>> Announcer: Live from Madrid, Spain, it's the Cube, covering HPE Discover Madrid 2017, brought to you by Hewlett Packard Enterprise. >> Welcome back to Madrid, Spain everybody, this is the Cube, the leader in live tech coverage. We're here covering HPE Discover 2017. I'm Dave Vellante with my cohost for the week, Peter Burris, Randy Meyer is back, he's the vice president and general manager Synergy and Mission Critical Solutions at Hewlett Packard Enterprise and Paul Shellerd is here, the director of the Center for Theoretical Cosmology at Cambridge University, thank you very much for coming on the Cube. >> It's a pleasure. >> Good to see you again. >> Yeah good to be back for the second time this week. I think that's, day stay outlets play too. >> Talking about computing meets the cosmos. >> Well it's exciting, yesterday we talked about Superdome Flex that we announced, we talked about it in the commercial space, where it's taking HANA and Orcale databases to the next level but there's a whole different side to what you can do with in memory compute. It's all in this high performance computing space. You think about the problems people want to solve in fluid dynamics, in forecasting, in all sorts of analytics problems, high performance compute, one of the things it does is it generates massive amounts of data that people then want to do things with. They want to compare that data to what their model said, okay can I run that against, they want to take that data and visualize it, okay how do I go do that. The more you can do that in memory, it means it's just faster to deal with because you're not going and writing this stuff off the disk, you're not moving it to another cluster back and forth, so we're seeing this burgeoning, the HPC guys would call it fat nodes, where you want to put lots of memory and eliminate the IO to go make their jobs easier and Professor Shallard will talk about a lot of that in terms of what they're doing at the Cosmos Institute, but this is a trend, you don't have to be a university. We're seeing this inside of oil and gas companies, aerospace engineering companies, anybody that's solving these complex computational problems that have an analytical element to whether it's comparative model, visualize, do something with that once you've done that. >> Paul, explain more about what it is you do. >> Well in the Cosmos Group, of which I'm the head, we're interested in two things, cosmology, which is trying to understand where the universe comes from, the whole big bang and then we're interested in black holes, particularly their collisions which produce gravitational waves, so they're the two main areas, relativity and cosmology. >> That's a big topic. I don't even know where to start, I just want to know okay what have you learned and can you summarize it for a lay person, where are you today, what can you share with us that we can understand? >> What we do is we take our mathematical models and we make predictions about the real universe and so we try and compare those to the latest observational data. We're in a particularly exciting period of time at the moment because of a flood of new data about the universe and about black holes and in the last two years, gravitational waves were discovered, there's a Nobel prize this year so lots of things are happening. It's a very data driven science so we have to try and keep up with this flood of new data which is getting larger and larger and also with new types of data, because suddenly gravitational waves are the latest thing to look at. >> What are the sources of data and new sources of data that you're tapping? >> Well, in cosmology we're mainly interested in the cosmic microwave background. >> Peter: Yeah the sources of data are the cosmos. >> Yeah right, so this is relic radiation left over from the big bang fireball, it's like a photograph of the universe, a blueprint and then also in the distribution of galaxies, so 3D maps of the universe and we've only, we're in a new age of exploration, we've only got a tiny fraction of the universe mapped so far and we're trying to extract new information about the origin of the universe from that data. In relativity, we've got these gravitational waves, these ripples in space time, they're traversing across the universe, they're essentially earthquakes in the universe and they're sound waves or seismic waves that propagate to us from these very violent events. >> I want to take you to the gravitational waves because in many respects, it's an example of a lot of what's here in action. Here's what I mean, that the experiment and correct me if I'm wrong, but it's basically, you create a, have two lasers perpendicular to each other, shooting a signal about two or three miles in that direction and it is the most precise experiment ever undertaken because what you're doing is you're measuring the time it takes for one laser versus another laser and that time is a function of the slight stretching that comes from the gravitational rays. That is an unbelievable example of edge computing, where you have just the tolerances to do that, that's not something you can send back to the cloud, you gotta do a lot of the compute right there, right? >> That's right, yes so a gravitational wave comes by and you shrink one way and you stretch the other. >> Peter: It distorts the space time. >> Yeah you become thinner and these tiny, tiny changes are what's measured and nobody expected gravitational waves to be discovered in 2015, we all thought, oh another five years, another five years, they've always been saying, we'll discover them, we'll discover them, but it happened. >> And since then, it's been used two or three times to discover new types of things and there's now a whole, I'm sure this is very centric to what you're doing, there's now a whole concept of gravitational information, can in fact becomes an entirely new branch of cosmology, have I got that right? >> Yeah you have, it's called multimessenger astronomy now because you don't just see the universe in electromagnetic waves, in light, you hear the universe. This is qualitatively different, it's sound waves coming across the universe and so combining these two, the latest event was where they heard the event first, then they turned their telescope and they saw it. So much information came out of that, even information about cosmology, because these signals are traveling hundreds of billions of light years across to us, we're getting a picture of the whole universe as they propagate all that way, so we're able to measure the expansion rate of the universe from that point. >> The techniques for the observational, the technology for observation, what is that, how has that evolved? >> Well you've got the wrong guy here. I'm from the theory group, we're doing the predictions and these guys with their incredible technology, are seeing the data, seeing and it's imagined, the whole point is you've gotta get the predictions and then you've gotta look in the data for a needle in the haystack which is this signature of these black holes colliding. >> You think about that, I have a model, I'm looking for the needle in the haystack, that's a different way to describe an in memory analytic search pattern recognition problem, that's really what it is. This is the world's largest pattern recognition problem. >> Most precise, and literally. >> And that's an observation that confirms your theory right? >> Confirms the theory, maybe it was your theory. >> I'm actually a cosmologist, so in my group we have relativists who are actively working on the black hole collisions and making predictions about this stuff. >> But they're dampening vibration from passing trucks and these things and correcting it, it's unbelievable. But coming back to the technology, the technology is, one of the reasons why this becomes so exciting and becomes practical is because for the first time, the technology has gotten to the point where you can assume that the problem you're trying to solve, that you're focused on and you don't have to translate it in technology terms, so talk a little bit about, because in many respects, that's where business is. Business wants to be able to focus on the problem and how to think the problem differently and have the technology to just respond. They don't want to have to start with the technology and then imagine what they can do with it. >> I think from our point of view, it's a very fast moving field, things are changing, new data's coming in. The data's getting bigger and bigger because instruments are getting packed tighter and tighter, there's more information, so we've got a computational problem as well, so we've got to get more computational power but there's new types of data, like suddenly there's gravitational waves. There's new types of analysis that we want to do so we want to be able to look at this data in a very flexible way and ingest it and explore new ideas more quickly because things are happening so fast, so that's why we've adopted this in memory paradigm for a number of years now and the latest incarnation of this is the HP Superdome flex and that's a shared memory system, so you can just pull in all your data and explore it without carefully programming how the memory is distributed around. We find this is very easy for our users to develop data analytic pipelines to develop their new theoretical models and to compare the two on the single system. It's also very easy for new users to use. You don't have to be an advanced programmer to get going, you can just stay with the science in a sense. >> You gotta have a PhD in Physics to do great in Physics, you don't have to have a PhD in Physics and technology. >> That's right, yeah it's a very flexible program. A flexible architecture with which to program so you can more or less take your laptop pipeline, develop your pipeline on a laptop, take it to the Superdome and then scale it up to these huge memory problems. >> And get it done fast and you can iterate. >> You know these are the most brilliant scientists in the world, bar none, I made the analogy the other day. >> Oh, thanks. >> You're supposed to say aw, chucks. >> Peter: Aw, chucks. >> Present company excepted. >> Oh yeah, that's right. >> I made the analogy of, imagine I.M. Pei or Frank Lloyd Wright or someone had to be their own general contractor, right? No, they're brilliant at designing architectures and imagining things that no one else could imagine and then they had people to go do that. This allows the people to focus on the brilliance of the science without having to go become the expert programmer, we see that in business too. Parallel programming techniques are difficult, spoken like an old tandem guy, parallelism is hard but to the extent that you can free yourself up and focus on the problem and not have to mess around with that, it makes life easier. Some problems parallelize well, but a lot of them don't need to be and you can allow the data to shine, you can allow the science to shine. >> Is it correct that the barrier in your ability to reach a conclusion or make a discovery is the ability to find that needle in a haystack or maybe there are many, but. >> Well, if you're talking about obstacles to progress, I would say computational power isn't the obstacle, it's developing the software pipelines and it's the human personnel, the smart people writing the codes that can look for the needle in the haystack who have the efficient algorithms to do that and if they're cobbled by having to think very hard about the hardware and the architecture they're working with and how they've parallelized the problem, our philosophy is much more that you solve the problem, you validate it, it can be quite inefficient if you like, but as long as it's a working program that gets you to where you want, then your second stage you worry about making it efficient, putting it on accelerators, putting it on GPUs, making it go really fast and that's, for many years now we've bought these very flexible shared memory or in memory is the new word for it, in memory architectures which allow new users, graduate students to come straight in without a Master's degree in high performance computing, they can start to tackle problems straight away. >> It's interesting, we hear the same, you talk about it at the outer reaches of the universe, I hear it at the inner reaches of the universe from the life sciences companies, we want to map the genome and we want to understand the interaction of various drug combinations with that genetic structure to say can I tune exactly a vaccine or a drug or something else for that patient's genetic makeup to improve medical outcomes? The same kind of problem, I want to have all this data that I have to run against a complex genome sequence to find the one that gets me to the answer. From the macro to the micro, we hear this problem in all different sorts of languages. >> One of the things we have our clients, mainly in business asking us all the time, is with each, let me step back, as analysts, not the smartest people in the world, as you'll attest I'm sure for real, as analysts, we like to talk about change and we always talked about mainframe being replaced by minicomputer being replaced by this or that. I like to talk in terms of the problems that computing's been able to take on, it's been able to take on increasingly complex, challenging, more difficult problems as a consequence of the advance of technology, very much like you're saying, the advance of technology allows us to focus increasingly on the problem. What kinds of problems do you think physicists are gonna be able to attack in the next five years or so as we think about the combination of increasingly powerful computing and an increasingly simple approach to use it? >> I think the simplification you're indicating here is really going to more memory. Holding your whole workload in memory, so that you, one of the biggest bottlenecks we find is ingesting the data and then writing it out, but if you can do everything at once, then that's the key element, so one of the things we've been working on a great deal is in situ visualization for example, so that you see the black holes coming together and you see that you've set the right parameters, they haven't missed each other or something's gone wrong with your simulation, so that you do the post-processing at the same time, you never need the intermediate data products, so larger and larger memory and the computational power that balances with that large memory. It's all very well to get a fat node, but you don't have the computational power to use all those terrabytes, so that's why this in memory architecture of the Superdome Flex is much more balanced between the two. What are the problems that we're looking forward to in terms of physics? Well, in cosmology we're looking for these hints about the origin of the universe and we've made a lot of progress analyzing the Plank satellite data about the cosmic microwave background. We're honing in on theories of inflation, which is where all the structure in the universe comes from, from Heisenberg's uncertainty principle, rapid period of expansion just like inflation in the financial markets in the very early universe, okay and so we're trying to identify can we distinguish between different types and are they gonna tell us whether the universe comes from a higher dimensional theory, ten dimensions, gets reduced to three plus one or lots of clues like that, we're looking for statistical fingerprints of these different models. In gravitational waves of course, this whole new area, we think of the cosmic microwave background as a photograph of the early universe, well in fact gravitational waves look right back to the earliest moment, fractions of a nanosecond after the big bang and so it may be that the answers, the clues that we're looking for come from gravitational waves and of course there's so much in astrophysics that we'll learn about compact objects, about neutron stars, about the most energetic events there are in the whole universe. >> I never thought about the idea, because cosmic radiation background goes back what, about 300,000 years if that's right. >> Yeah that's right, you're very well informed, 400,000 years because 300 is. >> Not that well informed. >> 370,000. >> I never thought about the idea of gravitational waves as being noise from the big bang and you make sense with that. >> Well with the cosmic microwave background, we're actually looking for a primordial signal from the big bang, from inflation, so it's yeah. Well anyway, what were you gonna say Randy? >> No, I just, it's amazing the frontiers we're heading down, it's kind of an honor to be able to enable some of these things, I've spent 30 years in the technology business and heard customers tell me you transformed by business or you helped me save costs, you helped me enter a new market. Never before in 30 plus years of being in this business have I had somebody tell me the things that you're providing are helping me understand the origins of the universe. It's an honor to be affiliated with you guys. >> Oh no, the honor's mine Randy, you're producing the hardware, the tools that allow us to do this work. >> Well now the honor's ours for coming onto the Cube. >> That's right, how do we learn more about your work and your discoveries, inclusions. >> In terms of looking at. >> Are there popular authors we could read other than Stephen Hawking? >> Well, read Stephen's books, they're very good, he's got a new one called A Briefer History of Time so it's more accessible than the Brief History of Time. >> So your website is. >> Yeah our website is ctc.cam.ac.uk, the center for theoretical cosmology and we've got some popular pages there, we've got some news stories about the latest things that have happened like the HP partnership that we're developing and some nice videos about the work that we're doing actually, very nice videos of that. >> Certainly, there were several videos run here this week that if people haven't seen them, go out, they're available on Youtube, they're available at your website, they're on Stephen's Facebook page also I think. >> Can you share that website again? >> Well, actually you can get the beautiful videos of Stephen and the rest of his group on the Discover website, is that right? >> I believe so. >> So that's at HP Discover website, but your website is? >> Is ctc.cam.ac.uk and we're just about to upload those videos ourselves. >> Can I make a marketing suggestion. >> Yeah. >> Simplify that. >> Ctc.cam.ac.uk. >> Yeah right, thank you. >> We gotta get the Cube at one of these conferences, one of these physics conferences and talk about gravitational waves. >> Bone up a little bit, you're kind of embarrassing us here, 100,000 years off. >> He's better informed than you are. >> You didn't need to remind me sir. Thanks very much for coming on the Cube, great pleasure having you today. >> Thank you. >> Keep it right there everybody, Mr. Universe and I will be back after this short break. (upbeat techno music)

>> Announcer: Live from Madrid, Spain, it's The Cube covering HPE Discover Madrid 2017. Brought to you by: Hewlett Packard Enterprise. >> Welcome back to HPE Discover, 2017 in Madrid. This is The Cube, the leader in live tech coverage, my name is Dave Vellante, I'm here to rap with my co-host, Peter Burris. >> Hey, Dave. >> Dave: Good couple a days. >> Oh, you know what I just discovered. I discovered The Cube is the antidote to jet lag. (laughs) >> That's right, when you get interesting people on. >> Oh, man. >> It pumps you up. >> Totally. Just unbelievable, exciting and it's all framed by... Well let's start where we talked about yesterday, we proposed that increasing what we're seeing in the industry is the new model of computing being established by Amazon and then the other poll, where it was known, we know that it's not all gonna be one cloud, it's not all gonna be a central cloud model, or essentialize cloud model. There's gonna be other places where data's gonna need to be processed. >> Dave: Well, that's what we believe. >> That's what we believe, and... There's physics behind that statement. There's legal regulations about data residency, behind that statement. But, we didn't know who was gonna step up and lead that other side and it's nice to see this conference indicate that HPE is in a position to help demonstrate, or help show the industry how cloud truly can go from centralized down to the edge. >> Yeah, and I think as I said a number of times, the strategy's coming into focus, you could debate it. You could say, "well, splitting it up was the wrong thing to do. "They lost their supply chain." But, Meg's argument, and then Antonio's argument always was, "look, we're gonna be more focused, "it's gonna allow us to do "a better job for our customers. "Yes, we're gonna be service's lead." They didn't say this. "Our margines are gonna be lower, "you don't have software anymore, "but that's okay, we can learn how to make money at that." And you know, the old HPE went through a similar transition. Kinda, got out of the HPEX business and got out of building it's own OS, and relying more on Microsoft and Intel and it made a lot of money. In those days. >> Peter: It did well. >> Did very well. It didn't invest under the herd regime the way it could have or should have and that hurt and then it spun out and made a lot of missteps but... Meg, to her credit, didn't make a lot of missteps. There was the initial entrance into the public cloud, they pulled back fast, they failed fast on that, good. Yeah, maybe there was some organizational issues early on but in general, the acquisitions have been solid, the strategy... >> And well integrated. >> And well integrated, absolutely. >> Peter: They've gotten value out of 'em. >> The strategies has been... I think clear internally, it wasn't always clear externally but they stayed calm about that, they didn't freak out about that. Helped that the stock price was going up a little bit, 'cause it was pretty depressed for a while. >> And shareholders weren't incontestable like they were for many years. >> That's right, and so, that gave them a little bit of time to bring it all together... It's finally here and I think Meg is stepping down at absolutely the right time. >> Or at a... She's stepping down at a good time, she's leaving a company that is much stronger than it was when she took it over. >> And that's what you want, one of the things I'm personally proud of when I left IDC it was in really good shape when I left, it wasn't a mess that I handed to somebody else. Had a lot of messes and IDC that I turned around as you well know. So, I think, I feel as though the company's in good shape and good hands. And, again, I think the... I don't know if you're a stock analyst or if you're pounding the table saying "buy this stock." 'Cause it is a relatively low margin business and there's a lot of competition, there's knife fights out there, it's not a high growth business, but on the flip side, it's clean, it throws off a lot of cash, they got a decent balance sheet and the customers love 'em. >> And that's the most important thing, it's the customers. Look, I... Disclosure, I actually did a significant consulting stint, here at HPE, right around the time of the compact acquisition and I saw what happened and for many years, the senior manager and team of HPE behaved as though they presumed that scale was it's own reward. If we get bigger, we'll find efficiencies, we'll find opportunities. Just being big, is the objective and I think that they have wandered in the desert trying to find those opportunities, that were the consequence of just being big and they never materialized. >> They weren't there. >> They never... It was like mirages on the horizon, they never materialized and I think if there's anything to your point that Meg has successfully done, is she's gotten the company to say, "don't chase the mirages, chase the customer. "Let's come back to what made HPE great for so long." And the idea that, if we stay focused on the customer and focus on technology, we can put them together in unique and interesting ways that will bind us to what customers are doing. And if you take a look at this event and the new messaging, and the things that they're focusing on it feels like, to me, that HPE is no longer wandering in the desert. You and I are smart guys, we are... Typically we can look at a company and we can see whether or not they know what they're doing and when you said, "well, you know what. "Maybe they had it all figured out inside, "and the rest of us couldn't see it." No, that's not the case. It was not figured out inside and that's what we saw but under Meg, it has become increasingly more figured out and the consequence of that... And it's been very, very plan full. She first was figured out and then she told Wall Street and Wall Street was happy with the numbers, and then she figured out and she started talking to customers when customers were there and now she's figuring it out, she's telling a broader market place. >> Well, and when she stopped by- >> And Antonio's got a great big story to tell. >> And both of those guys stopped by to see us. Meg spent 10 minutes with us, we were chatting here on the open mics and she was very good. Meg, one on one situation, in a small crowd is phenomenal. I've always said that about Meg. Not the greatest presence on stage, not a super dynamic speaker, she's not a Steve Jobs, obviously nobody is, but... But, man, is she credible in a one on one situation. One of the things she said to us was, "Y'know, we kinda got lucky..." My words, "with Aruba, we bought him "because we thought we could compete with "Cisco better, we bought him obviously "because it was a great business, a growth business," and boom all of a sudden, this intelligent edge thing hit. You sprinkle in a little Dr. Tom Bradicich and boom, off you go and you've got not only a great business, you got something that is becoming increasingly strategic for organizations. Great example, I mean the nimble acquisition. We heard, yesterday, Bill Philbin talking about, "well, when we got nimble-" was it Bill Philbin, no it was somebody else today it was... Alain Andreoli. He said, "we picked up nimble 'cause it was a great "flash company, but then we saw this inside thing, "we said, wow, we can spread this thing "across our entire portfolio." That's where- >> And the example he gave was: in six months, it's not running on... >> On three par and then it's gonna run... His goal, he says, "I'm not committing to this, "but my goal is by the end of the next year "it's gonna be running across the entire "server and storage and networking line." That would be a major accomplishment. If in fact, we'll see how much of this stuff is actually impactful to the business, how much it can actually save money you know, anticipate failures, I don't know. We'll see, it's AI, it's a perfect application. You guys have written a lot on the Wikibon team about AI for ITOM. >> Oh yeah, look... >> Dave: And this is a good example. >> I'm not the kinda guy, as you know, that gets all excited about technology for technology's sake. I like thinking about technology and how it's gonna be applied, more problems are gonna be solved and so as we, in Wikibon, started running around and getting all excited about AI, my challenge to the guys was: Well, show me the two concrete cases, where it's gonna have a material business impact and one of the most important cases is, it's got a material business impact and how IT runs itself because you cannot... IT cannot reduce the number of people it's got and take on these increasingly complex application, problems, and portfolios unless they get a lot of help and the best, most likely source of that help is by bringing a lot of these new AI technologies that are capable of taking concrete, real time action in response to what's happening within the infrastructure and the applications at any given time. >> Yeah, now... Couple other things, just observations. Ana Pinczuk came on, great leader, woman in tech, big proponent of advancing women's causes, especially in tech. She had mixed feelings about Meg stepping down, obviously you have a woman leader, I thought her comments there were... Were quite interesting, but she said, "But I am up for the challenge "to continue the mission." Which leads me to Antonio. Antonio is outwardly a humble guy, he may have a big ego I don't know, he's been on The Cube a number of times, but he certainly doesn't come across as a guy who's looking to get credit. He's a quiet but very competent leader, he knows the business very well. Really interesting to see what his relationship- >> Peter: Homegrown. >> Homegrown, which is 22 years at HPE, technology background, not a U.S... Born individual, now living in the U.S. obviously. But, somebody with international experience which is always been an attribute that's valued at HPE. Gonna be interesting to see what his relationship is with Wall Street. Will he be sort of a quiet leader that lets the CFO take front and center, which would be fine. Or will he slowly sort of advance, he's not been sitting on the earnings calls. I'm interested to see how he handles it, or he may just say, "you know what, "I'm gonna go execute in the business "and let the results speak for themselves." So, I'm kind of curious as to how that all... All plays out. It's a big job, it's a big role as you pointed out with me the other day. Big role for him, big job for him. Serious opportunities to make a mark in the industry. >> Again, and you raise a really great point. Meg had a very good reputation on Wall Street, the knock on her when she came on, was she didn't know customers. Antonio's got a great reputation with customers, you're asking the question: is he gonna get to know Wall Street? A great CEO has to be able to take care of customers and owners He seems very... Look, this is a, this whole simplification of how they're gonna bring cloud technologies to where their data's gonna require is apparently, based on what we heard, in large part Antonio's brain child. He conceived it, he invested in it, he nurtured it, he took risks for it, he put some skin in the game and now it's coming to fruition, that's great, and he's got customers lining up behind it. We'll see, this is another place where we'll see, but I don't think that there's... There's no reason to suspect, just looking at Antonio's track record, why Wall Street would abandon him. On the contrary, there's reasons to suspect that he will also be able to develop that set of skills that Wall Street needs to do their job. But, clearly this is a guy that's gonna turn on a lot of customers. >> Yeah, and as I say, it's gonna be interesting to see what his relationship, like look at a guy like Frank Slootman, who had a great relationship with Wall Street, everybody loved him 'cause he just performed but he's a hard-driving, in your face kinda guy, who developed close relationships with the street. It's gonna be, as they say, I gotta watch that, to see how Antonio interacts with them. I think it's important to have a relationship with... >> Peter: With your ownership, yeah it usually is. >> And I think that's the one big question mark here is, where has his presence been there but so we'll watch and I'm confident he'll step up to that. Okay. Let's see, The Cube... Next week? Cube-con? >> Peter: Yeah. >> Next week in Austin. Right, so development. You'll see The Cube expanding way beyond it's original infrastructure route, so obviously HPE Discover, big infrastructure show. But we're at Amazon Reinvent this week, it's our big cloud show. We obviously... All the IBM shows are being consolidated into one show called Think. This year The Cube will be there. But CES is gonna be January, we were there last year, likely be there again. Cisco live is on the radar, we're gonna be at Cisco live I think both in Barcelona and most likely in the states this year, so that's another big thing. A lot of developer shows, Docckercon, Kubecon, working with the Linux Foundation, developers are really the lynch pin, developers in cloud. Really big areas of growth. IOT, some IOT conferences that we're gonna be doin' this year. Obviously, our big data heritage we still do a lot of work there, so. It's been an unbelievable year, I think a 125 shows for The Cube. TheCube.net, new website, our new clipper tool, you see the clips that come out, so. A lot of innovation comin' out of Siliconangle Media, check out Siliconangle.com. Peter, the work that your team is doing on the Wikibon side, Wikibon.com. Unbelievable amounts of research that you guys are crackin' out. Digital business, AI, AI for ITOM stuff that we talked about, we still do some stuff in infrastructure, true private cloud. >> New computing architectures, memory based computer architectures. >> So, fantastic work there and... Yeah, so we're looking forward to another great year. Thanks everybody for these last two days, thanks to the crew, great job. Everybody at home. We're out. Dave Vellante for Peter Buriss from Madrid. Thanks for watching. (upbeat music)

>> Announcer: Live from Madrid Spain, it's theCUBE covering HPE Discover Madrid 2017. Brought to you by Hewlett Packard Enterprise. >> We're back. This is theCUBE that you're watching, the leader in live tech coverage. We're at HPE Discover 2017 in Madrid. My name is Dave Vellante, I'm here with my co-host for the week, Peter Burris. Peter, it's been great working with you this week. >> Indeed, it's been great. >> We're winding down, and we're really excited to have Ric Lewis, >> Great ideas. >> Senior Vice President and General Manger of the Software Defined and Cloud Group. Many time CUBE guest with HPE, and Jeff Wike of Dreamworks. CTO, thanks for coming on. >> Yeah. Thank you. Thanks for having me. >> Great to see you. You're welcome. Been a good week? >> It's been a fantastic week. >> Things are coming into focus? >> They are. >> You killed it on the keynote, how are you feeling? >> Feeling really good, feeling really good. I mean, the momentum in the software defined and cloud arena is just fantastic. You know, there were times when I used to visit with you guys and we were only talking about what's coming in the future. Now we're talking a lot about what we have, what customers are buying, where we have momentum. And still introducing new things, so it's just a whole lot of fun. >> Jeff, Senior Vice President, CTO, can we talk a little bit about your role? What the scope is? >> Sure. Sure, so Dreamworks Animation, you may have heard of it. >> Yeah. We do we make animated films. >> Good friend Kate Swanberg's been on a number of times. >> Kate's, love her. We make animated films, we do a lot more than that. We're a digital content creation company. So we, we're the largest TV animation studio in the world. We're doing theme park ride work, cause we've got, we're now under NBC Universal. So we're doing a lot of projects, it's a very busy time for us. >> So, Synergy, we talked about Synergy a lot, there's nothing >> Yeah. >> like Synergy we've heard. >> Yeah, yeah. >> Fluid pools of infrastructure. >> Yeah, it just gets better. >> Wait and see and so, what can you tell us? How's the momentum? >> Yeah let's talk a little bit about that. So the momentum on Synergy is fantastic. We started shipping in volume at this conference last year, basically December of last year. And the response has been fantastic. We've looked at Momentum for new infrastructure plays. You know if you look back at our history, whether it was the C7000 or whether it was UCS from Cisco or whether it was VCEs built on UCS, Nutanix. If you kind of look at the first year of a new infrastructure play, Synergy looks like it's the fastest growing thing ever. It's just fantastic, really growing for us. We have over 1100 customers on Synergy now. You know, and that's in 11 months of shipping. And the business, it just continues to grow quarter by quarter. Just really thrilled with the progress there, so happy. >> And you guys are customers? >> We're big customers, if we're not the biggest customer, we're certainly the biggest fan. >> One of the biggest, one of the biggest customers, maybe the biggest fan. >> Certainly the biggest fan. >> Okay so Jeff, tell us, take us back to sort of pre-Synergy, you know, what was it like before and after and what has it done for your business in particular? >> Well one of the things that that we face going forward is we developed, in our infrastructure, and inner data center, we do a lot of rendering to make a movie. That's our largest high performance compute. You know, 80 million render hours, CPU hours to make one of these films. And we're making a lot of them at the same time. We really defined that work flow, and how we optimize the data center hardware to be able to go through that work flow and be able to be as efficient as possible. The issue came with we have a lot of other projects that are coming in, and since we are now under NBC Universal, there's a lot of other work that's happening there. And also, different types of media that's coming, you know, around the corner. And we want to be able to prepare for that. What we would have done traditionally would be to buy to peak, you know because it is rather cyclical, and that's what we would do that on prem, peak. But if we had a special project, we might buy or segment a portion of that and say, you know, this is for this purpose. This is for that purpose, but that's very inefficient. So with Synergy, the beauty of it is we can purchase you know that hardware, but then if we want to be able to use it for another project, we can do that. And we can do that very very quickly. >> You said you repurpose that across your application portfolio. Or your project portfolio. >> Yeah. Yeah, it gives us, I like to say it future proofs us. Because now no matter what the parent company or our own creative ambitions are, we can handle that. We can't say no, well we never say no. We usually say not right now, or wait a couple of weeks or a couple of months to be able to provision that. And now it's, it's instantaneous. >> And I know what Ric's answer would be to this, but I want to hear from the customers. Is this really different than other products that you've experienced. >> It's totally unique. We haven't experienced it before. And I'll give you, I'll give you a little example. We just got our order. We got about 200 servers of Synergy that arrived a couple of months ago. And within seven working days, we were using it in production. And I just want to say, we took, I don't know if I told you this story, but we were able to provision all of that from the time we mounted in the racks within five hours, which is incredible. It would have taken us easily three weeks before. In fact, it took us longer to take it out of the cartons than it did to provision. >> Well, so let me see if I... You're talking about maybe 200 servers. You're probably talking about 8,000 individual tasks configured. To get it done in five hours you probably perform what, 40, 50 tasks? Administrative steps? >> By the way, first time doing it. And our engineers were saying, we could've used more parallelism. We could've done it faster. You know, it's almost a challenge to see just how easy you can do this. >> But I got that right? Is it really like 98 percent reduction in the administrative tasks? >> Absolutely. >> Really? >> That's incredible. >> It is. >> Huh, alright. >> That's before you start flexing work, flexing resources against different workloads and dynamically reprovisioning. This is just provisioning the first time. But it, if you think about it, if you're gonna do it dynamically, it can't take forever, so you've gotta make it, the first time it's gotta be super fast. >> Okay. >> So, I have to admit I'm a little stunned, I didn't know that. So, and as you said, the whole point is that you can reprovision >> Yes. >> Over and over. Which means that the... There's something in economics and technology that's known as an asset specificity. And an asset has high specificity when you buy it and can appropriate it to a specific purpose. And about the only thing in tech that makes something an asset specificity is the administrative tasks of changing it to prepare it to do something else. And you just told me that I can remove nearly 100% of the transaction costs associated with taking an asset from this and applying it to that. >> If you're gonna destroy silos in the data center, that's what you have to do. >> But that's... >> Right, so silo is this asset specificity. If you can repurpose it immediately. >> So I'm excited, that's my second question. How did your people respond to this? Because I talked to a lot of other CIOs that say one of the biggest challenges I'm having, or CTOs, one of the biggest challenges I'm having is I'm able to converge hardware, I'm able to converge to some software, I'm able to converge Administrative tasks, but my people don't like converge. What, they don't like to converge. How are you walking your people through some of these changes to liberate these opportunities? >> Well we've been moving toward, from more traditional, we'll call it IT for now. From traditional IT to dev ops environment and, you know what, it's change. So we've been bringing people along in that you know, to, and some people adapt to it. They say wow this is gonna be great for my career. And engineers want to always use the new stuff, so from that aspect of I know how I work, and I know what I do, to here's a better way of doing it to be more automated, it's been a good experience for people. And you know what, the chance of human error in configuring things... If I look to my long history at Dreamworks, 21 years, I look at any down time we've had or any problems, 90% of that has been from misconfiguration. And it's usually from somebody fat fingering, you know a parameter in the set up of the servers. And now, that's virtually eliminated. >> Did you have to go through some kind of organizational, internal sort of discussion, transformation, whatever you want to call it to actually get to the point where you could buy this way, buy a sort of single SKU of Synergy? Because you maybe previously you were buying bespoke, kind of roll your own components. A little server here, maybe some storage over there, maybe some networking here. Now maybe it's all HP that made it simpler, but you probably had specialist in each of those areas, did you not? >> We did. >> How did you deal with that organizational friction? >> You know, that was an issue as and by the way, there's so many, there's so much technology that's being developed some of it open source, some of it in this partner ecosystem that you have. And trying to stay abreast of that has been a real challenge. And one of the things that we always dreamed of is wouldn't it be nice if there was one way that you could control that. The single pane of glass, which is you know, to be able to have an API layer that everybody could hook in to. I think you've got a company like Hewlett Packard Enterprise that has that dominance in the market place to be able to dictate, I'm using that word. >> Yeah. >> Maybe dictate isn't the right word. >> Offer. >> Offer. (group laughing) >> That's the word we use. Enable. >> Enable, you know those APIs. And all of those are being developed you know almost in parallel. >> Yeah, yeah, yeah. >> So this stuff is really coming in. Now we have our own... We're a snowflake like everybody else is to your point. And what we've done is we brought in the Pointnext team to go in and write those northbound APIs so that we can hook in to one view. To be able to manage all of our legacy, I'll call it legacy, our previous infrastructure along with you know, the new tech that we're buying. So that it makes it easy to manage. >> They made it match the composable API that we put into Synergy. It's natively integrated. All the ecosystem partners are adapting to it. And they said we'll just use that as our standard to even manage our legacy infrastructure. Plus, since Oneview runs on legacy infrastructure, all of the HPE stuff, it just adapts like that. So it's been a very good, good project. >> So you've got a lot of experience with this now. Can you share with, maybe you can quantify it, maybe you can't, but even subjectively the developer impact or the animator impact, the business impact to Dreamworks? >> So the biggest impact... Well I have three things that are my, actually I got this from Meg Whitman, I had a list of 12 objectives for the studio for technology and she said at one of the CIO summits, you've gotta have three. So I said okay, I've gotta pare it down to three. And one of those is provide the technology, the software and infrastructure to meet the creative needs. The second one was innovate for competitive advantage. And the third one was drive efficiency into operations. And if you look at what Synergy provides, it hits every single one of those. So we've actually, you know, over the past year or two, we've actually reduced the number of people that we have maintaining our infrastructure, which is amazing if you consider the fact that this year we doubled the size of our infrastructure. In what other business, in what other area can you actually reduce the amount of people that are maintaining something while you're doubling the amount that you're maintaining. That never happens. And I think it's because of this software defined infrastructure and the fact that you can write these recipes or profiles, whatever you want to call them, personalities. >> Yep, yep, yep. >> To be able to... And test them and harden them. And by the way, that reminds me, one of the things I really like about this is our ability to do proofs of concept, to try different workflows and all that without having to take away resources from the main thing that we're doing which is the artistic community. So we can actually say, you know what? We're gonna go in, reimage these servers. We're gonna do that at night to run this test, in the morning they're back, they're back in the pool. And that's an amazing thing. >> That's dynamic provisioning. No one else can dynamically provision. >> Yeah. >> All the converge systems, all the hyper converge, they're provisioned a certain way. They run VMs a certain way. They stay that way for their lifetime. This stuff dynamically reprovisions, and you guys, you're not even talking about kind of doing containers with VMs and containers with your bare metal, you can dynamically reprovision across that as well. >> Yeah, what he said. (laughter) >> Listen, we're just getting started so just relax, okay. These guys are telling me we gotta wrap. We're not gonna wrap. >> No. >> We haven't even gotten to One Sphere yet. >> We have other topics. Exactly. >> So let's get to One Sphere. >> Yeah. >> Yeah I want to talk about One Sphere. But I do want to say. >> Go ahead, last thought. >> One more thing, so you talked about artists, but the other part of it is for developers so one of the things we don't want the engineering teams to be a hindrance to the developers. Because they want to be able to move quickly, they want to be able to be assessing, and I think one of the things that's not just an impact on our artists, to be able to do these new projects, but also it makes our developers more efficient. They don't have to wait. >> Yeah. >> Okay, great. Now let's talk multi cloud. >> Yep. >> A lot of complexity, the more things get simple, the more complex they seem to get. So, One Sphere. You guys announced yesterday. >> Yeah, so. A core pillar of the HP strategy, make hybrid IT simple, right. And you can see from this conversation we're making hybrid IT simple on-prem. Not only do we have Synergy, but we have a fantastic offering in our Simplivity space. And that platform's over 2,000 customers and growing like crazy as well. But after we did that, we said look, we've got fantastically simple virtualization clusters in Simplivity, we've got great dynamic reprovisioning and composable infrastructure, but customer are not... That's part of their hybrid IT problem, that's the on-prem part. They're also wrestling with I've got multiple cloud instances, I need to get insights into where I'm spending my money, where workloads are deployed and all that. So we started this program, HPE OneSphere. We've had it going for almost three years. We had a small team on it early on. We ramped up the staffing a couple years ago. And what it really does, it's pretty simple. It allows you to build clouds, deploy apps, and gain insights extremely fast. So it's designed for IT ops to be able to build and deploy a private cloud as fast as they can and assemble that with their public cloud assets. And provide one place to look at all of those. For developers, it provides a common multi-tenant environment that has all the services and tools they need to be able to deploy an application whether it's on-prem or off-prem, and you can choose, you can build applications that have some of both inside that developer environment. And then for the business, it shows insights into where's the money being spent? Where are those workloads running and what's it costing me? So, think of it almost as composable at that next level where it's not just resources within chassis, now it's resources across the hybrid IT estate. It actually is public cloud assets from any of the public clouds, whether it's AWS, Azure, Google, Cloud28+, as well as your private cloud assets. And it automates the life cycle stuff that we were just talking about through this application into OneView. It's a SaaS environment, so actually OneSphere is software as a service. It lives in the cloud, it's a subscription that our customers buy, and it does all of this capability to simplify their hybrid environment and taps into the capabilities we just talked about. It's fantastic, nobody has anything like it. >> Okay well we've heard that before, but now... >> Exactly. >> You're putting your money where your mouth is. >> So I was right on that one. >> Okay but it's early days for OneSphere. >> Okay. >> And your private cloud is what we call a true private cloud. >> Which you said on stage yesterday. >> I did that's exactly right. >> It's evidence by your ability to reduce staff to manage infrastructure. >> It's a con experience wherever the data requires is how we put it. >> Yes, yes. We want the simplicity of management and the availability of apps that you get in public cloud in the private cloud. >> And the pricing. Yeah? >> Well, yeah, well... No, cause it's actually more expensive to go public cloud. >> I mean pricing models. >> Oh yes, yeah. >> The consumption is what you're basically talking about, yeah. >> And so you, Jeff you guys are OneSphere or OneSphere betas? >> Yeah, you bet. >> So what were you trying to learn? What were you kicking the tires on, testing? Where'd you focus? >> We, you know, if we look at the future, we're not gonna be on-prem forever, and I certainly don't want to be on-prem forever, I want to take advantage of flexing to public cloud, but again, for our films, you know, we want to be able to provide the producers of those movies, what is that gonna cost me? What is that, how can I tell you what that costs? And where can we move as we start to do more different types of projects? Which ones should go to the public cloud? Which ones should stay inside? And be able to understand that. The other thing that made us nervous about public cloud. Was what they call the zombie cloud instances, you know where you went in, you provision something and then you forget about, and you, but you're paying, you know. And that's, a lot of money is made. >> Kind of like app subscriptions. >> Group: Yes, exactly. >> I'm still paying for that? (laughter) >> Exactly but this gives you all of that... >> 4,000 dollars a month. >> A little different right. >> Or 15,000 a month. (laughter) >> Yeah, that's for sure. That visibility is something that all... We talk about it, CFOs hate this thing... Some of the consumption model is shifting from cap ex to op ex, but CFOs hate surprise op ex. And that's where they're actually surprised by oh my gosh look at that bill. Well this provides visibility into all of those assets, whether they're on-prem or off-prem and what they're costing you. And it's always up to date, and it's always consistent across your entire farm, so you can choose and say that's costing me too much, I want to move those apps over here. And immediately do it. And for a lot of our customers, they're over-provisioned so they have spare capacity on-prem they're not taking advantage of. Why not use some of that and it's instantly provisioned. >> And that's where you initially, anyway, see the business value of OneSphere. >> Well, look, it's OneSphere to rule them all. And I believe whether it's private, public, you know we really want to have what is my total resource availability? So in the future, we never say no anymore. Really, we can tell them how much, but you don't have to say no. And the other thing is we can do this stuff instantly. So, we don't even say when, we just go now here's what you have to pay if you want to do it, we can provide those options. It's a new world. >> I love the demo of, I don't know if you guys saw it, there's a demo with Pong, you know, it's the IT guy of the past. >> Yeah the guy saying no. >> And then they made it vertical. It's the IT guy of the future. So, alright my last question. What cool movies can we anticipate? What's coming? >> Well you know what, How to drain... How to Train, how to drain your tragon I was gonna say. (laughter) How to Train Your Dragon 3 is our next film out and it's gonna be unbelievable. >> I'll bet. >> So my last question. Am I gonna have to continue to sit through 15 minutes of IT credits at the end of future Dreamworks movies as a consequence of Synergy? >> There's less, cause there's less resources required to manage your Synergy hardware. So it's less people. >> I know you don't sit through the credits. (laughter) >> I do. (laughter) I love credits. Alright guys, thanks very much for coming on. >> Thank you. >> It's been a great pleasure. >> Thank you, always fun. >> Alright keep it there everybody, Peter and I will be back to wrap up HPE Discover 2017 from Madrid, you're watching theCUBE. (upbeat music)

>> Narrator: Live from Madrid, Spain it's theCube, covering HPE Discover Madrid 2017. Brought to you by Hewlitt Packard Enterprise. >> Welcome back to Madrid everybody. This is theCube, the leader in live tech coverage. My name is Dave Vellante and I'm here with my co-host for the week, Peter Burris, otherwise known as Mr. Universe. This is HPE Discover Madrid 2017. Flynn Maloy is here as the Vice President of Marketing the HP Point Next. >> Hi guys. >> And John Treadway is here as the Senior Vice President of Strategy and Portfolio at Cloud Technology Partners, an HPE company. Gentlemen, great to see you again. Welcome to theCube. >> Great to see you. >> It's been a good week. We were just talking about the clarity that's coming to light with HPE, the portfolio, some of the cool acquisitions. You and I, Flynn, were at this event last year in London. You had the Cheshire Cat smile on your face. You said something big is coming. I can't really tell you about it partly because I can't tell you about it. The other part is we're still shaping it. Then Point Next came out of it. How are you feeling? Give us the update. >> It's been a really exciting year for services. This time last year we knew as Antonio announced, we're going to be bringing our services together after we announced that we're spinning out our outsourcing business. We're bringing technology services at the time forward. We had a new brand coming. We purchased Cloud Cruiser in February so we're investing in the business. We also invested in services back in the engine room all year long to really build up to our announcement this week with Green Lake which takes our consumption services to the next level. Then of course in September we continue to invest and acquire Cloud Technology Partners and by the way brought on our new leadership team with Ana Pinczuk and Parvesh Sethi. For us here at HP it's really been a banner year for services. It's really been transformative for the company and we're excited to lead it going into FY '18. >> John, Cloud Technology Partners specializing, deep technology expertise. You've got an affinity for AWS, you've got a bunch of guys that reinvent this week in close partnership with them. Interesting acquisition from your perspective coming into HPE. What's it been like? What has HP brought you and what have you brought HP? >> That's a fantastic question. We have really found that everything about this experience has exceeded our expectations across the board. When you go into these things you're kind of hoping for the best outcome, which is we're here because we want to be able to grow our business and scale it and HP gives us that scale. We also think that we have a lot of value to add to the credibility around public cloud and the capabilities we bring. You hope that those things turn out to be true. The level of engagement that we're getting across the business with the sellers, with the customers, with the partners is way beyond expectations. I like to say that we're about six months ahead of where we thought we'd be in terms of integration, in terms of capability and expertise. Really bringing that public cloud expertise, not just to AWS, we do a lot of Azure work, we do a lot of Google work as well, really does allow the HPE teams to be able to go into their clients and have a new conversation that they couldn't have a year ago. >> What is that new conversation? >> The new conversation is really about, and we like to use the term "the right mix." I.T. is not just one mode. You're gonna have internal I.T., you're gonna have private clouds. Public cloud is a reality. AWS is the fastest growing company in tech history ever. If you think about that it's a reality for our clients, HPE clients, that public cloud is there. That new capability that we could bring, that credibility is that we have done this for the last seven years with large enterprises across all sorts of industries and domains: Toko, healthcare, financial services in particular. We bring that to the table, combine that with the scale and operational capability of HPE and now we have something that's actually pretty special. >> Just to add, it is about the customers at the end of the day. It's about where do those workloads want to land? Public cloud, private cloud, traditional, those are all tools in your toolbox. What customers want to know is what is the right mix? There are workloads that are ideal for going to the public cloud. There are workloads that are ideal for staying on prem. Finding that right mix, especially by bringing in the capabilities of what needs to go to public cloud that really rounds out our portfolio for hybrid I.T. >> I'm starting to buy the story. The upstarts, the fastest growing company in the world would say old guard trying to hang onto the past. I like the way you framed it as look, we know our customers want to go to the cloud. They want certain workloads to be on prem. We want them to succeed. We're open, we're giving them choice. Maybe two years ago it sounded like bromide. But you're actually putting it into action acquiring a company like CTP. It's interesting what you were saying, John, about well no not just AWS, it's Google, it's Azure. You've got independent perspective on what should go where or on prem. >> We always have so even as a company that derived most of our revenue from public cloud over the last few years, we've never, ever been the company that said everything should go to public cloud. Toss it all, go to Amazon, toss it all, go to Azure. Never been our perspective. We've had methodology for looking through the application portfolio and helping determine where things should go. Very often a large percentage of the portfolio we say it's good where it is, don't move it. Don't move it right away. >> But in the past that's where it ended. You said okay, hey, go figure out, go talk to HPE. >> That's actually a funny thing because we've had this conversation. Literally when we would say okay we'll take care of this part for the public cloud, but you're on your own for the private cloud stuff, in the past HP would do the reverse. We'll help you with the private cloud stuff, and we think this could go to public cloud. But you're kind of on your own with that. Not that there wasn't any capability, but it wasn't really well developed. Now we can say this should go to private cloud, this should go to public cloud and guess what? We can do both. >> Dave: So now you've got a lean-in strategy. >> Absolutely right, as John said the funnels and the response from our customers have been outstanding. As you can imagine, Mike, all of our top customers are saying fantastic, come talk to us, come talk to us. They're having to prioritize where they go over the last few months. We are well ahead of where we were. >> We strongly believe over the years that the goal is not to bring your business to the cloud. It's to bring the cloud to your business. That ultimately means that public cloud will be a subset of the total although Amazon's done a wonderful job of putting forward the new mental model for the future of computing. Can you guys reliably through things like Green Lake and other, can you present yourselves as a cloud company that just doesn't have a public cloud component? >> Let me approach the response to that question in a slightly different way. When you look at our strategy around making hybrid I.T. simple it's not necessarily which cloud is the right cloud? It's not really about that. It's about where should the workloads land? We do believe that the pragmatic answer is you need to be a little bit above all of those choices. They're all in the toolbox. If you look at, for example, our announcement with One Sphere this week that's a perfect example of what customers are asking the industry to do which is to look across all of it. The reality is it's hybrid, it's multi-cloud and speaking at that length. >> But you're saying it's a super set of tools that each are chosen based on the characteristics of workloads, data, whatever it might be, that's right. So John look, as human beings we all get good at stuff. We say I know that person I can stereotype him. I can stereotype that. What's the euristic that your team is using to very quickly look at a workload? Give our audience, our clients a clue here so that they can walk away a little bit and say well that workload naturally probably is going to go here. And that workload's naturally going to go there. What's it like 30 second where you're able to generally get it right 80% of the time? >> It really comes down to a set of factors, right? One factor is just technical fit. Will it work at all? We can knock out a lot of workloads because they're on old Unix or just kind of generally the technical fit isn't there, right? Second thing is from a business case. Does it make sense? Is there gonna be any operational saving against the cost of doing the migration? Because migrating something isn't free right? It's never free. Third is what is the security and governance constraint within which I'm living? If I have a data residency requirement in a country and there's no hyper-scale public cloud presence in that country then that workload needs to stay in that country, right? It's those types of high-level factors we can very quickly go from the list of here's your entire list down to already these are candidates for further evaluation. Then we start to get into sort of deeper analysis. But the top level screen can happen very, very quickly. >> You do that across the, you take an application view, obviously. A workload view. Then how do avoid sort of boiling the ocean? Or do you boil the ocean? You have tools to help do that. >> We do, I mean we've invested a lot in IP, both service IP and software IP in both Point Next also comes with some strong IP in this as well that we've been able to merge in with. Our application assessment methodology is backed by a tool called Aura. Aura is a tool for taking that data, collecting it, and help providing individualization in reporting and decisioning at the high level on these items. Then every application that looks like a great candidate for something that I'm gonna invest in migration, we need to do a deeper analysis. Because it isn't lifting and shifting. It doesn't work for 90% of the applications, or 80%, or 70. It's certainly not anywhere near 50% of the applications. They require a little bit of work, sometimes a lot of work, to be able to have operational scale in a public cloud environment because they're expecting a certain performance and operational characteristic of their internal infrastructure and it's not there. It's a different model in the public cloud. >> A lot of organizations like yours would have a challenge presenting that to a customer because they can't get the attention of the senior leaders. How is it that you guys are able to do that? You were talking I think, off-camera, talking about 20-plus years of experience on average for each of your professionals. Is that one of the secrets to how you've succeeded? >> This is a big thing and why this integration's working so well is that the people, the early team all the way through today of CTP are all seasoned I.T. professionals. We're not kids straight out of school that have only known how to do I.T. in an Amazon way. We have CIOs of banks that are in our executive team, or in our architecture team that have that empathy and understanding of what it means to be in the shoes. Not having this arrogant approach of everything must be a certain way because that's what we believe. That doesn't work. The clients are all different. Every application is a snowflake and needs to be treated as such, needs to be treated like an individual, like a human. You want to be treated like an individual, not like -- >> Stalker! (laughing) >> Gezunheit. (laughing) >> Okay, so now the challenge is how you scale that. How you replicate that globally and scale it and get the word out. Talk about that challenge. >> That's right and one of the big things we're really excited to see is the merger of the IP that comes from CTP along with everything that we have inside of Point Next and then rolling that out to the 5,000 plus consultants that we've got inside of HP and our partners. That's really where we're expecting a lot of the magic to come from is once we really expose the integrated set of what those capabilities are we think, and Ana has said it on stage. We had heard from a couple of analysts that we believe that together we have the largest cloud advisory in the industry today. >> It was interesting we actually had, we've had challenges in the past where we've gone into clients and were starting to get into some pretty serious level of work. We were a younger company, didn't have the scale, and scope, and capability of HPE. Now we're being brought in to these opportunities and the clients are saying HP, you're right here. We can do that. We have the scale to now start doing the larger transformation programs and projects with these clients that we didn't have before. Now we're being invited back in, right? In addition to that being invited in because now we have the cloud competency that we can bring to the table. >> You know what, I kind of want to go back to the point you made earlier about how it's all cloud. That resonates with me. I think it is all cloud depending on where you want to land the various pieces. If that's what you want to call that umbrella I think it makes a ton of sense. You know, a lot of what we've announced this week with Green Lake is about trying to bridge the benefits gap with public cloud as the benchmark for the experience today for what needs to stay on prem. When you sit down and for all those reasons you outlined, whether it's ready, whether it isn't ready, where the data has to sit, or whether or not. There's gonna be x-workloads that need to stay on prem. We've been working hard in the engine room to really build out an experience that can feel to the customer a lot like what you get from the public cloud. That's gonna continue to be an investment area for us. >> If the goal is success for the business then you don't measure success by whether you got to Amazon. >> That's correct. >> The goal of success is the business. You measure success by whether or not the business successfully adopts the technology where the data requires. What's interesting about the change we're experiencing is in many respects for the first time the way of thinking about problems in this industry is going through a radical transformation. Let's credit AWS for catalyzing a lot of that change. >> Absolutely, setting that benchmark. I mean it really is a catalyst. >> But you look at this show, HP has adopted the thought process, it's adopted it. It's no longer in our position to say fine, you want to think this way, we'll help. >> Imagine this, as One Sphere comes up and as we really can manage multi-clouds and as we'll eventually be able to move workloads between the various clouds, manage the whole estate, view the whole estate and everything under it whether it's off-prem or on-prem is all consumption. I mean, how does that change central I.T.? Central I.T. radically changes. If everything's consumed, wherever it is and you've got a visibility to the whole estate and you can move stuff depending on what the right mix is, that's a fundamental change and we're not there yet as an industry. But that's a fundamental change to the role of Central I.T. >> But your CIOs are thinking along those lines. We can verify they are thinking along those lines. >> Again the strategy's coming into focus for me personally. I think us generally. We talked to Ana about services-led, outcome-led. And if it's big chewy outcome like kind of IBM talks well you've got partners to help you do that. Deloitte, we had PWC on. They're big, world-class organizations with deep expertise in retail and manufacturing and oil and gas. You're happy to work with those guys. If it is service-led or outcome-led you can make money whether you're going to Amazon, whether you're staying on prem, whether you're doing some kind of hybrid in between and you're happy to do that as an agnostic, independent player. Now yeah, of course you'd like to sell HP hardware and software, why not? >> I think that's really an important point. When it comes to the infrastructure itself we do believe we have the best infrastructure in the industry, but we play well with others and we always said HPE plays well with others. When it comes to the app layer we are app agnostic. A lot of our biggest competitors are not. When you go out and talk to CIOs today that's really, this is my app, this is my baby. This is the one that I want. They're not really looking for alternatives for that in many cases. When you're thinking agnostic that's really where we think partner, being agnostic, working with all the ad vendors, working with all the SIs, we think that's where the future-- >> And it's a key thing. You guys are younger, but you remember Unix is snake oil. I mean-- >> Designing is a Russian Trump. >> Unix is snake oil and then two years later it's like here our Unix. >> Flynn: It's the best thing ever. >> So you now are in a position to say great, wherever you wanna go we'll take you there. That's powerful because it can be genuine and it can be lucrative. >> What's unlocking here is the ability to actually execute a digital transformation program within the enterprise. One of the big things the public cloud providers brought to us and that HPE's now bringing in through the internal infrastructure is that agility and speed of innovation of the users. Their ability to actually get things done very quickly and reduce the cycle time of innovation. That frankly has always been the core benefit of the public cloud model, that pay-as-you-go, start with what you need, use the platform services as they grow. That model has been there since the beginning and it's over 11 years of AWS at this point. Now with enterprise technology adopting similar models of pay for it when you consume it, we'll provision it in advance, we'll get things going for you, we're giving that model. It's about unlocking the ability for the enterprise to do innovation at scale. >> I wanna end if I can on met Jonathan Buma last night, J.P., J.B., sorry. You're J.T. >> It's confusing. >> But one of the things I learned, a small organization, 200-250 people roughly when you got acquired, but you've got this thing called Doppler, right? Is that what it's called, Doppler? Explain that, explain the thought leadership angles that you guys have. >> Actually from the very beginning. >> The marketing team loves this, it's fantastic. >> So follow up with how. >> From day one there's a few things that we said were core principles, the way that we were going to grow and run the business. I'll talk about one other thing first which was that we were gonna be technology-enabled, technology-enabled services company. That we were gonna invest in IP both at the service level but as the technology level to accelerate the delivery of what we do. The second thing as a core principle is that we were going to lead through thought leadership. So we have been the most prolific producers of independent cloud content as a services firm bar none. Yeah, there's newspapers, magazines, analyst firms like yourself producing a lot of content. The stuff that we're producing is based on direct experience of implementing these solutions in the cloud with our clients so we can bring best practices. We're not talking about our services. We're talking about what is the best practice for any enterprise that wants to get to the cloud. How do you do security? How do you do organizational change? That has a very large following of Doppler both online where we have an email newsletter. But we also do printed publication of our quarterly Dopplers that goes out to a lot of our clients, the CIOs and key partners. That kind of thought leadership has really set us apart from all of the rest of the, even the born in cloud consultancies who never put that investment in. >> Flynn, you're a content guy. >> Absolutely. >> So you've got to really appreciate this. >> That's a dream, it's an absolute dream. One of the things, another proof point as a way to end, services first strategy is what we're doing in the market community at HP more money, energy, content, time is going into how we're talking, thought leadership and services than anything else in the company. We've got not just branding for Point Next and Green Lake, but bringing Doppler forward, bringing those great case studies forward. Putting that kind of content at the tip of the HPE sphere. It's not something you've seen from our company in the past. I think keep your eyes out over the next year. We'll have this conversation in six months and you'll see a lot more from us on that topic. >> Great stuff, congratulations on the process, the exit, the future. Good luck, exciting. >> Thanks guys. >> Really appreciate it. Keep it right there everybody, we'll be back right after this short break. Dave Vallente for Peter Burris from HPE Discover Madrid. This is theCube. (upbeat instrumental music)

>> Man: Live from Madrid, Spain, it's the Cube! Covering HPE Discover Madrid, 2017. Brought to you by: Hewlett Packard Enterprise. >> Welcome back to Madrid, everybody, this is The Cube, the leader in live tech coverage. My name is Dave Vellante, I'm here with my co-host, Peter Burris, and this is our second day of coverage of HPE's Madrid Conference, HPE Discover. Sharad Singhal is back, Director of Machine Software and Applications, HPE and Corps and Labs >> Good to be back. And Mike Woodacre is here, a distinguished engineer from Mission Critical Solutions at Hewlett-Packard Enterprise. Gentlemen, welcome to the Cube, welcome back. Good to see you, Mike. >> Good to be here. >> Superdome Flex is all the rage here! (laughs) At this show. You guys are happy about that? You were explaining off-camera that is the first jointly-engineered product from SGI and HPE, so you hit a milestone. >> Yeah, and I came into Hewett Packard Enterprise just over a year ago with the SGI Acquisition. We're already working on our next generation in memory computing platform. We basically hit the ground running, integrated the engineering teams immediately that we closed the acquisition so we could drive through the finish line and with the product announcement just recently, we're really excited to get that out into the market. Really represent the leading in memory, computing system in the industry. >> Sharad, a high performance computer, you've always been big data, needing big memories, lots of performance... How has, or has, the acquisition of SGI shaped your agenda in any way or your thinking, or advanced some of the innovations that you guys are coming up with? >> Actually, it was truly like a meeting of the minds when these guys came into HPE. We had been talking about memory-driven computing, the machine prototype, for the last two years. Some of us were aware of it, but a lot of us were not aware of it. These guys had been working essentially in parallel on similar concepts. Some of the work we had done, we were thinking in terms of our road maps and they were looking at the same things. Their road maps were looking incredibly similar to what we were talking about. As the engineering teams came about, we brought both the Superdome X technology and The UV300 technology together into this new product that Mike can talk a lot more about. From my side, I was talking about the machine and the machine research project. When I first met Mike and I started talking to him about what they were doing, my immediate reaction was, "Oh wow wait a minute, this is exactly what I need!" I was talking about something where I could take the machine concepts and deliver products to customers in the 2020 time frame. With the help of Mike and his team, we are able to now do essentially something where we can take the benefits we are describing in the machine program and- make those ideas available to customers right now. I think to me that was the fun part of this journey here. >> So what are the key problems that your team is attacking with this new offering? >> The primary use case for the Superdome Flex is really high-performance in memory database applications, typically SAP Hana is sort of the industry leading solution in that space right now. One of the key things with the Superdome Flex, you know, Flex is the active word, it's the flexibility. You can start with a small building block of four socket, three terabyte building block, and then you just connect these boxes together. The memory footprint just grows linearly. The latency across our fabric just stays constant as you add these modules together. We can deliver up to 32 processes, 48 terabytes of in-memory data in a single rack. So it's really the flexibility, sort of a pay as you grow model. As their needs grow, they don't have to throw out the infrastructure. They can add to it. >> So when you take a look ultimately at the combination, we talked a little bit about some of the new types of problems that can be addressed, but let's bring it practical to the average enterprise. What can the enterprise do today, as a consequence of this machine, that they couldn't do just a few weeks ago? >> So it sort of builds on the modularity, as Lance explained. If you ask a CEO today, "what's my database requirement going to be in two or three years?" they're like, "I hope my business is successful, I hope I'm gonna grow my needs," but I really don't know where that side is going to grow, so the flexibility to just add modules and scale up the capacity of memory to bring that- so the whole concept of in-memory databases is basically bringing your online transaction processing and your data-analytics processing together. So then you can do this in real time and instead of your data going to a data warehouse and looking at how the business is operating days or weeks or months ago, I can see how it's acting right now with the latest updates of transactions. >> So this is important. You mentioned two different things. Number one is you mentioned you can envision- or three things. You can start using modern technology immediately on an extremely modern platform. Number two, you can grow this and scale this as needs follow, because Hana in memory is not gonna have the same scaling limitations that you know, Oracle on a bunch of spinning discs had. >> Mike: Exactly. >> So, you still have the flexibility to learn and then very importantly, you can start adding new functions, including automation, because now you can put the analytics and the transaction processing together, close that loop so you can bring transactions, analytics, boom, into a piece of automation, and scale that in unprecedented ways. That's kind of three things that the business can now think about. Have I got that right? >> Yeah, that's exactly right. It lets people really understand how their business is operating in real time, look for trends, look for new signatures in how the business is operating. They can basically build on their success and basically having this sort of technology gives them a competitive advantage over their competitors so they can out-compute or out-compete and get ahead of the competition. >> But it also presumably leads to new kinds of efficiencies because you can converge, that converge word that we've heard so much. You can not just converge the hardware and converge the system software management, but you can now increasingly converge tasks. Bring those tasks in the system, but also at a business level, down onto the same platform. >> Exactly, and so moving in memory is really about bringing real time to the problem instead of batch mode processing, you bring in the real-time aspect. Humans, we're interactive, we like to ask a question, get an answer, get on to the next question in real time. When processes move from batch mode to real time, you just get a step change in the innovation that can occur. We think with this foundation, we're really enabling the industry to step forward. >> So let's create a practical example here. Let's apply this platform to a sizeable system that's looking at customer behavior patterns. Then let's imagine how we can take the e-commerce system that's actually handling order, bill, fulfillment and all those other things. We can bring those two things together not just in a way that might work, if we have someone online for five minutes, but right now. Is that kind of one of those examples that we're looking at? >> Absolutely, you can basically- you have a history of the customers you're working with. In retail when you go in a store, the store will know your history of transactions with them. They can decide if they want to offer you real time discounts on particular items. They'll also be taking in other data, weather conditions to drive their business. Suddenly there's going to be a heat wave, I want more ice cream in the store, or it's gonna be freezing next week, I'm gonna order in more coats and mittens for everyone to buy. So taking in lots of transactional data, not just the actual business transaction, but environmental data, you can accelerate your ability to provide consumers with the things they will need. >> Okay, so I remember when you guys launched Apollo. Antonio Neri was running the server division, you might have had networking to him. He did a little reveal on the floor. Antonio's actually in the house over there. >> Mike: (laughs) Next door. There was an astronaut at the reveal. We covered it on the Cube. He's always been very focused on this part of the business of the high-performance computing, and obviously the machine has been a huge project. How has the leadership been? We had a lot of skeptics early on that said you were crazy. What was the conversation like with Meg and Antonio? Were they continuously supportive, were they sometimes skeptical too? What was that like? >> So if you think about the total amount of effort we've put in the machine program, and truly speaking, that kind of effort would not be possible if the senior leadership was not behind us inside this company. Right? A lot of us in HP labs were working on it. It was not just a labs project, it was a project where our business partners were working on it. We brought together engineering teams from the business groups who understood how projects were put together. We had software people working with us who were working inside the business, we had researchers from labs working, we had supply chain partners working with us inside this project. A project of this scale and scope does not succeed if it's a handful of researchers doing this work. We had enormous support from the business side and from our leadership team. I give enormous thanks to our leadership team to allow us to do this, because it's an industry thing, not just an HP Enterprise thing. At the same time, with this kind of investment, there's clearly an expectation that we will make it real. It's taken us three years to go from, "here is a vague idea from a group of crazy people in labs," to something which actually works and is real. Frankly, the conversation in the last six months has been, "okay, so how do we actually take it to customers?" That's where the partnership with Mike and his team has become so valuable. At this point in time, we have a shared vision of where we need to take the thing. We have something where we can on-board customers right now. We have something where, frankly, even I'm working on the examples we were talking about earlier today. Not everybody can afford a 16-socket, giant machine. The Superdome Flex allows my customer, or anybody who is playing with an application to start small, something that is reasonably affordable, try that application out. If that application is working, they have the ability to scale up. This is what makes the Superdome Flex such a nice environment to work in for the types of applications I'm worrying about because it takes something which when we had started this program, people would ask us, "when will the machine product be?" From day one, we said, "the machine product will be something that might become available to you in some form or another by the end of the decade." Well, suddenly with Mike, I think I can make it happen right now. It's not quite the end of the decade yet, right? So I think that's what excited me about this partnership we have with the Superdome Flex team. The fact that they had the same vision and the same aspirations that we do. It's a platform that allows my current customers with their current applications like Mike described within the context of say, SAB Hana, a scalable platform, they can operate it now. It's also something that allows them to involve towards the future and start putting new applications that they haven't even thought about today. Those were the kinds of applications we were talking about. It makes it possible for them to move into this journey today. >> So what is the availability of Superdome Flex? Can I buy it today? >> Mike: You can buy it today. Actually, I had the pleasure of installing the first early-access system in the UK last week. We've been delivering large memory platforms to Stephen Hawking's team at Cambridge University for the last twenty years because they really like the in-memory capability to allow them, as they say, to be scientists, not computer scientists, in working through their algorithms and data. Yeah, it's ready for sale today. >> What's going on with Hawking's team? I don't know if this is fake news or not, but I saw something come across that said he says the world's gonna blow up in 600 years. (laughter) I was like, uh-oh, what's Hawking got going now? (laughs) That's gotta be fun working with those guys. >> Yeah, I know, it's been fun working with that team. Actually, what I would say following up on Sharad's comment, it's been really fun this last year, because I've sort of been following the machine from outside when the announcements were made a couple of years ago. Immediately when the acquisition closed, I was like, "tell me about the software you've been developing, tell me about the photonics and all these technologies," because boy, I can now accelerate where I want to go with the technology we've been developing. Superdome Flex is really the first step on the path. It's a better product than either company could have delivered on their own. Now over time, we can integrate other learnings and technologies from the machine research program. It's a really exciting time. >> Excellent. Gentlemen, I always love the SGI acquisitions. Thought it made a lot of sense. Great brand, kind of put SGI back on the map in a lot of ways. Gentlemen, thanks very much for coming on the Cube. >> Thank you again. >> We appreciate you. >> Mike: Thank you. >> Thanks for coming on. Alright everybody, We'll be back with our next guest right after this short break. This is the Cube, live from HGE Discover Madrid. Be right back. (energetic synth)

>> Announcer: Live from Madrid, Spain. It's theCube covering HPE Discover Madrid 2017. Brought to you by Hewlett-Packard Enterprise. >> And we're back at HPE Discover Madrid 2018. This is theCube, the leader in live tech coverage. I'm Dave Vellante, with my co-host Peter Burris. This is day two of the event. Johannes Koch is here, he's the Vice President and Managing Director of Central Eastern Europe, Middle East and Africa for Hewlett-Packard Enterprise, and he's joined by Ali Saleh, he's the Senior Vice President and Chief Commercial Officer at GE Digital for Middle East and Africa. Gentlemen, thanks so much for coming to theCube. >> Appreciate it. Thank you. >> Thank you for having us. >> Johannes, let's start off with you. GE, HPE, what are you guys all about, what are you doing together? Talk about the partnership and the alliance. >> So, you know, it started actually one month ago, I suppose and it was meetings that we had with General Electric to understand the customer requirements in cybersecurity, and what we figured is in this world of IoT, Internet of Things there is an increased requirement for security. And there was, from our perspective, lots of solutions out there but it's quite difficult for customers to understand the landscape and who to turn to. And we also figured that in this world, nobody can serve every requirement of a customer, so this is how we figured out with GED that we have a joint interest here, to serve in the Central/Eastern European and then mainly in the Middle East and Africa part our customer base. And this is how it started and I think what I can say is it has accelerated incredibly during the last two months since we signed the joint agreement. We've been building a channel, we've been having lots of meetings with customers and built a really nice pipeline in the meantime, also I think here the show reflects an incredible interest by our customers. So I think we are in a very good state at the moment having a lot of interest, probably all the key customers in our region having this on their agenda. >> Ali, maybe you could just describe the situation, the industrial expansion if you will in Middle East and Africa, what you guys are seeing in terms of the big trends, and what the opportunity looks like. >> Well thank you. You know, GE has been in the Middle East, Africa, for over 80 years in some countries, and we have deep relationships on industrial side, whether it's power, oil and gas, aviation, healthcare and others. And our customers are thinking a lot about cost, quality, and access, and productivity is top of mind, and they've discovered that their industrial assets are smart and capable but the data are not being collected. So when we collaborate with ecosystem of partners, and we fetch the data and get connected, and get insights from the machine to make them able to make the right decision at the right time and then it drives optimization. This is top of mind. They want to see how they can do better for less. >> Okay, so the customers at GE Digital, the customers are going digital, they have all these devices, instruments, machines, and they're moving in a new direction you guys are trying to lead in. What are the challenges that they're facing, what are they asking your help on, what are the big problems that they're trying to solve? >> So, everyone wants to talk about productivity and calls out. The challenge is that not everyone is ready for digital transformation. Some do not feel there is a burning platform, and those that are ready when they feel there is a burning platform, they don't have a plan, they don't have a playbook. So it's important that we collaborate and help our partners and customers understand their current state and heat map and desired state and pinpoint to quick wins so that they get it and they see incremental improvement. And asset performance management has been an easy way for us to say, "Your asset is underutilized "compared to your industrial entitlement "you can do 10x better," and this gets their attention, and this is where we see the power of one in the industrial age is relevant, one percent. In our market, in the world free market, when we talk to them about one or two percent productivity they laugh at us. They say, "Talk to us about ten or twenty." Because there has been a lot of gap in productivity and efficiency. >> Are you able to, I mean, it's only been nine months, but are you able to start to see any kind of customer results at this point? Do you have some examples even early wins with customers? >> So to be exact, the start of the relationship in a formal way-- >> Was it, what'd you say? >> --is two months. >> I thought I heard nine months. >> No, no, we started our first conversations and until it was over, it came to the agreement-- >> So it was brand new, in terms of... >> It is really brand new and I think what we can say is, I think we have 180 partners already engaged. We have probably more than 500 customer contacts in the region already so with large accounts. And we have a pipeline that is multi-million dollar in size. So we're expecting the first close within our first quarter, which ends at January 2018. I think there's no question that there is a big market opportunity out there, right? And I think the show here, I think for me, even accelerated things, because I think in the past, digital transformation was sort of limited to a few industries, we always give travel industry, we take banking sometimes but here, I think what became transparent to many of the customers that we had here, that there is no industry that is sort of immune against what is happening out there and specifically also that the sensors and the devices out there require special attention. And I think with the, specifically on the OT side, we have a solution now with GED that we can basically roll out across our territory. >> So I wanna talk about three things very quickly, I'm gonna lay this out and ask you if in fact this is going to catalyze that much more attention. Number one is a lot of the industry in the Middle East and Africa are natural resource industries, where the historical ways of doing things have been relatively inefficient. So there's a lot of opportunity to use IoT and related things to bring more efficiency, better practices, overall resource management. Number two is, that the technology's now capable of doing that in places where you don't necessarily have the best infrastructure. Aruba technology, for wireless, some of the other things are now possible, that adds to it. And number three, we've seen some recent steps in liberalizing some of the countries that have the most opportunity to do some things differently. You know, Robert Mugabe, no longer in Zimbabwe, the new prince in Saudi Arabia talking about liberalizing things. Are you seeing these come together in a way that would encourage people to think new ways, do new things, use information perhaps differently than it did before? What do you think, is this a confluence, is this a moment? >> Well I agree with you, and absolutely. Today, our customers and partners in region are more ready than before, and they're pulling hard. And when we put our act together as an ecosystem of partners we make it easier on them to make the right decision. When we talk productivity, productivity comes from people, from process operation, from industrial entitlement. And when we talk about the digital thread that brings it all together whether we look at the culture and vision and mission and people utilization, look at the process defined or not, and how we can optimize it, look at the industrial entitlement, and tell them, "Compared to your peers, "this is where you can be." We have their attention. And with the push from the government for productivity and utilization and do more for less, this is becoming top of mind, everyone is talking about it. So, when we partner together and we say, "This is the playbook, this is how you can start, "and this is the edge to cloud solution in a secure way." And we link it to the industrial entitlement, and let's underline industrial, because when we speak the healthcare language and the power language and the oil and gas language we get their attention. >> Excellent, so there's an increasing interest, and you anticipate that there's going to be new action with their pocketbooks. >> Johannes: Yes. And I would add, I think we, this is not an easy marketplace but you can have some outstanding projects. And we have, in the region, you may have heard about, there was in the private investment fund, when the crown prince did announce the NEOM project. Or, we have in Dubai Smart City as a project, with the city of Dubai which are all projects that probably would not happen in Western Europe. So there is potential, there are bigger things happening, and I think there is also an understanding that this is a way how to leapfrog, to your point, to leapfrog technology. And I think that is what can happen. What we need to be careful of is where to invest, because there are lots of ideas out there, and to understand what are the real things, and what are the things that we need to make happen. This is, I think, the challenge. >> And they wouldn't happen in Western Europe because, what? The maturity of the infrastructure, the space limitations, the appetite? >> Johannes: I think, to give the example of Smart City. >> Dave: Yeah. >> So I think we have a lot of, in my remit we have lots of discussion on Smart City. But it's usually you have to find the city that is willing to pay a POC. >> Pete: 12 layers of bureaucracy. >> And exactly. And you need to talk to each and every city individually, whereas here, if you have a decision maker to say, "Yes, we do this." >> Pete: Yep. >> Dave: Right. >> And then we do it. >> Dave: You cut the line. >> And the answer is about readiness. When you go to a large enterprise that's very successful, you meet the CEO and you quickly conclude whether they're ready for digital transformation or not, are they gonna make this top of mind for them? Are they gonna give you time? Are they gonna talk about productivity? Or is this going to be an IT discussion, and they're gonna treat you as a supplier? Those that are ready, we roll up our sleeves, and we put in our dedicated resources to help them look at the transformation. When the government official is pushing and mandating for calls out, then obviously everyone wants to copy and talk about it. And this makes it easier for us to execute. >> You're talking, again, big numbers. Not one percent, ten percent, so that's the nirvana. How confident are you that you can actually have that type of impact? >> So we've got data points, right? If you look at healthcare in the Saudi Ministry of Health, we've been collaborating on looking at operating room optimization or emergency room optimization, without touching digitization. Looking at the process and utilization of appointments, no-show, and the way the clinical governance is taking place, we're showing 40 percent improvement. If you look at, the factory of the future with Obeikan in Saudi Arabia, we've got asset performance improvement project, and they already yielded a 12 percent improvement, and the entitlement is up to 20, that we're working on. When you transform something, it's iterative, right? When you transform something that you have not pushed for efficiency before it's easy for the first iteration to show an incremental change. >> Pete: Yup. >> That challenge will be for the change to last. And this is where digitization makes it last and makes it impactful. And when we look at the HPE relationship with the MOH on the electronic medical record, we've got right now two active projects with two hospitals, and it's all powered by Predix, and HP peripherals are being deployed to the site. And if we go to the Saudi Electricity Company, we've got a project now on asset performance management across all their assets and again HPE peripherals are also deployed and it's all about GE ecosystem of Predix-enabled solutions. >> So I've had the pleasure and honor of speaking in front of a relatively large group of CIOs a couple times in Africa in the past few years. And I always was surprised by the degree to which they suggested that the necessity of change in this region, and the fact that a little bit of technology can have an enormous impact, the degree to which we might actually see some leadership technology come out of this region. What do you think, are the types of issues, the types of problems that could be solved with this technology in Africa, the types of problems that solving them there could actually start driving the industry in different directions? Solving new classes, whole new classes of problems. Do you think that this type of technology can have a transformative effect, not only in Africa but more broadly? >> Absolutely, this is a way for systems to leapfrog. If you look at Kenya right now, they've got a transformation project for 98 hospitals. And they've got massive shortage of radiologists. So right now, we're replacing equipment in 98 hospitals but tele-radiology is the answer for the shortage of radiologists. If you look to South African Discovery, what Discovery is doing is best in class, and I haven't seen any other insurance company looking at the ecosystem the way they do it. So, absolutely, we're seeing pockets of excellence in Africa, and this can be a way to leapfrog. >> You said you started the conversations around security. >> Johannes: Cybersecurity. >> What was that conversation like? Why was that the starting point? I mean, obviously it's important, but why? >> To be honest, I would have to leave this to you, but I think it was because mainly there was we saw the customer interest. >> Yeah. >> And I would say, probably a year or two years ago, you would have not seen this as a very typical HPE alliance. We were technology people. We were software or hardware people. What you see in, and I mentioned in the beginning, in the world of IoT, things are blurring a bit. What is happening on the edge is very much in the business of General Electric. So I think this builds automatically the new ecosystem. When you look here at Discovery, the alliance has become more and more industrial companies. It's linked to industrial 4.0, car industries and all that. Everywhere where data is being created, we need to have the partnership because and that is because the data that is being created at the edge also needs to be computed at the edge. If we want to be successful, we gotta say, "We cannot limit ourselves to the "data centers the rest is the others." And this is where I think we find the very good connection point because now we have software that actually can operate at the edge. I think you have good examples on that. >> Yeah absolutely, if you look at the pain point in Middle East, Africa, majority of our partners and customers are government entities and for them top of mind securing their large industrial assets is important. And in the operations space there hasn't been much done on security, where you can go into a hospital and simulate light flickering with a voltmeter. And you can take over the temperature and play with it. Today there's a lot of smart sensors out there, but we're securing the IT firewall, but within the hospital, or within the plant, we can do a lot of crazy stuff. And we owe it to our partners to show our capability. That's what we do within our factories, and our platform is designed around security for operations. So the easier interlock with HPE is our ability to get closer to the edge and peripherals, and ensure the operation is secure and that's the first experiment but then, obviously, we're expanding beyond that to other opportunities. >> Dave: Excellent. Alright, gentlemen, we have to leave it there. Thanks so much for coming to theCube. >> Johannes: Thank you very much. >> Sharing your story, good luck with the partnership. >> Thank you. >> Dave: Hope you can come back, maybe in Las Vegas or maybe next year at this event, give us the update. >> For sure. Thank you very much. >> Thank you. Appreciate it. >> Dave: Okay. Keep it right there, everybody. We'll be back with our next guest. Dave Vellante, Peter Burris, this is theCube live from Madrid HPE Discover 2017. (upbeat music)

>> Announcer: Live from Madrid, Spain. It's theCUBE! Covering HBE Discover Madrid 2017. Brought to you by Hewlett Packard Enterprise. >> Calls off just Rebecca. Hi, everybody, welcome back to Madrid. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante. I'm here with my cohost, Peter Burris. Day two of HPE Discover Madrid, 2017. Beena Ammanath is here. She's the Global Vice President of Big Data AI and new tech innovation at Hewlett Packard Enterprise. Beena, welcome to theCUBE, it's great to have you on. >> Thank you, Dave. >> Dave: First time on The Cube, right? >> Yes, thank you Dave, thank you Peter. I'm very glad to be here. >> Ah, you're very welcome. So, let's talk about what Hewlett Packard Enterprise is doing in AI, and you're new to the company, they brought you in. Why did Hewlett Packard tap your expertise? >> I think a lot of it is based on my previous experience and, honestly there is so much buzz going on with AI, and the hype around it, right? There is so much that we need to do with AI. There's so much potential and we are not tapping into it as much as we should. That was one of the big reasons and especially what Hewlett Packard Enterprise is doing now. We are going through this transformation, we can help our customers start on their AI journey, help them build out end to end solutions with AI, which is going to be one of my biggest charters. >> Well when we were young and started in this business, AI was the buzz, in the early to mid eighties. >> Beena: Yes. >> And that was the fifth or sixth time around with AI. >> Oh, yeah, yeah. >> That was 40 years ago. >> Yeah. >> It just obviously died, the processing power wasn't there, and I guess the data. >> Beena: Yeah. >> Why AI, why now? >> Yeah, so you know, I'll date myself here. When I was doing my undergrad, post-grad, we had AI as one of the courses and nobody wanted to do it because it was considered this very futuristic thing, never going to happen. Self-driving cars, boom. Personalized ads, even that was considered so hypothetical because we didn't have the compute, we didn't have the processing power, we didn't have the amount of data accessible to us. >> The acquisition of data was harder, the compute power wasn't there, So it was just, it was just always a science project. >> It was always a science project, it was a research, it was more ideas and it wasn't doable, but today, with the advances we've seen with cheap storage, easy access to compute, the whole game has changed. Lot of things we could only dream about is now becoming real, we are able to experiment more. And speaking to what you were saying earlier, AI has been through this hype cycle several times. If you think back, AI, the term itself was coined in 1956, and then we see those hype cycles when there is massive investment and there is nothing delivered, then it wanes down, so the AI winters keep happening. And now, I think it's again on a rise, but this time, we are actually seeing results. We are seeing self-driving cars, we are seeing first-rise marketing taken to a whole new level. We are seeing drones making deliveries, right? But if you think about it, when you started the business, you've seen about AI too right? It's still the narrow-intelligence part, right? It's not a super-intelligence or general-intelligence that scale that we've reached out to, and I think, given what I know about the analytic techniques available today or even the compute power available today, we are still going to be dabbling around in narrow-intelligence for at least the next few years, before we expand out to the next level. >> So that raises an interesting issue because, I first heard about AI back in the '70s reading Flagibon's fifth Generation Systems book, which, by then, they were talking about multiple generations of AI that supposedly already happened, but AI has, for technical reasons, for technology, for the acquisitions, has disappointed. Now, it's not disappointing, but there's still this perception of how much change is coming, and the impact of a change and let's talk about the people's side of this, Because the success of AI is going to be very closely tied to whether or not social groups abandon it because it doesn't deliver what was expected, or the impacts are greater in ways that weren't anticipated. Yeah. >> What's the people side of this change, the innovation, the social changes side? >> Yeah, yeah. So I like to look back at history, history always gives us an indication of where technology is taking us. And if you look back at the early 19th century, actually, the early 20th century when the steam engine was invented, right? What did it do? It enabled humans to expand their physical abilities. To move things, to drive things forward, so it was increasing the human muscle-power. And that whole industrial revolution that happened around that time with steam engine and the automation of lot of work that was being done by humans manually, right? And we see a similar revolution happening now because it's fundamentally changing how we work, how economies are made, and that causes a lot of fear and insecurities and, who knows, our jobs might be replaced or changed over the next few years, we don't know because this technology is coming at us very fast. The reason is because there are so many companies investing so heavily in AI. What that makes us do is it accelerates the development of the technology, it comes at us smarter and faster. And we are not prepared for it, like if you look back at our whole lives, right? I'm talking about a time when I was in my twenties and just thinking about AI, it was mythical and futuristic, and now, today, there are self-driving cars. It's happening in our lifetime where things have changed so rapidly and we don't know what it's going to look like 20 years from now. The piece that I am optimistic about is, unlike a number of luminaries who are spelling doom of mankind and elimination of human race and jobs and so much more, for me, it seems like, look, at the end of the day, we are building AI. We have the power to shape it the way we want. The fear exists because there is so much unknown. And it is also because it's a select few group of people who are shaping AI. So, how do we actually get more people involved? How do we truly democratize AI so that we get different view points? Like, should a computer scientist be building an AI product in isolation, without full partnership from a lawyer or for similar domain products? The domain experts have to be involved. And today that's not happening. So we don't, and if you're building... And I stick to legal just because something I can relate to is if a lawyer is actively involved in building an AI Legal product, he or she knows all the checks and balances we need to put in place so that AI doesn't go rogue. When a pure computer science person is driving that product and building the product, he or she may not be aware of all the checks and balances. And we may not put the right guard rails in place to prevent that program from going rogue. At the end of the day, AI is something that we own, and we should be able to build it in a way with the right guard rails in place. And if you look at, we are all so dependent on our phones, and what is that? That is AI today. But we are not afraid of it, we use it, we leverage it. And that's how I think AI will be 20, 30 years from now. Is really helping us extend our brain power, right? Remove the monotonous tasks we have to do and help us be more creative and really elevate the human aspects of all of us. >> So, let's carry that through. >> Beena: Yeah. >> So you mentioned the industrial revolution? >> Beena: Yeah. >> Machines have always replaced humans at certain tasks. >> Peter: There's always been substitution. >> Always. >> M-hm. >> But, for the first time, it's happening with cognitive tasks. >> M-hm. >> So, people get scared. And then you quote the statistics, median income in the United States has dropped since the late '90s from $55,000 down to $50,000. >> Yeah. >> Part of that is you can see it, and you know there aren't paper hangers on billboards anymore, or barely there are. Or you go the airports and kiosks have replaced tickets issuers. Hopefully, they can replace-- (laughing) And so people are concerned, as you rightly pointed out. But you also said that we have the opportunity to shape this so the answer, many of us feel, is education around creativity, how to combine different inputs to create value, but many people are afraid, they say, "Let's stop progress." That's not gonna happen. >> Right, yes. >> We know that, so what has to happen from a socio-economic, a public policy standpoint in order to create those borders that you talked about? >> Right, right. I think education itself has to fundamentally change where we infuse more creativity into the education system, where we start to allow it to be more focused on the science or math aspect, which is where you go for computer scientists, but you need that human aspect like built out in all of us, right? And so, but it's also an opportunity for us to leverage AI to make our education better. So, more personalized education. But, from a social aspect, I think one of the things that's missing is really the policy aspect. We don't know, this technology is coming at us so fast, we don't have all the policies figured out. We are building out the policies as the technology evolves. And, that is kind of causing that fear of friction, so to speak. So, I think there needs to be this group, or the governments actually need to take more ownership and start putting in those guard rails into place from a policy perspective and that needs to come from the industry themselves, right? >> Yeah, yeah, yeah. >> There needs to be these thought leaders. I think everybody who is scared of AI should be starting to take an active role to understand it and drive this policy forward. >> Well, it has to be bipartisan too. >> Beena: Yes. >> Which, right now, doesn't look too-- >> Well, whatever the partisan is 'cause in other areas it's not just bipartisan like it is in the US but, coming back to this question, I've got a couple quick questions for you. One is that you mentioned earlier that the computer scientists probably should not be the one that's necessarily making a decision about a legal issue. It suggests that there is going to be a renaissance of cross-disciplinary skills required within a, certainly within computing, so, for example, the people that are best at describing how human interactions evolve and maintain, might be philosophers, which gets turned into law. Talk a little bit about the renaissance of the whole promise of cross-discipline thinking in computing because we're attacking new kinds of problems that just aren't algorithmic. >> Exactly and you need to have deep domain experts deeply involved in building out these AI products, which is kind of a gap today, so I think you're absolutely right. >> So second thing is, related to that, is we've done some research and we're in the midst right now of a pretty sizeable project on envisioning what we call, or the needs and how it will be structured, we call Systems of Agency, so, you observe the collection of the data, the turning the data into value through big data, and then to have a consequential action in the real world, we think there are three different ways that's gonna happen. I won't bore you right now. >> Yeah, yeah. >> But really, we're asking these systems to do something on behalf of the brand. >> Ah. >> And increasingly do something in a complex, human-centered environment. >> Yes. >> What does, and so effectively the agents for the brand. We know how to distribute authority. I'm sorry, we know how to distribute data and we know how to distribute processing; how do we think about distributing authority? >> Mmm. >> Using AI, is that something people are starting to think about in your estimation, as we think about the people problems associated with this? >> I think so. I think people are beginning to think about it. There's a lot of investments going on, not only in the technology development part, but also the human side of things. It just doesn't get as much publicity as the technology piece does, right? A robot beating somebody at a goal is much more newsworthy than-- >> Doesn't have huge-- >> Yeah. >> Moral implications for something else. So I've got one more question. >> Dave: Well, wait, in a narrow sense, would fraud detection be an example of distributing authority? >> No, because, well, I'll ask you. Is fraud detection an example of distributing authority? >> It's narrow. >> Beena: Yeah. >> It's somebody, it's a machine making a decision not to fulfill a transaction. >> Right. But the machine is not making a decision to bring an indictment against someone >> Beena: Exactly. >> And were they doing fraud? So all the machine's doing is-- >> Flagging. >> Is seeing a pattern that might indicate a problem and taking a prophylactic step to avoid it, the machine is not declaring fraud. >> No, and there are two things to it, right? The machine, before it declares fraud, it's being trained, it's being built by a human, it's being trained by human, right? Before it declares, before it goes into production and declares fraud, there has been a lot of training done by human where they're saying yes, no, this is right, this is wrong. So that training is crucial, that comes from humans, and also once this is in production, there's a human in the loop who's watching it. >> Peter: Who still has agency rights. >> Exactly. So the human is still there. >> So I've got one more question, one more question. And that other question is, at least in the US, 'cause AI is software, at least in the US, most software is covered under copyright law. Which means what software does is a speech act, which has implications for whether or not you can go after a company because their software did something wrong. >> M-hm, m-hm. >> AI as an agent can't be a speech act. There's gotta be some other remediation, we have to expect more from brands that deploy this. How is that going to evolve in your estimation? >> I think the policy part, that's where it becomes more important, right? And if you recently heard the news of a robot being given citizenship, I mean, besides the marketing and hype, what does that entail? Making us question fundamental things and the policy aspect has to cover a lot of new scenarios which we just haven't had to think about-- >> Peter: Right. >> In our whole life, right? It's just arising a lot of new scenarios that are going to make us create new policies around it. >> Dave: So, I mean, this is a very interesting discussion and when I hear it I think about what can humans do that machines can't do? And you go back, it wasn't long ago that machines couldn't climb stairs. >> Beena: Yeah, yeah, yeah, they can do-- >> Yeah, now they can, sort of. >> Gymnastics. >> Yeah, right. Okay, so. I don't know, do you think in those terms. >> Yes. >> I mean, there's empathy. There's maybe negotiation, there's things like, ya know, decisions on a jury that require a human. >> Oh yes, I'll give you the simplest one. What it cannot do, even today, it can write music, which you probably see-- >> Sure. >> But, AI still can't tell a joke. (Dave laughs) It can't write a joke because-- >> Peter: It doesn't know irony. >> It doesn't know, it doesn't understand sarcasm. And it doesn't really have that human aspect of connecting with people, and taking conversations forward, like just talking to you, I have something called an intuition or perception which helps me guide this conversation. A machine can't do that. It's just black and white, it goes by data. >> Dave: Strange, yes. >> It's strange. >> Responses. >> Yes. >> So, I always struggled with the term Artificial Intelligence. I feel like machine intelligence is more-- >> Yeah. >> More accurate. >> I don't struggle with the artificial, I struggle with the intelligence. >> Beena: Yes, it's how you define intelligence. >> Alright, we have to leave it there. Last word, on a, let's bring it back to Discover 2018. >> Beena: Yes. >> Tie it into your future vision. >> Oh, yes, I am so excited to be here and be, and I don't know if you've had a chance to walk through the floors but we're doing some amazing things with AI, with Big Data, and really looking forward to helping our customers start and execute on their AI journeys. >> Beena, thanks very much for coming in theCUBE. >> Thank you. >> It was great to meet you. Alright, keep it right there, everybody. We'll be right back with our next guest, Dave Vellante. From Peter Burris, live from HPE Discover, Madrid 2018. You're watching theCUBE. (light music)

>> Announcer: Live from Madrid, Spain, it's theCUBE, covering HP Discover Madrid 2017. Brought to you by Hewlett-Packard Enterprise. >> We're back in Madrid, everybody. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante and I'm here with my colleague, Peter Burris, co-host for the week, covering HPE Discover Madrid 2017. McLeod Glass is here. He's the vice president of product management for software defined in the cloud group at Hewlett-Packard Enterprise, and he's joined by Ronald Veirweij, who is the managing partner with The Sourcing Company. >> Ronald: Yeah. >> Dave: Good to see you. >> Thank you. >> Dave: Thanks so much for coming on theCUBE. >> Thanks, thanks for-- >> So I'm excited about this. We've been hearing about Azure Stack for awhile now, and we've been talking about bringing the cloud model to your business for awhile now and it looks like it's here. >> Yeah, no, absolutely. We're excited. I mean, you know, I think we've worked hard with Microsoft to pull together what we believe is a very compelling solution with Azure Stack. I think this gentleman here can attest to the value behind it, but we basically pulled together a lot of capability and flexibility in the overall solution that allows our customers to be able to pull together a solution that lets you take Azure-centric type services and run them on premise for maybe conditions where you have data sovereignty issues or you maybe have edge applications where you can't actually have the connectivity you need to the Azure cloud and be able to start building on those capabilities. >> Well, Ronald, I wonder if you could come in. It's interesting to juxtapose, take the AWS strategy, which is hey, got the cloud here, bring it all over. Microsoft obviously has an on-prem estate already, recognizes the customer need for that, and says, alright, we can bring substantially that cloud model on-prem. Why does that appeal to you, and does it work? >> Well, actually, we do think that for the first time now it's possible to get control of cloud. To us, it's the connection between the devices and the Azure cloud, and Azure Stack, to us, is between in. As a company, we do have control of Azure Stack, but we can also give control to our clients for Azure Stack. So a user can decide to put things in the cloud, and the company can decide whether they go in the cloud, or whether they stay into Azure Stack. So they have control of their data, and they can keep control of their data. On top of that, it's our hardware. So the data they decide to store on Azure Stack is on our hardware, and it's not a US hardware company, it's a Dutch hardware company. >> So, should I ask you upfront? Talk about The Sourcing Company, what you guys do, what your role is. >> Well, we are a cloud service provider. We do deliver cloud service to end users. We have a strong vertical focus. We do lawyer companies. We do housing companies. And we do care companies. And especially for the lawyer companies, we have built our own proposition where we connected several applications together, called Magistra, and that's what we bring to companies to use. >> So the model is when you bring a solution on-prem, you bill it like it's a cloud, is that right? >> Absolutely, yeah, it's all pay per use. >> Dave: Okay, describe that a little bit more detail. What are my limitations of that pay per use? >> What's different between the on-prem version and the non-on-prem version? >> I can talk something about it. We have an Azure Pack, which is just a formal system cloud environment. We call it our legacy environment. That's in a pay-per-month model. So we do report to Microsoft what licenses are used, and we do that monthly. Azure and Azure Stack are different. Azure is in a pay-per-second model, and Azure Stack is in a pay-per-minute model. Actually, for the first time, we are also able to create more flexibility. If in our legacy environment, a machine is on for two minutes, we have to pay for it for a month. If we do the same in our Azure Stack environment, well, we have to pay for the minutes. For example, at lawyer offices, you'll have people supporting the lawyers while they work for maybe 16-20 hours a week. You know, the lawyers themselves try to. >> Dave: But they bill a lot more. >> They try to see if they can put 100 hours in a week. And we're now able to create more agility in that, and to make it more flexible. >> So you were an early Azure Stack customer. >> Yeah, we're three years in March of program now. We decided in March on the early Azure Stack, to acquire to buy the Azure Stack. >> So how's it working-- maybe take us through the journey. A lot of times, the first Microsoft product isn't quite right. The second one starts to get really good. And then after it's mature-- >> Ronald: Well, almost. >> Yeah. >> Ronald: Well, our company was founded almost 11 years ago. And we always have looked into ways to simplify our environment. We were founded on the estate of Nyenrode Business University. We were not able to put any service over there, so we decided to put in a data center, and that's what we now call our legacy cloud environment. But in that road, we were always searching to simplify our environment. And Azure Pack was a good step, but not good enough. And Azure Stack, actually, does simplify that. It's a box, and nothing more than that. And if the box runs, then the box runs, and we decide when to update it, and we decide what to put on it, and well, that helps us. Next to the simplification of our environment, we also wanted to be able to generate more standardization. And with Azure Stack, you are forced to use defaults. The best way to use Azure Stack is to create templates and with the creation of templates, you have a defaults environment. So that's also the biggest thing. >> So McLeod, what do you guys bring to the table? What does Microsoft bring into the table? >> Yeah, so obviously we've got a longstanding relationship, partnership, with Microsoft. We worked hand-in-hand with them on the solution. I mean, first of all, it's based on proliant hardware, which we all know and love, but then we've also worked very hard to engineer this solution. One of the things that separates our configuration, our solution, from some of the others, is the expandability. We allow you to scale it by node, so basically, you can add individual nodes. We have some capabilities around adding different memory, and different networking configurations that we support around that. And then also, wrapping some of our flexible capacity capabilities around that to allow a pay-as-you-go type of model, consumption model, very much in line with what he was talking about earlier, that really kind of builds together a complete solution. And the other thing that we've done, is we've co-invested with Microsoft in what we call our Azure Stack Innovation Centers. So there's one in Bellevue and one here in Switzerland, in Geneva, that allows customers to actually go and test and leverage the great capabilities of our solution in a controlled environment. They can actually go there and work with experts to kind of engineer their solution, or they can actually connect remotely to those. And we also spent a lot of time training a lot of individuals. I think somewhere in the neighborhood of about 6,000 individuals in the company from a service and support standpoint to support the solution. So we're very excited about it. >> So as I understand it, you're a cloud service provider. You're a service provider. So how does this granularity provided by Azure Stack translate into a superior experience for your customers? >> Well, it simplified our platform. And while simplifying our platform, we have time up. And we can, in that time, we can do other things. If you look to Magistra, Magistra is a complete workspace for lawyers, and while we are forced to keep it standard, in a default, and keeping the template up to date. So while doing that, we don't have to bother about the things below the template, because that's taken care of by HP and by Microsoft. So it gives us time to think of other things that helps lawyers. And we like to think of things what helps them enable more productivity. For example, for a lawyer, it is absolutely a thing to keep time writing right. And we just announced that we will extract the time-writing with artificial intelligence at keeping up what they do during the day, and at the end of the day, tells them, okay, you worked for 48 minutes on that document. We do take that from that client, and swipe to the right, and it's accepted. Swipe to the left, and that changes. And that, things we like to do to enable more productivity for our end users. >> So the advantages are at least that you can now put more time and energy into creating services. How do you go to market? Do you go to market, is it all self-service? Do you have a direct sales organization that's going out and meeting with law firms? How do you sell your service? >> The things we do most is go to events and sponsor events and tell people that Magistra is there. And then, second, is one-on-one meetings. >> Peter: That's person-to-person. >> Absolutely, yeah. We do think that we put a lot of time in finding out what they need, and what keeps them awake at night. And we try to translate that into software and into a product, Magistra, what's helped them not being awake at night. >> But for many years, one of the challenges of doing this approach for a partner like yourself was, you want to present the solution to the customer in a form that they understand, but the underlying provisioning of the assets and ultimately the costs end up being presented in infrastructure and technology terms, which means a salesperson's having a hard time, the customer's having a hard time. Does this kind of common, simplified approach allow the customer, the salesperson, and the business overall to use a common template to articulate and make commitments about what's going to be delivered, have conversations about what's needed, all of those things. It's just simplifying not only the technology, but the business and how the customer perceives value. >> Well, look at it this way. Implementation time is quite low, because when we go to an office and ask them what they want, we need at least two, maybe three months to implement that. But we have to think about the solution in Magistra, well, we just run the script. It runs for seven hours, and then it's there. The environment's there. 21 servers are enrolled. The SharePoint of the commencement system is enrolled. The things are put in place. So the functionality is there. And maybe it's not answering all the functionality. Maybe it's answering 60, 70, maybe 80%. But it's fast. And that's what they like. >> What is keeping your clients up at night? >> To a lawyer, we do think three things. They want to have a good office functionality. To us, that's Office 365. They want to have a good document management system. Being sure that they are not having two colleagues working on the same case. And time writing. And those three things were the first we enabled in Magistra. >> McLeod, so what's your expectation for this business? I mean you guys have been, the market's been waiting for it for a long time, and it looks like it's here and ready to roll. >> Yeah, we're very excited. I mean, the interest has been very high especially by, with customers, especially in the service provider space, and customers that are looking to deploy Edge applications. That's been really where we've seen the most uptake, at the beginning here. And also some of the other kind of common use cases are things like areas where compliance or data sovereignty is a concern, and we're very excited about it. It's been great so far, so we're looking forward to it this year. >> Do you think other large cloud service providers, namely AWS, are going to have to respond with something like Azure Stack? >> We think they will. >> I mean, I don't see how they could just let that big of a market go. But it's capitulating to the dogma of everything has to be in the cloud. >> Here's what we know. >> You would presumably welcome that. If AWS comes to you and says hey, we want to partner with HP >> Hey, we believe the world is hybrid, right. The world is hybrid, and it's going to be hybrid. >> Peter: This is not a belief. >> And that, yeah. >> Peter: It is. >> Yes. >> It is today. And there's not a lot of changes expected in the laws of physics that are going to change in the next couple of years to make it easier for AWS. I think it's going to be the same basic physics. So from that perspective, it suggests pretty strongly that while there's a lot of use cases and there's a lot of money to be made just on that central piece, and then introducing new technologies like serverless and functional to approximate the ability to serve, but you can't do an office environment easily in a serverless computing world. It's just not how it's going to work. >> True. >> So at the end of the day, AWS is going to be able to do a great business doing what it does, because there's a lot of open space, but if they want to claim that it's everything, if they want to get everything, they're not going to do it by just claiming that this is all going to go away. >> I mean, the TAM of this opportunity for HPE and Microsoft is quite large, right, I would think. >> Oh, it's enormous. >> Anyway, I'd be surprised if we don't see something-- >> They have to respond. >> Anyway, guys, last word on HPE Discover. What's the bumper sticker, pulling out of the show? >> Well they have it, it's stable. They have it all on the right note. >> Dave: On the right path. >> On the right path. >> We're just continuing to make hybrid IT simple, and you've seen more of it here at the show. There's been a lot of exciting announcements and a lot of the technologies that we're bringing together. Azure Stack's just one of many that we've got in our portfolio that we're extremely excited about. >> Gents, thanks so much for coming to theCUBE. It was a pleasure to have you. >> McLeod: Alright, thanks. >> You're welcome. Alright, keep it right there, buddy. Everybody. Peter and I will be back after this.

>> (Narrator) Live from Madrid, Spain. It's The Cube covering HP Discover Madrid 2017, brought to you by Hewlett Packard Enterprise. >> We're back at HPE Discover Madrid 2017. This is The Cube, the leader in live tech coverage. My name is Dave Vellante and I'm with my co-host for the week, Peter Burris. Cat Graves is here, she's a research scientist at Hewlett Packard Enterprises. And she's joined by Natalia Vassilieva. Cube alum, senior research manager at HPE. Both with the labs in Palo Alto. Thanks so much for coming on The Cube. >> Thank you for having us. >> You're welcome. So for decades this industry has marched to the cadence of Moore's Law, bowed down to Moore's Law, been subservient to Moore's Law. But that's changing, isn't it? >> Absolutely. >> What's going on? >> I can tell Moore's Law is changing. So we can't increase the number, of course, on the same chip and have the same space. We can't increase the density of the computer today. And from the software perspective, we need to analyze more and more data. We are now marching calls into the area of artificial intelligence when we need to train larger and larger models, we need more and more compute for that. And the only possible way today to speed up the training of those modules, to actually enable the AI, is to scale out. Because we can't put more cores on the chip. So we try to use more chips together But then communication bottlenecks come in. So we can't efficiently use all of those chips. So for us on the software side, on the part of people who works how to speed up the training, how to speed up the implementation of the algorithms, and the work of those algorithms, that's a problem. And that's where Cat can help us because she's working on a new hardware which will overcome those troubles. >> Yeah, so in our lab what we do is try and think of new ways of doing computation but also doing the computations that really matter. You know, what are the bottlenecks for the applications that Natalia is working on that are really preventing the performance from accelerating? Again exponentially like Moore's Law, right? We'd like to return to Moore's Law where we're in that sort of exponential growth in terms of what compute is really capable of. And so what we're doing in labs is leveraging novel devices so, you've heard of memristor in the past probably. But instead of using memristor for computer memory, non volatile memory for persistent memory driven computer systems, we're using these devices instead for doing computation itself in the analog domain. So one of our first target applications, and target core computations that we're going after is matrix multiplication. And that is a fundamental mathematical building block for a lot of different machine learning, deep learning, signal processing, you kind of name it, it's pretty broad in terms of where it's used today. >> So Dr. Tom Bradicich was talking about the dot product, and it sounds like it's related. Matrix multiplications, suddenly I start breaking out in hives but is that kind of related? >> That's exactly what it is. So, if you remember your linear algebra in college, a dot product is exactly a matrix multiplication. It's the dot in between the vector and the matrix. The two itself, so exactly right. Our hardware prototype is called the dot product engine. It's just cranking out those matrix multiplications. >> And can you explain how that addresses the problem that we're trying to solve with respect to Moore's Law? >> Yeah, let me. You mentioned the problem with Moore's Law. From me as a software person, the end of Moore's Law is a bad thing because I can't increase their compute power anymore on the single chip. But for Cat it's a good thing because it forced her to think what's unconventional. >> (Cat) It's an opportunity. >> It's an opportunity! >> It forced her to think, what are unconventional devices which she can come up with? And we also have to mention they understand that general purpose computing is not always a solution. Sometimes if you want to speed up the thing, you need to come up with a device which is designed specifically for the type of computation which you care about. And for machine learning technification, again as I've mentioned, these matrix-matrix multiplications matrix-vector multiplications, these are the core of it. Today if you want to do those AI type applications, you spend roughly 90% of the time doing exactly that computation. So if we can come up with a more power efficient and a more effective way of doing that, that will really help us, and that's what dot product engine is solving. >> Yes, an example some of our colleagues did in architectural work. Sort of taking the dot product engine as the core, and then saying, okay if I designed a computer architecture specifically for doing convolutional neural networks. So image classification, these kinds of applications. If I built this architecture, how would it perform? And how would it compare to GPUs? And we're seeing 10 to 100 X speed up over GPUs. And even 15 X speed up over if you had a custom-built, state of the art specialized digital Asic. Even comparing to the best that we can do today, we are seeing this potential for a huge amount of speed up and also energy savings as well. >> So follow up on that, if I may. So you're saying these alternative processors like GPUs, FGPAs, custom Asics, can I infer from that that that is a stop-gap architecturally, in your mind? Because you're seeing these alternative processors pop up all over the place. >> (Cat) Yes. >> Is that a fair assertion? >> I think that recent trends are obviously favoring a return to specialized hardware. >> (Dave) Yeah, for sure. Just look at INVIDIA, it's exploding. >> I think it really depends on the application and you have to look at what the requirements are. Especially in terms of where there's a lot of power limitations, right, GPUs have become a little bit tricky. So there's a lot of interest in the automotive industry, space, robotics, for more low power but still very high performance, highly efficient computation. >> Many years ago when I was actually thinking about doing computer science and realized pretty quickly that I didn't have the brain power to get there. But I remember thinking in terms of there's three ways of improving performance. You can do it architecturally, what do you do with an instruction? You can do it organizationally, how do you fit the various elements together? You can do it with technology, which is what's the clock speed, what's the underlying substrate? Moore's Law is focused on the technology. Risk, for example, focused on architecture. FPGAs, arm processors, GPUs focus on architecture. What we're talking about to get back to that doubling the performance every 18 months from a computing standpoint not just a chip standpoint, now we're talking about revealing and liberating, I presume, some of the organization elements. Ways of thinking about how to put these things together. So even if we can't get improvements that we've gotten out of technology, we can start getting more performance out of new architectures. But organizing how everything works together. And make it so that the software doesn't have to know, or the developer, doesn't have to know everything about the organization. Am I kind of getting there with this? >> Yes, I think you are right. And if we are talking about some of the architectural challenges of today's processors, not only we can't increase the power of a single device today, but even if we increase the power of a single device, then the challenge would be how do you bring the data fast enough to that device? So we will have problems with feeding that device. And again, what dot product engine does, it does computations in memory, inside. So you limit the number of data transfers between different chips and you don't face the problem of feeding their computation thing. >> So similar same technology, different architecture, and using a new organization to take advantage of that architecture. The dot product engine being kind of that combination. >> I would say that even technology is different. >> Yeah, my view of it we're actually thinking about it holistically. We have in labs software working with architects. >> I mean it's not just a clock speed issue. >> It's not just a clock speed issue. It's thinking about what computations actually matter, which ones you're actually doing, and how to perform them in different ways. And so one of the great things as well with the dot product engine and these kind of new computation accelerators, is with something like the memory driven computing architecture. We have now an ecosystem that is really favoring accelerators and encouraging the development of these specialized hardware pieces that can kind of slot in in the same architecture that can scale also in size. >> And you invoke that resource in an automated way, presumably. >> Yeah, exactly. >> What's the secret sauce behind that? Is that software that does that or an algorithm that chooses the algorithm? >> A gen z. >> A gen z's underlying protocol is to make the device talk to the data. But at the end of the system software, it's algorithms also which will make a decision at every particular point which compute device I should use to do a particular task. With memory driven computing, if all my data sits in the shared pool of memory and I have different heterogeneous compute devices, being able to see that data and to talk to that data, then it's up to the system management software to allocate the execution of a particular task to the device which does that the best. In a more power efficient way, in the fastest way, and everybody wins. >> So as a software person, you now with memory driven computing have been thinking about developing software in a completely different way. Is that correct? >> (Natalia) Yeah. You're not thinking about going through I/O stack anymore and waiting for a mechanical device and doing other things? >> It's not only the I/O stack. >> As I mentioned today, the only possibility for us to decrease the time of processing for the algorithms is to scale out. That means that I need to take into account the locality of the data. It's not only when you distribute the computation across multiple nodes, even if we have some number based which is we have different sockets in a single system. With local memory and the memory which is remote to that socket but which is local to another socket. Today as a software programmer, as a developer, I need to take into account where my data sits. Because I know in order to accept the data on a local memory it'll take me 100 seconds to accept my data. In the remote socket, it will take me longer. So when I developed the algorithm in order to prevent my computational course to stall and to wait for the data, I need to schedule that very carefully. With memory driven computing, giving an assumption that, again, all memory not only in the single pool, but it's also evenly accessible from every compute device. I don't need to care about that anymore. And you can't even imagine such a relief it is! (laughs) It makes our life so much easier. >> Yeah, because you're spending a lot of time previously trying to optimize your code >> Yes for that factor of the locality of the data. How much of your time was spent doing that menial task? >> Years! In the beginning of Moore's Law and the beginning of the traditional architectures, if you turn to the HPC applications, every HPC application device today needs to take care of data locality. >> And you hear about when a new GPU comes out or even just a slightly new generation. They have to take months to even redesign their algorithm to tune it to that specific hardware, right? And that's the same company, maybe even the same product sort of path lined. But just because that architecture has slightly changed changes exactly what Natalia is talking about. >> I'm interested in switching subjects here. I'd love to spend a minute on women in tech. How you guys got into this role. You're both obviously strong in math, computer backgrounds. But give us a little flavor of your background, Cat, and then, Natalia, you as well. >> Me or you? >> You start. >> Hm, I don't know. I was always interested in a lot of different things. I kind of wanted to study and do everything. And I got to the point in college where physics was something that still fascinated me. I felt like I didn't know nearly enough. I felt like there was still so much to learn and it was constantly challenging me. So I decided to pursue my Ph.D in that, and it's never boring, and you're always learning something new. Yeah, I don't know. >> Okay, and that led to a career in technology development. >> Yeah, and I actually did my Ph.D in kind of something that was pretty different. But towards the end of it, decided I really enjoyed research and was just always inspired by it. But I wanted to do that research on projects that I felt like might have more of an impact. And particularly an impact in my lifetime. My Ph.D work was kind of something that I knew would never actually be implemented in, maybe a couple hundred years or something we might get to that point. So there's not too many places, at least in my field in hardware, where you can be doing what feels like very cutting edge research, but be doing it in a place where you can see your ideas and your work be implemented. That's something that led me to labs. >> And Natalia, what's your passion? How did you arrive here? >> As a kid I always liked different math puzzles. I was into math and pretty soon it became obvious that I like solving those math problems much more than writing about anything. I think in middle school there was the first class on programming, I went right into that. And then the teacher told me that I should probably go to a specialized school and that led me to physics and mathematics lyceum and then mathematical department at the university so it was pretty straightforward for me since then. >> You're both obviously very comfortable in this role, extremely knowledgeable. You seem like great leaders. Why do you feel that more women don't pursue a career in technology. Do you have these discussions amongst yourselves? Is this something that you even think about? >> I think it starts very early. For me, both my parents are scientists, and so always had books around the house. Always was encouraged to think and pursue that path, and be curious. I think its something that happens at a very young age. And various academic institutions have done studies and shown when they do certain things, its surmountable. Carnegie Mellon has a very nice program for this, where they went for the percentage of women in their CS program went from 10% to 40% in five years. And there were a couple of strategies that they implemented. I'm not gonna get all of them, but one was peer to peer mentoring, when the freshmen came in, pairing them with a senior, feeling like you're not the only one doing what you're doing, or interested in what you're doing. It's like anything human, you want to feel like you belong and can relate to your group. So I think, yeah. (laughs) >> Let's have a last word. >> On that topic? >> Yeah sure, or any topic. But yes, I'm very interested in this topic because less than 20% of the tech business is women. Its 50W% of the population. >> I think for me its not the percentage which matters Just don't stay in the way of those who's interested in that. And give equal opportunities to everybody. And yes, the environment from the very childhood should be the proper one. >> Do you feel like the industry gives women equal opportunity? >> For me, my feeling would be yes. You also need to understand >> Because of your experience Because of my experience, but I also originally came from Russia, was born in St. Petersburg, and I do believe that ex-Soviet Union countries has much better history in that. Because the Soviet Union, we don't have man and woman. We have comrades. And after the Second World War, there was women who took all hard jobs. And we used to get moms at work. All moms of all my peers have been working. My mom was an engineer, my dad is an engineer. From that, there is no perception that the woman should stay at home, or the woman is taking care of kids. There is less of that. >> Interesting. So for me, yes. Now I think that industry going that direction. And that's right. >> Instructive, great. Well, listen, thanks very much for coming on the Cube. >> Sure. >> Sharing the stories, and good luck in lab, wherever you may end up. >> Thank you. >> Good to see you. >> Thank you very much. >> Alright, keep it right there everybody. We'll be back with our next guest, Dave Vallante for Peter Buress. We're live from Madrid, 2017, HPE Discover. This is the Cube.

>> Announcer: Live from Madrid, Spain, it's theCUBE, covering HPE Discover Madrid 2017, brought to you by Hewlett Packard Enterprise. >> Welcome back to Madrid, everybody, this is theCUBE, the leader in live tech coverage and my name is Dave Vellante, and I'm here with Peter Burris, this is day two of HPE Hewlett Packard Enterprise Discover in Madrid, this is their European version of a show that we also cover in Las Vegas, kind of six month cadence of innovation and organizational evolution of HPE that we've been tracking now for several years. Sharad Singal is here, he covers software architecture for the machine at Hewlett Packard Enterprise, and Matthias Becker, who's a postdoctoral researcher at the University of Bonn. Gentlemen, thanks so much for coming in theCUBE. >> Thank you. >> No problem. >> You know, we talk a lot on theCUBE about how technology helps people make money or save money, but now we're talking about, you know, something just more important, right? We're talking about lives and the human condition and >> Peter: Hard problems to solve. >> Specifically, yeah, hard problems like Alzheimer's. So Sharad, why don't we start with you, maybe talk a little bit about what this initiative is all about, what the partnership is all about, what you guys are doing. >> So we started on a project called the Machine Project about three, three and a half years ago and frankly at that time, the response we got from a lot of my colleagues in the IT industry was "You guys are crazy", (Dave laughs) right. We said we are looking at an enormous amount of data coming at us, we are looking at real time requirements on larger and larger processing coming up in front of us, and there is no way that the current architectures of the computing environments we create today are going to keep up with this huge flood of data, and we have to rethink how we do computing, and the real question for those of us who are in research in Hewlett Packard Labs, was if we were to design a computer today, knowing what we do today, as opposed to what we knew 50 years ago, how would we design the computer? And this computer should not be something which solves problems for the past, this should be a computer which deals with problems in the future. So we are looking for something which would take us for the next 50 years, in terms of computing architectures and what we will do there. In the last three years we have gone from ideas and paper study, paper designs, and things which were made out of plastic, to a real working system. We have around Las Vegas time, we'd basically announced that we had the entire system working with actual applications running on it, 160 terabytes of memory all addressable from any processing core in 40 computing nodes around it. And the reason is, although we call it memory-driven computing, it's really thinking in terms of data-driven computing. The reason is that the data is now at the center of this computing architecture, as opposed to the processor, and any processor can return to any part of the data directly as if it was doing, addressing in local memory. This provides us with a degree of flexibility and freedom in compute that we never had before, and as a software person, I work in software, as a software person, when we started looking at this architecture, our answer was, well, we didn't know we could do this. Now if, given now that I can do this and I assume that I can do this, all of us in the programmers started thinking differently, writing code differently, and we suddenly had essentially a toy to play with, if you will, as programmers, where we said, you know, this algorithm I had written off decades ago because it didn't work, but now I have enough memory that if I were to think about this algorithm today, I would do it differently. And all of a sudden, a new set of algorithms, a new set of programming possibilities opened up. We worked with a number of applications, ranging from just Spark on this kind of an environment, to how do you do large scale simulations, Monte Carlo simulations. And people talk about improvements in performance from something in the order of, oh I can get you a 30% improvement. We are saying in the example applications we saw anywhere from five, 10, 15 times better to something which where we are looking at financial analysis, risk management problems, which we can do 10,000 times faster. >> So many orders of magnitude. >> Many, many orders >> When you don't have to wait for the horrible storage stack. (laughs) >> That's right, right. And these kinds of results gave us the hope that as we look forward, all of us in these new computing architectures that we are thinking through right now, will take us through this data mountain, data tsunami that we are all facing, in terms of bringing all of the data back and essentially doing real-time work on those. >> Matthias, maybe you could describe the work that you're doing at the University of Bonn, specifically as it relates to Alzheimer's and how this technology gives you possible hope to solve some problems. >> So at the University of Bonn, we work very closely with the German Center for Neurodegenerative Diseases, and in their mission they are facing all diseases like Alzheimer's, Parkinson's, Multiple Sclerosis, and so on. And in particular Alzheimer's is a really serious disease and for many diseases like cancer, for example, the mortality rates improve, but for Alzheimer's, there's no improvement in sight. So there's a large population that is affected by it. There is really not much we currently can do, so the DZNE is focusing on their research efforts together with the German government in this direction, and one thing about Alzheimer's is that if you show the first symptoms, the disease has already been present for at least a decade. So if you really want to identify sources or biomarkers that will point you in this direction, once you see the first symptoms, it's already too late. So at the DZNE they have started on a cohort study. In the area around Bonn, they are now collecting the data from 30,000 volunteers. They are planning to follow them for 30 years, and in this process we generate a lot of data, so of course we do the usual surveys to learn a bit about them, we learn about their environments. But we also do very more detailed analysis, so we take blood samples and we analyze the complete genome, and also we acquire imaging data from the brain, so we do an MRI at an extremely high resolution with some very advanced machines we have, and all this data is accumulated because we do not only have to do this once, but we try to do that repeatedly for every one of the participants in the study, so that we can later analyze the time series when in 10 years someone develops Alzheimer's we can go back through the data and see, maybe there's something interesting in there, maybe there was one biomarker that we are looking for so that we can predict the disease better in advance. And with this pile of data that we are collecting, basically we need something new to analyze this data, and to deal with this, and when we heard about the machine, we though immediately this is a system that we would need. >> Let me see if I can put this in a little bit of context. So Dave lives in Massachusetts, I used to live there, in Framingham, Massachusetts, >> Dave: I was actually born in Framingham. >> You were born in Framingham. And one of the more famous studies is the Framingham Heart Study, which tracked people over many years and discovered things about heart disease and relationship between smoking and cancer, and other really interesting problems. But they used a paper-based study with an interview base, so for each of those kind of people, they might have collected, you know, maybe a megabyte, maybe a megabyte and a half of data. You just described a couple of gigabytes of data per person, 30,000, multiple years. So we're talking about being able to find patterns in data about individuals that would number in the petabytes over a period of time. Very rich detail that's possible, but if you don't have something that can help you do it, you've just collected a bunch of data that's just sitting there. So is that basically what you're trying to do with the machine is the ability to capture all this data, to then do something with it, so you can generate those important inferences. >> Exactly, so with all these large amounts of data we do not only compare the data sets for a single person, but once we find something interesting, we have also to compare the whole population that we have captured with each other. So there's really a lot of things we have to parse and compare. >> This brings together the idea that it's not just the volume of data. I also have to do analytics and cross all of that data together, right, so every time a scientist, one of the people who is doing biology studies or informatic studies asks a question, and they say, I have a hypothesis which this might be a reason for this particular evolution of the disease or occurrence of the disease, they then want to go through all of that data, and analyze it as as they are asking the question. Now if the amount of compute it takes to actually answer their questions takes me three days, I have lost my train of thought. But if I can get that answer in real time, then I get into this flow where I'm asking a question, seeing the answer, making a different hypothesis, seeing a different answer, and this is what my colleagues here were looking for. >> But if I think about, again, going back to the Framingham Heart Study, you know, I might do a query on a couple of related questions, and use a small amount of data. The technology to do that's been around, but when we start looking for patterns across brain scans with time series, we're not talking about a small problem, we're talking about an enormous sum of data that can be looked at in a lot of different ways. I got one other question for you related to this, because I gotta presume that there's the quid pro quo for getting people into the study, is that, you know, 30,000 people, is that you'll be able to help them and provide prescriptive advice about how to improve their health as you discover more about what's going on, have I got that right? >> So, we're trying to do that, but also there are limits to this, of course. >> Of course. >> For us it's basically collecting the data and people are really willing to donate everything they can from their health data to allow these large studies. >> To help future generations. >> So that's not necessarily quid pro quo. >> Okay, there isn't, okay. But still, the knowledge is enough for them. >> Yeah, their incentive is they're gonna help people who have this disease down the road. >> I mean if it is not me, if it helps society in general, people are willing to do a lot. >> Yeah of course. >> Oh sure. >> Now the machine is not a product yet that's shipping, right, so how do you get access to it, or is this sort of futures, or... >> When we started talking to one another about this, we actually did not have the prototype with us. But remember that when we started down this journey for the machine three years ago, we know back then that we would have hardware somewhere in the future, but as part of my responsibility, I had to deal with the fact that software has to be ready for this hardware. It does me no good to build hardware when there is no software to run on it. So we have actually been working at the software stack, how to think about applications on that software stack, using emulation and simulation environments, where we have some simulators with essentially instruction level simulator for what the machine does, or what that prototype would have done, and we were running code on top of those simulators. We also had performance simulators, where we'd say, if we write the application this way, this is how much we think we would gain in terms of performance, and all of those applications on all of that code we were writing was actually on our large memory machines, Superdome X to be precise. So by the time we started talking to them, we had these emulation environments available, we had experience using these emulation environments on our Superdome X platform. So when they came to us and started working with us, we took their software that they brought to us, and started working within those emulation environments to see how fast we could make those problems, even within those emulation environments. So that's how we started down this track, and most of the results we have shown in the study are all measured results that we are quoting inside this forum on the Superdome X platform. So even in that emulated environment, which is emulating the machine now, on course in the emulation Superdome X, for example, I can only hold 24 terabytes of data in memory. I say only 24 terabytes >> Only! because I'm looking at much larger systems, but an enormously large number of workloads fit very comfortably inside the 24 terabytes. And for those particular workloads, the programming techniques we are developing work at that scale, right, they won't scale beyond the 24 terabytes, but they'll certainly work at that scale. So between us we then started looking for problems, and I'll let Matthias comment on the problems that they brought to us, and then we can talk about how we actually solved those problems. >> So we work a lot with genomics data, and usually what we do is we have a pipeline so we connect multiple tools, and we thought, okay, this architecture sounds really interesting to us, but if we want to get started with this, we should pose them a challenge. So if they can convince us, we went through the literature, we took a tool that was advertised as the new optimal solution. So prior work was taking up to six days for processing, they were able to cut it to 22 minutes, and we thought, okay, this is a perfect challenge for our collaboration, and we went ahead and we took this tool, we put it on the Superdome X that was already running and stepped five minutes instead of just 22, and then we started modifying the code and in the end we were able to shrink the time down to just 30 seconds, so that's two magnitudes faster. >> We took something which was... They were able to run in 22 minutes, and that was already had been optimized by people in the field to say "I want this answer fast", and then when we moved it to our Superdome X platform, the platform is extremely capable. Hardware-wise it compares really well to other platforms which are out there. That time came down to five minutes, but that was just the beginning. And then as we modified the software based on the emulation results we were seeing underneath, we brought that time down to 13 seconds, which is a hundred times faster. We started this work with them in December of last year. It takes time to set up all of this environment, so the serious coding was starting in around March. By June we had 9X improvement, which is already a factor of 10, and since June up to now, we have gotten another factor of 10 on that application. So I'm now at a 100X faster than what the application was able to do before. >> Dave: Two orders of magnitude in a year? >> Sharad: In a year. >> Okay, we're out of time, but where do you see this going? What is the ultimate outcome that you're hoping for? >> For us, we're really aiming to analyze our data in real time. Oftentimes when we have biological questions that we address, we analyze our data set, and then in a discussion a new question comes up, and we have to say, "Sorry, we have to process the data, "come back in a week", and our idea is to be able to generate these answers instantaneously from our data. >> And those answers will lead to what? Just better care for individuals with Alzheimer's, or potentially, as you said, making Alzheimer's a memory. >> So the idea is to identify Alzheimer long before the first symptoms are shown, because then you can start an effective treatment and you can have the biggest impact. Once the first symptoms are present, it's not getting any better. >> Well thank you for your great work, gentlemen, and best of luck on behalf of society, >> Thank you very much >> really appreciate you coming on theCUBE and sharing your story. You're welcome. All right, keep it right there, buddy. Peter and I will be back with our next guest right after this short break. This is theCUBE, you're watching live from Madrid, HPE Discover 2017. We'll be right back.

(upbeat music) >> Announcer: Live from Madrid, Spain it's The Cube, covering HPE Discover Madrid 2017. Brought to you by Hewlett-Packard Enterprise. >> Welcome back to Madrid, everyone. This is The Cube, the leader in live tech coverage. We're here, this is Day Two of of HPE Discover 2017. My name is Dave Vellante and I'm here with my co-host for the week Peter Burris. Ana Pinczuk is here, she's the Senior Vice President and General Manager of HPE Pointnext Group. >> That's right, that's right. >> Welcome back to The Cube. >> Glad to be here. >> Many time Cube alum. >> That's right, that's right. >> Pre-HPE and second time since, when did you start, in February? >> Yes, I know it's been nine months, I'm a veteran. >> You're a vet, right. (laughs) How's the gig going, you hitting your groove swing? >> Yes. >> Dave: Looked great up on stage yesterday. >> Thank you so much, yeah I appreciate it. Yeah I think we are, I came on board in February and it's been a run ever since. We launched a brand in February, so that's when I think when we sort of talked last. And then since then, we've just launched another brand which is HPE GreenLake for flexible consumption model stuff. And we've been doing a lot of great things, we've been doing partnerships with folks, I've been going out to each one of the regions talking to different customers, it's been going really well. >> Well so Pointnext has become a linchpin of HPE strategy. After the spin-merges, things became more clear when you talk about making hybrid IT simple, getting to the intelligent edge, services is now front and center. Meg talks about it, Antonio talks about it. >> That's right. >> Why is services so important and how do you see that scaling in the organization? >> So first of all, I definitely believe the world is turning to be a services-led world and I tell folks that it's really two things, it's services-led and then advisory-led, really advisory. And particularly because our customers want to really undergo these new digital journeys. I was just on stage talking to one of our customers, the Tottenham Hotspurs, and they're redoing their whole stadium and they're trying to increase the interaction and the engagement that they have with fans. So that's where services come in, and so we're really services-led that way and the second thing that's a phenomenon is really the cloud has really helped us learn to want everything instantaneously and to want things when we need them and when we think we need them. And so a lot of services is really about enabling those experiences in a consumption model. So that's the transformation I think that HPE is going through right now, just being a product company, but really moving to being services-led to deliver these digital experiences. >> Well one of the things that we've observed over the years, as folks who work with customers in thinking about their technology, is that there's a co-mingling, a bringing together of the idea of invention. And one of the things that's most attractive to me about a services-led, or acknowledging the role of services, is it really, innovation, is a two-part process. There's an invention, which is the engineering element, and enters the innovation, which is the social, the change. And one of the beauties of taking a services as opposed to a product approach, is that you end up focusing on the social change. >> That's right. >> You end up focusing on what does it mean to use this, apply it, make it happen, and it accelerates the innovation process. I'm wondering if by having a more services-approach, HP's able to look at this significant new range of problems you're going to try to address, but address them as a social innovation challenge as opposed to just getting product into market. >> Yeah, no and that's absolutely right. I'll give you another cool example, we have a customer Yoox Net-A-Porter, and they're a digital sort of online experience provider. They support brands like all of the expensive luxury brands that we know and love. And they're trying to help stores innovate, so let's say you're Prada or Marni or Louis Vuitton, they're helping provide a social experience to their luxury brand consumer. And being able to do that, not just mirroring what you would get in a store, but really innovating in how do you engage with that kind of a consumer online. And so for example, they allow you to shop online but then they'll bring the product to you, it'll be all wrapped really nice, they wait for you to try it on to make sure it's okay. So that's an example of social innovation, not just thinking about how to provide product to enable a website, but how do you actually then help a customer innovate in that whole engagement model? >> It's innovation that is made possible by a whole lot of technology combined with simple ways of introduce change, not just to consumers, but also the people who are ultimately responsible for providing that service. >> Ana: That's right, that's right, that's exactly right. >> Peter: Is that one of the basis then for thinking about Pointnext? >> It is, yeah, it is because people ask me, you know we've always done services and a lot of our services were product-attached services, you do support services, operational services, data center care, those sorts of things. And then we decided to sort of launch Pointnext, and the idea is that this is more than just what we've traditionally done as product-attached. This is really coming at it from a completely different angle, which is recognizing that there is an element of social and management of change that comes through digital. And that's why we talk about advisory-led. Part of that advisory-led is really helping companies figure out what is that new phenomenon, how do I actually shift the experience that I want to enable and how do I bring social innovation with a set of partners, too, because experiences really require us to work not just with our own products, but with software providers, with inside and others. >> Peter: And your customer's partners too. >> And our customer's partners as well, I mean who the customer is is shifting as we put this together. I'll give you an example, when we work with automotive companies, we've gotta think not just about, let's say, the car company and their connected car, but we also have to think about how the consumer of the car is going to interact with the IT environment in the car. >> How the dealers are going to sell it, >> Ana: And how the dealers are gonna sell it. >> how they're gonna make money, the whole thing. >> How they're gonna do predictive maintenance on it >> Exactly. >> So you start to think not just about one experience, but all the elements that come from that single experience. >> Well we just had Deloitte on talking about retail experiences and transforming brick and mortar stores, so that's a key part of it. So partnerships is also something critical, 'cause you can't do everything. >> Ana: That's right. >> So I want to come back to some of the invention piece. When you were up on stage talking about flexible consumption models, you know, cloud, when we went into the downturn it was kinda a tap on the shoulder. Coming out of the downturn it became a kick in the butt to a lot of tradtional IT players. So you've had to respond to that. And you have, flexible consumption models, pay-as-you-go models. So I started to make a list because we've been talking all week about two ends of the spectrum. We've got here at HPE Discover, AWS re:Invent's going on this week, completely different philosophies about what customers want and how to serve those customers. And so you've got to a great degree mimic the cloud experience. And you can't do it 100%. At the same time, the cloud can't mimic what you guys can do. So I kinda wanted to go through a list and think about where have you closed those gaps, where do you still have advantages for customers. So things like pay-as-you-go, flexible capacity, you've done a lot of work there. Can you give us the update on that and how big is that gap when you talk to customers? >> So first of all, it's interesting because when some of our competitors talk about pay-as-you-go, they start by talking about just a leasing arrangement. They say "Okay, it's a lease." And this is far beyond a lease. I think I can eliminate quite a few of our competitors, (laughs) not cloud competitors, just by saying we've gone beyond that, right. And we provide a full service. So it's the hardware, the software, the data center care, the operational management. And then we turn that service into a pay-as-you-go model. So that's the first sort of innovation and differentiation. And we do that on-prem or in a hosted environment, that's the first thing. The second thing is that part of what we do is we help to manage that environment for the customer. So in a flexible capacity model, we over-provision in a sense and we have a buffer and we understand where the customer's going, how much their utilization is, and then we automatically sort of manage that whole thing for them, up or down depending on what happens. I think the third thing, which is part of the innovation, which is a little different, is we also do the integration of other technologies into the offer. So yesterday I was talking about private backup as a service. There we've got the hardware, the software, it could be Commvault let's say backup software, all the management associated with that, including the support that you need for that, offered in an outcome-based service. So what we're doing there is we're also innovating in the metering, what we're saying is we're going to really provide you an outcome, and that outcome is a successful backup. So you don't actually have to worry about the equipment, you don't have to worry about is it infrastructure-as-a-service? You know, AWS, whatever, we're actually providing a full solution in an outcome-based. And I think that's a little bit of what differentiates us from maybe some of the solutions that are out there, from others. That said, I view this as providing the right mix to our customers, so although, yes, you can say that we're competing with the public cloud, because customers have choice, at the same time part of what we're trying to do also is bring those two together, which I think is unique for us. >> Makes more same philosophy, different approaches. >> Different approaches, and by the way, if you're customer-centric, then what you wanna do is provide customer choice and do the right thing for the customer, and to say where does it make sense to be on the public cloud, or in a private environment, and optimize for the customer benefits that you're going after. >> Well I think it's fair to say that the world has learned a lot from what AWS has done, and said "Hey, we can take that "and we can apply it to our customers' businesses "on-prem or in a hybrid environment." >> And by the way, AWS, especially with our CTP acquisition, they've been a long-term AWS partner and we're having conversations with AWS that say okay, if we're going to really focus on customers, and we're really customer-centric, then how do we work together? Not just AWS, but Microsoft and Google and others, how do we work together and look at where we can optimize our solutions to be able to do the right thing for the customer. >> So our clients are sick and tired of hearing me say this, or us say this, but we believe that where we're going is the cloud experience for your data demands. >> That's right. >> So the way we think about it and I'm wondering if you would agree, is that the first conversation we have with a customer is what's the outcome, what data is required to serve that outcome, how're you gonna package it up as a workload, and where do you naturally need to run that based on latency, other types of issues. Is that kind of how Pointnext is working with customers as well? >> Yeah absolutely right, so we wanna come in, customer in, so you wanna be able to say "What is it that you're trying to do from an outcome?" I described a backup outcome, another outcome might be I'm trying to accelerate my ability to roll out new agile solutions, or microservices-based applications. So we have that conversation with a customer, we then say okay, for that kind of workload, what are you requirements? What are you trying to do? We might also come in and actually, 'cause sometimes what people think they do and what they actually do in their environment is different. So we can come in and say okay, let me actually measure what you're doing and see what you're doing and then bring that information back to them. And then have a conversation about what to do with your workload and what makes sense. So I think it's a very close engagement with the customer, it's based on real data about what the customer's trying to do. And frankly that was one of the reasons that we made the CTP acquisition, as well, because it started to complement our portfolio. A lot of the capabilities that we had were very robust, in particular around private cloud, but just having the public cloud angle there and sort of strengthening that piece was super important to be able to have that conversation and truly enable the right mix. >> Well now that brings up the topic of multi-cloud, which kinda, to use a sports analogy, it's jump ball. It's kind of a free-for-all, everybody wants that business. I guess with the exception of some of the big cloud guys aren't interested. But certainly, Hewlett-Packard >> Peter: Well don't believe it, want to avoid it. >> Yeah well, but that's the reality is there's gonna be multiple clouds, we know this. Particularly with SaaS. So a company like Hewlett-Packard Enterprise, obviously has to play in that space. So I wonder if you could talk about the strategy there, why you feel confident that HPE is in a good position. >> Yeah well a couple things, first of all I think it's really good to be, we're somewhat independent, we're not totally independent because we've got a whole set of products, but we're somewhat independent in the sense that if we wanna be truly hybrid and enable other public and private solutions, we wanna be able to give customers choice in terms of the public domains that they can work with. And so we're sort of in a great position as a large provider and with the relations that we have in the enterprise in particular, with our customer base, to be a little bit of Switzerland and be able to say, okay, let's have that conversation about the right mix and enable these multi-cloud solutions, that's the first thing. The second thing is we have relationships and great partnerships with many of these providers. So take Microsoft, we've got an Azure relationship, an Azure stack opportunity, so we've got the ability and by the way, we do many of their applications as well. So we've got the ability to help have that conversation with our customers to say okay, do you wanna be on-prem or do you wanna be in the cloud? Even with one provider, and to do that, and so we have the opportunity to provide robust solutions even with one private and public provider. And on top of that, we've got a consultancy with our professional services. We wanna be responsive to our customers, we've got now HPE OneSphere. And with HPE OneSphere we can be data-driven and actually provide our customers a view of their environment and help to be a little bit of that Switzerland to say look, here's what would be best for you and help to have workload mobility together with OneSphere. So I think we're well-positioned, I tend to call it my stairway to Heaven. In a sense we start out at the bottom talking about infrastructure and support, and we've got great relationships there with our customers. If I launch the flexible capacity offers, we're starting to deliver outcome-based solutions. When I bring in CTP, we'd go up the stack and we now provide advisory and the consumption solutions. And with OneSphere now you go up the stacks just a little bit more and say not only are we gonna advise you and provide you those executables with consumption models, but we now have capabilities that allow you to sort of optimally choose what's the right thing for you. So I think we're well-positioned, by the way, with CTP we've got sort of a managed, sort of cloud sort of capability as well. We manage compliance and other elements. So we're able to have in our portfolio sort of value-added services above and beyond that help with multi-cloud and making sure that customers can be compliant, secure, and have the right experience on a multi-cloud environment. >> Yeah I think a lot of people that don't know CTP don't understand how deep their expertise is. They're only a few hundred people, if that. But they're rockstars. >> They're over 200 people. >> Serious thought leaders with real deep connections. I've gotta change subjects to the last topic area. As you know, The Cube from day one has always been a fan of having women on, and promoting women in tech. We first met you at the Anita Borg Institute of the Grace Hopper Conference. Meg Whitman is obviously a woman leader in tech and she's leaving HP. We've got Meg and we've got Ginni. And Ginni's coming to the end, I don't know, she's getting to the age where typically IBM retires its CEOs. You've got two prominent women in tech now leaving. Now maybe IMB will replace Ginni with a woman. HPE has chosen Antonio, great choice. But your thoughts on a leader like Meg, obviously has done some great work. But we're losing one. >> I know, and so >> How do you feel about that? >> I mean, you know, I'm very conflicted if I've gotta be honest. One one hand, as I joined HPE I had never worked for a female CEO so I've really enjoyed watching. You know it's always great to have mentors and to have people that are advocating for women, so I really enjoyed being part of Meg's organization, I'm really sorry to see her go. And she's an icon as well, so she does a lot, in fact this afternoon we're gonna be doing a session for women just here at the conference. So very sad to see her go, at the same time I think we as women, and men by the way, have a responsibility to build the next generation of leaders. And I think that's where I focus my energy and I know that I'm gonna be sort of a high profile female in the HPE environment so I feel that sense of responsibility, not just within HPE, but within the industry, to help to cultivate an environment that takes advantage of half of the population and enables innovation through them as well. So I think we've gotta get more women up there. I think part of it is really bringing up the next generation and frankly this next generation, they don't have tolerance for waiting for things, whatever, and they feel like they're super entitled to have the right and the choice >> Peter: They are. >> And they are, right. But that seems like an easy thing to say, but in some sense we come from a generation, many women as well, which have had challenges especially in the tech world, in terms of really breaking that glass ceiling. And I think we've got some amazing women and some amazing leaders as well. I'm part of the Anita Borg Board of Trustees as well, and we were at Grace Hopper and we had Debbie Sterling, some really great women that are coming up the ranks that are CEOs, that are CTOs, that are really leading the way and so I'm very hopeful that the conversation, by the way, about women in tech is really prominent right now. And that I think it'll open up opportunities for women to shine going forward and I think that should happen for HPE as well. In fact right now its me and then Archie Deskus is the CIO for HPE. So we're trying to do our part to sort of make sure that there's other women in leadership as well. >> Well you're a great example of a current and future leader. >> Thank you so much. >> Really appreciate you coming onto The Cube, Ana. >> I appreciate it, thank you. >> Great to see you again. >> Great to see you, great to see you, thank you so much. >> Alright keep it right there everyone. This is The Cube, we're live from HPE Discover Madrid, we'll be right back. 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>>live from Madrid, Spain. It's the Q covering HP Discover Madrid 2017 Brought to you by Hewlett Packard Enterprise >>Welcome back to Madrid, Spain. Everybody, this is cute. The leader in live tech coverage And we have a day to HP discover Madrid. My name is Dave Volonte with my co host for the week Peter Verse. Jeff, Carla is here. He's the senior director of solutions. Go to market system integrators at Hewlett Packard Enterprise and Kalyan Gara Mela. Who is the i o t manager? Deloitte. Yes, Gentlemen, welcome to the Cube. Thanks for coming on, You bad love too deep here. It's always a great time. Yes. So you know, when you come on with Deloitte, we always sort of mentioned you guys. One of the top system integrators in the planet. You got deep expertise and vertical industries. You guys bring the technology expertise. Last time we were talking about manufacturing. This time we're gonna talk about retail. Yes. Why? Retail, You know, retails in turmoil. Everybody's got numbers on war room. But you guys are going after that, helping some of your customers so to take advantage of their physical presence, bringing in an online presence move into digital. Is there hope there's hope, their retail dead? You know, >>I hear all the time about this retail apocalypse retail is dead, and in reality, it's not dead at all. Still, 85 to 90% of purchases were being going through a brick and mortar store problem here, and the apocalypse will happen to those brick and mortar retailers that don't change. They don't digitize and change to the changing demands of a consumer and the way they want purchase something, give you an example, my son or even myself. Now I increasingly want to do things through an experience. My computer, my mobile phone. I do research. I I want to understand. I want recommendations. I want personalization. I want to be catered to. I don't want to go stand in line. Well, that experience can be done but are unique. Ability. Is taking that experience in a planet into a brick and mortar environment? >>Well, I got to say I love going Cabela's with my kid with my wife. I mean, I could spend all day. Hey, get that on Callie and tell us about your you're rolling. The Lloyd, obviously specializing in the retail practice. What, Your background? >>Yes, my name's Kalyan. Gotta Mila being a coyote manager from the >>delight you >>practice based out of San Francisco, and we have been working with our partners and friends. Hitch be Aruba over the past year or so, helping them dollar, I ot go to market projects, products that can be that we can take to market on Dhe. Recently. We're just working with manufacturing and retail industry. >>So what's the conversation >>like with your customers? As I said, everybody's got an Amazon war room they're trying to figure out. Okay, >>how do we leverage our physical presence as an advantage? What were the conversations like with clients with >>our clients? Mostly that talking about How did the mimic our online channels? Right. If I go to an online retailer, you know, if I go open, say amazon dot com, they know exactly what item I am for chasing where I'm going next. What? How much time I'm spending. So in order to differentiate the brick and mortars in order to differentiate themselves from the fellow retailers, they have to offer that customized shopping experience in order to get given a reason for the customers to come in store and make that purchase. So they're trying to look at what new technologies that we can can we can help with. What are some of the new processes that we can help with? And that's where most of our conversations have been going on, >>Really experience. Problem >>it is. And you talk about the bells and I moved into a new house, ready to buy my big >>lazy boy chair and watch Sunday >>football, and I'm not gonna go online just by here. I want to touch and feel that I was late and I want to understand. Well, that is a perfect opportunity of providing an experience. Allows me to do the research, get suggestions, go into a brick and mortar store. Try it out, then guess what? I'm getting personalized. Hey, you know what? There's a nice beer stand that I could put right next to that table. Be calm, perfectly complemented. Hey, there's a light that can look over So we have that ability of actually tying together and experience, actually predicting in advance what the customer really doesn't know they want next. But they really do want example. We just walked out of a client engagement. Beautiful example. Plan Engagement sells high end women's fashions, right dresses and shoes and accessories. Everything. And he's He basically said, We're dabbling around with R F I. D tags, um, inventory management, but we don't know what to do, right? Bingo. We now have a proven, referenced architecture called the Connected Consumer. This is a preview to be announced to be soon, but that can allow, actually that client to integrate and optimized and digitized the solution for a number of different use cases that spans a unique customer experience in store operations and efficiencies, and then providing insights through analytics in store analytics to make decisions quickly. So you've got by using this architecture building of solutions based with Deloitte Competence season capabilities in HPD Aruba technology. We can deliver that to increase top line revenue, increase basket side, decrease inventory costs, lost inventory and provide much greater brand loyalty to those customers by having a nice, personalized teachers. They know me by name. They know what I'm looking for in advance. Beautiful solution. >>So the online retail world did two crucial things. One is provided new way of customer to buy something and number two, it provided a new way for the retailer to learn something about the customer. Very, very powerful. But as you said, we're still last time. I checked physical things that move through space that used physical senses, too. Make decisions, Tactile. Do I like the color? You know the experience. I mean, I remember having arguments with people about whether the Apple stores are ever gonna have any impact in the world. And, boy, did they prove that experience of physically being there matters. So in many respects we're talking about, We're talking about creating spaces, the correspond to the experience that a customer wants in a way that doesn't force them into another channel. >>I think that is excellent. Thank you will hear security and character talks about who these are Aruba team. And they are renowned for taking a space and providing using technology and I, t and software and security to provide a total experience, an immersive experience for those that are occupying that. >>But that's not how retailers used to think. What they used to think was this is the space where I put my inventory where I show my product and then I'll put the catch register over here. What you guys I presume we're trying to do is show how. Show them how they could turn that physical space into a place that can bring in the online digital elements, complimented in a way that makes that door a source of different jack >>experience in the brick and mortar store and allows the comfort of Yeah, you know >>that makes it differentiated so that someone wants to go there, because that is a valuable experience in and of itself. >>And sadly, retailers of the past 40 years have always relied on big brand names to attract customers. If I have the best brands in the world, customers will come to me back. That scenario doesn't hold true anymore. You need to give them a reason. A personalized, curated experience for them to come in >>well, not least of which is the digital technology allows us to spin up new brands like overnight and so also so there's a there's it's having an erosion of effect on the other side of the inventory. So tell us a little bit about where you think over the next few years that differentiated in store experience is gonna be what is going to constitute great retail. >>I'll start enough shit. >>First and foremost, the expectations of millennials and other generations is more of that online experience. So I think I think retailers of the future have to be able to provide that customized experience. To be able to provide predicted people are not waiting in line is not an option in the future, right? I mean, even you. You look a waiting in line is not an option. I think that ability of you have to have more instantaneous gratification but allowing, if you will, the personalization being covered. I think that one expectation for those that want to sustain a business in retail in the future >>and add on to that right. I mean, the marketing managers are the store managers of the past have always relied on opinions rather than data and insights to make this better business stations. Where do I place my product? Where are my customer spending most of my time? It's just guess it's most of it was guessing. Now there is a technology out there where we can actually monitor what's happening inside your retail store and dead. While you can make better business nations to help you with your customer journeys, >>traffic, foot traffic, you know through video analytics and the data someone's hanging around the Nike booth or whatever you know financially, and you can purposely point them and give them suggestions of 20% off. And so you can personalize that experience. >>So wait. See Io client on DDE that's in the retail space on the way he described it is, you gotta break the whole thing down. Let me test you guys. You have a period of I want the experience of shopping on. The example that he gave me was a bike company a number of years ago who used flexible manufacturing to collapse the time high end bike to collapse, a time from order in the bike, getting the bike down to a few days. And they failed because the customer like waiting the process of buying, reducing time. Simple, straightforward, but also what they said. And this is the kind of flexibility we're talking about is some people don't wanna walk out of the store with the product they want to deliver to their home, so the store is again, not the place where the inventory is. It's the place where you experience the product and that they create an option. How would you like that? I like to be delivered to my house. No problem. There you go. Is that the kind of thing that we're talking about in the future? >>Absolutely. We call it the unified commerce of the Arm and channel shopping experience. You want to give the customer all the options available. Like you said, I could buy online shipping in store O. R. I can buy in store get into my house all the different options that a customer is looking for. A non online channel, which is easy and convenient. We want to do that in a brick and mortar as well, and our solution can help you do that as well. So you >>guys encounter a client that is, you know, declining same store sales management is concerned about, You know, the future. It seems like it's a tired sort of experience and, you know, that's sort of the end of the spectrum. And you want, you know, the to be his future. Stefano, the talk about where do you start so >>who brings what experts is. >>Actually, I'm gonna repeat what I said last time. Our mantra is First off, you gotta think big. Then you start small and then you scale fast. And what I mean, that what we mean by that is with the Lloyds capability. It's been a week and jointly come in and help a retailer. Let's think it through. Let's think you have how many branches looking to wear? What are your problems? What your inventory leak age. You know what your current experience, but you're in store WiFi. We can build a plan on what we can do. But the next big problem that we see is not about the technology is about the people in the process. How do you convince its How do you commit? Some who invest to change well, this through our proof of concept capabilities, we have the ability of starting small. Let's just go in and we can do through this architect modular proven architecture. We could do a starting Well, let's just start with some R F I. D tags and tags and start small. We can deliver the business value and calculate that and extrapolate that out if we apply that to your all your stores and scale fast. So we're making it. This be an on ramp for those retailers because they're saying what I do. I know I need to change, but what do I >>So you do like a test store model, right? Okay. And then what? That's your POC is actual. >>Yeah, And then So I wanna go back a little bit on this whole coyote offering. It's a composite offering, right? It takes a lot of technologies coming together and a lot of SMEs subject matter experts to come in and help you to build a whole solution. And that's where I think our solution is where it's ready to go, where all the pieces have been put together and can be easy from day one. The time to market has been drastically reduced because of this. Right? So we see a lot of value in that. >>So So you're able to say Okay, what kind of target customer? What kind of inventory? What's the cost of it? What's the turn? Take all those business attributes and then say we can map that back into a set of physical and system components that you can scale fast >>really comes around you. Three buckets were doing this to optimize an increase revenue, basket size conversions, everything timed revenue, decrease costs, efficiencies and inventory logistics people, uh, labor. And then providing a much greater experience of brand loyalty, which will also affect both costs and >>capture and capture additional data. So, for example, returns means two things costs, but also, somebody had a problem. >>So, uh, we're out of time, but so summarize kind of where you guys were at, >>uh, your solutions when it's gonna be available, you go to market, give us the >>tickets. That right now we're here at HP discovered we're previewing this connected consumer architecture. We're will deploy it. Calendar quarter one of next year will be the full announcement. We have contact information. We would love to engage in clients and start that discussion now around doing proof of concepts on dhe. We're going to be not only driving this collective retail solution that could be extrapolated into different use. Cases in markets were also continued to drive the Moorman industrial Internet of things and manufacturing offering around predicting maintenance, asset monitoring, maintenance that we talked about in Vegas. >>Great. Well, I hope next next Vegas come back with some examples and some a customer, and we could go through so that one of impact you've had, maybe you'll be through a POC. At that point. I'd >>love to get the cube into one of their poc >>a well loved. All right, guys. Thanks very much for coming on the Cube. All right. Good >>to see you. See? All right. Thanks. Keep it right there, >>buddy. We'll be back with our next guest day. Volonte for Peter Burke alive from Madrid 17.

>> Announcer: Live from Madrid, Spain. It's the Cube. Covering HPE Discover Madrid 2017, brought to you by Hewlett Packard Enterprise. >> Welcome back to Madrid everybody. This is the Cube, the leader in live tech coverage, and this is day two of HPE Discover 2017. I'm Dave Vellante with my co-host, Peter Burris, Alain Andreoli is here. He's the Senior Vice President and general manager of the hybrid IT group at HPE. Great to see you again. >> Great to see you David, great to see you Peter. >> So, a lot of good energy here, the story Alain is coming together. >> Alain: Yes. >> We've seen it over the last five years but really fine-tuned the organization and seems like things are going well. >> We have more clarity on our strategy than I've ever seen in a company, and this was not easy to do because the market is changing so fast. We addressing $120 billion market in hybrid IT, we lead the market in compute, we lead the market in storage, we lead the market with private cloud, we have invented composable, we are ramping up our Harper converge offering, and now on top of the infrastructure, we building these layers of one sphere, which is managing a multi-cloud environment for the data, and we are adjusting our services to become advisory and consumption models. This is having such an impact on our customers, 74 percent of our customers are going for hybrid IT journey. So we have organized ourselves to make this journey to be basically the partner of choice for our customers as they go through that. >> I mean so cloud of the last five, seven years, cloud and open-source software have really disrupted our industry. You've had to respond to that, and basically bringing cloud-like operating models to your customers. >> Alain: Yes. >> How have you done that, how do you rate your progress and where are you to date in that regard? >> So the first decision we had to make is are we a neutral party to our customers? (laughing) >> Dave: Yeah. >> We need to redo it. (laughing) >> They're getting you back, right? So, I don't know if you can see that, alright? Alain came by on his scooter, here we go, let's catch this. Here we go, this is called payback. (laughing) During Dr. Tom's interview, Alain came by with his scooter. (laughing) >> I will get you, I will get you for this. (laughing) >> It's great fun on the Cube. >> We can kid, that's alright. >> That's good. >> So the decision we had to make is are we the partner for our customers to go to the cloud or are we saying on PRIM is better? >> Dave: Yeah. >> And we 'vedecided to be this partner. Because we believe there is value for everyone and we believe it is not a one-way street. And we see actually that 32 percent of the customers who have moved work loads to the cloud are bringing these work loads back on PRIM. So we had to advise them. We helped them go through this journey, we really mean it, we helped them to go on Amazon, we helped them to go on Azure, we helped them to go on Google, and we helped them make it work, and this is why it's a service-led journey. The problem if you go on the public cloud is that we don't really know how much it is going to cost you, and you don't really have a single pane of glass to have all your data being managed across, what is now an ecosystem. We enabled them to do that. And the market we are directly addressing on PRIM is not shrinking. We still see huge pockets of growth, in flash storage, in HPC, you've seen the results we have in HPC. In Mission-Critical X86, in Hyperconvert, so we are basically moving from the one-size fits all type of organization of freeing X86 and start off storage, to become a company that offers value to customers, in specialized pools of compute, of storage, of networking, and offering them the end to end journey across the different stack. What I think is going to make a huge difference, if you look at the five-year horizon, is the growth of The Edge and the fact that 70 percent of the data are going to come from The Edge, and then you will really see the power of our strategy of private IT which goes from The Edge, to the core, to the cloud, because we will be able to enable our customers to have their data moving seamlessly across this journey. And we have exactly organized the company that way. >> One of the obvious use cases from what I like to call machine intelligence or artificial intelligence is really infusing artificial intelligence into infrastructure for predictive analytics and predictive maintenance, IT operations management, Infocyte, you got through an acquisition of Nimble and have been impressed with the pace at which you pushed that throughout the portfolio, I wondered if you could address that. >> We've been almost surprised. We looked at, we wanted to become the flash company because we saw that the market over three years, would completely move to flash. And when there is such a pendulum shift, you want to be at the forefront. >> Dave: Right. >> So we looked at all these companies who were having very strong positions on flash and Nimble intrigued us because they had, by far, when we talked to their customers, the highest customer satisfaction, I think it was something like 87 percent. >> The NPS is off the charts. >> The NPS is off the charts, right? And then we peeled the onion and we saw Infocyte, which was almost enough to start south because it was not part of our list, right? Initially of our list of this is how we are gonna select a company we want to acquire, and when we got into Infocyte, how it works, how we can actually port easily these to three power and then to SimpliVity and then to the rest of the portfolio we felt this is the crown jewel that is going to be the foundation of us making >> Dave: And not just the storage portfolio. >> No, end to end so we're gonna do these for everything, now we cannot do it in one day. The priority was to give a seamless experience to customers going three power or Nimble, so we've done that very quickly. We acquired the company six months ago and it's already there for three power. Next one will be Simplivity, very soon in a few weeks, then we go to the whole computes platform as well, then finally to networking. I hope, it's not a commitment, but I hope that by the end of next year, and under a year, we will be done for the whole infrastructure portfolio. >> And explain the benefit to customers. >> And then the benefit is that you basically have, you eliminate the need for level one and level two support because it's proactively, now you have to be wanting to have your device calling home, right? Because otherwise, if you want your device to be in the data center and insulated from communicating with the network effect, that is not going to work, so but assuming you want your device to be connected centrally, so that it can be monitored centrally the artificial intelligence that is embedded in Infocyte is basically going to monitor the behavior of your device compared with hundreds of thousands of other ones and therefore anything that is deviant will be flagged as a potential problem and resolved before you even know about it. That's one. So when you end up having a problem eventually, which is becoming very, very rare, then you directly call the level three engineer who is an expert and who has, on the screen, the behavior of your device for the last month compared to others, and the resolution is in less than a minute. So it's a revolution in the way to do service. >> So, one of the things that we've observed as we've talked to customers is that the characteristics of the problems that they're now trying to solve have real world elements, and that's really what The Edge is about in many respects. For the first 50 years of IT, we were doing accounting, and HR, and supply chain, and we were able to define what the data models looked like, we could therefore say, the data's going to be here, the processing is going to be here, we could build data centers. Now as you said, 70 percent of the data is going to be coming from The Edge. It's not clear, necessarily where the best place to process that data is. Where's the compute going to be? How's it going to integrate with people? In many respects, hybrid IT is about diminishing the degree to which infrastructure dictates the way the problem gets solved. Would you agree with that? It's kind of like where does, let the data reside where it needs to reside, and make sure that the business is a natural infrastructure that reflects and corresponds to the work that needs to get done. >> I totally agree with your problem statement, and the way you position the question. In terms of semantics, I would just say we need to make infrastructure invisible. It's still there because it's all running on infrastructure. The iPhone is infrastructure, your PC is infrastructure, your camera is infrastructure, it's all there. >> A C.I.O said to me not too long ago... >> But you know what? We are having this interview, we are not thinking about what makes it happen. >> Peter: Right, right, right. >> Our business is to talk and communicate right now, this all has got to be seamless and that's how we need to make IT, seamless. >> I had a conversation with a C.I.O. >> Invisible. >> Yeah, who said that the value of my infrastructure is inversely proportional to the degree to which anybody knows anything about it. So, is that kind of what the HP promise is, is we're gonna let the data and the work loads define where the infrastructure goes and ensure we have those options? >> It's exactly right and the vehicle to do that, we call it autonomous data centers. Your phone is a data center. Your data center is a data center. Your off-frame cloud is a data center that you are subcontracting, right? So we want all of these to be autonomous, in terms of self-healing and everything else, and then the intelligence of where these data are being moved and how you use what and when is the single pane of glass that we are developing around one sphere. And how to get the customers to move their work loads and their business around that is what we do with point next with services. This is our strategy. >> So let me break that down a little bit. So, we've got devices that are powerful enough that we could put new types of control, new types of work loads there if we wanted to, we've got now the ability to package infrastructure, and have a single pane of glass, and have a common management framework. >> Right. >> But when you say the autonomous data center, it's we have a common business approach thinking about policy, thinking about value, thinking about how we're gonna do things, and we can put that into this entire vision, and let it actually execute how that manifests itself from a business standpoint. >> Exactly right. >> Have I got that right? >> It's exactly right. I love the way you put it. That's exactly what we are trying to do. it's not going to be done in one day, but that is our strategy, and we have organized, once again, the whole company around it, to execute this strategy and to make it happen for our customers. >> So if we think about what an HPE customer is gonna look like in, you know a really good HPE customer in 2023, what.. >> Alain: That's a long time. >> That's, five years, but I'm giving you that much run way, because you're right, it's not there yet and if it's too ambitious then so be it, but how is a business person going to think differently about working, about the role that IT is going to play in the business, and what it means to have a great partnership with a company like HP? >> Yeah, so we are basically, our motto is One size doesn't fit all, so we are first trying to understand the business of the customer, and then we will apply solutions to enhance this business, or to empower this business, right? So, we have the biggest brace of infrastructure that you can think of, think about this infrastructure becoming self-healing, but this infrastructure is more and more specialized, there is HPC, there is Mission-Critical, we just found Superdome flex, or SAP, we have all these specializations that, for those customers to optimize their business outcome. Then we have the single pane of glass that allows everything to seamlessly operate the data around, and then our point-neck services are going to work with the customers to architect their IT model in a way that their work loads are optimized. And one of the key is the right mix. The right mix of what you do yourself, what you got from multi-cloud, how much do you pay for it, how much do you anticipate that you're gonna pay for it, do you want this to be CAPEX, do you want this to be OPEX? And then how do you manage The Edge, and with Aruba and with Edgeline, and then with all your IT platforms that can manage the data across The Edge. We have the capability to also let the customer decide, do I want a lot of analytics and decisions to be made at The Edge, in my devices, and this is highly valuable depending on what customer business model we are talking about, or, do I want all the data from the analog world through the censors to come straight back to the ranch. All of these decisions, we are gonna have platforms to allow customers to make these decisions, to decide, kind of templates if you want, this is how I want it to run, and to be executed, and then to be automatically, autonomously operated. That's our vision of how we can help our customers moving forward. >> Last question, so the attendees of Discover, your customers, when they go back and he or she talks to their boss, what do you want them to say about Discover 2018? >> I invested two or three days of my time to come to HPE Discover. It was really exciting because I felt that it's like a new company, it's the company I know. I know they are customer first and customer last, and they are the ones who help me when I have a problem, whether they created it or not, they are here to help me. This is not going away, but they are taking us to the new world. They are gonna help us to build our hybrid IT model, and I think we need to trust them to have a seat at the table when we make these decisions, boss. >> Intimacy, innovation... >> Alain: Yeah, innovation. >> Trust. >> HPE's no longer wandering in the desert. (laughing) >> Alain Andreoli thanks so much for coming on the Cube, it is always a pleasure. >> It was a pleasure. Take care, thanks Peter. >> Keep it right there, everybody, Peter and I will be back with our next guest, right after this short break, we're live from Madrid. You're watching the Cube. (techno music)

>> Narrator: Live from Madrid, Spain, it's theCUBE, covering HPE Discover Madrid 2017, brought to you by Hewlett Packard Enterprise. >> Welcome back to Madrid, Spain, everybody. This is theCUBE, the leader in live tech coverage, and this is day two of our exclusive coverage of HPE Discover 2017. I'm Dave Vellante with my co-host Peter Burris. Last night was a great night of customer meetings. We stumbled into the CIO meeting, we were at the-- >> And were quickly ushered out. (both laugh) >> We were at the analyst event, and of course we met our good friend Dr. Tom Bradicich at the analyst meeting. This is the man who brought a lot of the IOT Initiative into HPE. He's the general manager of the IOT and Systems division. Great to see you again, Dr. Tom. Thanks so much for coming on. >> Thank you Dave and Peter, it's great to be here at theCUBE, great to be here at HPE Discover Madrid. Lots of great things happening, I can't wait to tell you about 'em. >> So we're very excited to have you on. John Furg and I interviewed you in the very early days after you came over from your previous company, and you had this sort of vision of, you know, bringing the HPE into the intelligent edge. >> Yes. >> And we're like okay, this sounds really complicated. You got ecosystem, you got all kinds of technologies that you gotta develop. Hardware, software. And you're making it happen. It's become a meaningful portion of HPE's business, so I know you got a long way to go, but congratulations on the progress so far. >> Thank you. Give us the update on the-- >> Well, first of all, thank you for that, I appreciate it. I must give credit to my team, I tell them all the time that if you don't execute and do the work, I'm just a science fiction writer. (interviewers laugh) And the vision has come about, and we have real customer deployments of course that the, you know, the proof of it. >> Right. >> At first we had no products and no customers, now we have these products that we'll talk about, and we have the customer deployments, and we're changing things for businesses at the edge, and again the edge is just not the data center. And the manufacturing floor, we'll talk about refineries, oil rigs, those type of edges. We're doing a lot of work there. And it's been exciting to see the ideas that we have get adopted by not only customers, but the industry, so we're seeing other analysts pick up on two dimensions: computing at the edge, and a little more complicated one, a little more difficult to grasp, is converged OT and IT at the edge, the two worlds of operational technology converging with IT. We were on theCUBE talking with an OT partner, National Instruments, a long while ago, and now we literally have those products in the market in the hands of customers. National Instruments is reselling the Edgeline 1000, the Edgeline 4000 products, as well as of course us selling it, and it's pretty exciting to see this happening. >> Well what I love about that conversation is, you know, when we first started to talk to you, we said okay, let's play the skeptic, analysts are skeptic. >> Sure. >> And we said one of the big problems you're gonna face is bringing the organizations together, OT and IT. They're just different worlds, oil and water, you know, you got hardcore engineers and you got IT guys, and then subsequent to that conversation, you bring on National Instrument, right? >> Yes. >> And we have that conversation. Okay, so we sit down, I check that box, at least they're having conversations. Can you talk about how that convergence is actually occurring, and what's in it for the customer? >> Well great. To talk about this convergence, the best thing to do is say it can happen at several levels. It can happen at a solutions level, it can happen at a software level and a hardware, physical level. Let's talk about a physical level, it's a little more tangible to understand. Let me use the smartphone, which everybody has. Like Peter, you have one there. If you hold that up, you will notice inside the manufacturer of that phone converged, or integrated, those are synonyms, many consumer devices. Such as what? A music player, of course, the phone, of course. But also many other things. A GPS system. >> Camera. >> A camera. The list goes on, right? We can go on. Oh, the flashlight, and by the way, your wallet. Maybe not your wallet, but a millennial and younger's wallet-- >> Yeah, sure. >> Is in that phone. >> My wallet's in it. >> My wallet's in it. >> In it, and-- >> Venmo, baby. >> That's right. (all laugh) >> I have my kids' wallets in there too. >> Oh that's great, you've done that switch. So what is happening there obviously is the notion of we're, you know, software defining and we're converging. Now the benefits of that are irrefutable. One thing you buy, it's less energy. One thing to manage, the convenience of carrying it around. Let's take that metaphor and impute it at, let me say a manufacturing floor edge. There's lots of edges out there. We go to a manufacturing floor edge, we see several devices, just like the early pioneers of the smartphone saw a consumer with a camera around his neck, a GPS on his belt, text, right, a flashlight, a wallet, and all this. We see all these devices out there, and what are they? Some of 'em are OT, as you mentioned. Operational technology devices such as control systems, such as data acquisition systems. >> Real-time systems. >> Real-time systems, industrial networks. CAN, PROFIBUS, SCADA solutions and networks. And the second thing we see is some IT. Most of it's closed, so this is important. It's good IT, meaning computing and storage, but a lot of it is closed systems. It's not the open EXEDY 6 architecture that we so enjoy in the data center. So those things are out there. We looked at 'em and we put them all in one box, just like the smartphone is one device. What are the benefits? Lower space, there's not a lot of space at the edge. Lower energy, there's not a lot of energy, right, at the edge. But the more profound benefits that we're seeing, and we have a large auto manufacturer who has deployed this on their manufacturing line, is it keeps uptime higher. In other words, it reduces downtime. So if the manufacturing line stops, there's nothing worse than a manufacturing line stopped, except perhaps an empty one. But the point is, when a manufacturing line stops, you can't put out product. You can't put out product, you can't recognize revenue get it in the consumer's hands. It's very obvious. It's an air-tight business case, actually. So we're able to reduce any downtime, why? Because first of all, everything's together, and secondly, we're able to manage it just like we're managing the data center because it's an open EXEDY 6 architecture. >> So you're converging tasks as well as hardware. >> As well as hardware, and then the next step is software, you know, as well. We just launched a new class of software called the Edgeline Services Platform, and this is OT software. So we're talking OT functions like aggregators and things that do OT technologies and some IT, but because we have so much compute power and it's open, it's EXEDY 6, it can run software like VMware, Microsoft Products, even database products as well. But because we have that, we're able to software define. When you software define, and I'll use the wallet again. You don't have a billfold with your license anymore. Plastic and leather has been software defined, and therefore it's less to deal with. It's much more efficient. So that announcement of our software strategy along now with our hardware strategy is very exciting for us, and customers are very much interested in it. >> So do you have some examples, you know, some real world examples? Customers that you can talk about where you're bringing together OT and IT disciplines? >> Yeah, you bet. Yeah, you bet. Let me talk about a large global beverage and snack company, and they make snacks, and in this case, potato chips. So a potato chip is a product, and the idea of having them come out of the line in the bag and be a higher quality is important. So we took an Edgeline System, the EL 1000, and we put it at the edge, and we were able to software define several of their IT and OT components and get it to a consolidation and integration in one box. Now what that did is it allowed the, and will do, is allowed the foods to move faster. So if they move across the conveyor belt faster, you can bag them faster, get 'em out to the consumer. The second thing is because it's so powerful, this is interesting. Now they can use video cameras to inspect the quality. Now think about that. That's not necessarily a new idea, but what is new is the notion that you can take video, which I think you'd agree is the largest data, is that right? A video is big, big data. >> We know that well. >> Especially if it's high, Yeah, especially if it's higher resolution, and your hosting costs are telling you that as well, right? Of all these videos. But if it's high resolution, and because you're looking for, you know, defects, indeed, one has to process that not only in high resolution, massive data, number one. Number two, quickly, because the thing is moving, and you wanna know to knock it off or stop or whatever the case may be. So what has happened there is my team and I did not think of that. Our customers thought that, well because you gave us this platform, we can now enhance it with a new type of sensor called a camera, with a new type of data, called video, to enhance our quality and keep our process moving faster. >> So keeping this converged notion going, you're converging the hardware, which is, you know, important. You're converging a lot of the administrative tasks. >> Yes. >> Which reduces the likelihood of any single human failure bringing the whole system down, but now you're talking about, in the whole sense, infer, and act loop that typifies what happens at the edge, you're converging new technologies into that loop by being able to add new data type, bring modeling, machine learning, analytics, in the infer, and then being able to act right there, which allows you to think about new invention, new innovation very, very rapidly because you have the processing power to converge all that new function as it becomes better understood. Have I got that right? >> You got it right. I serve as an adjunct professor at university, so let me position it in an easy way to learn. You said sense, infer, and act. Let's just call 'em the three A's. Acquire, analyze, and act. >> Okay. >> It's just easier to remember. And let me talk to that too, but this is actually just synonyms. So the acquisition of the data is through sensors in D to A conversion, or let me say A to D, analog to digital. Because most of these phenomenon, video for example, it has to be, is a light phenomenon. Moisture, pressure. At Duke Energy, for example, the second largest energy provider I worked on that industrial internet of things solution, and vibration was the thing that needed to be acquired and then analog to digital. Now the analysis has to take place. There are seven reasons to analyze at the edge. There are seven reasons not to send the data to the cloud. In the past, we have talked about it. One of them's latency, one of them's cost, one of them's bandwidth, another one is security, another one is reliability, another one is geofencing and policy, another one is duplication and security, you know, hostile or just, you know, reliability drop packets. There's a lot of issues to do that analysis there. But because we have a non-compromised full EXEDY 6, in fact, 64 in one box. 64 Xeon, Intel Xeon product in one box. We don't have to compromise the stack. We can take it directly out of the data center and run things like artificial intelligence, machine learning algorithms. We can virtualize, we can containerize, we can run Citrix applications at the edge to have better access to the data and of course the application. But you're absolutely right, and then the second thing in this point is we move from the middle A, analysis right, to the action. The reason, I've learned this doing many IOT deployments. The reason people do an IOT deployment is to act. Yes, it's exciting to collect data. It's also exciting to analyze it. But have you ever been in a business meeting where you sit and you analyze data and you give tremendous insights, and one conclusion is pit against another conclusion and it cancels out all conclusiveness, and then you talk and you analyze, and you walk out and nothing happens, there's no action. Many of us have been in that. That's the idea here. You can't stop at the analysis, even though artificial intelligence, deep algorithms, moving averages, signatures that we can compare are very powerful. Well, what do you do when you do that? Because we have control and actuation systems built into Edgeline, we literally in a physically space, as well as in a logical process, as you pointed out, close that loop. >> Right. >> Acquire, analyze, act, acquire, analyze, act. Yes, connect to the cloud or the data center if we need to, but the issue is you don't have to. Now here's what's profound about that. This system at the edge can be managed and run the same stacks as any cloud or data center. I'm gonna use those as synonyms because a cloud is just a data center that nobody's supposed to know where it is. So a data center far away on the corporate campus or in a public or private cloud somewhere, is managed the same way. When that happens, we are revolutionizing workload management. Now, I spent a lot of years in my former time in IT and building data centers and building some of the first clouds, workload management's a big deal. How do you shift the workload to the free server? >> Peter: Right. >> Or to the free resources, right? To optimize, obviously. And it's a packing problem many times in the data center. Well now we've introduced another place to workload manage. >> Right. >> It's called the edge, it's far away. So we workload managed in the data center, then the cloud was invented, that's the first off premises. The next off premises is now the edge. So the other off premise is the edge. So now we have a workload management capability. Do you wanna do 100% processing at the edge where the action is, and where the acquisition is? Do you wanna do 100% in the cloud? That's still possible. Do you wanna do 50-50? Would you like to do 10-90? Would you like to do 30-70? You get my point. >> Totally. >> I can shift this, and depending on the season, depending on issues like disaster recovery, depending on your workloads, you can now do that, and again, you can do this with the Edgeline 1000, the Edgeline 4000, because of the processing power and the converged OT inside it. >> Well our observation is that it's not about bringing your business to the cloud, it's about bringing the cloud to your business. >> Yes. >> So bringing that sense of workload management. You know, you might say the cloud is just a virtualized data center when you come right down to it. So bringing all those capabilities and bringing them to wherever the data requires it. And there's gonna be a lot of instances where the data is gonna be at the edge, stay at the edge, but that doesn't mean you don't want all the benefits of how you run computing data at the edge where that data is. >> Yeah, and we're not obviating, we're offering choice. >> Right. >> But again, there are seven reason I went over why you do it here, but I've had a customer say none of those seven matter. So okay, we send everything to the cloud, and we have great cloud hybrid IT products that do that. >> Yeah. >> And we've envisioned a three-tier data model, you know, real time at the edge. >> Yes. >> Maybe you don't persist everything, but like you said, there are a lot of reasons not to move all the data back. But there is maybe a spot where you aggregate some of that data from discrete devices, and sure, if you wanna do some deep modeling in the cloud, go for it. And that cloud might be the public cloud, it might be your own private cloud. Does that seem reasonable to you? >> Very reasonable, and another reason for a cloud is it's an aggregation point for other, in this case, manufacturing lines where other smart cities to come together, because you're not gonna connect every city, every plant, any to any. You'll have a hub and spoke model where the cloud serves as that hub. So there are always reasons, and that's why, you know, if you look at our company, the pillars of our company, Pointnext services, the second pillar is hybrid IT, primarily focused on cloud and data centers, and the third is the intelligent edge. And those all play very, very closely together, in fact we have edge to core strategies, we have edge to core offerings with partners like NVIDEA, with partners like SAP, with partners like SAS, we have edge to core. For example, Schneider as well, Schneider Electric. All of them are looking at this idea, GE, Microsoft Azure, let's go to the edge. And two years ago, that was not the case, right? Let's go there, when you go to the edge, what are you gonna run it on? Well, let's not force our software partners to re-architect like they used to have to to run at the edge, which is like I'd call that drive-by analytics. You just have to cut out everything because it only ran on a wimpy core somewhere or a little device. No, let's move the entire data center capability out to the edge, when I was presenting this to one of our partners, the CEO of the company, I was presenting this vision, and he was texting during my talk 'cause I was boring. (interviewers laugh) And then I said this, this is a very powerful company, I won't mention names. Then I said, we're gonna move data center class technology out to the edge. It's not gonna be in compromised cores or limited memory or a little bit of storage. It's the very things in the data center we'll harden called Edgeline. We'll add controls systems and data acquisition, we'll put it out at the edge. He stopped texting. Then he looked up at me and said, "Wow, you're really moving a data center out to the edge." and you just said that, right? It's the cloud is coming. It's almost a reverse idea of what was happening before. >> Well you wrote a blog recently. >> Yes. >> About the space edge. So I wanted to ask you about that. What's going on in the space, and that's the ultimate edge, I guess. >> The infinite edge. >> The infinite edge. Explain what you guys are doing there and why it's important. >> Well, this is exciting. Space travel for exploration and eventually colonization, if you would believe that, is happening. We have the first supercomputer technology in a NASA spaceship now. It has orbited the Earth well over 1,000 times and it is doing thousands of benchmarks and is doing very well, isn't failing. Now, why is that profound? Because again, that edge is so far away and the ability to push that back to Earth now, which we could call the data centers on Earth, is limited. It takes minutes, sometimes even longer. There's issues with reliability as well. So we were able to do that, and then we've created a new thing called Project Extreme Edge, where we're going to build Edgeline systems that will fit better with lower energy, smaller size in spaceships, and eventually in colonization, but we're just going into space travel and exploration right now. And I'd like to mention that HP Labs is a great participant in this because they're working on a technology, and the name of it is called the Dot-Product Engine. And dot-product is a mathematical operation needed in high-performance computing and artificial intelligence. But we're able to use that technology because it's small, it's fast, faster than we believe anything else on the market, and also it has a low energy profile. And those are all any edge, obviously, but it's also great for the space edge, and I like to quote Frank Sinatra when he said if I can make it there, I can make it anywhere, New York, New York. (laughs) Well, if we can make it in the space edge, these Earth edges will benefit as well. Some of the same challenges. >> All right, we're out of time, but I gotta ask you. Meg stopped by yesterday, and was giving great support for the intelligence. >> She has, yes. >> The company's now reporting the intelligent edge is gonna be one of the main areas. What about the new guy? Antonio. >> Antonio Neri. >> You know, what's your relationship with him, experience? Has he been focused on this area? >> Support? >> He's been great, he supports in three ways, let me just sum up in three ways. Number one, he supports in customer visits. He and I have been on customer visits together, it's always wonderful to have the president and now the new CEO with you affirming what we're doing. That's number one of three, number two of three, he supports the work we're doing with our new global IoT innovation labs, in fact our first grand opening, the first one in Houston, we will have one in Singapore opening in February, and then we'll have one in Europe and perhaps one in India, we're opening these labs for innovation, but my point is, the one in Houston, our first grand opening, Antonio Neri came personally and did the ribbon cutting and sponsored that as well. And then third, he is of course funding my business unit, and he's been very, very supportive and I'm really happy that he's staying with us and he'll be CEO. >> Excellent, Dr. Tom, thanks so much for coming on theCUBE. Congratulations, as you say, I know there's a long way to go, but looks like you're off to a great start and have some real traction. >> Tom: Thank you very much. >> So we appreciate your time and your insights. Okay, keep it right there buddy, we'll be back with our next guest. 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