>>The cube presents HPE discover 2022 brought to you by HPE. >>Welcome back to the cube. Lisa Martin and Dave ante here covering day one of HPE discover 22 live from Las Vegas. We've been having some great conversations today. So far. We've got three full days coming at you. This is close to the end of day one. We're gonna have an interesting conversation next with a may the executive vice president and chief people officer at HPE. Alan. Welcome to the cube. >>Thanks Lisa. Great to be here. Thanks Dave. >>It's great to be back in person. The keynote this morning, standing room, only people are ready. People are ready to be back to hear what HPE has been doing the last couple of years since the last conference, but I've heard you and Antonio talk about the human side of change. It's challenging humans. Humans are uncomfortable with change, right? Unfortunately we are, but it is a big challenge. How, what are some of the things that you're seeing as organizations are really looking and have to transform digitally? They gotta bring the humans along >>Well. You know, our folks as anybody is just bombarded with data these days bombarded with issues and, and it's really down to share of mind when it comes down to it. If, if you're asking someone to change first, you gotta break through the clutter. There's so many things going on in their world and in their life as individuals. But to me, the foundation is you really have to define what is the mission and purpose of the organization, and then make sure that individual feels safe and comfortable participating at work. I know those sound like soft things, but at the end of the day, you're not gonna have the gumption or the desire to change unless you care about something bigger than yourself and that you feel safe bringing your authentic self to work. >>So you have to be kind of, yeah. Have to be good at sales, I guess, because you have to sell the mission. Well, what do you do if somebody's not comfortable? If they, how do you get them to align? I mean, you, you, it's always a challenge. And that's like, I think what makes the people side of the equation so hard? What's your sort of secret sauce there? >>Well, I wouldn't say it's secret sauce, but you know, we define our mission is improving the way people work and live. Now that sounds so general. But the good thing about that is I think about every one of our 60,000 associates around the world can write theirselves into that mission. Sign >>Up >>For that. Yeah. It's not so specific that you think am I opting in, I am opting out. The other piece is you've gotta give employee voice. You have to give your team members an opportunity, not just to, you know, follow blindly, whatever you're saying, you don't want that. You actually want them to challenge it. You want 'em to say, Hey, what does that really mean, Alan? What does that mean, Antonio? When you say we're improving the way, you know, people work and live, what does it mean when we say commit and go or force for good, these are not just pithy phrases. They're ways to engage dialogue. That's how you get people to think and to create and innovate and to change >>And creating that employee experience and changing that and, and transforming that as obviously the world change, especially the last couple of years, but the employee experience and what they think. And are they, are we part of the vision? Are we, part of the mission is directly relates to the customer experience. Those two things to me are inextricably linked well >>In, in our company, it's all about innovation, but not innovation just for the sake of designing things, but to meet customer needs. So you've actually gotta inculcate in your workforce. A couple of things. One obviously is that freedom to be creative, but the other piece is actually actively listening to our customers and recognizing and understanding what they need and how we can satisfy those needs. When you can bring both of those things together, I tell you, you can do all kinds of things with your workforce. >>So, I mean, I'm hearing you obviously look for common ground, but sometimes there's, there's the dissonance, right? The, you know, creating that great human condition might not necessarily be advantageous for short-term profits. You're a public company. So, so how what's that discussion like, and, and, and I presume there's gotta be a long-term vision, but still how, how do you handle those types of >>Things? Well, that, that's what I call dynamic tension. Good, great organizations, encourage dynamic tension. They don't simply ask people to blindly adhere to whatever they're saying. They actually ask their employees to think and to create and to debate and to argue respectfully, of course, that's how you really get to that virtuous circle of innovation and people moving forward. Now, look, I, I'm not naive. We don't have 60,000 team members totally aligned on every point every day. But I think we've got the vast majority knowing ultimately where we're going. And frankly, some of the fun is figuring out how to get there. And that means that you've gotta have those open discussions to get there. >>Well, you talk about dynamic tension and at the first sort of thought of it, it sounds like it can be a challenging thing, but it also sounds like it can really be an accelerant to the culture and the ability for the company to move forward and obviously meet those customer demands. >>Well, if you're defining what's new in the world, that's the only way you get there because nobody has, you know, basically a lock on innovation or what's the best next thing. When you have the power of bringing a diverse set of individuals together and really create forms for them to explore debate, argue, as I mentioned, that's where you really move forward. >>How do you think about how, how, how has your thinking changed? The company's thinking changed post pandemic with regard to hybrid work, you have something Elon Musk, you gotta come to work at least 40 hours, or you're gone. We, we, we heard someone on wall street have similar things, say similar things and others have said, Hey, we have no headquarters anymore. You know, we're moving to, you know, someplace remote what's H HP's point of view on >>That. Well, thanks for that question in particular. And Dave, what you outlined is we seem to have a world of two extremes out there. Yeah. Where either companies say it's back to the old days pre COVID and you're not working unless I see the sweat on your brow and you're there at eight o'clock on Monday morning, then we have other companies say, Hey, it doesn't matter. Mail it in wherever you are around the world. You know, we're in between that, we think it's important that people do get together face to face. And in fact, we've asked our team members to come back a couple of three days a week in the office, but to do it in a purposeful, thoughtful way, it doesn't mean just coming in, swiping your bad shame that you were there. It means coming in to collaborate, to meet with customers, to celebrate, to innovate, to work with groups. >>So what we're trying to do now is orchestrate those moments that matter for our team members, for a reason, for them to be together. Now, there's no reason for them to come in the office. If they're on zoom or team meetings all day, we know that. But the fact is we believe that our culture thrives when people get together at least a bit during the week. So we're looking for that happy medium. I can't say we've got it all figured out, but I can tell you right now, we're not on those two extremes. We're absolutely center the plate. >>So you encouraged that. That was a great description. If somebody says, Hey, but I, I really want to go move to Bozeman, you know, and hang out there and I'll work remote and I'll, I'll be productive. You, you enable that as well. Or would you discourage that? Yeah. We've >>Hired people all over the world and we actually have people based upon their job classified. If they're so-called hybrid employees, the, the, the situation I mentioned, which is you're not required to come to the office, but we encourage it. In some cases we do have telecomm commuters. If they have the kind of job where they can work from Bozeman or from Bangalore or from any place else, we're open to that as well. Cuz we don't wanna rule out that talent. But the vast majority of our folks, we'd like to be pretty close to one of our centers of excellence to one of our offices, to one of our locations because that fuels our customers, our, our culture, and really it creates community. Now I can't predict the future, but frankly, a lot of the issues that we've seen through a, the, the pandemic around mental health, around isolation, around increased stress, those are not all gonna go away because people are getting back together. But I do think that the pandemic accelerated and exacerbated, some of those conditions, people do want to be together and we're gonna make that happen. >>I, I can't predict the future, but I often try and I, I predict, I think the hybrid model, it will be the dominant model going forward. And I think that that, that smart organizations will put incentives in place to get people together. Not, oh, you won't get promoted. No, but you're gonna, you're gonna actually enjoy getting together periodically with, with your teammates and we're gonna support you, you know, wherever you want to live. >>Yeah. >>What are some of the key skill sets these days that HPE is looking for to attract these folks in an increasingly digital world? What are some of those things you say ABC gotta have it. >>Well, interesting. You ask that. Get asked that question a lot. And people expect me to say, they gotta know C plus plus they've gotta, you know, know all the latest on AI. They gotta be a mathematical wizard to do all kinds of things that we do in algorithms. You know, it's not, it's not those factors. It's the behavioral factors. We're looking for people. First of all, that have intellectual curiosity. They thrive to learn. They thrive to innovate. They don't want to just do a job. They want to come in and they want to create something big. So I think that's first and foremost, the second one is we look for people that have some resiliency have they had in their experience broadly in life ever had to deal with stress with resistance, with, you know, uncomfortable situations. Not because that's our work environment, but because that's the world, the world is an actively changing one. >>And we need people who have got that resilience and that intellectual curiosity to kind of move forward. And then last but not least. And this goes back to our founder DNA. I, I talk about this a lot. Our founders, bill and Dave talked a lot about basic things, respect for one another collaboration, teamwork. That's our culture. Now that's not the culture that a lot of other firms have out there. And in some companies they have maybe a harder edge, but those are the things that really propel our organization. Those are the things our customers appreciate the most. >>What's your point of view on the, the so-called great resignation? Is it a sort of a media created dynamic? Is it something that is maybe a, a somewhat of a knee jerk reaction in the post isolation economy? How do you think about that? >>You know, I, it's real obviously, and, and we've seen some uptick in our, our turnover, although I'm happy to say our turnovers a half to a third of what our competitors are. So we, we seem to have been able to retain our folks pretty well. But I do think that coming out of the pandemic was a, an inflection point. For many people. It was such a searing experience in so many ways. It caused people to really reflect and say, do I want to do something different now? That's great. But I'd like to have them do something different in HPE, if they're one of our employees. So what we're focusing on is gigs. What's your next opportunity to learn something new, do something new, move to a different area. You know, we used to call it career development. Those days are gone of just job ladders and wait for the next job and wait for the next promotion. It's all about how can you give someone a new opportunity and challenge them. And when you're able to do that, I think you can create a real positive dynamic that results in greater retention. But I do think the great resurrection it's real, it's gonna persist one other data point. Well, before COVID supply and demand, I'm an economist, not a psychologist. And at the end of the day, we have fewer and fewer people available to do the kinds of work that we're trying to hire. So those two factors together do create some, some turnover. >>You know, what's interesting is certainly pre pandemic. The prediction was that machines were gonna replace humans, which has always happened, but for the first time ever, it's in cognitive functions. And there's a lot of concern in the press about, you know, the impact on, on jobs and employment seems like the reverses happened, which is often the way, but, but, but longer term, what's your point of view on, on, on that, that piece of, of the equation, people are talking about digital transformation, AI, we see robotic process automation. Initially, a lot of employees are really concerned. Whoa, they're gonna replace my job. We've certainly seen that. And you know, if you were, you see kiosks at the airport, people used to actually put up, you know, billboards and with, with the glue and paper and you know, those jobs are gone, but now other jobs are, are, are at risk. How do you think about that? What, what should companies like HPE and society do to help people get to the point where they can thrive in that environment? >>Yeah. Look, I, it, it's an observation that, that I could say based upon my career, I've seen for many, many years, I can remember back manufacturing when at least from a us perspective, many of those manufacturing firms were shedding jobs because of automation. And there are short term disruptions and those are real. Those are human. And we do have to help people through those. Now I think one obligation an employer has, is let's start with our own folks. Let's make sure we retrain them. Let's make sure we expose them to the latest skills. Let's give them an opportunity to grow and develop. So I think if we do those things, we can help people through those transitions over the long haul. I'm actually very optimistic. I believe that over time, people self-select, these things don't happen overnight. I'll give you one tiny little anecdote before the pandemic. All of this world about autonomous vehicles can eliminate truck drivers. Well, you know, back in the day I actually drove a truck and it's not just driving. You've gotta interact with somebody in a dock door. You've gotta do all kinds of other tasks that can't be automated. And so things will happen over time, but I don't think we're gonna see this massive social disruption people were worried about. There is an incumbent responsibility on firms to train their own people and to keep them up to speed. And that's something we're deeply committed to at HPE >>Is information technology, employment, a parallel. I mean, everybody thought the cloud was gonna destroy the it, you know, worker that didn't happen. They just sort of changed their skillset. They became, you know, cloud experts or cloud architects. Is there a parallel there? >>Yeah, I think there's absolutely a parallel. And while probably the, the rate of change is quicker in, in some tech industries than perhaps others. All we're doing is creating new markets and new opportunities. And ultimately the lack of, of skill that we have, the lack of talent in the external marketplace is gonna mean that it's still very much an opportunity for people to learn, grow, develop, and be employed. >>Do you think that's, it's a matter of, of, of awareness on people not really understanding that whether it's still fear? >>Well, I, I think there may be some of that, but again, I, I, I do think from a, a broader social perspective, there's probably some things that in the public policy we can do to improve education and training, particularly for new entrants and make sure they're learning the skills for tomorrow's jobs and not just today's, but can I'm optimistic. And I actually think most responsible companies get this. And if you talk to their CEOs, the top tier or three issues that they have include access to talent. So why don't we recycle repurpose and reuse to use the sustainability phrase instead of throwing people outta work that have all kinds of intellectual property and capability to be very productive. And that's what we do at HPE. >>And that curiosity, that's something that you can't teach, right? Exactly. Have it, or >>You don't. Exactly. >>So last question, in terms of, of looking at culture corporate culture, as a, as an accelerant, as a catalyst of digital transformation, how do you advise leadership teams? >>Well, I, we've done a fair amount of work in the last five years, defining the culture in very small frankly soundbites. And the way you make that come to life is back what I mentioned before. You have to engage people and ask them to debate it. What does it mean to say, commit and go? What does it mean to say force for good, those kind of conversations, help your culture evolve, help your culture become real and not just a bunch of words on some piece of paper or, or posters someplace. I will say from my experience, and, and particularly with a new entrance to the workforce, if you can't define your culture quickly for this next generation coming in, you're, you're, there's no way you're gonna attract that talent. And so put me on the spot. HPE is here to help people live and grow and work better, and we try to be a force for good. We focus on that and we create work that fits your life. Not the other way around. That's my elevator speech. It sounds pithy, but it's an invitation to have a deeper discussion. >>I love it. And I think this is only day one for us here, but I think that we're, we're seeing, and we're feeling that culture there's 8,000 or so HP folks, executives, partners, customers, ready to come back and innovate with each other. I think that culture is palpable, that you've created. >>Great. It's exciting. And thank you so much for being part of it. >>Thanks, Alan. Pleasure. Thanks, Alan. We appreciate your insights. Okay. For our guests. I'm Dave ante. I Lisa Martin stick around. You're watching the cube, the leader in live tech coverage, and we're gonna be back after your short break.

>>Okay. Okay. Now we're gonna go into the Green Lake Power Panel. Talk about the cloud landscape hybrid cloud and how the partner ecosystem and customers are thinking about cloud hybrid cloud as a service and, of course, Green Lake. And with me or CR Houdyshell, president of Advise X. Ron Nemecek, Who's the business Alliance manager at C B. T s. Harry Zaric is president of competition, and Benjamin Clay is VP of sales and alliances at Arrow Electronics. Great to see you guys. Thanks so much for coming on the Cube. >>Thanks for having us >>would be here. >>Okay, here's the deal. So I'm gonna ask you guys each to introduce yourselves and your company's add a little color to my brief intro and then answer the following question. How do you and your customers think about hybrid cloud and think about it in the context of where we are today and where we're going? Not just the snapshot, but where we are today and where we're going. CR, why don't you start, please? >>Sure. Thanks a lot. They appreciate it. And, uh, again cr Howdy Shell President of advising. I've been with the company for 18 years the last four years as president. So had the great great opportunity here to lead a 45 year old company with a very strong brand and great culture. Uh, as it relates to advise X and where we're headed to with hybrid Cloud is it's a journey, so we're excited to be leading that journey for the company as well as HP. We're very excited about where HP is going with Green Lake. We believe it's it's a very strong solution when it comes to hybrid. Cloud have been an HP partner since since 1980. So for 40 years it's our longest standing OM relationship, and we're really excited about where HP is going with Green Lake from a hybrid cloud perspective. Uh, we feel like we've been doing the hybrid cloud solutions in the past few years with everything that we've focused on from a VM Ware perspective. But now, with where HP is going, we think really changing the game and it really comes down to giving customers at cloud experience with the on Prem solution with Green Lake, and we've had great response from our customers and we think we're gonna continue to see how that kind of increased activity and reception. >>Great. Thank you. Cr and yeah, I totally agree. It is. It is a journey. And we've seen it really come a long way in the last decade. Ron, I wonder if you could kick off your little first intro there, please? >>Sure. Dave, thanks for having me today. And it's a pleasure being here with all of you. My name is Ron Nemecek, business Alliance manager at C B. T. S. In my role, I am responsible for RHP Green Lake relationship globally. I've enjoyed a 33 year career in the I T industry. I'm thankful for the opportunity to serve in multiple functional and senior leadership roles that have helped me gather a great deal of education and experience that could be used to aid our customers with their evolving needs for business outcomes. The best position them for sustainable and long term success. I'm honored to be part of the C B. T s and Annex Canada Organization, C B T s stands for consult Bill transform and support. We have a 35 year relationship with HP or a platinum and inner circle partner. We're headquartered in Cincinnati Ohio. We service 3000 customers, generating over a billion dollars in revenue, and we have over 2000 associates across the globe. Our focus is partnering with our customers to deliver innovative solutions and business results through thought leadership. We drive this innovation VR team of the best and brightest technology professionals in the industry that have secured over 2800 technical certifications 260 specifically with HP and in our hybrid cloud business. We have clearly found the technology new market demands for instant responses and experiences evolving economic considerations with detailed financial evaluation and, of course, the global pandemic have challenged each of our customers across all industries to develop an optimal cloud strategy we have. We now play an enhanced strategic role for our customers as there Technology Advisor and their guide to the right mix of cloud experiences that will maximize their organizational success with predictable outcomes. Our conversations have really moved from product roadmaps and speeds and feeds to return on investment, return on capital and financial statements, ratios and metrics. We collaborate regularly with our customers at all levels and all departments to find an effective, comprehensive cloud strategy for their workloads and applications, ensuring proper alignment and costs with financial return. >>Great. Thank you, Ron. Yeah, Today it's all about the business value. Harry, please, >>I Dave. Thanks for the opportunity and greetings from the Great White North, where Canadian based company headquartered in Toronto, with offices across the country. We've been in the tech industry for a very long time. What we would call a solution provider hard for my mother to understand what that means. But our goal is to help our customers realize the business value of their technology investments just to give you an example of what it is we try and do. We just finished a build out of a new networking and point in data center technology for a brand new hospital is now being mobilized for covid high risk patients. So talk about are all being an essential industry, providing essential services across the whole spectrum of technology. Now, in terms of what's happening in the marketplace, our customers are confused. No question about it. They hear about cloud and cloud first, and everyone goes to the cloud. But the reality is there's lots of technology, lots of applications that actually still have to run on premises for a whole bunch of reasons. And what customers want is solid senior serious advice as to how they leverage what they already have in terms of their existing infrastructure but modernized and updated So it looks and feels a lot like a cloud. But they have the security. They have the protection that they need to have for reasons that are dependent on their industry and business to allow them to run on from. And so the Green Lake philosophy is perfect. That allows customers to actually have 1 ft in the cloud, 1 ft in their traditional data center, but modernize it so it actually looks like one enterprise entity. And it's that kind of flexibility that gives us an opportunity collectively, ourselves, our partners, HP to really demonstrate that we understand how to optimize the use of technology across all of the business applications they need to rest >>your hair. It's interesting about what you said is is cloud is it is kind of chaotic. My word not yours, but but there is a lot of confusion out there. I mean, it's what's cloud right? Is it Public Cloud is a private cloud the hybrid cloud. Now, now it's the edge. And of course, the answer is all of the above. Ben, what's your perspective on all this? >>Um, from a cloud perspective. You know, I think as an industry, you know, I think we we've all accepted that public cloud is not necessarily gonna win the day and were, in fact, in a hybrid world, there's certainly been some some commentary impress. Um, you know, that would sort of validate that. Not that necessarily needs any validation. But I think it's the linkages between on Prem, Um, and cloud based services have increased. Its paved the way for customers to more effectively deploy hybrid solutions in the model that they want that they desired. You know, Harry was commenting on that a moment ago. Um, you know, as the trend continues, it becomes much easier for solution providers and service providers to drive there services, initiatives, uh, you know, in particular managed services. So, you know, from from an arrow perspective, as we think about how we can help scale in particular from Greenland perspective, we've got the ability to stand up some some cloud capabilities through our aero secure platform. um that can really help customers adopt Green Lake. Uh, and, uh, benefit to benefit from, um, some alliances, opportunities as well. And I'll talk more about that as we go through >>that. I didn't mean to squeeze you on a narrow. I mean, you got arrows. Been around longer than computers. I mean, if you google the history of arrow, it'll blow your mind. But give us a little, uh, quick commercial. >>Yeah, absolutely. So, um, I've been with arrow for about 20 years. I've got responsibility for alliances, organization, North America for Global value, added distribution, business consulting and channel enablement Company. Uh, you know, we bring scope, scale and and, uh, expertise as it relates to the I t industry. Um, you know, I love the fast paced, the fast paced that comes with the market, that we're all all in, and I love helping customers and suppliers both, you know, be positioned for long term success. And, you know, the subject matter here today is just a great example of that. So I'm happy to be here and or to the discussion. >>All right, We got some good brain power in the room. Let's let's cut right to the chase. Ron, Where's the pain? What are the main problems that C B. T s. I love the what it stands for. Consult Bill Transform and support the What's the main pain point that that customers are asking you to solve when it comes to their cloud strategies. >>Third day of our customers' concerns and associated risk come from the market demands to deliver their products, services and experiences instantaneously. And then the challenges is how do they meet those demands because they have aging infrastructure processes and fiscal constraints. Our customers really need us now more than ever to be excellent listeners so we can collaborate on an effective map for the strategic placement of workloads and applications in that spectrum of cloud experiences, while managing their costs and, of course, mitigating risk to their business. This collaboration with our customer customers often identify significant costs that have to be evaluated, justified or eliminated. We find significant development, migration and egress charges in their current public cloud experience, coupled with significant over provisioning, maintenance, operational and stranded asset costs in their on premise infrastructure environment. When we look at all these costs holistically through our customized workshops and assessments. We can identify the optimal cloud experience for the respective workloads and applications through our partnership with HP and the availability of the HP Green Lake Solutions. Our customers now have a choice to deliver SLA's economics and business outcomes for their workloads and applications that best reside on premise in a private cloud and have that experience. This is a rock solid solution that eliminates, you know, the development costs at the experience and the egress charges that are associated with the public cloud while utilizing HP Green Lake to eliminate over provisioning costs and the maintenance costs on aging infrastructure hardware. Lastly, our customers only have to pay for actual infrastructure usage with no upfront capital expense. And now that achieves true utilization to cost economics. You know, with HP Green Lake solution from C B. T s. >>I love to focus on the business case because it's measurable. That sort of follow the money. That's where it's where the opportunity is. Okay, See, I got a question for you thinking about advise X customers. How are they? Are they leaning into Green Lake? You know, what are they telling you? Is the business impact when they when they experience Green Lake, >>I think it goes back to what Ron was talking about. We have to solve the business challenges first, and so far the reception's been positive. When I say that is, customers are open, everybody wants to. The C suite wants to hear about cloud and hybrid cloud fits, but what we're hearing, what we're seeing from our customers is we're seeing more adoption from customers that it may be their first put in, if you will. But as importantly, we're able to share other customers with our potentially new clients that that say, What's the first thing that happens with regard to Green like Well, number one, it works. It works as advertised and as a as a service. That's a big step. There are a lot of people out there dabbling today, but when you can say we have a proven solution, it's working in in in our environment today. That's key. I think the second thing is is flexibility. You know, when customers are looking for this, this hybrid solution, you've got to be flexible for again. I think Ron said it well, you don't have a big capital outlay but also what customers want to be able to. We're gonna build for growth, but we don't want to pay for it, so we'll pay as we grow. Not as not as we have to use because we used to do It was upfront of the capital expenditure, and I will just pay as we grow and that really facilitates. In another great examples, you'll hear from a customer, uh, this afternoon, but you'll hear where one of the biggest benefits they just acquired a $570 million company, and their integration is going to be very seamless because of their investment in Green Lake. They're looking at the flexibility to add the Green Lake as a big opportunity to integrate for acquisitions and finally is really we see it really brings the cloud experience and as a service to our customers bring. And with HP Green Lake, it brings best to breathe. So it's not just what HP has to offer. When you look at hyper converged, they have Nutanix kohi city, so I really believe it brings best to breathe. So, uh to net it out and close it out with our customers thus far, the customer experience has been exceptional with Green Lake Central has interface. Customers have had a lot of success. We just had our first customer from about a year and a half ago, just re up, and it was a highly competitive situation. But they just said, Look, it's proven it works and it gives us that cloud experience So I had a lot of great success thus far, looking forward to more. >>Thank you. So, Harry, I want to pick up on something, CR said, And get your perspectives. So when you when I talk to the C suite, they do all want to hear about, you know, Cloud, they have a cloud agenda and and what they tell me is it's not just about their I t transformation. They want, they want that. But they also want to transform their business. So I wonder if you could talk Harry about competence, perspective on the potential business impact of Green Lake, and and also, you know, I'm interested in how you guys are thinking about workloads, how to manage work, you know how to cost optimize in i t. But also the business value that comes out of that capability. >>Yes. So, Dave, you know, if you were to talk to CFO and I have the good fortune to talk to lots of CFOs, they want to pay the cost. When they generate the revenue, they don't want to have all the cost up front and then wait for the revenue to come through. A good example of where that's happening right now is related to the pandemic. Employees that used to work at the office have now moved to working from home, and now they have to. They have to connect remotely to run the same application. So use this thing called VD virtual interfacing to allow them to connect to the applications that they need to run in the off. Don't want to get into too much detail. But to be able to support that from an at home environment, they needed to buy a lot more computing capacity to handle this. Now there's an expectation that hopefully six months from now, maybe sooner than that people will start returning to the office. They may not need that capacity so they can turn down on the cost. And so the idea of having the capacity available when you need it, But then turning it off when you don't need it is really a benefit of a variable cost model. Another example that I would use is one in new development if a customer is going to implement and you, let's say, line of business application essay P is very, very popular, you know, it actually, unfortunately takes six months to two years to actually get that application setup installed, validated, test it and then moves through production. You know what used to happen before they would buy all that capacity at front and basically sit there for two years? And then when they finally went to full production, then they were really getting value out of that investment. But they actually lost a couple of years of technology, literally sitting almost idle. And so, from a CFO perspective, his ability to support the development of those applications as he scales it perfect Green Lake is the ideal solution that allows them to do that. >>You know, technology has saved businesses in this pandemic. There's no question about it and what Harry was just talking about with regard to VD. You think about that. There's the dialing up and dialing down piece, which is awesome from an i t perspective and then the business impact. There is the productivity of Of of the end users, and most C suite executives I've talked to said Productivity actually went up during covid with work from home, which is kind of astounding if you think about it. Ben, you know Ben, I We said Arrow has been around for a long, long time, certainly before all of us were born and it's gone through many, many industry transitions during our lifetimes. How does arrow and how do How do your partners think about building cloud experience experiences? And where does Green Lake fit in from your perspective? >>A great question. So from a narrow perspective, when you think about cloud experience and, of course, us taking a view as a distribution partner, we want to be able to provide scale and efficiency to our network of partners. So we do that through our aero screw platform. Um, just just a bit of a you know, a bit of a commercial. I mean, you get single quote single bill auto provision compared multi supplier, if you will Subscription management utilization reporting from the platform itself. So if we pivot that directly to HP, you're going to get a bit of a scoop here, Dave. So we're excited today to have Green Lake live in our platform available for our part of community to consume in particular the swift solutions that HP has announced. So we're very excited to to share that today, Um, maybe a little bit more on Green Lake. I think at this point in time, there it's differentiated, Um, in a sense that if you think about some of the other offerings in the market today and further with, um uh, having the solutions himself available in a row sphere So, you know, I would say, Do we identify the uniqueness, um, and quickly partner with HP to to work with our atmosphere platform? One other sort of unique thing is, you know, when you think about platform itself, you've got to give a consistent experience the different geographies around the world. So, you know, we're available in north of 20 countries. There's thousands of resellers and transacting on the platform on a regular basis, and frankly, hundreds of thousands and customers are leveraging today, so that creates an opportunity for both Arrow HP and our partner community. So we're excited. >>Uh, you know, I just want to open it up and we don't have much time left, but thoughts on on on differentiation. You know, when people ask me Okay, what's really different about H P E and Green Lake? As others you know are doing things that with with as a service to me, it's a I I always say cultural. It starts from the top with Antonio, and it's like the company's all in. But But I wonder from your perspective because you guys are hands on. Are there other differential factors that you would point to let me just open that up to the group? >>Yeah, if I could make a comment. You know, Green Lake is really just the latest invocation of the as a service model. And what does that mean? What that actually means is you have a continuous ongoing relationship with the customer. It's not a cell. And forget not that we ever forget about customers, but there are highlights. Customer buys, it gets installed, and then for two or three years, you may have an occasional engagement with them. But it's not continuous. When you move to a Green Lake model, you're actually helping them manage that you are in the core in the heart of their business. No better place to be if you want to be sticky and you want to be relevant, and you want to be always there for them. >>You know, I wonder if somebody else could add to and and and in your in your remarks from your perspective as a partner because, you know, Hey, a lot of people made a lot of money selling boxes, but those days are pretty much gone. I mean, you have to transform into a services mindset. But other thoughts, >>I think I think Dad did that day. I think Harry's right on right. What he the way he positioned Exactly. You get on the customer. Even another step back for us is we're able to have the business conversation without leading with what you just said. You don't have to leave with a storage solution to leave with a compute. You can really have step back, have a business conversation, and we've done that where you don't even bring up hp Green Lake until you get to the point of the customer says, So you can give me an on prem cloud solution that gives me scalability, flexibility, all the things you're talking about. How does that work then? Then you bring up. It's all through this HP Green link tool. It really gives you the ability to have a business conversation. And you're solving the business problems versus trying to have a technology conversation. And to me, that's clear differentiation for HP. Green length. >>All right, guys. CR Ron. Harry. Ben. Great discussion. Thank you so much for coming on the program. Really appreciate it. >>Thanks for having us, Dave. >>All >>right. Keep it right there for more great content at Green Lake Day. Right back? Yeah.

(bright upbeat music) >> Announcer: We are entering an age of insight where data moves freely between environments to work together powerfully, from wherever it lives. A new era driven by next generation cloud services. It's freedom that accelerates innovation and digital transformation, but it's only for those who dare to propel their business toward a new future that pushes beyond the usual barriers. To a place that unites all information under a fluid yet consistent operating model, across all your applications and data. To a place called HPE GreenLake. HPE GreenLake pushes beyond the obstacles and limitations found in today's infrastructure because application entanglements, data gravity, security, compliance, and cost issues simply aren't solved by current cloud options. Instead, HPE GreenLake is the cloud that comes to you, bringing with it, increased agility, broad visibility, and open governance across your entire enterprise. This is digital transformation unlocked, incompatibility solved, data decentralized, and insights amplified. For those thinkers, makers and doers who want to create on the fly scale up or down with a single click, stand up new ideas without risk, and view it all as a single agile system of systems. HPE GreenLake is here and all are invited. >> The definition of cloud is evolving and now clearly comprises hybrid and on-prem cloud. These trends are top of mind for every CIO and the space is heating up as every major vendor has been talking about as-a-Service models and making moves to better accommodate customer needs. HPE was the first to market with its GreenLake brand, and continues to make new announcements designed to bring the cloud experience to far more customers. Come here from HPE and its partners about the momentum that they're seeing with this trend and what actions you can take to stay ahead of the competition in this fast moving market. (bright soft music) Okay, we're with Keith White, Senior Vice President and General Manager for GreenLake at HPE, and George Hope, who's the Worldwide Head of Partner Sales at Hewlett Packard Enterprise. Welcome gentlemen, good to see you. >> Awesome to be here. >> Yeah. Thanks so much. >> You're welcome, Keith, last we spoke, we talked about how you guys were enabling high performance computing workloads to get green-late right for enterprise markets. And you got some news today, which we're going to get to but you guys, you put out a pretty bold position with GreenLake, basically staking a claim if you will, the edge, cloud as-a-Service all in. How are you thinking about its impacts for your customers so far? >> You know, the impact's been amazing and, you know, in essence, I think the pandemic has really brought forward this real need to accelerate our customer's digital transformation, their modernization efforts, and you know, frankly help them solve what was amounting to a bunch of new business problems. And so, you know, this manifests itself in a set of workloads, set of solutions, and across all industries, across all customer types. And as you mentioned, you know GreenLake is really bringing that value to them. It brings the cloud to the customer in their data center, in their colo, or at the edge. And so frankly, being able to do that with that full cloud experience. All is a pay per use, you know, fully consumption-based scenario, all managed for them so they get that as I mentioned, true cloud experience. It's really sort of landing really well with customers and we continue to see accelerated growth. We're adding new customers, we're adding new technology. And we're adding a whole new set of partner ecosystem folks as well that we'll talk about. >> Well, you know, it's interesting you mentioned that just cause as a quick aside it's, the definition of cloud is evolving and it's because customers, it's the way customers look at it. It's not just vendor marketing. It's what customers want, that experience across cloud, edge, you know, multiclouds, on-prem. So George, what's your take? Anything you'd add to Keith's response? >> I would, you've heard Antonio Neri say it several times and you probably saying it for yourself. The cloud is an experience, it's not a destination. The digital transformation is pushing new business models and that demands more flexible IT. And the first round of digital transformation focused on a cloud first strategy. For our customers we're looking to get more agility. As Keith mentioned, the next phase of transformation will be characterized by bringing the cloud speed and agility to all apps and data, regardless of where they live, According to IDC, by the end of 2021, 80% of the businesses will have some mechanism in place to shift the cloud centric, infrastructure and apps and twice as fast as before the pandemic. So the pandemic has actually accelerated the impact of the digital divide, specifically, in the small and medium companies which are adapting to technology change even faster and emerging stronger as a result. You know, the analysts agree cloud computing and digitalization will be key differentiators for small and medium business in years to come. And speed and automation will be pivotal as well. And by 2022, at least 30% of the lagging SMBs will accelerate digitalization. But the fair focus will be on internal processes and operations. The digital leaders, however, will differentiate by delivering their customers, a dynamic experience. And with our partner ecosystem, we're helping our customers embrace our as-a-Service vision and stand out wherever they are. on their transformation journey. >> Well, thanks for those stats, I always liked the data. I mean, look, if you're not a digital business today I feel like you're out of business only 'cause.... I'm sure there's some exceptions, but you got to get on the digital bandwagon. I think pre-pandemic, a lot of times people really didn't know what it meant. We know now what it means. Okay, Keith, let's get into the news when we do these things. I love that you guys always have something new to share. What do you have? >> No, you got it. And you know, as we said, the world is hybrid and the world is multicloud. And so, customers are expecting these solutions. And so, we're continuing to really drive up the innovation and we're adding additional cloud services to GreenLake. We just recently went to General AVailability of our MLOps, Machine Learning Operations, and our containers for cloud services along with our virtual desktop which has become very big in a pandemic world where a lot more people are working from home. And then we have shipped our SAP HEC, customer edition, which allows SAP customers to run on their premise whether it's the data center or the colo. And then today we're introducing our new Bare Metal capabilities as well as containers on Bare Metal as a Service, for those folks that are running cloud native applications that don't require any sort of hypervisor. So we're really excited about that. And then second, I'd say similar to that HPC as a Service experience we talked about before, where we were bringing HPC down to a broader set of customers. We're expanding the entry point for our private cloud, which is virtual machines, containers, storage, compute type capabilities in workload optimized systems. So again, this is one of the key benefits that HPE brings is it combines all of the best of our hardware, software, third-party software, and our services, and financial services into a package. And we've workload optimized this for small, medium, large and extra-large. So we have a real sort of broader base for our customers to take advantage of and to really get that cloud experience through HPE GreenLake. And, you know, from a partner standpoint we also want to make sure that we continue to make this super easy. So we're adding self-service capabilities we're integrating into our distributors marketplaces through a core set of APIs to make sure that it plugs in for a very smooth customer experience. And this expands our reach to over 100,000 additional value-added resellers. And, you know, we saw just fantastic growth in the channel in Q1, over 118% year over year growth for GreenLake Cloud Services through the channel. And we're continuing to expand, extend and expand our partner ecosystem with additional key partnerships like our colos. The colocation centers are really key. So Equinix, CyrusOne and others that we're working with and I'll let George talk more about. >> Yeah, I wonder if you could pick up on that George. I mean, look, if I'm a partner and and I mean, I see an opportunity here.. Maybe, you know, I made a lot of money in the old days moving iron. But I got to move, I got to pivot my business. You know, COVID's actually, you know, accelerating a lot of those changes, but there's a lot of complexity out there and partners can be critical in helping customers make that journey. What do you see this meaning to partners, George? >> So I completely agree with Keith and through and with our partners we give our customers choice. Right, they don't have to worry about security or cost as they would with public cloud or the hyperscalers. We're driving special initiatives via Cloud28 which we run, which is the world's largest cloud aggregator. And also, in collaboration with our distributors in their marketplaces as Keith mentioned. In addition, customers can leverage our expertise and support of our service provider ecosystem, our SI's, our ISV's, to find the right mix of hybrid IT and decide where each application or workload should be hosted. 'Cause customers are now demanding robust ecosystems, cloud adjacency, and efficient low latency networks. And the modern workload demands, secure, compliant, highly available, and cost optimized environments. And Keith touched on colocation. We're partnering with colocation facilities to provide our customers with the ability to expand bandwidth, reduce latency, and get access to a robust ecosystem of adjacent providers. We touched on Equinix a bit as one of them, but we're partnering with them to enable customers to connect to multiple clouds with private on-demand interconnections from hundreds of data center locations around the globe. We continue to invest in the partner and customer experience, you know, making ourselves easier to do business with. We've now fully integrated partners in GreenLake Central, and could provide their customers end to end support and managing the entire hybrid IT estate. And lastly, we're providing partners with dedicated and exclusive enablement opportunities so customers can rely on both HPE and partner experts. And we have a competent team of specialists that can help them transform and differentiate themselves. >> Yeah, so, I'm hearing a theme of simplicity. You know, I talked earlier about this being customer-driven. To me what the customer wants is they want to come in, they want simple, like you mentioned, self-serve. I don't care if it's on-prem, in the cloud, across clouds, at the edge, abstract, all that complexity away from me. Make it simple to do, not only the technology to work, you figure out where the workload should run and let the metadata decide and that's a bold vision. And then, make it easy to do business. Let me buy as-a-Service if that's the way I want to consume. And partners are all about, you know, reducing friction and driving that. So, anyway guys, final thoughts, maybe Keith, you can close it out here and maybe George can call it timeout. >> Yeah, you summed it up really nice. You know, we're excited to continue to provide what we view as the largest and most flexible hybrid cloud for our customers' apps, data, workloads, and solutions. And really being that leading on-prem solution to meet our customer's needs. At the same time, we're going to continue to innovate and our ears are wide open, and we're listening to our customers on what their needs are, what their requirements are. So we're going to expand the use cases, expand the solution sets that we provide in these workload optimized offerings to a very very broad set of customers as they drive forward with that digital transformation and modernization efforts. >> Right, George, any final thoughts? >> Yeah, I would say, you know, with our partners we work as one team and continue to hone our skills and embrace our competence. We're looking to help them evolve their businesses and thrive, and we're here to help now more than ever. So, you know, please reach out to our team and our partners and we can show you where we've already been successful together. >> That's great, we're seeing the expanding GreenLake portfolio, partners key part of it. We're seeing new tools for them and then this ecosystem evolution and build out and expansion. Guys, thanks so much. >> Yeah, you bet, thank you. >> Thank you, appreciate it. >> You're welcome. (bright soft music) >> Okay, we're here with Jo Peterson the VP of Cloud & Security at Clarify360. Hello, Jo, welcome to theCUBE. >> Hello. >> Great to see you. >> Thanks for having me. >> You're welcome, all right, let's get right into it. How do you think about cloud where we are today in 2021? The definitions evolve, but where do you see it today and where do you see it going? >> Well, that's such an interesting question and is so relevant because the labels are disappearing. So over the last 10 years, we've sort of found ourselves defining whether an environment was public or whether it was private or whether it was hybrid. Here's the deal, cloud is infrastructure and infrastructure is cloud. So at the end of the day cloud in whatever form it's taking is a platform, and ultimately, this enablement tool for the business. Customers are consuming cloud in the best way that works for their businesses. So let's also point out that cloud is not a destination, it's this journey. And clients are finding themselves at different places on that road. And sometimes they need help getting to the next milestone. >> Right, and they're really looking for that consistent experience. Well, what are the big waves and trends that you're seeing around cloud out there in the marketplace? >> So I think that this hybrid reality is happening in most organizations. Their actual IT portfolios include a mix of on-premise and cloud infrastructure, and we're seeing this blurred line happening between the public cloud and the traditional data center. Customers want a bridge that easily connects one environment to the other environment, and they want end-to-end visibility. Customers are becoming more intentional and strategic about their cloud roadmaps. So some of them are intentionally and strategically selecting hybrid environments because they feel that it affords them more control, cost, balance, comfort level around their security. In a way, cloud itself is becoming borderless. The major tech providers are extending their platforms in an infrastructure agnostic manner and that's to work across hybrid environments, whether they be hosted in the data center, whether it includes multiple cloud providers. As cloud matures, workload environments fit is becoming more of a priority. So forward thinking where the organizations are matching workloads to the best environment. And it's sort of application rationalization on this case by case basis and it really makes sense. >> Yeah, it does makes sense. Okay, well, let's talk about HPE GreenLake. They just announced some new solutions. What do you think it means for customers? >> I think that HPE has stepped up. They've listened to not only their customers but their partners. Customers want consumable infrastructure, they've made that really clear. And HPE has expanded the cloud service portfolio for clients. They're offering more choices to not only enterprise customers but they're expanding that offering to attract this mid-market client base. And they provided additional tools for partners to make selling GreenLake easier. This is all helping to drive channel sales. >> Yeah, so better granularity, just so it increases the candidates, better optionality for customers. And this thing is evolving pretty quickly. We're seeing a number of customers that we talked to interested in this model, trying to understand it better and ultimately, I think they're going to really lean in hard. Jo, I wonder if you could maybe think about or share with us which companies are, I got to say, getting it right? And I'm really interested in the partner piece, because if you think about the partner business, it's really, it's changing a lot, right? It's gone from this notion of moving boxes and there was a lot of money to be made over the decades in doing that, but they have to now become value-add suppliers and really around cloud services. And in the early days of cloud, I think the channel was a little bit freaked out, saying, uh-oh, they're going to cut out the middleman. But what's actually happened is those smart agile partners are adding substantial value, they've got deep relationships with customers and they're serving as really trusted advisors and executors of cloud strategies. What do you see happening in the partner community? >> Well, I think it's been a learning curve and everything that you said was spot on. It's a two way street, right? In order for VARs to sell residual services, monthly recurring services, there has to have been some incentive to do that and HPE really got it right. Because they, again listened to that partner community, and they said, you know what? We've got to incentivize these guys to start selling this way. This is a partnership and we expect it to be a partnership. And the tech companies that are getting right are doing that same sort of thing, they're figuring out ways to make it palatable to that VAR, to help them along that journey. They're giving them tools, they're giving them self-serve tools, they're incentivizing them financially to make that shift. That's what's going to matter. >> Well, that's a key point you're making, I mean, the financial incentives, that's new and different. Paying, you know, incentivizing for as-a-Service models versus again, moving hardware and paying for, you know, installing iron. That's a shift in mindset, isn't it? >> It definitely is. And HPE, I think is getting it right because I didn't notice but I learned this, 70% of their annual sales are actually transacted through their channel. And they've seen this 116% increase in HPE GreenLake orders in Q1, from partners. So what they're doing is working. >> Yeah, I think you're right. And you know, the partner channel it becomes super critical. It's funny, Jo, I mean, again, in the early days of cloud, the channel was feeling like they were going to get disrupted. I don't know about you, but I mean, we've both been analysts for awhile and the more things get simple, the more they get complicated, right? I mean the consumerization of IT, the cloud, swipe your credit card, but actually applying that to your business is not easy. And so, I see that as great opportunities for the channel. Give you the last word. >> Absolutely, and what's going to matter is the tech companies that step up and realize we've got this chance, this opportunity to build that bridge and provide visibility, end-to-end visibility for clients. That's what going to matter. >> Yeah, I like how you're talking about that bridge, because that's what everybody wants. They want that bridge from on-prem to the public cloud, across clouds, going to to be moving out to the edge. And that is to your point, a journey that's going to evolve over the better part of this coming decade. Jo, great to see you. Thanks so much for coming on theCUBE today. >> Thanks for having me. (bright soft music) >> Okay, now we're going to into the GreenLake power panel to talk about the cloud landscape, hybrid cloud, and how the partner ecosystem and customers are thinking about cloud, hybrid cloud as a Service and of course, GreenLake. And with me are C.R. Howdyshell, President of Advizex. Ron Nemecek, who's the Business Alliance Manager at CBTS. Harry Zarek is President of Compugen. And Benjamin Klay is VP of Sales and Alliances at Arrow Electronics. Great to see you guys, thanks so much for coming on theCUBE. >> Thanks for having us. >> Good to be here. >> Okay, here's the deal. So I'm going to ask you guys each to introduce yourselves and your companies, add a little color to my brief intro, and then answer the following question. How do you and your customers think about hybrid cloud? And think about it in the context of where we are today and where we're going, not just the snapshot but where we are today and where we're going. C.R., why don't you start please? >> Sure, thanks a lot, Dave, appreciate it. And again, C.R. Howdyshell, President of Advizex. I've been with the company for 18 years, the last four years as president. So had the great opportunity here to lead a 45 year old company with a very strong brand and great culture. As it relates to Advizex and where we're headed to with hybrid cloud is it's a journey. So we're excited to be leading that journey for the company as well as HPE. We're very excited about where HPE is going with GreenLake. We believe it's a very strong solution when it comes to hybrid cloud. Have been an HPE partner since, well since 1980. So for 40 years, it's our longest standing OEM relationship. And we're really excited about where HPE is going with GreenLake. From a hybrid cloud perspective, we feel like we've been doing the hybrid cloud solutions, the past few years with everything that we've focused on from a VMware perspective. But now with where HPE is going, we think, probably changing the game. And it really comes down to giving customers that cloud experience with the on-prem solution with GreenLake. And we've had great response for customers and we think we're going to continue to see that kind of increased activity and reception. >> Great, thank you C.R., and yeah, I totally agree. It is a journey and we've seen it really come a long way in the last decade. Ron, I wonder if you could kickoff your little first intro there please. >> Sure Dave, thanks for having me today and it's a pleasure being here with all of you. My name is Ron Nemecek, I'm a Business Alliance manager at CBTS. In my role, I'm responsible for our HPE GreenLake relationship globally. I've enjoyed a 33 year career in the IT industry. I'm thankful for the opportunity to serve in multiple functional and senior leadership roles that have helped me gather a great deal of education and experience that could be used to aid our customers with their evolving needs, for business outcomes to best position them for sustainable and long-term success. I'm honored to be part of the CBTS and OnX Canada organization. CBTS stands for Consult Build Transform and Support. We have a 35 year relationship with HPE. We're a platinum and inner circle partner. We're headquartered in Cincinnati, Ohio. We service 3000 customers generating over a billion dollars in revenue and we have over 2000 associates across the globe. Our focus is partnering with our customers to deliver innovative solutions and business results through thought leadership. We drive this innovation via our team of the best and brightest technology professionals in the industry that have secured over 2,800 technical certifications, 260 specifically with HPE. And in our hybrid cloud business, we have clearly found that technology, new market demands for instant responses and experiences, evolving economic considerations with detailed financial evaluation, and of course the global pandemic, have challenged each of our customers across all industries to develop an optimal cloud strategy. We now play an enhanced strategic role for our customers as their technology advisor and their guide to the right mix of cloud experiences that will maximize their organizational success with predictable outcomes. Our conversations have really moved from product roadmaps and speeds and feeds to return on investment, return on capital, and financial statements, ratios, and metrics. We collaborate regularly with our customers at all levels and all departments to find an effective comprehensive cloud strategy for their workloads and applications ensuring proper alignment and cost with financial return. >> Great, thank you, Ron. Yeah, today it's all about the business value. Harry, please. >> Hi Dave, thanks for the opportunity and greetings from the Great White North. We're a Canadian-based company headquartered in Toronto with offices across the country. We've been in the tech industry for a very long time. We're what we would call a solution provider. How hard for my mother to understand what that means but what our goal is to help our customers realize the business value of their technology investments. Just to give you an example of what it is we try and do. We just finished a build out of a new networking endpoint and data center technology for a brand new hospital. It's now being mobilized for COVID high-risk patients. So talk about our all being in an essential industry, providing essential services across the whole spectrum of technology. Now, in terms of what's happening in the marketplace, our customers are confused. No question about it. They hear about cloud, I mean, cloud first, and everyone goes to the cloud, but the reality is there's lots of technology, lots of applications that actually still have to run on premises for a whole bunch of reasons. And what customers want is solid senior serious advice as to how they leverage what they already have in terms of their existing infrastructure, but modernize it, update it, so it looks and feels a lot like the cloud. But they have the security, they have the protection that they need to have for reasons that are dependent on their industry and business to allow them to run on-prem. And so, the GreenLake philosophy is perfect. That allows customers to actually have one foot in the cloud, one foot in their traditional data center but modernize it so it actually looks like one enterprise entity. And it's that kind of flexibility that gives us an opportunity collectively, ourselves, our partners, HPE, to really demonstrate that we understand how to optimize the use of technology across all of the business applications they need to run. >> You know Harry, it's interesting about what you said is, the cloud it is kind of chaotic my word, not yours. But there is a lot of confusion out there, I mean, what's cloud, right? Is it public cloud, is it private cloud, the hybrid cloud? Now, it's the edge and of course the answer is all of the above. Ben, what's your perspective on all this? >> From a cloud perspective, you know, I think as an industry, I think we we've all accepted that public cloud is not necessarily going to win the day and we're in fact, in a hybrid world. There's certainly been some commentary and press that was sort of validate that. Not that it necessarily needs any validation but I think is the linkages between on-prem and cloud-based services have increased. It's paved the way for customers more effectively, deploy hybrid solutions in in the model that they want or that they desire. You know, Harry was commenting on that a moment ago. As the trend continues, it becomes much easier for solution providers and service providers to drive their services initiatives, you know, in particular managed services. >> From an Arrow perspective is we think about how we can help scale in particular from a GreenLake perspective. We've got the ability to stand up some cloud capabilities through our ArrowSphere platform that can really help customers adopt GreenLake and to benefit from some alliances opportunities, as well. And I'll talk more about that as we go through. >> And Ben, I didn't mean to squeeze you on Arrow. I mean, Arrow has been around longer than computers. I mean, if you Google the history of Arrow it'll blow your mind, but give us a little quick commercial. >> Yeah, absolutely. So I've been with Arrow for about 20 years. I've got responsibility for Alliance organization in North America, We're a global value added distribution, business consulting and channel enablement company. And we bring scope, scale and and expertise as it relates to the IT industry. I love the fast pace that comes with the market that we're all in. And I love helping customers and suppliers both, be positioned for long-term success. And you know, the subject matter here today is just a great example of that. So I'm happy to be here and look forward to the discussion. >> All right, we got some good brain power in the room. Let's cut right to the chase. Ron, where's the pain? What are the main problems that CBTS I love what it stands for, Consult Build Transform and Support. What's the main pain point that customers are asking you to solve when it comes to their cloud strategies? >> Sure, Dave. Our customers' concerns and associated risks come from the market demands to deliver their products, services, and experiences instantaneously. And then the challenge is how do they meet those demands because they have aging infrastructure, processes, and fiscal constraints. Our customers really need us now more than ever to be excellent listeners so we can collaborate on an effective map with the strategic placement of workloads and applications in that spectrum of cloud experiences while managing their costs, and of course, mitigating risks to their business. This collaboration with our customers, often identify significant costs that have to be evaluated, justified or eliminated. We find significant development, migration, and egress charges in their current public cloud experience, coupled with significant over provisioning, maintenance, operational, and stranded asset costs in their on-premise infrastructure environment. When we look at all these costs holistically, through our customized workshops and assessments, we can identify the optimal cloud experience for the respective workloads and applications. Through our partnership with HPE and the availability of the HPE GreenLake solutions, our customers now have a choice to deliver SLA's, economics, and business outcomes for their workloads and applications that best reside on-premise in a private cloud and have that experience. This is a rock solid solution that eliminates, the development costs that they experience and the egress charges that are associated with the public cloud while utilizing HPE GreenLake to eliminate over provisioning costs and the maintenance costs on aging infrastructure hardware. Lastly, our customers only have to pay for actual infrastructure usage with no upfront capital expense. And now, that achieves true utilization to cost economics, you know, with HPE GreenLake solutions from CBTS. >> I love focus on the business case, 'cause it's measurable and it's sort of follow the money. That's where the opportunity is. Okay, C.R., so question for you. Thinking about Advizex customers, how are they, are they leaning into GreenLake? What are they telling you is the business impact when they experience GreenLake? >> Well, I think it goes back to what Ron was talking about. We had to solve the business challenges first and so far, the reception's been positive. When I say that is customers are open. Everybody wants to, the C-suite wants to hear about cloud and hybrid cloud fits. But what we hear and what we're seeing from our customers is we're seeing more adoption from customers that it may be their first foot in, if you will, but as important, we're able to share other customers with our potentially new clients that say, what's the first thing that happens with regard to GreenLake? Well, number one, it works. It works as advertised and as-a-Service, that's a big step. There are a lot of people out there dabbling today but when you can say we have a proven solution it's working in our environment today, that's key. I think the second thing is,, is flexibility. You know, when customers are looking for this hybrid solution, you got to be flexible for, again, I think Ron said (indistinct). You don't have a big capital outlay but also what customers want to be able to do is we want to build for growth but we don't want to pay for it. So we'll pay as we grow not as we have to use, as we used to do, it was upfront, the capital expenditure. Now we'll just pay as we grow, and that really facilitates in another great example as you'll hear from a customer, this afternoon. But you'll hear where one of the biggest benefits they just acquired a $570 million company and their integration is going to be very seamless because of their investment in GreenLake. They're looking at the flexibility to add to GreenLake as a big opportunity to integrate for acquisitions. And finally is really, we see, it really brings the cloud experience and as-a-Service to our customers. And with HPE GreenLake, it brings the best of breed. So it's not just what HPE has to offer. When you look at Hyperconverged, they have Nutanix, they have Cohesity. So, I really believe it brings best of breeds. So, to net it out and close it out with our customers, thus far, the customer experience has been exceptional. I mean, with GreenLake Central, as interface, customers have had a lot of success. We just had our first customer from about a year and a half ago just reopened, it was a highly competitive situation, but they just said, look, it's proven, it works, and it gives us that cloud experience so. Had a lot of great success thus far and looking forward to more. >> Thank you, so Harry, I want to pick up on something C.R. said and get your perspectives. So when I talk to the C-suite, they do all want to hear about, you know, cloud, they have a cloud agenda. And what they tell me is it's not just about their IT transformation. They want that but they also want to transform their business. So I wonder if you could talk, Harry, about Compugen's perspective on the potential business impact of GreenLake. And also, I'm interested in how you guys are thinking about workloads, how to manage work, you know, how to cost optimize in IT, but also, the business value that comes out of that capability. >> Yeah, so Dave, you know if you were to talk to CFO and I have the good fortune to talk to lots of CFOs, they want to pay the costs when they generate the revenue. They don't want to have all the costs upfront and then wait for the revenue to come through. A good example of where that's happening right now is you know, related to the pandemic, employees that used to work at the office have now moved to working from home. And now, they have to connect remotely to run the same application. So use this thing called VDI, virtual interfacing to allow them to connect to the applications that they need to run in the office. I don't want to get into too much detail but to be able to support that from an an at-home environment, they needed to buy a lot more computing capacity to handle this. Now, there's an expectation that hopefully six months from now, maybe sooner than that, people will start returning to the office. They may not need that capacity so they can turn down on the costs. And so, the idea of having the capacity available when you need it, but then turning it off when you don't need it, is really a benefit of the variable cost model. Another example that I would use is one in new development. If a customer is going to implement a new, let's say, line of business application. SAP is very very popular. You know, it actually, unfortunately, takes six months to two years to actually get that application set up, installed, validated, tested, then moves through production. You know, what used to happen before? They would buy all that capacity upfront, and it would basically sit there for two years, and then when they finally went to full production, then they were really value out of that investment. But they actually lost a couple of years of technology, literally sitting almost sidle. And so, from a CFO perspective, his ability to support the development of those applications as he scales it, perfect. GreenLake is the ideal solution that allows him to do that. >> You know, technology has saved businesses in this pandemic. There's no question about it. When Harry was just talking about with regard to VDI, you think about that, there's the dialing up and dialing down piece which is awesome from an IT perspective. And then the business impact there is the productivity of the end users. And most C-suite executives I've talked to said productivity actually went up during COVID with work from home, which is kind of astounding if you think about it. Ben, we said Arrow's been around for a long, long time. Certainly, before all of us were born and it's gone through many many industry transitions during our lifetimes. How does Arrow and how do your partners think about building cloud experiences and where does GreenLake fit in from your perspective? >> Great question. So from an Arrow perspective, when you think about cloud experience in of course us taking a view as a distribution partner, we want to be able to provide scale and efficiency to our network of partners. So we do that through our ArrowSphere platform. Just a bit of, you know, a bit of a commercial. I mean, you get single quote, single bill, auto provision, multi supplier, if you will, subscription management, utilization reporting from the platform itself. So if we pivot that directly to HPE, you're going to get a bit of a scoop here, Dave. And we're excited today to have GreenLake live in our platform available for our partner community to consume. In particular, the Swift solutions that HPE has announced so we're very excited to share that today. Maybe a little bit more on GreenLake. I think at this point in time, that it's differentiated in a sense that, if you think about some of the other offerings in the market today and further with having the the solutions themselves available in ArrowSphere. So, I would say, that we identify the uniqueness and quickly partner with HPE to work with our ArrowSphere platform. One other sort of unique thing is, when you think about platform itself, you've got to give a consistent experience. The different geographies around the world so, you know, we're available in North of 20 countries, there's thousands of resellers and transacting on the platform on a regular basis. And frankly, hundreds of thousands end customers. that are leveraging today. So that creates an opportunity for both Arrow, HPE and our partner community. So we're excited. >> You know, I just want to open it up. We don't have much time left, but thoughts on differentiation. Some people ask me, okay, what's really different about HPE and GreenLake? These others, you know, are doing things with as-a-Service. To me, I always say cultural, it starts from the top with Antonio, and it's like the company's all in. But I wonder from your perspectives, 'cause you guys are hands on. Are there other differentiable factors that you would point to? Let me just open that up to the group. >> Yeah, if I could make a comment. GreenLake is really just the latest invocation of the as-a-Service model. And what does that mean? What that actually means is you have a continuous ongoing relationship with the customer. It's not a sell and forget. Not that we ever forget about customers but there are highlights. Customer buys, it gets installed, and then for two or three years you may have an occasional engagement with them but it's not continuous. When you move to our GreenLake model, you're actually helping them manage that. You are in the core, in the heart of their business. No better place to be if you want to be sticky and you want to be relevant and you want to be always there for them. >> You know, I wonder if somebody else could add to it in your remarks. From your perspective as a partner, 'cause you know, hey, a lot of people made a lot of money selling boxes, but those days are pretty much gone. I mean, you have to transform into a services mindset, but other thoughts? >> I think to add to that Dave. I think Harry's right on. The way he positioned it it's exactly where he did own the customer. I think even another step back for us is, we're able to have the business conversation without leading with what you just said. You don't have to leave with a storage solution, you don't have to lead with compute. You know, you can really have step back, have a business conversation. And we've done that where you don't even bring up HPE GreenLake until you get to the point where the customer says, so you can give me an on-prem cloud solution that gives me scalability, flexibility, all the things you're talking about. How does that work? Then you bring up, it's all through this HPE GreenLake tool. And it really gives you the ability to have a business conversation. And you're solving the business problems versus trying to have a technology conversation. And to me, that's clear differentiation for HPE GreenLake. >> All right guys, C.R., Ron, Harry, Ben. Great discussion, thank you so much for coming on the program. Really appreciate it. >> Thanks for having us, Dave. >> Appreciate it Dave. >> All right, keep it right there for more great content at GreenLake Day, be right back. (bright soft music) (upbeat music) (upbeat electronic music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Welcome to the Virtual Cube and our coverage of aws reinvent 2020. I'm Lisa Martin. I'm joined by Ali Siddiqui, the chief product officer of BMC Software. We're gonna be talking about what BMC and A W s are doing together. Ali, it's great to have you on the Cube. Thank >>you, Lisa. Get great to be here and be part off AWS treatment. Exciting times. >>They are exciting times. That is true. No, never a dull moment these days, right? So all he talked to me a little bit. About what? A w what BMC is doing with AWS. Let's dig into what you're doing there on the technology front and unpack the benefits that you're delivering to customers. Great >>questions, Lisa. So at BMC, we really have a close partnership with AWS. It's really about BMC. Placido Blue s better together for our customers. That's what it's really about. We have a global presence, probably the largest, uh, off any window out there in this in our industry with 15 data centers, AWS data centers around the globe. We just announced five more in South Africa. Brazil Latin Um, a P J. A couple of them amia across the globe. Really? The presence is very strong with these, uh, data centers because that lets us offered local presence, Take care of GDP are and we have great certification. That is Aw, sock to fedramp. I'll four Haifa dram. We even got hip certifications as well as a dedicated Canada certifications for our customers. Thanks to our partnership, close partnership with the WS and on all these datas into the cross. In addition, for our customers, really visibility into aws seamless capability toe do multi cloud management is key and with a recent partnership with AWS around specifically AWS >>s >>S m, which gives customers cream multi cloud capabilities around multi cloud management, total visibility seamlessly in AWS and all their services whether it's easy toe s s s three sage maker, whatever services they have, we let them discover on syphilis. Lee give them visibility into that. >>That 360 degree visibility is really key to understand the dependencies right between the software in the services and help customers to optimize their investments in a W s assume correct. >>Exactly. With the AWS s s m and r E I service management integration. We really give deep visibility on the dependency, how they're being used, what services are being impacted and and really, AWS s system is a key, unique technology which we've integrated with them very, very happy with the results are customers are getting from it. >>Can you share some of those results? Operational efficiencies, Cost savings? Yeah, >>Yeah, least another great question. So when I look at the general picture off E I service management in the eye ops, which we run with AWS across all these global dinner senses and specifically with AWS S S M people are able to do customers. And this is like the talkto hyper scale, as we're talking about, as well as large telcos like Ericsson and and some of the leading, uh, industry retail Or or, you know, other customers we have They're getting great value because they're able to do service modeling, automatically use ascend to get true deep visibility seamlessly to do service discovery with for for for all the assets that they run or using our S service management in the eye ops capabilities. It really is the neck shin and it's disrupting the service idea Some traditional service management industry with what we offering now with the service management, AWS s, S M and other AWS Cloud needed capabilities such as sage Maker and AWS, Lex and connect that we leverage in our AI service management ai absolution. We recently announced that as a >>single >>unified platform which allows our customers to go on BMC customers and joined with AWS customers to go on this autonomous digital enterprise journey Uh, this announcement was done by our CEO of BMC. I'm in Say it in BMC Exchange recently, where we basically launched a single lady foundation, a single platform for observe ability, engagement with automation >>for the autonomous digital enterprise. I presume I'd like to understand to, from your perspective, this disruption that you're enabling. How is it helping your customers not just survive this viral disruption that we're all living with but be able thio, get the disability into their software and services, really maximize and optimize their cloud investments so that their business can operate well during these unprecedented times, meet their customer demands, exceed them and meet their customers. Where? There. How is this like an accelerator of that >>great question, Lisa. So when we say autonomous digital enterprise, this is the journey All our customers they're taking on its focus on three trips, agility, customer center, city and action ability. So if you think about our solutions with AWS, really, it's s of its management. AI ops enables these enterprises to go on this autonomous digital enterprise journey where they can offer great engagement to the employees. All CEOs really care about employee engagement. Happy employees make for more revenue for for those enterprises, as well as offer great customer experience for the customers. Uh, using our AI service management and AI ops combined. 80 found in this single platform, which we are calling 80 foundation. >>Yeah, go ahead. Sorry. >>No, go ahead, please. >>I was going to say I always look at the employee experience, and the customer experience is absolutely inextricably linked with the employee experience is hampered. That's bride default. Almost going to impact the customer experience. And right now, I don't know if it's even possible to say both the employee experience and the customer experience are even mawr essential to really get right because now we've got this. You know this big scatter That happened a few months ago with some companies that were completely 100% on site to remote being able, needing to give their employees access to the tools to do their jobs properly so that they can deliver products and services and solutions that customers need. So I always see those two employees. Customer experience is just inextricably linked. >>Absolutely. That's correct, especially in this time, even if the new pandemic these epidemics time, uh, the chief human resource offers. The CEOs are really thick focused on keeping the employees engaged and retaining top talent. And that's where our yes service management any other solution helps them really do. Use our digital assistance chat boards, which are powered by a W X and Lex and AWS connect and and and our integration with, uh, helix control them, which is another service we launched on AWS Helix Control them, which is our South version off a leading SAS product automation product out there, a swell as RP integrations we bring to the table, which really allows them toe take employing, give management to the next level And that's top of mind for all CEOs and being driven by line of business like chief human resource officers. Such >>a great point. Are you? Are you finding that mawr of your conversations with customers are at that sea level as they look to things like AI ops to help find you in their business that it's really that that sea level not concerned but priority to ensure that we're doing everything we can within our infrastructure, wherever where our software and services are to really ensure that we're delivering and exceeding customer expectations? That a very tumultuous time? >>Yes, What we're finding is, uh, really at the CEO level CEO level the sea level. It's about machine learning ai adopting that more than the enterprise and specifically in our capabilities when I say ai ops. So those are around root cause predictive I t. And even using ai NLP for self service for self service is a big part, and we offer key capabilities. We just did an acquisition come around, which lets them do knowledge management self service. So these are specific capabilities, predictability, ai ops and knowledge management. Self service that we offer that really is resonating very well with CEOs who are looking to transform their I T systems and in I t ops and align it with business is much better and really do innovation in this area. So that's what's happening, and it's great to see that we will do that. Exact capabilities that come with R E Foundation. The unified platform forms of ability and lets customers go on this autonomous digital enterprise journey without keeping capabilities. >>Do you see this facilitating the autonomous digital enterprise as as a way to separate the winners and losers of tomorrow as so much of the world has changed and some amount of this is going to be permanent, imagine that's got to be a competitive advantage to customers in any industry. >>We believe enterprises that have the growth mindset and and want to go into the next generation, and that's most of them. Toe, to be honest, are really looking at the ready autonomous digital price framework that we offer and work with our customers on the way to grow revenue to get more customer centric, increase employee engagement. That's what we see happening in the industry, and that's where our capabilities with 80 Foundation as well as Helix. Whether it's Felix Air Service management, he likes a Iot or now recently launched Helix Control them really enable them toe keep their existing, uh, you know, tools as well as keep their existing investments and move the ICTY ops towards the next generation off tooling and as well as increase employee engagement with our leading industry leading digital assistant chat board and and SMS management solution that that's what we see. And that's the journey we're taking with most of our customers and really, the ones with the growth mindset are really being distinguished as the front runs >>talk to me about some validation from the customer's perspective, the industry's perspective. What are you guys hearing about? What you're doing s BMC and with a w s >>so validation from customer that I just talked about great validation. As I said, talk to off the hyper skills users for proactive problem management. Proactive incident management ai ops a same time independent validation from Gardner we are back wear seven years and I don't know in a row So seven years the longest street in Gartner MQ for I t s m and we are a leader in that for seven years the longest run so far by any vendor. We are scoring the top in the top number one position in 12 of the 15 critical capabilities. As you know, Gardner, I d s m eyes really about the critical capability that where most customers look. So that's a big independent validation. Where we score 12 off the way were number one in 12 of the 15 capability. So that was the awesome validation from Gardner and I. D. S M. We also recently E Mei Enterprise Management Associates published a new report on AI Ops and BMT scored the top spot on the charts with Business impact and business alignment. Use cases categories for AI ops. So think about what that means. It's really about your business, right? So So we being the top of the chart for business impact and business alignment for ai ops radar report from Enterprise Management associated with a create independent validation that we can point toe off our solutions and what it is, really, because we partner very closely with our customers. We also got a couple of more awards than we want a lot more, but just to mention two more I break breakthrough, which is a nursery leading third party sources out there for chat boards and e i base chat board solution lamed BMC Helix Chat Board as the best chat board solution out there. Uh, SAS awards another industry analysts from independent from which really, uh really shows the how we're getting third parties and independents to talk about our solutions named BMC SAS per ticket and event management, which is really a proactive problem and proactive incident solution Revolution system as as the best solution out there for ticketing and event management. >>So a lot of accolades. A. Yes. It sounds like a lot of alcohol. A lot of validation. How do customers get How do you get started? So customers looking to come to BMC to really understand get that 3 60 degree visibility. How did they get started? >>Uh, well, they can start with our BMC Discovery, which integrates very tightly with AWS s s M toe. Basically get the full visibility off assets from network to storage toe aws services. Whether there s three. Uh, easy to, uh doesn't matter what services they did. A Kafka service they're using whatever. So the hundreds of services they're using weaken seamlessly do that. So that's one way to do that. Just start with BMC Helix Discovery. Thea Other one is with BMC Knowledge Management on BMC Self Service. That's a quick win for most of our customers. I ai service management, tooling That's the Third Way and I I, off stooling with BMC, Helix Monitor and AI ops that we offer pretty much the best in the industry in those that customers can start So the many areas, and now with BMC, control them. If they want to start with automation, that's a great way to start with BMC control them, which is our SAS solution off industry leading automation product called Controlling. >>And so, for just last question from a go to market perspective, it sounds like direct through BMC Channel partners. What about through a. W. S? >>Yes, absolutely. I mean again, we it's all about BMC and AWS better together we offer cloud native AWS services for our solutions, use them heavily, and I just mentioned whether that S S M or chat boards or any of the above or sage maker for machine learning I and customers can contact the local AWS Rep toe to start learning about BMC and AWS. Better together. >>Excellent. Well, Ali, thank you for coming on the program, talking to us about what BMC is doing to help your customers become that autonomous digital enterprise that we think up tomorrow. They're going to need to be to have that competitive edge. I've enjoyed talking to you >>same year. Thank you so much, Lisa. Really. It's about our customers and partnering with AWS. So very proud of Thank you so much. >>Excellent for Ali Siddiqui. I'm Lisa Martin and you're watching the Cube.

>>from around the globe. It's the Cube with digital coverage of workplace Next made possible by Hewlett Packard Enterprise. >>Welcome to Workplace Next Brought to You by the Cube 3 65 and sponsored by Hewlett Packard Enterprise. We got a great show lineup for you today. If you like me, you've had to change the way you work this year, and so have your team's. Ah, lot of work has gone remote, of course, and very quickly we've had toe rethink how we operate on a day to day basis, and that's great. If, like me, you could do your job remotely. But let's not forget there are a lot of industries were going remote isn't an option, or at least it's not as much of an option. But the show has to go on, Of course, safely. This has brought about major Rethink Is leaders everywhere. Try to figure out how to adapt. How do you maintain productivity now and also positioned for the future? So let me run through today's lineup First, we'll look at some of these leaders who are adapting. We'll hear how they've taken remote work securely an unbelievably quickly and how they're keeping people safe. When the work has toe happen in person, in approximate locations. Well, look at what they've done the last six months or so and what learnings they'll take forward. Then we've got some great workplace experts to make sense of it all to talk through what the prescription is going forward. What's this hybrid world going to look like? And not just to survive the pandemic, but to use this moment as a launch point to transformation of the way in which we work that will serve us in the years and the decades to come. And finally, we'll delve into the practical. We'll look at some of the solutions that are available today and bring people and technology together with processes to help you realize this transformation. We have HBs best experts lined up to answer your questions on what the practical steps are to reinvent the ways in which you work in these unpredictable times. Whether you want to talk about security, I o. T at the edge ai technologies for safe workplaces or any of the things that you need to do to nag, navigate, change successfully. They've been there, they've done that and they're here to help. So with that, let's go to our first panel. I'll hand it over to our moderator, Maribel Lopez. She's with the independent analyst firm Lopez Associates and friend of the Cube over to you, Maribel.

>>Welcome to Workplace Next Brought to you by the Cube 3 65 and sponsored by Hewlett Packard Enterprise. We got a great show lineup for you today. If you like me, you've had to change the way you work this year, and so have your teams. A lot of work has gone remote, of course, and very quickly we've had toe rethink how we operate on a day to day basis, and that's great. If, like me, you could do your job remotely. But let's not forget there are. A lot of industries were going remote isn't an option, or at least it's >>not as much of an option. But the show has to go on, >>Of course, safely. This has brought about major Rethink Is leaders everywhere. Try to figure out how to adapt. How do you maintain productivity now and also positioned for the future? So let me run through today's lineup First, we'll look at some of these leaders who are adapting. We'll hear how they've taken remote work securely an unbelievably quickly and how they're keeping people safe when the work has toe happen in person, in approximate locations. Well, look at >>what they've done the last six months or so and what learnings they'll take forward. Then we've got some great workplace experts to make sense of it all to talk through what the prescription is going forward. What's this hybrid world going to look like? And not just to survive the pandemic, but to use this moment as a launch point to transformation of the way in which we work that will serve us >>in >>the years and the decade to come. And finally, we'll delve into the practical. We'll look at some of the solutions that are available today and bring people and technology together with processes to help you realize this transformation. We have HBs best experts lined up to answer your questions on what the practical steps are to reinvent the ways in which you work in these unpredictable times, whether you wanna talk about security, I o. T at the edge ai Technologies for safe workplaces >>or any of the >>things that you need to do to nag, navigate, change successfully. They've been there, they've done that, and they're here to help. So >>with that, let's go to our first panel. I'll hand it over to our >>moderator, Maribel Lopez. She's with the independent analyst firm Lopez Associates and friend of the Cube over to you, Maribel

>> From Cambridge, Massachusetts, it's theCUBE covering MIT Chief Data Officer and Information Quality Symposium 2019 brought to you by SiliconANGLE Media. (upbeat techno music) >> Hi everybody, welcome back to Cambridge, Massachusetts. You're watching theCUBE, the leader in tech coverage. We go out to the events. We extract the signal from the noise, and we're here at the MIT CDOIQ Conference, Chief Data Officer Information Quality Conference. It is the 13th year here at the Tang building. We've outgrown this building and have to move next year. It's fire marshal full. Gokula Mishra is here. He is the Senior Director of Global Data and Analytics and Supply Chain-- >> Formerly. Former, former Senior Director. >> Former! I'm sorry. It's former Senior Director of Global Data Analytics and Supply Chain at McDonald's. Oh, I didn't know that. I apologize my friend. Well, welcome back to theCUBE. We met when you were at Oracle doing data. So you've left that, you're on to your next big thing. >> Yes, thinking through it. >> Fantastic, now let's start with your career. You've had, so you just recently left McDonald's. I met you when you were at Oracle, so you cut over to the dark side for a while, and then before that, I mean, you've been a practitioner all your life, so take us through sort of your background. >> Yeah, I mean my beginning was really with a company called Tata Burroughs. Those days we did not have a lot of work getting done in India. We used to send people to U.S. so I was one of the pioneers of the whole industry, coming here and working on very interesting projects. But I was lucky to be working on mostly data analytics related work, joined a great company called CS Associates. I did my Master's at Northwestern. In fact, my thesis was intelligent databases. So, building AI into the databases and from there on I have been with Booz Allen, Oracle, HP, TransUnion, I also run my own company, and Sierra Atlantic, which is part of Hitachi, and McDonald's. >> Awesome, so let's talk about use of data. It's evolved dramatically as we know. One of the themes in this conference over the years has been sort of, I said yesterday, the Chief Data Officer role emerged from the ashes of sort of governance, kind of back office information quality compliance, and then ascended with the tailwind of the Big Data meme, and it's kind of come full circle. People are realizing actually to get value out of data, you have to have information quality. So those two worlds have collided together, and you've also seen the ascendancy of the Chief Digital Officer who has really taken a front and center role in some of the more strategic and revenue generating initiatives, and in some ways the Chief Data Officer has been a supporting role to that, providing the quality, providing the compliance, the governance, and the data modeling and analytics, and a component of it. First of all, is that a fair assessment? How do you see the way in which the use of data has evolved over the last 10 years? >> So to me, primarily, the use of data was, in my mind, mostly around financial reporting. So, anything that companies needed to run their company, any metrics they needed, any data they needed. So, if you look at all the reporting that used to happen it's primarily around metrics that are financials, whether it's around finances around operations, finances around marketing effort, finances around reporting if it's a public company reporting to the market. That's where the focus was, and so therefore a lot of the data that was not needed for financial reporting was what we call nowadays dark data. This is data we collect but don't do anything with it. Then, as the capability of the computing, and the storage, and new technologies, and new techniques evolve, and are able to handle more variety and more volume of data, then people quickly realize how much potential they have in the other data outside of the financial reporting data that they can utilize too. So, some of the pioneers leverage that and actually improved a lot in their efficiency of operations, came out with innovation. You know, GE comes to mind as one of the companies that actually leverage data early on, and number of other companies. Obviously, you look at today data has been, it's defining some of the multi-billion dollar company and all they have is data. >> Well, Facebook, Google, Amazon, Microsoft. >> Exactly. >> Apple, I mean Apple obviously makes stuff, but those other companies, they're data companies. I mean largely, and those five companies have the highest market value on the U.S. stock exchange. They've surpassed all the other big leaders, even Berkshire Hathaway. >> So now, what is happening is because the market changes, the forces that are changing the behavior of our consumers and customers, which I talked about which is everyone now is digitally engaging with each other. What that does is all the experiences now are being captured digitally, all the services are being captured digitally, all the products are creating a lot of digital exhaust of data and so now companies have to pay attention to engage with their customers and partners digitally. Therefore, they have to make sure that they're leveraging data and analytics in doing so. The other thing that has changed is the time to decision to the time to act on the data inside that you get is shrinking, and shrinking, and shrinking, so a lot more decision-making is now going real time. Therefore, you have a situation now, you have the capability, you have the technology, you have the data now, you have to make sure that you convert that in what I call programmatic kind of data decision-making. Obviously, there are people involved in more strategic decision-making. So, that's more manual, but at the operational level, it's going more programmatic decision-making. >> Okay, I want to talk, By the way, I've seen a stat, I don't know if you can confirm this, that 80% of the data that's out there today is dark data or it's data that's behind a firewall or not searchable, not open to Google's crawlers. So, there's a lot of value there-- >> So, I would say that percent is declining over time as companies have realized the value of data. So, more and more companies are removing the silos, bringing those dark data out. I think the key to that is companies being able to value their data, and as soon as they are able to value their data, they are able to leverage a lot of the data. I still believe there's a large percent still not used or accessed in companies. >> Well, and of course you talked a lot about data monetization. Doug Laney, who's an expert in that topic, we had Doug on a couple years ago when he, just after, he wrote Infonomics. He was on yesterday. He's got a very detailed prescription as to, he makes strong cases as to why data should be valued like an asset. I don't think anybody really disagrees with that, but then he gave kind of a how-to-do-it, which will, somewhat, make your eyes bleed, but it was really well thought out, as you know. But you talked a lot about data monetization, you talked about a number of ways in which data can contribute to monetization. Revenue, cost reduction, efficiency, risk, and innovation. Revenue and cost is obvious. I mean, that's where the starting point is. Efficiency is interesting. I look at efficiency as kind of a doing more with less but it's sort of a cost reduction, but explain why it's not in the cost bucket, it's different. >> So, it is first starts with doing what we do today cheaper, better, faster, and doing more comes after that because if you don't understand, and data is the way to understand how your current processes work, you will not take the first step. So, to take the first step is to understand how can I do this process faster, and then you focus on cheaper, and then you focus on better. Of course, faster is because of some of the market forces and customer behavior that's driving you to do that process faster. >> Okay, and then the other one was risk reduction. I think that makes a lot of sense here. Actually, let me go back. So, one of the key pieces of it, of efficiency is time to value. So, if you can compress the time, or accelerate the time and you get the value that means more cash in house faster, whether it's cost reduction or-- >> And the other aspect you look at is, can you automate more of the processes, and in that way it can be faster. >> And that hits the income statement as well because you're reducing headcount cost of your, maybe not reducing headcount cost, but you're getting more out of different, out ahead you're reallocating them to more strategic initiatives. Everybody says that but the reality is you hire less people because you just automated. And then, risk reduction, so the degree to which you can lower your expected loss. That's just instead thinking in insurance terms, that's tangible value so certainly to large corporations, but even midsize and small corporations. Innovation, I thought was a good one, but maybe you could use an example of, give us an example of how in your career you've seen data contribute to innovation. >> So, I'll give an example of oil and gas industry. If you look at speed of innovation in the oil and gas industry, they were all paper-based. I don't know how much you know about drilling. A lot of the assets that goes into figuring out where to drill, how to drill, and actually drilling and then taking the oil or gas out, and of course selling it to make money. All of those processes were paper based. So, if you can imagine trying to optimize a paper-based innovation, it's very hard. Not only that, it's very, very by itself because it's on paper, it's in someone's drawer or file. So, it's siloed by design and so one thing that the industry has gone through, they recognize that they have to optimize the processes to be better, to innovate, to find, for example, shale gas was a result output of digitizing the processes because otherwise you can't drill faster, cheaper, better to leverage the shale gas drilling that they did. So, the industry went through actually digitizing a lot of the paper assets. So, they went from not having data to knowingly creating the data that they can use to optimize the process and then in the process they're innovating new ways to drill the oil well cheaper, better, faster. >> In the early days of oil exploration in the U.S. go back to the Osage Indian tribe in northern Oklahoma, and they brilliantly, when they got shuttled around, they pushed him out of Kansas and they negotiated with the U.S. government that they maintain the mineral rights and so they became very, very wealthy. In fact, at one point they were the wealthiest per capita individuals in the entire world, and they used to hold auctions for various drilling rights. So, it was all gut feel, all the oil barons would train in, and they would have an auction, and it was, again, it was gut feel as to which areas were the best, and then of course they evolved, you remember it used to be you drill a little hole, no oil, drill a hole, no oil, drill a hole. >> You know how much that cost? >> Yeah, the expense is enormous right? >> It can vary from 10 to 20 million dollars. >> Just a giant expense. So, now today fast-forward to this century, and you're seeing much more sophisticated-- >> Yeah, I can give you another example in pharmaceutical. They develop new drugs, it's a long process. So, one of the initial process is to figure out what molecules this would be exploring in the next step, and you could have thousand different combination of molecules that could treat a particular condition, and now they with digitization and data analytics, they're able to do this in a virtual world, kind of creating a virtual lab where they can test out thousands of molecules. And then, once they can bring it down to a fewer, then the physical aspect of that starts. Think about innovation really shrinking their processes. >> All right, well I want to say this about clouds. You made the statement in your keynote that how many people out there think cloud is cheaper, or maybe you even said cheap, but cheaper I inferred cheaper than an on-prem, and so it was a loaded question so nobody put their hand up they're afraid, but I put my hand up because we don't have any IT. We used to have IT. It was a nightmare. So, for us it's better but in your experience, I think I'm inferring correctly that you had meant cheaper than on-prem, and certainly we talked to many practitioners who have large systems that when they lift and shift to the cloud, they don't change their operating model, they don't really change anything, they get a bill at the end of the month, and they go "What did this really do for us?" And I think that's what you mean-- >> So what I mean, let me make it clear, is that there are certain use cases that cloud is and, as you saw, that people did raise their hand saying "Yeah, I have use cases where cloud is cheaper." I think you need to look at the whole thing. Cost is one aspect. The flexibility and agility of being able to do things is another aspect. For example, if you have a situation where your stakeholder want to do something for three weeks, and they need five times the computing power, and the data that they are buying from outside to do that experiment. Now, imagine doing that in a physical war. It's going to take a long time just to procure and get the physical boxes, and then you'll be able to do it. In cloud, you can enable that, you can get GPUs depending on what problem we are trying to solve. That's another benefit. You can get the fit for purpose computing environment to that and so there are a lot of flexibility, agility all of that. It's a new way of managing it so people need to pay attention to the cost because it will add to the cost. The other thing I will point out is that if you go to the public cloud, because they make it cheaper, because they have hundreds and thousands of this canned CPU. This much computing power, this much memory, this much disk, this much connectivity, and they build thousands of them, and that's why it's cheaper. Well, if your need is something that's very unique and they don't have it, that's when it becomes a problem. Either you need more of those and the cost will be higher. So, now we are getting to the IOT war. The volume of data is growing so much, and the type of processing that you need to do is becoming more real-time, and you can't just move all this bulk of data, and then bring it back, and move the data back and forth. You need a special type of computing, which is at the, what Amazon calls it, adds computing. And the industry is kind of trying to design it. So, that is an example of hybrid computing evolving out of a cloud or out of the necessity that you need special purpose computing environment to deal with new situations, and all of it can't be in the cloud. >> I mean, I would argue, well I guess Microsoft with Azure Stack was kind of the first, although not really. Now, they're there but I would say Oracle, your former company, was the first one to say "Okay, we're going to put the exact same infrastructure on prem as we have in the public cloud." Oracle, I would say, was the first to truly do that-- >> They were doing hybrid computing. >> You now see Amazon with outposts has done the same, Google kind of has similar approach as Azure, and so it's clear that hybrid is here to stay, at least for some period of time. I think the cloud guys probably believe that ultimately it's all going to go to the cloud. We'll see it's going to be a long, long time before that happens. Okay! I'll give you last thoughts on this conference. You've been here before? Or is this your first one? >> This is my first one. >> Okay, so your takeaways, your thoughts, things you might-- >> I am very impressed. I'm a practitioner and finding so many practitioners coming from so many different backgrounds and industries. It's very, very enlightening to listen to their journey, their story, their learnings in terms of what works and what doesn't work. It is really invaluable. >> Yeah, I tell you this, it's always a highlight of our season and Gokula, thank you very much for coming on theCUBE. It was great to see you. >> Thank you. >> You're welcome. All right, keep it right there everybody. We'll be back with our next guest, Dave Vellante. Paul Gillin is in the house. You're watching theCUBE from MIT. Be right back! (upbeat techno music)

>> Announcer: Live from Miami Beach, Florida, it's theCUBE. Covering VeeamON 2019. Brought to you by Veeam. >> Welcome back to the Fontainebleau, Miami, everybody. My name is Dave Vellante, I'm here with my co-host for this segment, Justin Warren. Justin it's great to see you. This is theCUBE, the leader in live tech coverage, day two of our coverage of VeeamON 2019 here in Miami. Jason Buffington, @Jbuff is here, he's the vice president of solution strategy congratulations on the promotion and great to see you again, my friend. >> Thank you very much. >> Dave: And Nathan Hughes who is the IT director at Flex-N-Gate. Great to see you, thanks for coming on. We love to get the customer's perspective, so welcome. >> Great to be here. >> Okay, so, Jason let me start with you. Former analyst, you've been at Veeam now for long enough to A, get promoted, but also, get the Kool-Aid injection, you're wearing the green, and, what are the big trends that you're seeing in the market that are really driving this next era, what do guys call it? Act two of data protection? >> Sure. So, I preached on this even before I joined Veeam that every 10 years or so, when the industry shifts the platform of choice, the data protection vendors almost always reset, right? The people that lead in NetWare don't lead in Windows. The people that lead in Windows didn't lead in Vert. The next wave is we're moving from servers to services. Right, we're going from on prem into cloud and so, and every time the problem is the secret sauce doesn't line up, right? So you got to reinvent yourself each time. And what we saw in the past generations, what we learned from, is, you can't be so busy taking care of your install base that you forget to keep innovating on what that next platform is and so for us, act two is all about cloud. We're going to take everything we know about reliability but we're moving into cloud. The difference is, that in virtualization there was one hero scenario. VMs, right? This time around it's IaaS, it's SaaS, it's PaaS, it's using cloud storage, it's BaaS and DRaaS, there's not a single hero scenario which means we have a lot more innovation to do. That's round two. >> And you made that point today, you used the Archimedes quote, give me a lever and a fulcrum and I'll change the world. You used the analogy of backup as now becoming much more than just backup, it's data protection, it's data management, we're going to get into that. And test some of that with Nathan. So, Nathan, tell us about Flex-N-Gate what does the company do and what does your role as IT director entail? >> Okay, so Flex-N-Gate is a tier one automotive supplier. Which means that we provide parts, most of the things that go into a car besides electronics and glass, to the final automotive makers. So most of the companies that you're familiar with when you go to buy one. >> Okay, so you guys are global, I think you've got what, 24,000 associates worldwide, 64 locations. So what're some of the things that are, fundamental drivers of your business, that are rippling through to your IT strategy? >> Well, our business is varied in the sense that we do a lot of different things in house so, we do, obviously, manufacturing, that's a big part of what we do. And then, even that is broken down into different kinds and then beyond manufacturing we have advanced product development and engineering so we do a lot of that in house. >> Dave: You support it all? >> Yes. >> So you've got diverse lines of business, you've got different roles and personas, you know, engineers versus business people versus finance people. And you got to make 'em all happy. >> We've got to make 'em all happy. >> So, one of the things I love about manufacturing examples, is if you think about it it's the two extremes of high tech and low tech, right? On the low tech side of things you've got this manufacturing floor and it's just producing real stuff, not the zeros and ones that we live with, but real things come off this line. And then you have the engineering and R and D side. Where they're absolutely focused on stuff that comes out of some engineer's head into a computer, which is truly unique data, so, one of the things I love about the story is, talk about the downtime challenges you have around the manufacturing floor. Because I learned some things when we first met, that I think is phenomenal when it comes to manufacturing things that I didn't realize. >> Sure. So, we have a lot of different kinds of manufacturing environments. Some of them are more passive and some of them are more active. The most active environments are, a form of manufacturing known as sequencing. And it's sort of where you bring final assembly of parts together right before they go to the customer. The way that customers order up parts these days, it's not like they used to back in the 70s and 80s. Where they would warehouse huge volumes of everything on their site and then just draw it down if they needed it. And you just kept the queue full. Now they want everything just in time delivery. So they basically want parts to come to the line right when they're needed and actually in the order they're needed. So, a final car maker, they're not necessarily making, 300 of the same thing in a row, they're going to make one of this in blue and one of that in red and they're all going to be sequenced behind each other, one right after the other on the assembly line. And they want the parts from the suppliers to come in the exact right order for that environment. So, the challenge with that from our perspective is that we have trucking windows that are between 30, maybe 60 minutes on the high end, and if anything goes badly, you can put the customer down. And now you're talking about stopping production at Ford, Chrysler, GM, whatever. And that's a lot of money and a lot of other suppliers impacted. >> Dave: So this is a data problem isn't it? >> Yeah, it definitely is. And it's an interesting point, 'cause, you talk about sequencing. Veeam has their own sequence about how customers use the product and they start with backup, everything starts with backup, and then they move further to the right so that you get, ideally, to fully automated data protection. So, what are you actually using Veeam for today? And where do you see yourself going with Veeam? >> So, right now, we're using Veeam primarily as backup and recovery. It's how we started with it. We came from another product that was, great conceptually, but in the real world it had terrible reliability and its performance was very poor as time went on and so, when Veeam came on the scene it was a breath of fresh air because we got to the place where we knew that what we had was dependable, it was reliable. We got to understand how the product worked and to improve the way that we'd implemented it. And so, one of the key features in Veeam that really actually excited us, especially in those sequencing environments are these instant recovery options, right? So, we were used to the idea of having to write down a VM out of snapshot storage. And then being put in a position where it might take an hour, two hours, three hours before you could get that thing back online now, or again, to be able to launch that right out of snapshot storage was a blessing in the industry we're in. >> Yeah, did you see the tech demo yesterday where they were showing off how you could do an instant recovery directly from cloud storage? >> Yes, yeah. >> Did that get you excited? >> Yes. That is exciting. >> Are you using cloud at the moment or is this something that you're looking to move towards? >> Cloud is something we're sort of investigating but it's not something that we're actively utilizing right now. >> So this instance recovery, you guys obviously make a big deal out of that, I was talking to Danny Allan yesterday offline about it. He claims it's unique in the industry. And I asked him a question, I said specifically, if you lose the catalog, can I actually get the data back? And he said yes. And I'm like, that sounds like magic. So, so I guess my question to maybe both of you is, instant, how instant? And how does it actually work? (he laughs) >> It just works, isn't that? >> It just works! >> It's just magic, new tagline? >> I guess we don't have to get into the weeds but when you say, when I hear instant recovery, we're talking like, (fingers clicking) instantaneous recovery with, very short RTOs? >> To us what that means is that in practice, we can expect to have a VM from snapshot data back into production in about a five minute window. >> Dave: Five minutes? Okay. >> And that is sufficient for our needs in any environment. >> Okay, so now we're talking RTO, right? And then, what about, so we said 64 sites across the world, 24,000 associates, is Veeam your enterprise wide data protection strategy or are you rolling it out now? Where are you at? >> Yes, no. Veeam, we started with it in a handful of key sites. And we were using it to specifically back up SharePoint and a few other platforms. But once we understood what the product was capable of, and we were sort of reaching the end of our rope with this former product, yeah, we began an active roll out and we've now had Veeam in our facilities for five, six years. >> So you swept the floor of that previous product. And how complicated was it for you to move from the legacy product to Veeam? >> It was a challenge just rethinking the way that we do things, the previous product, one thing that it really had going for it, if this could be considered a positive, I guess, is that it was very very simple to set up. So, you could take an entry level IT administrator and they just next, next, next, next, next. And it would do all the things that they needed it to do. But the problem was that in the real world, that was sort of the Achilles' Heel, because, it meant that it wasn't very well customized and it meant also that, the way that they've developed that product, it became performance, it had poor performance. >> So the reason I ask that question is because, so many times customers are stuck. And it's like they don't want to move, because it's a pain. But the longer they go, the more costly it is, down the road. So I'm always looking to IT practitioners like, advice that you would give in terms of others, things that you might do differently if you had a mulligan, I don't know, maybe you would've started sooner, or maybe there were some things that you'd do differently. What would you advise? >> Yeah, I mean, if we'd understood, the whole context of what was happening with that other product, we would've moved sooner. And the one thing that I will say about Veeam is, it's not click and point. It does involve a little more setup. But the Veeam team is excellent when it comes to support. So there's nothing to fear in that category because they stand behind their product and it's very easy to get qualified technicians to help you out. >> Is that by design? >> I don't know if it's. Well, the being great to work with, yes, that's by design. >> Yeah, but I mean. >> I was talking to Danny yesterday and asked about the interface thing. Because there is always that tension between making it really really simple to use but then it doesn't have any knobs to change when you need to. >> That's what I'm asking. >> But it can't be too complex either. >> Our gap actually comes a little bit later in the process, right? So, you asked earlier about, in what ways do you use Veeam? And we think about Veeam as a progression, right? So, everybody if they're using Veeam at all, they're using it for Veeam backup and replication and because foundationally, until you can protect your stuff, right? Until you can reliably do that, all the other stuff that you'd like to do around data management is aspirational and unattainable at best, right? So, we think the journey comes in at yeah, it is pretty easy, to go next, next, next, finish. Just a few tweaks, right? To get backup going. But then when you go beyond that, now there's a whole range of other things you can do, right? So Danny, I'm sure, talked about DataLabs yesterday. The orchestration engine, those are not, next, next, next, finish. But anything that's worthwhile takes a little bit of effort, right? So as we pivot from, now that you've solved backup, then you can do those other things and that's where we really start going back into something which is really more expertise driven. >> Well, and it's early days too and as you get more data and more experience you can begin to automate things. >> Yeah, absolutely. So Justin was asking, Nathan, where the direction is. Today it's really backup. You've seen the stages where, talking about full automation. Is that something that, is on the horizon, it is sort of near term, midterm, longterm? >> I mean, coming to the conference, our experience with backup, or Veeam, is primarily backup and recovery operations but, I've seen a lot of things in the last few days that have piqued my interest. Particularly when it comes to the cloud integration. That's being actively baked into the product now. And, some of the automated, API stuff, that's being built into the product. Any place where I can get to where we simplify our procedures for recovery, that's a plus. So I'm really excited about the idea of the virtual labs, being able to actively test backup on a regular basis without human intervention and have reporting out of that. Those are things that I don't see in any other product that's out there. >> You know, there's another piece of the innovation that we should think through, and, so we've talked about the sequencing side which is where we focus on RTO, how fast can you get back and running again? And when you and I talked earlier, the example that we worked on was think of a zipper, right? You've got the bumpers coming in to a line of cars and if either side slows down, everything breaks, and at the end, by the way, is the truck, right? And everything has to come at the same time at the same rate, if there's downtime on either side of the source, you're done. But that's an RTO problem. The engineering side, for high tech, is an RPO problem, right? You have unique stuff coming out of somebody's brain into a PC and it'll never come out that way again. And so, when we look at backup and replication, that should be the next pieces to go on. And then as you mentioned, DataLabs becomes really interesting and orchestration, so. >> Well speaking of human brains, and you kind of touched on it, Nathan, that you came here to learn some things and you've learned things from different sessions. So, what is it about coming to VeeamON that is worth the time for IT practitioners like yourself? >> I think it's all those, I mean we were talking about Veeam, doing backup and recovery operations, fairly straightforwardly, in terms of getting in, but once you see some of this stuff here at a conference like this, you get a better sense of all the more, elaborate aspects of the product. And, you wouldn't get that >> See the possibilities. >> I think, if you were just sitting in front of it using it conventionally, this is a good place to really learn the depth and the level that you can go with it. >> And you're like most of your peers here, is that right, highly virtualized, is that right? Lot of Microsoft apps. And, they say, mid-sized global organization, actually kind of bumping up into big. >> Nathan: Sure. >> Yeah, cool. I asked about the data problem before, it sounds like the zipper's coming together, that's some funky math that you got to figure out to make sure everything's there. So, talk about the data angle. How important data is to your organization, we know much data's growing, data's the new oil, all those promides but, what about your organization specifically as it relates to a digital strategy? It's a buzzword that we hear a lot but, does it have meaning for you, and what does it mean? >> Data is vital in any organization. I mean, we were referencing earlier, how you've got low tech in manufacturing, or at least people think of it as lower tech. And then high tech in R and D, and how those things merge together in a single company. But the reality is all of that is data driven, right? Even when you go to the shop floor, all your scheduling, all your automation equipment, all this stuff is talking and it's all laying down data. You're putting rivets in the parts, you're probably taking pictures of that now with imagers when you're in manufacturing. And you do that so that if you get 300 bad ones you can see exactly when that started and what happened at the machine level, right? So, >> That's a good one. >> We're just constantly collecting massive volumes of new data, and being able to store that reliably is everything. >> Well, and the reason I'm asking is you guys have been around for a while and your a highly distributed organization so, in the old days, even still today, you'd build, you'd get a server for an application, you'd harden that application, you'd secure that box and the application running on it, you'd lock the data inside and, my question is, can, the backup approach, the data protection approach, the data management, or whatever we want to call it, can it help solve that data silo problem? Is that part of the strategy or is it just too early for that? >> I'm, sorry, I'm going to get you to repeat that question in a slightly different way. >> Yeah so, am I correct that you've got data in silos from all the years and years and years of building up applications and-- >> I mean, we have-- >> And can you use something like Veeam to help unify that data model? >> Draw that all together? Yeah. I think a lot of that has, it's more on the hosting side, right? So it depends on how those systems were rolled out originally and all that kind of thing. But yeah, as we've moved towards Veeam, we've necessarily rebuilt some of those systems in such a way that they are more aggregated and that Veeam can pick them up in an integrated kind of way. >> You see that as a common theme? Veeam as one of the levers of the fulcrum to new data architecture? >> We're getting there, so here's the trick. So, first you got to solve for basic protection, right? But the next thing along the way to really get towards data management is you got to know what you got, right? You got to know what's actually in those zeros and ones. And so, some of the things that you've already seen from us are around what we do around GDPR compliance, some of the things we do around sanitization of data for DevOps scenarios and reuse scenarios. All of that opens up a box of, okay, now that the data is curated. Now that it's ingested into our system, what else can you do with it? You know, when I talk to C-level execs, what I tell them is, data protection, no matter who it comes from, including Veeam, is really expensive if the only thing you do is put that data in a box and wait for bad things to happen, right? Now the good news is, bad things are going to happen, so you're going to get ROI. But better is don't just leave your data in a box, right? Do other stuff with that data, unlock the value of it and some of that value comes in, now that I'm more aware of it, let's reduce some of the copies, let's reduce some of the compliance mandates. Let's only put data that has sovereignty requirements where it goes, but to do all of that, you got to know what you got. >> Go ahead, please. >> There was some impressive demo yesterday about exactly that, so, we have the data. You can use the API to script it and you can do all kinds of, basically, you're limited by your imagination. So it's going to be fascinating to see what customers do with it once they've put it in place, they've got their data protected. And then they start playing with things, come to a conference like this and learn, ooh, I might just give that a try on my data when I get back home. >> That's right. >> We'll give the customer the last word, Nathan. Impressions of VeeamON 2019? >> It's been great. And like I say, if you're a company that's been using Veeam even for a while, and you have your entry level setup for backup and recovery and I think there's a lot of, probably, companies out there that use Veeam in that kind of way, this is a great place to have a better understanding of all that's available to you in that product. And there's a lot more than just meets the eye. >> And it's fun, good food, fun people. Thanks you guys for coming on, really appreciate it. >> Yeah, thank you. >> Alright, keep it right there, buddy, we'll be back with our next guest, you're watching theCUBE, Dave Vellante, Justin Warren, and Peter Burris is also here. VeeamON 2019, we'll be right back. (electronic music)

(upbeat theme music plays) >> Live from Atlanta, Georgia, it's theCube. Covering Citrix Synergy, Atlanta, 2019, brought to you by Citrix. >> Hi, welcome back to theCube. Lisa Martin with Keith Townsend, and we are live in Atlanta, Georgia for Citrix Synergy, 2019. We are pleased to welcome Jack Gold to The Cube, President and founder of Jack Gold & Associates. Jack, it's great to have you join Keith and me this afternoon. >> Thank you for having me. >> So, we had a great day. We've talked to eight or nine folks or so, lot's of really relevant exciting news from Citrix this morning. Talking about the employee experience as, and how I kind of interpreted it, as a catalyst for digital transformation, cultural transformation. You've been working with Citrix for a long time. I'd love to get your perspective on not just what you heard today from Citrix, and with Google and Microsoft, but in the last year or so since they've really kind of done a re-brand effort. What're your thoughts on that? >> Yeah, it's interesting from a Citrix perspective. Citrix, the old Citrix I guess I would put in quotes right, was always known as the VDI company. I've got, you know, the screen that will talk to the server, that will talk to whatever other apps I need it to talk to, and I can have a nice thin client sitting on my desktop and I don't have to spend a lot of money. And I also don't have to worry about if I'm going to bank people stealing stuff off the hard drive, or whatever. They've made a pretty significant transition that was the old work space, if you will. The modern work spaces which is where Citrix is really moving is one where, look we've all grown up with smart phones for the last ten or fifteen years, our kids don't know anything different. They're not going to deal with anything that's complex, anything where I have to log in and out of applications, anything where I have to switch between screens, this just doesn't make any sense for them. And so, what we're seeing Citrix do is move into an environment where, as I said, it's about the modern workspace, it's about being able to help me do my job not getting in my way of me doing my job, and that's really the transition. It's not just Citrix, the industry is moving in that direction as well, but Citrix is really at the forefront of making a lot of that work now. >> So, Jack, talk to us about the new promise of the new Citrix. The, if you remember me, it had to have be about seven years ago, I did a blog post of running Windows XP on your iPad. It was taking, you know, the then desktop solution and running it on your iPad. >> (Jack) Sure. >> And it was a cool trick. But we talked about, today, we would hope by today, that mobile technology would of forced companies to rewrite applications, for a mobile first experience. But that simply hasn't happened. So, presenting a bad application on to a mobile dot, to a mobile work station, or a mobile device, doesn't work. We end up packing in, trying it, and squeezing, and trying to get our work done, how is Citrix promising to change that experience, even versus their competitors? >> Sure. Well, first of all so two bad's don't make a good. Right. Having a bad app on a bad device doesn't make it good. >> (Keith) Right. >> Doesn't make it easy to use, doesn't help me get my job done. What we really are talking about, now, is the ability to build a workspace. Something where I sit and look at, that helps me get my job done, as opposed to getting in the way. Which means that, instead of having to punch fourteen different holes, or you know, icons and sit at my keyboard and type forty-eight different commands and do thirty-eight different log-ins as each one is different, and by the way I couldn't remember them so I just called the help desk in-between, and that's another half an hour of my time that I didn't want to, that I wasted. >> (laughs) Give me my word perfect templates. >> (Lisa laughs) >> (Jack) There you go, there you go, word perfect I remember that no so well. I remember it well not so nicely. What we're really trying to focus on now is user experience, right. What we're really trying to focus on is if, if you wanted to get your work done, I want to make it easy. Think about it as going to a grocery store. If you can't, if you've got a list of groceries and you can't find what you want in five minutes, you leave, you go somewhere else. You go to another grocery store where things are much easier to find. It's the same at work, or it should be the same at work. Now, that said, a lot of apps and organizations, especially big enterprises where they have, some can have literally thousands of apps, are not going away. The notion that everything is going to go into the modern workspace, where everything looks like a phone, it's a nice idea, it's properly not going to happen. Legacy apps will be legacy apps for a very long time, it's like mainframes are dead, guess what, they're still around. That said, that doesn't mean that you can't take some of those legacy apps and make them easier to use with the proper front-end. And that's really what Citrix is trying to do with the workspaces, and other's again, it's not just Citrix in this, we have to be fair there are lots people working in this space. But, if you can make the front-end workspace more attractive, easier to use, easier to navigate, even if I've got old, clunky stuff in the background. For me as a user, you can give me back fifteen, twenty, thirty minutes a day, an hour a day, that's really productivity. Look, if you're paying me a hundred dollars an hour, and you save me an hour a day you just made a hundred dollars every day that I'm working at that company, that sounds like a lot, but there are people who make that kind of money. Or even a fifty or twenty-five dollars, it all adds up. And so, what we're really doing is trying to move into an environment where if I can make you more productive but making things more easier for you to navigate, and getting in and our of applications more quickly, getting more information to me more quickly, which makes the overall organization more productive because I'm sharing more information with you, then that's a real win-win, and that's where I think Citrix is really trying to position itself, and doing a fairly good job at doing that. Clearly they don't have all of the components yet, but then no one does. This is an ongoing process. >> So, employee experience is table-stakes for any business, as we look at the modern workforce it's highly disrupted. >> (Jack) Yes. >> It's composed of five different generations. >> (Jack) Yes. >> Who have varying expertise with technology. It is also demanding because we're all consumers. >> (Jack) Yes. >> And so we have this expectation, or this, yeah I'd say expectation that I want to be able to go in and have this personalized experience. I don't want to have to become an expert in sales-force because I might need to understand, can I talk to that costumer and ask them to be a reference? How much time are you going to take? But this personalization is becoming more and more critical as we see this influence from the consumer side. >> Right. >> Were some of the things that you heard today from Citrix, what are your thoughts on how their going to be able to improve that more personalized employee experience? >> So people think of personalization, I think sometimes, too narrowly. For some people personalization is, you know, I've got my phone out, and I have the apps that I want on my phone and that's personalization. I think of it a little bit differently. We need to extend personalization. When I'm at work, what I want is not just the apps I want, clearly I want those, right, but also the ability, to get help with those apps as I need it, right. And so where Citrix is going is trying to put intelligence into the system, so that when I'm interacting with back-end solutions or my neighbors, or with teams collaboration, I get the assistance I need to make it easier for me to do that work. It's not just the apps, it's also help with the apps. And if we can do that, that's really what we want. We go, you know, if I have a problem with my laptop I'm going to come to you and say, hey, you did this yesterday what was the result, can you help me for five minutes? Five minutes is never five minutes, it's usually an hour and a half, but still. I'll come to you. Why can't I have an app on my desk that does the same thing? I'm having trouble. Help me. Fix it. Let me know what I'm doing wrong, or let me know how I can do it better. And that's where Citrix is trying to go with the analytics that they've got in place. Which is huge, I think they're underplaying that, because I think that the whole analytic space in making things easier for people to use, because in understanding where my problems are is huge, and that's going to pick up. The notion of having a nice pretty, pretty may not be the right word, but attractive at least, workspace for me to go in that doesn't get frustrated, frustration is a killer in productivity, as everyone knows. There are examples I've heard multiple people tell me now that they go out and hire, especially with millennials, that go out an hire twenty or thirty new employees, and half of them quit within a week because their systems are so bad that they get so frustrated that they're not going to work there. So, the notion of having a modern workspace where I get the applications that I need, I get the assistance I need, because of the analytics of that backend telling the systems what I need, and making it easier for me to do it. And then allowing me to be productive not just for myself, but for the organization, is where we all need to go and I think that Citrix is making some real progress going that way. >> (Keith) Well Jack, we're talking about products that haven't quite been released yet, so I'm trying to get a sense or, worth's the right built versus buy stage, in complexity Citrix should be? You know, I can make it apple pie by going out and picking the apple. >> (Jack laughs) Right. >> And making my own crust or, I can go buy filling, or I can just go buy any mince pie, stick it in the oven and warm it up. Three very different experiences. Three different layers of investment, and outcomes frankly. In this world, I can go hire application developers to write these many apps, to write these customizations, to write these integrations, but that's, I think that's akin to picking the apple and that just simply doesn't scale. But, also while any mince pie is okay every now and again, I want, you know, something of higher quality. Where do you think Citrix is on the kind of range of built versus buy with this intelligent experience? >> So built versus buy is a very interesting phenomenon. And it's interesting because a lot of it has to do with where you think you are right now in the world, right. You know you mention going out and getting developers and building your own, that's all well and good, it doesn't scale, and by the way in today's market you can't find them to begin with. So you often don't even have a choice. So that's number one. Number two is that there are companies out there that still think for competitive advantage that they have to do everything from scratch, like building your pie. Yes, you probably make the best pie in the world, but guess what, sometimes a good enough pie is good enough. Right, and if you're in business sometimes good enough is the only way you survive. It doesn't have to be a hundred percent perfect, ninety percent's okay too. People can deal with that. So that's the other piece. The third piece of it is, from an end-user perspective, right, if end-users are accustom to having an interaction in a certain way and the you go out and get developers that come in and do it, something completely different, which they're apt to do because each will have their own kind of flavor to it, then you just force them to learn one, two, three, four, five different interface interactions I'm not going to do that. I'm going to get frustrated as heck, and I'm going to go call the help desk or I'm going to go get my app and say go do this for me. Both of which are counterproductive to the company and to me. So, it really depends on where you are in the stage of where your company is, I would say built versus buy it's not a one or a zero. There's lots of shades of gray in between, it's also not all or nothing. So, some applications might be built internally, some you may want to buy externally, some you may have a hybrid, and the nice thing about where workspaces is going now is that you plug all of those into the same environment. That's really the ultimate goal, is to make it as easy and transparent for the organization as possible, and also for the user because the user ultimately is the end consumer. And if it's not good for the end consumer, it's not good for the company either. >> (Lisa) So delivering this great game-changing customer experience for this, as we talked about before this distributed modern work force that wants to be able to access mobile apps, Sass apps. >> (Jack) Right. >> Web apps from tablets, PC, phone, desktop. >> (Jack) Your car, your refrigerator >> Exactly. >> (Jack) Anything with a screen on it. >> Oh yeah, the refrigerators. Wherever you are, I think, okay people >> (Jack) Sure. >> We're people, and we are the biggest single security threat there is. >> (Jack laughs) >> So in your perspective, how is what Citrix is talking about balancing security as an essential component of this employee experience? >> So there are a few things, number one is a lot of companies think that if they limit the end user experience they're more secure. The truth of the matter is, yes, I mean if you don't let me get in to an app I can't steal application or information, or lose it somehow. But I also can't get my work done. So there's a balance between security and privacy which many companies don't talk about which is not exactly the same thing, there are two unique things, more and more privacy is becoming as big or bigger an issue than security, but you know we can get at that in a minute. But, the notion of security really relates to what I was talking about earlier which is analytics. If I know what you're suppose to be doing, you're here at Synergy. If someone just got your credentials and logged in from Los Angeles or New York or Chicago or Denver or wherever, I know it's not you. I can shut that thing down very quickly and not have to worry about them stealing information, also if you're, if I know you're not suppose to be in a certain version of SAP, you're not suppose to be doing some ERP system and you're in it, then again the analytics tells me that there's something going on, there's something anomalous going on that I need to investigate. So, having a system that protects because there's a kind of a front end to everything that's going on in the back end, and a realization of what's going on behind that screen gives me a much higher sense of security from a corporate perspective, it's not perfect there is no such thing as perfect security, but it's a lot better than just letting us kind of do our own thing, and loading, you know, semantic or McAfee or whatever on your PC. And that's where the industry ultimately has to go. That becomes part of the new modern workspace. It's not just about more productive it's about more secure. It's about more private. It's about not letting information escape that shouldn't be there to begin with. >> (Keith) So last question on data grabbing. Because we haven't talked about data and data is, you know, probably the most important thing in this topic. The importance of the (unintelligible) and Google announcement. You know, we, the yottabyte, the first time I've heard that term, yottabye of data that data's going to be spread across the world and this, this ideal of centralized compute and us being able to present, compute into data centers, no longer going to work, that we're going to have to, applications are going to be spread across the world. Where do you Citrix advancing that discipline of providing apps where they need to be with these relationships? >> So, it's an interesting phenomenon what we're going through right now, if you look back a couple of decades ago everything was centralized, people were centralized, they all work in one building, computing was centralized it was all in the data center, IT was centralized, it was all, you know, working around the servers. The Cloud is the opposite direction, although I would argue The Cloud isn't new, The Cloud is just time-share in a different environment, for us old people who remember the old IBM time-share computers. But everything is becoming distributed, data is distributed, people are distributed, applications are distributed, networks are distributed, you name it. The key critical factor for companies in keeping their productivity, keeping up the productivity is to make sure that the distributed environment doesn't get in the way of doing work. So you've got things like latency, if it takes me, if I'm in. (crowd cheers) >> They're having a party behind us. >> No, they agree with you! >> (Jack laughs) Yes, apparently. I, you know, if I'm here at Synergy but I have to work back at my offices near Boston, I can't wait five minutes for information to come back and forth, it's like the old days. Latency now has to be within five microseconds or people get frustrated, so that becomes a network issue, applications, same way, if I have to go to a data center, the data isn't local to my server here, it has to go to London, I'm not going to wait three minutes for it to come back like we use to, or ten minutes or an hour and a half. Or come back the next morning. You know, you want to book a flight on an airline, are you going to wait thirty minutes for them to find you a seat? You're going to go to another airline. So the whole notion of distributed means that it's very different now, even though it's distributed, everything is local. And by local, keeping it local means that you have to have latency below a certain point (crowd cheers) so that I don't realize that it's distributed, or I don't care that it's distributed. Yottabyte's of data means that we're going to have data everywhere, accessible all the time, and we're going to produce data like crazy. You know, a typical car, an autonomous car will produce a gigabyte of data every minute. Hundreds every, you know, hour. So, the amount of data is going to be fantastic that we have to deal with. Then, the big question becomes, okay so, I can't personally deal with all this data, it's impossible, I have to have the assistance, the intelligence within the system to go off and make something of that data so that I can actually interact with it in a meaningful fashion. That's where Citrix would like to go, that's where other's would like to go. They can't do it alone, because the problem is just too darn big. But, it will, we will get there, companies will get there eventually, not all of them perhaps, only the ones that are going to be successful long term are going to get there. >> Well, Jack, I wish we had more time to chat with you. This has, I just feel like going dot, dot, dot, to be continued. And I want to say, coincidence, I don't know, there were two rounds of applause when you talked about latency. (Keith laughs) >> There we go. They're just waiting for the bar to open, it's taking too long. >> (Lisa laughs) You think that's what it is? >> (Jack) Properly. >> All right well we'll get you over there, and thank-you again for joining Keith and me this afternoon. >> Thank-you very much. >> (Lisa) Our pleasure. For Keith Townsend, I'm Lisa Martin, you're watching theCube live from Citrix Synergy, 2019. Thanks for watching. (upbeat theme music plays)

>> From Yorktown Heights New York It's theCUBE, covering IBM Cloud Innovation Day. Brought to you by IBM. >> Hi, I'm Peter Burris and welcome theCUBE. We're broadcasting today from IBM innovation day at the Thomas J. Watson research labs in Yorktown New York. Having a number of great conversations about what's going on with the industry, what's going on with the cloud, and to bring that further, Judith Hurwitz, president of Hurwitz Associates, longtime analyst. Judith, welcome to theCUBE. >> Thank you, Peter. Great to be here. So, Judith, I'll just open it up. What do you think are the two or three most important things that people should be thinking about right now? >> Well, I think as we look at the maturation of cloud and computing and the changes that we see, I think one of the most important things is the movement towards open and standards, because what customers really want is computing. They don't really care if you tell them "Well, that service runs over there and this one runs over here." They don't care about that. What they care about all of the workloads, all of the applications they need to get their jobs done just work. So if a workload needs to move, it should be able to move because it's less expensive or more efficient or it handles a workload better in terms of performance or security. Customers want the freedom to be able to do what they want when they want it, and not to be locked in. So openness is really becoming the battlecry for the cloud. >> You're talking about two things there. Let me parse them out. You're talking about the breaking of the natural relationship between where the resources are and where the value of the work is provided. >> Yes. >> And there is a degree of openness to that, but then there's also this notion of openness which is how fast innovation, what model are we going to use? Let's break those apart. Let's start with the idea of the cloud breaking the traditional mold of this workload here, that workload there. How is cloud doing that and what's the future for that flexibility look like? >> I think if we were having this conversation ten years from now we wouldn't be talking about cloud. We would be talking about the elasticity and the way we do computing so it really meets the needs of whatever business change you're experiencing. What's held companies back and what's held IT back is the idea that you're stuck with the platform or the application or the technology that you've always been using, and it makes change really hard. So, the more flexibility you can have, and the cloud in terms of elasticity, the way you can create new workloads using cloud native and microservices and leveraging containers, all of these techniques will lead us into a world where you can create a bunch of services and choose and pick the ones you want to get your job done and it really adds a level of innovation and speed that we've never seen before with IT. >> So let's build on that. One of the things we tell our clients is to focus on what we call plasticity. It's a physics term. Elasticity is a single workload, scale it up and down. Plasticity is new workload changes, transforms, leads, perturbs the infrastructure, the infrastructure reforms around it. One of the reasons why that concept becomes so important is precisely because of the rate of increase in innovation, as you said. So now tie open back to that. What is it about open, that's not just about making sure we have system software standards, but is actually doing a better job of turning business into software at a higher level. >> In a sense, it's what I would call service as software. If you can take the business process or how you want to interact with your customers, and you can turn those into software services that are malleable, that you can change and innovate on without having to go from top to bottom and recode everything, which is what's held companies back for probably 40 or 50 years. As you modularize things, you can, for example, simple idea like the way you would calculate a 30 year mortgage. In most companies over the years there were 30 different ways you could do that and each application had its own way. What if you could have a single service that did that that you could apply it no matter what the use case and what the business case was, apply that same concept to any business logic or any business strategy, that's where you get what you're calling- something that's very plastic, very malleable, and allows you to change, because in the past we've always written applications or written systems as though they were based on how we do business right now. And when you do that, you can't change. >> So one of the ways, again, if I were to describe some of the big changes and let me test this on you, is that I say for the first 50 years of computing it was known process unknown technology. We knew we were going to do accounting, we knew we were going to exchange titles, became supply chain, et cetera, we knew we were going to do HR. But we didn't know if it was going to run on a mainframe or how to run on a mainframe, or client server or the internet or whatever else it was. We're entering into a world now where it's unknown process, relatively known computing, or technology. We know it's going to be a cloud or cloudlike thing. When we think about that unknown process, more data first applications, data driven applications, where do you foresee some of these magnificent changes that are on the horizon? >> So, I think one of the most important changes is that we start leading with data rather than process, because if you lead with process, that's the past. If you lead with data, data will lead you to process. So if we have data driven organizations where the data, using it in a predictive analytics way, really using machine learning, algorithms, and some of the emerging AI techniques, we can begin to have data drive us to process. >> So, Judith, I know you've gone to IBM Think every year for a number of years now. Probably almost as long as I have. If you step back and say San Francisco, 2019, February, 30,000 plus people, what are you looking to get out of Think this year that builds upon what you've gotten out of it in the past? >> Well, what I really like about Think and about IBM events is that it brings together so many people, both IBMs fantastic technical leadership with business leadership, and it brings together the programmers. It brings together the IT leaders with business leaders, so it's a really coming together of the minds across business organizations, really collaborating together to really get to the heart of key business problems. >> Excellent. Judith Hurwitz, president of Hurwitz and Associates, thanks for being on theCUBE. >> Thank you. >> And this is Peter Burris, we'll be back with more of theCUBE from IBM Innovation Day in a few minutes. (upbeat techno music)

(upbeat music) >> Okay, welcome to theCUBE everyone. This is a CUBE Conversation here in Palo Alto, California in the CUBE studios. I'm John Furrier, the co-founder of SiliconANGLE media, the host of the CUBE. I'm here with Yinglian Xie. She's the co-founder and CEO of data visor, entrepreneur, former Microsoft researcher. Thanks for joining me in CUBE conversation. >> My great pleasure to be here. >> So I'm excited to chat with you because you've got a really hot company, and a very hot space, but also as an entrepreneur, you're out competing against a huge wave of transformation. You've got big clouds out there, you've got IT enterprises moving to some sort of cloud operating model. You have global IOT market, huge security problem. You guys are trying to solve that with Data Visor, your company. So take me through the journey. First take a minute to explain what Data Visor is, and I want to ask you about how you got into this business, how it started. So what does Data Visor do, first give a one minute overview of the company. >> Sure, so Data Visor is a company that uses the AI machinery and big data, trying to detect and prevent a variety of fraud and abuse problems for all these consumer facing enterprises. So our mission is to really leverage these advance technology that you talk about in many of these, and to help these consumer facing enterprises to establish and restore trust to the end users like you and me, like every one of us. >> Yes, cyber security and security in general is a global issue. I mean, spear phishing is just so effective, you just come in and just send someone a LinkedIn message or an email, they click on a link and you're done. There's not much technology. People are struggling with this, but you guys have a unique approach that you taking with Data Visor so I want to dig into it. But first, how did it all start? When you started this company with your co-founder, did you just wake up one day and say, you know what we're going to go solve the security problems for the world. Where did the idea come from and how did it all start? >> So I would say it's probably, if you look at the background of me and my co-founder, it's probably the natural journey to it, because we actually came from a research and academia background. And me spending seven years of my post doc research in Silicon Valley before starting Data Visor, from there when we joined in 2006, actually it was where we kind of just see this parallel computing paradigm. Like Matt Purdue's paper just got published, and all the data is available, we have all these security problems and at that time we were partnering with a number of large consumer facing groups in Microsoft, and to see how we can use this big data to solve some of the challenges that they face in terms of for example the online fraud and abuse. And also we see the industry and was rapidly getting into the digital era where we have billions of users online, so everybody sees this unique challenge of, they have a variety of vulnerabilities they face, they're trying to bring more rich features to users. At the same time, they see new fraud are coming up also very rapidly. So everybody, when they see new fraud, they are trying to have point solutions. Where they say, let's just tackle this, but then afterwards there's another fraud, or another abuse coming up. >> Throw another tool at em. Build another tool. Buy another tool. >> Exactly. Kind of arms race, where they're being reactive, and catching in a cat and mouse game. So we decided, let's just come to see whether we build something different and leverage the AI machine learning, and then we see what this new cull computing, big data infrastructure can do. So let's build something a little bit more proactive, so that we've been in the security area for so long, that we feel something fundamental that can be a game changer. It's only when we don't make assumptions to see what kind of attacks we want to detect. But be a little bit more open to say, let's try to build something more robust, that can have the ability to automatically discover and detect these new type of unknown attacks more proactively. >> Yinglian, I want to talk about that point, about your time at Microsoft. At that time around 2006, I think it's notable because the environment of Microsoft scale was massive. They were powering, the browsers were everywhere, MSN, the online services that Microsoft had were certainly large scale, but they were built on what I would call gen one internet technology. Databases, big large scale. At the time there, the new entrants, Facebook, otherworlds, they were building all their own tech. So you had kind of the new entrant who had a clean sheet of paper, and they built their own large scale. And we know the history of that, those kinds of companies, that were natively at that time. That's the environment that Microsoft had, that a lot of customers today have. They have technologies that have been around, they have to transform very quickly. So when you learned about some of those data collection capabilities at scale of older technologies, and rushing to a new solution, this is a problem that a lot of end user enterprises have. CIOs, cloud architects, data architects, and they've been operating data warehouses for generations. Big fenced off databases, slow, big data lakes turning into swamps. So that's the current situation, how do you guys speak to that? Because this is the number one challenge we see. Is, I have all this data, I've got a data problem. I'm now full of data, I'm being taken advantage of with the fraud. Whether it's spear phishing or some other scams that are going on with email and all this stuff. How do you guys talk to that customer, that environment? >> You definitely very spot on the challenges and problems that we all face. So while we get into the digital era, everybody has this great sense of trying to collect data and story those data. So that has been, the amount of data we collect is tremendous nowadays. The next step everybody was looking at, the big challenge for us, is how to make value of these in a more effective way. And we also talk about a lot about the AI and machine learning, how they can transform some of the way we do things in the past. The analogy we know is how do we go from the manual driving cars to the self driving era of having all the automation intelligence, and making value out of this. So there are still a lot of challenges that you definitely touch upon. First of all, when they have the data there, does that mean we have the data, we have the data in a consistent, consolidated way. Many times, two different divisions, departments collecting data, they're still in silo mode. So how to bring the data together. And second is, we have the data, we have the computing power, how do we bring the algorithm that operate on top of that the framework to have a system that would let algorithm generating values. Like in the fraud detection space, be able to automatically process huge amount of data, and make decisions in real time. Instantly, detecting these new type of attacks. So we find that's a problem beyond the silo of just an IT problem, or just a data science problem, of just a business problem. So many times these three groups still sort of work separately, but in the end we needed the main knowledge, we need building a system, and we need good data architecture to solve them together. So that's where Datavisor is building a solution, the ecosystem to consider all of this. >> Okay, so let's talk about the ecosystem a little bit later. I want to get to the algorithm piece. That seems to be your secret sauce, right? The algorithms? Is that where the action is for you guys? The secret algorithms or is it setup in the environment first? It kind of makes sense, you've got to set the table first, get the data unified or addressable, and then apply software algorithms to them. That's where the AI comes. What's your secret sauce? >> Yeah, so that's a good question. A lot of our customers ask us the same question, is algorithm your secret sauce? And my answer is kind of partially yes, but also at the same time, not completely. Because we're all catching up very rapidly in algorithm, if you look at the new algorithm being published every year. There's a lot of great ideas out there, great algorithm there. So our unique algorithm is the differentiating technology is called unsupervised machine learning. So unsupervised means we don't need to require customers to have historical loss experience, or need to know the training labels of what past attacks look like. So to proactively discover new type of, unknown type attacks and automate it away. So that's what the algorithm part is, and it has its merit. >> And by the way, people want to know about this machine supervised and unsupervised machine learning, go Google search, there's some papers out there. But I think, most people know this, or might not know it, it's really hard to do unsupervised machine learning because supervised you just tell it what to look for, it finds it. Unsupervised is saying be ready for anything, basically. Oversimplifying. >> Exactly, unsupervised means we want it to make decisions without assumptions. And we want to be able to discover those patterns as the attackers evolve and be very adaptive. So that's definitely a great idea out there. I wouldn't say if you Google, like search unsupervised, and you would find in academia there are published articles about it.6 So I wouldn't say it's a completely new concept, it's a concept out there. >> It's been around for a while, but the compute is the value. Because now you have the computation accelerate all those calculations required that used to be stalling it, from 10 years ago. I mean it's been around for a couple decades. AI and machine learning, but it's been computation intensive. >> Very much so, very much so. So if you look at the gap where that keep the academia side of the world algorithm, to where it's working. It is something similar to deep learning requires a lot more computation complexity compared to the past algorithms. >> Yinglian, I've got to ask you, because this comes up and I'll skip back to the reality of the customer. Because I can geek out on this all day long, I love the conversation, and we should certainly do a follow up on Deep Dive with our team. But the reality is customers have been consolidating and outsourcing IT for generations. And just only few years ago did they wake up, and some woke up earlier than others and said, wow I have no intellectual property, I have no competitive advantage, my IT's all outsourced, I am getting killed with requests for top line revenue growth and I'm getting killed with security breaches, and where's my IT staff. So they don't have the luxury of just turning on a machine learning. Hey, give me some machine learning guys, and solve the problem. That's really hard to setup. You've got to kind of build a trajectory with economies of scale in IT. This is a huge problem. How do you work with companies that just say, look I got security problems but I don't have time or the capability to hire machine learning people, because that's an aspiration, that's not viable, not attainable. What do you say to the customers? Can you still work with those customers, are you a good fit for that kind of environment? Talk about that dynamic, because that seems to happen a lot. >> Yeah, so in that area, you really to bring a solution to solve their problem. Like us today, we have a lot of infrastructure capability, platforms where they can leverage. But you definitely talk about the challenge they face. They don't have people to leverage those underlying primitives and build something to immediately address their business challenges. >> Can you build it for them? >> That's where Datavisor is, to provide the platform and the service to the customers. Where we take data in, and tell them directly all the type of attacks they face, in real time. Constantly, all the time. >> I really want to get your opinion on something that I've been talking about publicly lately, and I've been interviewing folks in the industry about it, because if you look at the graphics market around AI, and nvidia has been doing very, very well. They broke into gaming, obviously is the vertical and using the graphics cards for block chain mining. Then nvidia kind of walked into these new markets because they had purpose built processor for floating point and graphic stuff that was very specialized but now becomes very popular. We're seeing the need for something around data, where you want to have agility, but you also want high performance. So people are making trade offs between agility and high performance and if you ask anyone they'll tell you that I'd love to have more performance in data. So there's no nvidia yet has come out and become the nvidia of data. There's no data processing unit out there yet. This is something that we see a need for. So what you're talking about here is customers have all these demands, it's almost like they need a data processing unit. >> What they need is a solution, like you said, when they have a business solution, they're not looking at something like a generic framework or generic paradigm. They're looking at something to tackle the specific need. For example when we talk about fraud prevention, we're talking about rebuilding a service, the ecosystem that combines the data element, combines the algorithm that address their problem right away. So that's where we talk about with your analogy with nvidia, they want something almost like that chip, directly solve their pain point. >> And that's what you guys are kind of doing, because let me see if I get this right. You guys have this kind of horizontal view of data, but you're going very vertically, and specializing on the vertical markets because that's where the need for the acute nature of the algorithms to be successful. Like say, financial services. Am I getting that right? So it's like horizontally scalable data, but very specialized purpose. >> Exactly. So horizontally scalable data, but then really mine the data and view the algorithms that optimize for the detection of these unknown type of fraud in this area. >> Because they're customized, I mean they have certain techniques that the financial guys will use to attack the banks, right? So you had to be really nimble and agile at the application. >> Right, so when we build the algorithm, we have in mind the specific application we need to target. So you don't want to be over general in the sense that it can do anything, but in the end it does nothing super, super well. So if we are solving that particular fraud detection problem, in the end it needs to be, everything needs to be optimized. The integration with data, the algorithm, the output, the integration with the customer, needs to be optimized for the scenario. In the long run, can it be even generalized. You talked about the agility, and the nimbleness to broaden out to other areas. Then they will say, we are taking approach I would love to see nvidia's approach gradually expanding to other verticals. That is something we are looking from the long term perspective. Our view is that we a layer above all the cloud computing, the data layer. We are the layer that is verticalize position and targeted to solve this specific business issues. And we want to do that really well. Solve that problem one at a time. And then leveraging that algorithm, the underlying infrastructure we built to see whether we can expand that to other verticals, other scenarios. >> So you don't get dependent upon the cloud players? You actually will draft off their success. >> So we leverage the cloud computing era aggressively. Who doesn't in this scenario? It definitely brings the scale, the agility, and the flexibility to expand. And there's a lot of great technology there. >> What do you think about the cloud players? When you look at multiple clouds and hybrid cloud is a trend happening right now. What's your opinion of how that's going? That comes up a lot. CIOs number one channel and cloud architects, and then data architects are all kind of working as the new personas we're seeing. How has the cloud and multi cloud or single cloud approach, for your customers, how do you see that evolving? Because we see trends where, for instance, the Department of Defense, probably going to go all in on Amazon. That's the single cloud solution, but it wasn't sourced as a single cloud. So it turns out that Amazon was better for that, versus spreading things around to multiple clouds. So there's a trade off, what's your thoughts on that as a technologist. >> Well you touched upon an interesting point, because actually, our position is multi cloud. Multi cloud as well as, we support even un-permissed deployment. I will talk about the reason why. The cloud is such a big space, and we see different players there. We definitely see different players, because of their historical working with different vendors, as well as their development you definitely see. Actually our position in this space was driven by the customer need. From that, what we saw is customers have these requirements of their favorite cloud environment. And then there's public cloud verses private cloud. We're not completely there to say there's one cloud that rules all. And you also see some very conservative areas, particularly financial services where their security is really their top priority, they're conservative. And from that perspective, they still are having un-permissed solutions. And we have to be considerate of all these different requirements. And also when we look at evolvement, we also see different geographic landscapes have different cloud deployment landscapes as well. And it's a dynamic environment. >> It's a new dynamic. >> It's a new dynamic. >> Especially the global component, the regions. >> Exactly, the regions. And the different regions, and we also have the GDPR, where does the data residence problem. So that also makes it also challenging to say, just deploy your solution on one type of cloud, that's a very rigid model. So definitely from very early days, we basically decide our data decision would be, we are going to support multi cloud very early on. >> And it makes sense, because people don't want to move a lot of data around. They're going to want to have data in multiple clouds, if that's where the app is. Latency in the threats around moving packets from point A to point B are a risk too. Not just latency, but hacks. Alright, great. I'm very impressed with your vision. I'm very impressed with what you guys are going. I think it's very relevant. Talk about the business. Where are you guys at in terms of customers, what kind of customers do you have, how many customers, can you talk about some of the metrics. How many customers you have, what kind of customers, what are they doing with you, what are the successes? Can you lay out some of the use cases? >> So we work with many of the largest enterprises in the world, and so the probably also the ones that face a lot of challenge of these large scale fraud at the same time they are the ones aggressively moving forward in adopting new technology solutions. They are a little bit more the early, pioneering, adopters. So our customer can be in three verticals, today. So we take a vertical approach. The first is those large social commerce, like Sector. And some of our customers, for example Yelp, Pinterest, kind of customers. And there is also the second vertical, is those mobile apps. There's a lot of fraudulence in stores, where these mobile apps are trying acquire users aggressively everywhere, but among the users acquired, those in stores there can be substantial amount that is fraudulent. So those are the separate segment we target. And the third segment, we talked about, and you mentioned the financial area, where traditionally people focus on the risk of control, the fraud detection definitely causes a big problem. Their challenge is when they move from the past existing era to the digital era, going online, and a lot of new attacks start coming up, and definitely a huge challenge problem for them as well. >> So you guys have some great funds, you have some great investors. NEA, New Enterprise Associates and sequoia capital. What's the growth plan for you? What's the goal for the company, what's your growth strategy? What's on your mind now? Hiring obviously, customer, what's the focus? What's the growth plan? >> So our focus is, we've been working with many of these large service providers. We mentioned our large enterprise customers. So globally today, we've already been protecting over a full billing end user accounts in total. So it's a lot of users at this moment, for our next step of growth and so we have two thoughts. A is we want to basically make the service even more scalable, and even more standardized in a sense that we can work with more than just the largest ones and be able to make it convenient, to be integrated with as many consumer facing providers. >> To expand the breadth. >> To expand the breadth, yes, of customers that we work with. The second aspect is, when looking at the fraud detection, we feel traditionally when the fraud market is segmented, we talk about when in the offline world, you would see financial sector fraud very different from somebody working on content. Nowadays, we can consolidate it, so in that area we're trying to build a more wholistic ecosystem. Where the device side of solutions and the analytical solutions can be consolidated together, to make it an ecosystem where we can have both sides of use and be able to provide to our customers different kind of needs. In the past, it was very point solutions. You would see data signal providers, then you would see some algorithm providers, and focusing on a specific type of fraud, and we wanted to make an ecosystem, so that, to your point in the past on the data, we will be able to connect the data, look at the use at account level and be able to detect a variety of types of fraud. As the enterprises are pushing out new features, and new flavors of these types. >> And the ecosystem participants will look like what? Ad networks, data services? Who is in the ecosystem that you want to build? >> Yeah, so that's a great question. In the ecosystem we talk about, for example, cull providers, can be an ecosystem basically. They actually power the computation layer, of all the resource there. We can also partner with data partners. That's another important element, so you're looking at technology data systems all integrated together. At the same time we can also look at the consulting firms that bring a bigger solution to the customers with the fraud being an important component that they want to address with system integrators. And so all these can fit together, and even some of the underlying algorithm solutions in the end can be plucked into the ecosystem to provide different aspects of use and make value out of data. So that different algorithms work together, and become defense area. >> It's like a security first strategy. First we had cloud first, data first, now security first. I mean, got to have the security. Well I really appreciate, we need more algorithms to police the algorithms. Algorithms for algorithms. So maybe that's next for you guys. Well with the business goal in mind we always take an open holistic view. I like you talking about security first, when we look at how to solve that problem more effectively, then we are very open minded to say, what is the best combinations we want to be three ultimately. And that's a single bit of real time, instant decision that is important at that time, because that matters with good users friction, they face whether we can be able to accurately detect attackers. So we are all optimizing for that, and then all the underlying data consolidation piece, the algorithm in combination working with each other, is just to make the barrier high, make it difficult for the attackers, and to make all of us good users easier. >> Well you're doing amazing things, and I think you're right. There's value in that data, new ways to use that data for better security is just the beginning of this new trend. Thanks for coming in and sharing your insights and congratulations on a great start up, and good luck to you and you co-founder. Thanks for sharing. >> Thank you, great to have this conversation. I'm here in theCUBE studios in Palo Alto, I'm John Furrier for CUBE Conversation with hot start up Data Visor Yinglian Xie CEO and co-founder. I'm John Furrier, thanks for watching. (bright music)

>> Live, from Las Vegas, it's theCUBE covering VMworld 2018. Brought to you by VMware and it's eco system partners. >> Welcome back, this is VMworld 2018, you're watching theCUBE. With Justin Warren, I'm Stu Miniman and we've got a nice presence here front of the VM village, right next to the solutions expo we're over the three days. We've got about 95 guests and 75 interviews. Happen to welcome back to the program Lynn Lucas is the CMO of Cohesity. People are commenting a little bit about our presence here but, I don't know, I think Cohesity has a little bit of a bigger footprint and a few more people have been talking about you there so. First of all, welcome back to the program. >> Well thanks very much for having us again. We've been so excited to be here at VMworld. So obviously of course, customers are the core of our business and yeah, we thought we'd make a little bit of noise the first night. >> Yeah, a little bit. The booth is hopping, people are lining up. You usually have some games you're doing. I know I'd seen it at Cisco Live. Maybe give people just a little taste of what Cohesity's doing at the show. >> Well sure, so you know, we do have a great amazing marketing team here and they've got some cool games going on in the booth but what it's really about is bringing folks in and talking about how we can help them with their, consolidating their data silos and so we've also got eight demo stations in there, live theater presentations, Adam Rasner, who was here on the program earlier. >> From AutoNations, yeah. >> From AutoNations, CIO is speaking again today and what we're here to do is really talk to customers about how we can help them really modernize their data center and move into a hybrid cloud world. >> Yeah, it's always interesting, at VMworld there's you know, customers, I want to spend a bunch of time understanding how to use even more the things that I've got. But then there's also the new stuff. So Cohesity, you have a mix of that. New product announcement Helios, maybe bring us through what customers are really digging into and bring us up to speed on Helios. >> Yeah absolutely, thank you for mentioning that. Well, there's a broader trend which of course you guys have been covering which is data fueling business, but data's fragmented. It's in all these silos. Cohesity's been addressing that for the last several years with Cohesity Data Platform, Hyper converged secondary storage. Helios now adds to that offering and provides a single global unified view of all your secondary data and applications. It's a sass space service but it's not just a dashboard or a monitoring. It is active management which is really going to bring about great efficiencies for the IT organization. >> I was speaking to your CEO, I think it was yesterday we had a briefing and he walked me through some of that and I actually spoke with your customer AutoNation last week as well and they gave me an update on things. And it became quite clear to me that I'd missed something I misunderstood about Cohesity that it wasn't just about data protection. That's just sort of the first thing that you do. And AutoNation mentioned this as well, that they'd started using Cohesity for data protection but then they realized that, well actually I could use this for secondary data storage and they'd already had the platform, they bought it a couple of years ago. And these additional features just arrive on the platform. So is Helios also one of these features that just gets added to something that you've already made an investment in? >> So thanks for kind of calling out those use cases. So Helios is actually available as a freemium offer right now and it's intended for customers that have multiple sites whether those sites be you know LA, Tokyo, Dubai. But also in the Cloud because Cohesity offers capabilities if you're in Adjure, if you're in AWS, and now Google Cloud as well. So our new freemium edition of Helios is going to give that global view, that ability for IT to at a glance see how all of their secondary data and apps are working, do they have any capacity needs coming up, and the ability to role out automated policies globally. And so this is where we hear a lot of interest from IT because infrastructure really frankly has been so primitive right. When you think about it most of it architected in the last century. And they spend so much time just trying to keep thing up to date and keep the complexity down. And Helios offers a single way to manage all of that. And there's a lot more coming in Helios overtime because it's got machine learning capability built into it so I think you're going to see just the beginning right now and a long list of things that'll be coming out over time that will really help IT advance their operational efficiency. >> Yeah Lynn, definitely, Multi cloud is one of the top things we've been seeing at this show and it's been short. The VMworld's position, their partnership with AWS and some of the other providers. Help us understand how Cohesity lives in this multi cloud world, the things like the VMC and all those, how do those tie together? >> Yeah and great questions. So you know, customers, you said, they're in a multi cloud world and what most organizations are understanding is that two things, one, they've got to choose the cloud for the right set of workloads, right. It's not a fantasy. It can be more expensive if it's not thought about in terms of the use cases that they're looking for. Obvious;y has a mass advantage in terms of elastic scalability, compute power. And the other thing I think that now is becoming more to the forefront is that the cloud is created for IT just has many silos. And if you're a multi cloud organization which most are, well now you've got silos of different types between the two clouds. So Cohesity is creating with Helios, a single operating model across any environment whether that be Cloud, Core or Edge. And that is really what we aim to do is create that invisible kind of layer so that IT can focus more on helping the business. You know, I was talking to a prospect here just a couple of days ago and because we're so today instant gratification oriented the CEO just says, hey, I need that file, or I need this deleted maybe because of GDPR. And the IT teams are obviously struggling with how is this happening when I have such complex infrastructure Silos. Helios is the first step in helping to solve that. >> Yeah, Lynn, I'm wondering, do you know, there's so many players that want to be that platform across the multi cloud. VMware put their case forward is to how they do this. You know Microsoft has pretty good positioning when we talk about hybrid Cloud. Can you speak to how Cohesity can be across these environments, partnerships, alliances, eco system, help put this together because no single company can do it all? >> Totally agree with you. I mean I don't think any vendor today could operate on their own. It is an eco system. So first and foremost, VMware is our partner. And the Cloud providers are our partners along with many other companies that are here, Nutanix, Pure. We operate in the secondary world, right. The secondary realm first and foremost and that's the 80 percent of the enterprise data and infrastructure that hasn't had a lot of innovation. You pointed out, it started with data protection. There's a been a lot of pain points there, but it extends to file, to test dev to analytics and we really provide that complement to VMware for customers that are looking for a way to modernize their data center where Cohesity can back up, instantly restore VM's in the case of a disaster. Also move them up to the Cloud for test dev, then spin them back when they're ready to come back into production. So we're a real complement to the primary environment. >> I wanted to get into that a little bit Lynn. So one of the things when I'm talking to vendors and particularly with customers, they sometimes take a solution to remove some pain points. But then once they've actually got something in place there's all of these new possibilities that opens up for them and particularly around the silo aspect. Could you maybe give us an example of a customer who's been able to realize a new opportunity once they use Cohesity to remove some of that siloing and now they can build things on this platform that they've purchased? >> Yeah and so great questions. So one of our customers, you talked about AutoNation, but let me bring up another one. Manhattan Associates, large organization, software organization, also started with Cohesity with data protection and then realized, we can use the same platform for consolidating file services. It allowed them to instead of adding Opex in the form of additional teams to manage their very massively growing environment to reinvest those teams in actually a new model for the business which is to bring out more capability for the business in a faster time than they would of otherwise. So a lot of what we talk about is the operational simplicity that we bring. For every business, what they invest that in or reinvest those resources is going to be different but it enables them in that case to do more in their core business which is serving their manufacturing supply chain customers in a more efficient way. >> And that's quite important I think for IT teams to be able to join with the business and to show that they're actually providing new value rather than being seen as just a cost center which we hear that from IT teams al the time. They're quite sick of being, well you're just a cost, you're not involved in strategic decisions that are important to the business. So having a platform something like this, means that you can be part of those conversations. You can get a seat at the table and be involved in creating new value for the business. >> Yeah absolutely, I mean the analyst community's been talking about this for a long time. I know right, that most of IT unfortunately has been investing, I think it's 80 percent, maybe 80 percent plus, and just keeping everything running and the business gets so frustrated and creates shadow IT. Another customer of ours, so Verizon Subsidiary, XO Communications, another example where instead of having to I believe, invest in seven more folks just to manage their data protection and their file storages, once they were able to invest in Cohesity because of the simplicity of not having so many vendors, not having the complexity of managing silos of infrastructure, they took that same budget and were able to invest it in doing more for their government clients. >> Lynn, wonder if you could give is some of the company updates? Number of customers, you know, we talked a little bit about the product but just kind of step it back at a corporate level. >> Yeah, so the solution's really resonating. We had the good fortune to put out some news about our physical year. We grew 300 percent year over year in revenue which is I think fantastic growth for any company. We're certainly super pleased that the confidence our customers have in us. We saw a 76 percent growth in new customers Q4 over Q3 and this is primarily folks that I think are seeing the benefit of moving to a modern, scale out platform for data protection. As you mentioned, there are others now starting to discover file services. We feel that we haven't even tapped that. And these are, we've mentioned some customers, but others like Hyatt, US Air force. So there are some very large enterprise and government customers that have seen the benefits in the secondary world of adopting the new scale out hyper converged platform. >> That's great. Last thing, we were talking about multi Cloud. I think you had some news you wanted to share about where else we might be seeing Cohesity in theCUBE. >> That's right and so let's break the news here. So we are super pleased to have theCUBE at Microsoft Ignite in the Cohesity booth. We are very excited about that opportunity. Microsoft and as you're obviously being a very strong partners with Cohesity. We do a lot of work with them. And we're excited to bring theCUBE to the Microsoft customer set and your global audience watching worldwide in about a months time I think. >> Yeah Lynn, absolutely. We really appreciate the partnership. And for those who don't know, we love to cover all the shows. We do over 110 shows. Microsoft shows have been on the top of our list and we've talked with Microsoft, we have lots of guests on the program from Microsoft. We've had Fonti Adele on, we've have Brad Anderson on. But it, through the partnership with Cohesity we're there, we're going to have lots of editorial guests from Microsoft, from the ecosystem, our independent coverage. But we have Cohesity as our host. So thanks again. >> Happy to have you guys there and make the opportunity. Microsoft obviously a massive player in the IT ecosystem and it's important that you guys cover what's going on at that show. >> Okay, great and so of course you can always check out at the Cohesity website all the places they're being. To find where we'll be, check out theCUBE.net. For Justin Warren, I'm Stu Miniman. Always great to catch up with you Lynn Lucas. Thank you so much. >> Thank you. >> And we'll be back with lots more. Thank you for watching theCUBE. (upbeat music)

>> Narrator: Live from Las Vegas. It's the CUBE covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. >> Welcome back everyone to the CUBE's live coverage of ServiceNow Knowledge 18 in Las Vegas Nevada. I'm your host Rebecca Knight. We've got two guests for this panel. We have Katie Benedict who is director advisory people in change at KPMG and Michelle Esposito who is the AVP technology planning for JM Family Enterprises. Welcome Katie and Michelle. >> Thank you for having us. >> So I want to start out with you Michelle, explain to our viewers what JM Family Enterprises is. >> Sure, JM Family's, we're a privately held company located in south Florida. We have about 4,200 associates across the country and I describe us as a diversified automotive company. So we started 50 years ago, it's actually our 50th anniversary. Distributing Toyotas in the U.S. We were the first distributor and we still distribute to the five south eastern states but since then we've grown and expanded into other sectors of the automotive industry. Including auto finance and warranty and insurance products. >> Okay so diversified portfolio of services. >> Yes. >> So recently you had a situation, an implementation situation. Can you tell our viewers a little bit about it and then I want you to chime in Katie with how you worked on it too. >> Sure. So we were an existing ServiceNow customer. We implemented the product back in 2011 and at the time we really just tried to make it look like our old product. We wanted to minimize the disruption to the organization so we said let's just make it look and behave like the old product did. Seemed like a good idea at the time but with that and with the change that happened over time it became very complex to use and it really just wasn't meeting our needs. So, after much consultation with a lot of experts in the field we decided to re-implement ServiceNow. We believed in the platform, we believed in its capabilities and what it could do for us but we needed to start over. So with that comes a lot of change for our organization. People re used to doing things a certain way, they're used to the processes that we already had in place. So trying to get them on board and understand the why to what we were doing was really important. >> And Katie that's where you fit in. So tell us a little bit about KPMG's approach to making this easier, because as Michelle said. We are human nature, we're just resistant to change and sort of we like it the old way. This is hard. So how, what, can you tell us a little bit about your approach. >> Exactly. We were thrilled that JM Family chose KPMG as their implementation partner and really some of things we brought specifically to the table for this re-implementation. Was some of our accelerators. Our process packs to really optimize the new processes that JM Family was using but then also our organizational change management and learning and development capabilities. We specialize in IT transformation from a people perspective and group of a specialized in ServiceNow. We've done, well over 50 implementations of ServiceNow. So we wanted to look from that people perspective, how do we get the right level of buy in. How do we make sure that people understand why we're doing the change. Get that early, quick adoption. A continuous feedback loop we implement a change agent network. Which I've found was one of the most effective things we could have done especially at JM Family given the nature of their organization and given some of the cultural considerations there and it was a tremendous success there I feel. I mean the people there, the associates there were so involved in the initiative and really partnered with our team. As a single team, it wasn't JM Family and KPMG it was one implementation team working together in tandem to make this change happen. >> So what did you learn in the sense of what were people's, what were the sticking points? And then how did you overcome them? >> Yeah. Sure I can take that. As much as people were supportive of the re-implementation and really knew we needed to do it we found that they were still very much embedded with the way we did it today. So even going into this knowing what a huge change management effort it was I was still surprised at how much effort we had to put into it. So it took a lot of communication, a lot of different methods of communication and engagement to get people to really understand what we were doing and why we were doing it. Repetition really explaining it, the change agent network was huge for us and what we did there was. We pulled in some of our bigger supporters and some of our detractors and they were able to kind of permeate the organization in the different departments within IT to really help sell what we were doing. To bring back questions and concerns. So that was really key. >> What was that like bringing in the people who were really butting heads? I mean and how do you navigate between those two factions? >> Honestly I think it was great because I'd rather get that feedback while we're going through the process than hear about it later and hear the implementation not be successful. So in some cases when people brought that feedback that maybe wasn't so positive it was just a matter of more communication, more training but in other times it was you know we really scratched our head and said maybe we really need the rethink about this. Maybe they've got something here and we may need to tweak our approach or do something a little differently. But it was as Katie mentioned, the engagement level was phenomenal. So the positive and the negative we really had a very engaged team. >> So coming out of this Katie, what would you say are sort of the best practices for other leaders that are doing implementation, re-implementations and maybe dealing with some resistance? >> I would say definitely whether it's the implementation or a re-implementation. Don't forget about your people. The technology, especially ServiceNow is fabulous and your processes are generally are standard. You can align to idle processes but getting the adoption is really key and so remembering that this is a transformation. It's not just an implementation of the technology. Paying attention to the people, making sure that they're on board. They know what you're doing, why you're doing it and really what's in it for them is vital to making this a successful project. >> As you're looking at the ServiceNow platform and what you do for JM Family Enterprises what do you see looking ahead as sort of ways you can augment and enhance? >> Oh they have a lot of ideas going forward right now which is very exciting. >> It is, you know we focused in, we're in a second phase implementation. Our first phase really focused on the core ITSM functions and now we're dipping our toe into some other areas. The PPM suite, vendor management, performance analytics. So we're really continuing to mature our use of the product and even looking beyond that. You know we have interest in some of the security operations and even further than that into some of the financial management capabilities. So we definitely plan to continue invest in the platform and see what it can do for us. >> You're evolving just as ServiceNow is evolving too. >> Yes we are. >> Well Michelle and Katie thanks so much for coming on the CUBE. It was great having you. >> Thank you so much. >> I'm Rebecca Knight, we will have much more of the CUBE's live coverage of ServiceNow Knowledge 18. Hashtag no 18 just after this.

(upbeat music) >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're in our Palo Alto studios. The conference season is just about ready to take off so we still have some time to get some Cube conversations in before we hit the road and spend the next several days and weeks and months in Las Vegas, Orlando, and points on the compass. So, we're excited to have our next guest. She's Lynn Lucas, Cube alumni, CMO of Cohesity, Lynn, great to see you again. >> Jeff, super to be here for the first time in Palo Alto. >> Yeah, how do you like the studio? >> I love it! >> It's a little different than the vibe at the conferences. >> A little quieter-- >> A little quieter. >> Than the conferences but I like it. >> Well good, good, welcome, so you have relatively recently joined a new company, Cohesity, so congratulations on that. >> Thanks, yeah. >> And just curious, one, give us just a quick overview on Cohesity but more importantly, what did you see that attracted you, to get you to join? >> Great, yeah, so Cohesity, just joined at the beginning of January, having a blast. And really what I saw that attracted me to Cohesity was three things. It's an incredible founder, Mohit Aron, who was formerly the CTO and co-founder of Nutanix called the Father of Hyperconvergence and before that the lead developer at Google File System. And, he really is doing what a lot of Silicon Valley is known for, which is he took a step back and is looking at this space in the data center that we call secondary data, back up, archives, replications, test dev, analytics, and said, "You know what? "The world doesn't need a better point solution. "We need to take a step back and look "at how this gold mine of data can be used "in a much more efficient way." Because data is after all, is what's powering the worlds businesses and their differentiation. So, the technology, Mohit himself is a founder and then it's just an incredible start up culture. It's fast growing, we're having fun every day, I love going to work. >> It's amazing, I was just doing some background and you guys have raised $160 million. The list of leadership and board and advisory is pretty amazing. It's like a who's who from this industry. So he pulled together a helluva team. >> He really has and you know Carl Eschenbach, former COO of VM Ware is on our board. >> Cube alumni, Patrick Rogers. >> Rob Salmon. >> Cube alumni, we could go on and on. >> Yes, Ned App, Dan Wormehoven, Google Ventures is invested, Sequoia. I think Sequoia said we're the fastest growing company in their portfolio. We grew 600% year over year last year, 40 to 50% growth in new customers every quarter, cause they're is just such a pent up demand to really solve some of the problems that haven't been addressed over the last, really, couple of decades for the inefficiencies and how all of this data for these secondary workloads is managed. >> So you got an interesting graphic on the website talking about secondary data. And that it's really the ugly part of the iceberg below the water and significantly bigger, heavier, and more expensive to manage than the primary data. So I wonder if you could take us into that a little bit deeper, how did it get to be such a problem? And why is this new approach a better way to attack that problem? >> Sure and an iceberg is really kind of a good metaphor when you think about the data center. You know, we've got our production, and applications, primary storage and that's what's floating above the water and we see that 20% but below is another 80%. And, according to most industry analysts, IDC, Gartner, that represents not just 80% of the data but 80% of the cost. On average, IDC says every organization has 12 to 14 copies of each piece of data. And that happens because what's grown up over time is point solutions for all the various work loads. You got one set of hardware and software for backup. You've got another set for test dev. You've got another set for analytics. There's been no sharing of the data. There's no single infrastructure, knowing even or operations knowing what you have and being able to tell you where the inefficiencies are and so you think about a developer in retail or in a bank organization, they're requesting a copy of a data to develop the new applications that copy gets instantiated. They do their work, never gets erased, just like in our consumer life. >> Right, Right. >> Do you ever erase photos off your phone? >> I can't tell you how many copies and copies and copies of, cause it's, cause it's often-- >> It's easy. >> You figure, it's easier to make another copy just in case, right? >> Exactly, so that never goes away and then you've got yet another copy for the next time they need an updated set. And so, this has been multiplying and it creates just an incredible expense to maintain and operate. And it also creates a lot of risk these days for organizations because of new regulations like GDPR. >> Right. >> Where are all those copies of personal information from e-use citizens, people don't even know anymore. >> Right, and then there's, you know, two other big factors that have come into play in recent years. Software to find and public cloud. Two really big, huge tidal waves of change that were not accommodated in prior architectures so you guys, obviously, saw that opportunity glommed on and are now offering something that can take care of the different types of needs based on what type of infrastructure you need, really not at a company level. But really at the application or the workload level right? >> Yeah, so I think it's a great point and I won't claim any credit for this, this is Mehit and his team of developers and really, as you pointed out, what do we see organizations looking for now? They now realize that, hey, if I can get a software to find platform, a lot of commodity hardware does a really good job for me and I want to have that flexibility to choose, you know, what vendor I might be using. So, Mohit developed a software to find platform that addresses, how do you bring all of these data and these workloads together in one platform so I can have a consistent set of infrastructure and a consistent operational model and, because of his heritage, working at Google, being one of the lead developers for Google File System, it comes with this cloud first mentality. So this is not a bolt on with a gateway to get to Amazon or get to Azure. >> Right. >> This is a software platform that natively understands and spans both your private cloud and your on premises data center and the public cloud. So it gives an IT organization the flexibility to choose how do I want to use the public cloud with my private data center and not have to think really about, kind of, that plumbing below. >> Right. >> Below the water line anymore. >> Because, because there is no either or, right? It's really workload specific where that particular workload lives and the storage that supports that. >> Yeah, so so let me be specific about what Cohesity offers. It's software defined and we offer a appliance so that it's very easy for an organization to go in and say, you know what, data protection, backup frankly, legacy architectures built 20 years ago, before the advent of the cloud. Biggest pain point we see right now can move in a Cohesity hyperconverged appliance and solve that problem and gain massive business benefits right away. We offer global deduplication, very advanced compression and erasure coding and we have customers that are telling us that they're seeing eight to one, ten to one, even 14 to one ratios that really then give them-- >> 14:1 ratio and a reduction of capacity to store the same amount of stuff? >> Versus from some of the current customer, or current vendors that they have been using, from what I would call these legacy architectures. >> Right, right, that's pretty significant. >> So they're getting an amazing storage efficiency. Then, they often next say, wow I'd like to give my developers the flexibility of spinning this up in the cloud. So we offer a cloud edition that allows them to choose whether they want to operate on Azure on Amazon on Google cloud and be able to move that data into the cloud, use it for a test dev instance, but again all under the same software interface all looks like one operating system. No bolt on gateway to manage. >> Right, so you get it-- >> And then. >> I'm sorry go ahead. >> And I was going to say in the third part is many organizations obviously have remote offices, branch offices so there's a virtual edition too. >> Right. So I'm just curious on the cloud side. So Andy Chassis' been on a ton of times, great guy. >> Yes. >> You know, one of the promises of cloud is spin up what you need and spin down when you - don't, as you said. >> Right. >> Nobody ever spins anything down so are you seeing customers have the same type of, of economic impact in managing their storage that's in the public clouds? Because now they're actually spinning down what they don't need or consolidating more efficiently. >> Yeah, so I think that we've seen, in general, in the industry that if you likened the data center it'd kind of been a messy garage where there was a lot of things in the garage and you weren't really sure what it was. A lot of folks, I would say five plus years ago, were like, kind of ran to the cloud cause it was clean and new and it was like that new shiny storage box. >> Right. >> You know, that you see parked on people's driveways sometimes and then realize that there can be a lot of expense, cause you're really replicating in the cloud, some of these same silos if you're not careful. >> Right. >> We're going to help customers avoid that. I think customers are much more sophisticated now than say five years ago. And they're now looking at what's the best way for me to incorporate public cloud. >> Right. >> So really common use case right now would be what I mentioned before test dev, let's move something there, get the benefit of the compute, do some analytics on it, build some new application, maybe get spun down after that but another really common use case is a lot of organizations worried about disaster recovery, bringing the cloud in as their second site. Because that's a very efficient way for them to do that and not build yet another on premises data center. >> Silo. >> Yeah. >> So, the company's been around, the a round is 2013, you're coming in as a CMO. You're brand new and fresh, what's your charter? You know, you didn't come in at a low level you came in with the C, what are you excited about, what you know, again why did they bring you in and what are you going to bring to the table and what are your priorities for the rest of 2018 and beyond? I still can't believe we're a third, a quarter of the way through 2018. >> Yes we are. We're going to be at those shows pretty soon. >> (laughing) I know, they're comin'. >> They are, so I'm here really to build on the good work that the team has done and I'm just really thrilled to be at the company. I think what my charter is is to continue the company's expansion. So, they've seen tremendous growth and in fact, we've just really launched into Asia so we now have a large sales presence in Australia, New Zealand and we're going to continue to expand into the rest of Asia. Significantly expanded in Europe as well recently. So part of my charter is to bring the marketing programs to all of these new regions and in general, to up our awareness level. I think Cohesity has an incredible opportunity to really be one of those companies that changes the data center landscape. >> Right. >> And I want to make sure the world knows about the incredible benefits the customers are seeing already with us. And do that in a way that really features the customer voice. I've been on theCUBE before and I talked about that. For me, that is all about ensuring that the customer voice is really front and center and so hopefully we'll bring a Cohesity customer here. >> Good, well and I just want to ask you kind of from a marketing professional in B2B business, it's a really challenging time in terms of, of the scarcity now is not information, which it used to be. Now the scarcity is in attention and people can get a lot of information before they ever make it to your website within peer groups and hopefully watching some Cube interviews, et cetera. So I'm just curious to get your perspective from a Chief Marketing Officer how are you kind of looking at the challenges of getting the message out. It's a really different world than it was years and years ago. >> Yeah. >> People aren't reading white paper so much and it's a different challenge. >> Yeah, and it's part of the fun actually in being in marketing and being in marketing and tech because a lot of that cool technology for marketing is invented right here in the Valley too. So, you know, I think that word of mouth still actually plays an incredible role and it is that customer voice but bringing that out in ways that are accessible for customers. You and I know, we're all very ADD, very time sliced-- >> Right. >> And so those small moments on social media where you can feature bits of information that get people's attention. In fact, we're running something right now, which I think has a lot of legs because at the end of the day I'm selling to a human. >> Right? >> Right. >> Right. >> So we've got B2B monikers but at the end of the day, folks are people that laugh, they cry, they want to have fun. >> Right. >> So we're running a break up with your legacy backup campaign right now. And I encourage the audience to go check it out. It's pinned on our Twitter feed at Cohesity but it pokes a little bit of fun at how you might break up with your older vendor-- >> Right. >> And that's a moment that we think captures folks attention and gets them interested so that maybe they do want to move down and read the white paper and so forth. So I look to do that through combinations of just, you know, bringing out Cohesity's incredible voice, our customer voice, and then sharing it on social because that's the way people really get their information these days. >> Right, this is really interesting cause I think the voice of the customer or the trusted referral's actually more valuable now because it's just a different problem. Before, I couldn't get information, so that was a good valuable sort, now it's really that person's my trusted filter cause I have too much information. >> Right. >> I can't, I can't take it in so that continues to be that trusted filter and conduit so I could just focus on my peers and not necessarily try to read everything that comes out. >> Exactly, you know, so as an example, Manhattan Associates is one of Cohesity's customers and we've been super thrilled to be able to feature them you know, through social, through our website, and let them talk about the benefits of moving to the platform and what they've seen. And I know, I hate to say it, but Gartner as well continues to be an incredible influence on most organizations and, but we're pleased to say that our customers chose Cohesity and we won the Gartner peer insights for data center backup software, just about a month ago. So, that again is another example of customers looking at the options that they have and voting with their voice and we'll continue to drive that message out in the variety of ways and hopefully get people engaged so that they can see that there really is a completely different way of managing your secondary data and getting a lot better efficiencies and a lot lower cost. >> Yeah, good exciting times, challenging times. The old marketing mantra, right? Half of my marketing budget's wasted, I just don't know which half. (laughing) So, you know you got to cover all your bases from the old school Gartner to the new school, having some fun, and some comedy. Well Lynn, really fun to sit down and spend a few minutes and to get deeper into the Cohesity story. >> Likewise, thank you and I'll be seeing you in Orlando, Vegas, and those other points on the compass. >> Alright, she's Lynn Lucas, I'm Jeff Frick. You're watchin' theCUBE from the Palo Alto studios. Great to see ya, we'll see ya next time. Thanks for watchin'. (upbeat music)

>> Announcer: Live from Las Vegas, it's theCube, covering the AWS re:Invent 2017 presented by AWS, Intel, and our ecosystem of partners. >> Welcome back, everyone. Here, in Las Vegas for TheCUBE's exclusive coverage of AWS re:Invent 2017 Amazon Web Service's Annual Conference. It's a zoo every year. Forty-five thousand people. Just seven years ago they couldn't get 4000 people to come, now the business exploding. Eighteen billion dollars of infrastructure, completely changing the game. I'm John Furrier. Our next two guests are Josh Stella who is the CEO of Fugue and Peter O'Donoghue, vice president of application services at Unisys Federal. Guys, welcome to theCUBE. Welcome back. >> Thank you. Welcome to theCUBE. >> Thank you, John. >> So you guys are in the heart of it. We've talked many times about the DevOps ethos at the Amazon public sector event. Man, it's a changing of the guard happening in front of our eyes. >> Peter: Absolutely. >> Seeing a whole new way of re-imagining how to deploy services in kind of the old school infrastructure world. But now taking over the applications. This is disruption. Share some insight, what's going on? >> Well, actually I really appreciate your lead in because, we're seeing like lots of big patterns happening at the same time, particularly with, my business that I look after, that I look at, is the federal sector. And actually, I think in the precursor we were talking about federal used to be slower and more staid. And that's still true in some cases, but actually the rate of acceleration is really taken off, but I think the big patterns we're seeing is you know, the CIO who looks at the Cloud as compute, network, and storage, that CIO is alive and well, and actually we sell and we provide managed services to that type of business. But we're seeing kind of like a greater focus and greater concentration on folks who actually want, they see the Cloud as really kind of an inflection point to break with tradition and actually to be able to consume native services, so they can actually affect and bring mission outcomes more effectively. In my own experience I've seen like two mission customers in a space of months to be able to solve like really serious problems and significant problems that they faced, but they wouldn't have been able to do it without being able to access RDS or ELB or Lamda, and these are becoming like the essential building blocks, the Lego blocks, if you will, of building modern Cloud native applications. >> So you're saying they solved the problem that was unique right there on the spot, which is great, you know, good job Cloud. But the question really is, would that ever have been solved in the old model? They would have been probably in a room arguing over architecture. I mean a lot of this is about looking at a solution, jumping on it, making it happen, not sitting in a room arguing. >> That's right. >> Get RFP out there. >> Josh: Yeah, exactly. Well, I think a lot of this has to do with infrastructure and policy as code. Because if you can express these thing as code, you can try things in an hour or two, instead of having to go through the RFP, sit on a whiteboard, waterfall design it. You can experiment without fear of failure because the costs are so low. And that's also bleeding over into public sector as well. >> I heard a quote this morning, I'll share it with you. I know it's a public sector kind of quote. The guy was up onstage really presenting how he transformed his entire, you know, I won't say the name to protect the innocent, but it was pretty massive transformation. He goes, "We couldn't use Amazon a few years ago because it was too cool." Meaning it's new. So the government's kind of like, well, you know, it's not yet tested, security. And security's always been the issue for this one group. He goes, "But now it's not cool to use it, so we can use it." Meaning, it's proven. So, it's kind of the opposite. When it's boring it's cool. When it's boring it must be good. Kind of a federal mindset, I won't try to pitch into the whole federal too much there, but the reality is tried, proven, tested, certified. There are some serious things that have to go on in both enterprise and now on federal. Amazon is continuing to move the needle while doing the heavy work. I mean, that's hard. So once they break through that, then you got the creativity going on. So take us through where we are in this, because the GovCloud is pretty disruptive. How far are we in the Cloud game in you guys' opinion of that getting the job done, checking the boxes of the certifications. I know there's FedRAMP, there's a bunch of other stuff. Is that mostly done right now? I mean how much more work is needed before everyone goes, okay Cloud is the standard? >> Well actually, (mumbles) >> Well let me try to answer, and I think Josh'll have an opinion on, too. Is I think the FedRAMP certification, I think, has been, I would say, probably if not the single most, one of the most important kind of factors in amplifying or accelerating Clouded option in the federal government. And actually, we've also seen that manifest in the state and local markets as well. Which is, we don't really have an equivalent, but if you're on FedRAMP, that's definitely good enough for us. That has become the defacto standard. But we find, though, is, and actually this is where, we really appreciate a product like Fugue, is actually folks find that, I mean actually, you asked about like major patterns or major trends. Like another major trend that we see, and actually I'll kind of come back to your question is, is that there's a significant shortage in talent and knowledge and skills to be able to manage the Cloud, and actually it's such a phenomenally different kind of mindset, so, like to properly govern and manage the Cloud, is actually a really difficult thing. So, you know, >> Good point. >> As a tradition, we got a lot of managed service provider business in our history. And if you look at, say Amazon, you know some folks would look at it as almost an existential threat. But in fact, we don't. It just means that you need to move into a different place in the stack to add value. And actually that place for us is that, you know, in terms of being able to amplify and accelerate that, the planning for, the migrating to, the running workloads in, in a scalable way, cost effective way, securely, and being able to build Cloud natively, our customers are really struggling with that. And they can do it, we've seen them do it one offs, but to be able to do a scale, so being able to really attack the knowledge gap from a human resource perspective? >> John: Great point. >> But also, encapsulating that into templatizing and putting nanny guardrails in are really important. Well that's a great point. There was a conversation I was involved in this morning with the CIA where they basically admitted, we got a lot of smart people, we can build a Cloud. Running and maintaining it... >> Josh: That's right. >> Are two different things. So this is kind of a false trap that a lot of people could fall into. Oh yeah, the Cloud, it's no problem. >> Josh: Yeah. >> So this is where the issues come in. Thoughts on that? >> Yes. >> And what you guys are doing? So I think we've entered the second phase of Clouded option. The first phase was kind of shadow IT bought them up. When I was at AWS I'd go into a customer, they'd say we're not on the Cloud. And then we found out we had 130 accounts that were swiped credit cards. >> John: Don't tell anyone. >> Yeah, don't tell anybody. Help us... >> Secret region. >> Help us sort this out. >> How is this the prototype? Honest, that's right. >> But now the market has changed. And so whether it's commercial or federal or other spaces, we're now in this phase two where these are strategic adoption of Cloud at an enterprise level. And to do that you need automation, you need repeatability, you need consistency, you need policy enforcement, and so that's where a system like Fugue packages all that together, which accelerates the whole operation of that. You know, I don't like the term centralized, because what Fugue allows you to do is assert some things and then decentralize the innovation aspect. >> It reminds me of the whole fabric and the whole grid days. But you bring up a good point, phase two is about kind of grownup Cloud. And so, that begs the question, now what are you guys working on, Unisys and, what's the story between your partnership? Talk about that. Because you know, people are relying on you guys as suppliers, so you have to stand alone and be successful. We talked about your company, but partnering is now important. >> Peter: That's right. >> Who you partner with and why, and what's the outcome options for the customers? >> Well, we're super excited about our relationship with Fugue. And actually primarily, as I talked earlier, we do see the big challenges that the market has right now. There is this huge gap from a knowledge and talent perspective. And also, the pioneers have gone into the Cloud, but now you have to have the settlers there. So how do we kind of attack those at the same time? So, when we're looking for a management platform, you know, we look for three things that really were important to us. The first is, is what I call expressiveness. So actually, I've got a lot of experience implementing kind of like more IT ops, like classical, like Cloud broker solutions, and we found that, you know, in order to be able to build a solution quickly for customers, you need to be able to express yourself. I mean, you can't manage and you can't govern, and you can't meter, you can't bill for, you can't apply policy for what Dr Vogels calls the primitives, right? So if I've only got like three or four primitives, my ability to manage and govern is really limited, right? It's almost like, the metaphor I would use would be maybe somebody gives you a keyboard, you got a half a dozen keys on there, and you're trying to write the great American novel. You can't do that, right? So, expressiveness, being able to articulate the right models and templatize and govern. That's kind of concern number one. Concern number two that we think is really important is, is it kind of goes at that knowledge management piece. We're making a major investment within Unisys Federal, and we're looking at hundreds and hundreds of our associates to be trained and certified, and we're building it a Cloud (mumbles) enablement. But we're looking to encapsulate our best practices and templatize those. So to the point... >> And Josh fits in there what, from a software standpoint? >> Well, he actually provides the way for us to capture that knowledge. So, in terms of what our policies in terms of governing say, you know, load balancers or EC2 instances or you know, how we're gonna manage S3 and gonna protect S3. You know, policies and best practices up and down the stack. Like, even governance processes around dev test environments. We're not gonna leave dev environments flapping in the wind for months on end where people are running up big bills, right? >> John: Got it. >> So Josh's product helps us manage that. And the third thing is what I call like the nanny rails. Now my daughter has just learned how to drive a car. And some of the choices that we made, we took into consideration like lane changing things and like crash avoidance and so and so forth. So, what we want, and actually Josh brought this up very elegantly is, is we want, the forward-leaning federal agencies to be able to go quick. But we want to put the guardrails behind them and have like that nanny kind of supervision behind them so that if things start drifting out of compliance we can drag them back. >> You can notify them, right? >> Some instrumentation. >> Absolutely. Absolutely. >> And management. >> Well, it's not just notification with Fugue. We don't let you do the wrong thing. And then if somebody goes in later and breaks it, we fix it. So instead of mean time to response of 15 minutes for your monitoring solution, then however long for somebody to pick up the notification, then to respond to it. With Fugue, within 30 seconds we've seen it and we've fixed it. And so that is a real game changer in terms of... >> Yeah, you guys are very impression with DevOps, they way you connect it. And the theme is connecting the tech to business. And in this case it's government, but that's your customer. What's new with you guys? Any announcements here? What's the story? >> Oh yeah, we have... >> Give us the update quick. >> Thank you very much. We have two big announcements. So it used to be, to use all the great management features of Fugue you had to build things using Fugue. So as of today you can download the new version of the system, you can point it at your existing AWS infrastructure. We autogenerate code and diagrams to show you what you're running. You can compare policy against that. So you don't have to write any code. And then when you've got it right, you can just apply Fugue to that. You can import that infrastructure into Fugue management. So a lot of our customers are telling us we have years of development on AWS. It was not done using best practices. We allow you to go back and fix that really quickly without recreating your infrastructure. >> So go in, do some maintenance without breaking it, tearing it down, building it up. >> and then you get all the benefits of Fugue enforcement. Every 30 seconds we examine the environment. If anything breaks we fix it. And so the ability to just pull that into Fugue and do it easily. >> Well it's great that you guys are successful. Congratulations on the partnership with Unisys. Big name, brand name. You guys obviously experienced, trusted advisors and partners to Federal. Personal question for you, Josh. You know, as you look back at the Amazon mothership. >> Josh: Yes. >> You gotta be like, damn that was a good ride. As an alumni and an extender, you're bringing that DNA to your company that you founded. What's it like? I mean, you feel good? You got a spring in your step? You kinda wish you were back on the mothership? >> Oh no, you know it's great working with AWS because I love doing what I'm doing now more than anything I've ever done. And they are great partners to us. They are so helpful. So I love coming back and seeing all my friends at Amazon. >> They're all bosses now. They're managers. >> Josh: That's right, that's right. >> Promoted. >> But being able to go out and do something that's really your vision, there's nothing like it in the world. >> John: I agree. >> Yeah. >> Being an entrepreneur certainly you can control your own destiny. It's a lot of fun, lot of passion. >> Josh: Yeah. >> Congratulations. >> Josh: Thank you. >> The Fugue CEO here with his partner in Unisys here in theCUBE. Live coverage day one. We've got two more live days. It'll be wall to wall. Big parties tonight. Lot of events, lot of action. Forty-five thousand people here in Las Vegas. I'm John Furrier. Thanks for watching. We'll be right back.

>> So as folks make their way over from Javits I'm going to give you the least interesting part of the evening and that's my segment in which I welcome you here, introduce myself, lay out what what we're going to do for the next couple of hours. So first off, thank you very much for coming. As all of you know Wikibon is a part of SiliconANGLE which also includes theCUBE, so if you look around, this is what we have been doing for the past couple of days here in the TheCUBE. We've been inviting some significant thought leaders from over on the show and in incredibly expensive limousines driven them up the street to come on to TheCUBE and spend time with us and talk about some of the things that are happening in the industry today that are especially important. We tore it down, and we're having this party tonight. So we want to thank you very much for coming and look forward to having more conversations with all of you. Now what are we going to talk about? Well Wikibon is the research arm of SiliconANGLE. So we take data that comes out of TheCUBE and other places and we incorporated it into our research. And work very closely with large end users and large technology companies regarding how to make better decisions in this incredibly complex, incredibly important transformative world of digital business. What we're going to talk about tonight, and I've got a couple of my analysts assembled, and we're also going to have a panel, is this notion of software is eating the Edge. Now most of you have probably heard Marc Andreessen, the venture capitalist and developer, original developer of Netscape many years ago, talk about how software's eating the world. Well, if software is truly going to eat the world, it's going to eat at, it's going to take the big chunks, big bites at the Edge. That's where the actual action's going to be. And what we want to talk about specifically is the entangling of the internet or the industrial internet of things and IoT with analytics. So that's what we're going to talk about over the course of the next couple of hours. To do that we're going to, I've already blown the schedule, that's on me. But to do that I'm going to spend a couple minutes talking about what we regard as the essential digital business capabilities which includes analytics and Big Data, and includes IIoT and we'll explain at least in our position why those two things come together the way that they do. But I'm going to ask the august and revered Neil Raden, Wikibon analyst to come on up and talk about harvesting value at the Edge. 'Cause there are some, not now Neil, when we're done, when I'm done. So I'm going to ask Neil to come on up and we'll talk, he's going to talk about harvesting value at the Edge. And then Jim Kobielus will follow up with him, another Wikibon analyst, he'll talk specifically about how we're going to take that combination of analytics and Edge and turn it into the new types of systems and software that are going to sustain this significant transformation that's going on. And then after that, I'm going to ask Neil and Jim to come, going to invite some other folks up and we're going to run a panel to talk about some of these issues and do a real question and answer. So the goal here is before we break for drinks is to create a community feeling within the room. That includes smart people here, smart people in the audience having a conversation ultimately about some of these significant changes so please participate and we look forward to talking about the rest of it. All right, let's get going! What is digital business? One of the nice things about being an analyst is that you can reach back on people who were significantly smarter than you and build your points of view on the shoulders of those giants including Peter Drucker. Many years ago Peter Drucker made the observation that the purpose of business is to create and keep a customer. Not better shareholder value, not anything else. It is about creating and keeping your customer. Now you can argue with that, at the end of the day, if you don't have customers, you don't have a business. Now the observation that we've made, what we've added to that is that we've made the observation that the difference between business and digital business essentially is one thing. That's data. A digital business uses data to differentially create and keep customers. That's the only difference. If you think about the difference between taxi cab companies here in New York City, every cab that I've been in in the last three days has bothered me about Uber. The reason, the difference between Uber and a taxi cab company is data. That's the primary difference. Uber uses data as an asset. And we think this is the fundamental feature of digital business that everybody has to pay attention to. How is a business going to use data as an asset? Is the business using data as an asset? Is a business driving its engagement with customers, the role of its product et cetera using data? And if they are, they are becoming a more digital business. Now when you think about that, what we're really talking about is how are they going to put data to work? How are they going to take their customer data and their operational data and their financial data and any other kind of data and ultimately turn that into superior engagement or improved customer experience or more agile operations or increased automation? Those are the kinds of outcomes that we're talking about. But it is about putting data to work. That's fundamentally what we're trying to do within a digital business. Now that leads to an observation about the crucial strategic business capabilities that every business that aspires to be more digital or to be digital has to put in place. And I want to be clear. When I say strategic capabilities I mean something specific. When you talk about, for example technology architecture or information architecture there is this notion of what capabilities does your business need? Your business needs capabilities to pursue and achieve its mission. And in the digital business these are the capabilities that are now additive to this core question, ultimately of whether or not the company is a digital business. What are the three capabilities? One, you have to capture data. Not just do a good job of it, but better than your competition. You have to capture data better than your competition. In a way that is ultimately less intrusive on your markets and on your customers. That's in many respects, one of the first priorities of the internet of things and people. The idea of using sensors and related technologies to capture more data. Once you capture that data you have to turn it into value. You have to do something with it that creates business value so you can do a better job of engaging your markets and serving your customers. And that essentially is what we regard as the basis of Big Data. Including operations, including financial performance and everything else, but ultimately it's taking the data that's being captured and turning it into value within the business. The last point here is that once you have generated a model, or an insight or some other resource that you can act upon, you then have to act upon it in the real world. We call that systems of agency, the ability to enact based on data. Now I want to spend just a second talking about systems of agency 'cause we think it's an interesting concept and it's something Jim Kobielus is going to talk about a little bit later. When we say systems of agency, what we're saying is increasingly machines are acting on behalf of a brand. Or systems, combinations of machines and people are acting on behalf of the brand. And this whole notion of agency is the idea that ultimately these systems are now acting as the business's agent. They are at the front line of engaging customers. It's an extremely rich proposition that has subtle but crucial implications. For example I was talking to a senior decision maker at a business today and they made a quick observation, they talked about they, on their way here to New York City they had followed a woman who was going through security, opened up her suitcase and took out a bird. And then went through security with the bird. And the reason why I bring this up now is as TSA was trying to figure out how exactly to deal with this, the bird started talking and repeating things that the woman had said and many of those things, in fact, might have put her in jail. Now in this case the bird is not an agent of that woman. You can't put the woman in jail because of what the bird said. But increasingly we have to ask ourselves as we ask machines to do more on our behalf, digital instrumentation and elements to do more on our behalf, it's going to have blow back and an impact on our brand if we don't do it well. I want to draw that forward a little bit because I suggest there's going to be a new lifecycle for data. And the way that we think about it is we have the internet or the Edge which is comprised of things and crucially people, using sensors, whether they be smaller processors in control towers or whether they be phones that are tracking where we go, and this crucial element here is something that we call information transducers. Now a transducer in a traditional sense is something that takes energy from one form to another so that it can perform new types of work. By information transducer I essentially mean it takes information from one form to another so it can perform another type of work. This is a crucial feature of data. One of the beauties of data is that it can be used in multiple places at multiple times and not engender significant net new costs. It's one of the few assets that you can say about that. So the concept of an information transducer's really important because it's the basis for a lot of transformations of data as data flies through organizations. So we end up with the transducers storing data in the form of analytics, machine learning, business operations, other types of things, and then it goes back and it's transduced, back into to the real world as we program the real world and turning into these systems of agency. So that's the new lifecycle. And increasingly, that's how we have to think about data flows. Capturing it, turning it into value and having it act on our behalf in front of markets. That could have enormous implications for how ultimately money is spent over the next few years. So Wikibon does a significant amount of market research in addition to advising our large user customers. And that includes doing studies on cloud, public cloud, but also studies on what's happening within the analytics world. And if you take a look at it, what we basically see happening over the course of the next few years is significant investments in software and also services to get the word out. But we also expect there's going to be a lot of hardware. A significant amount of hardware that's ultimately sold within this space. And that's because of something that we call true private cloud. This concept of ultimately a business increasingly being designed and architected around the idea of data assets means that the reality, the physical realities of how data operates, how much it costs to store it or move it, the issues of latency, the issues of intellectual property protection as well as things like the regulatory regimes that are being put in place to govern how data gets used in between locations. All of those factors are going to drive increased utilization of what we call true private cloud. On premise technologies that provide the cloud experience but act where the data naturally needs to be processed. I'll come a little bit more to that in a second. So we think that it's going to be a relatively balanced market, a lot of stuff is going to end up in the cloud, but as Neil and Jim will talk about, there's going to be an enormous amount of analytics that pulls an enormous amount of data out to the Edge 'cause that's where the action's going to be. Now one of the things I want to also reveal to you is we've done a fair amount of data, we've done a fair amount of research around this question of where or how will data guide decisions about infrastructure? And in particular the Edge is driving these conversations. So here is a piece of research that one of our cohorts at Wikibon did, David Floyer. Taking a look at IoT Edge cost comparisons over a three year period. And it showed on the left hand side, an example where the sensor towers and other types of devices were streaming data back into a central location in a wind farm, stylized wind farm example. Very very expensive. Significant amounts of money end up being consumed, significant resources end up being consumed by the cost of moving the data from one place to another. Now this is even assuming that latency does not become a problem. The second example that we looked at is if we kept more of that data at the Edge and processed at the Edge. And literally it is a 85 plus percent cost reduction to keep more of the data at the Edge. Now that has enormous implications, how we think about big data, how we think about next generation architectures, et cetera. But it's these costs that are going to be so crucial to shaping the decisions that we make over the next two years about where we put hardware, where we put resources, what type of automation is possible, and what types of technology management has to be put in place. Ultimately we think it's going to lead to a structure, an architecture in the infrastructure as well as applications that is informed more by moving cloud to the data than moving the data to the cloud. That's kind of our fundamental proposition is that the norm in the industry has been to think about moving all data up to the cloud because who wants to do IT? It's so much cheaper, look what Amazon can do. Or what AWS can do. All true statements. Very very important in many respects. But most businesses today are starting to rethink that simple proposition and asking themselves do we have to move our business to the cloud, or can we move the cloud to the business? And increasingly what we see happening as we talk to our large customers about this, is that the cloud is being extended out to the Edge, we're moving the cloud and cloud services out to the business. Because of economic reasons, intellectual property control reasons, regulatory reasons, security reasons, any number of other reasons. It's just a more natural way to deal with it. And of course, the most important reason is latency. So with that as a quick backdrop, if I may quickly summarize, we believe fundamentally that the difference today is that businesses are trying to understand how to use data as an asset. And that requires an investment in new sets of technology capabilities that are not cheap, not simple and require significant thought, a lot of planning, lot of change within an IT and business organizations. How we capture data, how we turn it into value, and how we translate that into real world action through software. That's going to lead to a rethinking, ultimately, based on cost and other factors about how we deploy infrastructure. How we use the cloud so that the data guides the activity and not the choice of cloud supplier determines or limits what we can do with our data. And that's going to lead to this notion of true private cloud and elevate the role the Edge plays in analytics and all other architectures. So I hope that was perfectly clear. And now what I want to do is I want to bring up Neil Raden. Yes, now's the time Neil! So let me invite Neil up to spend some time talking about harvesting value at the Edge. Can you see his, all right. Got it. >> Oh boy. Hi everybody. Yeah, this is a really, this is a really big and complicated topic so I decided to just concentrate on something fairly simple, but I know that Peter mentioned customers. And he also had a picture of Peter Drucker. I had the pleasure in 1998 of interviewing Peter and photographing him. Peter Drucker, not this Peter. Because I'd started a magazine called Hired Brains. It was for consultants. And Peter said, Peter said a number of really interesting things to me, but one of them was his definition of a customer was someone who wrote you a check that didn't bounce. He was kind of a wag. He was! So anyway, he had to leave to do a video conference with Jack Welch and so I said to him, how do you charge Jack Welch to spend an hour on a video conference? And he said, you know I have this theory that you should always charge your client enough that it hurts a little bit or they don't take you seriously. Well, I had the chance to talk to Jack's wife, Suzie Welch recently and I told her that story and she said, "Oh he's full of it, Jack never paid "a dime for those conferences!" (laughs) So anyway, all right, so let's talk about this. To me, things about, engineered things like the hardware and network and all these other standards and so forth, we haven't fully developed those yet, but they're coming. As far as I'm concerned, they're not the most interesting thing. The most interesting thing to me in Edge Analytics is what you're going to get out of it, what the result is going to be. Making sense of this data that's coming. And while we're on data, something I've been thinking a lot lately because everybody I've talked to for the last three days just keeps talking to me about data. I have this feeling that data isn't actually quite real. That any data that we deal with is the result of some process that's captured it from something else that's actually real. In other words it's proxy. So it's not exactly perfect. And that's why we've always had these problems about customer A, customer A, customer A, what's their definition? What's the definition of this, that and the other thing? And with sensor data, I really have the feeling, when companies get, not you know, not companies, organizations get instrumented and start dealing with this kind of data what they're going to find is that this is the first time, and I've been involved in analytics, I don't want to date myself, 'cause I know I look young, but the first, I've been dealing with analytics since 1975. And everything we've ever done in analytics has involved pulling data from some other system that was not designed for analytics. But if you think about sensor data, this is data that we're actually going to catch the first time. It's going to be ours! We're not going to get it from some other source. It's going to be the real deal, to the extent that it's the real deal. Now you may say, ya know Neil, a sensor that's sending us information about oil pressure or temperature or something like that, how can you quarrel with that? Well, I can quarrel with it because I don't know if the sensor's doing it right. So we still don't know, even with that data, if it's right, but that's what we have to work with. Now, what does that really mean? Is that we have to be really careful with this data. It's ours, we have to take care of it. We don't get to reload it from source some other day. If we munge it up it's gone forever. So that has, that has very serious implications, but let me, let me roll you back a little bit. The way I look at analytics is it's come in three different eras. And we're entering into the third now. The first era was business intelligence. It was basically built and governed by IT, it was system of record kind of reporting. And as far as I can recall, it probably started around 1988 or at least that's the year that Howard Dresner claims to have invented the term. I'm not sure it's true. And things happened before 1988 that was sort of like BI, but 88 was when they really started coming out, that's when we saw BusinessObjects and Cognos and MicroStrategy and those kinds of things. The second generation just popped out on everybody else. We're all looking around at BI and we were saying why isn't this working? Why are only five people in the organization using this? Why are we not getting value out of this massive license we bought? And along comes companies like Tableau doing data discovery, visualization, data prep and Line of Business people are using this now. But it's still the same kind of data sources. It's moved out a little bit, but it still hasn't really hit the Big Data thing. Now we're in third generation, so we not only had Big Data, which has come and hit us like a tsunami, but we're looking at smart discovery, we're looking at machine learning. We're looking at AI induced analytics workflows. And then all the natural language cousins. You know, natural language processing, natural language, what's? Oh Q, natural language query. Natural language generation. Anybody here know what natural language generation is? Yeah, so what you see now is you do some sort of analysis and that tool comes up and says this chart is about the following and it used the following data, and it's blah blah blah blah blah. I think it's kind of wordy and it's going to refined some, but it's an interesting, it's an interesting thing to do. Now, the problem I see with Edge Analytics and IoT in general is that most of the canonical examples we talk about are pretty thin. I know we talk about autonomous cars, I hope to God we never have them, 'cause I'm a car guy. Fleet Management, I think Qualcomm started Fleet Management in 1988, that is not a new application. Industrial controls. I seem to remember, I seem to remember Honeywell doing industrial controls at least in the 70s and before that I wasn't, I don't want to talk about what I was doing, but I definitely wasn't in this industry. So my feeling is we all need to sit down and think about this and get creative. Because the real value in Edge Analytics or IoT, whatever you want to call it, the real value is going to be figuring out something that's new or different. Creating a brand new business. Changing the way an operation happens in a company, right? And I think there's a lot of smart people out there and I think there's a million apps that we haven't even talked about so, if you as a vendor come to me and tell me how great your product is, please don't talk to me about autonomous cars or Fleet Managing, 'cause I've heard about that, okay? Now, hardware and architecture are really not the most interesting thing. We fell into that trap with data warehousing. We've fallen into that trap with Big Data. We talk about speeds and feeds. Somebody said to me the other day, what's the narrative of this company? This is a technology provider. And I said as far as I can tell, they don't have a narrative they have some products and they compete in a space. And when they go to clients and the clients say, what's the value of your product? They don't have an answer for that. So we don't want to fall into this trap, okay? Because IoT is going to inform you in ways you've never even dreamed about. Unfortunately some of them are going to be really stinky, you know, they're going to be really bad. You're going to lose more of your privacy, it's going to get harder to get, I dunno, mortgage for example, I dunno, maybe it'll be easier, but in any case, it's not going to all be good. So let's really think about what you want to do with this technology to do something that's really valuable. Cost takeout is not the place to justify an IoT project. Because number one, it's very expensive, and number two, it's a waste of the technology because you should be looking at, you know the old numerator denominator thing? You should be looking at the numerators and forget about the denominators because that's not what you do with IoT. And the other thing is you don't want to get over confident. Actually this is good advice about anything, right? But in this case, I love this quote by Derek Sivers He's a pretty funny guy. He said, "If more information was the answer, "then we'd all be billionaires with perfect abs." I'm not sure what's on his wishlist, but you know, I would, those aren't necessarily the two things I would think of, okay. Now, what I said about the data, I want to explain some more. Big Data Analytics, if you look at this graphic, it depicts it perfectly. It's a bunch of different stuff falling into the funnel. All right? It comes from other places, it's not original material. And when it comes in, it's always used as second hand data. Now what does that mean? That means that you have to figure out the semantics of this information and you have to find a way to put it together in a way that's useful to you, okay. That's Big Data. That's where we are. How is that different from IoT data? It's like I said, IoT is original. You can put it together any way you want because no one else has ever done that before. It's yours to construct, okay. You don't even have to transform it into a schema because you're creating the new application. But the most important thing is you have to take care of it 'cause if you lose it, it's gone. It's the original data. It's the same way, in operational systems for a long long time we've always been concerned about backup and security and everything else. You better believe this is a problem. I know a lot of people think about streaming data, that we're going to look at it for a minute, and we're going to throw most of it away. Personally I don't think that's going to happen. I think it's all going to be saved, at least for a while. Now, the governance and security, oh, by the way, I don't know where you're going to find a presentation where somebody uses a newspaper clipping about Vladimir Lenin, but here it is, enjoy yourselves. I believe that when people think about governance and security today they're still thinking along the same grids that we thought about it all along. But this is very very different and again, I'm sorry I keep thrashing this around, but this is treasured data that has to be carefully taken care of. Now when I say governance, my experience has been over the years that governance is something that IT does to make everybody's lives miserable. But that's not what I mean by governance today. It means a comprehensive program to really secure the value of the data as an asset. And you need to think about this differently. Now the other thing is you may not get to think about it differently, because some of the stuff may end up being subject to regulation. And if the regulators start regulating some of this, then that'll take some of the degrees of freedom away from you in how you put this together, but you know, that's the way it works. Now, machine learning, I think I told somebody the other day that claims about machine learning in software products are as common as twisters in trail parks. And a lot of it is not really what I'd call machine learning. But there's a lot of it around. And I think all of the open source machine learning and artificial intelligence that's popped up, it's great because all those math PhDs who work at Home Depot now have something to do when they go home at night and they construct this stuff. But if you're going to have machine learning at the Edge, here's the question, what kind of machine learning would you have at the Edge? As opposed to developing your models back at say, the cloud, when you transmit the data there. The devices at the Edge are not very powerful. And they don't have a lot of memory. So you're only going to be able to do things that have been modeled or constructed somewhere else. But that's okay. Because machine learning algorithm development is actually slow and painful. So you really want the people who know how to do this working with gobs of data creating models and testing them offline. And when you have something that works, you can put it there. Now there's one thing I want to talk about before I finish, and I think I'm almost finished. I wrote a book about 10 years ago about automated decision making and the conclusion that I came up with was that little decisions add up, and that's good. But it also means you don't have to get them all right. But you don't want computers or software making decisions unattended if it involves human life, or frankly any life. Or the environment. So when you think about the applications that you can build using this architecture and this technology, think about the fact that you're not going to be doing air traffic control, you're not going to be monitoring crossing guards at the elementary school. You're going to be doing things that may seem fairly mundane. Managing machinery on the factory floor, I mean that may sound great, but really isn't that interesting. Managing well heads, drilling for oil, well I mean, it's great to the extent that it doesn't cause wells to explode, but they don't usually explode. What it's usually used for is to drive the cost out of preventative maintenance. Not very interesting. So use your heads. Come up with really cool stuff. And any of you who are involved in Edge Analytics, the next time I talk to you I don't want to hear about the same five applications that everybody talks about. Let's hear about some new ones. So, in conclusion, I don't really have anything in conclusion except that Peter mentioned something about limousines bringing people up here. On Monday I was slogging up and down Park Avenue and Madison Avenue with my client and we were visiting all the hedge funds there because we were doing a project with them. And in the miserable weather I looked at him and I said, for godsake Paul, where's the black car? And he said, that was the 90s. (laughs) Thank you. So, Jim, up to you. (audience applauding) This is terrible, go that way, this was terrible coming that way. >> Woo, don't want to trip! And let's move to, there we go. Hi everybody, how ya doing? Thanks Neil, thanks Peter, those were great discussions. So I'm the third leg in this relay race here, talking about of course how software is eating the world. And focusing on the value of Edge Analytics in a lot of real world scenarios. Programming the real world for, to make the world a better place. So I will talk, I'll break it out analytically in terms of the research that Wikibon is doing in the area of the IoT, but specifically how AI intelligence is being embedded really to all material reality potentially at the Edge. But mobile applications and industrial IoT and the smart appliances and self driving vehicles. I will break it out in terms of a reference architecture for understanding what functions are being pushed to the Edge to hardware, to our phones and so forth to drive various scenarios in terms of real world results. So I'll move a pace here. So basically AI software or AI microservices are being infused into Edge hardware as we speak. What we see is more vendors of smart phones and other, real world appliances and things like smart driving, self driving vehicles. What they're doing is they're instrumenting their products with computer vision and natural language processing, environmental awareness based on sensing and actuation and those capabilities and inferences that these devices just do to both provide human support for human users of these devices as well as to enable varying degrees of autonomous operation. So what I'll be talking about is how AI is a foundation for data driven systems of agency of the sort that Peter is talking about. Infusing data driven intelligence into everything or potentially so. As more of this capability, all these algorithms for things like, ya know for doing real time predictions and classifications, anomaly detection and so forth, as this functionality gets diffused widely and becomes more commoditized, you'll see it burned into an ever-wider variety of hardware architecture, neuro synaptic chips, GPUs and so forth. So what I've got here in front of you is a sort of a high level reference architecture that we're building up in our research at Wikibon. So AI, artificial intelligence is a big term, a big paradigm, I'm not going to unpack it completely. Of course we don't have oodles of time so I'm going to take you fairly quickly through the high points. It's a driver for systems of agency. Programming the real world. Transducing digital inputs, the data, to analog real world results. Through the embedding of this capability in the IoT, but pushing more and more of it out to the Edge with points of decision and action in real time. And there are four capabilities that we're seeing in terms of AI enabled, enabling capabilities that are absolutely critical to software being pushed to the Edge are sensing, actuation, inference and Learning. Sensing and actuation like Peter was describing, it's about capturing data from the environment within which a device or users is operating or moving. And then actuation is the fancy term for doing stuff, ya know like industrial IoT, it's obviously machine controlled, but clearly, you know self driving vehicles is steering a vehicle and avoiding crashing and so forth. Inference is the meat and potatoes as it were of AI. Analytics does inferences. It infers from the data, the logic of the application. Predictive logic, correlations, classification, abstractions, differentiation, anomaly detection, recognizing faces and voices. We see that now with Apple and the latest version of the iPhone is embedding face recognition as a core, as the core multifactor authentication technique. Clearly that's a harbinger of what's going to be universal fairly soon which is that depends on AI. That depends on convolutional neural networks, that is some heavy hitting processing power that's necessary and it's processing the data that's coming from your face. So that's critically important. So what we're looking at then is the AI software is taking root in hardware to power continuous agency. Getting stuff done. Powered decision support by human beings who have to take varying degrees of action in various environments. We don't necessarily want to let the car steer itself in all scenarios, we want some degree of override, for lots of good reasons. They want to protect life and limb including their own. And just more data driven automation across the internet of things in the broadest sense. So unpacking this reference framework, what's happening is that AI driven intelligence is powering real time decisioning at the Edge. Real time local sensing from the data that it's capturing there, it's ingesting the data. Some, not all of that data, may be persistent at the Edge. Some, perhaps most of it, will be pushed into the cloud for other processing. When you have these highly complex algorithms that are doing AI deep learning, multilayer, to do a variety of anti-fraud and higher level like narrative, auto-narrative roll-ups from various scenes that are unfolding. A lot of this processing is going to begin to happen in the cloud, but a fair amount of the more narrowly scoped inferences that drive real time decision support at the point of action will be done on the device itself. Contextual actuation, so it's the sensor data that's captured by the device along with other data that may be coming down in real time streams through the cloud will provide the broader contextual envelope of data needed to drive actuation, to drive various models and rules and so forth that are making stuff happen at the point of action, at the Edge. Continuous inference. What it all comes down to is that inference is what's going on inside the chips at the Edge device. And what we're seeing is a growing range of hardware architectures, GPUs, CPUs, FPGAs, ASIC, Neuro synaptic chips of all sorts playing in various combinations that are automating more and more very complex inference scenarios at the Edge. And not just individual devices, swarms of devices, like drones and so forth are essentially an Edge unto themselves. You'll see these tiered hierarchies of Edge swarms that are playing and doing inferences of ever more complex dynamic nature. And much of this will be, this capability, the fundamental capabilities that is powering them all will be burned into the hardware that powers them. And then adaptive learning. Now I use the term learning rather than training here, training is at the core of it. Training means everything in terms of the predictive fitness or the fitness of your AI services for whatever task, predictions, classifications, face recognition that you, you've built them for. But I use the term learning in a broader sense. It's what's make your inferences get better and better, more accurate over time is that you're training them with fresh data in a supervised learning environment. But you can have reinforcement learning if you're doing like say robotics and you don't have ground truth against which to train the data set. You know there's maximize a reward function versus minimize a loss function, you know, the standard approach, the latter for supervised learning. There's also, of course, the issue, or not the issue, the approach of unsupervised learning with cluster analysis critically important in a lot of real world scenarios. So Edge AI Algorithms, clearly, deep learning which is multilayered machine learning models that can do abstractions at higher and higher levels. Face recognition is a high level abstraction. Faces in a social environment is an even higher level of abstraction in terms of groups. Faces over time and bodies and gestures, doing various things in various environments is an even higher level abstraction in terms of narratives that can be rolled up, are being rolled up by deep learning capabilities of great sophistication. Convolutional neural networks for processing images, recurrent neural networks for processing time series. Generative adversarial networks for doing essentially what's called generative applications of all sort, composing music, and a lot of it's being used for auto programming. These are all deep learning. There's a variety of other algorithm approaches I'm not going to bore you with here. Deep learning is essentially the enabler of the five senses of the IoT. Your phone's going to have, has a camera, it has a microphone, it has the ability to of course, has geolocation and navigation capabilities. It's environmentally aware, it's got an accelerometer and so forth embedded therein. The reason that your phone and all of the devices are getting scary sentient is that they have the sensory modalities and the AI, the deep learning that enables them to make environmentally correct decisions in the wider range of scenarios. So machine learning is the foundation of all of this, but there are other, I mean of deep learning, artificial neural networks is the foundation of that. But there are other approaches for machine learning I want to make you aware of because support vector machines and these other established approaches for machine learning are not going away but really what's driving the show now is deep learning, because it's scary effective. And so that's where most of the investment in AI is going into these days for deep learning. AI Edge platforms, tools and frameworks are just coming along like gangbusters. Much development of AI, of deep learning happens in the context of your data lake. This is where you're storing your training data. This is the data that you use to build and test to validate in your models. So we're seeing a deepening stack of Hadoop and there's Kafka, and Spark and so forth that are driving the training (coughs) excuse me, of AI models that are power all these Edge Analytic applications so that that lake will continue to broaden in terms, and deepen in terms of a scope and the range of data sets and the range of modeling, AI modeling supports. Data science is critically important in this scenario because the data scientist, the data science teams, the tools and techniques and flows of data science are the fundamental development paradigm or discipline or capability that's being leveraged to build and to train and to deploy and iterate all this AI that's being pushed to the Edge. So clearly data science is at the center, data scientists of an increasingly specialized nature are necessary to the realization to this value at the Edge. AI frameworks are coming along like you know, a mile a minute. TensorFlow has achieved a, is an open source, most of these are open source, has achieved sort of almost like a defacto standard, status, I'm using the word defacto in air quotes. There's Theano and Keras and xNet and CNTK and a variety of other ones. We're seeing range of AI frameworks come to market, most open source. Most are supported by most of the major tool vendors as well. So at Wikibon we're definitely tracking that, we plan to go deeper in our coverage of that space. And then next best action, powers recommendation engines. I mean next best action decision automation of the sort of thing Neil's covered in a variety of contexts in his career is fundamentally important to Edge Analytics to systems of agency 'cause it's driving the process automation, decision automation, sort of the targeted recommendations that are made at the Edge to individual users as well as to process that automation. That's absolutely necessary for self driving vehicles to do their jobs and industrial IoT. So what we're seeing is more and more recommendation engine or recommender capabilities powered by ML and DL are going to the Edge, are already at the Edge for a variety of applications. Edge AI capabilities, like I said, there's sensing. And sensing at the Edge is becoming ever more rich, mixed reality Edge modalities of all sort are for augmented reality and so forth. We're just seeing a growth in certain, the range of sensory modalities that are enabled or filtered and analyzed through AI that are being pushed to the Edge, into the chip sets. Actuation, that's where robotics comes in. Robotics is coming into all aspects of our lives. And you know, it's brainless without AI, without deep learning and these capabilities. Inference, autonomous edge decisioning. Like I said, it's, a growing range of inferences that are being done at the Edge. And that's where it has to happen 'cause that's the point of decision. Learning, training, much training, most training will continue to be done in the cloud because it's very data intensive. It's a grind to train and optimize an AI algorithm to do its job. It's not something that you necessarily want to do or can do at the Edge at Edge devices so, the models that are built and trained in the cloud are pushed down through a dev ops process down to the Edge and that's the way it will work pretty much in most AI environments, Edge analytics environments. You centralize the modeling, you decentralize the execution of the inference models. The training engines will be in the cloud. Edge AI applications. I'll just run you through sort of a core list of the ones that are coming into, already come into the mainstream at the Edge. Multifactor authentication, clearly the Apple announcement of face recognition is just a harbinger of the fact that that's coming to every device. Computer vision speech recognition, NLP, digital assistance and chat bots powered by natural language processing and understanding, it's all AI powered. And it's becoming very mainstream. Emotion detection, face recognition, you know I could go on and on but these are like the core things that everybody has access to or will by 2020 and they're core devices, mass market devices. Developers, designers and hardware engineers are coming together to pool their expertise to build and train not just the AI, but also the entire package of hardware in UX and the orchestration of real world business scenarios or life scenarios that all this intelligence, the submitted intelligence enables and most, much of what they build in terms of AI will be containerized as micro services through Docker and orchestrated through Kubernetes as full cloud services in an increasingly distributed fabric. That's coming along very rapidly. We can see a fair amount of that already on display at Strata in terms of what the vendors are doing or announcing or who they're working with. The hardware itself, the Edge, you know at the Edge, some data will be persistent, needs to be persistent to drive inference. That's, and you know to drive a variety of different application scenarios that need some degree of historical data related to what that device in question happens to be sensing or has sensed in the immediate past or you know, whatever. The hardware itself is geared towards both sensing and increasingly persistence and Edge driven actuation of real world results. The whole notion of drones and robotics being embedded into everything that we do. That's where that comes in. That has to be powered by low cost, low power commodity chip sets of various sorts. What we see right now in terms of chip sets is it's a GPUs, Nvidia has gone real far and GPUs have come along very fast in terms of power inference engines, you know like the Tesla cars and so forth. But GPUs are in many ways the core hardware sub straight for in inference engines in DL so far. But to become a mass market phenomenon, it's got to get cheaper and lower powered and more commoditized, and so we see a fair number of CPUs being used as the hardware for Edge Analytic applications. Some vendors are fairly big on FPGAs, I believe Microsoft has gone fairly far with FPGAs inside DL strategy. ASIC, I mean, there's neuro synaptic chips like IBM's got one. There's at least a few dozen vendors of neuro synaptic chips on the market so at Wikibon we're going to track that market as it develops. And what we're seeing is a fair number of scenarios where it's a mixed environment where you use one chip set architecture at the inference side of the Edge, and other chip set architectures that are driving the DL as processed in the cloud, playing together within a common architecture. And we see some, a fair number of DL environments where the actual training is done in the cloud on Spark using CPUs and parallelized in memory, but pushing Tensorflow models that might be trained through Spark down to the Edge where the inferences are done in FPGAs and GPUs. Those kinds of mixed hardware scenarios are very, very, likely to be standard going forward in lots of areas. So analytics at the Edge power continuous results is what it's all about. The whole point is really not moving the data, it's putting the inference at the Edge and working from the data that's already captured and persistent there for the duration of whatever action or decision or result needs to be powered from the Edge. Like Neil said cost takeout alone is not worth doing. Cost takeout alone is not the rationale for putting AI at the Edge. It's getting new stuff done, new kinds of things done in an automated consistent, intelligent, contextualized way to make our lives better and more productive. Security and governance are becoming more important. Governance of the models, governance of the data, governance in a dev ops context in terms of version controls over all those DL models that are built, that are trained, that are containerized and deployed. Continuous iteration and improvement of those to help them learn to do, make our lives better and easier. With that said, I'm going to hand it over now. It's five minutes after the hour. We're going to get going with the Influencer Panel so what we'd like to do is I call Peter, and Peter's going to call our influencers. >> All right, am I live yet? Can you hear me? All right so, we've got, let me jump back in control here. We've got, again, the objective here is to have community take on some things. And so what we want to do is I want to invite five other people up, Neil why don't you come on up as well. Start with Neil. You can sit here. On the far right hand side, Judith, Judith Hurwitz. >> Neil: I'm glad I'm on the left side. >> From the Hurwitz Group. >> From the Hurwitz Group. Jennifer Shin who's affiliated with UC Berkeley. Jennifer are you here? >> She's here, Jennifer where are you? >> She was here a second ago. >> Neil: I saw her walk out she may have, >> Peter: All right, she'll be back in a second. >> Here's Jennifer! >> Here's Jennifer! >> Neil: With 8 Path Solutions, right? >> Yep. >> Yeah 8 Path Solutions. >> Just get my mic. >> Take your time Jen. >> Peter: All right, Stephanie McReynolds. Far left. And finally Joe Caserta, Joe come on up. >> Stephie's with Elysian >> And to the left. So what I want to do is I want to start by having everybody just go around introduce yourself quickly. Judith, why don't we start there. >> I'm Judith Hurwitz, I'm president of Hurwitz and Associates. We're an analyst research and fault leadership firm. I'm the co-author of eight books. Most recent is Cognitive Computing and Big Data Analytics. I've been in the market for a couple years now. >> Jennifer. >> Hi, my name's Jennifer Shin. I'm the founder and Chief Data Scientist 8 Path Solutions LLC. We do data science analytics and technology. We're actually about to do a big launch next month, with Box actually. >> We're apparent, are we having a, sorry Jennifer, are we having a problem with Jennifer's microphone? >> Man: Just turn it back on? >> Oh you have to turn it back on. >> It was on, oh sorry, can you hear me now? >> Yes! We can hear you now. >> Okay, I don't know how that turned back off, but okay. >> So you got to redo all that Jen. >> Okay, so my name's Jennifer Shin, I'm founder of 8 Path Solutions LLC, it's a data science analytics and technology company. I founded it about six years ago. So we've been developing some really cool technology that we're going to be launching with Box next month. It's really exciting. And I have, I've been developing a lot of patents and some technology as well as teaching at UC Berkeley as a lecturer in data science. >> You know Jim, you know Neil, Joe, you ready to go? >> Joe: Just broke my microphone. >> Joe's microphone is broken. >> Joe: Now it should be all right. >> Jim: Speak into Neil's. >> Joe: Hello, hello? >> I just feel not worthy in the presence of Joe Caserta. (several laughing) >> That's right, master of mics. If you can hear me, Joe Caserta, so yeah, I've been doing data technology solutions since 1986, almost as old as Neil here, but been doing specifically like BI, data warehousing, business intelligence type of work since 1996. And been doing, wholly dedicated to Big Data solutions and modern data engineering since 2009. Where should I be looking? >> Yeah I don't know where is the camera? >> Yeah, and that's basically it. So my company was formed in 2001, it's called Caserta Concepts. We recently rebranded to only Caserta 'cause what we do is way more than just concepts. So we conceptualize the stuff, we envision what the future brings and we actually build it. And we help clients large and small who are just, want to be leaders in innovation using data specifically to advance their business. >> Peter: And finally Stephanie McReynolds. >> I'm Stephanie McReynolds, I had product marketing as well as corporate marketing for a company called Elysian. And we are a data catalog so we help bring together not only a technical understanding of your data, but we curate that data with human knowledge and use automated intelligence internally within the system to make recommendations about what data to use for decision making. And some of our customers like City of San Diego, a large automotive manufacturer working on self driving cars and General Electric use Elysian to help power their solutions for IoT at the Edge. >> All right so let's jump right into it. And again if you have a question, raise your hand, and we'll do our best to get it to the floor. But what I want to do is I want to get seven questions in front of this group and have you guys discuss, slog, disagree, agree. Let's start here. What is the relationship between Big Data AI and IoT? Now Wikibon's put forward its observation that data's being generated at the Edge, that action is being taken at the Edge and then increasingly the software and other infrastructure architectures need to accommodate the realities of how data is going to work in these very complex systems. That's our perspective. Anybody, Judith, you want to start? >> Yeah, so I think that if you look at AI machine learning, all these different areas, you have to be able to have the data learned. Now when it comes to IoT, I think one of the issues we have to be careful about is not all data will be at the Edge. Not all data needs to be analyzed at the Edge. For example if the light is green and that's good and it's supposed to be green, do you really have to constantly analyze the fact that the light is green? You actually only really want to be able to analyze and take action when there's an anomaly. Well if it goes purple, that's actually a sign that something might explode, so that's where you want to make sure that you have the analytics at the edge. Not for everything, but for the things where there is an anomaly and a change. >> Joe, how about from your perspective? >> For me I think the evolution of data is really becoming, eventually oxygen is just, I mean data's going to be the oxygen we breathe. It used to be very very reactive and there used to be like a latency. You do something, there's a behavior, there's an event, there's a transaction, and then you go record it and then you collect it, and then you can analyze it. And it was very very waterfallish, right? And then eventually we figured out to put it back into the system. Or at least human beings interpret it to try to make the system better and that is really completely turned on it's head, we don't do that anymore. Right now it's very very, it's synchronous, where as we're actually making these transactions, the machines, we don't really need, I mean human beings are involved a bit, but less and less and less. And it's just a reality, it may not be politically correct to say but it's a reality that my phone in my pocket is following my behavior, and it knows without telling a human being what I'm doing. And it can actually help me do things like get to where I want to go faster depending on my preference if I want to save money or save time or visit things along the way. And I think that's all integration of big data, streaming data, artificial intelligence and I think the next thing that we're going to start seeing is the culmination of all of that. I actually, hopefully it'll be published soon, I just wrote an article for Forbes with the term of ARBI and ARBI is the integration of Augmented Reality and Business Intelligence. Where I think essentially we're going to see, you know, hold your phone up to Jim's face and it's going to recognize-- >> Peter: It's going to break. >> And it's going to say exactly you know, what are the key metrics that we want to know about Jim. If he works on my sales force, what's his attainment of goal, what is-- >> Jim: Can it read my mind? >> Potentially based on behavior patterns. >> Now I'm scared. >> I don't think Jim's buying it. >> It will, without a doubt be able to predict what you've done in the past, you may, with some certain level of confidence you may do again in the future, right? And is that mind reading? It's pretty close, right? >> Well, sometimes, I mean, mind reading is in the eye of the individual who wants to know. And if the machine appears to approximate what's going on in the person's head, sometimes you can't tell. So I guess, I guess we could call that the Turing machine test of the paranormal. >> Well, face recognition, micro gesture recognition, I mean facial gestures, people can do it. Maybe not better than a coin toss, but if it can be seen visually and captured and analyzed, conceivably some degree of mind reading can be built in. I can see when somebody's angry looking at me so, that's a possibility. That's kind of a scary possibility in a surveillance society, potentially. >> Neil: Right, absolutely. >> Peter: Stephanie, what do you think? >> Well, I hear a world of it's the bots versus the humans being painted here and I think that, you know at Elysian we have a very strong perspective on this and that is that the greatest impact, or the greatest results is going to be when humans figure out how to collaborate with the machines. And so yes, you want to get to the location more quickly, but the machine as in the bot isn't able to tell you exactly what to do and you're just going to blindly follow it. You need to train that machine, you need to have a partnership with that machine. So, a lot of the power, and I think this goes back to Judith's story is then what is the human decision making that can be augmented with data from the machine, but then the humans are actually training the training side and driving machines in the right direction. I think that's when we get true power out of some of these solutions so it's not just all about the technology. It's not all about the data or the AI, or the IoT, it's about how that empowers human systems to become smarter and more effective and more efficient. And I think we're playing that out in our technology in a certain way and I think organizations that are thinking along those lines with IoT are seeing more benefits immediately from those projects. >> So I think we have a general agreement of what kind of some of the things you talked about, IoT, crucial capturing information, and then having action being taken, AI being crucial to defining and refining the nature of the actions that are being taken Big Data ultimately powering how a lot of that changes. Let's go to the next one. >> So actually I have something to add to that. So I think it makes sense, right, with IoT, why we have Big Data associated with it. If you think about what data is collected by IoT. We're talking about a serial information, right? It's over time, it's going to grow exponentially just by definition, right, so every minute you collect a piece of information that means over time, it's going to keep growing, growing, growing as it accumulates. So that's one of the reasons why the IoT is so strongly associated with Big Data. And also why you need AI to be able to differentiate between one minute versus next minute, right? Trying to find a better way rather than looking at all that information and manually picking out patterns. To have some automated process for being able to filter through that much data that's being collected. >> I want to point out though based on what you just said Jennifer, I want to bring Neil in at this point, that this question of IoT now generating unprecedented levels of data does introduce this idea of the primary source. Historically what we've done within technology, or within IT certainly is we've taken stylized data. There is no such thing as a real world accounting thing. It is a human contrivance. And we stylize data and therefore it's relatively easy to be very precise on it. But when we start, as you noted, when we start measuring things with a tolerance down to thousandths of a millimeter, whatever that is, metric system, now we're still sometimes dealing with errors that we have to attend to. So, the reality is we're not just dealing with stylized data, we're dealing with real data, and it's more, more frequent, but it also has special cases that we have to attend to as in terms of how we use it. What do you think Neil? >> Well, I mean, I agree with that, I think I already said that, right. >> Yes you did, okay let's move on to the next one. >> Well it's a doppelganger, the digital twin doppelganger that's automatically created by your very fact that you're living and interacting and so forth and so on. It's going to accumulate regardless. Now that doppelganger may not be your agent, or might not be the foundation for your agent unless there's some other piece of logic like an interest graph that you build, a human being saying this is my broad set of interests, and so all of my agents out there in the IoT, you all need to be aware that when you make a decision on my behalf as my agent, this is what Jim would do. You know I mean there needs to be that kind of logic somewhere in this fabric to enable true agency. >> All right, so I'm going to start with you. Oh go ahead. >> I have a real short answer to this though. I think that Big Data provides the data and compute platform to make AI possible. For those of us who dipped our toes in the water in the 80s, we got clobbered because we didn't have the, we didn't have the facilities, we didn't have the resources to really do AI, we just kind of played around with it. And I think that the other thing about it is if you combine Big Data and AI and IoT, what you're going to see is people, a lot of the applications we develop now are very inward looking, we look at our organization, we look at our customers. We try to figure out how to sell more shoes to fashionable ladies, right? But with this technology, I think people can really expand what they're thinking about and what they model and come up with applications that are much more external. >> Actually what I would add to that is also it actually introduces being able to use engineering, right? Having engineers interested in the data. Because it's actually technical data that's collected not just say preferences or information about people, but actual measurements that are being collected with IoT. So it's really interesting in the engineering space because it opens up a whole new world for the engineers to actually look at data and to actually combine both that hardware side as well as the data that's being collected from it. >> Well, Neil, you and I have talked about something, 'cause it's not just engineers. We have in the healthcare industry for example, which you know a fair amount about, there's this notion of empirical based management. And the idea that increasingly we have to be driven by data as a way of improving the way that managers do things, the way the managers collect or collaborate and ultimately collectively how they take action. So it's not just engineers, it's supposed to also inform business, what's actually happening in the healthcare world when we start thinking about some of this empirical based management, is it working? What are some of the barriers? >> It's not a function of technology. What happens in medicine and healthcare research is, I guess you can say it borders on fraud. (people chuckling) No, I'm not kidding. I know the New England Journal of Medicine a couple of years ago released a study and said that at least half their articles that they published turned out to be written, ghost written by pharmaceutical companies. (man chuckling) Right, so I think the problem is that when you do a clinical study, the one that really killed me about 10 years ago was the women's health initiative. They spent $700 million gathering this data over 20 years. And when they released it they looked at all the wrong things deliberately, right? So I think that's a systemic-- >> I think you're bringing up a really important point that we haven't brought up yet, and that is is can you use Big Data and machine learning to begin to take the biases out? So if you let the, if you divorce your preconceived notions and your biases from the data and let the data lead you to the logic, you start to, I think get better over time, but it's going to take a while to get there because we do tend to gravitate towards our biases. >> I will share an anecdote. So I had some arm pain, and I had numbness in my thumb and pointer finger and I went to, excruciating pain, went to the hospital. So the doctor examined me, and he said you probably have a pinched nerve, he said, but I'm not exactly sure which nerve it would be, I'll be right back. And I kid you not, he went to a computer and he Googled it. (Neil laughs) And he came back because this little bit of information was something that could easily be looked up, right? Every nerve in your spine is connected to your different fingers so the pointer and the thumb just happens to be your C6, so he came back and said, it's your C6. (Neil mumbles) >> You know an interesting, I mean that's a good example. One of the issues with healthcare data is that the data set is not always shared across the entire research community, so by making Big Data accessible to everyone, you actually start a more rational conversation or debate on well what are the true insights-- >> If that conversation includes what Judith talked about, the actual model that you use to set priorities and make decisions about what's actually important. So it's not just about improving, this is the test. It's not just about improving your understanding of the wrong thing, it's also testing whether it's the right or wrong thing as well. >> That's right, to be able to test that you need to have humans in dialog with one another bringing different biases to the table to work through okay is there truth in this data? >> It's context and it's correlation and you can have a great correlation that's garbage. You know if you don't have the right context. >> Peter: So I want to, hold on Jim, I want to, >> It's exploratory. >> Hold on Jim, I want to take it to the next question 'cause I want to build off of what you talked about Stephanie and that is that this says something about what is the Edge. And our perspective is that the Edge is not just devices. That when we talk about the Edge, we're talking about human beings and the role that human beings are going to play both as sensors or carrying things with them, but also as actuators, actually taking action which is not a simple thing. So what do you guys think? What does the Edge mean to you? Joe, why don't you start? >> Well, I think it could be a combination of the two. And specifically when we talk about healthcare. So I believe in 2017 when we eat we don't know why we're eating, like I think we should absolutely by now be able to know exactly what is my protein level, what is my calcium level, what is my potassium level? And then find the foods to meet that. What have I depleted versus what I should have, and eat very very purposely and not by taste-- >> And it's amazing that red wine is always the answer. >> It is. (people laughing) And tequila, that helps too. >> Jim: You're a precision foodie is what you are. (several chuckle) >> There's no reason why we should not be able to know that right now, right? And when it comes to healthcare is, the biggest problem or challenge with healthcare is no matter how great of a technology you have, you can't, you can't, you can't manage what you can't measure. And you're really not allowed to use a lot of this data so you can't measure it, right? You can't do things very very scientifically right, in the healthcare world and I think regulation in the healthcare world is really burdening advancement in science. >> Peter: Any thoughts Jennifer? >> Yes, I teach statistics for data scientists, right, so you know we talk about a lot of these concepts. I think what makes these questions so difficult is you have to find a balance, right, a middle ground. For instance, in the case of are you being too biased through data, well you could say like we want to look at data only objectively, but then there are certain relationships that your data models might show that aren't actually a causal relationship. For instance, if there's an alien that came from space and saw earth, saw the people, everyone's carrying umbrellas right, and then it started to rain. That alien might think well, it's because they're carrying umbrellas that it's raining. Now we know from real world that that's actually not the way these things work. So if you look only at the data, that's the potential risk. That you'll start making associations or saying something's causal when it's actually not, right? So that's one of the, one of the I think big challenges. I think when it comes to looking also at things like healthcare data, right? Do you collect data about anything and everything? Does it mean that A, we need to collect all that data for the question we're looking at? Or that it's actually the best, more optimal way to be able to get to the answer? Meaning sometimes you can take some shortcuts in terms of what data you collect and still get the right answer and not have maybe that level of specificity that's going to cost you millions extra to be able to get. >> So Jennifer as a data scientist, I want to build upon what you just said. And that is, are we going to start to see methods and models emerge for how we actually solve some of these problems? So for example, we know how to build a system for stylized process like accounting or some elements of accounting. We have methods and models that lead to technology and actions and whatnot all the way down to that that system can be generated. We don't have the same notion to the same degree when we start talking about AI and some of these Big Datas. We have algorithms, we have technology. But are we going to start seeing, as a data scientist, repeatability and learning and how to think the problems through that's going to lead us to a more likely best or at least good result? >> So I think that's a bit of a tough question, right? Because part of it is, it's going to depend on how many of these researchers actually get exposed to real world scenarios, right? Research looks into all these papers, and you come up with all these models, but if it's never tested in a real world scenario, well, I mean we really can't validate that it works, right? So I think it is dependent on how much of this integration there's going to be between the research community and industry and how much investment there is. Funding is going to matter in this case. If there's no funding in the research side, then you'll see a lot of industry folk who feel very confident about their models that, but again on the other side of course, if researchers don't validate those models then you really can't say for sure that it's actually more accurate, or it's more efficient. >> It's the issue of real world testing and experimentation, A B testing, that's standard practice in many operationalized ML and AI implementations in the business world, but real world experimentation in the Edge analytics, what you're actually transducing are touching people's actual lives. Problem there is, like in healthcare and so forth, when you're experimenting with people's lives, somebody's going to die. I mean, in other words, that's a critical, in terms of causal analysis, you've got to tread lightly on doing operationalizing that kind of testing in the IoT when people's lives and health are at stake. >> We still give 'em placebos. So we still test 'em. All right so let's go to the next question. What are the hottest innovations in AI? Stephanie I want to start with you as a company, someone at a company that's got kind of an interesting little thing happening. We start thinking about how do we better catalog data and represent it to a large number of people. What are some of the hottest innovations in AI as you see it? >> I think it's a little counter intuitive about what the hottest innovations are in AI, because we're at a spot in the industry where the most successful companies that are working with AI are actually incorporating them into solutions. So the best AI solutions are actually the products that you don't know there's AI operating underneath. But they're having a significant impact on business decision making or bringing a different type of application to the market and you know, I think there's a lot of investment that's going into AI tooling and tool sets for data scientists or researchers, but the more innovative companies are thinking through how do we really take AI and make it have an impact on business decision making and that means kind of hiding the AI to the business user. Because if you think a bot is making a decision instead of you, you're not going to partner with that bot very easily or very readily. I worked at, way at the start of my career, I worked in CRM when recommendation engines were all the rage online and also in call centers. And the hardest thing was to get a call center agent to actually read the script that the algorithm was presenting to them, that algorithm was 99% correct most of the time, but there was this human resistance to letting a computer tell you what to tell that customer on the other side even if it was more successful in the end. And so I think that the innovation in AI that's really going to push us forward is when humans feel like they can partner with these bots and they don't think of it as a bot, but they think about as assisting their work and getting to a better result-- >> Hence the augmentation point you made earlier. >> Absolutely, absolutely. >> Joe how 'about you? What do you look at? What are you excited about? >> I think the coolest thing at the moment right now is chat bots. Like to be able, like to have voice be able to speak with you in natural language, to do that, I think that's pretty innovative, right? And I do think that eventually, for the average user, not for techies like me, but for the average user, I think keyboards are going to be a thing of the past. I think we're going to communicate with computers through voice and I think this is the very very beginning of that and it's an incredible innovation. >> Neil? >> Well, I think we all have myopia here. We're all thinking about commercial applications. Big, big things are happening with AI in the intelligence community, in military, the defense industry, in all sorts of things. Meteorology. And that's where, well, hopefully not on an every day basis with military, you really see the effect of this. But I was involved in a project a couple of years ago where we were developing AI software to detect artillery pieces in terrain from satellite imagery. I don't have to tell you what country that was. I think you can probably figure that one out right? But there are legions of people in many many companies that are involved in that industry. So if you're talking about the dollars spent on AI, I think the stuff that we do in our industries is probably fairly small. >> Well it reminds me of an application I actually thought was interesting about AI related to that, AI being applied to removing mines from war zones. >> Why not? >> Which is not a bad thing for a whole lot of people. Judith what do you look at? >> So I'm looking at things like being able to have pre-trained data sets in specific solution areas. I think that that's something that's coming. Also the ability to, to really be able to have a machine assist you in selecting the right algorithms based on what your data looks like and the problems you're trying to solve. Some of the things that data scientists still spend a lot of their time on, but can be augmented with some, basically we have to move to levels of abstraction before this becomes truly ubiquitous across many different areas. >> Peter: Jennifer? >> So I'm going to say computer vision. >> Computer vision? >> Computer vision. So computer vision ranges from image recognition to be able to say what content is in the image. Is it a dog, is it a cat, is it a blueberry muffin? Like a sort of popular post out there where it's like a blueberry muffin versus like I think a chihuahua and then it compares the two. And can the AI really actually detect difference, right? So I think that's really where a lot of people who are in this space of being in both the AI space as well as data science are looking to for the new innovations. I think, for instance, cloud vision I think that's what Google still calls it. The vision API we've they've released on beta allows you to actually use an API to send your image and then have it be recognized right, by their API. There's another startup in New York called Clarify that also does a similar thing as well as you know Amazon has their recognition platform as well. So I think in a, from images being able to detect what's in the content as well as from videos, being able to say things like how many people are entering a frame? How many people enter the store? Not having to actually go look at it and count it, but having a computer actually tally that information for you, right? >> There's actually an extra piece to that. So if I have a picture of a stop sign, and I'm an automated car, and is it a picture on the back of a bus of a stop sign, or is it a real stop sign? So that's going to be one of the complications. >> Doesn't matter to a New York City cab driver. How 'about you Jim? >> Probably not. (laughs) >> Hottest thing in AI is General Adversarial Networks, GANT, what's hot about that, well, I'll be very quick, most AI, most deep learning, machine learning is analytical, it's distilling or inferring insights from the data. Generative takes that same algorithmic basis but to build stuff. In other words, to create realistic looking photographs, to compose music, to build CAD CAM models essentially that can be constructed on 3D printers. So GANT, it's a huge research focus all around the world are used for, often increasingly used for natural language generation. In other words it's institutionalizing or having a foundation for nailing the Turing test every single time, building something with machines that looks like it was constructed by a human and doing it over and over again to fool humans. I mean you can imagine the fraud potential. But you can also imagine just the sheer, like it's going to shape the world, GANT. >> All right so I'm going to say one thing, and then we're going to ask if anybody in the audience has an idea. So the thing that I find interesting is traditional programs, or when you tell a machine to do something you don't need incentives. When you tell a human being something, you have to provide incentives. Like how do you get someone to actually read the text. And this whole question of elements within AI that incorporate incentives as a way of trying to guide human behavior is absolutely fascinating to me. Whether it's gamification, or even some things we're thinking about with block chain and bitcoins and related types of stuff. To my mind that's going to have an enormous impact, some good, some bad. Anybody in the audience? I don't want to lose everybody here. What do you think sir? And I'll try to do my best to repeat it. Oh we have a mic. >> So my question's about, Okay, so the question's pretty much about what Stephanie's talking about which is human and loop training right? I come from a computer vision background. That's the problem, we need millions of images trained, we need humans to do that. And that's like you know, the workforce is essentially people that aren't necessarily part of the AI community, they're people that are just able to use that data and analyze the data and label that data. That's something that I think is a big problem everyone in the computer vision industry at least faces. I was wondering-- >> So again, but the problem is that is the difficulty of methodologically bringing together people who understand it and people who, people who have domain expertise people who have algorithm expertise and working together? >> I think the expertise issue comes in healthcare, right? In healthcare you need experts to be labeling your images. With contextual information where essentially augmented reality applications coming in, you have the AR kit and everything coming out, but there is a lack of context based intelligence. And all of that comes through training images, and all of that requires people to do it. And that's kind of like the foundational basis of AI coming forward is not necessarily an algorithm, right? It's how well are datas labeled? Who's doing the labeling and how do we ensure that it happens? >> Great question. So for the panel. So if you think about it, a consultant talks about being on the bench. How much time are they going to have to spend on trying to develop additional business? How much time should we set aside for executives to help train some of the assistants? >> I think that the key is not, to think of the problem a different way is that you would have people manually label data and that's one way to solve the problem. But you can also look at what is the natural workflow of that executive, or that individual? And is there a way to gather that context automatically using AI, right? And if you can do that, it's similar to what we do in our product, we observe how someone is analyzing the data and from those observations we can actually create the metadata that then trains the system in a particular direction. But you have to think about solving the problem differently of finding the workflow that then you can feed into to make this labeling easy without the human really realizing that they're labeling the data. >> Peter: Anybody else? >> I'll just add to what Stephanie said, so in the IoT applications, all those sensory modalities, the computer vision, the speech recognition, all that, that's all potential training data. So it cross checks against all the other models that are processing all the other data coming from that device. So that the natural language process of understanding can be reality checked against the images that the person happens to be commenting upon, or the scene in which they're embedded, so yeah, the data's embedded-- >> I don't think we're, we're not at the stage yet where this is easy. It's going to take time before we do start doing the pre-training of some of these details so that it goes faster, but right now, there're not that many shortcuts. >> Go ahead Joe. >> Sorry so a couple things. So one is like, I was just caught up on your incentivizing programs to be more efficient like humans. You know in Ethereum that has this notion, which is bot chain, has this theory, this concept of gas. Where like as the process becomes more efficient it costs less to actually run, right? It costs less ether, right? So it actually is kind of, the machine is actually incentivized and you don't really know what it's going to cost until the machine processes it, right? So there is like some notion of that there. But as far as like vision, like training the machine for computer vision, I think it's through adoption and crowdsourcing, so as people start using it more they're going to be adding more pictures. Very very organically. And then the machines will be trained and right now is a very small handful doing it, and it's very proactive by the Googles and the Facebooks and all of that. But as we start using it, as they start looking at my images and Jim's and Jen's images, it's going to keep getting smarter and smarter through adoption and through very organic process. >> So Neil, let me ask you a question. Who owns the value that's generated as a consequence of all these people ultimately contributing their insight and intelligence into these systems? >> Well, to a certain extent the people who are contributing the insight own nothing because the systems collect their actions and the things they do and then that data doesn't belong to them, it belongs to whoever collected it or whoever's going to do something with it. But the other thing, getting back to the medical stuff. It's not enough to say that the systems, people will do the right thing, because a lot of them are not motivated to do the right thing. The whole grant thing, the whole oh my god I'm not going to go against the senior professor. A lot of these, I knew a guy who was a doctor at University of Pittsburgh and they were doing a clinical study on the tubes that they put in little kids' ears who have ear infections, right? And-- >> Google it! Who helps out? >> Anyway, I forget the exact thing, but he came out and said that the principle investigator lied when he made the presentation, that it should be this, I forget which way it went. He was fired from his position at Pittsburgh and he has never worked as a doctor again. 'Cause he went against the senior line of authority. He was-- >> Another question back here? >> Man: Yes, Mark Turner has a question. >> Not a question, just want to piggyback what you're saying about the transfixation of maybe in healthcare of black and white images and color images in the case of sonograms and ultrasound and mammograms, you see that happening using AI? You see that being, I mean it's already happening, do you see it moving forward in that kind of way? I mean, talk more about that, about you know, AI and black and white images being used and they can be transfixed, they can be made to color images so you can see things better, doctors can perform better operations. >> So I'm sorry, but could you summarize down? What's the question? Summarize it just, >> I had a lot of students, they're interested in the cross pollenization between AI and say the medical community as far as things like ultrasound and sonograms and mammograms and how you can literally take a black and white image and it can, using algorithms and stuff be made to color images that can help doctors better do the work that they've already been doing, just do it better. You touched on it like 30 seconds. >> So how AI can be used to actually add information in a way that's not necessarily invasive but is ultimately improves how someone might respond to it or use it, yes? Related? I've also got something say about medical images in a second, any of you guys want to, go ahead Jennifer. >> Yeah, so for one thing, you know and it kind of goes back to what we were talking about before. When we look at for instance scans, like at some point I was looking at CT scans, right, for lung cancer nodules. In order for me, who I don't have a medical background, to identify where the nodule is, of course, a doctor actually had to go in and specify which slice of the scan had the nodule and where exactly it is, so it's on both the slice level as well as, within that 2D image, where it's located and the size of it. So the beauty of things like AI is that ultimately right now a radiologist has to look at every slice and actually identify this manually, right? The goal of course would be that one day we wouldn't have to have someone look at every slice to like 300 usually slices and be able to identify it much more automated. And I think the reality is we're not going to get something where it's going to be 100%. And with anything we do in the real world it's always like a 95% chance of it being accurate. So I think it's finding that in between of where, what's the threshold that we want to use to be able to say that this is, definitively say a lung cancer nodule or not. I think the other thing to think about is in terms of how their using other information, what they might use is a for instance, to say like you know, based on other characteristics of the person's health, they might use that as sort of a grading right? So you know, how dark or how light something is, identify maybe in that region, the prevalence of that specific variable. So that's usually how they integrate that information into something that's already existing in the computer vision sense. I think that's, the difficulty with this of course, is being able to identify which variables were introduced into data that does exist. >> So I'll make two quick observations on this then I'll go to the next question. One is radiologists have historically been some of the highest paid physicians within the medical community partly because they don't have to be particularly clinical. They don't have to spend a lot of time with patients. They tend to spend time with doctors which means they can do a lot of work in a little bit of time, and charge a fair amount of money. As we start to introduce some of these technologies that allow us to from a machine standpoint actually make diagnoses based on those images, I find it fascinating that you now see television ads promoting the role that the radiologist plays in clinical medicine. It's kind of an interesting response. >> It's also disruptive as I'm seeing more and more studies showing that deep learning models processing images, ultrasounds and so forth are getting as accurate as many of the best radiologists. >> That's the point! >> Detecting cancer >> Now radiologists are saying oh look, we do this great thing in terms of interacting with the patients, never have because they're being dis-intermediated. The second thing that I'll note is one of my favorite examples of that if I got it right, is looking at the images, the deep space images that come out of Hubble. Where they're taking data from thousands, maybe even millions of images and combining it together in interesting ways you can actually see depth. You can actually move through to a very very small scale a system that's 150, well maybe that, can't be that much, maybe six billion light years away. Fascinating stuff. All right so let me go to the last question here, and then I'm going to close it down, then we can have something to drink. What are the hottest, oh I'm sorry, question? >> Yes, hi, my name's George, I'm with Blue Talon. You asked earlier there the question what's the hottest thing in the Edge and AI, I would say that it's security. It seems to me that before you can empower agency you need to be able to authorize what they can act on, how they can act on, who they can act on. So it seems if you're going to move from very distributed data at the Edge and analytics at the Edge, there has to be security similarly done at the Edge. And I saw (speaking faintly) slides that called out security as a key prerequisite and maybe Judith can comment, but I'm curious how security's going to evolve to meet this analytics at the Edge. >> Well, let me do that and I'll ask Jen to comment. The notion of agency is crucially important, slightly different from security, just so we're clear. And the basic idea here is historically folks have thought about moving data or they thought about moving application function, now we are thinking about moving authority. So as you said. That's not necessarily, that's not really a security question, but this has been a problem that's been in, of concern in a number of different domains. How do we move authority with the resources? And that's really what informs the whole agency process. But with that said, Jim. >> Yeah actually I'll, yeah, thank you for bringing up security so identity is the foundation of security. Strong identity, multifactor, face recognition, biometrics and so forth. Clearly AI, machine learning, deep learning are powering a new era of biometrics and you know it's behavioral metrics and so forth that's organic to people's use of devices and so forth. You know getting to the point that Peter was raising is important, agency! Systems of agency. Your agent, you have to, you as a human being should be vouching in a secure, tamper proof way, your identity should be vouching for the identity of some agent, physical or virtual that does stuff on your behalf. How can that, how should that be managed within this increasingly distributed IoT fabric? Well a lot of that's been worked. It all ran through webs of trust, public key infrastructure, formats and you know SAML for single sign and so forth. It's all about assertion, strong assertions and vouching. I mean there's the whole workflows of things. Back in the ancient days when I was actually a PKI analyst three analyst firms ago, I got deep into all the guts of all those federation agreements, something like that has to be IoT scalable to enable systems agency to be truly fluid. So we can vouch for our agents wherever they happen to be. We're going to keep on having as human beings agents all over creation, we're not even going to be aware of everywhere that our agents are, but our identity-- >> It's not just-- >> Our identity has to follow. >> But it's not just identity, it's also authorization and context. >> Permissioning, of course. >> So I may be the right person to do something yesterday, but I'm not authorized to do it in another context in another application. >> Role based permissioning, yeah. Or persona based. >> That's right. >> I agree. >> And obviously it's going to be interesting to see the role that block chain or its follow on to the technology is going to play here. Okay so let me throw one more questions out. What are the hottest applications of AI at the Edge? We've talked about a number of them, does anybody want to add something that hasn't been talked about? Or do you want to get a beer? (people laughing) Stephanie, you raised your hand first. >> I was going to go, I bring something mundane to the table actually because I think one of the most exciting innovations with IoT and AI are actually simple things like City of San Diego is rolling out 3200 automated street lights that will actually help you find a parking space, reduce the amount of emissions into the atmosphere, so has some environmental change, positive environmental change impact. I mean, it's street lights, it's not like a, it's not medical industry, it doesn't look like a life changing innovation, and yet if we automate streetlights and we manage our energy better, and maybe they can flicker on and off if there's a parking space there for you, that's a significant impact on everyone's life. >> And dramatically suppress the impact of backseat driving! >> (laughs) Exactly. >> Joe what were you saying? >> I was just going to say you know there's already the technology out there where you can put a camera on a drone with machine learning within an artificial intelligence within it, and it can look at buildings and determine whether there's rusty pipes and cracks in cement and leaky roofs and all of those things. And that's all based on artificial intelligence. And I think if you can do that, to be able to look at an x-ray and determine if there's a tumor there is not out of the realm of possibility, right? >> Neil? >> I agree with both of them, that's what I meant about external kind of applications. Instead of figuring out what to sell our customers. Which is most what we hear. I just, I think all of those things are imminently doable. And boy street lights that help you find a parking place, that's brilliant, right? >> Simple! >> It improves your life more than, I dunno. Something I use on the internet recently, but I think it's great! That's, I'd like to see a thousand things like that. >> Peter: Jim? >> Yeah, building on what Stephanie and Neil were saying, it's ambient intelligence built into everything to enable fine grain microclimate awareness of all of us as human beings moving through the world. And enable reading of every microclimate in buildings. In other words, you know you have sensors on your body that are always detecting the heat, the humidity, the level of pollution or whatever in every environment that you're in or that you might be likely to move into fairly soon and either A can help give you guidance in real time about where to avoid, or give that environment guidance about how to adjust itself to your, like the lighting or whatever it might be to your specific requirements. And you know when you have a room like this, full of other human beings, there has to be some negotiated settlement. Some will find it too hot, some will find it too cold or whatever but I think that is fundamental in terms of reshaping the sheer quality of experience of most of our lived habitats on the planet potentially. That's really the Edge analytics application that depends on everybody having, being fully equipped with a personal area network of sensors that's communicating into the cloud. >> Jennifer? >> So I think, what's really interesting about it is being able to utilize the technology we do have, it's a lot cheaper now to have a lot of these ways of measuring that we didn't have before. And whether or not engineers can then leverage what we have as ways to measure things and then of course then you need people like data scientists to build the right model. So you can collect all this data, if you don't build the right model that identifies these patterns then all that data's just collected and it's just made a repository. So without having the models that supports patterns that are actually in the data, you're not going to find a better way of being able to find insights in the data itself. So I think what will be really interesting is to see how existing technology is leveraged, to collect data and then how that's actually modeled as well as to be able to see how technology's going to now develop from where it is now, to being able to either collect things more sensitively or in the case of say for instance if you're dealing with like how people move, whether we can build things that we can then use to measure how we move, right? Like how we move every day and then being able to model that in a way that is actually going to give us better insights in things like healthcare and just maybe even just our behaviors. >> Peter: Judith? >> So, I think we also have to look at it from a peer to peer perspective. So I may be able to get some data from one thing at the Edge, but then all those Edge devices, sensors or whatever, they all have to interact with each other because we don't live, we may, in our business lives, act in silos, but in the real world when you look at things like sensors and devices it's how they react with each other on a peer to peer basis. >> All right, before I invite John up, I want to say, I'll say what my thing is, and it's not the hottest. It's the one I hate the most. I hate AI generated music. (people laughing) Hate it. All right, I want to thank all the panelists, every single person, some great commentary, great observations. I want to thank you very much. I want to thank everybody that joined. John in a second you'll kind of announce who's the big winner. But the one thing I want to do is, is I was listening, I learned a lot from everybody, but I want to call out the one comment that I think we all need to remember, and I'm going to give you the award Stephanie. And that is increasing we have to remember that the best AI is probably AI that we don't even know is working on our behalf. The same flip side of that is all of us have to be very cognizant of the idea that AI is acting on our behalf and we may not know it. So, John why don't you come on up. Who won the, whatever it's called, the raffle? >> You won. >> Thank you! >> How 'about a round of applause for the great panel. (audience applauding) Okay we have a put the business cards in the basket, we're going to have that brought up. We're going to have two raffle gifts, some nice Bose headsets and speaker, Bluetooth speaker. Got to wait for that. I just want to say thank you for coming and for the folks watching, this is our fifth year doing our own event called Big Data NYC which is really an extension of the landscape beyond the Big Data world that's Cloud and AI and IoT and other great things happen and great experts and influencers and analysts here. Thanks for sharing your opinion. Really appreciate you taking the time to come out and share your data and your knowledge, appreciate it. Thank you. Where's the? >> Sam's right in front of you. >> There's the thing, okay. Got to be present to win. We saw some people sneaking out the back door to go to a dinner. >> First prize first. >> Okay first prize is the Bose headset. >> Bluetooth and noise canceling. >> I won't look, Sam you got to hold it down, I can see the cards. >> All right. >> Stephanie you won! (Stephanie laughing) Okay, Sawny Cox, Sawny Allie Cox? (audience applauding) Yay look at that! He's here! The bar's open so help yourself, but we got one more. >> Congratulations. Picture right here. >> Hold that I saw you. Wake up a little bit. Okay, all right. Next one is, my kids love this. This is great, great for the beach, great for everything portable speaker, great gift. >> What is it? >> Portable speaker. >> It is a portable speaker, it's pretty awesome. >> Oh you grabbed mine. >> Oh that's one of our guys. >> (lauging) But who was it? >> Can't be related! Ava, Ava, Ava. Okay Gene Penesko (audience applauding) Hey! He came in! All right look at that, the timing's great. >> Another one? (people laughing) >> Hey thanks everybody, enjoy the night, thank Peter Burris, head of research for SiliconANGLE, Wikibon and he great guests and influencers and friends. And you guys for coming in the community. Thanks for watching and thanks for coming. Enjoy the party and some drinks and that's out, that's it for the influencer panel and analyst discussion. Thank you. (logo music)

>> Narrator: Live from Las Vegas, it's theCUBE covering VMworld 2017. Brought to you by VMware and its ecosystem partners. (upbeat tech music) >> Hi, I'm Stu Miniman here with my co-host Justin Warren, And we're at vmworld 2017. You're watching theCUBE worldwide leader in tech coverage. Happy to welcome to the program, first time guest, Juan Gaviria who's with ADP, and he's the senior director of technical systems engineering. Juan, thank you so much for joining us. >> Thank you for having me. It's a pleasure to be here. >> So vmworld, it's my 8th year coming to the show. I've been part of the community for a long time, but one of the things that people love at this show, about 20,000 maybe a little north of that, it's peers talking to peers. People that dig into the technology, find out what works and how to do things better and everything. Tell us a little bit about your role. I think most of us know ADP. We've gotten checks with the logo on it, or lots of areas of other services. But what's you're role inside the org? >> Yeah, sure. So really quick about ADP to your point, the logo is pretty well known. We actually pay one in six people in the United States, so over 25 million employees we pay. We have over 650,000 clients, and our mobile app, which is really the way I recommend you look at your pay stubs, 401K, benefits, etc., has been downloaded over 12 million times. So the ADP brand is doing well. It's a healthy business. My role specifically is that I manage all computer at ADP, so think about servers, server operating systems, and server virtualization; that's my role. >> Yeah, you brought up mobile, so maybe start there. Pat Gelsinger this morning was talking about kind digital transformation. We look at financial services, how do you reach those users? What does that kind of ripple through to all of the things that you manage? How long have you been there, and what changes have you been seeing? >> I've been there 15 years, and I've seen a lot of changes. >> Stu: 15 years ago they probably weren't even virtualized so... >> No, no, in fact, I remember rolling out ESX2.X and using the good ole mooey, so we've come a long way. And mobile has just been explosive. Ya know, from a product perspective the goal now, it's mobile first, right? So even now if you think about your benefits, when you go enroll in your benefits every year, the goal is to make that experience translate to mobile, and that's a little harder than it seems, but that's the goal for ADP. It's everything mobile. >> Bring us in. What's kind of the scope of what you manage? You said ADP globally what you handle, but what's kind of the team size? How many devices or VMs or however you manage, what are you listed in? >> Sure, so my team is responsible for computers, I mentioned, so think of everything from demand management through operations. We have globally about 50 associates that are responsible for that. We have over 3000 ESXI hosts deployed across seven global data centers with well over 40000 VMs. So it's a pretty good size infrastructure. >> And bring us inside VMware. How long have you been using it? What pieces of VMware in the ecosystem have you been using? >> We have been using VMware, again, since the early days of server virtualization. We're a VROPS customer, a VRA customer, in fact, VRA, we're leveraging it for infrastructure as service to our deaf community. We have, for ADP, thousands and thousands of developers, so just the amount of churn in our private cloud is tremendous. Airwatch, we're a big Airwatch customer, as well. >> Expand a little bit on the developer piece. What do they look for? How does that impact what you're doin? >> Yeah, sure. I don't know what they're looking for cause it's always changing to be honest with you. But we have somewhere around 6000 developers, and they're obviously developing ADP's next generation products. So they're just looking for us to get out of their way, right? They want VMs; they want 'em now. They want containers; they want 'em now. And every day I turn around they want bigger VMs, bigger containers, and it's getting harder and harder. So through VRA, we provide those pools of capacity and then they're able to spin up, tear down, rebuild VMs as needed. On a monthly basis what I see through VRA just in the developer community lab is about 3000 or so actions a month. So it's pretty high amount of change in that environment. >> Based on what was announced in the Kali, particularly around the partnership with AWS, do you thing that's going to resonate with the developers? >> Yeah, absolutely. Most of our, not most, but a fair amount of our next generation products are being developed on AWS, right? So everyone wants to be on AWS. In fact, we're bringing in a lot of college hires, and as soon as they come in they say, "I want to work on AWS." So for us it resonates because what ADP does, security is key, and we want to have a hybrid cloud, so we were actually part of the Lighthouse Program. So we were an early customer. Got to see the logo during the KeyNote which was nice. So, yeah we plan on leveraging that relationship to help us. For example, burst in that DevCloud. >> Unpack that for us. One of the things we look at, when I hear hybrid cloud I need you to explain that because every customer I talk to, it means different things to me, especially, you mention things like bursting that's a little scary sometimes. So maybe explain what that actually means in your environment. >> Yeah, so, in the Dev environment specifically, what it means is, as I mentioned, we get requests that come out of left field, right? I need a 300 gig memory VM and 10 terabytes of storage. You're just like, "Where, I don't have this," right? I don't have hundreds of those. So we can put that capacity out on AWS much faster, and as those projects materialize, we can then bring that back in. So that's what I mean by hybrid cloud for us. >> So you're using the VMware on AWS, you've been testing that out, you said? My understanding is you're also using Vsan, is that separate from that? Cause Vsan's part of the VMware Cloud or cloud foundation suite, a piece of it, so what's your interest been in Vsan, and how does that fit into the entire picture? >> So it is different. For us, the AWS relationship is going to be more of a manage service, obviously. We're actually going to become a consumer. So we're going to feel like our own customers. To answer your question on Vsan, yeah, we've made a huge investment in Vsan, so all of our VM storage, which again is 40000 VMs worth, which is well over 4+ petabytes of storage, we're moving that all to Vsan. >> What's happenin to all those arrays? >> They're going to be gone. >> Yeah? >> They're going to be gone. >> That's a really big move. Can you, you got to take us back, ya know. How did you is this a top-down or, ya know, bottoms-up walk us through some of that. >> Yeah what started that? Like how did you come to even begin contemplating replacing all of your storage? >> So it's been both to answer your first question. Both top-down and bottom-up. We've been looking at the technologies for a while, and just kind of keepin close to them. At this point, they're mature enough that we feel they can run our business-critical products. And it's been a journey, right? For the last year, we've spent looking at all the different market leading technologies and figuring out which ones make sense in an environment our size. How do we operationalize this thing? So it's been a journey and this is the beginning for us, so we're actually, as I speak, we're starting to deploy our first Vsan clusters in production and we're deploying it in hundreds of servers at a time so it's exciting and interesting times for the team and I. >> Yeah, one of the interesting things, some people look at Vsan and they're like "Oh well it's kind of small deployments," but we had some of the VMware people on earlier today, and they're like, "We're deploying internally," but it's lots of clusters because if you tell me hundreds of servers, I'm like, "Well that's not a single cluster that's lots of clusters." How do you carve that up? How do you manage that? How do you roll that out? What does that look like? >> That's the trickiest part, right? And, by the way, as we look at different solutions, the cluster size became one of the reasons why we chose Vsan. >> Okay. >> A lot of the other solutions that are out there will limit you to about eight node clusters, and to your point, we have thousands of hosts. That's hundreds of clusters. So Vsan gives us the ability to have slightly larger clusters. Today we're going to look at about 16 node clusters to start. That seems to be where VMware is going as well, so we'll follow their lead. We figure they know what they're doin'. And we'll manage that using Vroms as well. >> Yeah I was curious as to what was actually driving the change to Vsan, and what was it about Vsan that said, "Yes! This is great! "This is the one that we're going to pick." You've mentioned cluster size, were there other things that made you sort of decide that Vsan was the right choice for you? >> So to me, the way I look at Vsan from a Vsphere perspective is that they've made storage a feature. And our Vsphere administrators, they know how to run Vsphere and now they just have another feature. So that was one of the main reasons, just the operational efficiencies from a team perspective. There are a lot of other reasons as well. Security: some of the other competitors out there, for example, didn't have encryption when we were looking at it, which is, everything we do revolves around security, so that was another key reason for Vsan for us. And what drove us at first was really, with the traditional models, we found ourselves to not be very agile. Because our business is growing so fast, we're building about six months of capacity at a time, and if you can think about the cost of that much capacity at a shot it's millions of dollars, it's kind of sitting idle. So with HCI technologies and Vsan, specifically, we think we're going to be much more modular in our approach and closer to just in time. So we expect significant capital benefits from that. >> So if I hear you right, it's the pooled nature of what you're doing and that the building blocks are small enough that you're not getting to what people usually have is like, "Oh yeah, I have all this capacity and I'm three years in "and I'm still not using a lot of what I run into, "ya know, I overbuy so much because of that." >> Exactly, and think about that first purchase. You've got to sit with finance and say, "Hey I've got to go buy an array "and I've got to go buy a couple hundred servers." Now I don't have to buy that much up front so it's a huge benefit for us. >> And it sounds like it's going to be cord deployments as well, cause there are a lot of like the HCI deployments, traditionally, have been for remote office things, or just particular work loads like VDI will be one thing that it runs on, but it sounds like this is going to underpin pretty much everything that you do. >> Pretty much everything, yeah. And in addition to VDI we have a very large VDI deployment that supports all of our customer support reps, and it's going to underpin that in addition to underpinning all of the business products that you use to view your pay statement. >> Alright, so you talked about the finance people, what about the storage people? I have to imagine you had storage admins, you look at it and you say, "Okay are they out of a job? "Are they going to work on new challenges?" Can you walk us through how you approach them? How they've looked at this whole migration? And what happens to them versus the VMware people? The virtualization admins I should say. >> It's a funny question cause I've become a little bit more popular now with the storage scene. They've actually knocked on my door and said, "Hey, anything we can help you with?" But, no, it's a good partnership. My peer and I who run storage, we actually built a team together that's going to help us roll out Vsan so we know that there are skills in the storage team that we can leverage, and our vision of it is that we're no longer going to have Vsphere administrators or storage administrators. We're going to have cloud engineers, and they have to know, compute network storage really cause we view the skills as converging as well. It's not just the software and the hardware. >> How about the management of that though? Are you essentially going to be managing a team together rather than it being separate people managing different people? >> Correct it's one team. >> One team? >> It's really interesting, Juan, I'm just curious, in your kind of evaluation phase, what did you learn that if you had known it at the beginning might have either accelerated or you might have positioned things a little bit differently now that you're ready to kind of this massive roll out? >> I think I would have had maybe stricter entrance criteria. You think about a company our size and all the partners we have. We looked at a lot of different solutions. We spent a lot of time in the lab. Where in the end we knew that, for example, an eight node cluster, or not having encryption, were showstoppers, but yet we spent the time in the lab to do that, so my recommendation or advice to my peers out there is come up with good criteria that you know you have to have, and then from there, do the paper exercise and bring in the ones that you know will actually be able to get to production. >> What was that entire kind of evaluation phase? How long did that take? >> More than six months. >> And can I ask what underlying deployment you're going to use for Vsan? >> From a hardware perspective? >> Yeah. >> Sure, HP servers. DL360s. >> Okay, and what led you to choose that versus, ya know, the Dell people are all lined up to say, ya know, come on we own VMware, ya know, you should do VXrails? >> Vxrail to me is a little bit different than just Vsan, but yeah absolutely Dell was pretty interested in that business as well, and the beauty of Vsan is that it gives us the choice. We've been a long-time, happy HP customer, so for this first phase we'll continue to be with HP, and for some reason, if something changes we know with Vsan we have that flexibility. >> You've been with VMware for quite a while, I'm sure you've been watching Vsan. What are you still asking them for? They've had a very aggressive road map. I think they've got most of the basic check blocks done. I've heard a little bit about the road map, but what's on your to-do list for Vsan or any kind of the associated pieces? >> You mentioned VXrail as an example and the automation that they've brought with rail is significant. It's very valuable. I think they need to bring some of that same automation to Vsan's standalone. So as I think about patching thousands of hosts with Vsan and the drivers and that entire matrix of things. They've got to help us there. I think they've got some work to do in terms of improving the performance management of that because environments this size, managing that manually is too much work. So I think we've got some work to do there. But they've been a great partner. They've been listening to us, so I'm pretty happy about where they're headed. >> Earlier you mentioned deploying VMs and containers, is that like Docker or how do containers fit in? >> So Docker has been sort of a religious debate internally to be honest. Do you deploy it on bare metal? Do you deploy it on VMs? I think right now, we're settled on deploying Docker on VMs, but very large VMs. We're thinking 200 gigs, and the goal will be, we're going to try to do that on Vsan. So we're still in early development there, but that seems to be where we're finally landing on. >> Interesting, and I'm assuming that's Linux on top of the VMs to allow that. >> Yes. >> Alright, well, Juan Gaviria, really appreciate you sharing that really interesting use case. I wish ya best of luck on the rollout, and thank you for being on theCUBE. >> Thank you. Thanks for having me. >> Alright, for Justin, I'm Stu, and we'll be back with lots more coverage here from VMworld 2017, you're watching theCUBE.