survey data that we capture and other modeling that we do now as we've said we'll end this year it's about 75 billion in revenue or maybe even a little bit more note that for these three note that we've we've slightly restated some of our earlier estimates for azure to reconcile some differences that we had between constant currency and actual growth we try to keep things in constant currency where possible sorry for that but sometimes that happens azure according to our estimates as we reported last week is now 18 of microsoft's overall revenue number we had it at 19 that last week but when i dug in we made some adjustments so we toned it down a bit aws represents a much smaller percentage of course of amazon's revenues at about 12 percent but it represents 56 percent of amazon's profits gcp on the other hand accounts for less than five percent of google's overall revenue which as we've stated a few weeks ago needs more attention from google but look at the growth rates for these three platforms and the respective size of their is and pass businesses hear all this talk about repatriation i.e that what i mean by that is people go to the cloud but they're unhappy or the bill is too high it's too expensive so then they come back on prem well you just don't see that in the numbers so you gotta be careful when vendor a vendor tries to sell you on that trend i don't buy it except for selective situations now let's bring in some of the etr data and compare the spending momentum for each of the big three you've seen these wheel graphs before they show the breakdown of net score for aws microsoft and google now one note these figures represent these three companies overall within the etr technology taxonomy so for example they don't include amazon's retail business of course but they do include for example microsoft's entire tech portfolio not just the cloud the green portion of the wheel represents increases in spending via new adoptions and increased spending whereas the red sections show decreases via lower spending and defections net score which i've highlighted in the orange is calculated by subtracting the two reds from the two true greens in other words adoptions and increase minus decrease and replacements the takeaway here is these are all pretty strong with aws leading the pack microsoft is exceptionally strong as we pointed out last last week because they're so huge and they still have net scores comparable to aws which is a pure play gcp is a laggard and is showing softness in the data despite a sanguine outlook that we had back in 2019 based on survey data i don't know perhaps google's smaller presence muted their customers ability to take advantage of the platform the thinking there is the customers maybe needed to pivot to the cloud so quickly and aws and azure were the incumbents and that was maybe the most expedient path hence the higher increases in the spend more category but you do see gcp um they had 13 new adoptions which is pretty good so we'll keep looking at that regardless again these are not pure play cloud comparisons but they give a good indication of spending momentum i'd also note that all three show very low defections well each is showing solid increases in new adoptions especially google as i mentioned so that's kind of interesting to see but again google much much smaller you would expect that now i want to turn our attention to one of the hottest areas in cloud which is serverless and this is a pure play comparison so serverless let me start there it's a strange term because it's not really accurate but it's stuck serverless computing is a model where the cloud platform dynamically delivers services as the application requires so so you don't have to configure the compute and the containers for example rather when an application needs resources it goes and gets them and you only pay for when the services are actually invoked and in use so it's really good for workloads that spin up and spin down very frequently it kind of reminds me in concept anyway of the component tree that we saw in the days of soa if you remember that services oriented architecture but now this is cloud it's cloud native it's a whole new world and it's increasingly a popular model and as we'll show in a moment there's a lot of spending momentum in this area but before we do that i want to share some comments made by andy jassy a while back about serverless take a listen it's a good question and you know i really the comment i made was really about um directionally what amazon would do you know in this in the very earliest days of aws jeff used to say a lot if i were starting amazon today i'd have built it on top of aws we didn't have all the capability and all the functionality at that very moment but he knew what was coming and he saw what people were still able to accomplish even with where the services were at that point i think the same thing is true here with lambda which is i think if amazon were starting today it's a given they would build it on the cloud and i think with a lot of the applications that comprise amazon's consumer business we would build those on on our serverless capabilities now now lambda of course jesse referring to lambda that's amazon's serverless offering and if you think about amazon's retail business and take for example the frequent spin up and spin down of resources for something like black monday serverless would be a much more cost effective approach same for a managed data warehouse service for example where you know you don't want to pay for the compute if it's idle the app just calls for the compute when it's needed so it's a very popular model and it's got increased momentum today and you see that in this slide it shows the net score breakdown for serverless for azure aws is lambda which is again is their serverless offering and google cloud functions again you're shipping functions to the application that's why it's called functions look at the net scores azure functions nearly 70 aws at 65 google again lagging and that's a bit of a concern because this is a really really hot space all right let's move on and look at the competitive landscape as we like to do often and update you on that this xy graph is one of our favorites and it shows net score or spending momentum on the vertical axis and market share on the horizontal market share is a measure of pervasiveness in the data set in the upper right you also see a table that ranks each vendor my net score and it includes the shared n in other words the number of mentions in this sector for each vendor now you can you can see up top in the middle i've selected on the cloud computing category so this represents only the cloud businesses for each of these players there's a little bit of nuance here and that we've selected on microsoft azure there's a category in the etr taxonomy for that and we're comparing that with aws overall so there's there are things in the aws overall number that fit into the other parts of the taxonomy like maybe ai collaboration etc whereas azures and gcp are just the cloud segments so i i know it's a bit strange because aws is all cloud but don't get caught up in the taxonomical nuance the point is it's good to be azure in aws it's shown there when you look at the upper right of the chart here they stand out and they stand alone in cloud leadership google cloud is they have nice elevated levels but they're much much smaller they don't have the presence in the market now look at that hybrid cloud zone emerging we've talked about this sometimes in the past and and i want to call it vmware cloud on aws red hat open shift and vmware cloud itself like vmware cloud foundation and their other cloud services all of these appear to be gaining traction and you can see in the number of occurrences in the upper right that shared end that i talked about we're starting to see real numbers that are meaningful in this space vmware cloud on aws for example has a net score of 53 percent with 116 accounts within that total respondent sample that you see there in the middle left of 1438 that's how many cios and technology buyers responded to the etr survey in october you look at open shift at 45 net score and that's with 82 accounts now openshift is in beta with what looked to be some really strong offerings on aws and you can see for context i've added dell emc's cloud offerings hpe's cloud offerings and the oracle cloud and ibm cloud and also rackspace dell actually pretty strong with a net score of 20 and 185 shared accounts much much higher than dell overall which is kind of in the red zone oracle ibm you see those rackspace you know organizing not killing it rackspace is kind of in the big negative so that's a concern but anyway we'd like for these guys we'd like to see the data match the marketing rhetoric for the the guys that are in the red and look alibaba is starting to to show up in the server there's only 26 shared ends but we thought we'd we'd put it in there those three key points again aws and microsoft keep on trucking google needs to do better hybrid is becoming real and that bodes well for multi-cloud and the legacy on-prem guys they got a lot of work to do they're under a lot of pressure the pivot to cloud has not been easy for them uh and it's still a case where they're i've talked about this a lot they're they're declines in their on-premises offerings they're not being offset by the new stuff the cloud momentum all right i want to close out by sharing some of the conversations and thoughts that we've had in the community around sas and its impact on cloud we really have been focusing on ias and pass of the sas layer obviously up the stack so let me first share that there's a lot of talk around and has been for years about aws they're slowing growth rates and whether or not they'll have to enter the sas market to expand their total available market and i've said consistently while i never say never about aws i don't think so at least not yet this chart plots the big three cloud players note aws is a bigger piece of this pie now that i've turned off the cloud computing filter and i know more nuances but the data wonks will will find you know see this and they'll ask me about it this is all of aws portfolio and again it's only the microsoft azure portfolio so you see it aws now overtakes azure on the x-axis i.e market share now we've plotted some of the major sas vendors and you can see servicenow and salesforce both very large and they have really strong spending momentum and servicenow's you know pushing 100 billion dollars in market value they've surpassed workday quite some time ago workday's got less presence but they've got really really solid net score and i got to say i'm impressed with sap despite some of the earnings challenges that they've been having they're right up there with splunk and tableau splunk has softened in recent surveys and i've i've also plotted in there netsuite and oracle fusion which are just okay and that is i think for now anyway aws is going to position as the best place and the most friendly and highest quality cloud in which to run your sas for example workday runs on aws aws is salesforce's preferred infrastructure platform so my premise here is just like retail companies might want not want to run on aws a number of sas companies that compete with microsoft they might think twice about running on azure so aws would be better off for now trying to attract those sas players and drive their services and sticking to infrastructure and the pass layer snowflake is actually kind of interesting and i've added them for context because their netscore is always kind of a bellwether it's really off the charts and they're an isv running on the cloud they're different from some of the other sas players and the snowflake is a database okay and most of snowflake's business runs on aws and aws competes with snowflake with redshift but aws has the best cloud and drives a lot of business for snowflake and vice versa so it's kind of interesting snow snowflake to redshift and a much smaller example is kind of like netflix to amazon prime video to compete they both thrive so i think aws is going to continue to grow by attracting sas players as the preferred platform and they'll also attract developers and try to disrupt sas players like servicenow which runs on its own cloud i remember years ago david floyer and i said that servicenow was it was awesome but at some point its infrastructure cost structure its infrastructure cost structure is going to be less competitive than those companies that are running on hyperscale clouds certainly the hyperscale clouds themselves and servicenow they have this multi-instance architecture which just can't easily port over to the cloud but it can charge a lot which it does now at some point some sharp developers are going to look at all this and say whoa see that service now i can build this for less and they'll attack servicenow and their seat base license model maybe with the consumption pricing model and a platform that's perhaps or a set of services that are perhaps less expensive you're seeing this to a you know a certain degree with like elastic inside the application performance management space so there's some some things to watch there but there are those who firmly believe that aws will and must enter the sas space directly we talked last week about how beneficial microsoft's application business is for azure and what a flywheel that is but for me i think we're not there yet let's give it some time i think maybe four to five years before aws may even start to think about filling some of the space up the stack now maybe they'll find some unique opportunities to do that for instance at the edge but i think that's way off okay so bottom line it's good to be in tech these days it's even better to be in the cloud and it's best if you're aws and microsoft and i don't see that changing for a while now remember these episodes are all available as podcasts wherever you listen i publish each week on wikibon.com and siliconangle.com you can get in touch with me through email it's david at siliconangle.com feel free to dm me on twitter at d vallante i post on linkedin love your comments there thank you and don't forget to check out etr plus for all the survey action thanks for watching this episode of thecube insights powered by etr this is dave vellante stay safe stay sane and we'll see you next time you

>>Well, thank you, everyone. And welcome to winning the roadmap race. How? Toe work with tech vendors to get the features that you need. We're here today with representatives or RBC Capital Markets. We will share some of their best practices for collaborating with technology vendors. I am Ada Mancini, solution architect here at Mirant us. And we're joined by Tina Bustamante, senior production manager, RBC Capital Markets and Minnows Agarwal, head of capital markets. Compute and data fabric. Um, RBC has been using docker since about 2016 and you've been closely involved with that effort. What moved you to begin, contain arising applications. >>Okay, uh, higher that. Thank you for having us. Um, back in 2016 when we started our journey one off our major focus, Syria was measuring develops capabilities And what we, uh what we found was it was challenging. Toe adopt develops across applications with different shapes and sizes, different text tax. And as the financial industry, we do have, um, a large presence of rental applications. So making it making that work was challenging. This is where containers were appealing. Tow us. In those early days, we started looking at containers as a possible solution to create a standardization across across different applications to have a consistent format. Other than that, we also saw containers as a potential technology that could be adopted across across enterprise, not just a small subset of applications. Uh, so that that was very interesting. Interesting. Tow us. In addition to that, uh, containers came with schedulers like kubernetes or swarm, which were, uh, which we're doing a lot more than all, which would do a lot more than the traditional traditional schedulers. As an example, resource management fell over management or scaling up and down, depending on a application or business requirements. So all those things were very appealing. It looked like a solutions to a number of problems that are number of challenges that we're facing. So that's when we got started with containers. >>So what subsequently motivated you to start utilizing swarm and then kubernetes? >>Yeah, other than resource management, the follower Management Aziz, you can imagine managing followers. D are Those are never difficult, Never easy on with containers. We saw that, as the container schedule is, we saw that it's a kind of becomes a manage manage service for us. Um, other aspect We are heavily regulated industry in capital markets, especially so creating an audit trail off events. Who did what? When? Uh, that's important. And containers seem to provide all those all those aspects tow us out of the box. Um, the another thing that we saw with containers under the schedulers, we could simplify our risk management. We could control what, what application on which container gets deployed, where, how they run on when they run. So all all those aspects of schedule er they simplify are seen to simplify at that time a lot off a lot off the traditional challenges, and that that's what was very appealing to us. >>Eso what kind of changes were required in the development culture and in operations in order to enable these new this new platform in this new delivery method? >>Yeah, that that's a good question, and any change obviously requires a lot of education. And this was not just a change across our developers or operations, but it was the change across throughout the change, starting with project managers, business analyst developers, Q A, uh, Cuba and our support personal. In addition, I talked about the risk and security Management so it it is. It is a change across the organization. It's, uh it's a cultural change. So the collaboration other than education collaboration was extremely, extremely important. So across those two, we started first with internal education, using something like internal lunch and learns. We did some external workshops or some hands on workshops. So a lot of those exercises were done in collaboration across all those all those things. The next item that we focused on is how do we get our high end developers the awareness of this technology on, uh, make sure they can. They can see, uh, the use cases. Or they can identify the use cases that can benefit from this technology. So we picked high end developers, noticed application and kind of try before you buy type of scenarios. So we ran through some applications to make sure they get their hands study. They feel comfortable with it on. Then they can broadcast that message. The broader organization, the next thing we did it waas getting the management buying. So obviously any change is going to require investment on uh, making sure there's a value proposition that's clear to our management as well as our business was critical very early on in in container option face. So that that that was that was another item that we focused heavily on. And the last thing I would say is a clearly defining strategy benefits so defining a roadmap off how we will proceed, How do we go from our low risk to high risk application or low risk medium risk applications? And what other strategy benefits are these purely operational? Are these purely cause best benefit? Or it's a modernization of the underlying technical facts. So if the containers do check all those three boxes So that that was that was our fourth item on the left that, uh, that, I would say, changed, um, in a container adoption journey. >>So as as people are getting onto the container ization process and as this is starting to gain traction, what things did your developers embrace as the real tangible benefits, um, of moving the containers of container platforms? >>It's interesting. The benefits are not just for developers. And the way I will answer this question is not from development operations. But let me answer it from the operations to developers. So operationally the moment developers saw that application can be deployed with containers relatively quickly without without having them on the collar without them writing a long release notes. They started seeing that benefit right away, but I don't need to be there late in the evening. I don't need to be there on call to create the environment or deploying, uh, deploying Q A versus production versus the are to them because, like do it right one on then repeat that success factor of different environments. So that was that. That was a big eye opening, um, eye opening for them. And they started realizing that Say, Look, I can free up my time now I can focus. I can focus on my core development, and I don't need to deal with the traditional traditional operational operational issues. So that's what that what? That was quite eye opening for all of us, not just for developers. And we started seeing those, uh, that are very early on. Another thing, I would say the developers talked about waas. Hey, I can validate this application on my laptop. I don't need to be I don't need to be on, uh, on on servers. I don't need all these servers. I don't need to share my service. I don't need to depend on infrastructure teams or other teams to get their check is done. Before I kept start my work, I can validate on my on my laptop. That was that was another very powerful feature. Um, that that empowered them. The last thing I would say is that the software defined aspect, uh, aspect off, um, off technology as an example, Network or storage. Although a lot of these traditional things that something Democrats have to call someone they have to wait on, then they have to deal with tickets. Now, they can do a lot of these things themselves. They can define it themselves, and that's very empowering. So they are perspective. Our move towards left, Um, s o the more control developers have, the better the product is. The better the quality of the product. The time to market improves on just the overall experience on the business benefits. They also start to They all start toe, um improved last part. One extra point. I would like to make here the success success of this waas so interesting, uh, to the development community even our developers from business. They they came along and they have shown interest in adopting containers. Whether it's, uh, the development developers from the quartz are the data science developers. They all started realizing the value value proposition of containers. So it was It was quite eye opening, I would have to say. >>And so while this while this process is happening while you're moving to container platforms, um, you started looking for new ways to try and deliver some of the benefits of containers and distributed systems orchestration more widely across the organization. And I think you identified a couple areas where, um, the doctor Enterprise kubernetes service wasn't meeting the features that you anticipated or it hadn't planned on integrating the features that you required. Um, can you tell us about that situation? >>Certainly. Haida. Thanks for having us again. Um, from the product management perspective, I would say products are always evolving and the capabilities can We have different stages of maturity. So when we reviewed what our application teams what are businesses looking to dio? One area that stood out was definitely the state of science space. Um, are quantum data science is really wanted to expand our risk analysis models. Um, they were looking for larger scales, uh, to compute like a lot more computing power. And we tried to see, um, come up with a way to be ableto facilitate their needs. Um, one thing, and it really, really came from like an early concept was the idea of being able to leverage GPU. Um, we stood up like a small R and D team, trying to see if there was something that would be feasible for our on our end. Um, but based on different factors and considerations and, you know, technical thinking involved in this we just realized that the complexity that it would bring to our you know, our overall technical back is not something, um that we would be, um, best suitable, I would say to do it on our own. So we reached out Thio Tim Aransas and brought forth, like, the concept of being able to scale the kubernetes pods on GPS. We relied on there authorities on their engineers Thio, you know, think about being able to expand, uh, kubernetes there kubernetes offering to be able to scale and potentially support running the pods and GPS um, definitely was not something that came from one day to the next that it did involve a number of conversations. Um, but, you know, I'm happy to say I was saying the recent months it has become part of the KUBERNETES product offering. >>Yeah, I believe that that effort, um, did take ah, while took a ah lot of engineering effort. Um, and I think initially all had done some internal r and D to try to work on those features, but ultimately, you decided to go with a different strategy and rely on the vendor to produce those assed part of the vendors product. Um, can you elaborate on the things that you found in that internal R and D? >>Well, we definitely saw the potential for there was definitely potential there. But, you know, the longevity of actually maintaining that GPU, uh, scaling using communities on our own was just not 100% like, in our expertise, expertise of something that we wanted to collaborate more closely with the vendor. Um, you know, technology is always evolving, So it's just the longevity of keeping up with, like, the the up to date features or capabilities testing que involved was just not something that we thought it would be. Something that we should be taking on on our own. >>Okay, So, like spending the time and engineering effort, focusing on the data science, the quantity of analysis parts I see. Um, and then ultimately, um, working with the vendor produced a release and where these features are now available. Um, how what did that engagement look like? Um, with RBC s involvement, >>I would say the engagement started off with, you know, discussing bringing it forth, being very open, you know, having transparency. So that delivery was always a little bit was the focus. Um, but it definitely, um, started office, you know, discussing what it would be like the business case. Why we would require the feature. Definitely the representative. Those and others engaged from them. A ransom side had their own, Um, you know, thoughts and opinions. Um, it had to be being able to run the work clothes, um, on GPU would be something that they would ultimately, as I mentioned, have to support on their end. Um, so we did work with them very closely. There was a very much a willingness collaborate we held a number of meetings. We discuss how the CPU support would would actually evolved. So it wasn't something that came about within like one sprint. No, that was never like our expectation. It did take a couple weeks to be able to see, like a beta product opine on it, see a demo, review it, discuss it further. Um, as you know, sometimes there might be a relief where this capability maybe offered, but there are delays. It's just, you know, part of off of our industry in a cent. Um, we're very much risk versus the nose mentioned, you know, >>when >>you are a financial institution. So we just wanted to make sure it was a viable product, that it was definitely available off the shelf, and then we would be able to leverage it. Um, but yeah, the key point, I would say, in terms of being able to bring the feature forward with definitely constant communication with Miranda, >>that's excellent. I'm glad that were ableto help bring that feature forward. I think that it's something that a lot of people have been asking for and like you said, it enables ah, whole new class of uh, problem solving. Okay. Uh, Meno je Tina, Thank you for your time today. It's been wonderful talking to you again. Uh, that is our session on working with your vendors. I want to thank everyone who's watching this for taking the time Thio contribute to our conference. Uh, awesome. Thank you, kitty.

>> Paige: Hello, everybody, and thank you for joining us today for the virtual Vertica BDC 2020. Today's breakout session is entitled, Vertica next generation architecture. I'm Paige Roberts, open social relationship Manager at Vertica, I'll be your host for this session. And joining me is Vertica Chief Architect, Chuck Bear, before we begin, I encourage you to submit questions or comments during the virtual session. You don't have to wait, just type your question or comment, in the question box that's below the slides and click submit. So as you think about it, go ahead and type it in, there'll be a Q&A session at the end of the presentation, where we'll answer as many questions, as we're able to during the time. Any questions that we don't get a chance to address, we'll do our best to answer offline. Or alternatively, you can visit the Vertica forums to post your questions there, after the session. Our engineering team is planning to join the forum and keep the conversation going, so you can, it's just sort of like the developers lounge would be in delight conference. It gives you a chance to talk to our engineering team. Also, as a reminder, you can maximize your screen by clicking the double arrow button in the lower right corner of the slide. And before you ask, yes, this virtual session is being recorded, and it will be available to view on demand this week, we'll send you a notification, as soon as it's ready. Okay, now, let's get started, over to you, Chuck. >> Chuck: Thanks for the introduction, Paige, Vertica vision is to help customers, get value from structured data. This vision is simple, it doesn't matter what vertical the customer is in. They're all analytics companies, it doesn't matter what the customers environment is, as data is generated everywhere. We also can't do this alone, we know that you need other tools and people to build a complete solution. You know our database is key to delivering on the vision because we need a database that scales. When you start a new database company, you aren't going to win against 30 year old products on features. But from day one, we had something else, an architecture built for analytics performance. This architecture was inspired by the C-store project, combining the best design ideas from academics and industry veterans like Dr. Mike Stonebreaker. Our storage is optimized for performance, we use many computers in parallel. After over 10 years of refinements against various customer workloads, much of the design held up and serendipitously, the fact that we don't store in place updates set Vertica up for success in the cloud as well. These days, there are other tools that embody some of these design ideas. But we have other strengths that are more important than the storage format, where the only good analytics database that runs both on premise and in the cloud, giving customers the option to migrate their workloads, in most convenient and economical environment, or a full data management solution, not just the query tool. Unlike some other choices, ours comes with integration with a sequel ecosystem and full professional support. We organize our product roadmap into four key pillars, plus the cross cutting concerns of open integration and performance and scale. We have big plans to strengthen Vertica, while staying true to our core. This presentation is primarily about the separation pillar, and performance and scale, I'll cover our plans for Eon, our data management architecture, Mart analytic clusters, or fifth generation query executer, and our data storage layer. Let's start with how Vertica manages data, one of the central design points for Vertica was shared nothing, a design that didn't utilize a dedicated hardware shared disk technology. This quote here is how Mike put it politely, but around the Vertica office, shared disk with an LMTB over Mike's dead body. And we did get some early field experience with shared disk, customers, well, in fact will learn on anything if you let them. There were misconfigurations that required certified experts, obscure bugs extent. Another thing about the shared nothing designed for commodity hardware though, and this was in the papers, is that all the data management features like fault tolerance, backup and elasticity have to be done in software. And no matter how much you do, procuring, configuring and maintaining the machines with disks is harder. The software configuration process to add more service may be simple, but capacity planning, racking and stacking is not. The original allure of shared storage returned, this time though, the complexity and economics are different. It's cheaper, even provision storage with a few clicks and only pay for what you need. It expands, contracts and brings the maintenance of the storage close to a team is good at it. But there's a key difference, it's an object store, an object stores don't support the API's and access patterns used by most database software. So another Vertica visionary Ben, set out to exploit Vertica storage organization, which turns out to be a natural fit for modern cloud shared storage. Because Vertica data files are written once and not updated, they match the object storage model perfectly. And so today we have Eon, Eon uses shared storage to hold Vertica data with local disk depot's that act as caches, ensuring that we can get the performance that our customers have come to expect. Essentially Eon in enterprise behave similarly, but we have the benefit of flexible storage. Today Eon has the features our customers expect, it's been developed in tune for years, we have successful customers such as Redpharma, and if you'd like to know more about Eon has helped them succeed in Amazon cloud, I highly suggest reading their case study, which you can find on vertica.com. Eon provides high availability and flexible scaling, sometimes on premise customers with local disks get a little jealous of how recovery and sub-clusters work in Eon. Though we operate on premise, particularly on pure storage, but enterprise also had strengths, the most obvious being that you don't need and short shared storage to run it. So naturally, our vision is to converge the two modes, back into a single Vertica. A Vertica that runs any combination of local disks and shared storage, with full flexibility and portability. This is easy to say, but over the next releases, here's what we'll do. First, we realize that the query executer, optimizer and client drivers and so on, are already the same. Just the transaction handling and data management is different. But there's already more going on, we have peer-to-peer depot operations and other internode transfers. And enterprise also has a network, we could just get files from remote nodes over that network, essentially mimicking the behavior and benefits of shared storage with the layer of software. The only difference at the end of it, will be which storage hold the master copy. In enterprise, the nodes can't drop the files because they're the master copy. Whereas in Eon they can be evicted because it's just the cache, the masters, then shared storage. And in keeping with versus current support for multiple storage locations, we can intermix these approaches at the table level. Getting there as a journey, and we've already taken the first steps. One of the interesting design ideas of the C-store paper is the idea that redundant copies, don't have to have the same physical organization. Different copies can be optimized for different queries, sorted in different ways. Of course, Mike also said to keep the recovery system simple, because it's hard to debug, whenever the recovery system is being used, it's always in a high pressure situation. This turns out to be a contradiction, and the latter idea was better. No down performing stuff, if you don't keep the storage the same. Recovery hardware if you have, to reorganize data in the process. Even query optimization is more complicated. So over the past couple releases, we got rid of non identical buddies. But the storage files can still diverge at the fifth level, because tuple mover operations are synchronized. The same record can end up in different files than different nodes. The next step in our journey, is to make sure both copies are identical. This will help with backup and restore as well, because the second copy doesn't need backed up, or if it is backed up, it appears identical to the deduplication that is going to look present in both backup systems. Simultaneously, we're improving the Vertica networking service to support this new access pattern. In conjunction with identical storage files, we will converge to a recovery system that instantaneous nodes can process queries immediately, by retrieving data they need over the network from the redundant copies as they do in Eon day with even higher performance. The final step then is to unify the catalog and transaction model. Related concepts such as segment and shard, local catalog and shard catalog will be coalesced, as they're really represented the same concepts all along, just in different modes. In the catalog, we'll make slight changes to the definition of a projection, which represents the physical storage organization. The new definition simplifies segmentation and introduces valuable granularities of sharding to support evolution over time, and offers a straightforward migration path for both Eon and enterprise. There's a lot more to our Eon story than just the architectural roadmap. If you missed yesterday's Vertica, in Eon mode presentation about supported cloud, on premise storage option, replays are available. Be sure to catch the upcoming presentation on sizing and configuring vertica and in beyond doors. As we've seen with Eon, Vertica can separate data storage from the compute nodes, allowing machines to quickly fill in for each other, to rebuild fault tolerance. But separating compute and storage is used for much, much more. We now offer powerful, flexible ways for Vertica to add servers and increase access to the data. Vertica nine, this feature is called sub-clusters. It allows computing capacity to be added quickly and incrementally, and isolates workloads from each other. If your exploratory analytics team needs direct access to the source data, they need a lot of machines and not the same number all the time, and you don't 100% trust the kind of queries and user defined functions, they might be using sub-clusters as the solution. While there's much more expensive information available in our other presentation. I'd like to point out the highlights of our latest sub-cluster best practices. We suggest having a primary sub-cluster, this is the one that runs all the time, if you're loading data around the clock. It should be sized for the ETL workloads and also determines the natural shard count. Additional read oriented secondary sub-clusters can be added for real time dashboards, reports and analytics. That way, subclusters can be added or deep provisioned, without disruption to other users. The sub-cluster features of Vertica 9.3 are working well for customers. Yesterday, the Trade Desk presented their use case for Vertica over 300,000 in 5 sub clusters running in the cloud. If you missed a presentation, check out the replay. But we have plans beyond sub-clusters, we're extending sub-clusters to real clusters. For the Vertica savvy, this means the clusters bump, share the same spread ring network. This will provide further isolation, allowing clusters to control their own independent data sets. While replicating all are part of the data from other clusters using a publish subscribe mechanism. Synchronizing data between clusters is a feature customers want to understand the real business for themselves. This vision effects are designed for ancillary aspects, how we should assign resource pools, security policies and balance client connection. We will be simplifying our data segmentation strategy, so that when data that originate in the different clusters meet, they'll still get fully optimized joins, even if those clusters weren't positioned with the same number of nodes per shard. Having a broad vision for data management is a key component to political success. But we also take pride in our execution strategy, when you start a new database from scratch as we did 15 years ago, you won't compete on features. Our key competitive points where speed and scale of analytics, we set a target of 100 x better query performance in traditional databases with path loads. Our storage architecture provides a solid foundation on which to build toward these goals. Every query starts with data retrieval, keeping data sorted, organized by column and compressed by using adaptive caching, to keep the data retrieval time in IO to the bare minimum theoretically required. We also keep the data close to where it will be processed, and you clusters the machines to increase throughput. We have partition pruning a robust optimizer evaluate active use segmentation as part of the physical database designed to keep records close to the other relevant records. So the solid foundation, but we also need optimal execution strategies and tactics. One execution strategy which we built for a long time, but it's still a source of pride, it's how we process expressions. Databases and other systems with general purpose expression evaluators, write a compound expression into a tree. Here I'm using A plus one times B as an example, during execution, if your CPU traverses the tree and compute sub-parts from the whole. Tree traversal often takes more compute cycles than the actual work to be done. Especially in evaluation is a very common operation, so something worth optimizing. One instinct that engineers have is to use what we call, just-in-time or JIT compilation, which means generating code form the CPU into the specific activity expression, and add them. This replaces the tree of boxes that are custom made box for the query. This approach has complexity bugs, but it can be made to work. It has other drawbacks though, it adds a lot to query setup time, especially for short queries. And it pretty much eliminate the ability of mere models, mere mortals to develop user defined functions. If you go back to the problem we're trying to solve, the source of the overhead is the tree traversal. If you increase the batch of records processed in each traversal step, this overhead is amortized until it becomes negligible. It's a perfect match for a columnar storage engine. This also sets the CPU up for efficiency. The CPUs look particularly good, at following the same small sequence of instructions in a tight loop. In some cases, the CPU may even be able to vectorize, and apply the same processing to multiple records to the same instruction. This approach is easy to implement and debug, user defined functions are possible, then generally aligned with the other complexities of implementing and improving a large system. More importantly, the performance, both in terms of query setup and record throughput is dramatically improved. You'll hear me say that we look at research and industry for inspiration. In this case, our findings in line with academic binding. If you'd like to read papers, I recommend everything you always wanted to know about compiled and vectorized queries, don't afraid to ask, so we did have this idea before we read that paper. However, not every decision we made in the Vertica executer that the test of time as well as the expression evaluator. For example, sorting and grouping aren't susceptible to vectorization because sort decisions interrupt the flow. We have used JIT compiling on that for years, and Vertica 401, and it provides modest setups, but we know we can do even better. But who we've embarked on a new design for execution engine, which I call EE five, because it's our best. It's really designed especially for the cloud, now I know what you're thinking, you're thinking, I just put up a slide with an old engine, a new engine, and a sleek play headed up into the clouds. But this isn't just marketing hype, here's what I mean, when I say we've learned lessons over the years, and then we're redesigning the executer for the cloud. And of course, you'll see that the new design works well on premises as well. These changes are just more important for the cloud. Starting with the network layer in the cloud, we can't count on all nodes being connected to the same switch. Multicast doesn't work like it does in a custom data center, so as I mentioned earlier, we're redesigning the network transfer layer for the cloud. Storage in the cloud is different, and I'm not referring here to the storage of persistent data, but to the storage of temporary data used only once during the course of query execution. Our new pattern is designed to take into account the strengths and weaknesses of cloud object storage, where we can't easily do a path. Moving on to memory, many of our access patterns are reasonably effective on bare metal machines, that aren't the best choice on cloud hyperbug that have overheads, page faults or big gap. Here again, we found we can improve performance, a bit on dedicated hardware, and even more in the cloud. Finally, and this is true in all environments, core counts have gone up. And not all of our algorithms take full advantage, there's a lot of ground to cover here. But I think sorting in the perfect example to illustrate these points, I mentioned that we use JIT in sorting. We're getting rid of JIT in favor of a data format that can be treated efficiently, independent of what the data types are. We've drawn on the best, most modern technology from academia and industry. We've got our own analysis and testing, you know what we chose, we chose parallel merge sort, anyone wants to take a guess when merge sort was invented. It was invented in 1948, or at least documented that way, like computing context. If you've heard me talk before, you know that I'm fascinated by how all the things I worked with as an engineer, were invented before I was born. And in Vertica , we don't use the newest technologies, we use the best ones. And what is noble about Vertica is the way we've combined the best ideas together into a cohesive package. So all kidding about the 1940s aside, or he redesigned is actually state of the art. How do we know the sort routine is state of the art? It turns out, there's a pretty credible benchmark or at the appropriately named historic sortbenchmark.org. Anyone with resources looking for fame for their product or academic paper can try to set the record. Record is last set in 2016 with Tencent Sort, 100 terabytes in 99 seconds. Setting the records it's hard, you have to come up with hundreds of machines on a dedicated high speed switching fabric. There's a lot to a distributed sort, there all have core sorting algorithms. The authors of the paper conveniently broke out of the time spent in their sort, 67 out of 99 seconds want to know local sorting. If we break this out, divided by two CPUs and each of 512 nodes, we find that each CPU so there's almost a gig and a half per second. This is for what's called an indy sort, like an Indy race car, is in general purpose. It only handles fixed hundred five records with 10 byte key. There is a record length can vary, then it's called daytona sort, a 10 set daytona sort, is a little slower. One point is 10 gigabytes per second per CPU, now for Verrtica, We have a wide variety ability in record sizes, and more interesting data types, but still no harm in setting us like phone numbers, comfortable to the world record. On my 2017 era AMD desktop CPU, the Vertica EE5 sort to store about two and a half gigabytes per second. Obviously, this test isn't apply to apples because they use their own open power chip. But the number of DRM channels is the same, so it's pretty close the number that says we've hit on the right approach. And it performs this way on premise, in the cloud, and we can adapt it to cloud temp space. So what's our roadmap for integrating EE5 into the product and compare replacing the query executed the database to replacing the crankshaft and other parts of the engine of a car while it's been driven. We've actually done it before, between Vertica three and a half and five, and then we never really stopped changing it, now we'll do it again. The first part in replacing with algorithm called storage merge, which combines sorted data from disk. The first time has was two that are in vertical in incoming 10.0 patch that will be EE5 or resegmented storage merge, and then convert sorting and grouping into do out. There the performance results so far, in cases where the Vertica execute is doing well today, simple environments with simple data patterns, such as this simple capitalistic query, there's a lot of speed up, when we ship the segmentation code, which didn't quite make the freeze as much like to bump longer term, what we do is grouping into the storage of large operations, we'll get to where we think we ought to be, given a theoretical minimum work the CPUs need to do. Now if we look at a case where the current execution isn't doing as well, we see there's a much stronger benefit to the code shipping in Vertica 10. In fact, it turns a chart bar sideways to try to help you see the difference better. This case also benefit from the improvements in 10 product point releases and beyond. They will not happening to the vertical query executer, That was just the taste. But now I'd like to switch to the roadmap first for our adapters layer. I'll start with a story about, how our storage access layer evolved. If you go back to the academic ideas, if you start paper that persuaded investors to fund Vertica, read optimized store was the part that had substantiation in the form of performance data. Much of the paper was speculative, but we tried to follow it anyway. That paper talked about the WS with RS, The rights are in the read store, and how they work together for transaction processing and how there was a supernova. In all honesty, Vertica engineers couldn't figure out from the paper what to do next, incase you want to try, and we asked them they would like, We never got enough clarification to build it that way. But here's what we built, instead. We built the ROS, read optimized store, introduction on steep major revision. It's sorted, ordered columnar and compressed that follows a table partitioning that worked even better than the we are as described in the paper. We also built the last byte optimized store, we built four versions of this over the years actually. But this was the best one, it's not a set of interrelated V tree. It's just an append only, insertion order remember your way here, am sorry, no compression, no base, no partitioning. There is, however, a tuple over which does what we call move out. Move the data from WOS to ROS, sorting and compressing. Let's take a moment to compare how they behave, when you load data directly to the ROS, there's a data parsing operation. Then we finished the sorting, and then compressing right out the columnar data files to stay storage. The next query through executes against the ROS and it runs as it should because the ROS is read optimized. Let's repeat the exercise for WOS, the load operation response before the sorting and compressing, and before the data is written to persistent storage. Now it's possible for a query to come along, and the query could be responsible for sorting the lost data in addition to its other processes. Effect on query isn't predictable until the TM comes along and writes the data to the ROS. Over the years, we've done a lot of comparisons between ROS and WOS. ROS has always been better for sustained load throughput, it achieves much higher records per second without pushing back against the client and hasn't Vertica for when we developed the first usable merge out algorithm. ROS has always been better for predictable query performance, the ROS has never had the same management complexity and limitations as WOS. You don't have to pick a memory size and figure out which transactions get to use the pool. A non persistent nature of ROS always cause headaches when there are unexpected cluster shutdowns. We also looked at field usage data, we found that few customers were using a lot, especially among those that studied the issue carefully. So how we set out on a mission to improve the ROS to the point where it was always better than both the WOS and the profit of the past. And now it's true, ROS is better than the WOS and the loss of a couple of years ago. We implemented storage bundling, better catalog object storage and better tuple mover merge outs. And now, after extensive Q&A and customer testing, we've now succeeded, and in Vertica 10, we've removed the whys. Let's talk for a moment about simplicity, one of the best things Mike Stonebreaker said is no knobs. Anyone want to guess how many knobs we got rid of, and we took the WOS out of the product. 22 were five knobs to control whether it didn't went to ROS as well. Six controlling the ROS itself, Six more to set policies for the typical remove out and so on. In my honest opinion is still wasn't enough control over to achieve excess in a multi tenant environment, the big reason to get rid of the WOS for simplicity. Make the lives of DBAs and users better, we have a long way to go, but we're doing it. On my desk, I keep a jar with the knob in it for each knob in Vertica. When developers add a knob to the product, they have to add a knob to the jar. When they remove a knob, they get to choose one to take out, We have a lot of work to do, but I'm thrilled to report that in 15 years 10 is the first release with a number of knobs ticked downward. Get back to the WOS, I've said the most important thing get rid of it for last. We're getting rid of it so we can deliver our vision of the future to our customer. Remember how he said an Eon and sub-clusters we got all these benefits from shared storage? Guess what can't live in shared storage, the WOS. Remember how it's been a big part of the future was keeping the copies that identical to the primary copy? Independent actions of the WOS took a little at the root of the divergence between copies of the data. You have to admit it when you're wrong. That was in the original design and held up to the a selling point of time, without onto the idea of a separate ROS and WOS for too long. In Vertica, 10, we can finally bid, good reagents. I've covered a lot of ground, so let's put all the pieces together. I've talked a lot about our vision and how we're achieving it. But we also still pay attention to tactical detail. We've been fine tuning our memory management model to enhance performance. That involves revisiting tens of thousands of satellite of code, much like painting the inside of a large building with small paintbrushes. We're getting results as shown in the chart in Vertica nine, concurrent monitoring queries use memory from the global catalog tool, and Vertica 10, they don't. This is only one example of an important detail we're improving. We've also reworked the monitoring tables without network messages into two parts. The increased data we're collecting and analyzing and our quality assurance processes, we're improving on everything. As the story goes, I still have my grandfather's axe, of course, my father had to replace the handle, and I had to replace the head. Along the same lines, we still have Mike Stonebreaker Vertica. We didn't replace the query optimizer twice the debate database designer and storage layer four times each. The query executed is and it's a free design, like charted out how our code has changed over the years. I found that we don't have much from a long time ago, I did some digging, and you know what we have left in 2007. We have the original curly braces, and a little bit of percent code for handling dates and times. To deliver on our mission to help customers get value from their structured data, with high performance of scale, and in diverse deployment environments. We have the sound architecture roadmap, reviews the best execution strategy and solid tactics. On the architectural front, we're converging in an enterprise, we're extending smart analytic clusters. In query processing, we're redesigning the execution engine for the cloud, as I've told you. There's a lot more than just the fast engine. that you want to learn about our new data support for complex data types, improvements to the query optimizer statistics, or extension to live aggregate projections and flatten tables. You should check out some of the other engineering talk that the big data conference. We continue to stay on top of the details from low level CPU and memory too, to the monitoring management, developing tighter feedback cycles between development, Q&A and customers. And don't forget to check out the rest of the pillars of our roadmap. We have new easier ways to get started with Vertica in the cloud. Engineers have been hard at work on machine learning and security. It's easier than ever to use Vertica with third Party product, as a variety of tools integrations continues to increase. Finally, the most important thing we can do, is to help people get value from structured data to help people learn more about Vertica. So hopefully I left plenty of time for Q&A at the end of this presentation. I hope to hear your questions soon.

>> Announcer: From Miami Beach, Florida it's theCUBE, covering Acronis Global Cyber Summit 2019. Brought to you by Acronis. >> Welcome back everyone to theCUBE coverage here at the Acronis Global Cyber Summit 2019 in Miami Beach at the Fontainebleau Hotel. Not a bad venue for an event. It's their first inaugural event around cyber protection. Our next guest is a great guest. He's going to go into great detail. Very fun job. Stressful job. Graeme Hackland, CIO of ROKiT Williams Racing Formula One team. Thanks for joining me. >> Thanks Joe. >> Great job you have. I mean, it's high pressure, high stakes, data's involved. You can nerd out on all the tech and it's a part of the business these days. Take a minute to explain the Williams Racing Team history and what are you guys up to these days. >> So Williams, this is Sir Frank Williams' 41st year with this team. 50 years in total he's been in Formula One. Won 16 world championships. Not recently, we want to do that again for him and that's the mission, right? Get up every day wanting to get back to the front of the grid and help Williams to win. I joined them in 2014. I've been 23 years in total in Formula One. I love the industry, the fast pace, everything you describe. There's a bit of stress obviously but I just love the industry and I joined Williams in 2014 to help with the digital transformation and it's been brilliant and now we're not using the transformation word anymore. We're on a digital journey. We've already put a lot of that infrastructure in place, moved to the cloud, and it's just been, it's been brilliant and we've had some success on the track. More recently it's been tough but we'll get back there. >> You know, I just had a conversation with Dan Havens who's the Chief Growth Officer, he's done all of the sports deals. We were talking about, you know, baseball and the other football, European football, and also Formula One. The competitive advantage edge is there in the data. AI is here, machine learning feeds AI, so now do you set up the infrastructure, you get operationalized properly. This is a big job. It's not just loading software. You got to really think about the wholistic system at work. >> That's the great thing, right? We've go to do the infrastructure right. So you've got to get the basics right. But then if we can do a better job with AI, with machine learning, with the analytic tools that are out there than the other teams are doing. We can beat them. We don't have the same funding levels that they do but we got really smart people, and people is our biggest asset. And then the second biggest is data and making sure that the right engineer has the right data at the right time so that they can do their job, so that we can set the fastest pit stop time or that we can challenge the cars in front of us. It is really important, so we put a lot of time and effort into data analytics, but especially video. Video has become huge for us and obviously then, the data size grows massively. But data and being able to analyze your competitors, analyze your own car, your two drivers against each other. There's a huge amount of data that we are dealing with. >> Without giving any secrets away Graeme, talk about some of the data dynamics that you have going on. What is some of the workflows? What are some of the things you're optimize... You said video. Where are you guys looking at? What are some of the key, cool things that you're seeing as an edge opportunity for you? >> So, Formula One team has this life cycle of a Formula One car where you start in aerodynamics, either in a wind tunnel with a physical model or you do virtual wind tunnel with computational fluid dynamics. There's CFD, so that computation power is really important. Then you go into design, CAD design, that really turns it into something that you can make so then we're into manufacturing. Then we got a race engineer, and all the tools that they use to get the optimum out of the car that they're given on a race weekend. And then you feed that back in so that every race were adding performance to the car, and all through the season. We'll add one and a half to two seconds per lap of performance onto that car every season. And so that's a really important loop that you need to be constantly doing. And if you don't, you know, we've had some issues in this year, if you don't get that completely right, you will lose time to your competitors. >> Give me an example where it didn't work out, where you've gone back to the drawing board. >> So, I think there's been, and it's been well publicized, Clay Williams has talked about it. There's been a bit of a gap between the results we were getting in the wind tunnel and the reality that was happening on the track. And so we've had to bring that back and make sure that there was a correlation between the tunnel and the track. And our engineering group will be working really hard on that, so that kind of thing can happen. >> Talk about the engineering backgrounds that are going on behind the scenes. A lot of people look at Formula One's, only the hardcore nerd that are nerding out and geeking out on the sport know that the depth but, what's going on in the engineering front because there's a lot of investment you guys are making on engineering. >> Yeah, and so, Formula One fans love the data. I think they really love to see the data and work with it and, fortunately, the people who run Formula One are opening more of that data to the fans. If you left it to the teams, we wouldn't share it with the fans because then our competitors see it and we see it as a competitor's advantage. But if something's shared for everyone then that's fair. So, I think the fans love to see the data and see what we're doing. What we're trying to look at now is automation. Humans making decisions has been okay up until probably the last couple of years where some errors have been made in strategy, in real-time where you've got a few seconds to make a decision. Are you going to pit? Virtual safety car has just been called. You've got three seconds to make a decision. Sometimes the humans are making the wrong decision. So we see automation, AI, as really having a role in that real-time decision making. But we think AI can help us in our factory. The things that we're making, something happens at the track, and now we have to change that design. We think introducing automation and AI into that process will really help us as well. >> Yeah, sports market, sports teams, and sports franchises, to me, optimize digital transformation or digital journey because the fans want it. >> Graeme: Yeah. >> There's competitive advantage in running the team. There's the player's decision making whether it's baseball or a driver. >> Graeme: Yup. >> And then there's the fans. So, I got to ask ya on, what are you guys thinking about the fan experience because now you got some data opening up, you got visualization, potentially apps that show you that cars in 3D space and some virtual reality potential. >> Yup. >> The old experience was, ooh, there's a car, goes by again, hey we're (giggles) comes by again. So, bringing, extending the digital fan-based experience, what do you guys, what's your view there? >> Oh, there's a huge amount of work happening in Formula One and it's great to see the people who are running Formula One talking about a digital transformation, not just the teams, right. And it was all about the fan experience. We want the fan to feel like they're a part of it. So Williams did a couple of experiments with virtual reality, so that you could either be one of the pit crews, so you could be the person holding the gun, feel the car coming in, and changing the tire. >> That's awesome. >> Or you could have the driver's view. So the cameras that are on the car are above the driver's head so you don't get an accurate view. So we brought that down into the helmet and now you're getting the view of what it's like to be the driver. >> Wow. >> So, there's been a lot of focus on that fan experience and making sure that you're not at a disadvantage sitting in this, you know, at the track, compared to someone who's at home with two televisions or multiple devices that they're tracking the data on. And the GPS data of where the cars are and hearing some of the commentary of why they're making the decisions they are and when the driver's challenge their engineers, I love that bit. So the engineers got all that data, tells the driver we're going to do this strategy and the driver challenges it because they're in the car feeling how the car feels. >> I think you guys have a great opportunity as an industry because, you look at Esports and the gaming culture, the confluence of that experience based product coming to Formula One. >> Graeme: Yup. >> It's just the perfect fit. >> Well, it's gone, the Esports Formula One has gone huge. We run a team as well. Most of the Formula One teams now have an Esports team. And actually, the people who are driving in the Esports teams, their skills are transferrable. I remember one of the competitions a couple of years ago was to win a drive in the simulator. You became a development driver for one of the Formula One teams. And that shows that those skills are transferrable, so it's great. >> Yeah, that's beautiful stuff. All right, I want to get back to the Acronis cyber.. >> Yup. >> Global Cyber Summit 2019. You're here talking to folks, also sharing knowledge, you guys were hit with ransomware. >> Graeme: Yup. >> Not once, but twice. >> Graeme: Yup. >> I think you had just joined, I think at that time before.. >> It was during 2014 when I first joined and we would, I know, we had put as much investment as we could into our cyber security and to our protection. But we had gaps and I think, so the first ransomware that we got hit by was inside our network and it encrypted 50,000 files before we discovered it. Now we were lucky. We were able to recover all the data from back-up, but we knew that, because it had happened in the middle of the day, someone had looked at some websites during their lunch break and within a couple of hours we had discovered it, contained it, corrected it, restored the data. But the second time we got hit, it was an individual on their computer off network, and we lost data. And that's the thing I hate the most. That data is so precious to us. Losing it was really upsetting. And so we went out into the market, how can we make sure that our data is being backed up? But more than that, how can we make sure that backed up data is protected? And there's a number of reasons we want to protect it. We want to protect it from things like ransomware, but also, the thing that people often don't thing about with their data is, how do we make sure that it's not tampered with at any point? So, when we're at the track, and the car's running around the track, we're pushing data locally, inside the network. We're pushing it to the cloud to do computation and we're sending it back to the UK so that engineers at base can work with it. >> Yeah. >> What it someone was in those stream of data tampering with it? >> Yeah. >> And we then had fake data? And as we go to more machine learning and automation, if those decisions are being made on bad data, that's going to be a real problem. So, we wanted to make sure that our data couldn't be tampered with, so we can adopt new technology. So that was really important. But, Williams also have an advanced engineering company, so beyond Formula One, we apply that knowledge and know how, to all sorts of other industries. From healthcare to retail to automotive. We've been helping Unilever with some really interesting projects to make ice cream better and more efficiently and to help with soap powder. We got to make sure that that customer data is never tampered with. If we're going to put technology into road cars, that's a very different challenge to Formula One. >> John: Yeah. >> We got to make sure that, that whole, the IP chain, how we develop that technology can be proven and isn't tampered with. >> It's interesting, supply chain concepts data protection merging together. Data protection used to be thought after.. Oh, we've got a design. Well let's brush up, we'll get back it, bolt it on. Not anymore. >> Now having to build it into the solutions up front. As we're preparing technology for customers, we're having to make sure that we're thinking about the data challenge. So if it's in a car, so we did battery technology, we won the supply for the first ever gas to electric model, right. As that car is driving around, there's going to be data that's important around the health of the battery. >> John: Yeah. >> And information that is going to be needed by the driver, but also for later for when they're doing the servicing on the car. We got to make sure that that data is protected properly. >> You guys are pushing the envelope on instrumentation, sensors, data, real-time telemetry? >> To be honest, Formula One has always been like that. We put our first data logger in 1979 on a Formula One car. Honestly, it's been an IOT device since then. (laughs) It's not a new thing for F Ones. I think we are really experienced. Our electronics group are real experienced in how to protect that data as it comes off the car and we've applied that knowledge to road cars as well. >> Well you, what's great about you guys and the whole industry is that, that innovation for the sport is now translating as a benefit for society. >> Exactly. >> And I think that is really kind of a, I think, an example of where innovation can come from. Places you least expect it. The people doing hard work pays off. >> It always worried me that Formula One, we spend all the money we spend, right, hundred million pounds, three hundred million pounds per year. And at the end of the year, the product that we created gets retired and we create a whole new product. It always worried me that that technology wasn't reused. Williams are reusing it. You know, we take the carbon fiber that we use to protect a driver in a Formula One car. We've now applied that to babies in hospitals when they get moved around. We built a carbon fiber unit that moves them around. Aerodynamics design, we've applied to fridges to make them more efficient. If you've got an open fridge, the cold air doesn't come out into the aisle of the supermarket. We push it back into the fridges. I love that. Reuse, taking loose end leaf batteries and putting them into a unit that you bought on the side of a house and it helps to power the house over night. >> You know, it's interesting Graeme, you mentioned digital transformation versus digital journey, you guys are operationalize it as it's used. >> Graeme: Exactly. >> Difference, there's nuance but transformation. You have yet transformed. >> Graeme: Yup. >> You guys up transformed so you're on a journey. I got to ask you, what is some learnings in your operationalize digital? I mean, obviously you got your sport, but now it's translating out to other areas. What's the big learnings that you take away from, as a professional and as an individual in the industry, from all this? >> I think, initially, we were quite conservative and we only went with big players that we were convinced were going to be around in three to five years. I think, there's a lot more established cloud providers now but early on we only went with the big guys because we wanted to make sure we could get our data out. If they disappeared, we weren't going to lose our data. I think what the partnership with Acronis and other partnerships we've done has helped us to be more aggressive in terms of our approach towards CAD vendors. We can now take risks with a smaller player. We've got a really niche product but it's something that could give us a competitive advantage for half a season, three, four races sometimes. We'd go for it. Whereas, I think we were a bit conservative at first. I think all CIOs have to think about what's their appetite for risk. We did a really good process of mapping that out, discussing it all the way to board level. What exactly are we prepared to risk? There's some things, you know, car data, we're just not prepared to risk that. >> Yeah. >> But there are some things that we can afford to take risks with. And I've talked to CIOs at finance institutes, they're starting to take risks now. There's core data that they won't be able to, either by regulation or just doesn't make sense. But there's a lot you can commoditize and put out into the cloud. >> And if you have a cyber protection foundation, you can take those risks. >> Graeme: Exactly. >> You don't want to be looking over your shoulder worrying. >> Because you own the data. And sometimes when you go with a cloud provider, it feels almost like they own the data. But when you've got a partnership like the one we have with Acronis, we know that we own the data. We're backing that data away from the cloud vendor so we can always get it back. >> Graeme, thanks so much for the insight. Love this conversation. I think it's really innovative, cutting edge, and great fun to talk about. Thanks for coming on theCUBE, appreciate it. >> Thank you very much, cheers. >> CUBE coverage here at Miami Beach at the Fontainebleau Hotel for Acronis Global Cyber Security 2019 Summit, I'm John Ferrier, stay with us for more CUBE day two coverage after this short break. (fun music)