(upbeat music) >> Hey everyone. Welcome back to theCUBE's live coverage of Women in Data Science, WiDS 2022, coming to live from Stanford University. I'm Lisa Martin. My next guest is here. Nandi Leslie, Doctor Nandi Leslie, Senior Engineering Fellow at Raytheon Technologies. Nandi, it's great to have you on the program. >> Oh it's my pleasure, thank you. >> This is your first WiDS you were saying before we went live. >> That's right. >> What's your take so far? >> I'm absolutely loving it. I love the comradery and the community of women in data science. You know, what more can you say? It's amazing. >> It is. It's amazing what they built since 2015, that this is now reaching 100,000 people 200 online event. It's a hybrid event. Of course, here we are in person, and the online event going on, but it's always an inspiring, energy-filled experience in my experience of WiDS. >> I'm thoroughly impressed at what the organizers have been able to accomplish. And it's amazing, that you know, you've been involved from the beginning. >> Yeah, yeah. Talk to me, so you're Senior Engineering Fellow at Raytheon. Talk to me a little bit about your role there and what you're doing. >> Well, my role is really to think about our customer's most challenging problems, primarily at the intersection of data science, and you know, the intersectional fields of applied mathematics, machine learning, cybersecurity. And then we have a plethora of government clients and commercial clients. And so what their needs are beyond those sub-fields as well, I address. >> And your background is mathematics. >> Yes. >> Have you always been a math fan? >> I have, I actually have loved math for many, many years. My dad is a mathematician, and he introduced me to, you know mathematical research and the sciences at a very early age. And so, yeah, I went on, I studied in a math degree at Howard undergrad, and then I went on to do my PhD at Princeton in applied math. And later did a postdoc in the math department at University of Maryland. >> And how long have you been with Raytheon? >> I've been with Raytheon about six years. Yeah, and before Raytheon, I worked at a small to midsize defense company, defense contracting company in the DC area, systems planning and analysis. And then prior to that, I taught in a math department where I also did my postdoc, at University of Maryland College Park. >> You have a really interesting background. I was doing some reading on you, and you have worked with the Navy. You've worked with very interesting organizations. Talk to the audience a little bit about your diverse background. >> Awesome yeah, I've worked with the Navy on submarine force security, and submarine tracking, and localization, sensor performance. Also with the Army and the Army Research Laboratory during research at the intersection of machine learning and cyber security. Also looking at game theoretic and graph theoretic approaches to understand network resilience and robustness. I've also supported Department of Homeland Security, and other government agencies, other governments, NATO. Yeah, so I've really been excited by the diverse problems that our various customers have you know, brought to us. >> Well, you get such great experience when you are able to work in different industries and different fields. And that really just really probably helps you have such a much diverse kind of diversity of thought with what you're doing even now with Raytheon. >> Yeah, it definitely does help me build like a portfolio of topics that I can address. And then when new problems emerge, then I can pull from a toolbox of capabilities. And, you know, the solutions that have previously been developed to address those wide array of problems, but then also innovate new solutions based on those experiences. So I've been really blessed to have those experiences. >> Talk to me about one of the things I heard this morning in the session I was able to attend before we came to set was about mentors and sponsors. And, you know, I actually didn't know the difference between that until a few years ago. But it's so important. Talk to me about some of the mentors you've had along the way that really helped you find your voice in research and development. >> Definitely, I mean, beyond just the mentorship of my my family and my parents, I've had amazing opportunities to meet with wonderful people, who've helped me navigate my career. One in particular, I can think of as and I'll name a number of folks, but Dr. Carlos Castillo-Chavez was one of my earlier mentors. I was an undergrad at Howard University. He encouraged me to apply to his summer research program in mathematical and theoretical biology, which was then at Cornell. And, you know, he just really developed an enthusiasm with me for applied mathematics. And for how it can be, mathematics that is, can be applied to epidemiological and theoretical immunological problems. And then I had an amazing mentor in my PhD advisor, Dr. Simon Levin at Princeton, who just continued to inspire me, in how to leverage mathematical approaches and computational thinking for ecological conservation problems. And then since then, I've had amazing mentors, you know through just a variety of people that I've met, through customers, who've inspired me to write these papers that you mentioned in the beginning. >> Yeah, you've written 55 different publications so far. 55 and counting I'm sure, right? >> Well, I hope so. I hope to continue to contribute to the conversation and the community, you know, within research, and specifically research that is computationally driven. That really is applicable to problems that we face, whether it's cyber security, or machine learning problems, or others in data science. >> What are some of the things, you're giving a a tech vision talk this afternoon. Talk to me a little bit about that, and maybe the top three takeaways you want the audience to leave with. >> Yeah, so my talk is entitled "Unsupervised Learning for Network Security, or Network Intrusion Detection" I believe. And essentially three key areas I want to convey are the following. That unsupervised learning, that is the mathematical and statistical approach, which tries to derive patterns from unlabeled data is a powerful one. And one can still innovate new algorithms in this area. Secondly, that network security, and specifically, anomaly detection, and anomaly-based methods can be really useful to discerning and ensuring, that there is information confidentiality, availability, and integrity in our data >> A CIA triad. >> There you go, you know. And so in addition to that, you know there is this wealth of data that's out there. It's coming at us quickly. You know, there are millions of packets to represent communications. And that data has, it's mixed, in terms of there's categorical or qualitative data, text data, along with numerical data. And it is streaming, right. And so we need methods that are efficient, and that are capable of being deployed real time, in order to detect these anomalies, which we hope are representative of malicious activities, and so that we can therefore alert on them and thwart them. >> It's so interesting that, you know, the amount of data that's being generated and collected is growing exponentially. There's also, you know, some concerning challenges, not just with respect to data that's reinforcing social biases, but also with cyber warfare. I mean, that's a huge challenge right now. We've seen from a cybersecurity perspective in the last couple of years during the pandemic, a massive explosion in anomalies, and in social engineering. And companies in every industry have to be super vigilant, and help the people understand how to interact with it, right. There's a human component. >> Oh, for sure. There's a huge human component. You know, there are these phishing attacks that are really a huge source of the vulnerability that corporations, governments, and universities face. And so to be able to close that gap and the understanding that each individual plays in the vulnerability of a network is key. And then also seeing the link between the network activities or the cyber realm, and physical systems, right. And so, you know, especially in cyber warfare as a remote cyber attack, unauthorized network activities can have real implications for physical systems. They can, you know, stop a vehicle from running properly in an autonomous vehicle. They can impact a SCADA system that's, you know there to provide HVAC for example. And much more grievous implications. And so, you know, definitely there's the human component. >> Yes, and humans being so vulnerable to those social engineering that goes on in those phishing attacks. And we've seen them get more and more personal, which is challenging. You talking about, you know, sensitive data, personally identifiable data, using that against someone in cyber warfare is a huge challenge. >> Oh yeah, certainly. And it's one that computational thinking and mathematics can be leveraged to better understand and to predict those patterns. And that's a very rich area for innovation. >> What would you say is the power of computational thinking in the industry? >> In industry at-large? >> At large. >> Yes, I think that it is such a benefit to, you know, a burgeoning scientist, if they want to get into industry. There's so many opportunities, because computational thinking is needed. We need to be more objective, and it provides that objectivity, and it's so needed right now. Especially with the emergence of data, and you know, across industries. So there are so many opportunities for data scientists, whether it's in aerospace and defense, like Raytheon or in the health industry. And we saw with the pandemic, the utility of mathematical modeling. There are just so many opportunities. >> Yeah, there's a lot of opportunities, and that's one of the themes I think, of WiDS, is just the opportunities, not just in data science, and for women. And there's obviously even high school girls that are here, which is so nice to see those young, fresh faces, but opportunities to build your own network and your own personal board of directors, your mentors, your sponsors. There's tremendous opportunity in data science, and it's really all encompassing, at least from my seat. >> Oh yeah, no I completely agree with that. >> What are some of the things that you've heard at this WiDS event that inspire you going, we're going in the right direction. If we think about International Women's Day tomorrow, "Breaking the Bias" is the theme, do you think we're on our way to breaking that bias? >> Definitely, you know, there was a panel today talking about the bias in data, and in a variety of fields, and how we are, you know discovering that bias, and creating solutions to address it. So there was that panel. There was another talk by a speaker from Pinterest, who had presented some solutions that her, and her team had derived to address bias there, in you know, image recognition and search. And so I think that we've realized this bias, and, you know, in AI ethics, not only in these topics that I've mentioned, but also in the implications for like getting a loan, so economic implications, as well. And so we're realizing those issues and bias now in AI, and we're addressing them. So I definitely am optimistic. I feel encouraged by the talks today at WiDS that you know, not only are we recognizing the issues, but we're creating solutions >> Right taking steps to remediate those, so that ultimately going forward. You know, we know it's not possible to have unbiased data. That's not humanly possible, or probably mathematically possible. But the steps that they're taking, they're going in the right direction. And a lot of it starts with awareness. >> Exactly. >> Of understanding there is bias in this data, regardless. All the people that are interacting with it, and touching it, and transforming it, and cleaning it, for example, that's all influencing the veracity of it. >> Oh, for sure. Exactly, you know, and I think that there are for sure solutions are being discussed here, papers written by some of the speakers here, that are driving the solutions to the mitigation of this bias and data problem. So I agree a hundred percent with you, that awareness is you know, half the battle, if not more. And then, you know, that drives creation of solutions >> And that's what we need the creation of solutions. Nandi, thank you so much for joining me today. It was a pleasure talking with you about what you're doing with Raytheon, what you've done and your path with mathematics, and what excites you about data science going forward. We appreciate your insights. >> Thank you so much. It was my pleasure. >> Good, for Nandi Leslie, I'm Lisa Martin. You're watching theCUBE's coverage of Women in Data Science 2022. Stick around, I'll be right back with my next guest. (upbeat flowing music)

(energetic music) >> From Washington D.C., It's theCUBE! Covering ScienceLogic Symposium 2019. Brought to you by ScienceLogic. >> Welcome back to TheCUBE's coverage of ScienceLogic Symposium 2019, I'm Stu Miniman, and we're here at the Ritz-Carlton in Washington, D.C. Happy to welcome to the program two first-time guests from ScienceLogic, to my left is Leslie Minnix-Wolfe, who is the Senior Director of Product Marketing. And to her left, is Russ Elsner, who's the Senior Director of Product Strategy. Thank you so much for joining us. >> Thank you sir. >> Good, good to be here. >> All right, so Leslie let's start with you. Talk a lot about the product, a whole lot of announcements, Big Ben on the keynote this morning. Everybody's in, getting a little bit more of injection in the keynote today. Tell us a little bit about your roll, what you work on inside of ScienceLogic. >> Okay, so I am basically responsible for enterprise product marketing. So my job is to spin the story and help our sales guys successfully sell the product. >> All right, and Russ. >> I'm part of the product strategy team. So, I have product management responsibilities. I work a lot with the analytics and applications. And I spend a lot of time in the field with our customers. >> All right so, Leslie let's start with enterprise, the keynote this morning. The themes that I hear at many of the shows, you know we talk about things like digital transformation. But, we know the only constant in our environment is change. You know, it's good. I've actually talked to a couple of your customers and one of them this morning he's like "Look, most people don't like change. "I do, I'm embracing it I'm digging in, It's good." But, you know, we have arguments sometimes in analyst circles. And it's like are customers moving any faster. My peers that have been in the industry longer, they're like, Hogwash Stu. They never move faster they don't want change, we can't get them to move anything. I'm like, come on, if they don't the alternative is often, You're going to be... You know, you're competitors are going to take advantage of data and do things better. So, bring us a little bit of insight as what you're hearing from your customers both here and in your day to day. >> Sure, yeah, change is constant now and so one of the big challenges that our customers are facing is how do I keep up with it. The traditional manual processes that they've had in place for years are just not sufficient anymore. So they're looking for ways to move faster, to automate some of the processes that they've been doing manually. To find ways to free up resources to focus on things that do require a human to be involved. But they really need to have more automation in their day to day operations. >> All right, so Russ when I look at this space you know, tooling, monitoring has been something that in my career, has been a little bit messy. (laughter) Guess a little bit of an understatement even. It's an interesting... When I look at, kind of, that balance between what's happening in the infrastructure space and the application space. I went through, one of your partners over here is like "from legacy to server lists and how many weeks." (laughter) And I'm like okay that sounds good on a slide but, these things take awhile. >> Absolutely. Bring us inside a little bit, kind of the the application space an how that marries with the underlying pieces and monitoring. >> Yeah, you have a lot of transformations happening. There's a lot of new technologies and trends happening. You hear about server lists or containers or microservices. And that does represent a part of the application world. There are applications being written with those technologies. But, one of the things is that those applications don't live in isolation. It's that there part of broader business services and we're not rewriting everything and so the new shiny application and the new framework has to work with the old legacy application. So, a big piece of what we see is how do we collapse those different silos of information? How do we merge that data into something meaningful? You can have the greatest Kubernetes based microservice application but, if it requires a SAP instance it's on PRIM it's on Bare Metal. Those things need to work together. So, how do you work with an environment that's like that? Enterprise, just by it's nature is incredibly heterogeneous, lot's of different technologies and that's not going to change. >> Yeah. It's going to be that way. >> You're preaching to the choir, here. You know, IT it always seems additive the answer is always and. And, unfortunately, nothing ever dies. By the way you want to run that wonderful Kubernetes Docker stuff and everything. I could do it on a mainframe with Z Linux. So, from that environment to the latest greatest hypercloud environment >> Right. Talk a little bit about your customers. Most of them probably have hundreds of applications. They're working through that portfolio. What goes where, how do I manage all of those various pieces, and not kill my staff? (laughter) One of the things we're spending a lot of time with this, is that obviously, we come from a background of infrastructure management. So, we understand the different technologies different layers and the heterogeneous nature and on top of that runs application. So they have their own data and there's APM space. So we're seeing a lot of interest in the work we're doing with taking our view of the infrastructure and marrying it to the application view that we're getting from tools like Appdynamics or Dynatrace or New Relic. And so, we're able to take that data and leverage it on top of the infrastructure to give you a single view which aids in root cause analysis, capacity planning and all the different things that people want to do. Which lead us to automation. So, this idea of merging data from lots of sources is a big theme for us. >> All right so, Leslie who are some of the key constituents that you're talking to, to messaging to. In the industry we talked about silos for so many time. And now it's like oh, we're going to get architects and generalists. And you know cloud changes everything, yes and no. (laughter) We understand where budgets sit for most CIO's today. So, bring us inside what you're seeing. >> Sure. Yeah, we're seeing a tremendous change. Where before we use to talk more to the infrastructure team, to the folks managing the servers, the storage the network. We're really seeing a broader audience. And a multiple constituent. We're looking at directors, VP's, CIO's, CEO's, architects. We're starting to see more people that are tools managers, folks that are involved in the application side of the house. So, it's really diverged. So, you're not going in and talking to one person you're talking to lots of different teams, lots of different organizations that need to work together. To Russ's point in about being able to bring all this data together. As you bring it together, those different stakeholders have more visibility into each others areas. And they also have a better understanding of what the impact is when something goes down in the infrastructure, how it effects the app and vice versa. >> Leslie, the other thing I'm wondering if you can help me squint through, when I looked at the landscape, it's, you know, my ITSM's I've got my logging, I've got all my various tools and silos. When I hear something like, actually, your CEO Dave just said "Oh, we just had a customer that replaced 50 tools." with there it's like, How do you target that? How does a customer know that they have a solution that they have a challenge that you fit, Because, you understand, you can't be all things to all people. You've got certain partners that might claim that kind of thing. >> Right But, where you fit in the marketplace how do you balance that? >> Well, so I think what we're seeing now is that there have been some big players for a long time. What we refer to fondly as the Big Four. And those companies really haven't evolved to the extent that they can support the latest technology. Certainly at the speed with which organizations are adopting them. So, they might be able to support some of the legacy but they've really become so cumbersome, so complicated and difficult to maintain people are wanting to move away from them. I would say five years ago, most organizations weren't willing to move down that path. But with some of the recent acquisitions, The Broadcom acquisition, Microfocus acquisition. You're seeing that more organizations are looking to replace those tools in their entirety. And as a result of that they're looking at how can I minimize my tool set. I'm not going to get rid of everything and only have one vendor. But, how do I pick the right tools and bring them together. And this is one of the areas where we do extremely well in that we can bring in data, we can integrate in other tools, we can give you the full picture. But, we're kind of that hub, that central. And I think we heard that earlier today from Bailey at Cisco, where he talked about ScienceLogic is really the core to their monitoring and management environment, because we're bringing the data and we're feeding the data in to other systems as well as managing it within ScienceLogic. >> Russ, I actually heard, data was emphasized more that I expect. I know enough about the management and monitoring space. We understand data was important to that, I'm a networking guy by background, we've been talking about leveraging the data for network and using some automation and things like that but it's a little bit different. Can you talk some about those relationships to data? We understand data's going to be everywhere and customers actually wrapping my arms around it make sure I can manage it, compliance and to hopefully get value out of that is one of the most important things in today. >> Absolutely, so one of the things we stress a lot when we talk about data, it use to be that data was hard to come by. We were data poor and so how do we get... We don't have a probe there so how do we get this data, Do we need agent? That's different now, data is... We are drowning in data, we have so much data. So, really the key is to give that data context. And so for us that means a lot of structure, and topology and dependencies across the layers of abstraction, across the application. And we think that's really the key to taking this, just vast unstructured mess of data that isn't useful to the business and actually be able to take... Apply analytics, and actually take action, and ultimately drive automation by learning and maintaining that structure in real time automatically, because that's something a human can't do. So, you need machine help, you need to automate that. >> So, Leslie, there was in the keynote this morning that to start discussion of the AI Ops maturity model >> Right >> And one of the things struck me is there was not a single person in the poll that said, yes I've gone fully automated. And first, there's the maturity of the technology, the term and where we are. But, there's also that, let's put it on the table. That fear sometimes, is to "Oh my gosh, the machines are taking our jobs" (laughter) You know, we laugh, but it is something that needs to be addressed. How are you addressing that, Where are your customers with at least that willingness, because I use to run operations for a number of years, and I told my team, look you're going to have more work next year, and you're going to have more things change, so if you can't simplify, automate. Get rid of things, I've got to have somebody helping me, and boy those robots would be a good help there. >> What we're seeing is, I mean let's be real, people don't like to do the mundane tasks, right. So you think about, When you report an issue to the service desk. Do you really want to open that ticket? Do you want to enter in all that information yourself? Do you want to provide all the details that they need in order to help you? No. People don't do it they put in the bare minimum and then what ends up happening is there's this back and forth, as they try collect more information. It's things like that, that you want to automate. You want to be able to take that burden off of the individuals And do the things, or at least allow them to do the things that they really need to do. The things that require their intelligence. So, we can do things like clean up storage disk space when your starting to run out of disk space. Or we can restart a service, or we might apply a configuration change that we know that is inconsistent in environment. So, there's lots of things like that that you can automate without actually replacing the individual. You're just freeing them up to do more high level thinking. >> Russ, anything else along the automation line. Great customer examples or any successes that you've seen that are worth sharing? >> Yeah, automation also comes in the form of connecting the breadcrumbs. So, we have a great example. A customer we worked with, they had an EPM tool, one of the great ones, you know, top of the magic quadrant kind of thing, and it kept on reporting code problems. The applications going down, affecting revenue, huge visibility. And it's saying code problem, code problem ,code problem. But the problem is jumping around. Sometimes it's here, sometimes it's there. So, it seemed like a ghost. So, when we connected that data, the APN data with the V center data and the network data what it turned out was, there was a packet loss in the hypervisor. So, it was actually network outage that was manifesting itself as a code problem, and as soon as they saw that, they said what's causing that network problem? They immediately found a big spike of traffic and were able to solve it. They always had the data. They had the network data, they had the VMware data they had the JVM data. They didn't know to connect the dots. And so, by us putting it right next to each other we connected the dots, and it was a human ultimately that said I know what's wrong, I can fix that. But it took them 30 seconds to solve a problem that they had been chasing after for months. That's a form of automation too is get the information to the human, so that they can make a smart decision. That's automation just as much as rebooting a >> Exactly server or cleaning a disk >> Well right, It's The Hitchhikers Guide to the Galaxy. Sometimes, the answers are easy if I know what question to ask. >> Exactly, yes. (laughter) >> And that's something we've seen from data scientists too. That's what their expertise is, is to help find that. All right, Leslie give us a little view forward. We heard a little bit, so many integrations, the AI ops journey. What should customers be looking for forward? What are they asking you, to help bring them along that journey? >> Oh gosh. They're asking us to make it easier on all counts. Whether it's easier to collect the data, easier to add the context to the data, easier to analyze the data. So, we're putting more and more analytics into our platform. So that their not having to do a lot of the analysis themselves. There's, as you said earlier, there's the folks that are afraid they're going to lose their job because the robots or the machines are taking over. That's not really where I see it. It's just that we're bringing the automation in ways and the analytics in ways that they don't want to have to do, so that they can look at it and solve the really gnarly problems and start focusing on areas that are not necessarily going to be automatable or predictable. It's the things that are unusual that their going to have to get involved in as opposed to the things that are traditional and constant. So, Russ, I'd love for you to comment on the same question. And just a little bit of feedback I got talking to some of the customers is they like directionally where it's going, but the term they through out was dynamic. Because, if you talk about cloud you talk about containers. Down the road things like serverless. It's if it pulls every five minutes it's probably out of date. >> oh, Absolutely. I remember back when we talked big data, real time was one of those misnomers that got thrown out there. Really, what we always said is what real time needs to mean is the data in the right place to the right people to solve the issue >> Absolutely. >> Exactly. So, where do you guys see this directionally, and how do you get more dynamic? >> Well see, dynamic exists in a bunch of different ways. How immediate is the data? How accurate is the dependency map, and that's changing and shifting all the time. So, we have to keep that up to date automatically in our product. It's also the analytics that get applied the recommendations you make. And one of the things you can talk to data scientists and they can build a model, train a model, test a model and find something. But if they find something that was true three weeks ago it's irrelevant. So, we need to build systems that can do this in real time. That they can in real time, meaning, gather data in real time, understand the context in real time, recognize the behavior and make a recommendation or take an action. There's a lot of stuff that we have to do to get there. We have a lot of the pieces in place, it's a really cool time in the industry right now because, we have the tools we have the technology. And it's a need that needs to be filled. That's really where we're spending our energy is completing that loop. Closed loop system that can help humans do their jobs better and in a more automated way. >> Awesome. Well, Leslie and Russ, thanks so much for sharing your visibility into what customers are doing and the progress with your platforms. >> All right, thank you Stu. >> And we'll be back with more coverage here from ScienceLogic Symposium 2019. I'm Stu Miniman, and thank you for watching theCUBE. (energetic music)

(hopeful futuristic music) >> Hey welcome back everybody, Jeff Frick here with theCUBE. We are really excited to have this cube conversation here in the Palo Alto studio with a real close friend of theCUBE, and repeat alumni, Leslie Berlin. I want to get her official title; she's the historian for the Silicon Valley archive at Stanford. Last time we talked to Leslie, she had just come out with a book about Robert Noyce, and the man behind the microchip. If you haven't seen that, go check it out. But now she's got a new book, it's called "Troublemakers," which is a really appropriate title. And it's really about kind of the next phase of Silicon Valley growth, and it's hitting bookstores. I'm sure you can buy it wherever you can buy any other book, and we're excited to have you on Leslie, great to see you again. >> So good to see you Jeff. >> Absolutely, so the last book you wrote was really just about Noyce, and obviously, Intel, very specific in, you know, the silicon in Silicon Valley obviously. >> Right yeah. >> This is a much, kind of broader history with again just great characters. I mean, it's a tech history book, but it's really a character novel; I love it. >> Well thanks, yeah; I mean, I really wanted to find people. They had to meet a few criteria. They had to be interesting, they had to be important, they had to be, in my book, a little unknown; and most important, they had to be super-duper interesting. >> Jeff Frick: Yeah. >> And what I love about this generation is I look at Noyce's generation of innovators, who sort of working in the... Are getting their start in the 60s. And they really kind of set the tone for the valley in a lot of ways, but the valley at that point was still just all about chips. And then you have this new generation show up in the 70s, and they come up with the personal computer, they come up with video games. They sort of launch the venture capital industry in the way we know it now. Biotech, the internet gets started via the ARPANET, and they kind of set the tone for where we are today around the world in this modern, sort of tech infused, life that we live. >> Right, right, and it's interesting to me, because there's so many things that kind of define what Silicon Valley is. And of course, people are trying to replicate it all over the place, all over the world. But really, a lot of those kind of attributes were started by this class of entrepreneurs. Like just venture capital, the whole concept of having kind of a high risk, high return, small carve out from an institution, to put in a tech venture with basically a PowerPoint and some faith was a brand new concept back in the day. >> Leslie Berlin: Yeah, and no PowerPoint even. >> Well that's right, no PowerPoint, which is probably a good thing. >> You're right, because we're talking about the 1970s. I mean, what's so, really was very surprising to me about this book, and really important for understanding early venture capital, is that now a lot of venture capitalists are professional investors. But these venture capitalists pretty much to a man, and they were all men at that point, they were all operating guys, all of them. They worked at Fairchild, they worked at Intel, they worked at HP; and that was really part of the value that they brought to these propositions was they had money, yes, but they also had done this before. >> Jeff Frick: Right. >> And that was really, really important. >> Right, another concept that kind of comes out, and I think we've seen it time and time again is kind of this partnership of kind of the crazy super enthusiastic visionary that maybe is hard to work with and drives everybody nuts, and then always kind of has the other person, again, generally a guy in this time still a lot, who's kind of the doer. And it was really the Bushnell-Alcorn story around Atari that really brought that home where you had this guy way out front of the curve but you have to have the person behind who's actually building the vision in real material. >> Yeah, I mean I think something that's really important to understand, and this is something that I was really trying to bring out in the book, is that we usually only have room in our stories for one person in the spotlight when innovation is a team sport. And so, the kind of relationship that you're talking about with Nolan Bushnell, who started Atari, and Al Alcorn who was the first engineer there, it's a great example of that. And Nolan is exactly this very out there person, big curly hair, talkative, outgoing guy. After Atari he starts Chuck E. Cheese, which kind of tells you everything you need to know about someone who's dreaming up Chuck E. Cheese, super creative, super out there, super fun oriented. And you have working with him, Al Alcorn, who's a very straight laced for the time, by which I mean, he tried LSD but only once. (cumulative laughing) Engineer, and I think that what's important to understand is how much they needed each other, because the stories are so often only about the exuberant out front guy. To understand that those are just dreams, they are not reality without these other people. And how important, I mean, Al Alcorn told me look, "I couldn't have done this without Nolan, "kind of constantly pushing me." >> Right, right. >> And then in the Apple example, you actually see a third really important person, which to me was possibly the most exciting part of everything I discovered, which was the importance of the guy named Mike Markkula. Because in Jobs you had the visionary, and in Woz you had the engineer, but the two of them together, they had an idea, they had a great product, the Apple II, but they didn't have a company. And when Mike Markkula shows up at the garage, you know, Steve Jobs is 21 years old. >> Jeff Frick: Right. >> He has had 17 months of business experience in his life, and it's all his attack for Atari, actually. And so how that company became a business is due to Mike Markkula, this very quiet guy, very, very ambitious guy. He talked them up from a thousand stock options at Intel to 20,000 stock options at Intel when he got there, just before the IPO, which is how he could then turn around and help finance >> Jeff Frick: Right. >> The birth of Apple. And he pulled into Apple all of the chip people that he had worked with, and that is really what turned Apple into a company. So you had the visionary, you had the tech guy, you also needed a business person. >> But it's funny though because in that story of his visit to the garage he's specifically taken by the engineering elegance of the board >> Leslie Berlin: Right. >> That Woz put together, which I thought was really neat. So yeah, he's a successful business man. Yes he was bringing a lot of kind of business acumen value to the opportunity, but what struck him, and he specifically talks about what chips he used, how he planned for the power supply. Just very elegant engineering stuff that touched him, and he could recognize that they were so far ahead of the curve. And I think that's such another interesting point is that things that we so take for granted like mice, and UI, and UX. I mean the Atari example, for them to even think of actually building it that would operate with a television was just, I mean you might as well go to Venus, forget Mars, I mean that was such a crazy idea. >> Yeah, I mean I think Al ran to Walgreens or something like that and just sort of picked out the closest t.v. to figure out how he could build what turned out to be Pong, the first super successful video game. And I mean, if you look also at another story I tell is about Xerox Park; and specifically about a guy named Bob Taylor, who, I know I keep saying, "Oh this might be my favorite part." But Bob Taylor is another incredible story. This is the guy who convinced DARPA to start, it was then called ARPA, to start the ARPANET, which became the internet in a lot of ways. And then he goes on and he starts the computer sciences lab at Xerox Park. And that is the lab that Steve Jobs comes to in 1979, and for the first time sees a GUI, sees a mouse, sees Windows. And this is... The history behind that, and these people all working together, these very sophisticated Ph.D. engineers were all working together under the guidance of Bob Taylor, a Texan with a drawl and a Master's Degree in Psychology. So what it takes to lead, I think, is a really interesting question that gets raised in this book. >> So another great personality, Sandra Kurtzig. >> Yeah. >> I had to look to see if she's still alive. She's still alive. >> Leslie Berlin: Yeah. >> I'd love to get her in some time, we'll have to arrange for that next time, but her story is pretty fascinating, because she's a woman, and we still have big women issues in the tech industry, and this is years ago, but she was aggressive, she was a fantastic sales person, and she could code. And what was really interesting is she started her own software company. The whole concept of software kind of separated from hardware was completely alien. She couldn't even convince the HP guys to let her have access to a machine to write basically an NRP system that would add a ton of value to these big, expensive machines that they were selling. >> Yeah, you know what's interesting, she was able to get access to the machine. And HP, this is not a well known part of HP's history, is how important it was in helping launch little bitty companies in the valley. It was a wonderful sort of... Benefited all these small companies. But she had to go and read to them the definition of what an OEM was to make an argument that I am adding value to your machines by putting software on it. And software was such an unknown concept. A, people who heard she was selling software thought she was selling lingerie. And B, Larry Ellison tells a hilarious story of going to talk to venture capitalists about... When he's trying to start Oracle, he had co-founders, which I'm not sure everybody knows. And he and his co-founders were going to try to start Oracle, and these venture capitalists would, he said, not only throw him out of the office for such a crazy idea, but their secretaries would double check that he hadn't stolen the copy of Business Week off the table because what kind of nut job are we talking to here? >> Software. >> Yeah, where as now, I mean when you think about it, this is software valley. >> Right, right, it's software, even, world. There's so many great stories, again, "Troublemakers" just go out and get it wherever you buy a book. The whole recombinant DNA story and the birth of Genentech, A, is interesting, but I think the more kind of unique twist was the guy at Stanford, who really took it upon himself to take the commercialization of academic, generated, basic research to a whole 'nother level that had never been done. I guess it was like a sleepy little something in Manhattan they would send some paper to, but this guy took it to a whole 'nother level. >> Oh yeah, I mean before Niels showed up, Niels Reimers, he I believe that Stanford had made something like $3,000 off of the IP from its professors and students in the previous decades, and Niels said "There had to be a better way to do this." And he's the person who decided, we ought to be able to patent recombinant DNA. And one of the stories that's very, very interesting is what a cultural shift that required, whereas engineers had always thought in terms of, "How can this be practical?" For biologists this was seen as really an unpleasant thing to be doing, don't think about that we're about basic research. So in addition to having to convince all sorts of government agencies and the University of California system, which co-patented this, to make it possible, just almost on a paperwork level... >> Right. >> He had to convince the scientists themselves. And it was not a foregone conclusion, and a lot of people think that what kept the two named co-inventors of recombinant DNA, Stan Cohen and Herb Boyer, from winning the Nobel Prize is that they were seen as having benefited from the work of others, but having claimed all the credit, which is not, A, isn't fair, and B, both of those men had worried about that from the very beginning and kept saying, "We need to make sure that this includes everyone." >> Right. >> But that's not just the origins of the biotech industry in the valley, the entire landscape of how universities get their ideas to the public was transformed, and that whole story, there are these ideas that used to be in university labs, used to be locked up in the DOD, like you know, the ARPANET. And this is the time when those ideas start making their way out in a significant way. >> But it's this elegant dance, because it's basic research, and they want it to benefit all, but then you commercialize it, right? And then it's benefiting the few. But if you don't commercialize it and it doesn't get out, you really don't benefit very many. So they really had to walk this fine line to kind of serve both masters. >> Absolutely, and I mean it was even more complicated than that, because researchers didn't have to pay for it, it was... The thing that's amazing to me is that we look back at these people and say, "Oh these are trailblazers." And when I talked to them, because something that was really exciting about this book was that I got to talk to every one of the primary characters, you talk to them, and they say, "I was just putting one foot in front of the other." It's only when you sort of look behind them years later that you see, "Oh my God, they forged a completely new trail." But here it was just, "No I need to get to here, "and now I need to get to here." And that's what helped them get through. That's why I start the book with the quote from Raiders of the Lost Ark where Sallah asks Indy, you know basically, how are you going to stop, "Stop that car." And he says, "How are you going to do it Indy?" And Indy says, "I don't know "I'm making it up as I go along." And that really could almost be a theme in a lot of cases here that they knew where they needed to get to, and they just had to make it up to get there. >> Yeah, and there's a whole 'nother tranche on the Genentech story; they couldn't get all of the financing, so they actually used outsourcing, you know, so that whole kind of approach to business, which was really new and innovative. But we're running out of time, and I wanted to follow up on the last comment that you made. As a historian, you know, you are so fortunate or smart to pick your field that you can talk to the individual. So, I think you said, you've been doing interviews for five or six years for this book, it's 100 pages of notes in the back, don't miss the notes. >> But also don't think the book's too long. >> No, it's a good book, it's an easy read. But as you reflect on these individuals and these personalities, so there's obviously the stories you spent a lot of time writing about, but I'm wondering if there's some things that you see over and over again that just impress you. Is there a pattern, or is it just, as you said, just people working hard, putting one step in front of the other, and taking those risks that in hindsight are so big? >> I would say, I would point to a few things. I'd point to audacity; there really is a certain kind of adventurousness, at an almost unimaginable level, and persistence. I would also point to a third feature at that time that I think was really important, which was for a purpose that was creative. You know, I mean there was the notion, I think the metaphor of pioneering is much more what they were doing then what we would necessarily... Today we would call it disruption, and I think there's a difference there. And their vision was creative, I think of them as rebels with a cause. >> Right, right; is disruption the right... Is disruption, is that the right way that we should be thinking about it today or are just kind of backfilling the disruption after the fact that it happens do you think? >> I don't know, I mean I've given this a lot of thought, because I actually think, well, you know, the valley at this point, two-thirds of the people who are working in the tech industry in the valley were born outside of this country right now, actually 76 percent of the women. >> Jeff Frick: 76 percent? Wow. >> 76 percent of the women, I think it's age 25 to 44 working in tech were born outside of the United States. Okay, so the pioneering metaphor, that's just not the right metaphor anymore. The disruptive metaphor has a lot of the same concepts, but it has, it sounds to me more like blowing things up, and doesn't really thing so far as to, "Okay, what comes next?" >> Jeff Frick: Right, right. >> And I think we have to be sure that we continue to do that. >> Right, well because clearly, I mean, the Facebooks are the classic example where, you know, when he built that thing at Harvard, it was not to build a new platform that was going to have the power to disrupt global elections. You're trying to get dates, right? I mean, it was pretty simple. >> Right. >> Simple concept and yet, as you said, by putting one foot in front of the other as things roll out, he gets smart people, they see opportunities and take advantage of it, it becomes a much different thing, as has Google, as has Amazon. >> That's the way it goes, that's exactly... I mean, and you look back at the chip industry. These guys just didn't want to work for a boss they didn't like, and they wanted to build a transistor. And 20 years later a huge portion of the U.S. economy rests on the decisions they're making and the choices. And so I think this has been a continuous story in Silicon Valley. People start with a cool, small idea and it just grows so fast among them and around them with other people contributing, some people they wish didn't contribute, okay then what comes next? >> Jeff Frick: Right, right. >> That's what we figure out now. >> All right, audacity, creativity and persistence. Did I get it? >> And a goal. >> And a goal, and a goal. Pong, I mean was a great goal. (cumulative laughing) All right, so Leslie, thanks for taking a few minutes. Congratulations on the book; go out, get the book, you will not be disappointed. And of course, the Bob Noyce book is awesome as well, so... >> Thanks. >> Thanks for taking a few minutes and congratulations. >> Thank you so much Jeff. >> All right this is Leslie Berlin, I'm Jeff Frick, you're watching theCUBE. See you next time, thanks for watching. (electronic music)

(technology music) >> Announcer: Live from Las Vegas, it's theCUBE. Covering VMworld 2017. Brought to you by VMware and it's ecosystem partners. (upbeat music) >> Welcome back to VMworld, we are live on theCUBE, day two of our continuing coverage here. We've had a great day and half so far. I'm Lisa Martin, with my cohost Keith Townsend. We're excited to be joined by two guests from HPE, who are new to theCUBE. We have Leslie Maher, the VP of North American Enterprise Servers and Converged Systems. Welcome to theCUBE. >> Thank you. >> And Satya Vardharajan, Senior Director of Strategic Alliances, from HPE. Welcome to you as well. >> Oh thank you, Lisa. Thanks for inviting us here. >> Absolutely. So guys, let's talk to each of you, Leslie we'll start with you. Tell us about your role, especially in the converged side for HPE, and what you're doing with VMware. >> Great. So, my role at HPE is I'm responsible for enterprise servers and converged systems. What that means is, really our value products. A couple of my key responsibility, one is our HPE Synergy Composable Infrastructure, I'm you've probably heard, people talk about here at the event. We also have converged products around offerings like SAP Hana, and some of the more mission critical servers. So here a lot of the focus has been on Synergy, and our relationship once with VMware, but also solutions around vSan and vCloud Foundation, where Synergy provides some really unique capabilities. >> Yes. And Satya tell us about your role in alliances, and your GTM strategy with VMware. >> Sure, so I manage the VMware Alliance globally, at Hewlett Packard Enterprise, and this is a very strategic relationship for each HPE. We have a long history with VMware, over 15 years. We've had a great run with VMware. And we continue to innovate everyday. My role at HPE is to make sure that we keep the customer trends in our radar as we copartner and innovate together, with VMware. At VMworld 2017, we've got lots of great and exciting announcements. And we're more than happy to share with them as we get to the discussion today. >> Fantastic >> A big question around converged systems, you guys will have the hyper-converged guys on shortly, but converged systems have kind of gotten a bad rap over the past couple of years. Like, "Oh, that's the legacy." But as you mentioned, SAP Hana systems, what's the relationship between converged systems and Vmware? >> To your point, about seven years ago the industry tried to simplify IT operations by doing converged systems. And that was putting together servers, storage, and networking fabric; and putting it all together for our customers. Where the industry has moved to now, is more software defined capabilities, where not just putting those hardware pieces together but enabling them through software. So hyper-converged as one flavor of that, and we're hearing a lot about that here in the conference, and then at HPE, what we did is we innovated around the best of converged and hyper-converged. Put them together into a new category of infrastructure, called composable. Fully software defined, it does compute, storage, and fabric. And the essential idea here is that you can have any combinations of these elements, through a software defined capability. So it really extends the ability of hyper-converge to multiple workloads. We use this term Composable Infrastructure, in addition to supporting through other products we have hyper-converged. That's where we're seeing the market trend. >> So Leslie, that's a unique concept. This composable concept. It sounds a lot like virtualization. How does the two relate? Can I run virtualization on top of these Synergy systems? >> Great questions. Absolutely. In fact one of the key things with hyper-converged or composable, is the ability to really have virtualized workloads. In addition to that, with our composable infrastructure, we let you also run bare metal, as well as containerized workload. So you have a real range of workloads you can run in one set of infrastructure. And so we can support lots of workloads, different kinds of storage in the environment in fabric. So you have a real range of opportunity. >> Let's talk a little bit about composable and your target market. What is the key message? A lot of, sounds like, flexibility and agility within the technology. What's the key message to your VMware customers that are using the VMware software? >> Sue, with customers who are using VMware software, it's the flexibility. For example, with vSan, we've talked a lot about here at the conference, our relationship with VMware. And vSan is the ability to have software defined storage. And what our HP offerings allow you to do, is to have the ability to scale, compute, and storage independently. So giving you this very flexible environment, to grow your capacity. And then you manage it with this virtualized vSan, software defined storage from VMware. Very simple for our customers to really have a simple operations, and really flexible scale; is what these new applications are requiring. >> As you guys have been talking from a VMware and HPE perspective to customers. How are they receiving this message? >> From a customer standpoint it's very clear. They need to move to the hybrid IT model. And that's kind of the mandate that's coming. They see it on the horizon. But they also want to do it in a very cost effective manner. They want to do it in a very scalable, efficient, and automated manner. And that's when customers look to HPE and VMware to solve the problem for them. And that's where our flagship composable platform Synergy comes in to play. Marrying the benefits of Synergy with VMware's Cloud Foundation Software, which is a very seamless and automated way of consuming a software-defined stack. You bring those two together, what you get is industry's first composable platform, that lets you set up your private cloud, in less than minutes. There also gives you the ability to allocate and reallocate. Again, compute, storage, and networking resources independent of each other, at will. Creating this very flexible platform for traditional workloads, cloud native workloads, private cloud workloads. That's what we're hearing from the customers, they want us to step in, solve this problem. But also give them the visibility on top. We were at a partner panel earlier today, and one of the partners got up and said, "Look. This is all fantastic. You're making the right moves, "You're building the right solutions for us, "But help us understand how you're going to build "That uber layer of visibility, "and more detailed predicative analytics. "To help us get the whole picture. "Because I don't want to use third party tools." And those are the frontiers that HPE and VMworld will continue to work on together, and create new solutions for our customers. >> One of the things this morning that Michael Dell mentioned when he was onstage with Pat Gelsinger, was that Dell EMC and VMware where like peanut butter and chocolate. (Satya laughs) Such a good combination. (Leslie laughs) So one year post combination, has that strengthened the HP VMware partnership with this now umbrella under Dell Technologies? In the last minute or so, talk to us about how that's helped you maybe differentiate. >> Yeah, absolutely. Look, right from the beginning, as soon as the acquisition was announced, there was a lot of skepticism. And that was industry wide. Everybody said, "Hey. How is this going to impact "The rest of the ecosystem?" VMware made it a point, and Michael as well, made it a point to come outright and say, "Look. We don't want to mess with the ecosystem. "The neutrality is very important to us." To make VMware not only thrive, survive, all of the above. So we've seen that in the market. We don't see any material change in the relationship with VMware. Just a few proof points I want to throw out there, we are still the largest OEM for VMware from a product perspective. We have over 500,000 customers together, who make demands on running workloads. Our channel overlap is over 80%. All of this continues to be recognized. We won a lot of awards in the past, the last two years, we won the OEM Innovation Award of the Year. Partner of the Year Award. >> I saw it. I think I may have a photo with you. >> Yeah >> To talk about synergy, pun intended. (group laughs) >> No it is. >> It sounds like it's only a strengthening. >> It is strengthening. 'Cause we took it upon ourselves at HPE, as a challenge to say, "Hey, look. "I know there's a new owner. "But this shouldn't materially change your business, "Because there's so much business at stake." And we cannot ignore the customers. The demand for our joint products is stronger than ever. >> Absolutely. Great, you guys. Thank you so much for coming on and sharing what's new, what's going on, and the commitment to customers. Outstanding Leslie and Satya. Thank you so much. You're now in the category of CUBE alumni. >> Alright, thank you so much. >> That's great. >> We look forward to having you back. >> Thank you. >> And for my cohost, Keith Townsend, I am Lisa Martin. You've been watching live continuing coverage by theCUBE of VMworld 2017, day two. Stick around, we'll be right back. (technology music)

>>Hello, fabulous cloud community and welcome to Las Vegas. We are the Cube and we will be broadcasting live from the AWS Reinvent Show floor for the next four days. This is our first opening segment. I am joined by the infamous John Furrier. John, it is your 10th year being here at Reinvent. How does >>It feel? It's been a great to see you. It feels great. I mean, just getting ready for the next four days. It's, this is the marathon of all tech shows. It's, it's busy, it's crowd, it's loud and the content and the people here are really kind of changing the game and the stories are always plentiful and deep and just it's, it really is one of those shows you kind of get intoxicated on the show floor and in the event and after hours people are partying. I mean it is like the big show and 10 years been amazing run People getting bigger. You're seeing the changing ecosystem Next Gen Cloud and you got the Classics Classic still kind of doing its thing. So getting a lot data, a lot of data stories. And our guests here are gonna talk more about that. This is the year the cloud kind of goes next gen and you start to see the success Gen One cloud players go on the next level. It's gonna be really fun. Fun week. >>Yes, I'm absolutely thrilled and you can certainly feel the excitement. The show floor doors just opened, people pouring in the drinks are getting stacked behind us. As you mentioned, it is gonna be a marathon and very exciting. On that note, fantastic interview to kick us off here. We're starting the day with Stripe. Please welcome nor and Brian, how are you both doing today? Excited to be here. >>Really happy to be here. Nice to meet you guys. Yeah, >>Definitely excited to be here. Nice to meet you. >>Yeah, you know, you were mentioning you could feel the temperature and the energy in here. It is hot, it's a hot show. We're a hot crew. Let's just be honest about that. No shame in that. No shame in that game. But I wanna, I wanna open us up. You know, Stripe serving 2 million customers according to the internet. AWS with 1 million customers of their own, both leading companies in your industries. What, just in case there's someone in the audience who hasn't heard of Stripe, what is Stripe and how can companies use it along with AWS nor, why don't you start us off? >>Yeah, so Stripe started back in 2010 originally as a payments company that helped businesses accept and process their payments online. So that was something that traditionally had been really tedious, kind of difficult for web developers to set up. And what Stripe did was actually introduce a couple of lines of code that developers could really easily integrate into their websites and start accepting those payments online. So payments is super core to who Stripe is as a company. It's something that we still focus on a lot today, but we actually like to think of ourselves now as more than just a payments company but rather financial infrastructure for the internet. And that's just because we have expanded into so many different tools and technologies that are beyond payments and actually help businesses with just about anything that they might need to do when it comes to the finances of running an online company. So what I mean by that, couple examples being setting up online marketplaces to accept multi-party payments, running subscriptions and recurring payments, collecting sales tax accurately and compliantly revenue recognition and data and analytics. Importantly on all of those things, which is what Brian and I focus on at Stripe. So yeah, since since 2010 Stripes really grown to serve millions of customers, as you said, from your small startups to your large multinational companies, be able to not only run their payments but also run complex financial operations online. >>Interesting. Even the Cube, the customer of Stripe, it's so easy to integrate. You guys got your roots there, but now as you guys got bigger, I mean you guys have massive traction and people are doing more, you guys are gonna talk here on the data pipeline in front you, the engineering manager. What has it grown to, I mean, what are some of the challenges and opportunities your customers are facing as they look at that data pipeline that you guys are talking about here at Reinvent? >>Yeah, so Stripe Data Pipeline really helps our customers get their data out of Stripe and into, you know, their data warehouse into Amazon Redshift. And that has been something that for our customers it's super important. They have a lot of other data sets that they want to join our Stripe data with to kind of get to more complex, more enriched insights. And Stripe data pipeline is just a really seamless way to do that. It lets you, without any engineering, without any coding, with pretty minimal setup, just connect your Stripe account to your Amazon Redshift data warehouse, really secure. It's encrypted, you know, it's scalable, it's gonna meet all of the needs of kind of a big enterprise and it gets you all of your Stripe data. So anything in our api, a lot of our reports are just like there for you to take and this just overcomes a big hurdle. I mean this is something that would take, you know, multiple engineers months to build if you wanted to do this in house. Yeah, we give it to you, you know, with a couple clicks. So it's kind of a, a step change for getting data out of Stripe into your data work. >>Yeah, the topic of this chat is getting more data outta your data from Stripe with the pipelining, this is kind of an interesting point, I want to get your thoughts. You guys are in the, in the front lines with customers, you know, stripes started out with their roots line of code, get up and running, payment gateway, whatever you wanna call it. Developers just want to get cash on the door. Thank you very much. Now you're kind of turning in growing up and continue to grow. Are you guys like a financial cloud? I mean, would you categorize yourself as a, cuz you're on top of aws? >>Yeah, financial infrastructure of the internet was a, was a claim I definitely wanna touch on from your, earlier today it was >>Powerful. You guys are super financial cloud basically. >>Yeah, super cloud basically the way that AWS kind of is the superstar in cloud computing. That's how we feel at Stripe that we want to put forth as financial infrastructure for the internet. So yeah, a lot of similarities. Actually it's funny, we're, we're really glad to be at aws. I think this is the first time that we've participated in a conference like this. But just to be able to participate and you know, be around AWS where we have a lot of synergies both as companies. Stripe is a customer of AWS and you know, for AWS users they can easily process payments through Stripe. So a lot of synergies there. And yeah, at a company level as well, we find ourselves really aligned with AWS in terms of the goals that we have for our users, helping them scale, expand globally, all of those good things. >>Let's dig in there a little bit more. Sounds like a wonderful collaboration. We love to hear of technology partnerships like that. Brian, talk to us a little bit about the challenges that the data pipeline solves from Stripe for Redshift users. >>Yeah, for sure. So Stripe Data Pipeline uses Amazon RedShift's built in data sharing capabilities, which gives you kind of an instant view into your Stripe data. If you weren't using Stripe data pipeline, you would have to, you know, ingest the state out of our api, kind of pull yourself manually. And yeah, I think that's just like a big part of it really is just the simplicity with what you can pull the data. >>Yeah, absolutely. And I mean the, the complexity of data and the volume of it is only gonna get bigger. So tools like that that can make things a lot easier are what we're all looking for. >>What's the machine learning angle? Cause I know there's lots of big topic here this year. More machine learning, more ai, a lot more solutions on top of the basic building blocks and the primitives at adds, you guys fit right into that. Cause developers doing more, they're either building their own or rolling out solutions. How do you guys see you guys connecting into that with the pipeline? Because, you know, data pipelining people like, they like that's, it feels like a heavy lift. What's the challenge there? Because when people roll their own or try to get in, it's, it's, it could be a lot of muck as they say. Yeah. What's the, what's the real pain point that you guys solve? >>So in terms of, you know, AI and machine learning, what Stripe Data Pipeline is gonna give you is it gives you a lot of signals around your payments that you can incorporate into your models. We actually have a number of customers that use Stripe radar data, so our fraud product and they integrate it with their in-house data that they get from other sources, have a really good understanding of fraud within their whole business. So it's kind of a way to get that data without having to like go through the process of ingesting it. So like, yeah, your, your team doesn't have to think about the ingestion piece. They can just think about, you know, building models, enriching the data, getting insights on top >>And Adam, so let's, we call it etl, the nasty three letter word in my interview with them. And that's what we're getting to where data is actually connecting via APIs and pipelines. Yes. Seamlessly into other data. So the data mashup, it feels like we're back into in the old mashup days now you've got data mashing up together. This integration's now a big practice, it's a becoming an industry standard. What are some of the patterns and matches that you see around how people are integrating their data? Because we all know machine learning works better when there's more data available and people want to connect their data and integrate it without the hassle. What's the, what's some of the use cases that >>Yeah, totally. So as Brian mentioned, there's a ton of use case for engineering teams and being able to get that data reported over efficiently and correctly and that's, you know, something exactly like you touched on that we're seeing nowadays is like simply having access to the data isn't enough. It's all about consolidating it correctly and accurately and effectively so that you can draw the best insights from that. So yeah, we're seeing a lot of use cases for teams across companies, including, a big example is finance teams. We had one of our largest users actually report that they were able to close their books faster than ever from integrating all of their Stripe revenue data for their business with their, the rest of their data in their data warehouse, which was traditionally something that would've taken them days, weeks, you know, having to do the manual aspect. But they were able to, to >>Simplify that, Savannah, you know, we were talking at the last event we were at Supercomputing where it's more speeds and feeds as people get more compute power, right? They can do more at the application level with developers. And one of the things we've been noticing I'd love to get your reaction to is as you guys have customers, millions of customers, are you seeing customers doing more with Stripe that's not just customers where they're more of an ecosystem partner of Stripe as people see that Stripe is not just a, a >>More comprehensive solution. >>Yeah. What's going on with the customer base? I can see the developers embedding it in, but once you get Stripe, you're like a, you're the plumbing, you're the financial bloodline if you will for the all the applications. Are your customers turning into partners, ecosystem partners? How do you see that? >>Yeah, so we definitely, that's what we're hoping to do. We're really hoping to be everything that a user needs when they wanna run an online business, be able to come in and maybe initially they're just using payments or they're just using billing to set up subscriptions but down the line, like as they grow, as they might go public, we wanna be able to scale with them and be able to offer them all of the products that they need to do. So Data Pipeline being a really important one for, you know, if you're a smaller company you might not be needing to leverage all of this big data and making important product decisions that you know, might come down to the very details, but as you scale, it's really something that we've seen a lot of our larger users benefit from. >>Oh and people don't wanna have to factor in too many different variables. There's enough complexity scaling a business, especially if you're headed towards IPO or something like that. Anyway, I love that the Stripe data pipeline is a no code solution as well. So people can do more faster. I wanna talk about it cuz it struck me right away on our lineup that we have engineering and product marketing on the stage with us. Now for those who haven't worked in a very high growth, massive company before, these teams can have a tiny bit of tension only because both teams want a lot of great things for the end user and their community. Tell me a little bit about the culture at Stripe and what it's like collaborating on the data pipeline. >>Yeah, I mean I, I can kick it off, you know, from, from the standpoint like we're on the same team, like we want to grow Stripe data pipeline, that is the goal. So whatever it takes to kind of get that job done is what we're gonna do. And I think that is something that is just really core to all of Stripe is like high collaboration, high trust, you know, this is something where we can all win if we work together. You don't need to, you know, compete with like products for like resourcing or to get your stuff done. It's like no, what's the, what's the, the team goal here, right? Like we're looking for team wins, not, you know, individual wins. >>Awesome. Yeah. And at the end of the day we have the same goal of connecting the product and the user in a way that makes sense and delivering the best product to that target user. So it's, it's really, it's a great collaboration and as Brian mentioned, the culture at Stripe really aligns with that as >>Well. So you got the engineering teams that get value outta that you guys are dealing with, that's your customer. But the security angle really becomes a big, I think catalyst cuz not just engineering, they gotta build stuff in so they're always building, but the security angle's interesting cuz now you got that data feeding security teams, this is becoming very secure security ops oriented. >>Yeah, you know, we are really, really tight partners with our internal security folks. They review everything that we do. We have a really robust security team. But I think, you know, kind of tying back to the Amazon side, like Amazon, Redshift is a very secure product and the way that we share data is really secure. You know, the, the sharing mechanism only works between encrypted clusters. So your data is encrypted at rest, encrypted and transit and excuse me, >>You're allowed to breathe. You also swallow the audience as well as your team at Stripe and all of us here at the Cube would like your survival. First and foremost, the knowledge we'll get to the people. >>Yeah, for sure. Where else was I gonna go? Yeah, so the other thing like you kind of mentioned, you know, there are these ETLs out there, but they, you know that that requires you to trust your data to a third party. So that's another thing here where like your data is only going from stripe to your cluster. There's no one in the middle, no one else has seen what you're doing, there's no other security risks. So security's a big focus and it kind of runs through the whole process both on our side and Amazon side. >>What's the most important story for Stripe at this event? You guys hear? How would you say, how would you say, and if you're on the elevator, what's going on with Stripe? Why now? What's so important at Reinvent for Stripe? >>Yeah, I mean I'm gonna use this as an opportunity to plug data pipelines. That's what we focus on. We're here representing the product, which is the easiest way for any user of aws, a user of Amazon, Redshift and a user of Stripe be able to connect the dots and get their data in the best way possible so that they can draw important business insights from that. >>Right? >>Yeah, I think, you know, I would double what North said, really grow Stripe data pipeline, get it to more customers, get more value for our customers by connecting them with their data and with reporting. I think that's, you know, my goal here is to talk to folks, kind of understand what they want to see out of their data and get them onto Stripe data pipeline. >>And you know, former Mike Mikela, former eight executive now over there at Stripe leading the charge, he knows a lot about Amazon here at aws. The theme tomorrow, Adams Leslie keynote, it's gonna be a lot about data, data integration, data end to end Lifeing, you see more, we call it data as code where engineering infrastructure as code was cloud was starting to see a big trend towards data as code where it's more of an engineering opportunity and solution insights. This data as code is kinda like the next evolution. What do you guys think about that? >>Yeah, definitely there is a ton that you can get out of your data if it's in the right place and you can analyze it in the correct ways. You know, you look at Redshift and you can pull data from Redshift into a ton of other products to like, you know, visualize it to get machine learning insights and you need the data there to be able to do this. So again, Stripe Data Pipeline is a great way to take your data and integrate it into the larger data picture that you're building within your company. >>I love that you are supporting businesses of all sizes and millions of them. No. And Brian, thank you so much for being here and telling us more about the financial infrastructure of the internet. That is Stripe, John Furrier. Thanks as always for your questions and your commentary. And thank you to all of you for tuning in to the Cubes coverage of AWS Reinvent Live here from Las Vegas, Nevada. I'm Savannah Peterson and we look forward to seeing you all week.

>>Hello, welcome back to the cubes coverage of Monaco, crypto summit presented by digital bits. It's a conference where a lot of the people using digital bits and the industry coming together around the future of crypto in the applicates got a great guest garage, rod cot, founder, and CEO of an innovative company. Love this co I love this company, Luke mogul, Rob, thanks for coming on the queue. Appreciate it. Oh, >>Thank you for having >>Us. Yeah. So I checked out what you guys are doing. You've got the sports metaverse angle going on with super valuable, cuz sports is super entertaining. Uh, people are engaged. There's huge fan base, huge online now, digital convergence going on with the physical, you know, you see all kinds of sports betting going on now everything's going digital. There's a whole nother consumer experience going on with sports and the game is still the same on the, on the field or so to, or the court. That's correct. Yeah. Now it's going to digital take a minute to explain what you guys are working on. >>Yeah, so yes, we are building out a sports ERs where we are bringing athletes, whether they're NBA stars, NFL stars, w N B a many of those athletes into meows giving them the ownership of the entire, um, meows commerce along with gameplay. So that's something from our perspective, this, uh, this is something that we're focused on. We're building out stadiums. Athletes can own stadiums. Athlete can create their own training centers, media hubs. Um, and imagine Lisa, Leslie for example, is building out a woman leadership sports academy, right? We have Michael Cooper building out defensive academy. So those are all the brands. We have 174 NBA w N B stars. And, um, and we are building out this, >>The brand is the brand, is the platform that's correct. That's the trend we're seeing. And it's, it's also an extension of their reach in community. So there's, they can convert their star power and athlete with owner's approval. If they probably write it on to the contracts, he, they can imagine all the complications, but they bring that online and extend that energy and brand equity yep. To fans and social network. Yeah. >>And many of these athletes are tremendous successful in their web two careers, right? Yeah. Um, some are current athletes, some are former athletes, but they have built such a brand persona where people are following them on Instagram. For example, Carlos Boozer. He has like almost 6 million followers between Twitter and Instagram and those kind of brands are looking or how do I give back to the community? How do I engage with my community and web three? And especially with our platform, we are giving that power back to the players. >>So you guys got some big names booers on there. You mentioned Carlos Boozer. You mentioned that Lisa, Leslie others among others, Michael Cooper throw back to the old Lakers, uh, magic. Johnson's kind actually here in crypto. We just saw him in the lobbies and in dinner and the other night, um, at Nobu, um, you got a lot of NBA support. Take a take, take, even explain how you're working this angle. Uh, you got some great traction, uh, momentum. Um, you got great pedigree, riot games in your career. Uh, you kind of get the world, the tech world, the media world, as it comes together. What's the secret sauce here? Is it the NBA relationship combination of the team explained >>It's really focusing on what, uh, we are building on me was focusing on players first, right? So players are literally, we call our platform as, uh, owned by the players, made for the players. Uh, and engagement is really all done through the players, right? So that's our key sauce. And when we worked out with NBA, we, we are part of the NBA BPA acceleration program for 2022 that is funded by a six Z, uh, and, and many others. Um, and our partnership with league is very critical. So it's not only partnered with player association partnered with leagues, whether it's NBA, w N B a NFL. So those are the venues. And this becomes almost a program, especially for athletes to really generate this lifetime engagement and royalty model because some of this famous athletes really want to give back to the communities. So like for example, I use Lisa Leslie a lot, but Lisa, Leslie really wants to empower women leadership, leadership, and really help, um, women in sports, for example. Right? So those are the angles that, um, that really people are excited about. >>Well, for the people watching that might not understand some of the ins and outs of sports and, and rod, your background in your team, it's interesting. The sports teams have been on the big day to train for many, many years. You look at all the stadiums. Now they've got mobile devices, they got wifi under the chairs. They use data and technology to manage the team. Mm-hmm, <affirmative> manage the stadium and venue and operations suppliers, whatnot. And then also the fans. So you, they, they got about a decade or so experience already in the digital world. This is not new to the, to the sports world. Yeah. So you guys come to the table kind of at a good time. >>Yeah. Especially the defi of the sports, right? So there's a defi of the finance, but this is the really, uh, a, a decentralization of the sports is something that there's a lot of traction. And there are many companies that are really focusing on that. Our focus obviously is players first, right? How do we give power to the players? Uh, and those are really driving the entire engagement. And also the brands >>How's the NBA feel about this because, you know, you got the NBA and you get the team, you got the owners. I mean, the democratization of the players, which I love by the way that angle kind of brings their power. Now's the new kind of balance of power. How is the NBA handling this? What's some of the conversations you've had with the, the organization. >>Yeah. So obviously there are a lot of things that, uh, people have to be careful about, right? They have existing contracts, existing, digital media rights. Um, so that's something that, uh, we have to be very tactful when we are working with NBA and NPA, uh, on what we can say, we cannot say. So that is obviously they have a lot of existing multimillion or billion dollar contracts that they cannot void with the web because the evolution of web three, >>You know, I love, uh, riffing on the notion of contract compliance when there's major structural change happening. Remember back in baseball, back in the days before the internet, the franchise rights was geographic territory. Mm-hmm <affirmative> well, if you're the New York Yankees, you're doing great. If you're Milwaukee, you're not doing too good, but then comes the internet. That's good. That's no geography. There's no boundaries. That's good. So you're gonna have stadiums have virtual Bo. So again, how do they keep up with the contracts? Yeah. I mean, this is gonna be a fundamental issue. >>That's >>Good. Good. And I think if they don't move, the players are gonna fill that void. >>That's correct. Yeah. And especially with this, this an IL deal, right. That happened for the players, uh, especially college athletes. So we are in process of onboarding 1.5 million college athletes. Uh, and those athletes are looking for not only paying for the tuition for the colleges, but also for engagement and generating this early on, uh, >>More okay. Rod, we're gonna make a prediction here in the cube, 20, 20 we're in Monaco, all the NBA, NHL, the teams they're gonna be run by player Dows. Yeah. What do you think? A very good prediction. Yeah. Very good prediction. Yeah. I mean you, I mean, that's a joke, I'm joking aside. I mean, it's kind of connecting the dots, but you know, whether that happens or not, what this means is if this continues to go down this road, that's correct. Get the players collectively could come together. Yeah. And flip the script. >>Yeah. And that's the entire decentralization, right. So it's like the web three has really disrupted this industry as you know. Um, and, and I know your community knows that too. >>Of course, course we do. We love it. >>Something from sports perspective, we are very excited. >>Well, I love it. Love talking. Let's get to the, to the weeds here on the product, under the hood, tell about the roadmap, obviously NFTs are involved. That's kind of sexy right now. I get the digital asset model on there. Uh, but there's a lot more under the coverage. You gotta have a platform, you gotta have the big data and then ultimately align into connecting other systems together. How do you view the tech roadmap and the product roadmap? What's your vision? >>Yeah. So the, the one thing that you had to be T full, uh, as a company, whether it's LUT, mogul or any other startup, is you have to be really part of the ecosystem. So the reason why we are here at Monaco is that we obviously are looking at partnership with digital bits, um, and those kind of partnership, whether it's fourth centric, centric are very critical for the ecosystem in the community to grow. Um, and that's one thing you cannot build a, another, uh, isolated metaverse right? So that's one thing. Many companies have done it, but obviously not. >>It's a wall garden doesn't work. >>Exactly. So you have to be more open platform. So one things that we did early on in our platform, we have open APIs and SDKs where not only you as an athlete can bring in your, uh, other eCommerce or web, uh, NFTs or anything you want, but you can also bring in other real estate properties. So when we are building out this metaverse, you start with real estate, then you build out obviously stadiums and arenas and academies training academies, but then athletes can bring their, uh, web commerce, right. Where it's NFT wearables shoe line. So >>Not an ecosystem on top of Luke Mo. So you're like, I'm almost like you think about a platform as a service and a cloud computing paradigm. Yeah. Look different, not decentralized, but similarly enabling others, do the heavy lifting on their behalf. Yeah. Is that right? >>So that's correct. Yes. So we are calling ourself as the sports platform as a service, right. So we want to add the word sports because we, uh, in, in many contexts, right. When you're building metaverse, you can get distracted with them, especially we are in Los Angeles. Right. >>Can I get a luxury box for the cube and some of the metaverse islands and the stadiums you're doing? >>We, we are working >>On it. We're >>Definitely working on, especially the, uh, Los Angeles, uh, stadium. Yeah. >>Well, we're looking for some hosts, anyone out there looking for some hosts, uh, for the metaverse bring your avatar. You can host the cube, bro. Thanks for coming on the cube. Really appreciate. What's the, what's next for you guys, obviously, continuing to build momentum. You got your playful, how many people on the team what's going on, give a plug for the company. What are you looking for share with the audience, some of the, some of your goals. Yeah. >>So, uh, the main thing we're looking for is really, um, from a brand perspective, if you are looking at buying properties, this would be an amazing time to buy virtual sports stadium. Um, so we are, obviously we have 175 stadiums in roadmap right now. We started with Los Angeles. Then we are in San Francisco, New York, Qatar, Dubai. So all those sports stadiums, whether they're basketball, football, soccer are all the properties. And, uh, from a community perspective, if you want to get an early access, we are all about giving back to the community. Uh, so you can buy it at a much better presale price right now. Uh, so that's one, the second thing is that if you have any innovative ideas or a player that you want to integrate into, we have an very open platform from a community engagement perspective. If you have something unique from a land sale perspective yeah. Or the NFD perspective plug, contact us at, at Raj lumo.com. >>And I'm assuming virtual team, you in LA area where where's your home. >>So, yeah, so I live in Malibu, um, and our office is in Santa Monica. We have an office in India. Uh, we have few developers also in Europe. So, uh, and then we are team of 34 people right now >>Looking to hire some folks >>We are looking for, what >>Are you, what are you looking for? >>So, uh, we are looking for a passionate sports, uh, fanatics. >>It's a lot, not hard to find. Yeah. >><laugh> who knows how to also code. Right? So from blockchain perspective, we are, uh, chain agnostic. Uh, but obviously right now we are building on polygon, but we are chain agnostic. So if you have any blockchain development experience, uh, that's something we, we are looking for. Yeah. >>RA, thanks for coming out. Luke Mo check him out. I'm John furry with the cube here in Monaco for the mono crypto summit presented by digital bits. We got all the action, a lot of great guests going on, stay with us for more coverage. Um, John furrier, thanks for watching.

(soft instrumental music) >> Welcome back everyone, to theCUBE's coverage of AWS re:Invent 2021. I'm John Furrier, your host of theCUBE. We're here in person at a live physical event with real people. Of course, it's a hybrid event. Great stuff online. Check it out on the Amazon site, as well as theCUBE zone. We've got great guests, talking about the cloud vision for getting talent in to the marketplace, in being productive and for society Accenture always great content. Denise Reese, Managing Director of the South Market Unit Lead at Accenture, AABG, which stands for "Accenture Area Business Group" and Gina Gina Fratarcangeli who is also the managing director of Midwest sales leader. Ladies, thanks for coming, I appreciate you coming on and talking about the vision of talent. >> I guess >> Thanks for having us. >> Yes, absolutely. It's a pleasure to be here. >> So, Amazon's got this dangerous goal, to train 29 million people. Maureen Lonergan came on yesterday, who I've known for a long time, doing a great job. It's hard to get the talent in. First of all, it sounds harder than it really is, that's my opinion. You know, you get some training certifications and you're up and running. So, talent's a big thing. What do you guys do? Give us the overview. >> Sure. Well, we're having a lot of activity at Accenture trying to get talent in. Across the entire country we're spending a tremendous amount of effort to do that. A couple of critical things we're doing in the Midwest is bringing in and searching for different talent streams that we haven't typically done in the past. For instance, one thing that we're doing is, we set up an apprentice program where we're reaching out into the market to find diverse talent, who aren't coming through the critical normal college path and bringing folks in like that. And we've got 1200 people that we've brought in that way, just in the Midwest. Which has been a phenomenal new talent stream for us. And supporting our inclusion and diversity. One of the other exciting things is what we call "The Mom Project", where we're intentionally working with an organization called the Mom Project, to bring women back into the workplace who may have left while they were taking care of their families and helping them get certified in all the new cloud technology and getting back to work. >> I love how you guys are going after this whole places that not everyone's looking at, because what I love about Cloud is that, it's a level up kind of opportunity where you don't really have to have that pedigree, or that big-big school. Of course, I went to a different school. So, I have a little chip on my shoulder. I didn't go to MIT, wasn't North-east but still good school. But, I mean, you could really level up from anywhere. >> Gina: That's right. >> And the opportunities with Cloud are so great. This is like a huge thing. No I'm surprised no one knows about it. >> Absolutely. I would add to that. So, we've in the South, in Georgia in particular. We've just launched an initiative with the technical college system of Georgia and AWS. So, it's a public-private partnership, where we're actually helping to set the curriculum for those students that are going through programs, through the technical colleges. It's one of the largest parts of the university system of Georgia. And, we're actually helping to frame the curriculum. And, giving folks what they need, to your point. It is an opportunity to level up. It's a great way to get talent in non-traditional spaces. It helps us to achieve our inclusion and diversity roles or goals, rather. But, then it also allows us to really continue to fill that pipeline with folks that we may not have had access to otherwise. >> Is there a best practice that you see developing in the acquisition of talent? Or enticing people to come in? Because that's just economics you know, Maureen was telling me that it was this person she was unemployed, and she got certified and she's making six figures. >> Both: Yeah. >> She's like oh my God, this is great. So, that's the Cloud growth. Is there a way to entice people? Is there a pattern? Is it more economic? Is it more, hey, be part of something. What's the data showing? >> There's definitely a war for talent out there. And so in this space we continuously hear from our clients that they can't hire enough people. So in the past, in the technology space, a lot of clients were hiring their own teams and here they just can't get the skills fast enough. So we're spending a tremendous amount of time being proactive. We started a women in Cloud organization where we're proactively reaching out to the community to bring women in, let them know that we will help them get those certifications and partnering with organizations like Women in Cloud, which is a global organization to create new funnels of talent. >> I think the women angle is great. The mom network coming out of the work for back into the workforce, because things change. Like we were talking about how Amazon just changed over the past five years now that this architectural approach is changing. So that's cool. Also we were involved in the women in data science, out of Stanford University, they have that great symposium. This is power technical women. >> Yes >> And it's got a global following. So the women networks that are developing are phenomenal. So that's not just an Accenture thing, right? That's outside of Accenture. >> I think it's a combination because I think we do a really good job inside of Accenture to create opportunities for women of various ethnicities lived experiences to be able to come together to network internally, but then also to pour some of that talent that they have into the communities where we live and we all do business as well. So I think I'm seeing definitely a two-pronged approach there. >> Let me ask you a question, I don't mean to put you on the spot, but I kind of will, Accenture's known as a pretty great firm. So working at Accenture is kind of a big deal. Does that scare people? Because if you could work at a Accenture I mean, that's good pedigree right there. So like, when you're trying to get people coming into the cloud, do they get the Accenture mojo or does it work for them? And can you share your experiences on that? >> I've been here five years and it's been a phenomenal ride for me. I've really enjoyed the fact having a female CEO, I think, and having a CEO who is so committed to diversity on all aspects, right? Her commitment is 50% diversity parody by 2025 at every level of our organization. And that doesn't happen without really intentional efforts at the entry-level and everywhere through the process to ensure that women are not only promoted, but really given the support network among all of our leaders and mentorship to be successful. And it's not just words, it's something that we're really spending a lot of time doing with intention. And that word is out in the space now, as women come in, they're loving it and they're recruiting their other women into the organization and diverse groups as well as what I'm seeing. >> And so I actually just started at Accenture in March. So I've been around eight months. I actually joined from AWS, interestingly enough. And I can tell you from my own experience, the intentionality that Gina spoke to you is it's evident at all levels. I feel like the way that I was courted to the firm was nothing short of amazing. That's another story for another day, but I feel like my being where I am, being hired in as a managing director, as an experienced hire, I think my presence is a testament to the focus that Accenture has on inclusion diversity and the equity component as well. And then also in Atlanta, we are exceptionally fortunate. We have close to 30 black and Latin X managing directors and senior managing directors out of the Atlanta office. So what we're doing there is pretty magical and it's something that I've never experienced in my 25 years. >> It's contagious I hope, the magic is contagious. >> Yeah. >> Yes, absolutely. >> And it's exciting because we're known as a management consulting business, right? So our product is the people >> That's right. >> And so there is intention from day one as to what you want from your career and setting your career plan. So everyone is given those career counselors and the expectation that someone is thinking about your business and your personal business, and what is your role today and what should your role be in two years, and what skills do you need to get there? Which is awesome, it's a lot of fun. >> It's also walking the talk too, right? I mean, Amazon here, they had a 50% women on stage. I don't know if you noticed on the keynote, they was two men and two women, 50%. Of course the United Airlines, it's got to be three. We got to get a 51%,, 'cause technically 51% So it should be three to one, but yeah, like, okay, that was cute notice but that's good. But this is real, I've been a big proponent of software development. Customers are women too that's 51%. So I think this whole representation thing has to be more real and more intentional. And so I want to ask you, how would you share the best practice of making that real from the essential playbook? What could people learn and what mistakes should they avoid? I think people who do want to try with it, but they don't know what to do. >> You know, I think get started, right. Do the work. I feel like since I started in technology, we've been having this conversation about diversity and inclusion and bringing more people into the space. And now it's time for us to just do that. And I feel like Accenture is doing that in spades. I think also again, I've been using this word. I was on a breakout panel yesterday talking about our partnership with AWS and intentionality keeps coming up. But I think also it helps to have a CEO who's creating diversity as an imperative at the most senior levels of the firm and folks are being incentivized as a result. So you've got to put the mechanisms in place to ensure that folks understand that this is not just lip service. >> That's a great point. It's not only just the people, but the mechanisms. And one of the things that I've been saying early on in the top of the interview was Cloud is an instant leveler there, because if you can be so capable so fast. So like when you start thinking about getting people in the market, producing talent, this notion of meritocracy isn't lip service, because if you have the capabilities and the people side lineup, then it truly can be like that. 'Cause your game does the talking, right. >> And we're doing it with intention at every level in the organization so much though, that every people leader, one of their metrics is the diversity. And as we look at the promotions, making sure that that parody is there, but every person who's managing people has diversity as a metric that they're being measured on. And so I think that's really critical as well as having the people who are being the advocates and being the allies and really asking the questions as the teams are getting put together. You know, my job is to review all the deals in the Midwest. And when the teams come forward, I say, "Great where are the women on the team? Who are we putting it?" We're all talking about the diversity. So when we're going to a client meeting, where are the women who are you're taking to that meeting? And if the answer is well, there's not one who's technical yet, the most senior, the most technical, well, great bring her on and use this as a training opportunity. We need to walk the walk and talk the talk and show that to our clients. >> I think that's really good. You guys are senior leaders, one can do that, demonstrate that, but also you're in the field for Accenture. You're in front of your customers. What are you seeing out there and what excites you about being in these industry? >> Yeah, I love the fact that there are so many more women in this space. I love that we're having so many women out there with intention. We've had six female CEOs do women in Cloud panel discussions with us and with our team. So you made the comment early about cloud moving so fast. That's the most exciting thing for me and the fact that it is moving at such a pace that no one client is going to be able to get the skills fast enough. They need companies like Accenture. They need companies like AWS to help them where we're leveraging all the knowledge from our own other clients and bringing that together so we can help them accelerate their development. What about you? >> Absolutely. Now I would echo that as we used to say at AWS plus one to that. But I'm really hopeful because what I'm seeing is the number of folks with my lived experience better at senior executive levels, not only within Accenture and AWS, but in our customers. And I think going back to the point that you were making earlier regarding Cloud being a level up and giving folks opportunity, folks have to be able to see a path, right? It's one thing to just get a certification and tick a box, that's great. But if you don't see a pathway to being able to utilize that in a way that allows you to move up and seeing where we are now, just as a firm, just really, really excites me that every time I get onto a call and I see another strong, amazing woman, I'm like, man, this is amazing. And it's something that... I think it's a phenomenon that I've started to see maybe within the last like five years or so. And probably even within the last two to three years, I've started to see that even more so, so that really excites me. >> Well, first of all, you guys are great. You're contagious, okay? Which is good, a good thing. I love how you brought the whole path thing because path finders was a big part of Adam's Leslie's keynote, and it must be really fun to see people taking the path that you guys are pioneering- >> We're ploughing, we're ploughing >> Yes we are. We're ploughing and you know what else we're doing? We're lifting, as we climb. That is important. I would say that, we don't have all of these amazing opportunities and blessings just to talk about what we have, but if you're not actually bringing somebody else along and giving those opportunities to folks, then it's all for not. >> You got people and the Cloud, to get them people, which is, we're humans and the mechanisms software to bring it together, magic. >> Absolutely >> Congratulations. Thanks for coming on theCUBE. >> Both: Thanks for having us. >> Okay this is theCUBE, I'm John Furrier, host of theCUBE. You're watching theCUBE, the leader in global tech coverage from re:Invent 2021 AWS web services. Thanks for watching (soft instrumental music)

>>Mhm Yes. Hello and welcome back to the cube coverage of red hat summit 2021 Virtual. I'm john furrier host of the Q. We've got a great lineup here. We've got two great guests just bad padan, E. S. V. P. Of cloud platforms at red hat and Tracy ranking VP of open shift engineering at Red Hat folks. Thanks for coming on. Good to see. You got some big news, you guys have made some acquisitions. Uh stack rocks you guys bought into red hat was a really big deal. People want to know, what's the story? How's it going? What's the uptake? What's the integration, how's it going? >>Right, thanks john, thanks for having us on. Um so yeah, we're really excited with stack rocks acquisition being the team on board. Uh Well, the first thing to note before even why we did it uh was for for you and and then the beers have been following us closely. This is our first acquisition as red Hat being part of IBM. So, so, so quite big for us from that perspective as well. Right? Continue to maintain our independence um within uh IBM uh and I really appreciate that way of working together. Um but saying all of that aside, you know, as a company have always been focused on ensuring that were direct enterprise capabilities to just sort of doing that for two decades. With with Lennox, security has always been a big part of our story, right, ensuring that, you know, we're finding cbs updating uh and sending out patches to our customers and doing that in a reliable fashion running mission critical applications. We applied that same if you will um security mindset on the community side with the open ship platform. Um we've invested insecurity ourselves organically, right, you know, uh in various areas and making it more secure, all right, can't run containers uh as Root by default, uh investing in things like role based access control and so on. And we really felt like we want to deepen our commitment to security. Uh and so, you know, in conversations with stack rocks, we found just a great fit, just a great team building a really interesting approach to community security, right? You know, very declared of approach to it. Uh you know, focus on a vision around this notion of shift left. But you've probably been hearing from that because we're a little bit right. Which is this uh idea that, you know, we're in the world moving from devops to death setups. Uh and the approach that sack rocks were saying, so great team, great product, really great vision with regard to kind of weather going forward and finding a nice alignment between, you know what, you know, they've been thinking about the value that we want to bring >>Yeah, I want to dig into the depths cops, piece of it. But you brought up the IBM acquisition as part of now Red Hat bought IBM you know it's just you remember back in 2019 I interviewed Arvin on the cube when he was at IBM you guys were still independent and he had a smile on his face. He is pro cloud, he is all about cloud Native and even that interview I had no idea what was going on behind the scenes but I was kind of drilling him on some of the things that were important at that time which are now certainly relevant today which is cloud Native, Agile development Programmable infrastructure. I don't think we touched on security that much was kind of inherent in the conversation. He was like all smiling, he loves the cloud Native and and this is where it comes into the relevant, I have to ask you, what was it like to get this through? IBM where they're like girl green light or was it, was it different? What was different about this acquisition? >>John great, great question for you to ask. And you know, I will say that, uh, you know, everyone's heard the stories they're telling us. They get, you know, part of IBM, you know, it's definitely working on red hat jOHn the cube we've talked to you and several of your colleagues about that. Um, the great thing has been that, look, the redhead way of working, uh, are still pushing forward with regard to our commitment to open source, uh, and our culture, you know, is still the way it is. And I have to give huge credit not just to urban and his and his team, but definitely to orbit right. He's always champion, He's champion rather acquisition. He's champion kind of, you know, the independence that we've had and he takes very, very firm stance around it. Um, and look, IBM uh, story company uh, in the United States and really in the world, um, they have, there was working and you know, for redhead, they've kind of said, look, we'll give you a pass path, right? So, uh, getting the acquisition through, if you will, diarrhea processes, um, really was, was hugely supported by, you know, from mormon, but all the way down. Russian strategic >>strategic bet with the dollars involved trace, they want to get you in this because, you know, one of the things about shift left and getting security built in by default, which has always been part of red hat, that's never been an issue. It just extends as developers want to have native security built in. There's a technology angle to this as well. So, um, obviously cloud native is super important. What investments are you guys making with this acquisition and how does that translate to customer benefits? >>Yeah, I mean the one thing that is really important about the stock rocks acquisition and kind of, you know, key for us is, you know, this was a cube native solution and I think that's really, you know, was important piece as to why stock rocks might have been, you know, was a great fit for us. Um, and so you know, what we've been trying to do in the short time that that team has been on board with us is really, you know, taken a deep look and understanding where are the intersection points of some of the things that we have been trying to focus on, you know, just with inside of, you know, open shift in red hat in general and where do they have bring the additional value. Um, and really trying to make sure that when we create this solution and ultimately it is a solution that's cohesive across the board. Um, we don't add confusion too. You know what, some of the things that maybe we already do this team knows, you know, how to they know their customer base. They really know what the customers are looking for. And we are just trying to absorb, I would say so much of this information uh as we are trying to, you know, create what the right road map will be uh for stack rocks from a long term and infrared had ultimately in the security space. I mean, as the chef said, I mean we are red hats known for being, you know, security mind focus built on top of realm, you know, uh the leader and so we want to make sure that what we've got that actually serves, you know, the developers being able to not just secure the environment and the platform, but also the workloads, customers need that security from us. Um and build it in so that we have, you know, into the cube native >>controls. >>So stack rocks was known for reinventing and security enterprise security with cloud native. How is it complimentary? How does it fit in? Can you guys just quickly talk to that point because um like you said, you guys had security but as kubernetes and containers in general continue to rise up and and kubernetes continue to become a hybrid cloud kind of linchpin for applications. Um where's the synergy? Where's where does this connect? And what are some of the uh the part of the areas where it's it's fitting in nicely or or any overlaps that you can talk about as well? >>Yeah, I can start and then maybe Tracy if you want to add to that securities of it's a wide space. Right? So, you know, just saying security is like, well, you know what security you're talking about, you're talking about, you know, and use the security, like what your desktop are you talking about? You know, intrusion prevention? I mean, it's a huge, huge, you know, space. Uh you know, many companies devoted to the entire spectrum, you know, self has a very robust security business. We're very focused on uniting Tracy. Was talking about this, the Kubernetes Native security part of this. Right. You know, do we have the appropriate runtime uh, controls in place? Uh You know, our policies configured appropriately Well, if they're in one cluster, are they being applied consistently across, you know, every cluster? How do we make sure that, you know, we make security the domain, not just of the operators but also uh in in uh make it easier for it to be adopted at development time. So, you know, there's a, there's a, if you will, a very sort of uh a lot of surface area for security, we're trying to really think about the pieces that are most relevant for our enterprise customers and the ones that are deploying it at scale. And I'm sure we can build on it. Having said that, john what I do want to add also is that because expands even of Cuban any security is so large, there is a lot of room for our partners to play. Right? And so before you asked me that question, I want to say that there is space. Right? So you know, I've had conversations with you know, all the other folks in the cloud native security space. We know them well, we've been working with them over the years and we could do to look forward to ensure that they're building over and above the foundation of Berlin. >>So plenty of beachhead, what you're saying from a, from a security sample, you guys hit the table stakes added into the product, but there's so much surface area going on with this hybrid cloud and soon to be multi cloud that you're saying this room for partners to play. >>Exactly, right, >>okay. Tracy quick under the hood, you know, actually shift left. That's kind of the mindset for developers who are writing modern applications might not want to get under the hood, who just wanted all the program ability of security and not have to come back to it. I mean that seems to be the complaint that I hear. It's like okay I gotta come back and do a security, more security work. I just wrote the code that was last week or yesterday and that seems to be the developer productivity. Then there's also under the hood devops what how does this all fit? >>Yeah, so it's uh let's take a take a step back and this is how I kind of like to think about it. So we are trying to look at, you know, how do we just enable in some of the C. I. C. D. The tooling that we have? How do we actually take and enable some of the technology that was already available in stock rocks today and actually put it into those tools. Because if we can make it easy for you to not just develop your application and, you know, integrated in with what you're, the tooling is that you're trying to use for the entire life cycle of developing your application. It then becomes exactly what you didn't say, you know, what they're doing now is it's an after thought. We don't need it to be an afterthought. Um and I think, you know, we're seeing the changing from a customer mindset where um they're become customers are becoming a lot more aware of these things. So if we actually get this into, you know, some of the Argo and the ci cd pipe pipeline work, then it just becomes something natural and not a secondary thought because actually when it's a secondary thought, uh we have exposures and that's not what a customer wants when they're creating, you know, creating these workloads, they're trying to rapidly create the workloads, so we need to make it um to have those integration points in as quickly >>as possible. >>Totally nailed. I mean there's productivity issues and there's also the top line which is security. Great stuff. Congratulations on that acquisition. Security continues to be built in from the beginning. That's what people want. They want productivity want want security, great stuff, Great acquisition. Congratulations. Um Next next segment I want to get into is uh open shifts around telemetry. Tell us about telemetry for open shift. What is this about? >>Yeah, another big interesting topic for us. So over a year ago we released open Ship for and you know, we learned a lot of lessons, you know, shipping open ship three up and over the years and really getting feedback from hundreds of customers around the globe. One of the things obviously we heard from a lot was you know, make install the upgrade experience better. Right. But you know, we were thinking about how can we take that forward to the next level, which is is there a way for us to say, you know, let these clusters they connected up so we can get a better sense of cluster help and help with remote health monitoring will be able to proactively provide information back to our customers around, let's say, you know, if applications are healthy clusters healthy and how they're running and how we can help them um could figure them if they're not. Um And so that led us to introducing uh inflammatory remote health monitoring directly into open ship for as a value that we can provide to customers. Um And what that really starts doing is starts bringing this notion of a public cloud, like experience to customers with clusters run across the hybrid cloud. Right? So you have the expectation that, you know, your clusters are monitored and watched over in the public cloud and we want to make sure we can provide that to customers regardless of, you know, where they're running in. So, so that's just >>a quick question on that insights for open shit. That's what you're getting to. Is that on premise? And in the cloud? So it's hybrid environment, is that correct? >>Exactly. Right. So, the insights for open ship is all about that, Right? So how can be proactively, you know, uh identify risk helped remediated? How can we uh do things like, for example, give you recommendations, cost optimization, right insights around around around that. Uh and to your point, right? The goal is to make it completely hybrid. So, it's obviously a new area right for customers want Leslie used to that, you know, in an on premise environment, they're used to that in a public cloud or cloud native environment. And we're trying to make sure we bring that consistently across to our customers, you know, regardless of where they're running apart. >>Tracy. Talk about the the developer productivity involved because if you have telemetry and you have insight into what's going on in the infrastructure and the data, what's going on the application, you can be more proactive, You don't have to get pulled into these rabbit holes of troubleshooting. Oh, is a trace over here or something going on over here. Are clusters going down or should I could have caught that there's a lot of, you know, good intentions with with the code and then all of a sudden new code gets pushed and then also that triggers this to go off and you have all these kind of dependencies, day two operations, many people call this kind of that phenomenon where everything looks good and then you start pushing more stuff more code and then the cluster goes down and then it's like wait, that could have been avoided. That was a dumb error, we could have fixed that this is kind of the basic what I call human software error kind of stuff that's not intended. The telemetry help this area. >>Yeah, it does. And actually one point that even to take it further, that I think it's important is our customers can learn from each other not even having to talk to each other, which is the beauty of what telemetry is and what redhead insights, rope and shift is. You know, what we have been able to see is you know, there are certain characteristics that happen even across, you know, certain groups of customers but they don't know that they don't talk to each other, but the telemetry is giving us a night into what some of those patterns are. And so when a customer in one site starts to have, we start to see telemetry, you know, you know, maybe a. T. D. Is going down for a certain reason and and we can determine that we then have the ability to take that telemetry and you know, be able to send alerts back to all the other customers and say, hey we recognize this might be becoming an issue, You know, here's how you might re mediate it or hey we've already put a fix out for this issue that we're starting to see you having an issue, you should probably take action on. So it's an increasing the the efficiency of customers without them necessarily having to, you know, constantly be understanding, monitoring, you know, watching everything like they had had to do from of the three perspective, we're now giving them some of the insights of what we know as developers back to them, >>you know, that's interesting. I think that's really key because it's talking to a friend last night we just talked about cybersecurity and we're talking about how a lot of these things are patterns that have that are the same and people just don't talk to each other. There's no shared insights. I think this is an interesting dynamic where you can get the collective intelligence of other patterns and then share that. So the question that I mean that's that's a game changer in my opinion. So that's awesome. The question I have is can you guys push alerts and recommendations to the customers? So from this data? So how does that work? Is that built into the product? Can I get some proactive notifications and saying, hey, you know, your cluster might go down and we've seen this before, we've seen this movie. I mean she is that built in. >>Yeah, so john you're keeping it exactly where we're taking this, right? And I think Tracy started putting out some breadcrumbs for you there. So uh, first get comfortable with the foundation was laid out, get clusters connected right. Then information starts going, reported, we start getting exactly to what you said, john write a set of patterns that we can see Tracy, start talking about what we can, if we see pattern on one end, we can go off and help customers on other end. Now, if you take this forward interest for your viewers today, um introduce a I you know, into this, right? And then we can start almost starting to proactive now of saying, look, you know, following actions are going to be committed or we expect them to be committed. You know, here's what the outcome is a result of that. Here's what we recommend for you to do, right? So start proactive remediation along that. So that is exactly, you know, the surface that we're trying to lay down here and I think this is a huge, >>huge game changer. Well, great stuff, want to move on the next we're getting go on for hours on that one topic. I think telemetry is a super important trend. Uh you guys are on top of a great, great job to bring in the Ai piece. I think that's super cool. Let's get back to the end of blocking and tackling Tracy. You know, one of the things that we're seeing with devops as it goes mainstream now, you've got def sec apps in there too, is you've got the infrastructure and you've got the modern application development, modern application developers, just wanna code, be productive, all that security shifting left, everyone's all happy that things are going great under the hood. You have a whole set of developers working on infrastructure. The end of the customers don't want to manage their own infrastructure. How is red hat focused on these two groups? Because you got this SRE like cloud Ops persona developing in the enterprise and you got the developers, it's kind of like almost two worlds coming together, how you, how you helping customers, you know, control their infrastructure and manage it better. >>Yeah, so great question. And you know, this really plays to the strength of what, you know, we have been trying to champion here at red hat for for many years now around the hybrid cloud and this, you know, hopefully everybody's recently heard about the announcement we've made with our new offering Rosa in partnership with amazon. Um you know, we've got different offerings that enables customers to really focus, as you mentioned on the key aspects that they are concerned about, which is how do they drive their businesses, how do they create their applications, their workloads that they need to and offload, you know, the need for having to understand all of the I. T. Infrastructure that's underneath. Um We want to red hat to reduce the operational complexity that customers are having um and give them the ability to really focus on what's important for them. Um how can they be able to scale out their applications, their businesses and continue to add value where they need to have and so um I think it's great we're seeing a huge uptake right now and we've got customers and they understand completely this hybrid cloud model where they're, you know, purchasing open shift um for certain, you know, applications and workloads that they want to run inside their own data centers. And then for those that they know that they don't, you know, don't have to be inside their own data centers. They don't want to have all of that operational complexity. They want to utilize some of the clouds. That's when they're starting to look at other things like rosa or open shift dedicated and and really starting to find the right mix that works well for their business. >>So are you saying that you guys are going to the next level because the previous, I won't say generation but the current situation was okay, you're born in the cloud or you lift and shift to the cloud, You do that manually, then you go on premise to build that cloud operations. Now you're in a hybrid environment. So you're saying if I get this right that you guys are providing automation around standing up in building services on AWS and cloud, public cloud and hybrid, is that kinda what you're getting at? >>Yeah. So the to go to the higher multi cloud world, right? You want platform consistency, right? Running my application running on a platform consistently, you know, where we go. Right. Tracy started talking about this idea of in some cases you say, well I've got the infrastructure team, I've got the ops team, johnny talked about this notion of, well the dwarves can be hard, sometimes right to some groups. Um, and so hey, red hat or hey redhead, plus, you know, my hyper scale of choice, you know, take that off of my hands, Right. Run that for me consistently yourself. Right. So I focused on my application uh and the management of infrastructure is something that's on you Tracy talked about rosa, that's our joint uh first party service that you know, we've got with amazon were directly available in amazon's console, you can go pull that down, right. You'll see red hat open shift on AWS, right on their uh we've got a similar one with Microsoft Azure Tracy mentioned open dedicated, we stand up the platform, we have our own sorry team that manages it with IBM as well as with google. So you pick your cloud of choice and we'll make sure, you know, we'll give you a platform that if you as a customer so choose to self manage. Great, go for it. If you'd like for us to manage it directly ourselves or in conjunction with the cloud provider and provided to you as a native service, you know, we can do that for you as well. Right? So that day to obsolete, you know, challenge that we're talking about. You know, it's something that we can get your hands if you want us to. >>That's really cool. You gotta manage service. They can do it themselves whatever they want. They can do it on public cloud and hybrid. Great stuff. Yeah, I think that's the key. Um, and that's, that's, that's killer. Now, the next question is my favorite. I want to ask you guys both pretend I'm a customer and I'm like, okay, Tracy shit, tell me what's in it for me. What is open shifts and red hat doing for me is the customer? What are you bringing to the table for me? What are you gonna do for me? What is red hat doing for me today? So if you have the kind of bottom line we were in the elevator or probably I ask you, I like what I'm hearing. Why? Why are you cool? Why are you relevant? What's in it for me? >>You >>already start? Okay. Yeah, so I mean I think it's a couple of things that we let's just tie it back to the first initial blend. I mean we've got, we're enabling the customers to choose like where do they want to work that run their workloads, what do they want to focus on? I think that's the first thing. Um we're enabling them to also determine like what workloads do they want to put on there. We continue to expand the workloads that we are providing um capabilities to customers. You know most, you know one of the more recent ones we've had is you know, enablement of Windows containers a huge plus for us. Um, you know, it's just kind of talked about, dropped the buzzword ai you know, recently, you know, we're looking at that, we're talking about, you know, moving workloads need to go to the edge now. It's not just about being in the data centers, so it's about enablement. That's really what open shift as you know, bread and butter is, is, you know, let us, you know, create the ability for you to drive your workloads, whichever, whatever your workloads is, modernize those workloads um, in place them wherever you want to. >>Yes, your your answer. How would you say to that? >>I'll build on what Tracy said, right. She obviously took the, you know, build up tribal Benjamin perspective and I'll sort of talk about a business thing you're introducing, actually add threat at summit. So, you know, we go up and acquire stock rocks, you know, further deepen investment in communities or containment of security. Uh if you recall, john, we've talked to you about, you know, advanced cluster management team that we actually got from IBM incorporate that within red hat, um, to start providing, you know, those capabilities are consistent, you know, cluster policy, immigration management. Um, and you know, in the past we've made an acquisition of Core West, we've got a lot of technology from that incorporated the platform and also things like the quake container registry. What we're introducing address had some it is a way for us to package all of that together. So a customer doesn't say, look, you know, let me pick out a container platform here, let me go find, you know, somebody manage it over there. You let me see, you know what security you adhere. We introduced something called open shift platform plus right. Which is the packaging of, you know, core Open shift contain a platform uh, capabilities within uh, stack rocks, which we're calling advanced cluster security capabilities of cluster management, which is called advanced cluster management. And the quake container registry always want to make it much easier for customers to consume that. And again, you know, the goal is, you know, run that consistently in your hybrid multi club >>chef Tracy. Great, great segment, great insight. Um, here on the cloud platform and open shift under the hood. Uh, you guys are well positioned and I was talking about Arvin and idea who acquired red hat. You know, it's pretty clear that cloud native hybrid is the new cloud operating environment. That's clear. You guys are well positioned. And congratulations. Final question Chef. Take a minute to quickly put the plug in for open shift. What's next? Um, looking forward, what do you guys building on? Um, what's on the roadmap if you can negative share the road map, but yeah, tell us what you're thinking about. I mean you're innovating out in the open, love your shirt by the way and that's the red hat way, looking ahead. What's coming for? Open shift? >>So john I will say this, our roadmap is out in the open every quarter. Our product managers host the session right open to anybody, right? You know, customers prospect, competitors, anybody can can come on. Um, and uh, you hear about our road map, lots of interesting things they're working on uh, as you can imagine investments on the edge front, right? So that's across our portfolio, right on the open shift side, but also on learning platform as well as on the open stack front, make it easier to have, you know, slim down open shift. we'll run that you won't be able to run uh open ship in remote locations and then manage it. Um So expect for us uh you know, just to show you more work there, drinking things like uh ai and more workloads directly onto the platform, but you'll see what they're doing to get more Alex on what we're doing to take uh technologies that we've got called Open data hub to make it easier to run more data intensive, more ai ml types of frameworks directly a platform. Um And so that's a great interest, more workloads Tracy, start talking about that. Right, so Windows containers, support has G eight, uh and what's really awesome about that is that we've done that with Microsoft, right, so that offering is jointly supported by both us and our partners over at Microsoft uh virtualization, which is taking much machines and being able to run them as dangerous orchestrated by communities Um, and and doing more work, you know, on that front as well. So just a lot of different areas uh, were investigated and really, really excited to bring more workloads on 2:00. >>Well, Chef Tracy, great segment with a lot of data in there. Thanks for spending time in and providing that insight and uh, sharing the information. A lot of flowers blooming um, here in the cloud native environment, a lot of action. A lot of new stuff going on. Love the shift left. I think that's super relevant. You guys do a great job. Thanks for coming on. I appreciate it. >>Okay. >>This the cubes coverage of red hat summit. I'm john for a host of the cube. Thank you for watching.

>>Hello, everyone. Welcome to Entities Summit. My name is Ellen Komarovsky and I will talk about sparks So simple realizable arguments of knowledge. This talk is based on the joint work No, me, Frank, Cody, Freytag and Raphael past. Let me start by telling you what's the same documents are that's the same argument is a special type of interactive protocol between the prove prove er and the verifier who share some instance X, >>which is allegedly in some language. And the goal of the protocol is for the proper toe convince the very far that access indeed in the language for completeness, the guarantees that their guarantees that if X is indeed in the language, the verifier will in the end of the protocol indeed be convinced. On the other hand, for sadness we require that if X is not in the language, that no matter what the proper does, as long as it is bounded to run in polynomial time, the verifier will not be convinced. There is a stronger notion of sadness called an argument of knowledge, which says that the only way for the approval to continue the verifier is by knowing some witness there is a mathematical way to formalize this notion, but I will not get into it for efficiency. And what makes this protocol succinct is that we require the very far is running time and communication complexity between the program, the verifier Toby, both mounted by some political written function in T, where T is the time to verify the empty statement. In terms of the proof is running time, we don't require anything except that it's, for example, in normality. The goal of this work is to improve this polygonal overhead of the prove er, to explain why this is an important task. Let me give you a motivating example, which is just the concept of delegation of computation. So considering some small device, like a laptop or smartphone, that we used to perform some complicated computation which it cannot do. Since it is a big device, it wishes to delegate the computation to some service or cloud to perform the computation for it. Since the small device does not fully trust the service, it may want to ask the device the service to also issue a proof for correctness of the computation. And the problem is that if the proof it takes much more time than just performing the computation. It's not clear that this is something that will be useful in practice thinking. Think off an overhead, which is square of the time it takes to perform the computation. This will become, very quickly a very big number or very, very large delay for generating the We're not the >>first to study this problem. It has been studied for several decades, and at least from a theoretical point of view, the problem is almost solved or essentially solved. We have constructions off argument systems is great overhead, just bottle of arrhythmic multiplicity of overhead. This is obtained by combining efficient disappears. Together with Killian's arguments is there's a >>huge open problem in complexity. Theory of constructing PCP is with constant over namely, running just in linear time in the running, in the running time off just running the computation. But we argued that even if we had such a PCP and the constant was great, let's say it was just too. This would already be too much, because if you delegate the computation to takes a month toe complete, then waiting another month just for the proof might not be so reasonable. There is a solution in the literature for this problem in what we call using what we call a reliable PCP medicine. And I'll show that there is a recipe construction that has the following very useful property. Once you perform the computation itself without the just the computation and write down the computation to blow, then there is the way to generate every simple off the PCP in just only logarithmic time. So this means that you can, in parallel after computing the function itself, you can empire led, compute the whole PCP in just falling over it. Next time this gives you this gives us a great argument system with just t plus Polly locked parallel time instead of three times for luck tea time. But for this we need about the process service, which is prohibitively large. This is where sparks come in. We introduced the notion, or the paradigm off, computing the proof in part to the computation, not after the computation is done slightly more formally. What spark is it's just a succinct argument of knowledge, like what we said before, with the very fired and communication of Leslie being small but now we also require approval for which is super efficient. Namely, it can be paralyzed able. And it has to finish the proof together with the computation in Time T plus volatility, which essentially the best you can hope for. And we want to prefer to do so only with political rhythmic number off processors. You can also extend the definition to handling computations, which are to begin with a paralyze herbal. But I will not touch upon this. In the stock, you can see the paper. For the >>girls, we have two main results. The first main result is the construction of an interactive spark. It's just four rounds, and it is assumes Onley collisions is not hash functions. The second result is a non interactive spark. This result also assumes career resistant hash functions and in addition, the existence off any snark and namely succinct, non interactive argument of college that does not have to be a super efficient in terms of programming time. Slightly more generally, the two theories follow from >>combined framework, which takes essentially any argument of knowledge and turns it into a spark by assuming on a collision system, hash functions and maybe the multi behind the construction could be viewed as a trade off between computation time and process. Source. Winston. She ate theorem one using Killings protocol, which is an argument of knowledge, which is a four round argument of knowledge. And we insensate you're into using its not which is an argument knowledge. Just by definition, let me tell you what are the main ideas underlying our construction before telling you to control the ideas. Let me make some simplifying assumptions. The first assumption I will only be talking about the non interactive regime. The second example assumption is that I'm going to assume snark, which is a non interactive 16 argument of knowledge. And then we'll assume that's not the snark which is super efficient. So it will consumed other time to t for computation that takes 20 so almost what we want, but just not yet, not not yet there. I will assume that the computation that we want to perform a sequential and additionally I will assume that the computation has no >>space, namely its ah, or it has very low space. So think about the sequential computation, which MM doesn't have a lot of space or even zero for the for the time being, I would like to discuss how to simplify, how to remove this simplifying assumptions. So the starting idea is based on two works off a nettle and duckling. It'll from a couple of years ago. And here's how it works. So >>remember, we want toe performative time. Computation generated proof and we need to finish roughly by time. T. So the idea is to run half of the computation, which is what we can afford because we have a snark that can generate a proof in additional to over two steps so we can run the complete half of the computation and prove that half of the computation all in time T. And the idea is that now we can recursive Lee computer improve the rest of the computation in Parliament. Here's how it looks like. So you run half of the computation, started proof, and then you run a quarter of the remaining half of the remaining computation, which is a quarter of the original one, and prove it. And in parallel again, you take another eighth of the computation, which is one half of what's left and so on. And so forth. As you can see, that eventually will finish the whole computation. And you only need something like logarithmic Lee. Many parallel processors and the communication complexity and verifies running time only grow by algorithmic >>factor. So this is the main idea. Let's go back to the simplifying assumptions we have. So the first one was that I'm only gonna talk about the new interactive regime. You have to believe me that the same ideas extend to the interactive case, which is a little bit more massive with notation. But the ideas extent so I will not talk about it anymore. The second assumption I had was that I have a super efficient start, so it had over had two T >>40 time computation again. You have to believe me that if you work out the math, then the ideas extend to starts with quasi linear overhead. Namely, starts that working time tee times, Polly locked e and then the result extends to any snark because of a result because of a previous work will be tense. Kettle, who showed that a snark with the proof it runs in polynomial time can be generically translated into a snark where the programs in quasi linear with quasi linear overhead. So this gives a result from any stark not only from pretty efficient starts. The last bullet was about the fact that we're dealing with only with sequential Ram computations. And again, you have to believe me that the ideas can be extended toe tyrants And the last assumption which is the focus of this work is how to get rid of the small space assumption. This is what I'm gonna be talking next. Let's see what goes wrong. If the if the computation has space, remember what we did in the previous. In a couple of slides ago, the construction was toe perform. Half of the computation prove it and then half of the remaining computation prove it. And >>so on. If you write down the statement that each of these proofs proofs, it's something like that a machine m on input X executed for some number of steps starting from some state ended at some other state. And if you notice the statement itself depends on the space of the computation, well and therefore, if the space of the computation is nontrivial, the statements are large and therefore the communication will be large and therefore the very fire will have toe be running time, proportional to the space and so on. So we don't even get a saint argument if we do it. Neighborly. Here's a solution for this problem. You can say, Well, you don't have to include the space in the whole space. In the statement, you can include only a digest of the space. Think about some hash function of the space. So indeed, you can modify the statement to not include the space, but only a digest. And now the statement will be a little bit more complicated. It will be that there exists some initial state end state such that there hush is consistent with digest in the statement. And if you run the machine M for K state and for K steps starting from the initial space, you end up with the final space. So this is great. It indeed solves the communication complexity problem in the very far complexity problem. But notice that from the proof for site, we didn't actually do anything because we just move, pushed the complexity in tow. The weakness. So the proof is running. Time is still very large with this solution. Yeah. Our final solution, if in a very high level, is to compress the witness. So instead of using the whole space is the witness. We will be using the computation itself in the computation that we ran as the witness. So now the statement will be off the same form, so it will still be. It will still consist off to digests and machine. But now the the witness will be not the whole state. But it will be the case steps that we performed. Namely, it will be that there exists case steps that I performed such that if I run >>the machine m on this case steps and I started with a digest and I just start and I applied this case steps on the digest. I will end up with the Final Digest. In order to implement this, we need some sort off a nap. Datable digest. This is not really hard, not so hard to obtain because you could just do something like a miracle tree. It's not hard to see that you can add the locations in the medical tree quite efficiently. But the problem is that we need toe toe to compute those updates. Not only not only we need toe be ableto update the hash browns, the hush or the largest which don't also be able to compute the updates in parallel to the computation. And to this end, we introduce a variant of Merkle trees and show how to perform all of those updates level by level in the in the Merkel tree in a pipeline in fashion. So namely, we push the updates off the digest in toe the Merkel tree, one after the other without waiting for the previous ones to end. And here we're using the tree structure off Merkle trees. So that's all I'm gonna say about the protocol. I'm just gonna end with showing you how the final protocol looks like We run case steps of computations. Okay, one steps of computation and we compute the K updates for those case steps in violent the computation. So every time we run a step of computation, we also update start an update off our digest. And once we are finished computing all the updates, we can start running a proof using those updates as witness and were forcibly continuing this way as a conclusion this results with the spark namely 1/16 argument system with the proof is running Time t plus for you Look, team and no times and all we need is something like quality of arrhythmic number of processors. E would like to mention that this is a theoretical result and by no means should be should be taken as a za practical thing that should be implemented. But I think that it is important to work on it. And there is a lot of interesting questions on how to make this really practical and useful. So with that, I'm gonna end and thank you so much for inviting me and enjoy the sandwich.

>>thank you so much that sake for inviting me to the Entity Research Summit. And I'm really excited to talk to all of them today. So I will be talking about achieving indistinguishability obfuscation from well founded assumptions. And this is really the result of a wonderful two year collaboration with But now it's standing. Graduate student I use chain will be graduating soon on my outstanding co author, Rachel Lynde from the University of Washington. So let me jump right into it. We all know that constructing indistinguishable the obfuscation. Constructing Io has been perhaps the most consequential open problem in the foundations of photography. For several years now, they've seen over 100 papers written that show how to use Iot to achieve a number of remarkable cryptographic goals. Um, that really expand the scope of cryptography in addition to doing just remarkable, really interesting new things. Unfortunately, however, until this work, I told the work I'm about to tell you about all known constructions of Iove. All required new hardness, assumptions, heart assumptions that were designed specifically to prove that Iowa secure. And unfortunately, uh, this has a torture of history. And many of the assumptions were actually broken, which led to just a lot of doubt and uncertainty about the status of Iot, whether it really exists or doesn't exist. And the work I'm about to tell you about today changes that state of affairs in the continental way in that we show how to build io from the combination of four well established topographic assumptions. Okay, let me jump right into it and tell you how we do it. So before this work that I'm about to tell you about over the last two years with Rachel and Ayush, we actually constructed a whole sequence of works that have looked at this question. And what we showed was that if we could just build a certain special object, then that would be sufficient for constructing Io, assuming well established assumptions like L W E P R g s and M C zero and the 68 assumption of a violin. Your mouths. Okay, So what is this object? The object first starts with a P. R G and >>S zero. In other words, of trg with constant locality that stretches end bits of seed to M bits of output where am is ended one plus Epsilon for any constant Epsilon zero. Yes, but in addition to this prg, we also have these l w we like samples. So as usual, we have an elder Bluey Secret s which is random vector z b two k, where K is the dimension of the secret, which is much smaller than any way also have this public about vectors ai which are also going to be okay. And now what is given out is are the elderly samples where the error is this X I that is just brilliant value. Uh, where these excise air Also the input to our prg. Okay, unfortunately, we needed to assume that these two things together, this y and Z together is actually pseudo random. But if you think about it, there is some sort of kind of strange assumption that assumes some kind of special leakage resilience, property of elderly, we where elderly samples, even with this sort of bizarre leakage on the errors from all debris, is still surround or still have some surrounding properties. And unfortunately, we had no idea how to prove that. And we still don't have any idea how to prove this. Actually, So this is just a assumption and we didn't know it's a new assumption. So far, it hasn't been broken, but that's pretty much it. That's all we knew about it. Um and that was it. If we could. If this is true, then we could actually build. I'll now to actually use this object. We needed additional property. We needed a special property that the output of this prg here can actually be computed. Every single bit of the output could be computed by a polynomial over the public. Elder Louise samples Why? And an additional secret w with the property that this additional secret w is actually quite small. It's only excise em to the one minus delta or some constant delta gradients. Barroso polynomial smaller from the output of the prg. And crucially, the degree of this polynomial is on Lee to its violin e er can this secret double that's where the bottle in your mouth will come. Okay. And in fact, this part we did not approve. So in this previous work, using various clever transformations, we were able to show that in fact we are able to construct this in a way to this Parliament has existed only degree to be short secret values. Double mhm. So now I'm gonna show you how using our new ideas were actually gonna build. That's a special object just like this from standard assumptions. We're just gonna be sufficient for building io, and we're gonna have to modify it a little bit. Okay? One of the things that makes me so excited is that actually, our ideas are extremely simple. I want to try to get that across today. Thanks. So the first idea is let's take thes elder movie samples that we have here and change them up a little bit when it changed them up. Start before I get to that in this talk, I want you to think of K the dimension of the secret here as something very small. Something like end of the excellent. That's only for the stock, not for the previous work. Okay. All right. So we have these elderly samples right from the previous work, but I'm going to change it up instead of computing them this way, as shown in the biggest slide on this line. Let's add some sparse hair. So let's replace this error x i with the air e i plus x I where e is very sparse. Almost all of these IIs or zero. But when the I is not zero is just completely random in all of Z, pizza just completely destroys all information. Okay, so first I just want to point out that the previous work that I already mentioned applies also to this case. So if we only want to compute P R g of X plus E, then that can still be computer the polynomial. That's degree to in a short W that's previous work the jail on Guess work from 2019. I'm not going to recall that you don't have time to tell you how you do it. It's very simple. Okay, so why are we doing this? Why are we adding the sparse error? The key observation is that even though I have changed the input of the PRG to the X Plus E because he is so sparse, prg of explosive is actually the same as P. R. G of X. In almost every outlet location. It's only a tiny, tiny fraction of the outputs that are actually corrupted by the sparse Arab. Okay, so for a moment Let's just pretend that in fact, we knew how to compute PRGF X with a degree to polynomial over a short seeking. We'll come back to this, I promise. But suppose for a moment we actually knew how to compute care to your ex, Not just scared of explosive in that case were essentially already done. And the reason is there's the L. P n over zp assumption that has been around for many years, which says that if you look at these sort of elderly like samples ai from the A, I s but plus a sparse air e I where you guys most zero open when it's not serious, completely random then In fact, these samples look pseudo random. They're indistinguishable from a I r r. I just completely uniform over ZP, okay? And this is a long history which I won't go because I don't have time, but it's just really nice or something. Okay, so let's see how we can use it. So again, suppose for the moment that we were able to compute, not just appeared you've explosive but appeared to you that well, the first operation that since we're adding the sparse R E I This part the the L P N part here is actually completely random by the LP an assumption so by L P and G. P, we can actually replace this entire term with just all right. And now, no, there is no more information about X present in the samples, The only place where as is being used in the input to the prg and as a result, we could just apply to sit around this of the prg and say this whole thing is pseudo random and that's it. We've now proven that this object that I wanted to construct it is actually surrounded, which is the main thing that was so bothering us and all this previous work. Now we get it like that just for the snap of our fingers just immediately from people. Okay, so the only thing that's missing that I haven't told you yet is Wait, how do we actually compute prg attacks? Right? Because we can compute p r g of X plus e. But there's still gonna be a few outputs. They're gonna be wrong. So how can we correct those few corrupted output positions to recover PRGF s? So, for the purpose of this talks because I don't have enough time. I'm gonna make sort of a crazy simplifying assumption. Let's just assume that in fact, Onley one out the position of P r g of X plus e was correct. So it's almost exactly what PR gox. There's only one position in prg of Ecstasy which needs to be corrected to get us back to PR gox. Okay, so how can we do that? The idea is again really, really simple. Okay, so the output of the PRG is an M. Becker and so Dimension and Becker. But let's actually just rearrange that into a spirit of them by spirit of them matrix. And as I mentioned, there's only one position in this matrix that actually needs to be corrected. So let's make this correction matrix, which is almost everywhere. Zero just in position. I j it contains a single correction factor. Why, right? And if you can add this matrix to prg of explosive, then we'll get PR dribbles. Okay, so now the Onley thing I need to do is to compute this extremely sparse matrix. And here the observation was almost trivia. Just I could take a spirit of em by one maker That just has why in position I and I could take a one by spirit of them matrix. I just have one in position J zero everywhere else. If I just take the tensor product was music the matrix product of these two of these two off this column vector in a row vector. Then I will get exactly this correction matrix. Right? And note that these two vectors that's called them you and be actually really, really swamped their only spirit of n dimensional way smaller than them. Right? So if I want to correct PRGF Expo see, all I have to do is add you, Tenzer V and I can add the individual vectors u and V to my short secret w it's still short. That's not gonna make W's any sufficiently bigger. And you chancery is only a degree to computation. So in this way, using a degree to computation, we can quickly, uh, correct our our computation to recover prg events. And now, of course, this was oversimplifying situation, uh, in general gonna have many more areas. We're not just gonna have one error, like as I mentioned, but it turns out that that is also easy to deal with, essentially the same way. It's again, just a very simple additional idea. Very, very briefly. The idea is that instead of just having one giant square to them by sort of a matrix, you can split up this matrix with lots of little sub matrices and with suitable concentration bound simple balls and pins arguments we can show that we could never Leslie this idea this you Tenzer v idea to correct all of the remaining yet. Okay, that's it. Just, you see, he's like, three simple >>ah ha moments. What kind of all that it took, um, that allowed >>us to achieve this result to get idol from standard assumptions. And, um, of course I'm presenting to you them to you in this very simple way. We just these three little ideas of which I told you to. Um, but of course, there were only made possible because of years of struggling with >>all the way that didn't work, that all that struggling and mapping out all the ways didn't work >>was what allowed us toe have these ideas. Um, and again, it yields the first I'll construction from well established cryptographic assumptions, namely Theo Elgon, assumption over zp learning with errors, assumption, existence of PR GS and then zero that is PR juice with constant death circuits and the SX th assumption over by linear notes, all of which have been used many years for a number of other applications, including such things as publicly inversion, something simple public inversion that's the That's the context in which the assumptions have been used so very far from the previous state of affairs where we had assumptions that were introduced on Lee Professor constructing my own. And with that I will conclude, uh and, uh, thank you for your attention. Thanks so much.

>> From our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Welcome to the special. Keep conversation here in Palo Alto, California. I'm John for a co host of the Cube. Were Arsenal was a C I S O. C. So for Microsoft also corporate vice President, Chief information security. Thanks for joining me today. >> Thank you. >> Appreciate it. Thanks. So you have a really big job. You're a warrior in the industry, security is the hardest job on the planet. >> And hang in sight >> of every skirt. Officer is so hard. Tell us about the role of Microsoft. You have overlooked the entire thing. You report to the board, give us an overview of what >> happens. Yeah. I >> mean, it's you know, obviously we're pretty busy. Ah, in this world we have today with a lot of adversaries going on, an operational issues happening. And so I have responsibility. Accountability for obviously protecting Microsoft assets are customer assets. And then ah, And for me, with the trend also responsibility for business continuity Disaster recovery company >> on the sea. So job has been evolving. We're talking before the camera came on that it's coming to CEO CF roll years ago involved to a business leader. Where is the sea? So roll now in your industry is our is a formal title is it establishes their clear lines of reporting. How's it evolved? What's the current state of the market in terms of the sea? So it's roll? >> Yeah, the role is involved. A lot. Like you said, I think like the CIA or twenty years ago, you know, start from the back room of the front room and I think the, you know, one of things I look at in the role is it's really made it before things. There's technical architecture, there's business enablement. There's operational expert excellence. And then there's risk management and the older ah, what does find the right word? But the early see so model was really about the technical architecture. Today. It's really a blend of those four things. How do you enable your business to move forward? How do you take calculated risks or manage risks? And then how do you do it really effectively and efficiently, which is really a new suit and you look at them. You'LL see people evolving to those four functions. >> And who's your boss? Would you report to >> I report to a gentleman by the name of a curtain. Little Benny on DH. He is the chief digital officer, which would be a combination of Seo did officer and transformation as well as all of Microsoft corporate strategy >> and this broad board visibility, actually in security. >> Yeah, you >> guys, how is Microsoft evolved? You've been with the company for a long time >> in the >> old days ahead perimeters, and we talk about on the Cube all the time. When a criminalist environment. Now there's no perimeter. Yeah, the world's changed. How is Microsoft evolved? Its its view on security Has it evolved from central groups to decentralize? How is it how how was it managed? What's the what's the current state of the art for security organization? >> Well, I think that, you know, you raise a good point, though things have changed. And so in this idea, where there is this, you know, perimeter and you demanded everything through the network that was great. But in a client to cloak cloud world, we have today with mobile devices and proliferation or cloud services, and I ot the model just doesn't work anymore. So we sort of simplified it down into Well, we should go with this, you know, people calls your trust, I refer to It is just don't talk to strangers. But the idea being is this really so simplified, which is you've got to have a good identity, strong identity to participate. You have to have managed in healthy device to participate, to talk to, ah, Microsoft Asset. And then you have to have data in telemetry that surrounds that all the time. And so you basically have a trust, trust and then verify model between those three things. And that's really the fundamental. It's really that simple. >> David Lava as Pascal senior with twenty twelve when he was M. C before he was the C E O. V M. Where he said, You know his security do over and he was like, Yes, it's going to be a do over its opportunity. What's your thoughts on that perspective? Has there been a do over? Is it to do over our people looking at security and a whole new way? What's your thoughts? >> Yeah, I mean, I've been around security for a long time, and it's there's obviously changes in Massa nations that happened obviously, at Microsoft. At one point we had a security division. I was the CTO in that division, and we really thought the better way to do it was make security baked in all the products that we do. Everything has security baked in. And so we step back and really change the way we thought about it. To make it easier for developers for end users for admin, that is just a holistic part of the experience. So again, the technology really should disappear. If you really want to be affected, I think >> don't make it a happy thought. Make it baked in from Day one on new product development and new opportunity. >> Yeah, basically, shift the whole thing left. Put it right in from the beginning. And so then, therefore, it's a better experience for everyone using it. >> So one of things we've observed over the past ten years of doing the Cube when do first rolled up with scene, you know, big data role of date has been critical, and I think one of the things that's interesting is, as you get data into the system, you can use day that contextually and look at the contextual behavioral data. It's really is create some visibility into things you, Meyer may not have seen before. Your thoughts and reaction to the concept of leveraging data because you guys get a lot of data. How do you leverage the data? What's the view of data? New data will make things different. Different perspectives creates more visibility. Is that the right view? What's your thoughts on the role of Data World Data plays? >> Well, they're gonna say, You know, we had this idea. There's identity, there's device. And then there's the data telemetry. That platform becomes everything we do, what there's just security and are anomalous behavior like you were talking about. It is how do we improve the user experience all the way through? And so we use it to the service health indicator as well. I think the one thing we've learned, though, is I was building where the biggest data repositories your head for some time. Like we look at about a six point five trillion different security events a day in any given day, and so sort of. How do you filter through that? Manage? That's pretty amazing, says six point five trillion >> per day >> events per day as >> coming into Microsoft's >> that we run through the >> ecosystem your systems. Your computers? >> Yeah. About thirty five hundred people. Reason over that. So you can Certainly the math. You need us. Um, pretty good. Pretty good technology to make it work effectively for you and efficiently >> at RC A Heard a quote on the floor and on the q kind of echoing the same sentiment is you can't hire your way to success in this market is just not enough people qualified and jobs available to handle the volume and the velocity of the data coming in. Automation plays a critical role. Your reaction to that comment thoughts on? >> Well, I think I think the cure there, John, those when you talk about the volume of the data because there's what we used to call speeds and feeds, right? How big is it? And I used to get great network data so I can share a little because we've talked, like from the nineties or whatever period that were there. Like the network was everything, but it turns out much like a diverse workforce creates the best products. It turns out diverse data is more important than speeds and feeds. So, for example, authentication data map to, you know, email data map to end point data map. TEO SERVICE DATA Soon you're hosting, you know, the number of customers. We are like financial sector data vs Healthcare Data. And so it's the ability Teo actually do correlation across that diverse set of data that really differentiates it. So X is an example. We update one point two billion devices every single month. We do six hundred thirty billion authentications every single month. And so the ability to start correlating those things and movement give us a set of insights to protect people like we never had before. >> That's interesting telemetry you're getting in the marketplace. Plus, you have the systems to bring it in >> a pressure pressure coming just realized. And this all with this consent we don't do without consent, we would never do without consent. >> Of course, you guys have the terms of service. You guys do a good job on that, But I think the point that I'm seeing there is that you guys are Microsoft. Microsoft got a lot of access. Get a lot of stuff out there. How does an enterprise move to that divers model because they will have email, obviously. But they have devices. So you guys are kind of operating? I would say tear one of the level of that environment cause you're Microsoft. I'm sure the big scale players to that. I'm just an enterprising I'm a bank or I'm an insurance company or I'm in oil and gas, Whatever the vertical. Maybe. What do I do if I'm the sea? So they're So what does that mean, Diversity? How should they? >> Well, I think they have a diverse set of data as well. Also, if they participate, you know, even in our platform today, we you know, we have this thing called the security graph, which is an FBI people can tap into and tap into the same graph that I use and so they can use that same graph particular for them. They can use our security experts to help them with that if they don't have the all the resource and staff to go do that. So we provide both both models for that to happen, and I think that's why a unique perspective I should think should remind myself of which is we should have these three things. We have a really good security operations group we have. I think that makes us pretty unique that people can leverage. We build this stuff into the product, which I think is good. But then the partnership, the other partners who play in the graph, it's not just us. So there's lots of people who play on that as well. >> So like to ask you two lines of questions. Wanting on the internal complex is that organizations will have on the external complexity and realities of threats and coming in. How do they? How do you balance that out? What's your vision on that? Because, you know, actually, there's technology, his culture and people, you know in those gaps and capabilities on on all three. Yeah, internally just getting the culture right and then dealing with the external. How does a C so about his company's balance? Those realities? >> Well, I think you raised a really good point, which is how do you move the culture for? That's a big conversation We always have. And that was sort of, you know, it's interesting because the the one side we have thirty five hundred people who have security title in their job, But there's over one hundred thousand people who every day part of their job is doing security, making sure they'LL understand that and know that is a key part we should reinforce everyday on DSO. But I think balancing it is, is for me. It's actually simplifying just a set of priorities because there's no shortage of, you know, vendors who play in the space. There's no shortage of things you can read about. And so for us it was just simplifying it down and getting it. That simplifies simplified view of these are the three things we're going to go do we build onerous platform to prioritize relative to threat, and then and then we ensure we're building quality products. Those five things make it happen. >> I'd like to get your thoughts on common You have again Before I came on camera around how you guys view simplification terminal. You know, you guys have a lot of countries, the board level, and then also you made a common around trust of security and you an analogy around putting that drops in a bucket. So first talk about the simplification, how you guys simplifying it and why? Why is that important? >> You think we supply two things one was just supplying the message to people understood the identity of the device and making sure everything is emitting the right telemetry. The second part that was like for us but a Z to be illustrative security passwords like we started with this technology thing and we're going to do to FAA. We had cards and we had readers and oh, my God, we go talk to a user. We say we're going to put two FAA everywhere and you could just see recoil and please, >> no. And then >> just a simple change of being vision letters. And how about this? We're just going to get rid of passwords then People loved like they're super excited about it. And so, you know, we moved to this idea of, you know, we always said this know something, know something new, how something have something like a card And they said, What about just be something and be done with it? And so, you know, we built a lot of the capability natively into the product into windows, obviously, but I supported energies environment. So I you know, I support a lot of Mac clinics and IOS and Android as well So you've read it. Both models you could use by or you could use your device. >> That's that. That's that seems to be a trend. Actually, See that with phones as well as this. Who you are is the password and why is the support? Because Is it because of these abuses? Just easy to program? What's the thought process? >> I think there's two things that make it super helpful for us. One is when you do the biometric model. Well, first of all, to your point, the the user experience is so much better. Like we walk up to a device and it just comes on. So there's no typing this in No miss typing my password. And, you know, we talked earlier, and that was the most popular passwords in Seattle with Seahawks two thousand seventeen. You can guess why, but it would meet the complexity requirements. And so the idea is, just eliminate all that altogether. You walk up machine, recognize you, and you're often running s o. The user experience is great, but plus it's Actually the entropy is harder in the biometric, which makes it harder for people to break it, but also more importantly, it's bound locally to the device. You can't run it from somewhere else. And that's the big thing that I think people misunderstanding that scenario, which is you have to be local to that. To me, that's a >> great example of rethinking the security paradigm. Exactly. Let's talk about trust and security. You you have an opinion on this. I want to get your thoughts, the difference between trust and security so they go hand in hand at the same time. They could be confused. Your thoughts on this >> well being. You can have great trust. You can, so you can have great security. But you generally and you would hope that would equate like a direct correlation to trust. But it's not. You need to you build trust. I think our CEO said it best a long time ago. You put one bucket of water, one bucket. Sorry, one truffle water in the bucket every time. And that's how you build trust. Over time, my teenager will tell you that, and then you kick it over and you put it on the floor. So you have to. It's always this ratcheting up bar that builds trust. >> They doing great you got a bucket of water, you got a lot of trust, that one breach. It's over right, >> and you've got to go rebuild it and you've got to start all over again. And so key, obviously, is not to have that happen. But then, that's why we make sure you have operational rigor and >> great example that just totally is looking Facebook. Great. They have massive great security. What really went down this past week, but still the trust factor on just some of the other or societal questions? >> Yeah, >> and that something Do it. >> Security. Yeah, I think that's a large part of making sure you know you're being true. That's what I said before about, you know, we make sure we have consent. We're transparent about how we do the things we do, and that's probably the best ways to build trust. >> Okay, so you guys have been successful in Microsoft, just kind of tight the company for second to your role. It's pretty well documented that the stock prices at an all time high. So if Donatella Cube alumni, by the way, has been on the cue before he he took over and clear he didn't pivot. He just said we'd go in the cloud. And so the great moves, he don't eat a lot of great stuff. Open source from open compute to over the source. And this ship has turned and everything's going great. But that cheering the cloud has been great for the company. So I gotta ask you, as you guys move to the cloud, the impact to your businesses multi fold one products, ecosystem suppliers. All these things are changing. How has security role in the sea? So position been impact that what have you guys done? How does that impact security in general? Thoughts? >> Yeah, I think we obviously were like any other enterprise we had thousands of online are thousands of line of business applications, and we did a transformation, and we took a method logical approach with risk management. And we said, Okay, well, this thirty percent we should just get rid of and decommission these. We should, you know, optimize and just lifting shifting application. That cloud was okay, but it turns out there's massive benefit there, like for elasticity. Think of things that quarterly reporting or and you'll surveys or things like that where you could just dynamically grow and shrink your platform, which was awesome linear scale that we never had Cause those events I talk about would require re architectures. Separate function now becomes linear. And so I think there is a lot of things from a security perspective I could do in a much more efficient must wear a fish. In fact, they're then I had to have done it before, but also much more effective. I just have compute capability. Didn't have I have signal I didn't have. And so we had to wrap her head around that right and and figure out how to really leverage that. And to be honest, get the point. We're exploited because you were the MySpace. I have disaster and continent and business. This is processed stuff. And so, you know, everyone build dark fiber, big data centers, storage, active, active. And now when you use a platform is a service like on that kind of azure. You could just click a Bach and say, I want this thing to replicate. It also feeds your >> most diverse data and getting the data into the system that you throw a bunch of computer at that scale. So What diverse data? How does that impact the good guys and the bad guys? That doesn't tip the scales? Because if you have divers date and you have his ability, it's a race for who has the most data because more data diversity increases the aperture and our visibility into events. >> Yeah, I you >> know, I should be careful. I feel like I always This's a job. You always feel like you're treading water and trying to trying to stay ahead. But I think that, um, I think for the first time in my tenure do this. I feel there's an asymmetry that benefits. They're good guys in this case because of the fact that your ability to reason over large sets of data like that and is computed data intensive and it will be much harder for them like they could generally use encryption were effectively than some organization because the one the many relationship that happens in that scenario. But in the data center you can't. So at least for now, I feel like there's a tip This. The scales have tipped a bit for the >> guy that you're right on that one. I think it's good observation I think that industry inside look at the activity around, from new fund adventures to overall activity on the analytics side. Clearly, the data edge is going to be an advantage. I think that's a great point. Okay, that's how about the explosion of devices we're seeing now. An explosion of pipe enabled devices, Internet of things to the edge. Operational technologies are out there that in factory floors, everything being I P enables, kind of reminds me of the old days. Were Internet population you'd never uses on the Internet is growing, and >> that costs a lot >> of change in value, creation and opportunities devices. Air coming on both physical and software enabled at a massive rate is causing a lot of change in the industry. Certainly from a security posture standpoint, you have more surface area, but they're still in opportunity to either help on the do over, but also create value your thoughts on this exploding device a landscape, >> I think your Boston background. So Metcalfe's law was the value the net because the number of the nodes on the network squared right, and so it was a tense to still be true, and it continues to grow. I think there's a huge value and the device is there. I mean, if you look at the things we could do today, whether it's this watch or you know your smartphone or your smart home or whatever it is, it's just it's pretty unprecedented the capabilities and not just in those, but even in emerging markets where you see the things people are doing with, you know, with phones and Lauren phones that you just didn't have access to from information, you know, democratization of information and analysis. I think it's fantastic. I do think, though, on the devices there's a set of devices that don't have the same capabilities as some of the more markets, so they don't have encryption capability. They don't have some of those things. And, you know, one of Microsoft's responses to that was everything. Has an M see you in it, right? And so we, you know, without your spirit, we created our own emcee. That did give you the ability to update it, to secure, to run it and manage it. And I think that's one of the things we're doing to try to help, which is to start making these I, O. T or Smart devices, but at a very low cost point that still gives you the ability because the farm would not be healed Update, which we learn an O. T. Is that over time new techniques happen And you I can't update the system >> from That's getting down to the product level with security and also having the data great threats. So final final talk Tracking one today with you on this, your warrior in the industry, I said earlier. See, so is a hard job you're constantly dealing with compliance to, you know, current attacks, new vector, new strains of malware. And it's all over the map. You got it. You got got the inbound coming in and you got to deal with all that the blocking and tackling of the organization. >> What do you What do >> you finding as best practice? What's the what if some of the things on the cso's checklist that you're constantly worried about and or investing in what some of >> the yeah, >> the day to day take us through the day to day life >> of visited a lot? Yeah, it >> starts with not a Leslie. That's the first thing you have to get used to, but I think the you know again, like I said, there's risk Manager. Just prioritize your center. This is different for every company like for us. You know, hackers don't break and they just log in. And so identity still is one of the top things. People have to go work on him. You know, get rid of passwords is good for the user, but good for the system. We see a lot in supply chain going on right now. Obviously, you mentioned in the Cambridge Analytical Analytics where we had that issue. It's just down the supply chain. And when you look at not just third party but forthe party fifth party supply and just the time it takes to respond is longer. So that's something that we need to continue to work on. And then I think you know that those are some of the other big thing that was again about this. How do you become effective and efficient and how you managed that supply chain like, You know, I've been on a mission for three years to reduce my number of suppliers by about fifty percent, and there's still lots of work to do there, but it's just getting better leverage from the supplier I have, as well as taking on new capability or things that we maybe providing natively. But at the end of the day, if you have one system that could do what four systems going Teo going back to the war for talent, having people, no forces and versus one system, it's just way better for official use of talent. And and obviously, simplicity is the is the friend of security. Where is entropy is not, >> and also you mentioned quality data diversity it is you're into. But also there's also quality date of you have quality and diverse data. You could have a nice, nice mechanism to get machine learning going well, but that's kind of complex, because in the thie modes of security breaches, you got pre breached in breech post breach. All have different data characteristics all flowing together, so you can't just throw that answer across as a prism across the problem sets correct. This is super important, kind of fundamentally, >> yeah, but I think I >> would I would. The way I would characterize those is it's honestly, well, better lessons. I think I learned was living how to understand. Talk with CFO, and I really think we're just two things. There's technical debt that we're all working on. Everybody has. And then there's future proofing the company. And so we have a set of efforts that go onto like Red Team. Another actually think like bad people break them before they break you, you know, break it yourself and then go work on it. And so we're always balancing how much we're spending on the technical, that cleanup, you know, modernizing systems and things that are more capable. And then also the future proofing. If you're seeing things coming around the corner like cryptography and and other other element >> by chain blockchain, my supply chain is another good, great mechanism. So you constantly testing and R and D also practical mechanisms. >> And there in the red team's, which are the teams that attacking pen everything, which is again, break yourself first on this super super helpful for us >> well bred. You've seen a lot of ways of innovation have been involved in multiple ways computer industry client server all through the through the days, so feel. No, I feel good about this you know, because it reminds me and put me for broken the business together. But this is the interesting point I want to get to is there's a lot of younger Si SOS coming in, and a lot of young talent is being attractive. Security has kind of a game revived to it. You know, most people, my friends, at a security expert, they're all gamers. They love game, and now the thrill of it. It's exciting, but it's also challenging. Young people coming might not have experience. You have lessons you've learned. Share some thoughts over the years that scar either scar tissue or best practices share some advice. Some of the younger folks coming in breaking into the business of, you know, current situation. What you learned over the years it's Apple Apple. But now the industry. >> Yeah, sadly, I'd probably say it's no different than a lot of the general advice I would have in the space, which is there's you value experience. But it turns out I value enthusiasm and passion more here so you can teach about anybody whose passion enthusiastic and smart anything they want. So we get great data people and make them great security people, and we have people of a passion like you know, this person. It's his mission is to limit all passwords everywhere and like that passion. Take your passion and driver wherever you need to go do. And I >> think the nice >> thing about security is it is something that is technically complex. Human sociology complex, right? Like you said, changing culture. And it affects everything we do, whether it's enterprise, small, medium business, large international, it's actually a pretty It's a fasten, if you like hard problem. If you're a puzzle person, it's a great It's a great profession >> to me. I like how you said Puzzle. That's I think that's exactly it. They also bring up a good point. I want to get your thoughts on quickly. Is the talent gap is is really not about getting just computer science majors? It's bigger than that. In fact, I've heard many experts say, and you don't have to be a computer scientist. You could be a lot of cross disciplines. So is there a formula or industry or profession, a college degree? Or is it doesn't matter. It's just smart person >> again. It depends if your job's a hundred percent. Security is one thing, but like what we're trying to do is make not we don't have security for developers you want have developed to understand oppa security and what they build is an example on DSO. Same with administrators and other components. I do think again I would say the passion thing is a key piece for us, but But there's all aspects of the profession, like the risk managers air, you know, on the actuarial side. Then there's math people I had one of my favorite people was working on his phD and maladaptive behavior, and he was super valuable for helping us understand what actually makes things stick when you're trying to train their educate people. And what doesn't make that stick anthropologist or super helpful in this field like anthropologist, Really? Yeah, anthropologist are great in this field. So yeah, >> and sociology, too, you mentioned. That would think that's a big fact because you've got human aspect interests, human piece of it. You have society impact, so that's really not really one thing. It's really cross section, depending upon where you want to sit in the spectrum of opportunity, >> knowing it gives us a chance to really hire like we hire a big thing for us has been hard earlier in career and building time because it's just not all available. But then also you, well, you know, hire from military from law enforcement from people returning back. It's been actually, it's been a really fascinating thing from a management perspective that I didn't expect when I did. The role on has been fantastic. >> The mission. Personal question. Final question. What's getting you excited these days? I mean, honestly, you had a very challenging job and you have got attend all the big board meetings, but the risk management compliance. There's a lot of stuff going on, but it's a lot >> of >> technology fund in here to a lot of hard problems to solve. What's getting you excited? What what trends or things in the industry gets you excited? >> Well, I'm hopeful we're making progress on the bad guys, which I think is exciting. But honestly, this idea the you know, a long history of studying safety when I did this and I would love to see security become the air bags of the technology industry, right? It's just always there on new president. But you don't even know it's there until you need it. And I think that getting to that vision would be awesome. >> And then really kind of helping move the trust equation to a whole other level reputation. New data sets so data, bits of data business. >> It's total data business >> breath. Thanks for coming on the Q. Appreciate your insights, but also no see. So the chief information security officer at Microsoft, also corporate vice president here inside the Cuban Palo Alto. This is cute conversations. I'm John Career. Thanks for watching. >> Thank you.

>> Live from Miami Beach, Florida, it's theCUBE. Covering UiPathForward Americas. Brought to you by UiPath. >> Welcome back to Miami everybody. You're watching theCUBE, the leader in live tech coverage. We go out to events, we extract the signal from the noise. A lot of noise here but the signal's all around automation and robotic process automation. I'm Dave Vellante, he's Stu Miniman, my co-host. Guy Kirkwood's here he's the UiPath chief evangelist otherwise known as the chief injector of Kool-Aid. Welcome. (guests chuckling) And Craig LeClair, the Vice President at Forrester. Covers this market, wrote the seminal document on this space. Knows it inside out. Craig, great to see you again. >> Yeah, nice to see you again. It's great to be back at theCUBE. >> So let's start with the analyst perspective. Take us back to when you first discovered RPA, why you got excited about it, and what Forrester Research is all about in that space. >> Yeah, it's been a very a interesting ride. Most of these companies, at least that are the higher value ones in the category they've been around for a long time. They've been around for over a decade, and no one ever heard of them three years ago. So I had covered at Forrester, business process management and some of the business rules engines, and I've always been in process. I just got this sense that there was a way that companies could make progress and digital transformation and overcome the technical debt that they had. A lot of the progress has been tepid in digital transformation because it takes tremendous amount of time and tons of consultants to modernize that core system that really runs the company. So along comes this RPA technology that allows you to build human equivalence that patch up the inefficiencies without touching. I came in on American Airlines and the system that cut my ticket was designed in 1960. It's the same Sabre reservation system. That's the big obstacle that a lot of companies have been struggling to really take advantage of AI in general. A lot of the more moonshot and more sophisticated promises haven't been realized. RPA is a very practical form of automation that companies can get a handle on right now, and move the dial for digital transformation. >> So Guy we heard a vision set forth by Daniel this morning. Basically a chicken in every pot, I call it, a robot for every person. Now what Craig was just saying about essentially cutting the line on technical debt, do you have clear evidence of that in your customer base? Maybe you could give some examples. >> What we're really seeing is that as organizations have to deal with the stresses, what Leslie Wilcox professor at LSE describes as the stresses within organizations and particularly in environments where the demographics are changing. What we're seeing is that organizations have to automate. So the best example of that is in Japan where the Japanese population peaked in 2010. It's now falling as a whole, plus all the baby boomers, people of Craig's and my age are now retiring. So we're now in a position where they measure levels of dangerous overwork as being more that 106 hours a week. That isn't 106 hour a week in total, that's 106 hours a week in addition to the 60 hours a week the Japanese people normally work. And there is a word in Japanese, which is (speaking in foreign language), which means to work oneself to death. So there really is no choice. So what we're seeing happening in Japan will be replicated in Western Europe and certainly in the US over the next few years. So what's driving that is the rise of the ecosystems of technologies of which RPA and AI are part, and that's really what we're seeing within the market. >> Craig, sometimes these big waves particularly in infrastructure, you kind of saw it with virtualization and some other wonky techs, like data reduction. They could be a one-time step function, and not an ongoing business value creator. Where does RPA fit in there? How can organizations make sure that this is a continuous business value generator as opposed to a one time hit? >> Good question. >> Well, I like the concept of RPA as a platform that can lead to more intelligence and more integration with AI components. It allows companies to build an automation center or a center of excellence focused on automation. But the next thing they're going to do after building some simple robots that are doing repetitive tasks, is they're going to say "Oh well wouldn't it be better "if my employee could have a textual chat with a chatbot "that then was interacting with the digital worker "that I built with the bot." Or they're going to say "You know what? I really want to use that machine learning algorithm "for my underwriting process, but I can use these bots "to go out and collect all the data from the core systems "and elsewhere and from the web and feed the algorithms "so that I could make a better decision." So again it goes back to that backing off the moonshot approach that we've been talking about that AI has been taking because of the tremendous amount of money spent by the major players to lay out the promise of AI has really been a little dysfunctional in getting organizations' eye off the ball in terms of what could be done with slightly more intelligent automation. So RPA will be a flash in the pan unless it starts to embed these more learning-capable AI modules. But I think it has a very good chance of doing that particularly now with so much investment coming into the category right. >> Craig, it's really interesting. When I heard you describe that it reminds me of the home automation. The Cortanas and Alexas and consumer side where you're seeing this. You've got the consumer side where you can build skills yourself, you know teenagers people can do that. One of the challenges always on the business side is how do you get the momentum when you don't have the consumer side. How do those interact? >> It's the technical debt issue and it's just like the mobile peak in 2011. Consumers in their hands had much better mobility right away than businesses. It took businesses five, they're still not there in building a great mobile environment. So these Alexa in our kitchen snooping on our conversation and to some extent Netflix that observes our behavior. That's a light form of AI. There is a learning from that behavior that's updating an algorithm autonomously in Netflix to understand what you want to watch. There's no one with a spreadsheet back there right. So this has given us in a sense a false sense of progress with all of AI. The reality is business is just getting started. Business is nowhere with AI. RPA is an initial foray on that path. We're in Miami so I'll call it a gateway drug. >> In fact there's also an element that the Siris, the Cortanas, the Alexas, are very poor at understanding specific ontologies that are required for industry, and that's where the limitation is right now. We're working with an organization called Humly, they're focused on those ontologies for specific industries. So if the robot doesn't understand something, then you could say to the robot Okay sit that in the Wells account, if you're in a bank, and it understands that Wells in that case means Wells Fargo it doesn't mean a hole in the ground with water at the bottom or a town in Somerset in the UK, 'cause they're all wells. So it's getting that understanding correct. >> I wonder if you guys could comment on this. Stu and I were at Splunk earlier this week and they were talking up NLP and we were saying one of the problems is that NLP is sometimes not that great. And they made a comment that I thought was very interesting. They said frankly a lot of the stuff that we're ingesting is text and it's actually pretty good. I would imagine the same is true for RPA. Is that what you see? >> You were talking about that on stage. With regards to the text analytics. >> Yes. So RPA doesn't handle unstructured content the way that NLP does. So NLP can handle voice, it can handle text. For the bots to work in RPA today you have to have a layer of analytics that understands those documents, understands those emails and creates a nice clean file that the bots can then work with. But what's happening is the text analytics layer is slowly merging with the RPA bots platforms so it's going to be viewed as one solution. But it's more about categories of use cases that deal with forms and documents and emails rather than natural language, which is where it's at. >> So known business processes really is the starting point. >> Known business-- >> One example we've got live is an insurance company in South Africa called Hollard, and they've used a combination of Microsoft Cognitive Toolkit, plus IBM Watson and it's orchestrated doing NLP and orchestrated by UiPath. So that's dealing with utterly unstructured data. That's the 1.5 million emails that that organization gets in a year. They've managed to automate 98% of that, so it never sees a human. And their reduction in cost is 91% cost in reduction per transaction. And that's done by one of our implementation partners, a company called LarcAI down there. It's superb. >> Yeah, so text analytics is hard. Last several years we have that sentiment out of it, but if I understand it correctly Craig, you're saying if you apply it to a known process it actually could have outcomes that can save money. >> Yes, absolutely yes. >> As Guy was just saying. >> I think it's moving from that rules-based activity to more experience-based activity as more of these technologies become merged. >> Will the technology in your view advance to the point, because the known processes. okay, there's probably a lot of work to be done there, but today there's so many unknown processes. It's like this messy, unpredictable thing. Will machine intelligence combined with robotic process automation get to the point, and if so when, that we can actually be more flexible and adapt to some of these unknown processes or is that just decades off? >> No, no, I think we talk at Forrester about the concept of convergence. Meaning the convergence of the physical world and the digital world. So essentially digital's getting embedded in everything physical that we have right. Think of IoT applications and so forth. But essentially that data coming from those physical devices is unstructured data that the machine learning algorithms are going to make sense of, and make decisions about. So we're very close to seeing that in factory environments. We're seeing that in self-driving cars. The fleet managers that are now understanding where things are based on the signals coming from them. So there's a lot of opportunity that's right here on the horizon. >> Craig, a lot of the technologies you mentioned, we may have had a lot of the technical issues sorted out, but it's the people interactions some things like autonomous vehicles, there's government policies going to be one of the biggest inhibitors out there. When you look at the RPA space, what should workers how do they prepare for this? How do companies, make sure that they can embrace this and be better for it? >> That's a really tough and thoughtful question. The RPA category really attacks what we call the cubicle population. And there are we're estimating four million cubicles will be emptied out in five years by RPA technology specifically. That's how we built the market forecast 'cause each one of the digital workers replacing a cubicle worker will cost $11,000 or what. That's how we built up the market forecast. They're going to be automation deficits. It's not all going to be relocating people. We think that there's going to be a lot of disruption in the outsource community first. So companies are going to look at contractors. They're going to look at the BPO contract. Then they're going to look at their internal staff. Our numbers are pretty clear. We think they're going to be four million automation deficits in five years due to RPA technology specifically. Now there will be better jobs for those that are remaining. But I think it's a big change management issue. When you first talk about robots to employees you can tell them that their jobs are going to get better, they're going to be more human. They're going to have a much more exhilarating experience. And their response to you is, What they're thinking is, "Damn robot's going to take my job." That's what they're thinking. So you have to walk them up the mountain and really understand what their career path is and move them into this motion of adaptive and continual learning and what we call constructive ambition. Which is another whole subject. But there are employees that have a higher level of curiosity and are more willing to adapt to get on the other side of the digital divide. Yep. >> You mentioned the market. You guys did a market forecast. I've seen, read stats, a little over a billion today. I don't know if that's consistent with your numbers? >> Yeah that's about right. >> Is this a 10X market? When does it get to 10 billion? Is it five, seven, 10 years? >> So we go out five years and have it be close to three billion. I think the numbers I presented on stage were 3.2 billion in five years. Now that's just software licenses and it's not the services community that surround that. >> You'd probably triple it if you add in services. >> I think two to three times service license ratio. There's always an issue at this point in emerging markets. Some of the valuations that are there, that market three billion has to be a bit bigger than that in eight or nine years to justify those valuations. That's always the fascinating capital structure questions we create with these sorts of things. >> So you describe this sort of one for one replacement. I'm presuming there's other potential use cases, or maybe not, that you forecast. Is that right? >> Oh no for the cubicles? >> Yes, it's not just cubicle replacement in that three billion right? It's other uplifts. >> No there are use cases that help in factory automation, in supply chain, in guys carrying around clipboards in warehouses. There are a tremendous number of use cases, but the primary focus are back office workers that tend to be in cubicles and contact center employees who are always in cubicles. >> And then we'll see if the non-obvious ones emerge. >> I think ultimately what's going to happen is the number of people doing back office corporate functions, so that's both finance and accounting procurement, HR type roles and indeed the industry specific roles. So claims processing insurance will diminish over time. But I think what we're going to see is an increase in the number of people doing customer experience, because it's the customer intimacy that is really going to differentiate organizations going forward. >> The market's moving very fast. Reading your report, it's like you were saying yesterday's features are now table steaks. Everybody's watching everybody else. You heard Daniel today saying, "Hey our competitors are watching. "We're open they're going to steal from us so be it." The rising tide lifts all boats. What do you advise clients in terms of where they should start, how they should get started? Obviously pick some quick wins. But what do you tell people? >> I always same pretty much the same advice you give almost on any emerging technology. Start with a good solution provider that you trust. Focus on a proof of concept, POC and a pilot. Start small and grow incrementally, and walk people up the mountain as you do that. That's the solution. I also have this report I call The Rule of Fives, that there are certain tasks that are perfect for RPA and they should meet these three rules of five. A relatively small number of decisions, relatively small number of applications involved, and a relatively small number of clicks in the click stream. 500 clicks, five apps, five decisions. Look for those in high volume that have high transaction volume and you'll hit RPA goal. You'll be able to offset 2 1/2 to four FTE's for one bot. And if you follow those rules, follow the proof of concept, good solution partner everyone's winning. >> You have practical advice to get started and actually get to an outcome. Anything you'd add to that? >> In most organizations what they're now doing, is picking one, two, or three different technologies to actually play with to start. And that's a really good way. So we recommend that organizations pick three, four, five processes and do a hackathon and very quickly they work out which organizations they want to work with. It's not necessarily just the technology and in a lot of cases UiPath isn't the right answer. But that is a very good way for them to realize what they want to do and the speed with which they'll want to do it. >> Great, well guys thanks for coming on theCUBE, sharing your knowledge. >> Thank you. >> Pleasure. >> Appreciate your time. >> Thanks very much indeed. >> Alright keep it right there everybody. Stu and I will be back from UiPathForward Americas. This is theCUBE. Be right back. (upbeat music)

>> Announcer: Live, from Stanford University, it's theCUBE, covering the Women in Data Science Conference 2017. (upbeat music) >> Hi and welcome back to theCUBE, I'm Lisa Martin and we are at Stanford University for the second annual Women in Data Science Conference. Fantastic event with leaders from all different industries. Next we're joined by Finale Doshi-Velez. You are the Associate Professor of Computer Science at Harvard University. Welcome to the program. >> Excited to be here. >> You're a technical speaker so give us a little bit of insight as to what some of the attendees, those that are attending live and those that are watching the livestream across 75 locations. What are some of the key highlights from your talk that they're going to learn? >> So my main area is working on machine learning for healthcare applications and what I really want people to take away from my talk is all the needs and opportunities there are for data science to benefit patients in very very tangible ways. There's so much power that you can use with data science these days and I think we should be applying it to problems that really matter, like healthcare. >> Absolutely, absolutely. So talking about healthcare you kind of see the intersection, that's your big focus, is the intersection of machine learning and healthcare. What does that intersection look like from a real world applicability perspective? What are some of the big challenges? And can you talk about maybe specific diseases that you're maybe working on-- >> Sure, absolutely. So I'll tell you about two examples. One example that we're working on is with autism spectrum disorder. And as the name suggests, it's a really broad spectrum. And so things that might work well for one sort of child might not work for a different sort of child. And we're using big data and machine learning to figure out what are the natural categories here and once we can divide this disease into subgroups, we can maybe do better treatment, better prognosis for these children, rather than lumping them into this big bucket-- >> Lisa: And treating everybody the same? >> Exactly. >> Lisa: Right. >> And another area we're working on is personalizing treatment selection for patients with HIV and with depression. And again, in these cases, there's a lot of heterogeneity in how people respond to the diseases. >> Lisa: Right. >> And with the large data sets that we now have available, we actually have huge opportunities in getting the right treatments to the right people. >> That's fantastic, so exciting. And it's really leveraging data as a change agent to really improve the lives of patients. >> Finale: Absolutely. >> From a human interaction perspective, we hear that machine learning is going to replace jobs. It's really kind of a known fact. But human insight is still quite important. Can you share with us-- >> Finale: Absolutely. >> where the machines and the humans come into play to help some of these dis-- >> Yes, so a big area that we work on is actually in formalizing notions of interpretability because in the healthcare setting, the data that I use is really really poor quality. There's lots of it. It's collected in a standard of care everyday but it's biased, it's messy. And you really need the clinician to be able to vet the suggestions that the agent is making. Because there might be some bias, some confounder, some reason why the suggestions actually don't make sense at all. And so a big area that we're looking at is how do you make these algorithms interpretable to domain experts such as clinicians, but not data experts. And so this is a really important area. And I don't see that clinician being replaced anytime soon in this process. But what we're allowing them to do is look at things that they couldn't look at before. They're not able to look at the entire patient's record. They certainly can't look at all the patient records for the entire hospital system when making recommendations. But they're still going to be necessary because you also need to talk to the patient and figure out what are their needs, do they care about a drug, that might cause weight gain for example, when treating depression. And all of these sorts of things. Those are not factors again that the machine are going to be able to take over. >> Lisa: Right. >> But it's really an ecosystem where you need both of these agents to get the best care possible. >> Got it, that's interesting. From an experimentation perspective, are you running these different experiments simultaneously, how do you focus your priorities, on the autism side, on the depression side? >> I see, well I have a lab, so that helps makes things easy. >> Lisa: Yup, you got it. >> I have some students working on some projects-- >> Lisa: Excellent >> And some students working on other projects, And we really, we follow the data. My collaborations are largely chosen based on areas where there are data available and we believe we can make an impact. >> Fantastic, speaking of your students, I'd love to understand a little bit more. You teach computer science to undergrads. >> Yes. >> As we look at how we're at this really inflection point with data science; there's so much that can be done in that, to your point, in tangible ways the differences that we can make. Kids that are undergrads at Harvard these days grew up with technology and the ability to get something like that; we didn't. So what are some of the things that have influenced them to want to become the next generation of computer or data scientists? >> I mean, I think most of them just realize that computers and data are essential in whatever field they are. They don't necessarily come to Harvard thinking that they're going to become data scientists. But in whatever field that they end up in, whether it's economics or government, they quickly realize, or business, they quickly realize that data is very important. So they end up in my undergraduate machine learning course. And for these students, my main focus is just to teach them, what the science, what the field can do, and also what the field can't do. And teach them that with great power comes great responsibility. So we're really focused on evaluation and just understanding on how to use these methods properly. >> So looking at kind of traditional computer, data science skills: data analytics, being able to interpret, mathematics, statistics, what are some of the new emerging skills that the future generation of data and computer scientists needs to have, especially related to the social skills and communication? >> So I think that communication is absolutely essential. At Harvard, I think we're fortunate because most of these people are already in a different field. They're also taking data science so they're already very good at communicating. >> Lisa: Okay. >> Because they're already thinking about some other area they want to apply in. >> So they've got, they're getting really a good breadth. >> They're getting a really great breadth, but in general, I think it is on us, the data scientists, to figure out how do we explain the assumptions in our algorithms to people who are not experts again in data science, because that could have really huge downstream effects. >> Absolutely. I like what you said that these kids understand that the computers and technology are important whatever they do. We've got a great cross section of speakers at this event that are people of, that are influencing this in retail, in healthcare, in education, and as well as in sports technology, on the venture capital side. And it really shows you that this day and age, everything is technology, every company we're in, we're sitting in Silicon Valley of course, where a car company is a technology company. But that's a great point that the next generation understands that it's prolific. I can't do anything without understanding this and knowing how to communicate it. So from your background perspective, were you a STEM kid from way back and you really just loved math and science? Is that what shaped your career? >> So I grew up in a family with like 15 generations back, accounting, finance, small business, and I was like, I'm never going to do any of this. (Lisa laughs) I am going to do something completely different. >> Lisa: You were determined, right. >> And so now I'm a data scientist. (laughing) >> At Harvard, that's pretty good, they must be proud. >> Working on healthcare applications. So I think numbers were definitely very much part of my upbringing, from the beginning. But one thing that I think did take a while for me to put together is that I came from a family where my great uncle was part of India's independence movement. My role models were people like Martin Luther King and Mother Teresa and I liked numbers. >> Lisa: Yeah. >> And, like how to put those together? And I think it definitely took me a while to figure out okay, how do you deliver those warm fuzzies with like cold hard facts. >> Lisa: Right. >> And I'm really glad that we're in a place today where the sort of skills that I have can be used to do enormous social good. >> What are some of the things that you're most excited about about this particular conference and being involved here? >> So I think conferences like these, like the Women in Data Science, I'm also involved in the Women in Machine Learning Conference, are a tremendous opportunity for people to find mentors and cohorts. So I went to my first Women in Machine Learning Conference over 10 years ago, and those are the people I still talk to whenever I need career advice, when I'm trying to figure out what I want to do with my research and what directions, or just general support. And when you're in a field where you maybe don't see that many women around you, it's great to have this connection so that you can draw on that wherever you end up. Your workplace may or may not have that many women but you know that they're out there and you can get support. >> Now that there's so much data available, a lot of the spirit of corporations that use data as a change agent have adopted cultures or tried, of try it, it might fail, but we're going to learn something from this. Do you see that mentality in your students about being free or being confident enough to try experiments and if they fail, take learnings from it and move forward as a positive? >> I mean, certainly that's what I try to teach my students. >> Lisa: Yeah, yeah. >> My graduate students I tell them, I expect you to make consistent progress. Progress includes failure if you can explain why it failed. And that's huge, that's how we learn and that's how we develop new algorithms, absolutely. >> Yeah, and I think that confidence is a key factor. You mention that Women in Machine Learning Conference, you've been involved in that for 10 years, how have you seen women's perspectives, maybe confidence evolve and change and grow as a result of this continued networking? Are you seeing people become more confident-- >> Finale: I think so. >> To be able to try things and experiments. >> I mean certainly, as people stay involved in the field, I've noticed that you develop that network, you develop that confidence, it's amazing. The first event had less than a hundred people. The last event that we had had over 500 people. The number of people at just the Women in Machine Learning event, was the same as the number of people at the entire conference 10 years ago. >> Right. >> Right, and so the field has grown but the number of women involved that you see through this events like WIDS and WIML I think is enormous. >> And the great thing that's happening here at WIDS 2017 is it's being live streamed. >> Finale: Right. >> Over 75 locations. >> So it's accessible to so many people. >> Exactly. >> Yes. >> We're expecting up to 6,000 people on the live stream. So the reach and the extension is truly global. >> Which is fantastic. >> It is fantastic and just the breadth of speakers that are here to influence. You mentioned a couple of your key influencers: Martin Luther King and Mother Teresa. From an education perspective, when you were trying to figure out your love of math and numbers and that, who were some of the people in your early career that were really inspiring and helped you gain that confidence that you would need to do what you're doing? >> So I think if I had to pick one person, it was probably a professor at MIT that I interacted quite a bit in my undergrad and continued to mentor me, Leslie Kaelbling, who is just absolutely fearless in just telling people to follow their passions. Because we really are super privileged as was mentioned earlier: we lose our jobs, we can just get another one. >> Lisa: Right. >> Right? And our skills are so in need that we can and we should try to do amazing things that we care about. And I think that message really stayed with me. >> Absolutely. >> So you got research going on in autism. You mentioned depression. What's next for you? What are some of your next interests? Cancer research, other things like that? >> So I'm actually really interested in mental health because I think that that's, you know, talk about messy spaces, in terms of data. (laughing) It's very hard to quantify but it has a huge, huge burden both to the people who suffer from mental health disorders, which is like close to 15 percent, 20 percent, depending on how you count. But also it has a huge burden on everyone else too, on like lost work, on the people around them. And so we're working with depression and autism, as I mentioned. And we're hoping to branch out into other neurodevelopmental disorders, as well as adult psychiatric disorders. And I feel like in this phase, it's even harder to find the right treatments. And the treatments take so long to test, six to eight weeks. And it can be so hard to keep up the morale, to keep trying out a treatment when your disorder is one that makes it hard to keep up trying whatever you need to try. >> Lisa: Right. >> So that's an area that I'm really focusing on these days. >> Well then your passion is clearly there. That intersection of machine learning and healthcare. You're right, you're talking about something that maybe isn't talked about nearly as much as some of other big diseases but it's one that is prolific. It affects so many. And it's exciting to know that there are people out there like you who really have a passion for that and are using data as a change agent to help current generations and future to come. So Finale, such a pleasure to have you on theCUBE. We wish you the best of luck in your technical talk and know that you're going to be mentoring a lot of people from far and wide. >> Thank you, my pleasure to be here. >> Absolutely, so I'm Lisa Martin. You've been watching theCUBE. We are live at the Women in Data Science Conference at Stanford University, but stick around, we'll be right back. (upbeat music)

>> Announcer: Live from New York, it's theCUBE covering BigData New York City 2016, brought to you by headline sponsors Cisco, IBM, Nvidia, and our ecosystem sponsors. Now here are your hosts, Dave Vellante and Peter Burris. >> Welcome back to theCUBE everybody. This is BigData NYC and we are covering wall to wall, we've been here since Monday evening. We we're with Nvidia, Nvidia talking about deep learning, machine learning. Yesterday we had a full slate, we had eight data scientists up on stage yesterday and then we covered the IBM event last night, the rooftop party. Saw David Richards there, hanging out with him, and wall to wall today and tomorrow. Jagane Sundar is here, he is the CTO of WANdisco, great to see you again Jagane. >> Thanks for having me Dave. >> You're welcome. It's been a while since you and I sat down and I know you were on theCUBE recently at Oracle Headquarters, which I was happy to see you there and see the deals that are going on you've got good stuff going on with IBM, good stuff going on with Oracle, the Cloud is eating the world as we sort of predicted and knew but everybody wanted to put their head in the sand but you guys had to accommodate that didn't you. >> We did and if you remember us from a few years ago we were very very interested in the Hadoop space but along the journey we realized that our replication platform is actually much bigger than Hadoop. And the Cloud is just a manifestation of that vision. We had this ability to replicate data, strongly consistent, across wide area networks in different data centers and across storage systems so you can go from HDFS to a Cloud storage system like S3 or Azure Wasabi and we will do it with strong consistency. And that turned out to be a bigger deal than actually providing just replication for the Hadoop platform. So we expanded beyond our initial Hadoop Forex and now we're big in the Cloud. We replicate data to many Cloud providers and customers use us for many use cases like disaster recovery, migration, active/active, Cloud bursting, all of those interesting use cases. >> So any time I get you on theCUBE I like to refresh the 101 for me and for the audience that may not be familiar with it but you say strongly consistent, versus you hear the term eventual consistency, >> Jugane: Correct. >> What's the difference, why is the latter inadequate for the applications that you're serving. >> Right so when people say eventually consistent, what they don't remember is that eventually consistent systems often have different data in the different replicas and once in a while, once every five minutes or 15 minutes, they have to run an anti-entropy process to reconcile the differences and entropy is the total randomness right if you go back to your physics, high school physics. What you're really talking about is having random data and once every 10 minutes making it reconcile and the reconciliation process is very messy, it's like last right winds and the notion of time becomes important, how do you keep time accurate between those. Companies like Google have wonderful infrastructure where they have GPS and atomic clocks and they can do a better job but for the regular enterprise user that's a hard problem so often you get wrong data that's reconciled. So asking the same query you may get different answers and your different replicas. That's a bad sign, you want it consistent enough so you can guarantee results. >> Dave: And you've done this with math, right? >> Exactly, our basis is an algorithm called Paxos, which was invented by a gentleman called Leslie Lamport back in '89 but it took many decades for that algorithm to be widely understood. Our own chief scientists spent over a decade developing those, adding enhancements to make it run over the wide area network. The end result is a strongly consistent system, mathematically proven, that runs over the wide area network and it's completely resistant to failure of all sorts. >> That allows you to sort of create the same type of availability, data consistency as you mentioned Google with the atomic clocks, Spanner I presume, is this fascinating, I mean when the paper came out I was, my eyes were bleeding reading it and but that's the type of capability that you're able to bring to enterprises right? >> That's exactly right, we can bring similar capabilities across diverse networks. You can have regular networking gear, time synchronized by NTP, out in the Cloud, things are running in a virtual machine where time adrift most of the time, people don't realize that VMs are pretty bad at keeping time and all you get up in the Cloud is VMS. Across all those enviroments we can give you strongly consistent replication at the same quality that Google does with their hardware. So that's the value that we bring to the Fortune 500. >> So increasingly enterprises are recognizing that data has an, I don't want to say intrinsic value but data is a source of value in context all by itself. Independent of any hardware, independent of any software. That it's something that needs to be taken care of and you guys have an approach for ensuring that important aspects of it are better taken care of. Not the least of which, is that you can provide an option to a customer who may make a bad technology choice one day to make a better technology choice the next day and not be too worried about dead ending themselves. I'm reminded of the old days when somebody who was negotiating an IBM main frame deal would put an Amdahl coffee cup in front of IBM or put an Oracle coffee cup in front of SAP. Do you find customers metaphorically putting a WANdisco coffee cup in front of those different options and say these guys are ensuring that our data remains ours? >> Customers are a lot more sophisticated now, the scenarios that you pointed out are very very funny but what customers come to us for is the exact same thing, the way they ask it is, I want to move to Cloud X, but I want to make sure that I can also run on Cloud Y and I want to do it seamlessly without any downtime on my on-prem applications that are running. We can give them that. Not only are they building a disaster recovery environment, often they're experimenting with multiple Clouds at the same time and may the better Cloud win. That puts a lot of competition and pressure on the actual Cloud applications they're trying. That's a manifestation in modern Cloud terms of the coffee cup competitor in the face that you just pointed out. Very funny but this how customers are doing it these days. >> So are you using or are they starting to, obviously you are able to replicate with high fidelity with strong fidelity, strong consistency, large volumes of data. Are you starting to see customers, based on that capability actually starting to redesign how they set up their technology plant? >> Absolutely, when customers were talking about hybrid Cloud which was pretty well hyped a year or so ago, they basically had some data on-prem and some other data in the Cloud and they were doing stuff but what we brought to them was the ability to have the same data both on-prem and in the Cloud, maybe you had a weekly analytics job that took a lot of resources. You'd burst that out into the Cloud and run it up there, move the result of that analytics job back on-prem. You'd have it with strong consistency. The result is that true hybrid Cloud is enabled when only when you have the same exact data available in all of your Cloud locations. We're the only company that can provide that so we've got customers that are expanding their Cloud options because of the data consistency we offer. >> And those Cloud options are obviously are increasing >> Jugane: They are. >> But there's also a recognition that it's as we gain more experience with Cloud, that different workloads are better than others as we move up there. Now Oracle with some of their announcements last week may start to push the envelope on that a little bit but as you think about where the need for moving large volumes of data with high, with strong consistency what types of applications do you think people are focusing on? Is it mainly big data or are there other application styles or job types that you think are going to become increasingly important? >> So we've got much more than big data, one of the big sources of leads for us now is our capability to migrate netapp filers up into the Cloud and that has suddenly become very important because an example I'd like to give is a big financial firm that has all of its binaries and applications and user data and netapp filers, the actual data is in HDFS on-prem. They're moving their binaries from the netapp up into the Cloud in a specific Cloud windows equal into the filer and the big data part of it from HDFS up into Cloud object store, we are the only platform that can deal with both in the strong consistent manner that I've talked about and we're a single replication platform so that gives them the ability to make the sort of a migration with very low risk. One of the attributes of our migration is that we do it with no downtime. You don't have to take your online, your on-prem environment offline in order to do the migration so they are doing that so we see a lot of business from that sort of migration efforts where people have data in mass filers, people have data in other non-HDFS storage systems. We're happy to migrate all of those. Our replication platform approach, which we've taken in the last year and a half or so is really paying off in that respect. >> And you couldn't do that with conventional migration techniques because it would take too long, you'd have to freeze the applications? >> A couple of things, one you'd probably have to take the applications offline, second you'd be using tools of periodic synchronization variety such as RSYNC and anybody in the devops or operations whose ever used RSYNC across the wide area network will tell you how bad that experience is. It really is a very bad experience. We've got capability to migrate netapp filer data without imposing a load on the netapp's on-prem so we can do it without pounding the crap out of the netapp's server such that they can't offer service to their existing customers. Very low impact on the network configuration, application configuration. We can go in, start the migration without downtime, maybe it takes two, three days for the data to get up over there because of mavenlink. After that is done, you can start playing with it up in the Cloud. And you can cut over seamlessly so there's so real downtime, that's the capability we've seen. >> But you've also mentioned one data type, binaries, they can't withstand error propagation. >> Jugane: Absolutely. >> And so being able to go to a customer and say you're going to have to move these a couple times over the course of the next n-months or years, as a consequence of the new technology that's now available and we can do so without error propagation is going to have a big impact on how well their IT infrastructure, their IT asset base runs in five years. >> Indeed, indeed. That's very important. Having the ability to actually start the application, having the data in a consistent and true form so you can start, for example, the data base and have it mount the actual data so you can use it up in the Cloud, those are capabilities that are very important to customers. >> So there's another application. If you think about, you tend to be more bulk, the question I'm going to ask is and at what point in time is the low threshold in terms of specific types of data movement. Here's why I'm asking. IOT data is a data source or is a use-case that has often the most stringent physical constraints possible. Time, speed of light, has an implication but also very importantly, this notion of error propagation really matters. If you go from a sensor to a gateway to another gateway to another gateway you will lose bits along the way if you're not very careful. >> Correct. >> And in a nuclear power plant, that doesn't work that way. >> Jugane: Yeah. >> Now we don't have to just look at a nuclear power plant as an example but there's increasingly industrial IOTs starting to dramatically impact not just life and death circumstances but business success or failure. What types of smaller batch use-cases do you guys find yourselves operating in, in places like IOT where this notion of error or air control strong consistency is so critical? >> So one of the most popular applications that use our replication is Spark and Spark Streaming which as you can imagine is a big part of most IOT infrastructure, we can do replication such that you ingest into the closest data center, you go from your server or your car or whatever to the closest data center, you don't have to go multiple hops. We will take care consistency from there on. What that gives you is the ability to say I have 12 data centers with my IOT infrastructure running, one data center goes down, you don't have a downtime at all. It's only the data that was generated inside the data center that's lost. All client machines connecting to that data center will simply connect to another data center, strong replication continues, this gives you the ability to ingest at very large volumes while still maintaining the consistency and IOT is a big deal for us, yes. >> We're out of time but I got a couple of last minute questions if I may. So when you integrate with IBM, Oracle, what kind of technical issues do you encounter, what kind of integration do you have to do, is it lightweight, heavyweight, middleweight? >> It's middleweight I would say. IBM is a great example, they have a deep integration with our product and some of the authentication technology they use was more advanced than what was available in open source at that time. We did a little of work, and they did a little bit of work to make that work, but other than that, it's a pretty straight forward process. The end result is that they have a number of their applications where this is a critical part of their infrastructure. >> Right, and then road map. What can you tell us about, what should we look for in the future, what kind of problems are you going to be solving? >> So we look at our platform as the best replication engine in the world. We're building an SDK, we expect custom plugins for different other applications, we expect more high-speed streaming data such as IOT data, we want to be the choice for replication. As for the plugins themselves, they're getting easier and easier to build so you'll see wide coverage from us. >> Jugane, thanks so much for coming to theCUBE, always a pleasure to have you. >> Thank you for having me. >> You're welcome. Alright keep it right there everybody, we'll be back to wrap. This is theCUBE, we're live from NYC. We'll be right back. (upbeat electronic music)

>> Presenter: Live from the Wynn Resort in Las Vegas, it's theCUBE, covering .NEXT Conference 2016. Brought to you by Nutanix. Now here are your hosts, Dave Vellante and Stu Miniman. >> Welcome back to Las Vegas everybody. Mark Templeton is here, industry legend, former CEO of Citrix. Mark, really a pleasure having you on. >> Thanks, thanks, really great to be here. >> So what are you doing these days? (laughter) >> Enjoying retirement, right, way more than I thought. But earlier today at the Nutanix NEXT Conference, Mark Leslie, the legend, icon, talked about the Ark of Life. And he had this one slide that said, "There is no finish line." And I think anyone who is blessed to have worked their career around their passion, he just captured it all in that one slide. And so there's no finish line, it's just sort of continuing the journey with maybe some new friends and colleagues. >> Right, no hammock, no umbrella drinks. >> Oh plenty of drinks-- >> But it doesn't end there. >> No, plenty of drinks as always but no hammock. >> So we heard your keynote yesterday, which is outstanding. You're spending a lot of time thinking about the future. >> Yes. >> So you've got time to do that now, what are you seeing? What's in the binoculars of Mark Templeton. >> Well, a big thing for me is people and how generations of people actually influence changes in our environment and how they drive different ages in the sense of descriptions of time. So I think for me, I was born analog, I'm a boomer, and boomers generally, born analog, but I fell in love with digital and made it my career. My children are XY geners. They were born digital mainly because of my career, but many in their generation we're actually born analog but learned digital pretty quickly. Now Millennials, they're born digital and they're not interested in how things work from a computing perspective. They want to know what can it do. And so the question is now what's next? And as I sort of talked to a lot of Millennials, talked to a lot of companies that are out there with ideas, I've concluded that we're actually at the end of the digital age because we're on digital overload. There are too many devices, there are too many apps, too much data, too many social connections. I mean, no one can handle and manage it all and the only way we can keep going in terms of leveraging technology to the benefit of humankind is for it to become invisible. And the way it becomes invisible is to take what we've accepted as analog for a long, long time, human emotion, relationships, location of people, intersections amongst people, et cetera, and start creating context out of that through digital mechanisms. So I think this next, where things are going, is away from digital, toward contextual. And it's through contextual that we can actually have a greater experience with technology underneath. And yeah, tremendous opportunities for invention, innovation, et cetera. You asked the question yesterday to the audience, who can program an assembler. I put my hand up. I don't know if I still could, but I certainly have. But your point was that everybody who's programming today is programming an assembler, it's just invisible. >> It's invisible, that's right. Every layer of extraction makes the layers below invisible. And that's one of the things I love about Nutanix because they're making cloud infrastructure, hypervisors, kind of all this componentry, invisible, allowing the focus on a common set of services that are exposed. And for a whole set of people, that's great, right? And that means you can move on to the higher layers of the stack. Same thing goes for contextuality. Contextuality will create layers of abstraction that when you enter the room, the right things happen. You don't have to think about oh, I'm using Lutron switches or I've got a nest going on here, did it move from away to home? All of that, it becomes invisible and goes away. It's just early in the cycle of getting there. >> Yeah, so what do you see that having an impact on the jobs that people are having? You talked about moving up the stack. Even in IT here and for Nutanix, it's oh wow, this is what my job's been for years and now I don't need to do that, I'm retraining, moving up the stack, those challenges. >> Well, I think history shows that every generation where there's a layer of abstraction that has lots of staying power, what it does is it takes a bunch of people and it says okay, you stay below that stack if you're a specialist and you stay deep on it. I mean, let's face it, you put Nutanix technology in place, you have to have deep specialists under that. It's just that the DevOps people don't have to know anything about how it works underneath. The business units don't have to know anything about that, and so they can take all of that stuff that's cluttering their time and mind and focus on the missions that are important to them. So it creates layers of specialization along the way, and then it pushes generalists up, up, up. And look, I mean I think the Nutanix team I think adequately talked about the notion of what do we do when we get time back, whether we're admins or whether we're CIOs or whether we're CEOs or whether we're just individuals? And I think that's where humankind seems to not have a problem in consuming that extra time, whether it's recreational or maybe more return to some of the basic values of families and relationships, or new levels of innovation and invention. I think there are a lot of things that get done with that extra time. >> If I infer from your talk yesterday, you don't like the term consumerization of IT. You used a different term. >> Yeah, I actually... Jeff with Slack made that point around consumerization of IT, and he said really, it's about humanization of IT. I think these terms serve purposes along the way, and I think that we're still in the process of consumerizing IT. It's just that the purpose of the consumerization is to humanize it. And the consumerization basically is making things, making the IT experience much more retail, right? Where people get choice, where they get self service, and IT organizations actually describe themselves in a way where they're merchandising services that benefit the business. So I don't dislike consumerization as much as I really like the idea of moving the idea forward to humanization, because that's the outcome you're looking for. >> So square or circle for me because you said something that surprised me, the end of the digital age, right? And you defended that position, but I want to ask you about something like autonomous vehicles. I was talking to my teenage daughters the other day, and one of them made the point that turning 16 is a symbol of freedom. And one of the pieces of that freedom is you get to drive a car. And so I thought you were going to say this is just the beginning of the digital age. What do you make of that in terms of the impact on society and its humanization aspect? >> Well, so the end of the digital age includes it's the end of the visibility of digital, because it's just peaked out. And so digital and technologies around digital, you're just becoming more and more and more invisible as machines do more work that humans used to do. I mean, here's a question. Why is it so hard for older people to adopt new technologies? If they're so simple and they're so great, why do they have a hard time adopting? >> Dave: Because they're complicated. >> They're complicated, right? When you're doing it over and over, you don't realize how much knowledge you're applying to something that's so simple, all right? So all I'm saying is that the test will be when a generation that's behind us can actually consume it in pretty ubiquitous ways. And so it's the boomerang kind of effect, all right? >> So Stu, you were talking a little bit about the work that we did with the guys at MIT and Brynjolfsson and McAfee of The Second Machine Age. So do you think much about, I'm sure you do, about the impact of machines? Machines have always replaced humans. They seem to be now doing it at a cognitive level. What are your thoughts and the state of education in this country in particular? >> Well, I mean there are two ways to answer that, half-full, half-empty. I'm an optimist, and I think that these kinds of things I'm talking about actually will serve to make education more personalized by individual. When I look at the things like Khan Academy, right, and the impact the Khan Academy has made in public school systems, and you squint at it so that you only see the shapes and forms, here's what it's done. It's allowed the teachers to focus on the students by exception and where they need help as opposed to mass kind of education, an entire classroom. That's been one of the big effects of Sal Kahn's work. So I'm optimistic about machines, contextuality, and the intersection of all of that when it comes to education. Because I think the more context a teacher has around a student, what's going on at home, what's happening in other classes, extracurricular activities or lack thereof gives them a better ability to actually teach them, and gives them a better ability to learn if the systems are set up to make that connection. >> And we're optimists too. I mean, I think the observation is that the industry has marched to the cadence of Moore's Law for decades, and that's what's driven innovation. And it's not driving innovation anymore, it's the combination of technologies. We think that creativity, teaching, I don't know if you could teach creativity, I guess you can-- >> Yes you can. >> Why can't you, right? That seems to be the new frontier of education, in our view anyways. That make sense to you? >> It makes total sense. By the way, you travel the world and you characterize various educational methodologies and priorities around the world. I mean, a lot of people throw rocks at the educational system in the U.S. It's actually a system that promotes creativity more than any other educational system in the world, okay? You go to certain countries in Asia and they promote knowledge and knowing facts and being able to state facts and correlate fact, all right? And there's nothing fundamentally wrong with that, it's just that you're not driving a creative sort of process, you aren't teaching creativity. So yes, I'm optimistic about where we're headed in the sense of how this age of contextuality can actually propel us forward as a nation around education. >> And that's, Stu, why I hear so much criticism about teaching the test. You got little young kids and you hear a lot of that backlash. >> Yeah, yeah absolutely. Mark, I want to go back. You talked a lot about kind of generations and journeys. When we look in the IT space, the pace of change is just faster than ever. What advice do you give to, how do you get, by now, by the time you're relevant, you're almost irrelevant soon after. So how do you plan for that? >> So first of all, I think you always have to start with an opinion about the future that you believe in so strongly that you're willing to make bets, okay? And some of the bets, there are low-risk bets, there are high-risk bets. Mark Leslie talked about transformation, et cetera, today, and that's really about having an opinion about the future and making a bet. And he gave some great case studies. But if you look at those case studies, you ask the CEOs, the leaders there, they didn't think they were high risk because they thought the greater risk was not betting, right? And it's because of their opinion of the future. So I think you have to start there. Too many, my observation, opinion, is too many people read too many books, too much of the net and form their opinions based upon what they read as opposed to forming an opinion on their own through some amount of introspection and experience, okay? And I think that, I'll give you an example. I remember, it was probably 1999. I was newly CEO of Citrix and I had a whole faction of our dev team saying, Mark it's all about WAP. (host chuckles) I was like, what do you mean it's all about WAP? It's like, it's all about WAP. I said, what's WAP? Well, it's the wireless, I can't remember what it stood for, something protocol. Access protocol. (crosstalk) So okay, I said fine, all right. Let's meet on that like next week. Okay, fine. So over the weekend, I go somewhere and I bought a WAP phone, a Nokia WAP phone that supported WAP. So I get on there over the weekend and blah, blah, blah, blah, blah, fine. I go to the meeting next week, sit down, and the whole team comes, it's all about WAP, here's why. I said okay, let me start with a question. Can everyone showed me their WAP phones? No one had one. And I pulled mine out and I said hey, let me give you a demo. So yeah, you form an opinion about something and then you can, and so I said we're not spending one nickel on WAP, right? Right. So I think that's the number one advice I would give. Because then when you have a belief and an opinion about the future, you feel they're low risk for the right reasons. >> I want to ask you as a CEO, a former CEO of a public company, you heard Mark Leslie talk about, today, the short-term focus. A lot of people talk about that. Ever since I've been in the business, people talk about, particularly US companies, short-term focus, Wall Street, now you're seeing activist investors. Maybe it's gone to a new level. I presume you agree, but it's worked. United States is dominant, and they've always had the short-term focus. Have we gone beyond a point though of rationality? >> Well, I think this is a semantical problem. So I think I probably don't agree with Mark, all right? And along the way, when people said public CEO, go with the PE guys, do that. Well, why would I do that? Well, because you don't have the short-term focus like the quarterly thing. I was like, are you kidding me? (host chuckles) You don't know PE guys, first of all. Secondly, I disagree because you're measured as a public company against the expectations that you set. So if you set the wrong expectations and miss them, then you're in trouble. If you set the right expectations, whether those expectations are financial, strategic, operational, and you exceed them, there's no problem with it. And our system is successful because there's a quarterly rhythm to measuring the path of companies that are public. And so there's no law out there that says every time you measure, it has to be something prescribed. It is prescribed, it's prescribed by the CEO and board-- >> Dave: And the expectations that get set. >> And the expectations that get set. So I was CEO of Citrix for many, many years. And when I retired, it was my 70th earnings report, all right? And I figured, I figured 70 years in jail is enough. I applied for parole a few times and it was denied. But seriously, the idea of a quarterly report against the expectations you set is not a bad thing. >> Yeah, Michael Dell talks about the 90-day shot clock, but I bet you he has a 90-day shot clock internally. >> Sure. I mean, absolutely. >> I don't know if this is the case, but it seems to me that some of the companies that I observed today, that are successful, in particular, Nutanix, I would put service now in that category, Tableau, Splunk, they seem to be highly transparent, maybe more transparent than I'm used to. Maybe I just wasn't paying attention before. Have you observed that? Do you think it's just a function of their success and their size, allows them to be more transparent than-- >> I think that... I think that's a big change that's taken place. So more newly public companies like Splunk, for example, have to be more transparent around the core metrics they use to measure success. So if you look at some of the, like Adobe, hugely successful transformation story. They did it through obviously the right strategic mechanisms to move to a different business model, but they had to create a level of transparency to get there in order to successfully make that transformation. Companies like Splunk started there, all right? And so that is the standard for a more of a subscription cloud-based SAS-oriented type business model. And investors reward that, I think. And so therefore, it's confirms, it's like positive strokes to transparency, which I'm all for. >> I wish we had more time to talk about things like culture. There's so many different different topics, but we'll leave it at what's next for you, what are you spending your time on, any fun projects that you're working on? >> Yeah, I'm spending all my time on technologies that increase contextuality. So for example, one of them is a web psychographics company. So when you surf the web now, their web analytics really does more demographical kinds of things, right? But the science of psychographics actually takes a lot of that and actually figures out what's the why, your behavior, what's in your head. So I think that's a context that's important to add, again, to make the technology more invisible. Spending time on autonomic security, security that actually not only dynamically sees attacks and discontinuities, it fixes them and then tells you later, okay? Spending time on something really exciting called human location analytics, which basically is technology that can passively track human motion, and very precisely, so that as people occupy various spaces and have paths and interactions, systems around it can respond. So like in a retail environment, maybe if you're spending a lot of time at an N cap, somebody will come and help you. And if you combine some of these things, the psychographics and the human location, you'll get the right kind of help and so forth. And that all becomes invisible and we just have a great experience. >> Combining innovations, right, taking advantage of this invisible digital matrix. >> Yeah. And the thing that I'm really psyched about, and most people that have known me for some time know that I have a particular weakness for things that have round rubber tires, okay? So deeply involved in a company, an e-bike company that is called Vintage Electric Bikes. It's an e-bike you love and you want to ride because of the joy that it gives you, all right? So yeah, so things that... Greater context, so technology can be invisible, and things that bring out emotional kinds of pleasure and joy. That's where I'm spending my time. By the way, it's fun, which is the first bar I have. Number two, great people, the second bar, all right? And then the third bar is I think they actually, these things are important for a better world and creating opportunity for people, et cetera. And I like doing that. >> Well, thanks for coming on theCUBE and delighting our audiences. It was really a pleasure having you. You look great, you sound great, congratulations. >> Mark: Thanks, thanks. Having a great time, thank you very much-- >> You're welcome. All right, keep it right there everybody. Stu and I will be back with our next guest. This is SiliconANGLE's theCUBE. We'll be right back. (upbeat electronic music)