>> Hi, I'm Stu Miniman, and this is special On the Ground of theCUBE here at the VTUG Summer Slam 2019. We've had the pleasure of knowing the VTUG team for quite awhile back actually, when it was the New England VMUG was when I started attending. When it switched to the VTUG at Gillette Stadium's when we started doing theCUBE there. And happy to bring back to the program first, Chris Harney, who is the one who created this as a true user event. And joining him is his wife Dawn Harney, who we know is behind the scenes organizing all of this event. So, Dawn and Chris, thank you so much for joining us and thank you for sharing this community and educational process with all of us. >> Thanks Stu, it's been a pleasure. >> All right, so, Chris, we really want this, it's a celebration. Sixteen years; back in 2003 the number one movie of the year was actually Finding Nemo. Of course we waited a long time for there. It goes without saying that all of us were a little bit younger. And boy, in those days, I started working with VMR in 2002, so that journey of virtualization was real early. There was no cloud talking we had kind of the XSP's and some of the earlier things. But so much has changed, and what I have loved is this journey that the users that are attending here. We're actually here in the Expo hall, and if you look, why are there no people in here right now? Because they are all in the break out sessions understanding what are the skill sets that they need today and tomorrow to help them in their journey; virtualization, cloud, DevOps, all of these changes there. Chris, you started this as a user to help share with your peers, so, we've had you on the program many times, bring us back. >> Yeah, so think back to 2003. There was no way to share information. There's no Google, no YouTube, no Facebook groups, Meetups, no Game of Thrones. >> We had to go to books and stuff like that. >> Exactly. >> Read the paper. >> So white papers, those were the big deal. You had the Microsoft books that were two inches thick and glossy. >> Yeah, I wonder how many of our younger audience would know the acronym RTFM? Read The Fine Manual please, is what we're doing. Dawn, this event, as I said, we've been at the winter event at Gillette Stadium, you brought in some of the Patriot players we've had the pleasure of interviewing. This Summer event is epic. I know people that come from very long distances to swim in the community, get the information. There's a little bit of lobster at the end of the day. >> There's a lot of lobster at the end of the day. >> So give us the community that you look to help build and foster, and what this event has meant to you over the years. >> For me it's really a place for everybody in the community to come together and share their knowledge with their peers. Something may work for me maybe it will work for you. Let's get together and talk about it. The best way to learn something is from somebody that may have done it, or done it, messed it up, learned something, like to share it with you. So, it really is about working with your peers, learning something from your sponsors and all these companies that you work with everyday. What's new, what's going on. So this is the place to go to get all that. >> Wait, Dawn, I thought you weren't a tech person. >> I'm not a tech person. >> That answer was spot on because one of the things I loved about the virtualization community, is we were all learning in the early days. And it required a little bit of work. There's this theory known as the IKEA effect. Sometimes if you actually help build it a little bit, you actually like it a little bit more. And this community really epitomizes that in the virtualization community and cloud. We've been talking about cloud now for a decade but it's still relatively early days on how this multi-hybrid cloud fits together, how operations are changing, so, Chris, bring us through a little bit of that arc. >> Well, I'll think about it, back in 2003, there was only VMwire. There was only one virtualization platform, if you didn't use VMwire, you were doing bare metal Windows install or Unix install on physical servers. Well, back when we changed, there was Hyper-V, that was coming out, AWS was just coming out, so that's when we kind of made the jump from just being a VMwire user to a virtual technology. So we could talk about the cloud, we could share those experiences and have that same journey together, and hopefully learn and lead, get smarter together as a group, you can learn faster as a group than you can by yourself. >> Yeah, and as we know, Chris, and we've talked about this, the IT industry is never "Hey, give me a clean "sheet of paper and we'll start everything." We know it is additive and all of these things go together, so cloud did not obviate the need for virtualization, so all of these things go together, and how do I make sure as my job doesn't get completely eliminated or, I was talking to somebody who said "If I've been doing the same thing for 10 years, "will I be out of a job?" They said, "Well hopefully you really really like "what you're doing cause if you think "you can keep doing what you're doing, "that is all you will ever be able to do." And I thought that was a very poignant comment. >> Yeah, Matt Broberg's talk this morning about what's your next job going to be, what skillsets do you need to be relevant in 10 years, and it's the same thing, I mean we said the same thing 10, 15 years ago. You can't be a Windows admin anymore, you can't be a VMwire admin anymore, you can't be a cloud admin anymore in five years. >> Yeah, so Dawn, give our audience a little bit of the scope of this event, as I said, I know people that have flown in from the Carolinas, from Colorado, from all over, from California and the like, 16 years of this event, this community is not just New England, it really has had a broad impact. >> Right, and it's huge, people plan their vacations around this, I've had people come from Europe, they fly over here, stay in the state of Maine, they go to L.L. Bean, they do all those things because they plan their vacation, they know they need to be here for the VTUG event, so it's meant a lot, because you do get so many different variety of people, you have the sponsors, you have the end users, you have media, you have bloggers, you have pretty much just everybody comes together to really be that community, so it's meant a lot to me, it's been a long 16 years but it's meant a lot. >> All right, so the question people are asking, this is the final VTUG, so no more winter event at Gillette, this is the final event tonight at Gritty's, so explain to us how that happened. >> It is the final event, 16 years, we're all getting older, it's bittersweet, but we've just realized that it takes a lot of time to put these together, it takes a lot of sponsors, it takes a lot of users, the users continue to come, but unfortunately the sponsors pay for it, and really don't have that following with the sponsors that we used to have, unfortunately. >> There are a lot more events, there are a lot more ways to find customers, so they're going to the meetups and they're doing their own events. >> Yeah, to your opening point Chris, 16 years ago it was much tougher to find sources. Now the challenge we have is there's too many options out there, there are too many events, trust me, I go to too many events, but this one has always been one that we've always looked forward, so please from the community, want to say thank you so much, it has always been one of our favorite things to kick off the year with when we do the winter one, and the summer one, I've made this trip a couple of times, it is a little warm in here, I think brings back to the roots of this event, remember it was four or five years ago it was 110 degrees out, and then you switched to this facility, so of course the air conditioning decides to go out, because we know in IT, sometimes things break. >> Start in the heat, end in the heat. >> So Chris, want to give you the final word for the final VTUG. >> You know, I'm just very proud and happy with this community, it truly is a community, it wasn't us, it wasn't theCUBE, it wasn't the vendors, it was everyone working together to make a community that helped each other out, so thanks to everyone. >> Chris and Dawn, thank you so much, we're happy to be a small piece of this community, and look forward to staying in touch with you in your future endeavors. Thanks so much, I'm Stu Miniman, we have a full day of coverage here, keynote speaker, some of the users that have traveled around, really focusing on the community here at the VTUG Summer Slam, as always, thank you for watching theCUBE.

>> [Announcer] Live from Los Angeles, it's The Cube, covering E3 2018. Brought to you by Silicon Angle Media. >> Hey, welcome back here, we're ready, Jeff Frick here with the Cube. We're at the Los Angeles Convention Center at E3, it's amazing. It's like 68,000 people. They're in every single hall, they're out in the streets, they're in the hotels, they're at LA Live, they're all over the place, for really the biggest gaming conference I think in the world, and we're excited to have our next guest, he's Dana Jan, he's a design director for Ready at Dawn, and you just introduced a new game, right? Great to see ya. >> Thank you very much for having me. Yes, that's true. We just announced Echo Combat last year in October, and today we're showing off on the floor. >> Private beta still or are you going to public beta you said soon? >> Yeah, we just had a closed beta actually. We're moving into open beta, and that's gonna be June 21st. >> Right, pretty amazing though, you guys have not been around that long, and this is already your third game. >> Well, the studio's been around for a while, so we've been making games for a long time. This is actually kind of a new foray for us though going into VR. We released a game called Lone Echo last year, and Echo Arena was a multi-player mode that we also launched simultaneously with Lone Echo, so yeah, this game is new and fresh, but it's, we've been developing VR now for a little over two years. >> Right, so from a design perspective in the VR space, what is some of the special considerations you have to be thinking about, either challenges and opportunities? >> Yeah, I mean some of the challenges are obviously, performance is a big deal for us. The game has to run at 90 frames per second on Oculus per eye, so that's rendering essentially like two different-- >> [Jeff] 90 frames per eye? >> Yeah, it's really fast. You have to render 90 frames per second, otherwise it gets really uncomfortable for the user, so we optimize a lot of our experiences. And it's even like, some of the ideas that we have, we have to figure out how to make them viable at that frame rate. And we have a lot of high-fidelity body movement going on in Lone Echo, Echo Arena, and now Echo Combat. We do a lot of IK work to kind of represent a full body avatar that honors essentially head, hands, and because our game takes place in zero G, we have this floating body that has to convincingly flow behind you wherever you go. >> [Jeff] Right >> Yeah, it's actually, it's a pretty big challenge for us as both designers, developers, and just on a technical standpoint to get all that to kind of harmoniously work together. >> Right, so other thing, just in terms of the game play inside VR, 'cause the other thing is right, you don't necessarily control which direction they're looking. I mean, how do you kind of direct the player to where you want them to look, and where you want them to participate? >> That's a great question. Actually, so part of the beauty of VR, is we try to do some of that like you would for a conventional game, trying to use lighting, trying to basically design environments with things, cues, details that would maybe help people along, but ultimately you're as free as you are, just like right now you and I, we can look all over the place. >> [Jeff] Right. >> We don't really want to restrict that. Part of the beauty of VR is that ultimate freedom. If you wanna of kind of go, look in that little corner underneath you for the whole game, you really can, and we try to as much as possible make that something that's beneficial too. We try to code every little bit of our world with something that's interesting to find, discover, so. >> Right, right. >> Yeah, it's freedom of movement, freedom of wherever you wanna be, whatever you wanna do. >> Right, so we're doing this as part of the Western Digital data makes possible program, and really as we get closer and closer to infinite store, infinite compute, infinite networking, you just said you've got designs, and you've got ideas that even today you can't necessarily put into place. So as you look forward for the opportunities when all these things are basically gonna be close to infinite, at close to zero cost, what are some of the things that excite you? Where do you see kind of using that power to do a better job, or different job in your storytelling? >> Yeah, I mean the horsepower that you need to run these kind of games is actually pretty staggering. We compute a lot of stuff on the GPUs, the CPUs, we have a lot of physics-oriented things in the game because VR is really big into like letting you kind of touch everything, and manipulate stuff, and it doesn't feel like you're really somewhere, you don't feel present unless you can actually interact with the environments. And for that we have to basically create tons, and tons, and tons of objects, we have physics constraints and things that are costly for the computation cycles. And then there's like memory issues. We have streaming that we have to kind of get better at. These worlds are very large, and so to store the things that you're gonna see and do, takes a lot of actual hard drive space, and the speed at which we can load and unload things, is a critical factor in terms of unlocking the freedom of your experience. >> Right, so when you get more horsepower, a new processor comes out, and you get more memory, whatever, I mean do you already have stuff keyed up where you want to use that? Is it more a realistic nature of the graphics, is it speed, I mean what are some of the priorities that you would immediately apply if you had some more horsepower tomorrow? >> Yeah, certainly I mean there are things that we absolutely know about like there's texture resolution, there's like I said, there's physics objects, there are just things that we end up going, that's too costly to do, we're gonna have to maybe stop doing that or cut back on it, or scope back. We do look at creating settings and things where our users who actually have more high-end machines, to actually turn that stuff back on, but I think every time we kind of go into another design kind of exercise and sort of looking at what do we want to do in VR, I think we're surprised at what does it take to actually accomplish it. And so I'm not sure I know right now fully what we're gonna start getting into and what kind of hardware that might require, but every day's just a different challenge, and that's part of the excitement of working in VR. >> Right, right and I was gonna say and also obviously the trade-offs. I mean you could go bananas on the texture, but at some point is it the law of diminishing returns in terms of the storytelling, in terms of the experience 'cause you can't optimize across all the potential variables. >> Yeah, no, you have to pick and choose, and you're right, like basically we look at what are our goals, what are we trying to get out of this experience, what do we want the user to really get out of it? And then we have to compromise. We have to make some of those smart choices. But I do think at some point, we'll have to make less compromises as the technology gets better, and certainly things like resolution, if the headsets have higher resolution then it makes sense to put more resolution into the textures because now you can actually see it, and so we kind of hit that synergy where both of those are unlocked, it'll never be infinite obviously, but to where they're more in sync with each other, maybe we can make that compromise now, but maybe in the future we won't. >> Yeah, the headset's a whole 'nother bucket of technology. >> It is yeah. >> That you guys have to account. >> But they're awesome I mean, yeah we're doing, I think it's really impressive to me how far we've come with the headset technology. And I think in the next few years, we're just gonna see even crazier advances. So I'm really excited about that. >> Not just slap on the phone in the cardboard box, like a couple years ago. Here's your VR box. >> I know, right? That's not that long ago if you think about it really. >> All right, Dana, well give a shout out, what's the date for the public beta so people know where to go and how to get involved. >> Yeah, our open beta's gonna be starting June 21st. They can sign up on oculus.com. And yeah, we're looking forward to people getting in there and seeing what their impressions are, and taking the feedback. >> All right, well, Dana, thanks for taking a few minutes and stopping by. >> Great, thank you very much. >> All right, he's Dana, and I'm Jeff. You're watching The Cube from E3 at the LA Convention Center. Thanks for watching. (upbeat music)

>> Announcer: Live from Stanford University in Palo Alto, California, it's theCUBE! Covering Women In Data Science Conference 2018. Brought to you by-- >> Coverage of Women in Data Science 2018. I am Lisa Martin. We're at Stanford University. This is where the big in-person event is, but there are more than 177 regional WiDS events going on around the globe today. They are in 53 countries, and they're actually expecting to have about 100,000 people engaged with WiDS 2018. Pretty awesome. I'm joined by one of the speakers for WiDS 2018, Dawn Woodard, the senior data science manager of maps at Uber. Welcome to theCUBE! >> Thank you so much, Lisa. >> It's exciting to have you here. This is your first WiDS, and you are already a speaker. Tell us a little bit about what attracted you to WiDS. What was it that kind of spoke to you as a female leader in data science? >> Well, I tried to do a fair amount of reach-out to women in data science. I really feel like I've been blessed throughout my career with inspiring female mentors, including my mother, for example. Not every woman comes into her career with that kind of mentorship, so I really wanted to reach out and help provide that to some of the younger folks in our community. >> That's fantastic. One of the things that's remarkable about WiDS, one, is the growth and scale that they've achieved reaching such big, broad audiences in such a short time period. But it's also from a thematic perspective, aiming to inspire and to educate data scientists worldwide, and of course, to support females in that. What are some of the, tell us a little bit about your talk is Dynamic Pricing and Matching in Ride Sharing. What are some of the takeaways that the audience watching the livestream and here in person are going to hear from your talk? >> There are two technical takeaways, and then there's one non-technical takeaway. The first technical takeaway is that the matching algorithms that we use are really designed to reduce the amount of time that riders and drivers have to spend waiting in the app. For drivers, that means that we're working to increase the amount of time that they spend on-trip and getting paid. For riders, that means that we're reducing the amount of time that they have to wait to be picked up by a car. That's the first takeaway. The second takeaway is around dynamic pricing, and why it's important in ride-hailing services in particular. It turns out that it's really important in creating a seamless and reliable experience, both for riders and for drivers, so I talk through the technical reasons for that. Interestingly, these technical arguments are based not just on machine learning and statistics, but also on economic analyses and some optimization concepts. The third takeaway is really that data science is this incredibly interdisciplinary environment in which we have economics, statistics, optimization, machine learning, and more. >> It's really, data sciences has the opportunity, or really is, very horizontal. Every sector, every area of our lives is impacted by it. I mean, we think of all of us that use Uber and ride-sharing apps. I think that's one of the neat things that we're hearing from the event and from the speakers like yourself is these demarcated lines of career paths are blurring, or some of 'em are evaporating. And so, I think having the opportunity to talk to the younger generation, showing them how much impact they can make in this field has got to sort of be maybe, I would even guess, invigorating for you, as someone who's been in the tech in both industry and academia for a while. >> Absolutely. I think about data science as being the way that we learn about the world, statistics and data science. So, how do we use data to learn about the world, and how do we use data to improve, to make great products, to make great apps, for example. >> Exactly. Tell me a little bit about your career path. You have your PhD in statistics from Duke University. Tell me about how you got there, and then how you also got into industry. Were you always a STEM fan as a kid, or was it something that you had a passion for early on, or developed over time? >> I was always passionate about math and science. When I was an undergraduate, I did an internship with a defense contractor. That's how I got interested in machine learning in particular. That's where it took off. I decided to get a PhD in statistics from there. Statistics and machine learning are really closely related. And then, continued down that path throughout my academic career, and now my career in tech. >> What are some of the things that you think that prepared you for a being a female leader? Was it those mentors that you mentioned before? Was it the fact that you just had a passion for it and thought, "If I'm one of the only females in the room, I don't care. "This is something that's interesting to me." What were some of those foundational elements that really guided you? >> One is the inspiration of some women in my life, and if we have to be completely honest, I'm a person who, when, the very rare times in my career when somebody has acted like I couldn't hack it or couldn't make it, it always really got me angry. The way that I channeled that was really to turn it around and to say, "No problem. "I'm going to show you that I can go well beyond "anything that you had conceived of." >> You know, I love that you said that, 'cause Margot Gerritsen, one of the founders of WiDS actually said a couple hours ago, a few years ago, when they had this idea, from concept to first conference was six months, and she said she almost thought of it like a revenge conference. Like, "We can do this!" I think it's kind of, when they had this idea in 2015, the fact that even in 2015, there's still not only demand for, but the demand is growing. As we're seeing, the statistics that show a low percentage of women that have degrees in engineering, I want to say 20%, but only 11% of them are actually working in their field. We still have a lot of work to do to ignite the fire in this next generation of prospective leaders in technology. There's still a lot of groundwork to make up there. I think we're hearing that a lot at WiDS. Are you hearing that in your peer groups as well? >> Absolutely. I think one of the things that I've really focused on is mentoring women as leaders and managers within my organization, and I really find that that's an amazing way to reach out, is not just to reach out myself, but also to do that through female leaders in my own organization. For example, I've mentored and managed two women through the transition from individual contributor to manager. Just watching their trajectory afterwards is incredibly inspiring. But then, of course, those female managers bring in additional female contributors, and it grows from there. >> Right. And you have a pretty good, pretty diverse team at Uber. Tell us a little bit about your rise at Uber. One of the things that I saw on your LinkedIn profile, that you achieved pretty quickly in the first three years, or probably less, was that you led the marketplace data science team through a period of transformative growth. You started that team with 10 data scientists, and by the time you transitioned into your next role, there were 49 data scientists, including seven managers. How were you able to come in and make such a big impact so quickly? >> Well, the whole team chipped in in terms of hiring and reaching out. But at the time when I joined Uber, data science was still relatively small. Those 10 people were being asked to do all of the pricing and matching algorithms, all of the data science for Uber Pool, all of the data science for Uber Eats. We just had one person in each of these areas, and those people very quickly stepped up to the plate and said, "Okay, I need help." We worked together to help grow their teams. It's really a collaborative effort involving the whole team. >> The current team that you're managing, what does that look like from a male/female ratio standpoint? >> The current team is more than 50% female at this point, which is something that I'm really proud of. It's definitely not only my achievement. There was a manager who was leading the team just before I switched to leading maps, and that person also helped increase the presence of women in data science for Uber's mapping organization. The first data scientist on maps at Uber was a woman, actually. >> That's fantastic. And you were saying before we went live that there's a good-sized contingent of women data scientists at Uber today that are participating in WiDS up in San Francisco? >> That's right, yes. We're live-streaming it. There's a Women in Data Science organization at Uber, and that organization is sponsoring the internal events for the live stream, not just for my talk, but really, the whole conference. >> That's one of the things that Margot Gerritsen was also saying, that from a timing perspective, they really knew they were on to something pretty quickly, and being able to take advantage of technology, live streaming, they're also doing it on Facebook, gives them that opportunity to reach a bigger audience. It also is, for you and your peers as speakers, gives you an even bigger platform to be able to reach that audience. But one of the things I find interesting about WiDS is it's not just the younger audience. Like Maria Klawe had said in her opening remarks this morning and before, that the optimal time that she's found of reaching women to get them interested in STEM subjects is first year college, first semester of college. I actually had the same exact experience many years ago, and I didn't realize that was a timing that was actually proven to be the most successful. But it's not just young women at that stage of their university career. It's also those who've been in tech, academia, and industry for a while who, we're hearing, are feeling invigorated by events like WiDS. Do you feel the same? Is this something that just sort of turns up that bunsen burner maybe a little bit higher? >> Oh, it's incredibly empowering to be in a room full of such technically powerful women. It's a wonderful opportunity. >> It really is, and I think that reinvigoration is key. Some of the things like, as we look at what you've already achieved at Uber so far, and we're in 2018, what are some of the things that you're looking forward to your team helping to impact for Uber in 2018? >> In 2018, we're looking to magnify the impact of data science within Uber's mapping organization, which is my main focus right now. Maps at Uber does several things. Think of Uber as being a physical logistics platform. We move people and things from point A to point B. Maps, as our physical world, really impacts every aspect of the user experience, both for riders and for drivers. And then, whenever we're making a dispatch decision or a pricing decision, we need to know something about how long it would take this driver to get to this rider, for example, which is really a mapping prediction. We are looking at increasing the presence of data science within the mapping organization, really bringing that perspective to the table, both at the individual contributor level, but really also growing leadership of data science within the mapping organization so that we can help drive the direction of maps at Uber through data-driven insights. >> Data-driven insights, I'm glad that you brought that up. That's something that, as we talk about data science. Data science is helping to make decisions on policy, healthcare, so many different things, you name it. It really seems like these blurred lines of job categories, as businesses use data science, and even Uber, to extend, grow the business, open new business models, so can the next generation leverage data science to just open up this infinite box, if you will, of careers that they can go into and industries they can impact by having this foundation of data science. >> Absolutely. Well, any time we have to make a decision about what direction we go in, right, as a business, for example, as an organization, then doing that starting from data, understanding what is the world really like, what are the opportunities, what are the places in which we as a company are not doing very well, for example, and can make a simple change and get an incredible impact? Those are incredibly powerful insights. What do you think, last question-ish, 'cause we're getting low on time. We talk a lot about, there's the hard skills/soft skills. Soft is kind of a weird word these days to describe that. You know, statistical analysis, data mining. But there's also this, the softer skills, empathy, things like that. How do you find those two sides, maybe it's right brain/left brain, as being essential for people to become well-rounded data scientists? >> The couple of soft skills that I really look for heavily when I'm hiring a data scientist, one is being really focused on impact, as opposed to focused on building a new shiny thing. That's quite a different approach to the world, and if we stay focused on the product that we're creating, that means that we're willing to chip in, even if the work that's being done is not as glamorous, or is not going to get as much attention, or is not as fancy of a model. We can really stay focused on what are some simple approaches that we can use that can really drive the product forward. That kind of impact focus, and also, that great attitude about being willing to chip in on something, even if it's not that fancy or if I'm not going to get in the limelight for doing this. Those are the kinds of soft skills that really are so critical for us. >> Attitude and impact. I've heard impact a number of times today. Dawn, thank you so much for carving out some time to chat with us on theCUBE. We congratulate you on being a speaker at this year's event, and look forward to talking to you next year. >> Thank you, Lisa. >> We want to thank you for watching theCUBE. We are live at Stanford for the third annual Women in Data Science Conference, hashtag #WiDS2018. Get involved in the conversation. It is happening in over 53 countries. After this short break, I will be right back with my next guest. (fast electronic music)

>> Announcer: Live from Austin, Texas it's the Cube. Covering South by Southwest 2017. Brought to you by Intel. Now here's John Furrier. Okay we're back live here at the South by Southwest Intel AI Lounge, this is The Cube's special coverage of South by Southwest with Intel, #IntelAI where amazing starts with Intel. Our next guest is Dr. Dawn Nafus who's with Intel and you are a senior research scientist. Welcome to The Cube. >> Thank you. >> So you've got a panel coming up and you also have a book AI For Everything. And looking at a democratization of AI we had a quote yesterday that, "AI is the bulldozer for data." What bulldozers were in the real world, AI will be that bulldozer for data, surfacing new experiences. >> Right. >> This is the subject of your book, kind of. What's your take on this and what's your premise? >> Right well the book actually takes a step way back, it's actually called Self Tracking, the panel is AI For Everyone. But the book is on self tracking. And it's really about actually getting some meaning out of data before we start talking about bulldozers. So right now we've got this situation where there's a lot of talk about AI's going to sort of solve all of our problems in health and there's a lot that can get accomplished, whoops. But the fact of the matter is is that people are still struggling with gees, like, "What does my Fitbit actually mean, right?" So there's this, there's a real big gap. And I think probably part of what the industry has to do is not just sort of build new great technologies which we've got to do but also start to fill that gap in sort of data education, data literacy, all that sort of stuff. >> So we're kind of in this first generation of AI data you mentioned wearable, Fitbits. >> Dawn: Yup. >> So people are now getting used to this, so that it sounds this integration into lifestyle becomes kind of a dynamic. >> Yeah. >> Why are people grappling >> John: with this, what's your research say about that? >> Well right now with wearables frankly we're in the classic trough of disillusionment. (laughs) You know for those of you listening I don't know if you have sort of wearables in drawers right now, right? But a lot of people do. And it turns out that folks tend to use it, you know maybe about three or four weeks and either they've learned something really interesting and helpful or they haven't. And so there's actually a lot of people who do really interesting stuff to kind of combine it with symptoms tracking, location, right other sorts of things to actually really reveal the sorts of triggers for medical issues that you can't find in a clinical setting. It's all about being out in the real world and figuring out what's going on with you. Right, so then when we start to think about adding more complexity into that, which is the thing that AI's good at, we've got this problem of there's only so many data sets that AI's any actually any good at handling. And so I think there's going to have to be a moment where sort of people themselves actually start to say, "Okay you know what? "This is how I define my problem. "This is what I'm going to choose to keep track of." And some of that's going to be on a sensor and some of it isn't. Right and sort of being really intervening a little bit more strongly in what this stuff's actually doing. >> You mentioned the Fitbit and you were seeing a lot of disruption in the areas, innovation and disruption, same thing good and bad potentially. But I'll see autonomous vehicles is pretty clear, and knows what Tesla's tracking with their hot trend. But you mentioned Fitbit, that's a healthcare kind of thing. AIs might seem to be a perfect fit into healthcare because there's always alarms going off and all this data flying around. Is that a low hanging fruit for AI? Healthcare? >> Well I don't know if there's any such thing as low hanging fruit (John laughs) in this space. (laughs) But certainly if you're talking about like actual human benefit, right? That absolutely comes the top of the list. And we can see that in both formal healthcare in clinical settings and sort of imaging for diagnosis. Again I think there's areas to be cautious about, right? You know making sure that there's also an appropriate human check and there's also mechanisms for transparency, right? So that doctors, when there is a discrepancy between what the doctor believes and what the machine says you can actually go back and figure out what's actually going on. The other thing I'm particularly excited about is, and this is why I'm so interested in democratization is that health is not just about, you know, what goes on in clinical care. There are right now environmental health groups who are looking at slew of air quality data that they don't know what to do with, right? And a certain amount of machine assistance to sort of figure out you know signatures of sort of point source polluters, for example, is a really great use of AI. It's not going to make anybody any money anytime soon, but that's the kind of society that we want to live in right? >> You are the social good angle for sure, but I'd like to get your thoughts 'cause you mentioned democratization and it's kind of a nuance depending upon what you're looking at. Democratization with news and media is what you saw with social media now you got healthcare. So how do you define democratization in your context and you're excited about.? Is that more of freedom of information and data is it getting around gatekeepers and siloed stacks? I mean how do you look at democratization? >> All of the above. (laughs) (John laughs) I'd say there are two real elements to that. The first is making sure that you know, people are going to use this for more than just business, have the ability to actually do it and have access to the right sorts of infrastructures to, whether it's the environmental health case or there are actually artists now who use natural language processing to create art work. And people ask them, "Why are you using deblurting?" I said, "Well there's a real access issue frankly." It's also on the side of if you're not the person who's going to be directly using data a kind of a sense of, you know... Democratization to me means being able to ask questions of how the stuff's actually behaving. So that means building in mechanisms for transparency, building in mechanisms to allow journalists to do the work that they do. >> Sharing potentially? >> I'm sorry? >> And sharing as well more data? >> Very, very good. Right absolutely, I mean frankly we still have a problem right now in the wearable base of people even getting access to their own data. There's a guy I work with named Hugo Campos who has an arterial defibrillator and he's still fighting to get access to the very data that's coming out of his heart. Right? (laughs) >> Is it on SSD, in the cloud? I mean where is it? >> It is in the cloud. It's going back to the manufacturer. And there are very robust conversations about where it should be. >> That's super sad. So this brings up the whole thing that we've been talking about yesterday when we had a mini segment on The Cube is that there are all these new societal use cases that are just springing up that we've never seen before. Self-driving cars with transportation, healthcare access to data, all these things. What are some of the things that you see emerging on that tools or approaches that could help either scientists or practitioners or citizens deal with these new critical problem solving that needs to apply technology to. I was talking just last week at Stanford with folks that are looking at gender bias and algorithms. >> Right, uh-huh it's real. >> Something I would never have thought of that's an outlier. Like hey, what? >> Oh no, it's happened. >> But it's one of those things were okay, let's put that on the table. There's all this new stuff coming on the table. >> Yeah, yeah absolutely. >> What do you see? >> So they're-- >> How do we solve that >> John: what approaches? >> Yeah there are a couple of mechanisms and I would encourage listeners and folks in the audience to have a look at a really great report that just came out from the Obama Administration and NYU School of Law. It's called AI Now and they actually propose a couple of pathways to sort of making sure we get this right. So you know a couple of things. You know one is frankly making sure that women and people of color are in the room when the stuff's getting built, right? That helps. You know as I said earlier you know making sure that you know things will go awry. Like it just will we can't predict how these things are going to work and catching it after the fact and building in mechanisms to be able to do that really matter. So there was a great effort by ProPublica to look at a system that was predicting criminal recidivism. And what they did was they said, "Look you know "it is true that "the thing has the same failure rate "for both blacks and whites." But some hefty data journalism and data scraping and all the rest of it actually revealed that it was producing false positives for blacks and false negatives for whites. Meaning that black people were predicted to create more crime than white people right? So you know, we can catch that, right? And when we build in more system of people who had the skills to do it, then we can build stuff that we can live with. >> This is exactly to your point of democratization I think that fascinates me that I get so excited about. It's almost intoxicating when you think about it technically and also societal that there's all these new things that are emerging and the community has to work together. Because it's one of those things where there's no, there may be a board of governors out there. I mean who is the board of governors for this stuff? It really has to be community driven. >> Yeah, yeah. >> And NYU's got one, any other examples of communities that are out there that people can participate in or? >> Yup, absolutely. So I think that you know, they're certainly collaborating on projects that you actually care about and sort of asking good questions about, is this appropriate for AI or not, right? Is a great place to start of reaching out to people who have those technical skills. There are also the Engineering Professional Association actually just came out a couple months ago with a set of guidelines for developers to be able to... The kinds of things you have to think about if you're going to build an ethical AI system. So they came out with some very high level principles. Operationalizing those principles is going to be a real tough job and we're all going to have to pitch in. And I'm certainly involved in that. But yeah, there are actually systems of governance that are cohering, but it's early days. >> It's great way to get involved. So I got to ask you the personal question. In your efforts with the research and the book and all of your travels, what's some of the most amazing things that you've seen with AI that are out there that people may know about or may not know about that they should know about? >> Oh gosh. I'm going to reserve judgment, I don't know yet. I think we're too early on the curve to be able to talk about, you know, sort of the magic of it. What I can say is that there is real power when ordinary people who have no coding skills whatsoever and frankly don't even know what the heck machine learning is, get their heads around data that is collected about them personally. That opens up, you can teach five year olds statistical concepts that are learned in college with a wearable because the data applies to them. So they know how it's been collected. >> It's personal. >> Yeah they know what it is already. You don't have to tell them what a outlier effect is because they know because they wear that outlier. You know what I mean. >> They're immersed in the data. >> Absolutely and I think that's where the real social change is going to come from. >> I love immersion as a great way to teach kids. But the data's key. So I got to ask you with the big pillars of change going on and at Mobile World Congress I saw you, Intel in particular, talking about autonomous vehicles heavily, smart cities, media entertainment and the smart home. I'm just trying to get a peg a comparable of how big this shift will be. These will be, I mean the '60s revolution when chips started coming out, the PC revolution and server revolution and now we're kind of in this new wave. How big is it? I mean in order of magnitude, is it super huge with all of the other ships combined? Are we going to see radical >> I don't know. >> configuration changes? >> You know. You know I'm an anthropologist, right? (John laughs) You know everything changes and nothing changes at the same time, right? We're still going to wake up, we're still going to put on our shoes in the morning, right? We're still going to have a lot of the same values and social structures and all the rest of it that we've always had, right. So I don't think in terms of plonk, here's a bunch of technology now. Now that's a revolution. There's like a dialogue. And we are just at the very, very baby steps of having that dialogue. But when we do people in my field call it domestication, right? These become tame, they become part of our lives, we shape them and they shape us. And that's not radical change, that's the change we always have. >> That's evolution. So I got to ask you a question because I have four kids and I have this conversation with my wife and friends all the time because we have kids, digital natives are growing up. And we see a lot of also work place domestication, people kind of getting domesticated with the new technologies. What's your advice whether it's parents to their kids, kids to growing up in this world, whether it's education? How should people approach the technology that's coming at them so heavily? In the age of social media where all our voices are equal right now, getting more filters are coming out. It's pretty intense. >> Yeah, yeah. I think it's an occasion where people have to think a lot more deliberately than they ever have about the sources of information that they want exposure to. The kinds of interaction, the mechanisms that actual do and don't matter. And thinking very clearly about what's noise and what's not is a fine thing to do. (laughs) (John laughs) so yeah, probably the filtering mechanisms has to get a bit stronger. I would say too there's a whole set of practices, there are ways that you can scrutinize new devices for, you know, where the data goes. And often, kind of the higher bar companies will give you access back, right? So if you can't get your data out again, I would start asking questions. >> All right final two questions for you. What's your experiences like so far at South by Southwest? >> Yup. >> And where is the world going to take you next in terms of your research and your focus? >> Well this is my second year at South by Southwest. It's hugely fun, I am so pleased to see just a rip roaring crowd here at the Intel facility which is just amazing. I think this is our first time as in Dell proper. I'm having a really good time. The Self Tracking book is in the book shelf over in the convention center if you're interested. And what's next is we are going to get real about how to make, how to make these ethical principles actually work at an engineering level. >> Computer science meets social science, happening right now. >> Absolutely. >> Intel powering amazing here at South by Southwest. I'm John Furrier you're watching The Cube. We've got a great set of people here on The Cube. Also great AI Lounge experience, great demos, great technologists all about AI for social change with Dr. Dawn Nafus with Intel. We'll be right back with more coverage after this short break. (upbeat digital beats)

>>Welcome to this cube conversation. I'm Dave Nicholson, and this is part of the AWS startup showcase season two. I'm very happy to have with me Webb brown CEO of Qube cost web. Welcome to the program. How are you? I'm doing >>Great. It's great to be here, Dave. Thank you so much for having me really excited for the discussion. >>Good to see you. I guess we saw each other last down in Los Angeles for, for coop con, >>Right? Exactly. Right. Still feeling the energy from that event. Hoping we can be back together in person. Not, not too long from now. >>Yeah. Well I'll second that, well, let, let's get straight to it. Tell us, tell us about Q cost. What do you guys do? And I think just central to that question is what gives you guys the right to exist? What problem are you solving? >>Yeah, I love the question. So first and foremost coupe costs, we provide cost monitoring and cost management solutions for teams running Kubernetes or cloud native workloads. Everything we do is, is built on open source. Our founding team was working on infrastructure monitoring solutions at Google before this. And, and what we saw was as we had several teammates join the Kubernetes effort very early days at Google, we saw teams really struggling even just to, to monitor and understand Kubernetes costs, right? There's lots of complexity with the Kubernetes scheduler and being able to answer the question of what is the cost of an application or what is the cost of, you know, a team department, et cetera. And the workloads that they're deploying was really hard for most teams. If you look at CNCF study from late last year, still today, about two thirds of teams, can't answer where they are spending money. And what we saw when digging in there is that when you can't answer that question, it's really hard to be efficient. And by be efficient, we, we mean get the right balance between cost and performance and reliability. So we help teams in, in these areas and more where, you know, now have thousands of teams using our product. You know, we feel where we're just getting started on our mission as well. >>So when people hear it, when people think of coop costs, they w they naturally associate that with Kubernetes. And they think, well, Kubernetes is open-source wait, isn't that free? So what, so what costs are you tracking? Exactly. >>Yeah. Great question. We would track costs in any environment where you can run Kubernetes. So if that's on-prem, you can bring a custom pricing sheet to monitor, say the cost of your underlying CPU course, you know, GPU's memory, et cetera. If you're running in a cloud environment, we have integrations with Azure, GCP and AWS, where we would be able to reflect all the complexity of, you know, whatever deployment you have, whether you're using a spot and multiple regions where you have complex enterprise discounts are eyes savings plans, you name it, we'd be reflecting it. So it's really about, you know, not just generic prices, it's about getting the right price for your organization. >>So the infrastructure that goes into this calculation can be on premises or off premises in the form of cloud. I heard that, right? >>Yeah, that's exactly right. So all of those environments, we'd give you a visibility into all the resources that your Kubernetes clusters are consuming. Again, that's, you know, nodes, load balancers, every resource that it's directly touching also have the ability for you to pull in external costs, right? So if you have Kubernetes tenants that are using S3 or cloud sequel, or, you know, another external cloud service, we would make that connection for you. And then lastly, if you have shared costs, sometimes even like the cost of a dev ops team, we'd give you the ability to kind of allocate that back to your core infrastructure, which may be used for showback or even charged back across your, your, >>So who are the folks in an organization that are tapping into this, are these, you know, our, our, our, our developers being encouraged to be cognizant of these costs throughout the process, or is this just sort of a CFO on down visibility tool? >>Yeah, it's a great, it's a great question. And what we see is a major transformation here where, you know, kind of shift left from a cost perspective where more and more engineering teams are interested in just being aware or having transparency. So they can build a culture of accountability with costs, right, with the amazing ability to rapidly push to production and iterate, you know, with microservices and Kubernetes, it's hard to have this kind of, you know, just wait for say the finance team to review this at the end of the month or the end of the quarter. We see this increasingly be being viewed in real time by infrastructure teams, by engineering teams. Now finance is still a very important stakeholder and, you know, absolutely has a very important like seat at the table in these conversations. But increasingly these are, again, real time or near real time engineering decisions that are really moving the needle on cost and cost efficiency, overtime and performance as well. >>Now, can you use this to model what costs might be, or is this, or is this, you know, you, you mentioned monitoring in real time, is this only for pulling information as it exists, or could you do, could you use some of the aspects of, of, of your toolset to make a decision, whether something makes more sense to run on your existing infrastructure on premises versus moving into, you know, working in a cloud? Is that something that is designed for or not? >>Great question. So we do have the ability to predict cost cost going forward, based on everything we've learned about your environment, whether you're in multi-cloud hybrid cloud, et cetera. So some really interesting functionality there and a lot more coming later this year, because we do see more and more teams wanting to model the state of the future, right? As you deploy really complex technologies, like say the cluster auto scale or, or HPA in different environments, it can really challenging to do an apples to apples comparison, and we help teams do exactly that. And again, gonna have a lot more interesting announcements here later this year. >>So later that later this year, meaning not in the next few minutes while we're together, >>Nothing new to announce on that front today, but I would say, you know, expect later this quarter for us to have more. >>Okay, that sounds good. Now, now you touched on this a little bit, but I want to hone in on why this is particularly relevant now and moving into the future. You know, we've always tracking costs has always been important, you know, even before the Dawn of cloud, but why is it increasingly important? And, and, you know, there are, there are alternatives for cost tracking legacy alternatives that are out there. So talk about why it's particularly relevant now and tell us what your super power is. You know, what's the, all right. All right. >>Secrets, >>Secret sauce is something you can't share super power. You can talk about >>Absolutely >>NDA. So yes, >>Your superpower. Yeah. Great questions. So for support, just to, to, to touch on, what's fundamentally changing to make a company like ours, you know, impactful or relevant. There's really three things here first and foremost is the new abstractions or complexities that come with Kubernetes, right. Super powerful, but from a cost standpoint, make it considerably harder to accurately track costs. And the big transformation here is, you know, with Kubernetes, you can, at any given moment have 50 applications running on a single node or a single VM, you can fast forward five minutes and there could be 50 entirely new applications, right? So just assigning that VM or, you know, tagging that VM back to an application or team or department really is not relevant in those places. So just the new complexity related to costs makes this problem harder for teams. Second is what we touch on. >>Just again, the power of Cooney. Kubernetes is the ability to allow distributed engineering teams to work on many microservices concurrently. So you're no longer in a lot of ways managing this problem where they centralized kind of single point of decision-making. Oftentimes these decisions are distributed across not only your infrastructure team, but your engineering team. So just the way these decisions and, you know, innovation is happening is changing how you manage these. And lastly, it's just scale, right? The, the cloud and, you know, Kubernetes continue to be incredibly successful. You know, where as goop costs now managing billions of dollars as these numbers get bigger and bigger just becomes more of a business focus and business critical issue. So those are the, you know, the three kind of underlying themes that are changing. When I talk about what we do, that makes us special. It's really this like foundational layer of visibility that we build. >>And what we can do is in real time with a very high degree of accuracy at the largest Kubernetes clusters in the world, give you visibility at any dimension. And so from there, you can do things like have real-time monitoring. You can have real-time insights, you can allow automation to make decisions on these, you know, inputs or data feeds. You can set alerts, you can set recurring reports. All of these things are made possible because of, you know, the, the, I would say really hard work that we've done to, again, give this real-time visibility with a high degree of accuracy at, at crazy scale. >>So if we were to play little make-believe for a moment, pretend like I'm a skeptical sitting on the fence. Not sure if I want to go down this path kind of person. And I say, you know what, web, I think I have a really good handle on all of my costs so far. What would you hit me with as, as, as an example of something that people really didn't expect until they, until they were running coup costs and they had actually had that visibility, what are some of the things that people are surprised by? >>Yeah. Great question. There'd be a number, number one. I'd have, you know, one data point I want to get from you, which is, you know, for your organization or for all of your clusters, what is your cost efficiency? Can you answer that with a high degree of accuracy and by cost efficiency? >>And the answer is now. So tell me, tell me, tell me how to sign up for coupons. >>Yeah. And so the answer, the answer there is you can go get our community version, you know, you can be up and running in minutes, you don't have to share any data, right? Like it is, you know, simply a helmet install, but cost efficiency is this notion of, of every dollar that you are spending on provision resources. What percentage of those dollars are you actually utilizing? And we have, you know, we, we now have, you know, thousands of teams using our product and we've worked with, you know, hundreds of them really closely, you know, this is, you know, that's not the entire market, but in our large sample sizes, we regularly see teams start in the low 20% cost efficiency, meaning that approximately 80% is quote waste time and time. Again, we see teams just be shocked by this number. And again, most of it is not because they were measuring it and accurately or anything like that. Most teams again today still just don't have that visibility until they start working with this. >>So is that, is that sort of the, I in my house household, certain members seem to only believe that there is one position for a light switch, and that would be the on position. Is there, is this a bit of a parallel where, where folks are, are spinning up resources and then just out of sight, out of mind, maybe not spinning them down when not needed. Yeah. >>Yeah. It's, it's, that's definitely one class of the challenges I would say, you know, so today, if you look at our product, we have 14 different insights across like different dimensions of your infrastructure one, or, or I would say several of those relate to exactly what you just described, which is you spin up a VM, you spend a bit load balancer, you spin up an external IP address. You're using it. You're not paying for it. Another class is this notion of, again, I don't have an understanding of what my resources cost. I also don't have a great sense for how much my microservice or application will need. So I'm just going to turn on all the lights, which is, or I'm going to drastically over provision again, I don't know the cost, so I'm just going to kind of set it and forget it. And if my application is performing, you know, then you know, we're doing well here. Again, with this visibility, you can get much more specific, much more accurate, much more actionable with making that trade off, you know, again, down to the individual pod workload, you know, deployment, et cetera. >>So we've, we've touched on this a bit peripherally, but give me an example. You know, you, you run into someone who happens to be a happy user of coop cost. What's the dream story that you love to hear from them about what life was before was before coop costs and what life was like after? >>Yeah, there's a lot, a lot of different dimensions there. You know, one, one is, you know, working with an infrastructure team that, that used to get asked these questions a lot about, you know, why does this cost so much, or why are we spending this and Kubernetes or, or wire expenses growing the rate that they are, you know, like when this, when this works, you know, engineering teams or infrastructure teams, aren't getting asked those questions, right? The tool could cost itself is getting asked that and answering that. So I think one is infrastructure teams, not fielding those types of questions as much. Secondly, is just, you know, more and more teams rolling this out throughout their organization. And ultimately just getting, building a culture of awareness, like ownership, accountability. And then, you know, we just increasingly are seeing teams, you know, find this right balance between cost and performance again. So, you know, in certain cases, improving performance, when are resource bottlenecks in places and other places, you know, reducing costs, you know, by 10 plus million dollars, ultimately at the end of the day, we like to see just teams being more comfortable running their workloads in Kubernetes, right? That is the ultimate sign of success is just an organization, feels comfortable with how they're deploying, how they're managing, how they're spending in Kubernetes. Again, whether that be, you know, on-prem or transitioning from on-prem to a cloud in multiple clouds, et cetera. >>So we're talking to you today as part of the second season of the AWS startup showcase. What's, what's the relationship there with, with AWS? >>So it is the, the largest platform for coop costs being run today. So I believe, you know, at this point, at least a thousand different organizations running our product on AWS hosted clusters, whether they're, you know, ETS or, or self-managed, but you know, a growing number of those on, on EKS. And, you know, we've just, you know, absolutely loved working with the team across, I think at this point, you know, six or seven different groups from marketplace to their containers team, you know, obviously, you know, ETS and others, and just very much see them continuing to push the boundaries on what's possible from a scale and, you know, ease of use and, you know, just breadth of, of offering to this market. >>Well, we really look forward to having you back and hearing about some of these announcements, things that are, that are coming down the line. So we'll definitely have to touch base in the future, but just one, one final, more general question for you, where do you see Kubernetes in general going in 2022? Is it sort of a linear growth? Is there some, is there an inflection point that we see, you know, a good percentage of software that's running enterprises right now is already in that open source category, but what are your thoughts on Kubernetes in 2022? >>Yeah, I think, you know, the one word is everywhere is where I see Kubernetes in 2022, like very deep in the like large and really complex enterprises. Right. So I think you'll see just, you know, major bets there. And I think you'll continue to see more engineers adopted. And I think you'll also continue to see, you know, more and more flavors of it, right? So, you know, some teams find that running Kubernetes anymore serverless fashion is, is right for them. Others find that, you know, having full control, you know, at every part of the stack, including running their own autoscaler for example is really powerful. So I think just, you know, you'll see more and more options. And again, I think teams increasingly adopting the right, you know, abstraction level on top of Kubernetes that works for their workloads and their organizations >>Sounds good. We'll we'll, we'll come back in 2023 and we'll check and see how that, how that all panned out. Well, it's been great talking to you today as part of the startup showcase. Really appreciate it. Great to see you again. It's right about the time where I can still tell you happy new year, because we're still, we're still in January here. Hope you have a great 2022 with that from me, Dave Nicholson, part of the cube part of AWS startup showcase season two, I'd like to thank everyone for joining and stay with us for the best in hybrid tech coverage.

(upbeat soft intro music) >> Hey everyone. Welcome back to theCube's live coverage from Los Angeles of KubeCon and CloudNativeCon 2021. Lisa Martin with Dave Nicholson, Dave and I are pleased to welcome our next guest remotely. Steven Huels joins us, the senior director of Cloud Services at Red Hat. Steven, welcome to the program. >> Steven: Thanks, Lisa. Good to be here with you and Dave. >> Talk to me about where you're seeing traction from an AI/ML perspective? Like where are you seeing that traction? What are you seeing? Like it. >> It's a great starter question here, right? Like AI/ML is really being employed everywhere, right? Regardless of industry. So financial services, telco, governments, manufacturing, retail. Everyone at this point is finding a use for AI/ML. They're looking for ways to better take advantage of the data that they've been collecting for these years. It really, it wasn't all that long ago when we were talking to customers about Kubernetes and containers, you know, AI/ML really wasn't a core topic where they were looking to use a Kubernetes platform to address those types of workloads. But in the last couple of years, that's really skyrocketed. We're seeing a lot of interest from existing customers that are using Red Hat open shift, which is a Kubernetes based platform to take those AI/ML workloads and take them from what they've been doing for additionally, for experimentation, and really get them into production and start getting value out of them at the end of it. >> Is there a common theme, you mentioned a number of different verticals, telco, healthcare, financial services. Is there a common theme, that you're seeing among these organizations across verticals? >> ^There is. I mean, everyone has their own approach, like the type of technique that they're going to get the most value out of. But the common theme is really that everyone seems to have a really good handle on experimentation. They have a lot of very brig data scientists, model developers that are able to take their data and out of it, but where they're all looking to get, get our help or looking for help, is to put those models into production. So ML ops, right. So how do I take what's been built on, on somebody's machine and put that into production in a repeatable way. And then once it's in production, how do I monitor it? What am I looking for as triggers to indicate that I need to retrain and how do I iterate on this sequentially and rapidly applying what would really be traditional dev ops software development, life cycle methodologies to ML and AI models. >> So Steve, we're joining you from KubeCon live at the moment. What's, what's the connection with Kubernetes and how does Kubernetes enable machine learning artificial intelligence? How does it enable it and what are some of the special considerations to in mind? >> So the immediate connection for Red Hat, is Red Hat's open shift is basically an enterprise grade Kubernetics. And so the connection there is, is really how we're working with customers and how customers in general are looking to take advantage of all the benefits that you can get from the Kubernetes platform that they've been applying to their traditional software development over the years, right? The, the agility, the ability to scale up on demand, the ability to have shared resources, to make specialized hardware available to the individual communities. And they want to start applying those foundational elements to their AI/Ml practices. A lot of data science work traditionally was done with high powered monolithic machines and systems. They weren't necessarily shared across development communities. So connecting something that was built by a data scientist, to something that then a software developer was going to put into production was challenging. There wasn't a lot of repeatability in there. There wasn't a lot of scalability, there wasn't a lot of auditability and these are all things that we know we need when talking about analytics and AI/ML. There's a lot of scrutiny put on the auditability of what you put into production, something that's making decisions that impact on whether or not somebody gets a loan or whether or not somebody is granted access to systems or decisions that are made. And so that the connection there is really around taking advantage of what has proven itself in kubernetes to be a very effective development model and applying that to AI/ML and getting the benefits in being able to put these things into production. >> Dave: So, so Red Hat has been involved in enterprises for a long time. Are you seeing most of this from a Kubernetes perspective, being net new application environments or are these extensions of what we would call legacy or traditional environments. >> They tend to be net new, I guess, you know, it's, it's sort of, it's transitioned a little bit over time. When we first started talking to customers, there was desire to try to do all of this in a single Kubernetes cluster, right? How can I take the same environment that had been doing our, our software development, beef it up a little bit and have it applied to our data science environment. And over time, like Kubernetes advanced rights. So now you can actually add labels to different nodes and target workloads based on specialized machinery and hardware accelerators. And so that has shifted now toward coming up with specialized data science environments, but still connecting the clusters in that's something that's being built on that data science environment is essentially being deployed then through, through a model pipeline, into a software artifact that then makes its way into an application that that goes live. And, and really, I think that that's sensible, right? Because we're constantly seeing a lot of evolution in, in the types of accelerators, the types of frameworks, the types of libraries that are being made available to data scientists. And so you want the ability to extend your data science cluster to take advantage of those things and to give data scientists access to that those specialized environments. So they can try things out, determine if there's a better way to, to do what they're doing. And then when they find out there is, be able to rapidly roll that into your production environment. >> You mentioned the word acceleration, and that's one of the words that we talk about when we talk about 2020, and even 2021, the acceleration in digital transformation that was necessary really a year and a half ago, for companies to survive. And now to be able to pivot and thrive. What are you seeing in terms of customers appetites for, for adopting AI/ML based solutions? Has it accelerated as the pandemic has accelerated digital transformation. >> It's definitely accelerated. And I think, you know, the pandemic probably put more of a focus for businesses on where can they start to drive more value? How can they start to do more with less? And when you look at systems that are used for customer interactions, whether they're deflecting customer cases or providing next best action type recommendations, AI/ML fits the bill there perfectly. So when they were looking to optimize, Hey, where do we put our spend? What can help us accelerate and grow? Even in this virtual world we're living in, AI/ML really floated to the top there, that's definitely a theme that we've seen. >> Lisa: Is there a customer example that you think that you could mention that really articulates the value over that? >> You know, I think a lot of it, you know, we've published one specifically around HCA health care, and this had started actually before the pandemic, but I think especially, it's applicable because of the nature of what a pandemic is, where HCA was using AI/ML to essentially accelerate diagnosis of sepsis, right. They were using it for, for disease diagnoses. That same type of, of diagnosis was being applied to looking at COVID cases as well. And so there was one that we did in Canada with, it's called 'how's your flattening', which was basically being able to track and do some predictions around COVID cases in the Canadian provinces. And so that one's particularly, I guess, kind of close to home, given the nature of the pandemic, but even within Red Hat, we started applying a lot more attention to how we could help with customer support cases, right. Knowing that if folks were going to be out with any type of illness. We needed to be able to be able to handle that case, you know, workload without negatively impacting work-life balance for, for other associates. So we looked at how can we apply AI/ML to help, you know, maintain and increase the quality of customer service we were providing. >> it's a great use case. Did you have a keynote or a session, here at KubeCon CloudNative? >> I did. I did. And it really focused specifically on that whole ML ops and model ops pipeline. It was called involving Kubernetes and bracing model ops. It was for a Kubernetes AI day. I believe it aired on Wednesday of this week. Tuesday, maybe. It all kind of condenses in the virtual world. >> Doesn't it? It does. >> So one of the questions that Lisa and I have for folks where we sit here, I don't know, was it year seven or so of the Dawn of Kubernetes, if I have that, right. Where do you think we are, in this, in this wave of adoption, coming from a Red Hat perspective, you have insight into, what's been going on in enterprises for the last 20 plus years. Where are we in this wave? >> That's a great question. Every time, like you, it's sort of that cresting wave sort of, of analogy, right? That when you get to top one wave, you notice the next wave it's even bigger. I think we've certainly gotten to the point where, where organizations have accepted that Kubernetes can, is applicable across all the workloads that they're looking to put in production. Now, the focus has shifted on optimizing those workloads, right? So what are the things that we need to run in our in-house data centers? What are things that we need, or can benefit from using commodity hardware from one of the hyperscalers, how do we connect those environments and more effectively target workloads? So if I look at where things are going to the future, right now, we see a lot of things being targeted based on cluster, right? We say, Hey, we have a data science cluster. It has characteristics because of X, Y, and Z. And we put all of our data science workloads into that cluster. In the future, I think we want to see more workload specific, type of categorization of workloads so that we're able to match available hardware with workloads rather than targeting a workload at a specific cluster. So a developer or data scientist can say, Hey, my particular algorithm here needs access to GPU acceleration and the following frameworks. And then it, the Kubernetes scheduler is able to determine of the available environments. What's the capacity, what are the available resources and match it up accordingly. So we get into a more dynamic environment where the developers and those that are actually building on top of these platforms actually have to know less and less about the clusters they're running on. It just have to know what types of resources they need access to. >> Lisa: So sort of democratizing that. Steve, thank you for joining Dave and me on the program tonight, talking about the traction that you're seeing with AI/ML, Kubernetes as an enabler, we appreciate your time. >> Thank you. >> Thanks Steve. >> For Dave Nicholson. I'm Lisa Martin. You're watching theCube live from Los Angeles KubeCon and CloudNativeCon 21. We'll be right back with our next guest. (subtle music playing) >> Lisa: I have been in the software and technology industry for over 12 years now. So I've had the opportunity as a marketer to really understand and interact with customers across the entire buyer's journey. Hi, I'm Lisa Martin and I'm a host of theCube. Being a host on the cube has been a dream of mine for the last few years. I had the opportunity to meet Jeff and Dave and John at EMC World a few years ago and got the courage up to say, Hey, I'm really interested in this. I love talking with customers...

>>Have you ever wondered how we sequence the human genome, how your smartphone is so well smart, how we will ever analyze all the patient data for the new vaccines or even how we plan to send humans to Mars? Well, at Cloudera, we believe that data can make what is impossible today possible tomorrow we are the enterprise data cloud company. In fact, we provide analytics and machine learning technology that does everything from making your smartphone smarter, to helping scientists ensure that new vaccines are both safe and effective, big data, no problem out era, the enterprise data cloud company. >>So I think for a long time in this country, we've known that there's a great disparity between minority populations and the majority of population in terms of disease burden. And depending on where you live, your zip code has more to do with your health than almost anything else. But there are a lot of smaller, um, safety net facilities, as well as small academic medical colleges within the United States. And those in those smaller environments don't have the access, you know, to the technologies that the larger ones have. And, you know, I call that, uh, digital disparity. So I'm, Harry's in academic scientist center and our mission is to train diverse health care providers and researchers, but also provide services to underserved populations. As part of the reason that I think is so important for me hearing medical college, to do data science. One of the things that, you know, both Cloudera and Claire sensor very passionate about is bringing those height in technologies to, um, to the smaller organizations. >>It's very expensive to go to the cloud for these small organizations. So now with the partnership with Cloudera and Claire sets a clear sense, clients now enjoy those same technologies and really honestly have a technological advantage over some of the larger organizations. The reason being is they can move fast. So we were able to do this on our own without having to, um, hire data scientists. Uh, we probably cut three to five years off of our studies. I grew up in a small town in Arkansas and is one of those towns where the railroad tracks divided the blacks and the whites. My father died without getting much healthcare at all. And as an 11 year old, I did not understand why my father could not get medical attention because he was very sick. >>Since we come at my Harry are looking to serve populations that reflect themselves or affect the population. He came from. A lot of the data you find or research you find health is usually based on white men. And obviously not everybody who needs a medical provider is going to be a white male. >>One of the things that we're concerned about in healthcare is that there's bias in treatment already. We want to make sure those same biases do not enter into the algorithms. >>The issue is how do we get ahead of them to try to prevent these disparities? >>One of the great things about our dataset is that it contains a very diverse group of patients. >>Instead of just saying, everyone will have these results. You can break it down by race, class, cholesterol, level, other kinds of factors that play a role. So you can make the treatments in the long run. More specifically, >>Researchers are now able to use these technologies and really take those hypotheses from, from bench to bedside. >>We're able to overall improve the health of not just the person in front of you, but the population that, yeah, >>Well, the future is now. I love a quote by William Gibson who said the future is already here. It's just not evenly distributed. If we think hard enough and we apply things properly, uh, we can again take these technologies to, you know, underserved environments, um, in healthcare. Nobody should be technologically disadvantage. >>When is a car not just a car when it's a connected data driven ecosystem, dozens of sensors and edge devices gathering up data from just about anything road, infrastructure, other vehicles, and even pedestrians to create safer vehicles, smarter logistics, and more actionable insights. All the data from the connected car supports an entire ecosystem from manufacturers, building safer vehicles and fleet managers, tracking assets to insurers monitoring, driving behaviors to make roads safer. Now you can control the data journey from edge to AI. With Cloudera in the connected car, data is captured, consolidated and enriched with Cloudera data flow cloud Dara's data engineering, operational database and data warehouse provide the foundation to develop service center applications, sales reports, and engineering dashboards. With data science workbench data scientists can continuously train AI models and use data flow to push the models back to the edge, to enhance the car's performance as the industry's first enterprise data cloud Cloudera supports on-premise public and multi-cloud deployments delivering multifunction analytics on data anywhere with common security governance and metadata management powered by Cloudera SDX, an open platform built on open source, working with open compute architectures and open data stores all the way from edge to AI powering the connected car. >>The future has arrived. >>The Dawn of a retail Renaissance is here and shopping will never be the same again. Today's connected. Consumers are always on and didn't control. It's the era of smart retail, smart shelves, digital signage, and smart mirrors offer an immersive customer experience while delivering product information, personalized offers and recommendations, video analytics, capture customer emotions and gestures to better understand and respond to in-store shopping experiences. Beacons sensors, and streaming video provide valuable data into in-store traffic patterns, hotspots and dwell times. This helps retailers build visual heat maps to better understand custom journeys, conversion rates, and promotional effectiveness in our robots automate routine tasks like capturing inventory levels, identifying out of stocks and alerting in store personnel to replenish shelves. When it comes to checking out automated e-commerce pickup stations and frictionless checkouts will soon be the norm making standing in line. A thing of the past data and analytics are truly reshaping. >>The everyday shopping experience outside the store, smart trucks connect the supply chain, providing new levels of inventory visibility, not just into the precise location, but also the condition of those goods. All in real time, convenience is key and customers today have the power to get their goods delivered at the curbside to their doorstep, or even to their refrigerators. Smart retail is indeed here. And Cloudera makes all of this possible using Cloudera data can be captured from a variety of sources, then stored, processed, and analyzed to drive insights and action. In real time, data scientists can continuously build and train new machine learning models and put these models back to the edge for delivering those moment of truth customer experiences. This is the enterprise data cloud powered by Cloudera enabling smart retail from the edge to AI. The future has arrived >>For is a global automotive supplier. We have three business groups, automotive seating in studios, and then emission control technologies or biggest automotive customers are Volkswagen for the NPSA. And we have, uh, more than 300 sites. And in 75 countries >>Today, we are generating tons of data, more and more data on the manufacturing intelligence. We are trying to reduce the, the defective parts or anticipate the detection of the, of the defective part. And this is where we can get savings. I would say our goal in manufacturing is zero defects. The cost of downtime in a plant could be around the a hundred thousand euros. So with predictive maintenance, we are identifying correlations and patterns and try to anticipate, and maybe to replace a component before the machine is broken. We are in the range of about 2000 machines and we can have up to 300 different variables from pressure from vibration and temperatures. And the real-time data collection is key, and this is something we cannot achieve in a classical data warehouse approach. So with the be data and with clouded approach, what we are able to use really to put all the data, all the sources together in the classical way of working with that at our house, we need to spend weeks or months to set up the model with the Cloudera data lake. We can start working on from days to weeks. We think that predictive or machine learning could also improve on the estimation or NTC patient forecasting of what we'll need to brilliance with all this knowledge around internet of things and data collection. We are applying into the predictive convene and the cockpit of the future. So we can work in the self driving car and provide a better experience for the driver in the car. >>The Cloudera data platform makes it easy to say yes to any analytic workload from the edge to AI, yes. To enterprise grade security and governance, yes. To the analytics your people want to use yes. To operating on any cloud. Your business requires yes to the future with a cloud native platform that flexes to meet your needs today and tomorrow say yes to CDP and say goodbye to shadow it, take a tour of CDP and see how it's an easier, faster and safer enterprise analytics and data management platform with a new approach to data. Finally, a data platform that lets you say yes, >>Welcome to transforming ideas into insights, presented with the cube and made possible by cloud era. My name is Dave Volante from the cube, and I'll be your host for today. And the next hundred minutes, you're going to hear how to turn your best ideas into action using data. And we're going to share the real world examples and 12 industry use cases that apply modern data techniques to improve customer experience, reduce fraud, drive manufacturing, efficiencies, better forecast, retail demand, transform analytics, improve public sector service, and so much more how we use data is rapidly evolving as is the language that we use to describe data. I mean, for example, we don't really use the term big data as often as we used to rather we use terms like digital transformation and digital business, but you think about it. What is a digital business? How is that different from just a business? >>Well, digital business is a data business and it differentiates itself by the way, it uses data to compete. So whether we call it data, big data or digital, our belief is we're entering the next decade of a world that puts data at the core of our organizations. And as such the way we use insights is also rapidly evolving. You know, of course we get value from enabling humans to act with confidence on let's call it near perfect information or capitalize on non-intuitive findings. But increasingly insights are leading to the development of data, products and services that can be monetized, or as you'll hear in our industry, examples, data is enabling machines to take cognitive actions on our behalf. Examples are everywhere in the forms of apps and products and services, all built on data. Think about a real-time fraud detection, know your customer and finance, personal health apps that monitor our heart rates. >>Self-service investing, filing insurance claims and our smart phones. And so many examples, IOT systems that communicate and act machine and machine real-time pricing actions. These are all examples of products and services that drive revenue cut costs or create other value. And they all rely on data. Now while many business leaders sometimes express frustration that their investments in data, people, and process and technologies haven't delivered the full results they desire. The truth is that the investments that they've made over the past several years should be thought of as a step on the data journey. Key learnings and expertise from these efforts are now part of the organizational DNA that can catapult us into this next era of data, transformation and leadership. One thing is certain the next 10 years of data and digital transformation, won't be like the last 10. So let's get into it. Please join us in the chat. >>You can ask questions. You can share your comments, hit us up on Twitter right now. It's my pleasure to welcome Mick Holliston in he's the president of Cloudera mic. Great to see you. Great to see you as well, Dave, Hey, so I call it the new abnormal, right? The world is kind of out of whack offices are reopening again. We're seeing travel coming back. There's all this pent up demand for cars and vacations line cooks at restaurants. Everything that we consumers have missed, but here's the one thing. It seems like the algorithms are off. Whether it's retail's fulfillment capabilities, airline scheduling their pricing algorithms, you know, commodity prices we don't know is inflation. Transitory. Is it a long-term threat trying to forecast GDP? It's just seems like we have to reset all of our assumptions and make a feel a quality data is going to be a key here. How do you see the current state of the industry and the role data plays to get us into a more predictable and stable future? Well, I >>Can sure tell you this, Dave, uh, out of whack is definitely right. I don't know if you know or not, but I happen to be coming to you live today from Atlanta and, uh, as a native of Atlanta, I can, I can tell you there's a lot to be known about the airport here. It's often said that, uh, whether you're going to heaven or hell, you got to change planes in Atlanta and, uh, after 40 minutes waiting on algorithm to be right for baggage claim when I was not, I finally managed to get some bag and to be able to show up dressed appropriately for you today. Um, here's one thing that I know for sure though, Dave, clean, consistent, and safe data will be essential to getting the world and businesses as we know it back on track again, um, without well-managed data, we're certain to get very inconsistent outcomes, quality data will the normalizing factor because one thing really hasn't changed about computing since the Dawn of time. Back when I was taking computer classes at Georgia tech here in Atlanta, and that's what we used to refer to as garbage in garbage out. In other words, you'll never get quality data-driven insights from a poor data set. This is especially important today for machine learning and AI, you can build the most amazing models and algorithms, but none of it will matter if the underlying data isn't rock solid as AI is increasingly used in every business app, you must build a solid data foundation mic. Let's >>Talk about hybrid. Every CXO that I talked to, they're trying to get hybrid, right? Whether it's hybrid work hybrid events, which is our business hybrid cloud, how are you thinking about the hybrid? Everything, what's your point of view with >>All those descriptions of hybrid? Everything there, one item you might not have quite hit on Dave and that's hybrid data. >>Oh yeah, you're right. Mick. I did miss that. What, what do you mean by hybrid data? Well, >>David in cloud era, we think hybrid data is all about the juxtaposition of two things, freedom and security. Now every business wants to be more agile. They want the freedom to work with their data, wherever it happens to work best for them, whether that's on premises in a private cloud and public cloud, or perhaps even in a new open data exchange. Now this matters to businesses because not all data applications are created equal. Some apps are best suited to be run in the cloud because of their transitory nature. Others may be more economical if they're running a private cloud, but either way security, regulatory compliance and increasingly data sovereignty are playing a bigger and more important role in every industry. If you don't believe me, just watch her read a recent news story. Data breaches are at an all time high. And the ethics of AI applications are being called into question every day and understanding the lineage of machine learning algorithms is now paramount for every business. So how in the heck do you get both the freedom and security that you're looking for? Well, the answer is actually pretty straightforward. The key is developing a hybrid data strategy. And what do you know Dave? That's the business cloud era? Is it on a serious note from cloud era's perspective? Adopting a hybrid data strategy is central to every business's digital transformation. It will enable rapid adoption of new technologies and optimize economic models while ensuring the security and privacy of every bit of data. What can >>Make, I'm glad you brought in that notion of hybrid data, because when you think about things, especially remote work, it really changes a lot of the assumptions. You talked about security, the data flows are going to change. You've got the economics, the physics, the local laws come into play. So what about the rest of hybrid? Yeah, >>It's a great question, Dave and certainly cloud era itself as a business and all of our customers are feeling this in a big way. We now have the overwhelming majority of our workforce working from home. And in other words, we've got a much larger surface area from a security perspective to keep in mind the rate and pace of data, just generating a report that might've happened very quickly and rapidly on the office. Uh, ether net may not be happening quite so fast in somebody's rural home in, uh, in, in the middle of Nebraska somewhere. Right? So it doesn't really matter whether you're talking about the speed of business or securing data, any way you look at it. Uh, hybrid I think is going to play a more important role in how work is conducted and what percentage of people are working in the office and are not, I know our plans, Dave, uh, involve us kind of slowly coming back to work, begin in this fall. And we're looking forward to being able to shake hands and see one another again for the first time in many cases for more than a year and a half, but, uh, yes, hybrid work, uh, and hybrid data are playing an increasingly important role for every kind of business. >>Thanks for that. I wonder if we could talk about industry transformation for a moment because it's a major theme of course, of this event. So, and the case. Here's how I think about it. It makes, I mean, some industries have transformed. You think about retail, for example, it's pretty clear, although although every physical retail brand I know has, you know, not only peaked up its online presence, but they also have an Amazon war room strategy because they're trying to take greater advantage of that physical presence, uh, and ended up reverse. We see Amazon building out physical assets so that there's more hybrid going on. But when you look at healthcare, for example, it's just starting, you know, with such highly regulated industry. It seems that there's some hurdles there. Financial services is always been data savvy, but you're seeing the emergence of FinTech and some other challenges there in terms of control, mint control of payment systems in manufacturing, you know, the pandemic highlighted America's reliance on China as a manufacturing partner and, and supply chain. Uh it's so my point is it seems that different industries they're in different stages of transformation, but two things look really clear. One, you've got to put data at the core of the business model that's compulsory. It seems like embedding AI into the applications, the data, the business process that's going to become increasingly important. So how do you see that? >>Wow, there's a lot packed into that question there, Dave, but, uh, yeah, we, we, uh, you know, at Cloudera I happened to be leading our own digital transformation as a technology company and what I would, what I would tell you there that's been arresting for us is the shift from being largely a subscription-based, uh, model to a consumption-based model requires a completely different level of instrumentation and our products and data collection that takes place in real, both for billing, for our, uh, for our customers. And to be able to check on the health and wellness, if you will, of their cloud era implementations. But it's clearly not just impacting the technology industry. You mentioned healthcare and we've been helping a number of different organizations in the life sciences realm, either speed, the rate and pace of getting vaccines, uh, to market, uh, or we've been assisting with testing process. >>That's taken place because you can imagine the quantity of data that's been generated as we've tried to study the efficacy of these vaccines on millions of people and try to ensure that they were going to deliver great outcomes and, and healthy and safe outcomes for everyone. And cloud era has been underneath a great deal of that type of work and the financial services industry you pointed out. Uh, we continue to be central to the large banks, meeting their compliance and regulatory requirements around the globe. And in many parts of the world, those are becoming more stringent than ever. And Cloudera solutions are really helping those kinds of organizations get through those difficult challenges. You, you also happened to mention, uh, you know, public sector and in public sector. We're also playing a key role in working with government entities around the world and applying AI to some of the most challenging missions that those organizations face. >>Um, and while I've made the kind of pivot between the industry conversation and the AI conversation, what I'll share with you about AI, I touched upon a little bit earlier. You can't build great AI, can't grow, build great ML apps, unless you've got a strong data foundation underneath is back to that garbage in garbage out comment that I made previously. And so in order to do that, you've got to have a great hybrid dated management platform at your disposal to ensure that your data is clean and organized and up to date. Uh, just as importantly from that, that's kind of the freedom side of things on the security side of things. You've got to ensure that you can see who just touched, not just the data itself, Dave, but actually the machine learning models and organizations around the globe are now being challenged. It's kind of on the topic of the ethics of AI to produce model lineage. >>In addition to data lineage. In other words, who's had access to the machine learning models when and where, and at what time and what decisions were made perhaps by the humans, perhaps by the machines that may have led to a particular outcome. So every kind of business that is deploying AI applications should be thinking long and hard about whether or not they can track the full lineage of those machine learning models just as they can track the lineage of data. So lots going on there across industries, lots going on as those various industries think about how AI can be applied to their businesses. Pretty >>Interesting concepts. You bring it into the discussion, the hybrid data, uh, sort of new, I think, new to a lot of people. And th this idea of model lineage is a great point because people want to talk about AI, ethics, transparency of AI. When you start putting those models into, into machines to do real time inferencing at the edge, it starts to get really complicated. I wonder if we could talk about you still on that theme of industry transformation? I felt like coming into the pandemic pre pandemic, there was just a lot of complacency. Yeah. Digital transformation and a lot of buzz words. And then we had this forced March to digital, um, and it's, but, but people are now being more planful, but there's still a lot of sort of POC limbo going on. How do you see that? Can you help accelerate that and get people out of that state? It definitely >>Is a lot of a POC limbo or a, I think some of us internally have referred to as POC purgatory, just getting stuck in that phase, not being able to get from point a to point B in digital transformation and, um, you know, for every industry transformation, uh, change in general is difficult and it takes time and money and thoughtfulness, but like with all things, what we found is small wins work best and done quickly. So trying to get to quick, easy successes where you can identify a clear goal and a clear objective and then accomplish it in rapid fashion is sort of the way to build your way towards those larger transformative efforts set. Another way, Dave, it's not wise to try to boil the ocean with your digital transformation efforts as it relates to the underlying technology here. And to bring it home a little bit more practically, I guess I would say at cloud era, we tend to recommend that companies begin to adopt cloud infrastructure, for example, containerization. >>And they begin to deploy that on-prem and then they start to look at how they may move those containerized workloads into the public cloud. That'll give them an opportunity to work with the data and the underlying applications themselves, uh, right close to home in place. They can kind of experiment a little bit more safely and economically, and then determine which workloads are best suited for the public cloud and which ones should remain on prem. That's a way in which a hybrid data strategy can help get a digital transformation accomplish, but kind of starting small and then drawing fast from there on customer's journey to the we'll make we've >>Covered a lot of ground. Uh, last question. Uh, w what, what do you want people to leave this event, the session with, and thinking about sort of the next era of data that we're entering? >>Well, it's a great question, but, uh, you know, I think it could be summed up in, uh, in two words. I want them to think about a hybrid data, uh, strategy. So, uh, you know, really hybrid data is a concept that we're bringing forward on this show really for the, for the first time, arguably, and we really do think that it enables customers to experience what we refer to Dave as the power of, and that is freedom, uh, and security, and in a world where we're all still trying to decide whether each day when we walk out each building, we walk into, uh, whether we're free to come in and out with a mask without a mask, that sort of thing, we all want freedom, but we also also want to be safe and feel safe, uh, for ourselves and for others. And the same is true of organizations. It strategies. They want the freedom to choose, to run workloads and applications and the best and most economical place possible. But they also want to do that with certainty, that they're going to be able to deploy those applications in a safe and secure way that meets the regulatory requirements of their particular industry. So hybrid data we think is key to accomplishing both freedom and security for your data and for your business as a whole, >>Nick, thanks so much great conversation and really appreciate the insights that you're bringing to this event into the industry. Really thank you for your time. >>You bet Dave pleasure being with you. Okay. >>We want to pick up on a couple of themes that Mick discussed, you know, supercharging your business with AI, for example, and this notion of getting hybrid, right? So right now we're going to turn the program over to Rob Bearden, the CEO of Cloudera and Manny veer, DAS. Who's the head of enterprise computing at Nvidia. And before I hand it off to Robin, I just want to say for those of you who follow me at the cube, we've extensively covered the transformation of the semiconductor industry. We are entering an entirely new era of computing in the enterprise, and it's being driven by the emergence of data, intensive applications and workloads no longer will conventional methods of processing data suffice to handle this work. Rather, we need new thinking around architectures and ecosystems. And one of the keys to success in this new era is collaboration between software companies like Cloudera and semiconductor designers like Nvidia. So let's learn more about this collaboration and what it means to your data business. Rob, thanks, >>Mick and Dave, that was a great conversation on how speed and agility is everything in a hyper competitive hybrid world. You touched on AI as essential to a data first strategy and accelerating the path to value and hybrid environments. And I want to drill down on this aspect today. Every business is facing accelerating everything from face-to-face meetings to buying groceries has gone digital. As a result, businesses are generating more data than ever. There are more digital transactions to track and monitor. Now, every engagement with coworkers, customers and partners is virtual from website metrics to customer service records, and even onsite sensors. Enterprises are accumulating tremendous amounts of data and unlocking insights from it is key to our enterprises success. And with data flooding every enterprise, what should the businesses do? A cloud era? We believe this onslaught of data offers an opportunity to make better business decisions faster. >>And we want to make that easier for everyone, whether it's fraud, detection, demand, forecasting, preventative maintenance, or customer churn, whether the goal is to save money or produce income every day that companies don't gain deep insight from their data is money they've lost. And the reason we're talking about speed and why speed is everything in a hybrid world and in a hyper competitive climate, is that the faster we get insights from all of our data, the faster we grow and the more competitive we are. So those faster insights are also combined with the scalability and cost benefit they cloud provides and with security and edge to AI data intimacy. That's why the partnership between cloud air and Nvidia together means so much. And it starts with the shared vision making data-driven, decision-making a reality for every business and our customers will now be able to leverage virtually unlimited quantities of varieties, of data, to power, an order of magnitude faster decision-making and together we turbo charge the enterprise data cloud to enable our customers to work faster and better, and to make integration of AI approaches a reality for companies of all sizes in the cloud. >>We're joined today by NVIDIA's Mandy veer dos, and to talk more about how our technologies will deliver the speed companies need for innovation in our hyper competitive environment. Okay, man, you're veer. Thank you for joining us over the unit. >>Thank you, Rob, for having me. It's a pleasure to be here on behalf of Nvidia. We are so excited about this partnership with Cloudera. Uh, you know, when, when, uh, when Nvidia started many years ago, we started as a chip company focused on graphics, but as you know, over the last decade, we've really become a full stack accelerated computing company where we've been using the power of GPU hardware and software to accelerate a variety of workloads, uh, AI being a prime example. And when we think about Cloudera, uh, and your company, a great company, there's three things we see Rob. Uh, the first one is that for the companies that will already transforming themselves by the use of data, Cloudera has been a trusted partner for them. The second thing seen is that when it comes to using your data, you want to use it in a variety of ways with a powerful platform, which of course you have built over time. >>And finally, as we've heard already, you believe in the power of hybrid, that data exists in different places and the compute needs to follow the data. Now, if you think about in various mission, going forward to democratize accelerated computing for all companies, our mission actually aligns very well with exactly those three things. Firstly, you know, we've really worked with a variety of companies today who have been the early adopters, uh, using the power acceleration by changing the technology in their stacks. But more and more, we see the opportunity of meeting customers, where they are with tools that they're familiar with with partners that they trust. And of course, Cloudera being a great example of that. Uh, the second, uh, part of NVIDIA's mission is we focused a lot in the beginning on deep learning where the power of GPU is really shown through, but as we've gone forward, we found that GPU's can accelerate a variety of different workloads from machine learning to inference. >>And so again, the power of your platform, uh, is very appealing. And finally, we know that AI is all about data, more and more data. We believe very strongly in the idea that customers put their data, where they need to put it. And the compute, the AI compute the machine learning compute needs to meet the customer where their data is. And so that matches really well with your philosophy, right? And Rob, that's why we were so excited to do this partnership with you. It's come to fruition. We have a great combined stack now for the customer and we already see people using it. I think the IRS is a fantastic example where literally they took the workflow. They had, they took the servers, they had, they added GPS into those servers. They did not change anything. And they got an eight times performance improvement for their fraud detection workflows, right? And that's the kind of success we're looking forward to with all customers. So the team has actually put together a great video to show us what the IRS is doing with this technology. Let's take a look. >>My name's Joanne salty. I'm the branch chief of the technical branch and RAs. It's actually the research division research and statistical division of the IRS. Basically the mission that RAs has is we do statistical and research on all things related to taxes, compliance issues, uh, fraud issues, you know, anything that you can think of. Basically we do research on that. We're running into issues now that we have a lot of ideas to actually do data mining on our big troves of data, but we don't necessarily have the infrastructure or horsepower to do it. So it's our biggest challenge is definitely the, the infrastructure to support all the ideas that the subject matter experts are coming up with in terms of all the algorithms they would like to create. And the diving deeper within the algorithm space, the actual training of those Agra algorithms, the of parameters each of those algorithms have. >>So that's, that's really been our challenge. Now the expectation was that with Nvidia in cloud, there is help. And with the cluster, we actually build out the test this on the actual fraud, a fraud detection algorithm on our expectation was we were definitely going to see some speed up in prom, computational processing times. And just to give you context, the size of the data set that we were, uh, the SMI was actually working, um, the algorithm against Liz around four terabytes. If I recall correctly, we'd had a 22 to 48 times speed up after we started tweaking the original algorithm. My expectations, quite honestly, in that sphere, in terms of the timeframe to get results, was it that you guys actually exceeded them? It was really, really quick. Uh, the definite now term short term what's next is going to be the subject matter expert is actually going to take our algorithm run with that. >>So that's definitely the now term thing we want to do going down, go looking forward, maybe out a couple of months, we're also looking at curing some, a 100 cards to actually test those out. As you guys can guess our datasets are just getting bigger and bigger and bigger, and it demands, um, to actually do something when we get more value added out of those data sets is just putting more and more demands on our infrastructure. So, you know, with the pilot, now we have an idea with the infrastructure, the infrastructure we need going forward. And then also just our in terms of thinking of the algorithms and how we can approach these problems to actually code out solutions to them. Now we're kind of like the shackles are off and we can just run them, you know, come onto our art's desire, wherever imagination takes our skis to actually develop solutions, know how the platforms to run them on just kind of the close out. >>I rarely would be very missed. I've worked with a lot of, you know, companies through the year and most of them been spectacular. And, uh, you guys are definitely in that category. The, the whole partnership, as I said, a little bit early, it was really, really well, very responsive. I would be remiss if I didn't. Thank you guys. So thank you for the opportunity to, and fantastic. And I'd have to also, I want to thank my guys. My, uh, my staff, David worked on this Richie worked on this Lex and Tony just, they did a fantastic job and I want to publicly thank him for all the work they did with you guys and Chev, obviously also. Who's fantastic. So thank you everyone. >>Okay. That's a real great example of speed and action. Now let's get into some follow up questions guys, if I may, Rob, can you talk about the specific nature of the relationship between Cloudera and Nvidia? Is it primarily go to market or you do an engineering work? What's the story there? >>It's really both. It's both go to market and engineering and engineering focus is to optimize and take advantage of invidious platform to drive better price performance, lower cost, faster speeds, and better support for today's emerging data intensive applications. So it's really both >>Great. Thank you. Many of Eric, maybe you could talk a little bit more about why can't we just existing general purpose platforms that are, that are running all this ERP and CRM and HCM and you know, all the, all the Microsoft apps that are out there. What, what do Nvidia and cloud era bring to the table that goes beyond the conventional systems that we've known for many years? >>Yeah. I think Dave, as we've talked about the asset that the customer has is really the data, right? And the same data can be utilized in many different ways. Some machine learning, some AI, some traditional data analytics. So the first step here was really to take a general platform for data processing, Cloudera data platform, and integrate with that. Now Nvidia has a software stack called rapids, which has all of the primitives that make different kinds of data processing go fast on GPU's. And so the integration here has really been taking rapids and integrating it into a Cloudera data platform. So that regardless of the technique, the customer's using to get insight from that data, the acceleration will apply in all cases. And that's why it was important to start with a platform like Cloudera rather than a specific application. >>So I think this is really important because if you think about, you know, the software defined data center brought in, you know, some great efficiencies, but at the same time, a lot of the compute power is now going toward doing things like networking and storage and security offloads. So the good news, the reason this is important is because when you think about these data intensive workloads, we can now put more processing power to work for those, you know, AI intensive, uh, things. And so that's what I want to talk about a little bit, maybe a question for both of you, maybe Rob, you could start, you think about the AI that's done today in the enterprise. A lot of it is modeling in the cloud, but when we look at a lot of the exciting use cases, bringing real-time systems together, transaction systems and analytics systems and real time, AI inference, at least even at the edge, huge potential for business value and a consumer, you're seeing a lot of applications with AI biometrics and voice recognition and autonomous vehicles and the like, and so you're putting AI into these data intensive apps within the enterprise. >>The potential there is enormous. So what can we learn from sort of where we've come from, maybe these consumer examples and Rob, how are you thinking about enterprise AI in the coming years? >>Yeah, you're right. The opportunity is huge here, but you know, 90% of the cost of AI applications is the inference. And it's been a blocker in terms of adoption because it's just been too expensive and difficult from a performance standpoint and new platforms like these being developed by cloud air and Nvidia will dramatically lower the cost, uh, of enabling this type of workload to be done. Um, and what we're going to see the most improvements will be in the speed and accuracy for existing enterprise AI apps like fraud detection, recommendation, engine chain management, drug province, and increasingly the consumer led technologies will be bleeding into the enterprise in the form of autonomous factory operations. An example of that would be robots that AR VR and manufacturing. So driving quality, better quality in the power grid management, automated retail IOT, you know, the intelligent call centers, all of these will be powered by AI, but really the list of potential use cases now are going to be virtually endless. >>I mean, this is like your wheelhouse. Maybe you could add something to that. >>Yeah. I mean, I agree with Rob. I mean he listed some really good use cases. You know, the way we see this at Nvidia, this journey is in three phases or three steps, right? The first phase was for the early adopters. You know, the builders who assembled, uh, use cases, particular use cases like a chat bot, uh, uh, from the ground up with the hardware and the software almost like going to your local hardware store and buying piece parts and constructing a table yourself right now. I think we are in the first phase of the democratization, uh, for example, the work we did with Cloudera, which is, uh, for a broader base of customers, still building for a particular use case, but starting from a much higher baseline. So think about, for example, going to Ikea now and buying a table in a box, right. >>And you still come home and assemble it, but all the parts are there. The instructions are there, there's a recipe you just follow and it's easy to do, right? So that's sort of the phase we're in now. And then going forward, the opportunity we really look forward to for the democratization, you talked about applications like CRM, et cetera. I think the next wave of democratization is when customers just adopt and deploy the next version of an application they already have. And what's happening is that under the covers, the application is infused by AI and it's become more intelligent because of AI and the customer just thinks they went to the store and bought, bought a table and it showed up and somebody placed it in the right spot. Right. And they didn't really have to learn, uh, how to do AI. So these are the phases. And I think they're very excited to be going there. Yeah. You know, >>Rob, the great thing about for, for your customers is they don't have to build out the AI. They can, they can buy it. And, and just in thinking about this, it seems like there are a lot of really great and even sometimes narrow use cases. So I want to ask you, you know, staying with AI for a minute, one of the frustrations and Mick and I talked about this, the guy go problem that we've all studied in college, uh, you know, garbage in, garbage out. Uh, but, but the frustrations that users have had is really getting fast access to quality data that they can use to drive business results. So do you see, and how do you see AI maybe changing the game in that regard, Rob over the next several years? >>So yeah, the combination of massive amounts of data that have been gathered across the enterprise in the past 10 years with an open API APIs are dramatically lowering the processing costs that perform at much greater speed and efficiency, you know, and that's allowing us as an industry to democratize the data access while at the same time, delivering the federated governance and security models and hybrid technologies are playing a key role in making this a reality and enabling data access to be hybridized, meaning access and treated in a substantially similar way, your respect to the physical location of where that data actually resides. >>That's great. That is really the value layer that you guys are building out on top of that, all this great infrastructure that the hyperscalers have have given us, I mean, a hundred billion dollars a year that you can build value on top of, for your customers. Last question, and maybe Rob, you could, you can go first and then manufacture. You could bring us home. Where do you guys want to see the relationship go between cloud era and Nvidia? In other words, how should we, as outside observers be, be thinking about and measuring your project specifically and in the industry's progress generally? >>Yeah, I think we're very aligned on this and for cloud era, it's all about helping companies move forward, leverage every bit of their data and all the places that it may, uh, be hosted and partnering with our customers, working closely with our technology ecosystem of partners means innovation in every industry and that's inspiring for us. And that's what keeps us moving forward. >>Yeah. And I agree with Robin and for us at Nvidia, you know, we, this partnership started, uh, with data analytics, um, as you know, a spark is a very powerful technology for data analytics, uh, people who use spark rely on Cloudera for that. And the first thing we did together was to really accelerate spark in a seamless manner, but we're accelerating machine learning. We accelerating artificial intelligence together. And I think for Nvidia it's about democratization. We've seen what machine learning and AI have done for the early adopters and help them make their businesses, their products, their customer experience better. And we'd like every company to have the same opportunity. >>Okay. Now we're going to dig into the data landscape and cloud of course. And talk a little bit more about that with drew Allen. He's a managing director at Accenture drew. Welcome. Great to see you. Thank you. So let's talk a little bit about, you know, you've been in this game for a number of years. Uh, you've got particular expertise in, in data and finance and insurance. I mean, you know, you think about it within the data and analytics world, even our language is changing. You know, we don't say talk about big data so much anymore. We talk more about digital, you know, or, or, or data driven when you think about sort of where we've come from and where we're going. What are the puts and takes that you have with regard to what's going on in the business today? >>Well, thanks for having me. Um, you know, I think some of the trends we're seeing in terms of challenges and puts some takes are that a lot of companies are already on this digital journey. Um, they focused on customer experience is kind of table stakes. Everyone wants to focus on that and kind of digitizing their channels. But a lot of them are seeing that, you know, a lot of them don't even own their, their channels necessarily. So like we're working with a big cruise line, right. And yes, they've invested in digitizing what they own, but a lot of the channels that they sell through, they don't even own, right. It's the travel agencies or third party, real sellers. So having the data to know where, you know, where those agencies are, that that's something that they've discovered. And so there's a lot of big focus on not just digitizing, but also really understanding your customers and going across products because a lot of the data has built, been built up in individual channels and in digital products. >>And so bringing that data together is something that customers that have really figured out in the last few years is a big differentiator. And what we're seeing too, is that a big trend that the data rich are getting richer. So companies that have really invested in data, um, are having, uh, an outside market share and outside earnings per share and outside revenue growth. And it's really being a big differentiator. And I think for companies just getting started in this, the thing to think about is one of the missteps is to not try to capture all the data at once. The average company has, you know, 10,000, 20,000 data elements individually, when you want to start out, you know, 500, 300 critical data elements, about 5% of the data of a company drives 90% of the business value. So focusing on those key critical data elements is really what you need to govern first and really invest in first. And so that's something we, we tell companies at the beginning of their data strategy is first focus on those critical data elements, really get a handle on governing that data, organizing that data and building data products around >>That day. You can't boil the ocean. Right. And so, and I, I feel like pre pandemic, there was a lot of complacency. Oh yeah, we'll get to that. You know, not on my watch, I'll be retired before that, you know, is it becomes a minute. And then of course the pandemic was, I call it sometimes a forced March to digital. So in many respects, it wasn't planned. It just ha you know, you had to do it. And so now I feel like people are stepping back and saying, okay, let's now really rethink this and do it right. But is there, is there a sense of urgency, do you think? Absolutely. >>I think with COVID, you know, we were working with, um, a retailer where they had 12,000 stores across the U S and they had didn't have the insights where they could drill down and understand, you know, with the riots and with COVID was the store operational, you know, with the supply chain of the, having multiple distributors, what did they have in stock? So there are millions of data points that you need to drill down at the cell level, at the store level to really understand how's my business performing. And we like to think about it for like a CEO and his leadership team of it, like, think of it as a digital cockpit, right? You think about a pilot, they have a cockpit with all these dials and, um, dashboards, essentially understanding the performance of their business. And they should be able to drill down and understand for each individual, you know, unit of their work, how are they performing? That's really what we want to see for businesses. Can they get down to that individual performance to really understand how their business >>Is performing good, the ability to connect those dots and traverse those data points and not have to go in and come back out and go into a new system and come back out. And that's really been a lot of the frustration. W where does machine intelligence and AI fit in? Is that sort of a dot connector, if you will, and an enabler, I mean, we saw, you know, decades of the, the AI winter, and then, you know, there's been a lot of talk about it, but it feels like with the amount of data that we've collected over the last decade and the, the, the low costs of processing that data now, it feels like it's, it's real. Where do you see AI fitting? Yeah, >>I mean, I think there's been a lot of innovation in the last 10 years with, um, the low cost of storage and computing and these algorithms in non-linear, um, you know, knowledge graphs, and, um, um, a whole bunch of opportunities in cloud where what I think the, the big opportunity is, you know, you can apply AI in areas where a human just couldn't have the scale to do that alone. So back to the example of a cruise lines, you know, you may have a ship being built that has 4,000 cabins on the single cruise line, and it's going to multiple deaths that destinations over its 30 year life cycle. Each one of those cabins is being priced individually for each individual destination. It's physically impossible for a human to calculate the dynamic pricing across all those destinations. You need a machine to actually do that pricing. And so really what a machine is leveraging is all that data to really calculate and assist the human, essentially with all these opportunities where you wouldn't have a human being able to scale up to that amount of data >>Alone. You know, it's interesting. One of the things we talked to Nicolson about earlier was just the everybody's algorithms are out of whack. You know, you look at the airline pricing, you look at hotels it's as a consumer, you would be able to kind of game the system and predict that they can't even predict these days. And I feel as though that the data and AI are actually going to bring us back into some kind of normalcy and predictability, uh, what do you see in that regard? Yeah, I think it's, >>I mean, we're definitely not at a point where, when I talked to, you know, the top AI engineers and data scientists, we're not at a point where we have what they call broad AI, right? You can get machines to solve general knowledge problems, where they can solve one problem and then a distinctly different problem, right? That's still many years away, but narrow why AI, there's still tons of use cases out there that can really drive tons of business performance challenges, tons of accuracy challenges. So for example, in the insurance industry, commercial lines, where I work a lot of the time, the biggest leakage of loss experience in pricing for commercial insurers is, um, people will go in as an agent and they'll select an industry to say, you know what, I'm a restaurant business. Um, I'll select this industry code to quote out a policy, but there's, let's say, you know, 12 dozen permutations, you could be an outdoor restaurant. >>You could be a bar, you could be a caterer and all of that leads to different loss experience. So what this does is they built a machine learning algorithm. We've helped them do this, that actually at the time that they're putting in their name and address, it's crawling across the web and predicting in real time, you know, is this a address actually, you know, a business that's a restaurant with indoor dining, does it have a bar? Is it outdoor dining? And it's that that's able to accurately more price the policy and reduce the loss experience. So there's a lot of that you can do even with narrow AI that can really drive top line of business results. >>Yeah. I liked that term, narrow AI, because getting things done is important. Let's talk about cloud a little bit because people talk about cloud first public cloud first doesn't necessarily mean public cloud only, of course. So where do you see things like what's the right operating model, the right regime hybrid cloud. We talked earlier about hybrid data help us squint through the cloud landscape. Yeah. I mean, I think for most right, most >>Fortune 500 companies, they can't just snap their fingers and say, let's move all of our data centers to the cloud. They've got to move, you know, gradually. And it's usually a journey that's taking more than two to three plus years, even more than that in some cases. So they're have, they have to move their data, uh, incrementally to the cloud. And what that means is that, that they have to move to a hybrid perspective where some of their data is on premise and some of it is publicly on the cloud. And so that's the term hybrid cloud essentially. And so what they've had to think about is from an intelligence perspective, the privacy of that data, where is it being moved? Can they reduce the replication of that data? Because ultimately you like, uh, replicating the data from on-premise to the cloud that introduces, you know, errors and data quality issues. So thinking about how do you manage, uh, you know, uh on-premise and, um, public as a transition is something that Accenture thinks, thinks, and helps our clients do quite a bit. And how do you move them in a manner that's well-organized and well thought of? >>Yeah. So I've been a big proponent of sort of line of business lines of business becoming much more involved in, in the data pipeline, if you will, the data process, if you think about our major operational systems, they all have sort of line of business context in them. And then the salespeople, they know the CRM data and, you know, logistics folks there they're very much in tune with ERP, almost feel like for the past decade, the lines of business have been somewhat removed from the, the data team, if you will. And that, that seems to be changing. What are you seeing in terms of the line of line of business being much more involved in sort of end to end ownership, if you will, if I can use that term of, uh, of the data and sort of determining things like helping determine anyway, the data quality and things of that nature. Yeah. I >>Mean, I think this is where thinking about your data operating model and thinking about ideas of a chief data officer and having data on the CEO agenda, that's really important to get the lines of business, to really think about data sharing and reuse, and really getting them to, you know, kind of unlock the data because they do think about their data as a fiefdom data has value, but you've got to really get organizations in their silos to open it up and bring that data together because that's where the value is. You know, data doesn't operate. When you think about a customer, they don't operate in their journey across the business in silo channels. They don't think about, you know, I use only the web and then I use the call center, right? They think about that as just one experience and that data is a single journey. >>So we like to think about data as a product. You know, you should think about a data in the same way. You think about your products as, as products, you know, data as a product, you should have the idea of like every two weeks you have releases to it. You have an operational resiliency to it. So thinking about that, where you can have a very product mindset to delivering your data, I think is very important for the success. And that's where kind of, there's not just the things about critical data elements and having the right platform architecture, but there's a soft stuff as well, like a, a product mindset to data, having the right data, culture, and business adoption and having the right value set mindset for, for data, I think is really >>Important. I think data as a product is a very powerful concept and I think it maybe is uncomfortable to some people sometimes. And I think in the early days of big data, if you will, people thought, okay, data is a product going to sell my data and that's not necessarily what you mean, thinking about products or data that can fuel products that you can then monetize maybe as a product or as a, as, as a service. And I like to think about a new metric in the industry, which is how long does it take me to get from idea I'm a business person. I have an idea for a data product. How long does it take me to get from idea to monetization? And that's going to be something that ultimately as a business person, I'm going to use to determine the success of my data team and my data architecture. Is that kind of thinking starting to really hit the marketplace? Absolutely. >>I mean, I insurers now are working, partnering with, you know, auto manufacturers to monetize, um, driver usage data, you know, on telematics to see, you know, driver behavior on how, you know, how auto manufacturers are using that data. That's very important to insurers, you know, so how an auto manufacturer can monetize that data is very important and also an insurance, you know, cyber insurance, um, are there news new ways we can look at how companies are being attacked with viruses and malware. And is there a way we can somehow monetize that information? So companies that are able to agily, you know, think about how can we collect this data, bring it together, think about it as a product, and then potentially, you know, sell it as a service is something that, um, company, successful companies, you're doing great examples >>Of data products, and it might be revenue generating, or it might be in the case of, you know, cyber, maybe it reduces my expected loss and exactly. Then it drops right to my bottom line. What's the relationship between Accenture and cloud era? Do you, I presume you guys meet at the customer, but maybe you could give us some insight. >>Yeah. So, um, I, I'm in the executive sponsor for, um, the Accenture Cloudera partnership on the Accenture side. Uh, we do quite a lot of business together and, um, you know, Cloudera has been a great partner for us. Um, and they've got a great product in terms of the Cloudera data platform where, you know, what we do is as a big systems integrator for them, we help, um, you know, configure and we have a number of engineers across the world that come in and help in terms of, um, engineer architects and install, uh, cloud errors, data platform, and think about what are some of those, you know, value cases where you can really think about organizing data and bringing it together for all these different types of use cases. And really just as the examples we thought about. So the telematics, you know, um, in order to realize something like that, you're bringing in petabytes and huge scales of data that, you know, you just couldn't bring on a normal, uh, platform. You need to think about cloud. You need to think about speed of, of data and real-time insights and cloud era is the right data platform for that. So, um, >>Having a cloud Cloudera ushered in the modern big data era, we kind of all know that, and it was, which of course early on, it was very services intensive. You guys were right there helping people think through there weren't enough data scientists. We've sort of all, all been through that. And of course in your wheelhouse industries, you know, financial services and insurance, they were some of the early adopters, weren't they? Yeah, absolutely. >>Um, so, you know, an insurance, you've got huge amounts of data with loss history and, um, a lot with IOT. So in insurance, there's a whole thing of like sensorized thing in, uh, you know, taking the physical world and digitizing it. So, um, there's a big thing in insurance where, um, it's not just about, um, pricing out the risk of a loss experience, but actual reducing the loss before it even happens. So it's called risk control or loss control, you know, can we actually put sensors on oil pipelines or on elevators and, you know, reduce, um, you know, accidents before they happen. So we're, you know, working with an insurer to actually, um, listen to elevators as they move up and down and are there signals in just listening to the audio of an elevator over time that says, you know what, this elevator is going to need maintenance, you know, before a critical accident could happen. So there's huge applications, not just in structured data, but in unstructured data like voice and audio and video where a partner like Cloudera has a huge role to play. >>Great example of it. So again, narrow sort of use case for machine intelligence, but, but real value. True. We'll leave it like that. Thanks so much for taking some time. Yes. Thank you so much. Okay. We continue now with the theme of turning ideas into insights. So ultimately you can take action. We heard earlier that public cloud first doesn't mean public cloud only, and a winning strategy comprises data, irrespective of physical location on prem, across multiple clouds at the edge where real time inference is going to drive a lot of incremental value. Data is going to help the world come back to normal. We heard, or at least semi normal as we begin to better understand and forecast demand and supply and balances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processes, and applications. And now we're going to get into some of the foundational principles that support the data and insights centric processes, which are fundamental to digital transformation initiatives. And it's my pleasure to welcome two great guests, Michelle Goetz. Who's a Kuba woman, VP and principal analyst at Forrester, and doing some groundbreaking work in this area. And Cindy, Mikey, who is the vice president of industry solutions and value management at Cloudera. Welcome to both of >>You. Welcome. Thank you. Thanks Dave. >>All right, Michelle, let's get into it. Maybe you could talk about your foundational core principles. You start with data. What are the important aspects of this first principle that are achievable today? >>It's really about democratization. If you can't make your data accessible, um, it's not usable. Nobody's able to understand what's happening in the business and they don't understand, um, what insights can be gained or what are the signals that are occurring that are going to help them with decisions, create stronger value or create deeper relationships, their customers, um, due to their experiences. So it really begins with how do you make data available and bring it to where the consumer of the data is rather than trying to hunt and Peck around within your ecosystem to find what it is that's important. Great. >>Thank you for that. So, Cindy, I wonder in hearing what Michelle just said, what are your thoughts on this? And when you work with customers at Cloudera, does, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >>Yeah, there's, there's quite a few. And especially as we look across, um, all the industries that we're actually working with customers in, you know, a few that stand out in top of mind for me is one is IQ via and what they're doing with real-world evidence and bringing together data across the entire, um, healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making the ed accessible by both internally, as well as for their, um, the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, there are a European car manufacturer and how do they make sure that they have, you know, efficient and effective processes when it comes to, uh, fixing equipment and so forth. And then also, um, there's, uh, an Indonesian based, um, uh, telecommunications company tech, the smell, um, who's bringing together, um, over the last five years, all their data about their customers and how do they enhance our customer experience? How do they make information accessible, especially in these pandemic and post pandemic times, um, uh, you know, just getting better insights into what customers need and when do they need it? >>Cindy platform is another core principle. How should we be thinking about data platforms in this day and age? I mean, where does, where do things like hybrid fit in? Um, what's cloud era's point >>Of view platforms are truly an enabler, um, and data needs to be accessible in many different fashions. Um, and also what's right for the business. When, you know, I want it in a cost and efficient and effective manner. So, you know, data needs to be, um, data resides everywhere. Data is developed and it's brought together. So you need to be able to balance both real time, you know, our batch historical information. It all depends upon what your analytical workloads are. Um, and what types of analytical methods you're going to use to drive those business insights. So putting and placing data, um, landing it, making it accessible, analyzing it needs to be done in any accessible platform, whether it be, you know, a public cloud doing it on-prem or a hybrid of the two is typically what we're seeing, being the most successful. >>Great. Thank you, Michelle. Let's move on a little bit and talk about practices and practices and processes as the next core principles. Maybe you could provide some insight as to how you think about balancing practices and processes while at the same time managing agility. >>Yeah, it's a really great question because it's pretty complex. When you have to start to connect your data to your business, the first thing to really gravitate towards is what are you trying to do? And what Cindy was describing with those customer examples is that they're all based off of business goals off of very specific use cases that helps kind of set the agenda about what is the data and what are the data domains that are important to really understanding and recognizing what's happening within that business activity and the way that you can affect that either in, you know, near time or real time, or later on, as you're doing your strategic planning, what that's balancing against is also being able to not only see how that business is evolving, but also be able to go back and say, well, can I also measure the outcomes from those processes and using data and using insight? >>Can I also get intelligence about the data to know that it's actually satisfying my objectives to influence my customers in my market? Or is there some sort of data drift or detraction in my, um, analytic capabilities that are allowing me to be effective in those environments, but everything else revolves around that and really thinking succinctly about a strategy that isn't just data aware, what data do I have and how do I use it, but coming in more from that business perspective to then start to be, data-driven recognizing that every activity you do from a business perspective leads to thinking about information that supports that and supports your decisions, and ultimately getting to the point of being insight driven, where you're able to both, uh, describe what you want your business to be with your data, using analytics, to then execute on that fluidly and in real time. And then ultimately bringing that back with linking to business outcomes and doing that in a continuous cycle where you can test and you can learn, you can improve, you can optimize, and you can innovate because you can see your business as it's happening. And you have the right signals and intelligence that allow you to make great decisions. >>I like how you said near time or real time, because it is a spectrum. And you know, one of the spectrum, autonomous vehicles, you've got to make a decision in real time, but, but, but near real-time, or real-time, it's, it's in the eyes of the holder, if you will, it's it might be before you lose the customer before the market changes. So it's really defined on a case by case basis. Um, I wonder Michelle, if you could talk about in working with a number of organizations, I see folks, they sometimes get twisted up and understanding the dependencies that technology generally, and the technologies around data specifically can have on critical business processes. Can you maybe give some guidance as to where customers should start, where, you know, where can we find some of the quick wins and high return, it >>Comes first down to how does your business operate? So you're going to take a look at the business processes and value stream itself. And if you can understand how people and customers, partners, and automation are driving that step by step approach to your business activities, to realize those business outcomes, it's way easier to start thinking about what is the information necessary to see that particular step in the process, and then take the next step of saying what information is necessary to make a decision at that current point in the process, or are you collecting information asking for information that is going to help satisfy a downstream process step or a downstream decision. So constantly making sure that you are mapping out your business processes and activities, aligning your data process to that helps you now rationalize. Do you need that real time near real time, or do you want to start grading greater consistency by bringing all of those signals together, um, in a centralized area to eventually oversee the entire operations and outcomes as they happen? It's the process and the decision points and acting on those decision points for the best outcome that really determines are you going to move in more of a real-time, uh, streaming capacity, or are you going to push back into more of a batch oriented approach? Because it depends on the amount of information and the aggregate of which provides the best insight from that. >>Got it. Let's, let's bring Cindy back into the conversation in your city. We often talk about people process and technology and the roles they play in creating a data strategy. That's that's logical and sound. Can you speak to the broader ecosystem and the importance of creating both internal and external partners within an organization? Yeah. >>And that's, uh, you know, kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but you know, the fuel behind the process, um, and how do you actually become insight-driven? And, you know, you look at the capabilities that you're needing to enable from that business process, that insight process, um, you're extended ecosystem on, on how do I make that happen? You know, partners, um, and, and picking the right partner is important because a partner is one that actually helps under or helps you implement what your decisions are. Um, so, um, looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data, um, you know, for within process on that, if you need to do it in a time fashion, that they can actually meet those needs of the business, um, and enabling on those, those process activities. So the ecosystem looking at how you, um, look at, you know, your vendors are, and fundamentally they need to be that trusted partner. Um, do they bring those same principles of value of being insight driven? So they have to have those core values themselves in order to help you as a, um, an end of business person enable those capabilities. So, so yeah, I'm >>Cool with fuel, but it's like super fuel when you talk about data, cause it's not scarce, right? You're never going to run out. So Michelle, let's talk about leadership. W w who leads, what does so-called leadership look like in an organization that's insight driven? >>So I think the really interesting thing that is starting to evolve as late is that organizations enterprises are really recognizing that not just that data is an asset and data has value, but exactly what we're talking about here, data really does drive what your business outcomes are going to be data driving into the insight or the raw data itself has the ability to set in motion. What's going to happen in your business processes and your customer experiences. And so, as you kind of think about that, you're now starting to see your CEO, your CMO, um, your CRO coming back and saying, I need better data. I need information. That's representative of what's happening in my business. I need to be better adaptive to what's going on with my customers. And ultimately that means I need to be smarter and have clearer forecasting into what's about ready to come, not just, you know, one month, two months, three months or a year from now, but in a week or tomorrow. >>And so that's, how is having a trickle down effect to then looking at two other types of roles that are elevating from technical capacity to more business capacity, you have your chief data officer that is shaping the exp the experiences, uh, with data and with insight and reconciling, what type of information is necessary with it within the context of answering these questions and creating a future fit organization that is adaptive and resilient to things that are happening. And you also have a chief digital officer who is participating because they're providing the experience and shaping the information and the way that you're going to interact and execute on those business activities, and either running that autonomously or as part of an assistance for your employees and for your customers. So really to go from not just data aware to data driven, but ultimately to be insight driven, you're seeing way more, um, participation, uh, and leadership at that C-suite level. And just underneath, because that's where the subject matter expertise is coming in to know how to create a data strategy that is tightly connected to your business strategy. >>Right. Thank you. Let's wrap. And I've got a question for both of you, maybe Cindy, you could start and then Michelle bring us home. You know, a lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a, of a maturity model. Uh, you know, I'd love to go there, but I'm not going to get there anytime soon, but I want to take some baby steps. So when you're performing an analysis on, on insight driven organization, city, what do you see as the major characteristics that define the differences between sort of the, the early, you know, beginners, the sort of fat middle, if you will, and then the more advanced, uh, constituents. >>Yeah, I'm going to build upon, you know, what Michelle was talking about as data as an asset. And I think, you know, also being data where, and, you know, trying to actually become, you know, insight driven, um, companies can also have data and they can have data as a liability. And so when you're data aware, sometimes data can still be a liability to your organization. If you're not making business decisions on the most recent and relevant data, um, you know, you're not going to be insight driven. So you've got to move beyond that, that data awareness, where you're looking at data just from an operational reporting, but data's fundamentally driving the decisions that you make. Um, as a business, you're using data in real time. You're, um, you're, you know, leveraging data to actually help you make and drive those decisions. So when we use the term you're, data-driven, you can't just use the term, you know, tongue in cheek. It actually means that I'm using the recent, the relevant and the accuracy of data to actually make the decisions for me, because we're all advancing upon. We're talking about, you know, artificial intelligence and so forth. Being able to do that, if you're just data where I would not be embracing on leveraging artificial intelligence, because that means I probably haven't embedded data into my processes. It's data could very well still be a liability in your organization. So how do you actually make it an asset? Yeah, I think data >>Where it's like cable ready. So, so Michelle, maybe you could, you could, you could, uh, add to what Cindy just said and maybe add as well, any advice that you have around creating and defining a data strategy. >>So every data strategy has a component of being data aware. This is like building the data museum. How do you capture everything that's available to you? How do you maintain that memory of your business? You know, bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing and you're managing and you're maintaining it. And this is really where you're starting to think about the fact that it is an asset. It has value, but you may not necessarily know what that value is. Yet. If you move into a category of data driven, what starts to shift and change there is you're starting to classify label, organize the information in context of how you're making decisions and how you do business. It could start from being more, um, proficient from an analytic purpose. You also might start to introduce some early stages of data science in there. >>So you can do some predictions and some data mining to start to weed out some of those signals. And you might have some simple types of algorithms that you're deploying to do a next next best action for example. And that's what data-driven is really about. You're starting to get value out of it. The data itself is starting to make sense in context of your business, but what you haven't done quite yet, which is what insight driven businesses are, is really starting to take away. Um, the gap between when you see it, know it and then get the most value and really exploit what that insight is at the time when it's right. So in the moment we talk about this in terms of perishable insights, data and insights are ephemeral. And we want to ensure that the way that we're managing that and delivering on that data and insights is in time with our decisions and the highest value outcome we're going to have, that that insight can provide us. >>So are we just introducing it as data-driven organizations where we could see, you know, spreadsheets and PowerPoint presentations and lots of mapping to help make sort of longer strategic decisions, or are those insights coming up and being activated in an automated fashion within our business processes that are either assisting those human decisions at the point when they're needed, or an automated decisions for the types of digital experiences and capabilities that we're driving in our organization. So it's going from, I'm a data hoarder. If I'm data aware to I'm interested in what's happening as a data-driven organization and understanding my data. And then lastly being insight driven is really where light between business, data and insight. There is none it's all coming together for the best outcomes, >>Right? So people are acting on perfect or near perfect information or machines or, or, uh, doing so with a high degree of confidence, great advice and insights. And thank you both for sharing your thoughts with our audience today. It's great to have you. Thank you. Thank you. Okay. Now we're going to go into our industry. Deep dives. There are six industry breakouts, financial services, insurance, manufacturing, retail communications, and public sector. Now each breakout is going to cover two distinct use cases for a total of essentially 12 really detailed segments that each of these is going to be available on demand, but you can scan the calendar on the homepage and navigate to your breakout session for choice of choice or for more information, click on the agenda page and take a look to see which session is the best fit for you. And then dive in, join the chat and feel free to ask questions or contribute your knowledge, opinions, and data. Thanks so much for being part of the community and enjoy the rest of the day.

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. >>Welcome back to the cubes coverage of AWS reinvent 2020 live I'm Lisa Martin. Really exciting topic coming up for you next, please. Welcome Blake shoulda, founder and CEO of boom supersonic Blake. It's great to have you on the program. Thank you for having me, Lisa, and your background gives me all the way with what we're going to talk about in the next few minutes or so, but supersonic flight has existed for quite a long time, like 50 or so years. I think those of us in certain generations remember the Concorde for example, but the technology to make it efficient and mainstream is only recently been approved by or accepted by regulators. Tell us a little bit about boom, your mission to make the world more accessible with supersonic commercial flight. Well, a supersonic flight has >> actually been around since 1949 when Chuck Yeager broke the speed barrier or sorry, the sound barrier. >>And as, as many of you know, he actually passed yesterday, uh, 97. So very, very sad to see one of the supersonic pioneers behind us. Uh, but, uh, but as I say goodbye to Jaeger, a new era of supersonic flight is here. And if you look at the history of progress and transportation, since the Dawn of the industrial revolution, uh, we used to make regular progress and speed. As we went from, uh, the horse to the iron horse, to the, the boats, to the, the early propeller airplanes that have the jet age. And what happened was every time we made transportation faster, instead of spending less time traveling, we actually spent more time traveling because there were more places to go, more people to meet. Uh, we haven't had a world war since the Dawn of the jet age. Uh, places like Hawaii have become, uh, a major tourist destination. >>Uh, but today, uh, today it's been 60 years since we've had a mainstream re uh, step forward and speed. So what we're doing here at boom is picking up where Concord left off building an aircraft that flies faster by factor to the, anything you can get a ticket on today. And yet is 75% more affordable than Concorde was. So we want to make Australia as accessible as a why yesterday. We want to enable you to cross the Atlantic, do business, be home in time, detect your kids into bed, or take a three-day business trip to Asia and let you do it in just 24 >> hours. I like the sound of all of that. Even getting on a plane right now in general. I think we all do so, so interesting that you, you want to make this more accessible. And I did see the news about Chuck Yeager last night. >>Um, designing though the first supersonic airliner overture, it's called in decades, as you said, this dates back 60 years, rolling it out goal is to roll it out in 2025 and flying more than 500 trans oceanic routes. Talk to me about how you're leveraging technology and AWS to help facilitate that. Right. Well, so one of the really fascinating things is the new generation of airplanes, uh, are getting born in the cloud and then they're going to go fly through actual clouds. And so there are, there are a bunch of revolutions in technology that have happened since Concord's time that are enabling what we're doing now, their breakthroughs and materials. We've gone from aluminum to carbon fiber they're breakthroughs and engines. We've gone from after burning turbo jets that are loud and inefficient to quiet, clean, efficient turbo fans. But one of the most interesting breakthroughs has been in a available to do design digitally and iteration digitally versus, uh, versus physically. >>So when conquer was designed as an example, they were only able to do about a dozen wind tunnel tests because they were so expensive. And so time consuming and on, uh, on our XP one aircraft, which is our prototype that rolled out in October. Um, uh, we did hundreds of iterations of the design in virtual wind tunnels, where we could spin up a, uh, a simulation and HPC cluster in AWS, often more than 500 cores. And then we'd have our airplanes flying through virtual wind tunnels, thousands of flights scenarios you can figure out which were the losers, which were the winners keep iterating on the winners. And you arrive at an aerodynamic design that is more efficient at high speed. We're going very safely, very quickly in a straight line, but also a very smooth controllable for safe takeoff and landing. And the part of the artist supersonic airplane design is to accomplish both of those things. One, one airplane, and, uh, being able to design in the cloud, the cloud allows us to start up to do what previously only governments and militaries could do. I mentioned we rolled out our XP one prototype in October. That's the first time anyone has rolled out a supersonic civil aircraft since the Soviet union did it in 1968. And we're able to do as a startup because of computing. >>That's incredible born in the cloud to fly in the cloud. So talk to me about a lot of, of opportunity that technology has really accelerated. And we've seen a lot of acceleration this year in particular digital transformation businesses that if they haven't pivoted are probably in some challenging waters. So talk to us about how you're going all in with AWS to facilitate all these things that you just mentioned, which has dramatic change over 12, uh, when tone test for the Concord and how many times did it, >>Uh, I mean for 27 years, but not that many flights, never, it never changed the way mainstream, uh, never, never district some of you and I fly. Right. Um, so, so how, how are we going all in? So we've, you know, we've been using AWS for, uh, you know, basically since the founding of the company. Uh, but what we, what we're doing now is taking things that we were doing outside of the cloud and cloud. Uh, as an example, uh, we have 525 terabytes of XP one design and test data that what used to be backed up offsite. Um, and, and what we're doing is migrating into the cloud. And then your data is next. Your compute, you can start to do these really interesting things as an example, uh, you can run machine learning models to calibrate your simulations to your wind tunnel results, which accelerates convergence allows you to run more iterations even faster, and ultimately come up with a more efficient airplane, which means it's going to be more affordable for all of us to go to go break the sound barrier. >>And that sounds like kind of one of the biggest differences that you just said is that it wasn't built for mainstream before. Now, it's going to be accessibility affordability as well. So how are you going to be leveraging the cloud, you know, design manufacturing, but also other areas like the beyond onboard experience, which I'm already really excited to be participating in in the next few years. >>Yeah. So there's so many, so many examples. We've talked about design a little bit already. Uh, it's going to manifest in the manufacturing process, uh, where the, the, the, the, the supply chain, uh, will be totally digital. The factory operations will be run out of the cloud. You know, so what that means concretely is, uh, you know, literally there'll be like a million parts of this airplane. And for any given unit goes through their production line, you'll instantly know where they all are. Um, you'll know which serial numbers went on, which airplanes, uh, you'll understand, uh, if there was a problem with one of it, how you fixed it. And as you continue to iterate and refine the airplane, this, this is one of things that's actually a big deal, uh, with, with digital in the cloud is, you know, exactly what design iteration went into, exactly which airplane and, uh, and that allows you to actually iterate faster and any given airline with any given airplane will actually know exactly what, what airplane they have, but the next one that rolls off the line might be even a little bit better. >>And so it allows you to keep track of all of that. It allows you to iterate faster, uh, it allows you to spot bottlenecks in your supply chain before they impact production. Um, and then it allows you to, uh, to do preventive maintenance later. So there's to be digital interpretation all over the airplane, it's going to update the cloud on, you know, uh, are the engines running expected temperature. So I'm gonna run a little bit hot, is something vibrating more than it should vibrate. And so you catch these things way before there's any kind of real maintenance issue. You flag it in the cloud. The next time the airplane lands, there's a tech waiting for the airplane with whatever the part is and able to install it. And you don't have any downtime, and you're never anywhere close to a safety issue. You're able to do a lot more preventively versus what you can do today. >>Wow. So you have to say that you're going to be able to, to have a hundred percent visibility into manufacturing design, everything is kind of an understatement, but you launched XQ on your prototype in October. So during the pandemic, as I mentioned, we've been talking for months now on the virtual cube about the acceleration of digital transformation. Andy, Jassy talked about it in his keynote at AWS reinventing, reinventing this year, virtual, what were some of the, the, the advantages that you got, being able to stay on track and imagine if you were on track to launch in October during a time that has been so chaotic, uh, everywhere else, including air travel. >>Well, some of it's very analog, uh, and some of it's very digital. So to start with the analog, uh, we took COVID really seriously at Bo. Uh, we went into that, the pandemic first hit, we shut the company down for a couple of weeks, so we'd kind of get our feet underneath of us. And then we sort of testing, uh, everyone who had to work on the airplane every 14 days, we were religious about wearing masks. And as a result, we haven't had anyone catch COVID within the office. Um, and I'm super proud that we're able to stay productive and stay safe during the pandemic. Um, and you do that, but kind of taking it seriously, doing common sense things. And then there's the digital effort. And, uh, and so, you know, part of the company runs digitally. What we're able to do is when there's kind of a higher alert level, we go a little bit more digital when there's a lower alert level. >>Uh, we have more people in the office cause we, we still really do value that in-person collaboration and which brings it back through to a bigger point. It's been predicted for a long time, that the advent of digital communication is going to cause us not to need to travel. And, uh, what we've seen, you know, since the Dawn of the telephone is that it's actually been the opposite. The more you can know, somebody even a little bit, uh, at distance, the hungry you are to go see them in person, whether it's a business contact or someone you're in love with, um, no matter what it is, there's still that appetite to be there in person. And so I think what we're seeing with the digitization of communication is ultimately going to be very, um, uh, it's very complimentary with supersonic because you can get to know somebody a little bit over a long distance. You can have some kinds of exchanges and then you're, and then the friction for be able to see them in person is going to drop. And that is, uh, that's a wonderful combination. >>I think everybody on the planet welcomes that for sure, given what we've all experienced in the last year, you can have a lot of conversations by zoom. Obviously this was one of them, but there is to your point, something about that in-person collaboration that really takes things can anyway, to the next level. I am curious. So you launched XB one in October, as I mentioned a minute ago, and I think I read from one of your press releases planning to launch in 2025, the overture with over 500 trans oceanic routes. What can we expect from boom and the next year or two, are you on track for that 2025? >>Yeah. Things are going, things are going great. Uh, so to give a sense of what the next few years hold. So we rolled out the assembled XB one aircraft this year, uh, next year that's going to fly. And so that will be the first civil supersonic, uh, flying aircraft ever built by an independent company. Uh, and along the way, we are building the foundation of overture. So that design efforts happening now as XB one is breaking the sound barrier. We'll be finalizing the overture design in 22, we'll break ground in the factory in 23, we'll start building the first airplane and 25, we'll roll it out. And 26 we'll start flight tests. And, uh, and then we'll go through the flight test methodically, uh, systematically as carefully as we can, uh, and then be ready to carry passengers as soon as we are convinced that safe, which will be right around the end of the decade, most likely. >>Okay. Exciting. And so it sounds like you talked about the safety protocols that you guys put in place in the office, which is great. It's great to hear that, but also that this, this time hasn't derailed because you have the massive capabilities of, to be able to do all of the work that's necessary, way more than was done with before with the Concorde. And that you can do that remotely with cloud is a big facilitator of that communication. >>Yeah. You're able to do the cloud enables a lot of computational efficiencies. And I think about the, um, many times projects are not measured in how many months or years exactly does it take you to get done, but it's actually much easier to think about in terms of number of iterations. And so every time we do an airplane iteration, we look at the aerodynamics high speed. We look at the low speed. We look at the engine, uh, we look at the, the weights. Uh, we look at stability and control. We look at pilots, light aside, et cetera, et cetera. And every time you do an iteration, you're kind of looking around all of those and saying, what can I make better? But each one of those, uh, lines up a little bit differently with the rest now, for example, uh, uh, to get the best airplane aerodynamically, doesn't have a good view for the pilot. >>And that's why Concord had that droop nose famously get the nose out of the way so we can see the runway. And so we're able to do digital systems for virtual vision to let the pilot kind of look through the nose of the runway. But even then they're, trade-offs like, how, how good of an actual window do you need? And so your ability to make progress in all of this is proportional to how quickly you can make it around that, that iteration loop, that design cycle loop. And that's, that's part of where the cloud helps us. And we've, we've got some, uh, uh, some stuff we've built in house that runs on the cloud that lets you basically press a button with a whole set of airplane parameters. And bam, it gives you a, it gives you an instant report. I'm like, Oh, was it that this is a good change or bad change, uh, based on running some pretty high fidelity simulations with a very high degree of automation. And you can actually do many of those in parallel. And so it's about, you know, at this stage of the program, it's about accelerating, accelerating your design iterations, uh, giving everyone of the team visibility into those. And then, uh, I think you get together in person as it makes sense to now we're actually hitting a major design milestone with over-treat this week and we're, COVID testing everybody and get them all in the same room. Cause sometimes that in-person collaboration, uh, is really significant, even though you can still do so much digitally. >>I totally agree. There's there's certain things that you just can't replicate. Last question since my brother is a pilot for Southwest and retired Lieutenant Colonel from the air force, any special training that pilots will have to have, or are there certain pilots that are going to be maybe lower hanging fruit, if they have military experience versus commercial flight? Just curious. >>Yeah. So our XB one aircraft is being flown by test pilots. There's one ex Navy one ex air force on our crew, but, uh, overture, uh, will be accessible to any commercial pilot. So, uh, think about it as if you're, if you're used to flying Boeing, it'd be like switching to Airbus, uh, or vice versa. So the, uh, Concord is a complicated aircraft to fly because they didn't have computers. And all the complexity, the soup of supersonic flight was right there and the pilots and an overture, all that gets extracted by software. And, uh, you know, the, the, the ways the flight controls change over speed regimes. You don't have to worry about it, but the airplane is handled beautifully, no matter what you're doing. And so, uh, and so there are many, many places to innovate, but actually pilot experience, not one of them, >>Because the more conventional you can make it for people like your brother, the easier it's going to be for them to learn the aircraft. And therefore the safer it's going to be to fly. I'll let them know, like this has been fantastic, really exciting to see what boom supersonic is doing and the opportunities to make supersonic travel accessible. And I think at a time when everybody wants the world to open up, so by 20, 26, I'm going to be looking for my ticket. Awesome. Can't wait to have you on board. Likewise for Blake shul, I'm Lisa Martin. You're watching the QS live coverage of AWS reinvent 2020.

>>It's the queue covering the virtual vertical Big Data Conference 2020. Brought to you by vertical. >>Hello, everybody. Welcome to the new Normal. You're watching the Cube, and it's remote coverage of the vertical big data event on digital or gone Virtual. My name is Dave Volante, and I'm here with Colin Mahoney, who's a senior vice president at Micro Focus and the GM of Vertical Colin. Well, strange times, but the show goes on. Great to see you again. >>Good to see you too, Dave. Yeah, strange times indeed. Obviously, Safety first of everyone that we made >>a >>decision to go Virtual. I think it was absolutely the right all made it in advance of how things have transpired, but we're making the best of it and appreciate your time here, going virtual with us. >>Well, Joe and we're super excited to be here. As you know, the Cube has been at every single BDC since its inception. It's a great event. You just you just presented the key note to your to your audience, You know, it was remote. You didn't have that that live vibe. And you have a lot of fans in the vertical community But could you feel the love? >>Yeah, you know, it's >>it's hard to >>feel the love virtually, but I'll tell you what. The silver lining in all this is the reach that we have for this event now is much broader than it would have been a Z you know, you know, we brought this event back. It's been a few years since we've done it. We're super excited to do it, obviously, you know, in Boston, where it was supposed to be on location, but there wouldn't have been as many people that could participate. So the silver lining in all of this is that I think there's there's a lot of love out there we're getting, too. I have a lot of participants who otherwise would not have been able to participate in this. Both live as well. It's a lot of these assets that we're gonna have available. So, um, you know, it's out there. We've got an amazing customers and of practitioners with vertical. We've got so many have been with us for a long time. We've of course, have a lot of new customers as well that we're welcoming, so it's exciting. >>Well, it's been a while. Since you've had the BDC event, a lot of transpired. You're now part of micro focus, but I know you and I know the vertical team you guys have have not stopped. You've kept the innovation going. We've been following the announcements, but but bridge the gap between the last time. You know, we had coverage of this event and where we are today. A lot has changed. >>Oh, yeah, a lot. A lot has changed. I mean, you know, it's it's the software industry, right? So nothing stays the same. We constantly have Teoh keep going. Probably the only thing that stays the same is the name Vertical. Um and, uh, you know, you're not spending 10 which is just a phenomenal released for us. So, you know, overall, the the organization continues to grow. The dedication and commitment to this great form of vertical continues every single release we do as you know, and this hasn't changed. It's always about performance and scale and adding a whole bunch of new capabilities on that front. But it's also about are our main road map and direction that we're going towards. And I think one of the things have been great about it is that we've stayed true that from day one we haven't tried to deviate too much and get into things that are barred to outside your box. But we've really done, I think, a great job of extending vertical into places where people need a lot of help. And with vertical 10 we know we're going to talk more about that. But we've done a lot of that. It's super exciting for our customers, and all of this, of course, is driven by our customers. But back to the big data conference. You know, everybody has been saying this for years. It was one of the best conferences we've been to just so really it's. It's developers giving tech talks, its customers giving talks. And we have more customers that wanted to give talks than we had slots to fill this year at the event, which is another benefit, a little bit of going virtually accommodate a little bit more about obviously still a tight schedule. But it really was an opportunity for our community to come together and talk about not just America, but how to deal with data, you know, we know the volumes are slowing down. We know the complexity isn't slowing down. The things that people want to do with AI and machine learning are moving forward in a rapid pace as well. There's a lot talk about and share, and that's really huge part of what we try to do with it. >>Well, let's get into some of that. Um, your customers are making bets. Micro focus is actually making a bet on one vertical. I wanna get your perspective on one of the waves that you're riding and where are you placing your bets? >>Yeah, No, it's great. So, you know, I think that one of the waves that we've been writing for a long time, obviously Vertical started out as a sequel platform for analytics as a sequel, database engine, relational engine. But we always knew that was just sort of takes that we wanted to do. People were going to trust us to put enormous amounts of data in our platform and what we owe everyone else's lots of analytics to take advantage of that data in the lots of tools and capabilities to shape that data to get into the right format. The operational reporting but also in this day and age for machine learning and from some pretty advanced regressions and other techniques of things. So a huge part of vertical 10 is just doubling down on that commitment to what we call in database machine learning and ai. Um, And to do that, you know, we know that we're not going to come up with the world's best algorithms. Nor is that our focus to do. Our advantage is we have this massively parallel platform to ingest store, manage and analyze the data. So we made some announcements about incorporating PM ML models into the product. We continue to deepen our python integration. Building off of a new open source project we started with uber has been a great customer and partner on This is one of our great talks here at the event. So you know, we're continuing to do that, and it turns out that when it comes to anything analytics machine learning, certainly so much of what you have to do is actually prepare the big shape the data get the data in the right format, apply the model, fit the model test a model operationalized model and is a great platform to do that. So that's a huge bet that were, um, continuing to ride on, taking advantage of and then some of the other things that we've just been seeing. You continue. I'll take object. Storage is an example on, I think Hadoop and what would you point through ultimately was a huge part of this, but there's just a massive disruption going on in the world around object storage. You know, we've made several bets on S three early we created America Yang mode, which separates computing story. And so for us that separation is not just about being able to take care of your take advantage of cloud economics as we do, or the economics of object storage. It's also about being able to truly isolate workloads and start to set the sort of platform to be able to do very autonomous things in the databases in the database could actually start self analysing without impacting many operational workloads, and so that continues with our partnership with pure storage. On premise, we just announced that we're supporting beyond Google Cloud now. In addition to Amazon, we supported on we've got a CFS now being supported by are you on mode. So we continue to ride on that mega trend as well. Just the clouds in general. Whether it's a public cloud, it's a private cloud on premise. Giving our customers the flexibility and choice to run wherever it makes sense for them is something that we are very committed to. From a flexibility standpoint. There's a lot of lock in products out there. There's a lot of cloud only products now more than ever. We're hearing our customers that they want that flexibility to be able to run anywhere. They want the ease of use and simplicity of native cloud experiences, which we're giving them as well. >>I want to stay in that architectural component for a minute. Talk about separating compute from storage is not just about economics. I mean apart Is that you, you know, green, really scale compute separate from storage as opposed to in chunks. It's more efficient, but you're saying there's other advantages to operational and workload. Specificity. Um, what is unique about vertical In this regard, however, many others separate compute from storage? What's different about vertical? >>Yeah, I think you know, there's a lot of differences about how we do it. It's one thing if you're a cloud native company, you do it and you have a shared catalog. That's key value store that all of your customers are using and are on the same one. Frankly, it's probably more of a security concern than anything. But it's another thing. When you give that capability to each customer on their own, they're fully protected. They're not sharing it with any other customers. And that's something that we hear a lot of insights from our customers. They want to be able to separate compute and storage. But they want to be able to do this in their own environment so that they know that in their data catalog there's no one else is. You share in that catalog, there's no single point of failure. So, um, that's one huge advantage that we have. And frankly, I think it just comes from being a company that's operating on premise and, uh, up in the cloud. I think another huge advantages for us is we don't know what object storage platform is gonna win, nor do we necessarily have. We designed the young vote so that it's an sdk. We started with us three, but it could be anything. It's DFS. That's three. Who knows what what object storage formats were going to be there and then finally, beyond just the object storage. We're really one of the only database companies that actually allows our customers to natively operate on data in very different formats, like parquet and or if you're familiar with those in the Hadoop community. So we not only embrace this kind of object storage disruption, but we really embrace the different data formats. And what that means is our customers that have data pipelines that you know, fully automated, putting this information in different places. They don't have to completely reload everything to take advantage of the Arctic analytics. We can go where the data is connected into it, and we offer them a lot of different ways to take advantage of those analytics. So there are a couple of unique differences with verdict, and again, I think are really advance. You know, in many ways, by not being a cloud native platform is that we're very good at operating in different environments with different formats that changing formats over time. And I don't think a lot of the other companies out there that I think many, particularly many of the SAS companies were scrambling. They even have challenges moving from saying Amazon environment to a Microsoft azure environment with their office because they've got so much unique Band Aid. Excuse me in the background. Just holding the system up that is native to any of those. >>Good. I'm gonna summarize. I'm hearing from you your Ferrari of databases that we've always known. Your your object store agnostic? Um, it's any. It's the cloud experience that you can bring on Prem to virtually any cloud. All the popular clouds hybrid. You know, aws, azure, now Google or on Prem and in a variety of different data formats. And that is, I think, you know, you need the combination of those I think is unique in the marketplace. Um, before we get into the news, I want to ask you about data silos and data silos. You mentioned H DFs where you and I met back in the early days of big data. You know, in some respects, you know, Hadoop help break down the silos with distributing the date and leave it in place, and in other respects, they created Data Lakes, which became silos. And so we have. Yet all these other sales people are trying to get to, Ah, digital transformation meeting, putting data at their core virtually obviously, and leave it in place. What's your thoughts on that in terms of data being a silo buster Buster, How does verdict of way there? >>Yeah, so And you're absolutely right, I think if even if you look at his due for all the new data that gets into the do. In many ways, it's created yet another large island of data that many organizations are struggling with because it's separate from their core traditional data warehouse. It's separate from some of the operational systems that they have, and so there might be a lot of data in there, but they're still struggling with How do I break it out of that large silo and or combine it again? I think some some of the things that verdict it doesn't part of the announcement just attend his migration tools to make it really easy. If you do want to move it from one platform to another inter vertical, but you don't have to move it, you can actually take advantage of a lot of the data where it resides with vertical, especially in the Hadoop brown with our external table storage with our building or compartment natively. So we're very pragmatic about how our customers go about this. Very few customers, Many of them tried it with Hadoop and realize that didn't work. But very few customers want a wholesale. Just say we're going to throw everything out. We're gonna get rid of our data warehouse. We're gonna hit the pause button and we're going to go from there. Just it's not possible to do that. So we've spent a lot of time investing in the product, really work with them to go where the data is and then seamlessly migrate. And when it makes sense to migrate, you mentioned the performance of America. Um, and you talked about it is the variety. It definitely is. And one other thing that we're really proud of this is that it actually is not a gas guzzler. Easy either One of the things that we're seeing, a lot of the other cloud databases pound for pound you get on the 10th the hardware vertical running up there. You get over 10 x performance. We're seeing that a lot, so it's Ah, it's not just about the performance, but it's about the efficiency as well. And I think that efficiency is really important when it comes to silos. Because there's there's just only so much horsepower out there. And it's easier for companies to play tricks and lots of servers environment when they start up for so many organizations and cloud and frankly, looking at the bills they're getting from these cloud workloads that are running. They really conscious of that. >>Yeah. The big, big energy companies love the gas guzzlers. A lot of a lot of cloud. Cute. But let's get into the news. Uh, 10 dot io you shared with your the audience in your keynote. One of the one of the highlights of data. What do we need to know? >>Yeah, so, you know, again doubling down on these mega trends, I'll start with Machine Learning and ai. We've done a lot of work to integrate so that you can take native PM ml models, bring them into vertical, run them massively parallel and help shape you know your data and prepare it. Do all the work that we know is required true machine learning. And for all the hype that there is around it, this is really you know, people want to do a lot of unsupervised machine learning, whether it's for healthcare fraud, detection, financial services. So we've doubled down on that. We now also support things like Tensorflow and, you know, as I mentioned, we're not going to come up with the best algorithms. Our job is really to ensure that those algorithms that people coming up with could be incorporated, that we can run them against massive data sets super efficiently. So that's that's number one number two on object storage. We continue to support Mawr object storage platforms for ya mode in the cloud we're expanding to Google G CPI, Google's cloud beyond just Amazon on premise or in the cloud. Now we're also supporting HD fs with beyond. Of course, we continue to have a great relationship with our partners, your storage on premise. Well, what we continue to invest in the eon mode, especially. I'm not gonna go through all the different things here, but it's not just sort of Hey, you support this and then you move on. There's so many different things that we learn about AP I calls and how to save our customers money and tricks on performance and things on the third areas. We definitely continue to build on that flexibility of deployment, which is related to young vote with. Some are described, but it's also about simplicity. It's also about some of the migration tools that we've announced to make it easy to go from one platform to another. We have a great road map on these abuse on security, on performance and scale. I mean, for us. Those are the things that we're working on every single release. We probably don't talk about them as much as we need to, but obviously they're critically important. And so we constantly look at every component in this product, you know, Version 10 is. It is a huge release for any product, especially an analytic database platform. And so there's We're just constantly revisiting you know, some of the code base and figuring out how we can do it in new and better ways. And that's a big part of 10 as well. >>I'm glad you brought up the machine Intelligence, the machine Learning and AI piece because we would agree that it is really one of the things we've noticed is that you know the new innovation cocktail. It's not being driven by Moore's law anymore. It's really a combination of you. You've collected all this data over the last 10 years through Hadoop and other data stores, object stores, etcetera. And now you're applying machine intelligence to that. And then you've got the cloud for scale. And of course, we talked about you bringing the cloud experience, whether it's on Prem or hybrid etcetera. The reason why I think this is important I wanted to get your take on this is because you do see a lot of emerging analytic databases. Cloud Native. Yes, they do suck up, you know, a lot of compute. Yeah, but they also had a lot of value. And I really wanted to understand how you guys play in that new trend, that sort of cloud database, high performance, bringing in machine learning and AI and ML tools and then driving, you know, turning data into insights and from what I'm hearing is you played directly in that and your differentiation is a lot of the things that we talk about including the ability to do that on from and in the cloud and across clouds. >>Yeah, I mean, I think that's a great point. We were a great cloud database. We run very well upon three major clouds, and you could argue some of the other plants as well in other parts of the world. Um, if you talk to our customers and we have hundreds of customers who are running vertical in the cloud, the experience is very good. I think it would always be better. We've invested a lot in taking advantage of the native cloud ecosystem, so that provisioning and managing vertical is seamless when you're in that environment will continue to do that. But vertical excuse me as a cloud platform is phenomenal. And, um, you know, there's a There's a lot of confusion out there, you know? I think there's a lot of marketing dollars spent that won't name many of the companies here. You know who they are, You know, the cloud Native Data Warehouse and it's true, you know their their software as a service. But if you talk to a lot of our customers, they're getting very good and very similar. experiences with Bernie comic. We stopped short of saying where software is a service because ultimately our customers have that control of flexibility there. They're putting verdict on whichever cloud they want to run it on, managing it. Stay tuned on that. I think you'll you'll hear from or more from us about, you know, that going going even further. But, um, you know, we do really well in the cloud, and I think he on so much of yang. And, you know, this has really been a sort of 2.5 years and never for us. But so much of eon is was designed around. The cloud was designed around Cloud Data Lakes s three, separation of compute and storage on. And if you look at the work that we're doing around container ization and a lot of these other elements, it just takes that to the next level. And, um, there's a lot of great work, so I think we're gonna get continue to get better at cloud. But I would argue that we're already and have been for some time very good at being a cloud analytic data platform. >>Well, since you open the door I got to ask you. So it's e. I hear you from a performance and architectural perspective, but you're also alluding two. I think something else. I don't know what you can share with us. You said stay tuned on that. But I think you're talking about Optionality, maybe different consumption models. That am I getting that right and you share >>your difficult in that right? And actually, I'm glad you wrote something. I think a huge part of Cloud is also has nothing to do with the technology. I think it's how you and seeing the product. Some companies want to rent the product and they want to rent it for a certain period of time. And so we allow our customers to do that. We have incredibly flexible models of how you provision and purchase our product, and I think that helps a lot. You know, I am opening the door Ah, a little bit. But look, we have customers that ask us that we're in offer them or, you know, we can offer them platforms, brawl in. We've had customers come to us and say please take over systems, um, and offer something as a distribution as I said, though I think one thing that we've been really good at is focusing on on what is our core and where we really offer offer value. But I can tell you that, um, we introduced something called the Verdict Advisor Tool this year. One of the things that the Advisor Tool does is it collects information from our customer environments on premise or the cloud, and we run through our own machine learning. We analyze the customer's environment and we make some recommendations automatically. And a lot of our customers have said to us, You know, it's funny. We've tried managed service, tried SAS off, and you guys blow them away in terms of your ability to help us, like automatically managed the verdict, environment and the system. Why don't you guys just take this product and converted into a SAS offering, so I won't go much further than that? But you can imagine that there's a lot of innovation and a lot of thoughts going into how we can do that. But there's no reason that we have to wait and do that today and being able to offer our customers on premise customers that same sort of experience from a managed capability is something that we spend a lot of time thinking about as well. So again, just back to the automation that ease of use, the going above and beyond. Its really excited to have an analytic platform because we can do so much automation off ourselves. And just like we're doing with Perfect Advisor Tool, we're leveraging our own Kool Aid or Champagne Dawn. However you want to say Teoh, in fact, tune up and solve, um, some optimization for our customers automatically, and I think you're going to see that continue. And I think that could work really well in a bunch of different wallets. >>Welcome. Just on a personal note, I've always enjoyed our conversations. I've learned a lot from you over the years. I'm bummed that we can't hang out in Boston, but hopefully soon, uh, this will blow over. I loved last summer when we got together. We had the verdict throwback. We had Stone Breaker, Palmer, Lynch and Mahoney. We did a great series, and that was a lot of fun. So it's really it's a pleasure. And thanks so much. Stay safe out there and, uh, we'll talk to you soon. >>Yeah, you too did stay safe. I really appreciate it up. Unity and, you know, this is what it's all about. It's Ah, it's a lot of fun. I know we're going to see each other in person soon, and it's the people in the community that really make this happen. So looking forward to that, but I really appreciate it. >>Alright. And thank you, everybody for watching. This is the Cube coverage of the verdict. Big data conference gone, virtual going digital. I'm Dave Volante. We'll be right back right after this short break. >>Yeah.

>>live from Las Vegas. It's the Cube covering Splunk dot com. 19. Brought to you by spunk >>Welcome back Everyone's two cubes Live coverage from Las Vegas. Four Splunk dot com 2019 The 10th anniversary of their and user conference I'm John Free host of the key that starts seventh year covering Splunk Riding the wave of Big Data Day three of our three days were winding down. Our show are great to have on next guest Didn't Medoc executive director be Ibis Intelligence? Advanced Data Analytics at Clemson University Big A C C. Football team Everyone knows that. Great stadium. Great to have you on. Thanks for spending the time to come by and on Day three coverage. >>Thanks, John, for having me over. >>So, you know, hospitals, campuses, some use cases just encapsulate the digital opportunities and challenges. But you guys air have that kind of same thing going on. You got students, you got people who work there. You got a I ot or campus to campus is you guys are living the the real life example of physical digital coming together. Tell us about what's going on in your world that Clemson wouldn't your job there. What's your current situation? >>So, like you mentioned, we have a lot of students. So Clemson's about 20,000 undergraduate, children's and 5000 graduate students way faculty and staff. So you're talking about a lot of people every semester. We have new devices coming in. We have to support the entire network infrastructure, our student information systems on and research computing. So way we're focused on how convene make students lives better than experience. Better on how convene facilitated education for them. So way try toe in my role. Specifically, I'm responsible for the advanced eight analytics, the data that we're collecting from our systems. How can we? How can you use that on get more insides for better decision making? So that's that's >>Is a scope university wide, or is it specifically targeted for certain areas? >>So it does interest divide. So we have. We have some key projects going on University wide way, have a project for sure and success. There's a project for space utilization and how how, how we can utilize space and campus more efficiently. And then we're looking at energy energy usage across buildings campus emergency management idea. So we've got a couple of projects, and then Pettersson projects that most hired edge motion overseas work on this father's retention enrollment, graduation rates. How how the academics are. So so we're doing the same thing. >>What's interesting is that the new tagline for Splunk is data to everything. You got a lot of things. Their data. Ah, lot of horizontal use cases. So it seems to me that you have, ah, view and we're kind of talking on camera before we went live here was Dana is a fluid situation is not like just a subsystem. It's gotta be every native everywhere in the organization on touched, touches everything. How do you guys look at the data? Because you want to harness the data? Because data getting gathering on, say, energy. Your specialization might be great data to look at endpoint protection, for instance. I don't know. I'm making it up, but data needs to be workable. Cross. How do you view that? What's what's the state of the art thinking around data everywhere? >>So the key thing is, we've got so many IOC's. We've got so many sensors, we've got so many servers, it's it's hard when you work with different technologies to sort of integrate all of them on in the industry that have bean Some some software companies that try to view themselves as being deking, but really the way to dress it does you look at each system, you look at how you can integrate all of that, all of that data without being deking. So you basically analyze the data from different systems. You figured out a way to get it into a place where you can analyze it on, then make decisions based on that. So so that's essentially what we've been focused on. Working on >>Splunk role in all this is because one of things that we've been doing spot I've been falling spunk for a long time in a very fascinated with law. How they take log files and make make value out of that. And their vision now is that Grew is grow is they're enabling a lot of value of the data which I love. I think it's a mission that's notable, relevant and certainly gonna help a lot of use cases. But their success has been about just dumping data on display and then getting value out of it. How does that translate into this kind of data space that you're looking at, because does it work across all areas? What should what specifically are you guys doing with Splunk and you talk about the case. >>So we're looking at it as a platform, like, how can we provide ah self service platform toe analysts who can who can go into system, analyze the data way not We're not focusing on a specific technology, so our platform is built up of multiple technologies. We have tableau for visual analytics. We're also using Splunk. We also have a data warehouse. We've got a lot of databases. We have a Kafka infrastructure. So how can we integrate all of these tools and give give the choice to the people to use the tools, the place where we really see strong helping us? Originally in our journey when we started, our network team used to long for getting log data from switches. It started off troubleshooting exercise of a switch went down. You know what was wrong with it? Eventually we pulled in all for server logs. That's where security guard interested apart from the traditional idea of monitoring security, saw value in the data on. And then we talked about the whole ecosystem. That that's one provides. It gives you a way to bring in data withdrawal based access control so you can have data in a read only state that you can change when it's in the system and then give access to people to a specific set of data. So so that's that's really game changing, even for us. Like having having people be comfortable to opening data to two analysts for so that they can make better decisions. That's that's the key with a lot of product announcements made during dot com, I think the exciting thing is it's Nargis, the data that you index and spunk anymore, especially with the integration with With Dew and s three. You don't have to bring in your data in response. So even if you have your data sitting in history, our audio do cluster, you can just use the data fabric search and Sarge across all your data sets. And from what I hear that are gonna be more integrations that are gonna be added to the tool. So >>that's awesome. Well, that's a good use. Case shows that they're thinking about it. I got to ask you about Clemson to get into some of the things that you guys do in knowing Clemson. You guys have a lot of new things. You do your university here, building stuff here, you got people doing research. So you guys are bringing on new stuff, The network, a lot of new technology. Is there security concerns in terms of that, How do you guys handle that? Because you want to encourage innovation, students and faculty at the same time. You want gonna have the data to make sure you get the security without giving away the security secrets are things that you do. How do you look at the data when you got an environment that encourages people to put more stuff on the network to generate more data? Because devices generate data project, create more data. How do you view that? How do you guys handle that? >>So our mission and our goal is not to disrupt the student experience. Eso we want to make it seem less. And as we as we get influx of students every semester, we have way have challenges that the traditional corporate sector doesn't have. If you think about our violence infrastructure. We're talking about 20 25,000 students on campus. They're moving around. When, when? When they move from one class to another, they're switching between different access points. So having a robust infrastructure, how can we? How can we use the data to be more proactive and build infrastructure that's more stable? It also helps us plan for maintenance is S O. We don't destruct. Children's so looking at at key usage patterns. How what time's Our college is more active when our submissions happening when our I. D. Computing service is being access more and then finding out the time, which is gonna be less disruptive, do the students. So that's that's how we what's been >>the biggest learnings and challenges that you've overcome or opportunities that you see with data that Clemson What's the What's the exciting areas and or things that you guys have tripped over on, or what I have learned from? We'll share some experiences of what's going on in there for you, >>So I think Sky's the limit here. Really like that is so much data and so less people in the industry, it's hard to analyze all of the data and make sense of it. And it's not just the people who were doing the analysis. You also need people who understand the data. So the data, the data stores, the data trustees you need you need buy in from them. They're the ones who understand what data looks like, how how it should be structured, how, how, how it can be provided for additional analysis s Oh, that's That's the key thing. What's >>the coolest thing you're working on right now? >>So I'm specifically working on analyzing data from our learning management system canvas. So we're getting data informer snapshots that we're trying to analyze, using multiple technologies for that spunk is one of them. But we're loading the data, looking at at key trends, our colleges interacting, engaging with that elements. How can we drive more adoption? How can we encourage certain colleges and departments, too sort of moved to a digital classroom Gordon delivery experience. >>I just l a mess part of the curriculum in gym or online portion? Or is it integrated into the physical curriculum? >>So it's at this time it's more online, But are we trying to trying to engage more classes and more faculty members to use the elements to deliver content. So >>right online, soon to be integrated in Yeah, you know, I was talking with Dawn on our team from the Cube and some of the slum people this week. Look at this event. This is a physical event. Get physical campuses digitizing. Everything is kind of a nirvana. It's kind of aspiration is not. People aren't really doing 100% but people are envisioning that the physical and digital worlds are coming together. If that happens and it's going to happen at some point, it's a day that problem indeed, Opportunity date is everything right? So what's your vision of that as a professional or someone in the industry and someone dealing with data Clemson Because you can digitize everything, Then you can instrument everything of your instrument, everything you could start creating an official efficiencies and innovations. >>Yes, so the way I think you you structure it very accurately. It's amalgam of the physical world and the digital world as the as the as the world is moving towards using more more of smartphones and digital devices, how how can we improve experience by by analyzing the data on and sort of be behind the scenes without even having the user. The North is what's going on trading expedience. If the first expedience is in good that the user has, they're not going to be inclined to continue using the service that we offer. >>What's your view on security now? Splunk House League has been talking about security for a long time. I think about five years ago we started seeing the radar data. Is driving a lot of the cyber security now is ever Everyone knows that you guys have a lot of endpoints. Security's always a concern. How do you guys view the security of picture with data? How do you guys talk about that internally? How do you guys implement data without giving me a secret? You know, >>way don't have ah ready Good Cyber Security Operation Center. That's run by students on. And they do a tremendous job protecting our environment. Way monitored. A lot of activity that goes on higher I deserve is a is a challenge because way have in the corporate industry, you can you can have a set of devices in the in the higher education world We have students coming in every semester that bringing in new, important devices. It causes some unique set of challenges knowing where devices are getting on the network. If if there's fishing campaigns going on, how can be, How can we protect that environment and those sort of things? >>It is great to have you on. First of all, love to have folks from Clemson ons great great university got a great environment. Great Great conversation. Congratulations on all your success on their final question for you share some stories around some mischief that students do because students or students, you know, they're gonna get on the network and most things down. Like when when I was in school, when we were learning they're all love coding. They're all throwing. Who knows? Kitty scripts out there hosting Blockchain mining algorithms. They gonna cause some creek. Curiosity's gonna cause potentially some issues. Um, can you share some funny or interesting student stories of caught him in the dorm room, but a server in there running a Web farm? Is there any kind of cool experiences you can share? That might be interesting to folks that students have done that have been kind of funny mistress, but innovative. >>So without going into Thio, I just say, Like most universities, we have, we have students and computer science programs and people who were programmers and sort of trying to pursue the security route in the industry. So they, um, way also have a lot of research going on the network on. And sometimes research going on may affect our infrastructure environment. So we tried toe account for those use cases and on silo specific use cases and into a dedicated network. >>So they hit the honeypot a lot. They're freshmen together. I'll go right to the kidding, of course. >>Yes. So way do we do try to protect that environment on Dhe. Makes shooting experience better. >>I know you don't want to give any secrets. Thanks for coming on. I always find a talk tech with you guys. Thanks so much appreciated. Okay. Cube coverage. I'm shot for a year. Day three of spunk dot com for more coverage after this short break

>>Live from Denver, Colorado. It's the cube covering com vault go 2019 brought to you by Combolt. >>Hey, welcome back to the cube at Lisa Martin with Steve men and men and we are coming to you alive from combo go 19 please to welcome to the cube, a gent from Microsoft Azure. We've got Ben call principal program manager. Ben, welcome. Thank you. Thanks for having me on. Thanks for coming on. So Microsoft combo, what's going on with the partnership? >>They wouldn't have have great storage pond is in data management space. We've been working with Convult for 20 years now in Microsoft and and they've been working with us on Azure for that as long as I can remember not being on that the Azure business for about seven years now. So just a long time in cloud terms like dog ears and it's sort of, they've been doing a huge amount there around getting customer data into the cloud, reducing costs, getting more resiliency and then also letting them do more with the data. So they're a pretty good partner to have and they make it much easy for their customers to to go and leverage cloud. >> So Ben, you know, in my career I've had lots of interactions with the Microsoft storage team. Things have changed a little bit when you're now talking about Azure compared to more, it was the interaction with the operating system or the business suite at had. >>So maybe bring us up to date as those people that might not have followed where kind of the storage positioning inside of Microsoft is now that when we talk about Azure and your title. Yeah, we, we sort of can just, just briefly, we worked very heavily with our own premises brethren, they are actually inside the O team is inside of the Azure engineering old male, which is kind of funny, but we do a load of things there. If he started looking at, firstly on that, that hybrid side, we have things like Azure files. It's a highly resilient as a service SMB NFS file Shafter a hundred terabytes, but that interacts directly with windows server to give you Azure file sync. So there is sort of synergies there as well. What I'm doing personally, my team, we work on scale storage. The big thing we have in there is owl is out blood storage technology, which really is the underpinning technology fault. >>Preapproval storage and Azure, which is an including our SAS offerings, which are hosted on Azure too. So disc is on blood storage of files on blood storage. You look at Xbox live, all these kind of stuff is a customer to us. So we build that out and we were doing work there and that's, that's really, really interesting. And how we do it. And that's not looking at going, we're gonna buy some compute, we're going to buy some storage, we're going to build it out, we're going to run windows or hyper V or maybe VM-ware with hoc with windows running on the VMware, whatever else. This is more a story about we're gonna provide you storage as a service. You didn't get a minimum of like 11 nines at your ability. And and be able to have that scale to petabytes of capacity in one logical namespace and give you multiple gigabytes, double digit gigabytes of throughput to that storage. >>And now we're even that about to multiple protocols. So rest API century. Today we've got Azure stack storage, EU API, she can go and use, but we give you that consistency of the actual backend storage and the objects and the data available via more than just one protocol. You can go and access that via HDFS API. We talk about data lakes all the time. For us, our blood storage is a data Lake. We turn on hierarchal namespace and you can go and access that via other protocols like as I mentioned HDFS as well. So that is a big story about what we want to do. We want to make that data available at crazy scale, have no limits in the end to the capacity or throughput or performance and over any protocol. That's kind of our lawn on the Hill about what we want to get to. >>And we've been talking to the Combolt team about some of the solutions that they are putting in the cloud. The new offering metallic that came out. They said if my customer has Azure storage or storage from that other cloud provider, you could just go ahead and use that. Maybe how familiar and how much I know you've been having about run metallic. >> We were working, we work pretty tightly with the product team over Convolt around this and my team as well around how do we design and how do we make it work the best and we're going to continue working to optimize as they get to beyond initial launch to go, wow, we've got data sets we we can analyze. We knew how to, we wanted out of tune it. Now really we love the solution particularly more because you know the default if you don't select the storage type where you want to go, you will run on Azure. >>So really sort of be cued off to the relationship there. They chose us as a first place we'll go to, but they've also done the choice for customers. So some customers may want to take it to another cloud. That's fine. It's reasonable. I mean we totally understand it's going to be a multicloud world and that's a reality for any large company. Our goal is to make sure we're growing faster than the competitors, not to knock out the competitors altogether because that just won't happen. So they've got that ability to go and, yeah, Hey, we'll use Azure as default because they feel we're offering the best support and the best solution there. But then if they have that customer, same customer wants to turn around and use a competitor of ours, fine as well. And I see people talking about that today where they may want to mitigate risks and say, I'm going to do, I'm doing off office three, six five on a, taken off this three 65 backup. It's cool. You use metallic, it'll take it maybe to a different region in Asia and they're backing up. They still going, well, I'm still all in on Microsoft. They may want to take it to another cloud or even take it back to on premise. So that does happen too because just in case of that moment we can get that data back in a different location. Something >>so metallic talking about that is this new venture is right. It's a Convolt venture and saw that the other day and thought that's interesting. So we dug into it a little bit yesterday and it's like a startup operating within a 20 year old company, which is very interesting. Not just from an incumbent customer perspective, but an incumbent partner perspective. How have you seen over the last few years and particularly bad in the last nine months with big leadership and GTM changes for condo? How has the partnership with Microsoft evolved as a result of those changes? >>Um, it's always been interesting. I guess when you start looking at adventure and everything seems to, things change a little bit. Priorities may change just to be fair, but we've had that tight relationship for a long time and a relationship level and an exec leadership level, nothing's really changed. But in the way they're building this platform, we, we sit down out of my team at the Azure engineering group and we'll sit down and do things like ideations. Like here's where we see gaps in the markets, here's what we believe could happen. And look back in July, we had inspire, which is our partner conference in Las Vegas and we sat down with their OT, our OT in a room, we'll talking about these kinds of things. And this is I think about two months after they may have started the initial development metallic from what I understand, but we're talking about exactly what they're doing with metallic offered as a service in Azure as, Hey, how about we do this? So we think it's really cool. It opens up a new market to convert I think too. I mean they're so strong in the enterprise, but they don't do much in the smaller businesses because with the full feature product, it also has inherent complexibility complexity around it. So by doing metallic, is it click, click, next done thing. They really opening I think new markets to them and also to us as a partner. >>I was going to add, you know, kind of click on that because they developed this very quickly. This is something that I think what student were here yesterday, metallic was kind of conceived, designed, built in about six months. So in terms of like acceleration, that's kind of a new area for Combolt. >>Yeah, and I think, I think they're really embracing the fact about let's release our code in production for, for products which are sort of getting the, getting to the, Hey, the product is at the viable stage now, not minimum viable, viable, let's release in production, let's find out how customers are using it and then let's keep optimizing and doing that constant iteration, taking that dev ops approach to let's get it out there, let's get it launched, and then let's do these small batches of changes based on customer need, based on tele telemetry. We can actually get in. We can't get the telemetry without having customers. So that's how it's going to keep working. So I think this initial product we see today, it's just going to keep evolving and improving as they get more data, as they get more information, more feedback, which is exactly what we want to see. >>Well, what will come to the cloud air or something you've been living in for a number of years. Ben, I'd love to hear you've been meeting with customers, they've been asking you questions, gives us some of the, you know, some of the things that, what's top of mind for some of the customers? What kinds of things did they come into Microsoft, Dawn, and how's that all fit together? >>There's many different conferences of interrelate, many different conversations and there'll be, we'll go from talking about, you know, Python machine learning or AI fits in PowerPoint. >>Yeah. >>It's a things like, you know, when are we gonna do incremental snapshots from the manage disks, get into the weeds on very infrastructure centric stuff. We're seeing range of conversations there. The big thing I think I see, keep seeing people call out and make assumptions of is that they're not going to be relevant because cloud, I don't know cloud yet. I don't know this whole coup cube thing, containers, I don't really understand that as well as I think I need to. And an AI, Oh my gosh, what do we even do there? Cause everyone's throwing the words and terms around. But to be honest, I think would still really evident is cloud is still is tiny fraction of enterprise workloads. So let's be honest, it's growing at a huge rate because it is that small fraction. So again, there's plenty of time for people to learn but they shouldn't go and try. >>And so it's not like you go and learn everything in the technology stack from networking to development to database management to, to running a data set of power and cooling. You learn the things that are applicable to what you're trying to do. And the same thing goes to cloud. Any of these technologies go and look at what you need to build for your business. Take it that step and then go and find out the details and levels you want to know. And as someone who's been on Azure for, like I said, almost seven years, which is crazy long. That was, that was literally like being in a startup instead of Microsoft when I joined and I wasn't sure if I wanted to join a licensing company. It's been very evident to me. I will not say I'm an Azure expert and I've been seven years in the platform. >>There are too many things for for me to be an expert in everything on, and I think people sort of just have to realize that anyone's saying that it's bravado. Nothing else. Oh, people. The goal is Microsoft as a platform provider. Hopefully you've got the software and the solution does make a lot of this easier for the customer, so hopefully they shouldn't need to become a Coobernetti's expert because it's baked into your platform. They shouldn't have to worry about some of these offerings because it's SAS. Most customers are there. Some things you need to learn between going from exchange to go into Oh three 65 absolutely. There's some nuances and things like that, but once you get over that initial hurdle, it should be a little easier. I think it's right and I think going back to that, sort of going back to bear principles going, what is the highest level of distraction that's viable for your business or that application or this workload has to always be done with everything. If it's like, well, class, not even viable, running on premises, don't, don't need to apologize for not running in cloud. If I as this, what's happening for you because of security, because of application architecture, run it that way. Don't feel the need and the pressure to have to push it that way. I think too many people get caught up in this shiny stuff up here, which is what you know 1% of people are doing versus the other 99% which is still happening in a lot of the areas we work and have challenges in today. >>That's a great point that you bring up because there is all the buzz words, right? AI, machine learning cloud. You've got to be cloud ready. You've got to be data-driven to customer. To your point going, I just need to make sure that what we have set up for our business is going to allow our business one to remain relevant, but to also be able to harness the power of the data that they have to extract new opportunities, new insights, and not get caught up with, shoot, should we be using automation? Should we be using AI? Everybody's talking about it. I liked that you brought up and I find it very respectfully, he said, Hey, I'm not an Azure expert. You'd been there seven, seven dog years like you said. And I think that's what customers probably gained confidence in is hearing the folks like you that they look to for that guidance and that leadership saying, no, I don't know everything to know. But giving them the confidence that their tribe, they're trusting you with that data and also helping look, trusting you to help them make the right decisions for their business. >>Yeah, and that's, we've got to do that. I mean, I as a tech guy, it's like I've, I've loved seeing the changes. When I joined Microsoft, I, I wasn't lying. I was almost there go enough. I really want to join this company. I was going to go join a startup instead and I got asked to one stage in an interview going, why do you want to join Microsoft? We see you've never applied to, I'd never wanted to. A friend told me to come in and it's just been amazing to see those changes and I'm pretty proud on that. So when we talk about those things we're doing, I mean, I think there is no shame going, I'm just going to lift and shift machines because cloud's about flexibility. If you're doing it just on cost, probably doing it for the wrong reason. It's about that flexibility to go and do something. >>Then change within months and slowly make steps to make things better and better as you find a need as you find the ability, whatever it may be. And some of the big things that we focus on right now with customers is we've got a product called Azure advisor. It'll go until people, when one, you don't build things in a resilient manner. Hey, do you know this has not ha because of this and you can do this. It's like, great. We'll also will tell you about security vulnerabilities that maybe should a gateway here for security. Maybe you should do this or this is not patched. But the big thing of that, it also goes and tells you, Hey, you're overspending. You don't need this much. It provisions, you provision like a Ferrari, you need a, you just need a Prius. Go and run a Prius because it's going to do what you need. >>I need a paler list and that's part of that trusted suit. Getting that understanding, and it's counterintuitive, but we're now like, it's coming out of mozzarella too, which is great. But seeing these guys were dropping contracts and licenses and basically, you know, once every three years I may call the customer, Hey, how about renewal? Now, go from that to now being focused on the customer's actual success. I've focused on their growth in Azure as a platform. Our services growth, like utilization not in sales has been a huge change. It scared some people away, but it's brought a lot more people in and and that sort of counterintuitive spend less money thing actually leads in the longterm to people using more. >>Absolutely. That's definitely not the shrink wrap software company of Microsoft that I remember from the 90s yeah. might be similar to, you know, just as to get Convolt to 2019 is not the same combo that many of us know from 15 years ago. A good >>mutual friend of ours, sort of Simon and myself before I took this job, he and I sat down, we're having a beer and discussing the merits, all not Yvette go to things like that. Same with Convolt there. They're changing such such a great deal with, you know, what they're putting in the cloud, what they're doing with the data, where they're trying to achieve with things like for data management across on premises and cloud with microservices applications and stuff going, Hey, this won't work like this anymore. When you now are doing it on premises and with containers, it's pretty good to see. I'm interested to see how they take that even further to their current audience, which is product predominantly. You know the it pro, the data center admin, storage manager. >>It's funny when you talked about just the choice that customers have and those saying, aye, we shouldn't be following the trends because they're the trends. We actually interviewed a couple of hours ago, one of customers that is all on prime healthcare company and said, he's like, I want to make a sticker that says no cloud and proud and it just what there was, we don't normally hear from them. We always talk about cloud, but for a company to sit down and look at what's best for our business, whether it's, you know, FedRAMP certification challenges or HIPAA or GDPR, other compelling requirements to keep it on prem, it was just refreshing to hear this customer say, >>yeah, I mean it's just appropriate for them. You do what's right for you. I, yeah, it's no shame in any of it. It's, I mean you don't, you definitely don't get fans by it by shaming people about not doing something right. And I mean I've, I'm personally very happy to fee fee, you know, see sort of hype around things like blockchain die down a little bit. So it's a slow database and we should use it for this specific case of that shared ledger. You know, things like that where people don't have to know blockchain. Now I have to know IOT. It's like, yeah, and that hype gets people there, but it also causes a lot of anxiety and it's good to see someone actually not be ashamed of it. And they agree the ones when they do take a step and use cloud citizen may be in the business already, they're probably going to do it appropriately because have a reason, not just because we think this would be cool, right? >>Well not. And how much inherit and complexity does that bring in if somebody is really feeling pressured to follow those trends. And maybe that's when you end up with this hodgepodge of technologies that don't work well together. You're spending way more in as as business it folks are consumers, you know, consumers in their personal lives, they expect things to be accessible, visible, but also cost efficient because they have so much choice. >>Yeah, the choice choice is hard. It's just a, just the conversation I was having recently, for example, just we'll take the storage cause of where we are, right? It's like I'm running something on Azure, I'm a, I'm using Souza, I want an NFS Mount point, which is available to me in Fs. Great, perfect. what do I use as like, well you can use any one of these seven options like that, but what's the right choice? And that's the thing about being a platform can be, we give you a lot of choices, but it's still up to you or up to app hotness. It can really help the customers as well to make the most appropriate choice. And, and I, I pushed back really hard in terms of best practices and things. I hate it because again, it's making the assumption this is the best thing to do. >>It's not. It's always about, you know, what are the patterns that have worked for other people? What are the anti-patterns and what's the appropriate path for me to take? And that's actually how we're building our docs now too. So we, we keep, we keep focusing on our Azure technology and we're bringing out some of the biggest things we've done is how we manage our documentation. It's all open sourced, it's all in markdown on get hub. So you can go in and read a document from someone like myself is doing product management going, this is how to use this product and you're actually, this bit's wrong, this bit needs to be like this and you can go in yourself even now, make a change and we can go, Oh yeah and take that committed in and dual this kind of stuff in that way. So we're constantly taking those documents in that way and getting realtime feedback from customers who are using it, not just ourself in an echo chamber. >>So you get this great insight and visibility that you never had before. Well, Ben, thank you, Georgie stew and me on the queue this afternoon. Excited to hear what's coming up next for Azure. Makes appreciate your time. Thank you for steam and event. I, Lisa Martin, you're watching the cue from Convault go 19.

>>Live from Denver, Colorado. It's the cube covering com vault go 2019 brought to you by Combolt. >>Hey, welcome back to the Q but Lisa Martin with men and men and we are coming to you alive from Conn logo 19 please to welcome to the cube, a gent from Microsoft Azure. We've got Ben Nichol, principal program manager. Ben, welcome. Thank you. Thanks for having me on. Thanks for coming on. So Microsoft combo, what's going on with the partnership? >>They wouldn't have have great storage pond is in data management space. We've been working with Convolt for 20 years now in Microsoft and and they've been working with us on Azure for about as long as I can remember not being on that the Azure business RET seven years now. So just a long time in cloud terms like doggies and it sort of, they'd been doing a huge amount of their around getting customer data into the cloud, reducing costs, getting more resiliency and then also letting them do more with the data. So they were a pretty good partner to have and they make it much easy for their customers to to go and leverage cloud. So Ben, you know, in my career I've had lots of interactions with the Microsoft storage team. Things have changed a little bit when you're now talking about Azure compared to, you know, more. >>It was the interaction with the operating system or the business suite had. So maybe bring us up to date as those people that might not have followed. You know, we're kind of the storage positioning inside of Microsoft is now that when we talk about Azure and your title. Yeah, we, we sort of look and just just briefly, we worked very heavily with our on premises brethren. They actually inside the O S team is inside of the Azure engineering old male, which is kind of funny, but we do a lot of things there. If he started looking at, firstly on that hybrid side, we have things like Azure files. It's a highly resilient as a service SMB NFS file share up to a hundred terabytes but that interacts directly with windows server to give you Azure file sync. So there is sort of synergies there as well. When I'm doing personally my team, we work on scale storage. >>The big thing we have in there is Al is out blood storage technology, which really is the underpinning technology, full Priya tool storage and Azure which is including our SAS offerings which are hosted on Azure too. So disc is on blood storage, our files on blood storage, you look at Xbox live, all these kinds of stuff is a customer to us. So we build that out and we, we are doing work there and that's really, really interesting and how we do it and that's not looking at going we're going to buy some compute, we're going to buy some storage, we're going to build it out, we're going to run windows or hyper V or maybe VMware with windows running on the VMware, whatever else. This is more a story about wigging to provide you storage as a service. You didn't get a minimum of like 11 nines at your ability and and be able to have that scale to petabytes of capacity in one logical namespace and give you multiple gigabytes, double digit gigabytes of throughput to that storage. >>And now we're even moving about to model multiple protocols. So rest API century today we've got Azure stack storage, you pay API, she can go and use, but we give me that consistency of the actual back end storage and the objects and the data available via more than just one protocol. You can go and access that via HDFS API. As we talk about data lakes all the time. For us, our blood storage is a data Lake. We turn on hierarchal namespace and you can go and access that via our other protocols like as I mentioned HDFS as well. So that is a big story about what we want to do. We want to make that data available at crazy scale, have no limits in the end to the capacity or throughput or performance and over any protocol. That's kind of our line in the Hill about what we want to get to. >>And we've been talking to vault team about some of the solutions that they are putting in the cloud. The new offering metallic that came out. They said if my customer has Azure storage or storage from that other cloud provider, you could just go ahead and use that. Maybe how familiar and how much, I know you've been having a run metallic. We were working, we were pretty tightly with the product team over Convolt around this and my team as well around how do we design and how do we make it work the best and we're going to continue working to optimize as they get beyond initial launch to go, wow, we've got data sets we can analyze, we know how to, we wanted out of tune it. Now really we love the solution particularly more because the default, if you don't select the storage type where you want to go, you will run on Azure. >>So really sort of be kudos to the relationship there. They chose us as a first place we'll go to, but they've also done the choice for customers. Say some customers may want to take it to another cloud. That's fine. It's reasonable. I mean, we totally understand it's going to be a multi-cloud world and that's a reality for any large company. Our goal is to make sure we're growing faster than the competitors, not to knock out the competitors all together because that just won't happen. So they've got that ability to go and yet, Hey, we'll use Azure as default because they feel that way, offering the best support and the best solution there. But then if they have that customer, same customer wants to turn around and use a competitor, Val's fine as well. And I see people talking about that today where they may want to mitigate risks and say, I'm going to do, I'm doing all of office three, six, five on a taken office, three, six, five backup. It's cool. Use metallic, it'll take it maybe to a different region in Asia and they're backing up and they still going, well I'm still all in on Microsoft. They may want to take it to another cloud or even take it back to on premises. So that does happen too because just in case of that moment we can get that data back in a different location. Something happens. >>So metallic talking about that is this new venture is right. It's a Combolt venture and saw that the other day and thought that's interesting. So we dug into it a little bit yesterday and it's like a startup operating within a 20 year old company, which is very interesting. Not just from an incumbent customer perspective, but an incumbent partner perspective. How have you seen over the last few years and particularly bad in the last nine months with big leadership and GTM changes for combo? How has the partnership with Microsoft evolved as a result of those changes? >>Um, it's always been interesting. I guess when you start looking at adventure and everything, since things change a little bit, priorities may change just to be fair, but we've had that tight relationship for a long time. At a relationship level and an exec leadership level, nothing's really changed. But in the way they're building this platform, we sit down out of my team, out of the Azure engineering group and we'll sit down and do things like ideations, like here's where we see gaps in the markets, here's what we believe could happen. And look back in July, we had inspire, which is our partner conference in Las Vegas. When we sat down with their OT, our OT in a room, we'll talking about these kinds of things and this is I think about two months after they may have started the initial development metallic from what I understand, but we will talking about exactly what they're doing with metallic offered as a service in Azure is, Hey, how bout we do this? So we think it's really cool. It opens up a new market to Convolt I think too. I mean they're so strong in the enterprise, but they don't do much in smaller businesses because with a full feature product, it also has inherent complexibility complexity around it. So by doing metallic, is it click, click, next done thing. They're really opening, I think, new markets to them and also to us as a partner. >>I was going to ask, you know, kind of click on that because they developed this very quickly. This is something that I think what student were here yesterday, metallic was kind of conceived design built in about six months. So in terms of like acceleration, that's kind of a new area for Combalt. >>Yeah, and I think, I think they're really embracing the fact about um, let's release our code in production for products, which are sort of getting, getting to the, Hey that product is at the viable stage now, not minimum viable, viable, let's release in production, let's find out how customers are using Atlin, let's keep optimizing and doing that constant iteration, taking that dev ops approach to let's get it out there, let's get it launched. And then let's do these small batches of changes based on customer need, based on tele telemetry. We can actually get in. We can't get the telemetry without having customers. So that's how it's going to keep working. So I think this initial product we see today, it's just going to keep evolving and improving as they get more data, as they get more information, more feedback. Which is exactly what we want to see. >>Well, what will come to the cloud air or something you've been living in for a number of years. Ben, I'd love to hear you've been meeting with customers. They've been asking you questions, gives us some of the, you know, some of the things that, what's top of mind for some of the customers? What kinds of things did they come into Microsoft, Dawn, and how's that all fit together? >>There's many different conferences of interrelate, many different conversations and they'll, we will go from talking about, you know, Python machine learning or AI PowerPoint. >>Yeah. >>It's a things like, you know, when are we going to do incremental snapshots from a manage disks? Get into the weeds on very infrastructure century staff. We're seeing range of conversations there. The big thing I think I see, keep seeing people call out and make assumptions of is that they're not going to be relevant because cloud, I don't know cloud yet. I don't know this whole coup cube thing. Containers. I don't, I don't really understand that as well as I think I need to. And an AI, Oh my gosh, what do I even do there? Because everyone's throwing the words and terms around. But to be honest, I think what's still really evident is cloud is still is tiny fraction of enterprise workloads. Let's be honest, it's growing at a huge rate because it is that small fraction. So again, there's plenty of time for people to learn, but they shouldn't go and try and slip. >>It's not like you're going to learn everything in a technology stack, from networking to development to database management to, to running a data set of power and cooling. You learn the things that are applicable to what you're trying to do. And the same thing goes to cloud. Any of these technologies, go and look at what you need to build for your business. Take it to that step and then go and find out the details and levels you want to know. And as someone who's been on Azure for like a cinema seven years, which is crazy long. That was a, that was literally like being in a startup instead of Microsoft when I joined and I wasn't sure if I wanted to join a licensing company. It's been very evident to me. I will not say I'm an Azure expert and I've been seven years in the platform. >>There are too many things throughout my for me to be an expert in everything on and I think people sort of just have to realize that anyone saying that it's bravado, nothing else. The goal is Microsoft as a platform provider. Hopefully you've got the software and the solution to make a lot of this easier for the customer, so hopefully they shouldn't need to become a Kubernetes expert because it's baked into your platform. They shouldn't have to worry about some of these offerings because it's SAS. Most customers are there some things you need to learn between going from, you know, exchange to go into oath bricks, these five. Absolutely. There are some nuances and things like that, but once you get over that initial hurdle, it should be a little easier. I think it's right and I think going back to that, sort of going back to bare principles going, what is the highest level of distraction that's viable for your business or that application or this workload has to always be done with everything. >>If it's like, well, class, not even viable, run it on premises. Don't, don't need to apologize for not running in cloud. If I as is what's happening for you because of security, because of application architecture, run it that way. Don't feel the need and the pressure to have to push it that way. I think too many people get caught up in the shiny stuff up here, which is what you know 1% of people are doing versus the other 99% which is still happening in a lot of the areas we work and have challenges in today. >>That's a great point that you bring up because there is all the buzz words, right? AI, machine learning cloud. You've got to be cloud ready. You've gotta be data-driven to customer, to your point going, I just need to make sure that what we have set up for our business is going to allow our business one to remain relevant, but to also be able to harness the power of the data that they have to extract new opportunities, new insights, and not get caught up with, shoot, should we be using automation? Should we be using AI? Everybody's talking about it. I liked that you brought up and I find it very respectfully, he said, Hey, I'm not an Azure expert. You'd been there seven, seven dog years like you said. And I think that's what customers probably gained confidence in is hearing the folks like you that they look to for that guidance and that leadership saying, no, I don't know everything. To know that giving them the confidence that they're true, they're trusting you with that data and also helping trusting you to help them make the right decisions for their business. >>Yeah. And that that's, we've got to do that. I mean, I, as a tech guy, it's like I've, I've loved seeing the changes. When I joined Microsoft, I, I wasn't lying. I was almost there go inf I really want to join this company. I was going to go join a startup instead. And I got asked to one stage in an interview going, why do you want to join Microsoft? We see you've never applied to that. I never wanted to, a friend told me to come in and it's just been amazing to see those changes and I'm pretty proud on that. Um, so when we talk about, you know, those, the things we're doing, I mean I think there is no shame going, I'm just going to lift and shift machines because cloud is about flexibility. If you're doing it just on cost, probably doing it for the wrong reason, it's about that flexibility to go and do something. >>Then change within months of slowly make steps to make things better and better as you find a need as you find the ability, whatever it may be. And some of the big things that we focus on right now with customers is we've got a product called Azure advisor. It'll go until people want one. You know, you don't build things in a resilient manner. Hey, do you know this is not ha because of this and you can do this. It's like great. Also will tell you about security vulnerabilities that maybe she had a gateway here for security. Maybe you should do this or this is not patched. But the big thing is that it also goes and tells you, Hey, you're overspending. You don't need this much. It provisions, you provision like a Ferrari, you need a, you just need a Prius, go and run a Prius because it's going to do what you need and need to pay a lot less. >>And that's part of that trust. Getting that understanding. And it's counterintuitive that we're now like it's coming out of my team a lot too, which is great. But seeing these guys were dropping contracts and licenses and basically, you know, once every three years I may call the customer, Hey, how bout a renewal now go from that to now being focused on the customer's actual success and focused on their growth in Azure as a platform of our vast services growth like utilization not in sales has been a huge change. It scared some people away but it's brought a lot more people in and and that sort of counterintuitive spin less money thing actually leads in the longterm to people using more. >>Absolutely. That's definitely not the shrink wrap software company of Microsoft that I remember from the 90s yeah, very might be similar to you know, just as volt to 2019 is not the same combo, but many of us know from with 15 >>years and a good mutual friend of ours, sort of Simon and myself before I took this job, he and I sat down, we're having a beer and discussing the merits, all the not evacuate and things like that. Same with. They are changing such, such a great deal with, you know, what they're putting in the cloud, what they're doing with the data, where they're trying to achieve with things like Hedvig for data management across on premises and cloud with microservices applications and stuff going, Hey, this won't work like this anymore. When you now are doing an on premises and we containers, it's pretty good to see. I'm interested to see how they take that even further to their current audience, which is product predominantly, you know, the it pro, the data center admin, storage manager. >>It's funny when you talked about, um, just the choice that customers have and those saying I, we shouldn't be following the trends because they're the trends. We actually interviewed a couple of hours ago, one of Combolt's customers that is all on prime healthcare company and said, he's like, I want to make a secret that says no cloud and proud and it just, what that was, we don't normally hear from them. We always talk about cloud, but for a company to sit down and look at what's best for our business, whether it's, you know, FedRAMP certification challenges or HIPAA or GDPR or other compelling requirements to keep it on prem, it was just refreshing to hear this customer say, >>yeah, I mean it's, it's appropriate for the do what's right for you. I, yeah, it's no shame in any of them. It's, I mean, you don't, you definitely don't get fans by, by shaming people and not doing something right. And I mean, I, I'm personally very happy with the feet, you know, see sort of hype around things like blockchain died down a little bit. So it's a slow database unless you're using for the specific case of that shared ledger, you know, things like that where people don't have to know blockchain. Now I have to know IOT. It's like, yeah. And that hype gets people there, but it also causes a lot of anxiety and it's good to see someone actually not be ashamed of and like, and they grade the ones when they do take a step and use cloud citizen may be in the business already. They're probably going to do it appropriately because have a reason, not just because we think this would be cool. >>Well not and how much inherent and complexity does that bring in if somebody is really feeling pressured to follow those trends and maybe that's when you end up with this hodgepodge of technologies that don't work well together, you're spending way more in as as business it folks are consumers, you know, consumers in their personal lives, they expect things to be accessible, visible, but also cost efficient because they have so much choice. >>Yeah, the choice choice is hard. It's just a, just the conversation is having recently, for example, just we'll take the storage cause of where we are, right? It's like I'm running something on Azure. I'm a, I'm using Souza. I want an office Mount point, which is available to me in Fs. Great. Perfect. what do I use? It's like, well you use any one of these seven options, like what's the right choice? And that's the thing about being a platform company. We give you a lot of choices but it's still up to you or up to harness. It can really help the customers as well to make the most appropriate choice. And I pushed back really hard on terms like best practices and things. I hate it because again, it's making the assumption this is the best thing to do. It's not. It's always about, you know, what are the patterns that have worked for other people, what are the anti-patterns and the appropriate path for me to take. >>And that's actually how we're building our docs now too. So we keep, we keep focusing at our Azure technology and we're bringing out some of the biggest things we've done is how we manage our documentation. It's all open sourced. It's all in markdown on get hub. So you can go and read a document from someone like myself is doing product management going, this is how to use this product and you're actually this bits wrong. This bit needs to be like this, and you can go in yourself, even now, make a change and we can go, Oh yeah, and take that committed in and do all this kind of stuff in that way. So we're constantly taking those documents in that way, in getting real time feedback from customers who are using it, not just ourself and an echo chamber. >>So you get this great insight and visibility that you never had before. Well, Ben, thank you, Georgie stew and me on the Q this afternoon. Excited to hear what's coming up next for Azure. May appreciate your time. Thank you for streaming event. I, Lisa Martin, you're watching the cue from convo. Go 19.

>>Live from Denver, Colorado. It's the cube covering comm vault. Go 2019 brought to you by Combolt. >>Hey, you welcome back to the cubes coverage of combo go 19. We're in Colorado this year. Lisa Martin with Stu Miniman and we have a couple of gents joining us alumni of the cube. We're gonna have a really spirited conversation. Please welcome Avinash locks from the CEO of Hedvig, one of our alumni and Don Foxer, the VP of storage solutions from combo and Ann Campbell. Oh gee, Dawn, you can say that, right? Yes, yes. So guys, just a little bit of news coming out with combo and Hedvig in the last month or so, you guys announced combo, announced they were acquiring Hedvig. We last had you on the cube of an Asha DockerCon 18 talking about had veg. And here we are, the announcement comes in September. Acquisitions already closed, lots of buzz, lots of excitement. I'll finish. Let's start with you. Why Convolt >>good question. Uh, first of all, thanks for having me. Uh, C, the way I look at it, I believe the enterprises are gravitating towards complete solutions. If you look at, uh, data management and backup Conwell's clearly the leader in that space, I don't have to say it, I think the analysts have all attested to it. We bring in a very complimentary set of tools that I think coupled together could be a complete solution for a large variety of workloads in the modern data center. And hence it makes it a ideal fit. And also the cultures from an engineering perspective, being Hedwig being a small company in Cornwall is also a small company. But you know, definitely big when compared to what we are at. Um, the cultures were more or less aligned in terms of the engineering culture, so to speak. And that makes it, uh, it made it a very natural choice. Do you know, feel comfortable going into a bigger company. So it worked out really well. >>So Don way we've seen the slides given in the keynote, they talked about the two halves of the brain, the storage management and the data management. Talked to us a little bit about of Hedwig plus con vault and how that goes together. Yeah, for sure. I mean, if you start to look at the, I mean, I guess you look at the marketplace today and you can tell that, uh, kind of the, the lines of delineation of what vendor a versus vendor B versus vendor seat is doing is completely blurred, right? And you'll see that with the attachment of secondary storage, you see that with way backup companies and are driving more towards sort of, you know, uh, the app dev space. And we really start to look at where, what Combolt's doing and, and, and I always say when we talked about the acquisition of Hedvig, it's accelerating the vision that we've had on be able to provide a really super scalable backend for where you can land information that Combalt protects, but the really interesting and cool part, but as you start to realize the tool set that it has within it, it also keeps us very relevant for the future, for where it, in the shifts with applications are going. >>Then it gives us a chance to really give that complete solution from giving the storage, taking the information as it's being created and storing it in a compliance form, storing it off to the cloud, maybe re-purposing it, reusing it into the future. So that's how this really starts to come together. You have the index in control and management, the understanding of what Combolt provides the data management and you have all the flexibility and control that the Hedvig platform provides and Miriam together just gives you that much more agility for how you can use that information, that data. I want to understand what being part of Convolt will be different for Hedvig. I think back to, I've been talking to you since the company came out of stealth. We're huge proponents of the learnings that the hyperscalers had. You came from Amazon and Facebook. Bringing that to the enterprise is great, but building something that is highly scalable versus frigging something that has repeatability and scalability through thousands of deployment, like convoluted have are two separate issues. So, you know, we'll, we'll being part of Convolt, how will that impact your business and your group? >>I think the latter is what is going to make it really exciting for us. I think we added a point where the product that we are bringing into the market or we are brought into the market, it's pretty mature and most of the customers would deploy it and use it. They've been extremely happy with the way it performs and the way it has performed over time. And I think with the combo, they have a larger footprint in the enterprise, large channel infrastructure already in place makes it a lot more easy to push the product out there into the market. And uh, we will be given and VR given complete autonomy plus you what it would it is at Viva doing. And obviously, you know, when you go into any other organization that has got to be some cross-pollination, which is also something that we will be pursuing. But these two things I think, uh, make it very exciting times for us. >>Didn't you? You mentioned the word acceleration a few minutes ago. I'm just wondering from your perspective being called on as long as you have, do you and maybe customers and partners see the Hedvig acquisition as? Sanjay was saying something that's trending on Twitter today is the hashtag new comm dolls. Yep. So it's actually interesting. At first when the acquisition was announced, there were some partners that were like, Hmm, okay, I need to think about this a little bit. And then as we kind of went through the talk track and sort of explain some of the power with the head of the platform delivers, there were a number of, there was suddenly aha. Like you could just tell the light bulb went off. I get where this is going. And then you see what we're doing from Convolt metallic as well, right? The the SAS offering. And you see how we're continuing to drive all of the innovation in that core product. I don't know if you want to call it a combo to Datto, but I do think we've entered a new era of what we're delivering back to our customers from a solutions perspective. And it's really exciting because you can talk to a customer about backup and give them the best solution in the world, but we can also start to expand and get a whole heck of a lot more strategic and help be thought leaders and some of these new spaces, >>well, some of the commentary that I was reading about the acquisition from analysts say, Hey, this is a potentially, this is going to give Combalt opened the door for a bigger presence in the surge defined space, a big market. Also elevate comm vault from a Tam perspective. Talk to us about those perspectives. As some of the analysts said, when Sanjay came onboard nine months ago, Hey combo, you really got to expand your market share and get a kick out of just cultivating the large enterprises. How do you see that? >>Yeah, sure. I mean that's the easiest place to point to the secondary storage market place, right? So the secondary to storage marketplace, it's double digits in billions of market share. And that can be anything from things like object storage. It could just be scale-out, NAS. It could be, um, it could be, you know, companies like Cohesity and others that have a platform that build out secondary storage is a whole slew of people that play in that space. Uh, it also goes back to like appliances in a whole form of other storage types that are purpose built. So the secondary storage is a fairly broad sort of brush that people paint. You know, something is not running production workloads. But the interesting thing is, and this is kind of something that when the we've talked about we see those lines of private production or primary, secondary, tertiary, that's starting to really blur out. >>Um, so that market share that is in secondary storage, that market share that attaches also to object for where your, where we're going from a even a scale out backup perspective. You know, those are I going to be target areas that we can start to give customers solutions into in a really integrated and complete way. Uh, one of the customer areas that I've heard from Convolt that I'm curious if it might be applicable for your, for your team of an option is the service providers, you know, they've sold and you talk about how many end users actually leverage Convolt technology. It's like almost an order of magnitude more when you go through the service providers and when you talk about scalability and the requirements that that seems to be like it could be a fit for a. >>Yeah, you could even think of someone who is running a private cloud in their own on premise data center as being a service provider for their own internal consumption. Grateful folks working in tunnel. I guess going to an MSB or even do a larger service providers is an extrapolation of the same thing. So it'll obviously make it a very natural fit because you know, everyone understands the cap X game. Operational efficiency is the harder problem to actually crack. And with systems like this you can actually address that very simplistically. And it also allows them to kind of scale with their growth in a very effortless fashion. So it makes an agile mix a lot of natural sense. >>And that's an interesting point cause that aligns well too with the way the Combalt software themselves also attack attaches, right? We do a much better job of running that value back to the larger enterprise or those that are seeing more of that operational efficiency challenge. So it's another reason why this is a great intersection or you know, great, great marriage of the two technologies, um, what want to speak with, I think we talked about Sanjay about he of being at puppet worked a lot with dev ops in that environment. I heard from Convolt COO that five of the 45 developers that are here doing whiteboard session come from Hedvig. So speak a little bit of that, that customer base, the developer community microservices, you know, that kind of modern >>I think we have a, a demo session. I don't know what time, but we're going to give you a comprehensive overview of how, uh, you know, kind of Kubernetes orchestrated containers works with Hedwig. I think if people are here, they're hearing me, they should definitely check it out. And, uh, if you look at some of our larger customers, they deploy us in environments where they want to have practically zero touch provisioning capability, right? Which means that you got your infrastructure ought to be completely programmable, which bitches, what the DevOps movement is all about. And uh, the comprehensive set of APIs that be exposed for control and data plane, it actually makes that pipe dream a reality. >>Let's talk a little bit about the integrations. I mentioned a minute ago. The announcement was in September, the acquisitions close and you guys have already really started to buckle down into the integration between the technologies. Can you talk to us about that? And then I'd love to get your perspective on existing had big customers, you know, what door does this open for them? >>So for an existing customers, they are very happy because they now are convinced that we have a larger footprint and we have a lot more people to help to help support them as they grow and they don't have this field anymore as to how perhaps a small startup would be able to support them. So that fear factor goes away. So they're all very relieved on that front. Second, from an integration perspective, uh, there's a lot of things that we are working on from a technological perspective that is getting deep into the roadmap. I dunno if he can talk much about it at this point, but a non-technology we're all well integrated in, we are all Commonwealth employees now Gunwale badges come while emails so well integrated at this point. >>I guess maybe from a high level perspective, what we probably can say is probably number one, we want to make sure the experiences across both products are merged. So it truly views as you know, one one true company vault and providing that experience. And that's everything from installation to support to how we communicate and manage the, the ongoing relationship with the customer. So that's one there's always work to do there. Right? And the next core piece is just how we can make the two technologies basically make, you know, the had big platform, a part of the combo data platform and make sure those two integrations are as tight as possible. And that will be a longterm path, right? Because as that becomes more integrated, there's going to be new ideas, new innovations, and she's gonna come up with a whole lot of new things that we could potentially do that will meet the needs for the customer. And I think the third piece that ties back into the dev ops conversation is we've got two really solid API stacks. So bringing those together is going to be important in the future as well. So that it really is a crisp and clean sort of programmable infrastructure for customers from how the storage is delivered all the way to how it's managed and potentially even deleted out the back end. >>Well, with how quickly we're seeing Convolt move in the last nine months, I mean this year there's so much innovation from leadership changes in sales and marketing, new GTM routes, et cetera. What can, what can combo customers expect in terms of, I know you can't divulge too much on the roadmap, but you know with faster, shorter cycles of development. >>So I'll go first. I mean I think as you look at the sort of sort of where maybe the easiest way to answers is we're staying in front of where the market is heading and we're making sure we're providing solutions that can get customers to solve those challenges when they hit them. We don't want them to have to hit those challenges, have to then struggle, fight, figure out what it is they can do while everyone in their market moves past them. We want to be there with a solution that answers some of those challenges that day. They hit them more preemptive, preemptive, absolutely more preempted to react. That's a perfect way to put it. Thank you. So that's part of what they can expect from us and we do a lot of research and working with our customers and understanding where their future needs are, where they're going. We spend a lot of time with industry experts and analysts too for what they're seeing across the globe. Obviously we can only go so far and travel and talk to so many people. So we leveraged the collective of the industry to also kind of have a pretty good gauge and I'll say we've got leaders like Amash and Sanjay that are also awesome at just kind of having a really good pulse on where the industry is going and what we should be doing as a company. >>I'm just getting pickled in so too early for me to answer how that roadmap may Michio or how customers may perceive. But I think, uh, what should be very encouraging is that we bring so many, so much more capabilities. The enterprise has always been in this mindset of procuring things with a single throat to choke and this makes it very easy for them. >>What's the question of done for you is some of the things that Sue and I have talked about with guests today is from a partner perspective, there's been a lot of positive feedback in Navarro community we talked with and think we're talking with Rick de Blasio tomorrow. Want to understand, you know, some of the new partner programs, how are his Convolt traditional channel, your VARs, but also all the way up to your. Their reaction to all of the changes and the acceleration that Cohmad is driving is particularly with respect to head veg. >>For the most part it's been incredibly positive and even though the technology partner side, it's, it's fairly positive and also it forces us to have a much closer conversation on. All right, let's continue to talk about how we're successful together in the marketplace because we understand that our customers will need more than one vendor, more than two vendors to be successful as they kind of tackle the challenges that are in front of them. So you know, we're not going to stop our innovation and partnering and technology ecosystem development because that's so important to allow the customer to have the choice. We know that we're only one of many players and so we want to give them the choice to use whatever they need. We just want to help them control and manage the data >>and help them maybe simplify their operations. And especially as you know, we don't, we don't go to any event without talking about multi-cloud. It's the world that most businesses are living in. And, and I'll say, if you're not you Willy, how can what combat is doing now, not just with Hedvig but also just with some of the structural changes and directions that you guys have made it help customers embrace multicloud actually be able to protect, recover that data and >>you know, shift, sift insights from it. Yeah, sure. Please. All right, so multi-cloud, so first it starts off in tying in the ecosystems of the different cloud players offer, right? You need to be able to sort the support their platforms. You need to be able to continue to abstract out the information, the data itself that may be tied to an application or tied to a platform and give that level of portability. It's actually something that Hedvig does a fantastic job on as well and when you start to have that level of portability, well then it becomes a heck of a lot easier to either use other platforms within that cloud or a separate cloud or something you might homegrown build yourself as. That's part of the big value prop. We're doing all of these things not to have the best infrastructure but to make it easier for customers to use that data. So that means integrating and being strong partners to cloud players. It means continuing to be a really technological leader in how you can support all the platforms and services they offer and really allow the data to rise to the top as far as the value perspective goes and that's really where we continue to drive our innovation, at least on the on the data management side. >>That's a good Commonwealth perspective. The Hedvig perspective comes from a different angle. We always look at data portability, be it multicloud or even be at hybrid via met a lot of customers who went down the hybrid pot and then had to pull back. And when you pull back, you don't want to be in a situation where you're rewriting your entire application because your data is persisted in a very different way. But providing that data portability with an abstraction that sits between the application and the underlying physical infrastructure, I think is going to be a very important solution to take. You know, view often in this mix and hence together it becomes a comprehensive solution. >>Well guys, we thank you so much for stopping by joining soon and be on the program telling us a little bit more about this exciting new venture that you guys are going in together and we look forward to hearing more about it as it unfolds and maybe getting some customers on the cube next year. Absolutely. All right. Thank you. Thanks for Sumeta, man. I am Lisa Martin. You're watching the cube from convo. Go 19.

>> Live from Seattle, Washington. It's theCUBE, covering KubeCon and CloudNativeCon North America 2018, brought to you by Red Hat, the Cloud Native Computing Foundation and its ecosystem of partners. >> Hello everyone, welcome to theCUBE. We are at CubeCon 2018 in Seattle, CloudNativeCon as well. We've been to every KubeCon and CloudNativeCon since inception. I'm John Furrier. My co-host Stu Miniman want to break down the three days of wall to wall coverage of the rise of kubernetes and the ecosystem and the industry consolidation and standardization around kubernetes for multi cloud, for hybrid cloud. We're here breaking down day one keynote, kicking everything off. Stu, it's fun to come here and watch words like expansion, Moore's law, expansive growth, doubling down. The attendance for KubeCon, CloudNativeCon, hockey stick growth chart on Twitter. 1200, 4000, 8000 up into the right. Global phenomenon, the team at CNC at KubeCon, huge presence in China this year, total expansion all to save, hold the line on the cloud tsunami that is Amazon's web services. >> Yeah. >> This is the massive cloud game going on, your thoughts. >> Yeah, John first of all. You have to start out just expansive growth and you can just feel the energy here. We're in the middle of the show floor. You were here two years ago in Seattle when I think they said, they were, was it 16? There weren't that many sponsors here. There's 180 booths at this show. The joke in the keynote this morning was if you want to replace your entire T-shirt wardrobe that's what you can do here. Everybody's got fun stickers. It's a good crowd. Those alpha geeks, this is where they are. >> And Stu, you're sporting a new T-shirt. >> Yeah, John so I want to thank our friends. >> Make sure they can see that. >> Our friends here, Women Who Go. They do the GoLang languages, the gopher is what they're doing here. So love that, if you're at the show, come by. Get our stickers. If you look up Women Who Go on thread list. They actually have an artist shop. The CNCF has their logo up there. We have their logo. There is blockchain. There's docker, there's all these and you can buy the shirts and the money for buying these shirts actually goes to bring women and underserved people to events like this. We also love John when they're supporting this. The CNCF actually, I think it was a 130 or so people that they brought to this conference through charitable donations from many of the sponsors. >> And that's one of the highlights I want to get to later is the mission driven and the social responsibility, scholarships, the money that's being donated to fund diversity inclusion in all walks of life to make CloudNative, but Stu lets get back to the core thing that's going on here at KubeCon, CloudNativeCon. A couple years ago, I said, we said on theCUBE that the Tsunami, that is Amazon Web Service is just going to just hit ashore and just wipe out the industry in IT as much as it can go unless someone builds a seawall. Builds a wall to stop that momentum. Kubernetes and KubeCon specifically has had that moment. This is the industry saying look it. Cloud is awesome. It's full validation of cloud but there is more than just AWS. This is about multi cloud, hybrid cloud, and a lot of forces are at play competitively to make sure that Amazon doesn't run the table. >> Yeah, John, it's good to do a little bit of compare and contrast here because if you go back to OpenStack, it was OpenStack is the hail Mary against Amazon, and it's going to help you get off your VMware licenses. Well that's not what kubernetes is, if you look both VMware required Heptio, and Amazon have a big presence at this show. Amazon, their hands were forced to be able to actually work with kubernetes. I remember I read an article that said, there were about 14 different ways you can run kubernetes on Amazon before they supported it. Now they fully support it. They're going even deeper, AWS Fargate. I know you spend a lot of time at re:Invent digging into some of this environment here so this isn't, portability is a piece of kubernetes. Kubernetes won the orchestrator battles out there. It is the de facto standard out there, and we're seeing how this stack can really be built up on top of it. The thing that I've been keying in on coming into this year is how Serverless plays into it. You heard a big push for Knative on the keynote which is Google, who of course brought us to kubernetes. IBM, SAP, Red Hat all there but I don't see Microsoft or AWS yet embracing how we can match up Serverless and kubernetes today with the Knative. >> I think if I'm Amazon or Microsoft, I might be a little bit afraid of this movement because when, we went through the multi vendor days. You had multi vendoring decades ago. Now, multi cloud is the multi vendoring story, and what's interesting is that choice becomes the key word in all this and a real enterprise that's out there. They got Cisco routers, they got tons of stuff that's actually running their business, powering their business. They need to integrate that so this idea that one cloud fits all certainly has been validated. I think to me the winner takes most but what this community is doing Stu around kubernetes is galvanizing around a new stack configuration with kubernetes at the center of it, and that will disintermediate services at AWS and at Microsoft. Microsoft stock price has put that company in a higher value position than Google or Apple. What has Microsoft actually done to make them go from a $26 stock price to $100 and change? What did they actually invent? What did they actually do? What did they disrupt? Was it just go in a cloud? Is it Office 365? This begs the question is it just the business model shift so certainly there is business in the cloud and it's showing here at KubeCon. >> Yeah John, there was a major cultural shift inside of Microsoft I was really excited. One of the shows I got to go to this year was Microsoft Ignite, and in many ways it's interesting. That show has been around for decades and in many ways, it was the Windows admin just getting the latest and greatest. From my standpoint, I think it was Microsoft fully embracing the move to SaaS. They're pushing everybody to Office 365. They are aggressively moving to expand their cloud that that hybrid environment Microsoft has the applications, and they're not waiting for customers to just leave them or hold onto whatever revenue stream. They're switching to that writable model. They're switching to SaaS model. They're pushing really hard on Azure. They're here in force. They're really embracing developers, all the .NET folks, they were-- >> They're moving the ball inch by inch down the fields slowly to that cadence and that in totality with social responsibility and commencement of the cloud. I think has been, there's not one thing that's happened. It's just a total transformation for Microsoft, and the results and the valuation are off the charts. Google, the same way. Diane Greene has, I think was unfairly categorized by the press in terms of her exit. She's been wanting to retire for years Stu. She has turned Google around. You look at Google where they are right now verses where they were two years ago. Two years ago, they were slinging cloud the Google way. Now they're saying hey, you know what. We know the enterprise. Jennifer Lin, Sarah Novotny, Dawn Chen. All those people over there are leading the way real enterprise just with tech and they got some big moves to make, and they're doing it. So Diane Greene did not fail. So that was one thing that's interesting in the ecosystem and in Amazon as you know just kick it out. So given all that Stu, how does that relate to this? >> Yeah, let's bring it back here. So first of all, kubernetes. It was interesting the keynote this morning. We spent a lot of time talking about things that built on top of and around what's happening with kubernetes. Talking about things like how Helm is moving forward. Onvoy, Prometheus all of these projects. There are a couple dozen incubating projects and a few of them are graduating up to be full flanked projects. We talked about the ecosystem and how many partners are here. There's around 80 service providers and about 80 platforms that have kubernetes baked in. I want to point out an interesting distinction. Some people said, it's like oh they're 75 or 80 different distributions of it. I don't think that anybody thinks that they're going to make a differentiated platform that people are going to buy what I'm doing because I have the best kubernetes. Really what the CNCF has done a good job is saying you're fully supported. You're inoperable, you meet the guidelines to say, I am kubernetes and therefore it's baked into what we're doing. So why do we have so many of them? It's well, there's a lot of clouds out there. There's service providers and even the infrastructure players are making sure that they're in there. Everybody from Intel, all the way through. Servers and storage and networking all making sure that they're doing they're pieces to make sure that they work in the kubernetes environment. >> So Stu, I got to ask you a question on the keynote. You were in the front row. I was watching online here. Kind of distraction, sold out in the keynote. I didn't get the whole gist of it. How much of the keynote was vendor or project expansion verses end user traction? Can you give some color on that? >> Yeah, so a lot of it was the projects. What's really good is there's not a lot of vendors. Sure there is here's the logo slide. Let's everybody give a big round of applause and thank you. But when they put the projects up there, many of these projects came out of a group but some of that is well Lyft. Lyft created one of these projects and who's involved in that. One of the big news announcement was FCD is being donated to the CNCS, and well that came out of CoreOS to solve a really needed problem that they had to make sure that when you're rolling upgrades that you don't reboot the entire cluster all at once, and then your application isn't able to be there. So why are they donating? Well it has reached the maturity level, and while CoreOS is inside of Red Hat, there is a broad adoption. Lots of people contributing and it just makes sense to hand it over. Red Hat, everything they've done always is 100% open source, so them making sure that they have a good relationship with the foundation and who should have the governs of that. Red Hat has a strong track record on that. I know we'll be talking a lot-- >> All right so Stu get your perspective on the big players. We saw Google up on Saint-operno. We saw VMware. Cisco is here. I saw some of the Cisco executives here earlier. You got Red Hat, you got the big dogs here, Microsoft. What's the trend on the big players and then what's the trend on the hot startups either companies and or new wave in here? You mentioned Knative. So big companies, what's the general trend there and then what are you seeing on the interests around startups. >> So John, last year when I talked to users at this show. It was most of the people that were using kubernetes were building their own stack. The exception to that was oh if I'm a Red Hat customer, open shift makes sense for me. I can built it into what my model is. Azure had just come out with their AKS support. AWS had just been figuring out their ECS verse EKS and what they had. We're going to do before Fargate was down there. Today, what I hear is maturation of the platform so I expect Amazon and Microsoft to win more, and just I'm on those platforms. I'm using it, oh I want to use their kubernetes service that's going to make sense. So the rich get richer in this a lot way. Red Hat is going to do well, IBM is a strong player here, and of course sometime in 2019, we expect that acquisition of Red Hat to close. From a start up standpoint, there are so many niches that can be filled here. The question is how many of them are going to end up as acquisitions inside some of these big ones. How much of them can monetize because as I said with kubernetes John, I don't see a company that's going to say oh, I'm going to be the kubernetes company and monetize. Mirantis for a year or so ago was pivoting to be from the OpenStack company to the kubernetes company. Heptio was an early player and they had a quick exit. They're bringing strong skill set to the VMware team to help VMware accelerate their CloudNative activities. So in many ways John, this is an evolution more than a revolution so I do not see a drastic change in the landscape. >> Well evolution is cloud computing. We know that's going to yield the edge of the network and then on premise is complete conversions. This evolution is interesting Stu because this is an open source community vibe. You have now two other things going on around it. You have the classic open source community event, and you've got on the other spectrum, normal app developers that just want to right code. Then you got this IT dynamic. So what's happening and that will be interesting and we'll be watching this is how does the CNCF KubeCon, CloudNativeCon involve, and you start to cross pollinate app developers who just want our infrastructure as code. IT people who want to take over a new IT and then pure open source community players. This has now become a melting pot. Is that an opportunity or a challenge for the CNCF and the Linux Foundation? >> The danger is that this just gets overruned by vendors. It becomes another OpenStack that people get disenfranchised through what they're doing so absolutely there's a threat here. To their credit, I think the CNCF has done a really good job of managing things. They're smart is how they're doing. They're community focused. I have to say in the keynote John, if we noticed the diversity was phenomenal. Most of the speakers were women. They were one from end users. There are a couple of dozen end users that are now members of the CNCF. >> I think they're all CUBE alumnis too. >> Absolutely, and John, we've been here since the early days been tracking the whole thing. >> It's fun to watch. My opinion on the whole the melting pot of those personas is I think the CNCF and the Linux Foundation has a winning formula by owning and nurturing the open source community side of it. I think that's where the data is going to be, that's where the action is and I think as a downstream benefit, the IT market and developers will win. I would not try to get enamored by the money, and the vendor participation hype. I don't think they are. I'm just saying I would advise them to stay the course. Make this the open source community show of course, that's what we believe and of course we're CubeNative this week. We are here at the CloudNative and now we're CubeNative. This is the first day of three days of coverage. I'm John Furrier and Stu Miniman breaking down the analysis, talking to the smartest people we can find, and also talk about some of the key players that are sponsoring the show. We'll be back with more coverage after this short break. (uptempo techno music)

>> Live from Washington, D.C., it's theCUBE! Covering Inforum D.C. 2018. Brought to you by Infor. >> Well, welcome back to Washington, D.C., We are alive here at the Convention Center at Inforum 18, along with Dave Vellante, I'm John Walls. It's a pleasure now, welcome to theCUBE, Leigh Martin, who is the Senior Director of the Dynamic Science Labs at Infor, and good afternoon to you Leigh! >> Good afternoon, thank you for having me. >> Thanks for comin' on. >> Thank you for being here. Alright, well tell us about the Labs first off, obviously, data science is a big push at Infor. What do you do there, and then why is data science such a big deal? >> So Dynamic Science Labs is based in Cambridge, Massachusetts, we have about 20 scientists with backgrounds in math and science areas, so typically PhDs in Statistics and Operations Research, and those types of areas. And, we've really been working over the last several years to build solutions for Infor customers that are Math and Science based. So, we work directly with customers, typically through proof of concept, so we'll work directly with customers, we'll bring in their data, and we will build a solution around it. We like to see them implement it, and make sure we understand that they're getting the value back that we expect them to have. Once we prove out that piece of it, then we look for ways to deliver it to the larger group of Infor customers, typically through one of the Cloud Suites, perhaps functionality, that's built into a Cloud Suite, or something like that. >> Well, give me an example, I mean it's so, as you think-- you're saying that you're using data that's math and science based, but, for application development or solution development if you will. How? >> So, I'll give you an example, so we have a solution called Inventory Intelligence for Healthcare, it's moving towards a more generalized name of Inventory Intelligence, because we're going to move it out of the healthcare space and into other industries, but this is a product that we built over the last couple of years. We worked with a couple of customers, we brought in their loss and data, so their loss in customers, we bring the data into an area where we can work on it, we have a scientist in our team, actually, she's one of the Senior Directors in the team, Dawn Rose, who led the effort to design and build this, design and build the algorithm underlying the product; and what it essentially does is, it allows hospitals to find the right level of inventory. Most hospitals are overstocked, so this gives them an opportunity to bring down their inventory levels, to a manageable place without increasing stockouts, so obviously, it's very important in healthcare, that you're not having a lot of stockouts. And so, we spent a lot of time working with these customers, really understanding what the data was like that they were giving to us, and then Dawn and her team built the algorithm that essentially says, here's what you've done historically, right? So it's based on historic data, at the item level, at the location level. What've you done historically, and how can we project out the levels you should have going forward, so that they're at the right level where you're saving money, but again, you're not increasing stockouts, so. So, it's a lot of time and effort to bring those pieces together and build that algorithm, and then test it out with the customers, try it out a couple of times, you make some tweaks based on their business process and exactly how it works. And then, like I said, we've now built that out into originally a stand-alone application, and in about a month, we're going to go live in Cloud Suite Financials, so it's going to be a piece of functionality inside of Cloud Suite Financials. >> So, John, if I may, >> Please. >> I'm going to digress for a moment here because the first data scientist that I ever interviewed was the famous Hilary Mason, who's of course now at Cloudera, but, and she told me at the time that the data scientist is a part mathematician, part scientist, part statistician, part data hacker, part developer, and part artist. >> Right. (laughs) >> So, you know it's an amazing field that Hal Varian, who is the Google Economist said, "It's going to be the hottest field, in the next 10 years." And this is sort of proven true, but Leigh, my question is, so you guys are practitioners of data science, and then you bring that into your product, and what we hear from a lot of data scientists, other than that sort of, you know, panoply of skill sets, is, they spend more time wrangling data, and the tooling isn't there for collaboration. How are you guys dealing with that? How has that changed inside of Infor? >> It is true. And we actually really focus on first making sure we understand the data and the context of the data, so it's really important if you want to solve a particular business problem that a customer has, to make sure you understand exactly what is the definition of each and every piece of data that's in all of those fields that they sent over to you, before you try to put 'em inside an algorithm and make them do something for you. So it is very true that we spend a lot of time cleaning and understanding data before we ever dive into the problem solving aspect of it. And to your point, there is a whole list of other things that we do after we get through that phase, but it's still something we spend a lot of time on today, and that has been the case for, a long time now. We, wherever we can, we apply new tools and new techniques, but actually just the simple act of going in there and saying, "What am I looking at, how does it relate?" Let me ask the customer to clarify this to make sure I understand exactly what it means. That part doesn't go away, because we're really focused on solving the customer solution and then making sure that we can apply that to other customers, so really knowing what the data is that we're working with is key. So I don't think that part has actually changed too much, there are certainly tools that you can look at. People talk a lot about visualization, so you can start thinking, "Okay, how can I use some visualization to help me understand the data better?" But, just that, that whole act of understanding data is key and core to what we do, because, we want to build the solution that really answers the answers the business problem. >> The other thing that we hear a lot from data scientists is that, they help you figure out what questions you actually have to ask. So, it sort of starts with the data, they analyze the data, maybe you visualize the data, as you just pointed out, and all these questions pop out. So what is the process that you guys use? You have the data, you've got the data scientist, you're looking at the data, you're probably asking all these questions. You get, of course, get questions from your customers as well. You're building models maybe to address those questions, training the models to get better and better and better, and then you infuse that into your software. So, maybe, is that the process? Is it a little more complicated than that? Maybe you could fill in the gaps. >> Yeah, so, I, my personal opinion, and I think many of my colleagues would agree with me on this is, starting with the business problem, for us, is really the key. There are ways to go about looking at the data and then pulling out the questions from the data, but generally, that is a long and involved process. Because, it takes a lot of time to really get that deep into the data. So when we work, we really start with, what's the business problem that the customer's trying to solve? And then, what's the data that needs to be available for us to be able to solve that? And then, build the algorithm around that. So for us, it's really starting with the business problem. >> Okay, so what are some of the big problems? We heard this morning, that there's a problem in that, there's more job openings than there are candidates, and productivity, business productivity is not being impacted. So there are two big chewy problems that data scientists could maybe attack, and you guys seem to be passionate about those, so. How does data science help solve those problems? >> So, I think that, at Infor, I'll start off by saying at Infor there's actually, I talked about the folks that are in our office in Cambridge, but there's quite a bit of data science going on outside of our team, and we are the data science team, but there are lots of places inside of Infor where this is happening. Either in products that contains some sort of algorithmic approach, the HCM team for sure, the talent science team which works on HCM, that's a team that's led by Jill Strange, and we work with them on certain projects in certain areas. They are very focused on solving some of those people-related problems. For us, we work a little bit more on the, some of the other areas we work on is sort of the manufacturing and distribution areas, we work with the healthcare side of things, >> So supply chain, healthcare? >> Exactly. So some of the other areas, because they are, like I said, there are some strong teams out there that do data science, it's just, it's also incorporated with other things, like the talent science team. So, there's lots of examples of it out there. In terms of how we go about building it, so we, like I was saying, we work on answering the business, the business question upfront, understanding the data, and then, really sitting with the customer and building that out, and, so the problems that come to us are often through customers who have particular things that they want to answer. So, a lot of it is driven by customer questions, and particular problems that they're facing. Some of it is driven by us. We have some ideas about things that we think, would be really useful to customers. Either way, it ends up being a customer collaboration with us, with the product team, that eventually we'll want to roll it out too, to make sure that we're answering the problem in the way that the product team really feels it can be rolled out to customers, and better used, and more easily used by them. >> I presume it's a non-linear process, it's not like, that somebody comes to you with a problem, and it's okay, we're going to go look at that. Okay now, we got an answer, I mean it's-- Are you more embedded into the development process than that? Can you just explain that? >> So, we do have, we have a development team in Prague that does work with us, and it's depending on whether we think we're going to actually build a more-- a product with aspects to it like a UI, versus just a back end solution. Depends on how we've decided we want to proceed with it. so, for example, I was talking about Inventory Intelligence for Healthcare, we also have Pricing Science for Distribution, both of those were built initially with UIs on them, and customers could buy those separately. Now that we're in the Cloud Suites, that those are both being incorporated into the Cloud Suite. So, we have, going back to where I was talking about our team in Prague, we sometimes build product, sort of a fully encased product, working with them, and sometimes we work very closely with the development teams from the various Cloud Suites. And the product management team is always there to help us, to figure out sort of the long term plan and how the different pieces fit together. >> You know, kind of big picture, you've got AI right, and then machine learning, pumping all kinds of data your way. So, in a historical time frame, this is all pretty new, this confluence right? And in terms of development, but, where do you see it like 10 years from now, 20 years from now? What potential is there, we've talked about human potential, unlocking human potential, we'll unlock it with that kind of technology, what are we looking at, do you think? >> You know, I think that's such a fascinating area, and area of discussion, and sort of thinking, forward thinking. I do believe in sort of this idea of augmented intelligence, and I think Charles was talking a little bit about, about that this morning, although not in those particular terms; but this idea that computers and machines and technology will actually help us do better, and be better, and being more productive. So this idea of doing sort of the rote everyday tasks, that we no longer have to spend time doing, that'll free us up to think about the bigger problems, and hopefully, and my best self wants to say we'll work on famine, and poverty, and all those problems in the world that, really need our brains to focus on, and work. And the other interesting part of it is, if you think about, sort of the concept of singularity, and are computers ever going to actually be able to think for themselves? That's sort of another interesting piece when you talk about what's going to happen down the line. Maybe it won't happen in 10 years, maybe it will never happen, but there's definitely a lot of people out there, who are well known in sort of tech and science who talk about that, and talk about the fears related to that. That's a whole other piece, but it's fascinating to think about 10 years, 20 years from now, where we are going to be on that spectrum? >> How do you guys think about bias in AI and data science, because, humans express bias, tribalism, that's inherent in human nature. If machines are sort of mimicking humans, how do you deal with that and adjudicate? >> Yeah, and it's definitely a concern, it's another, there's a lot of writings out there and articles out there right now about bias in machine learning and in AI, and it's definitely a concern. I actually read, so, just being aware of it, I think is the first step, right? Because, as scientists and developers develop these algorithms, going into it consciously knowing that this is something they have to protect against, I think is the first step, for sure. And then, I was just reading an article just recently about another company (laughs) who is building sort of a, a bias tracker, so, a way to actually monitor your algorithm and identify places where there is perhaps bias coming in. So, I do think we'll see, we'll start to see more of those things, it gets very complicated, because when you start talking about deep learning and networks and AI, it's very difficult to actually understand what's going on under the covers, right? It's really hard to get in and say this is the reason why, your AI told you this, that's very hard to do. So, it's not going to be an easy process but, I think that we're going to start to see that kind of technology come. >> Well, we heard this morning about some sort of systems that could help, my interpretation, automate, speed up, and minimize the hassle of performance reviews. >> Yes. (laughs) >> And that's the classic example of, an assertive woman is called abrasive or aggressive, an assertive man is called a great leader, so it's just a classic example of bias. I mentioned Hilary Mason, rock star data scientist happens to be a woman, you happen to be a woman. Your thoughts as a woman in tech, and maybe, can AI help resolve some of those biases? >> Yeah. Well, first of all I want to say, I'm very pleased to work in an organization where we have some very strong leaders, who happen to be women, so I mentioned Dawn Rose, who designed our IIH solution, I mentioned Jill Strange, who runs the talent science organization. Half of my team is women, so, particularly inside of sort of the science area inside of Infor, I've been very pleased with the way we've built out some of that skill set. And, I'm also an active member of WIN, so the Women's Infor Network is something I'm very involved with, so, I meet a lot of people across our organization, a lot of women across our organization who have, are just really strong technology supporters, really intelligent, sort of go-getter type of people, and it's great to see that inside of Infor. I think there's a lot of work to be done, for sure. And you can always find stories, from other, whether it's coming out of Silicon Valley, or other places where you hear some, really sort of arcane sounding things that are still happening in the industry, and so, some of those things it's, it's disappointing, certainly to hear that. But I think, Van Jones said something this morning about how, and I liked the way he said it, and I'm not going to be able say it exactly, but he said something along the lines of, "The ground is there, the formation is starting, to get us moving in the right direction." and I think, I'm hopeful for the future, that we're heading in that way, and I think, you know, again, he sort of said something like, "Once the ground swell starts going in that direction, people will really jump in, and will see the benefits of being more diverse." Whether it's across, having more women, or having more people of color, however things expand, and that's just going to make us all better, and more efficient, and more productive, and I think that's a great thing. >> Well, and I think there's a spectrum, right? And on one side of the spectrum, there's intolerable and unacceptable behavior, which is just, should be zero tolerance in my opinion, and the passion of ours in theCUBE. The other side of that spectrum is inclusion, and it's a challenge that we have as a small company, and I remember having a conversation, earlier this year with an individual. And we talk about quotas, and I don't think that's the answer. Her comment was, "No, that's not the answer, you have to endeavor to reach deeper beyond your existing network." Which is hard sometimes for us, 'cause you're so busy, you're running around, it's like okay it's the convenient thing to do. But you got to peel the onion on that network, and actually take the extra time and make it a priority. I mean, your thoughts on that? >> No, I think that's a good point, I mean, if I think about who my circle is, right? And the people that I know and I interact with. If I only reach out to the smallest group of people, I'm not getting really out beyond my initial circle. So I think that's a very good point, and I think that that's-- we have to find ways to be more interactive, and pull from different areas. And I think it's interesting, so coming back to data science for a minute, if you sort of think about the evolution of where we got to, how we got to today where, now we're really pulling people from science areas, and math areas, and technology areas, and data scientists are coming from lots of places, right? And you don't always have to have a PhD, right? You don't necessary have to come up through that system to be a good data scientist, and I think, to see more of that, and really people going beyond, beyond just sort of the traditional circles and the traditional paths to really find people that you wouldn't normally identify, to bring into that, that path, is going to help us, just in general, be more diverse in our approach. >> Well it certainly it seems like it's embedded in the company culture. I think the great reason for you to be so optimistic going forward, not only about your job, but about the way companies going into that doing your job. >> What would you advise, young people generally, who want to crack into the data science field, but specifically, women, who have clearly, are underrepresented in technology? >> Yeah, so, I think the, I think we're starting to see more and more women enter the field, again it's one of those, people know it, and so there's less of a-- because people are aware of it, there's more tendency to be more inclusive. But I definitely think, just go for it, right? I mean if it's something you're interested in, and you want to try it out, go to a coding camp, and take a science class, and there's so many online resources now, I mean there's, the massive online courses that you can take. So, even if you're hesitant about it, there are ways you can kind of be at home, and try it out, and see if that's the right thing for you. >> Just dip your toe in the water. >> Yes, exactly, exactly! Try it out and see, and then just decide if that's the right thing for you, but I think there's a lot of different ways to sort of check it out. Again, you can take a course, you can actually get a degree, there's a wide range of things that you can do to kind of experiment with it, and then find out if that's right for you. >> And if you're not happy with the hiring opportunities out there, just start a company, that's my advice. >> That's right. (laughing together) >> Agreed, I definitely agree! >> We thank you-- we appreciate the time, and great advice, too. >> Thank you so much. >> Leigh Martin joining us here at Inforum 18, we are live in Washington, D.C., you're watching the exclusive coverage, right here, on theCUBE. (bubbly music)