>> Hello everyone and welcome back to theCUBE, where we're live from Dallas, Texas here at Super computing 2022. I am joined by my cohost David Nicholson. Thank you so much for being here with me and putting up with my trashy jokes all day. >> David: Thanks for having me. >> Yeah. Yes, we are going to be talking about AI this morning and I'm very excited that our guest has has set the stage for us here quite well. Please welcome Andrea Booker. Andrea, thank you so much for being here with us. >> Absolutely. Really excited to be here. >> Savannah: How's your show going so far? >> It's been really cool. I think being able to actually see people in person but also be able to see the latest technologies and and have the live dialogue that connects us in a different way than we have been able to virtually. >> Savannah: Oh yeah. No, it's all, it's all about that human connection and that it is driving towards our first question. So as we were just chit chatting. You said you are excited about making AI real and humanizing that. >> Andrea: Absolutely. >> What does that mean to you? >> So I think when it comes down to artificial intelligence it means so many different things to different people. >> Savannah: Absolutely. >> I was talking to my father the other day for context, he's in his late seventies, right. And I'm like, oh, artificial intelligence, this or that, and he is like, machines taking over the world. Right. >> Savannah: Very much the dark side. >> A little bit Terminator. And I'm like, well, not so much. So that was a fun discussion. And then you flip it to the other side and I'm talking to my 11 year old daughter and she's like, Alexa make sure you know my song preferences. Right. And that's the other very real way in which it's kind of impacting our lives. >> Savannah: Yeah. >> Right. There's so many different use cases that I don't think everyone understands how that resonates. Right. It's the simple things from, you know, recommend Jason Engines when you're on Amazon and it suggests just a little bit more. >> Oh yeah. >> I'm a little bit to you that one, right. To stuff that's more impactful in regards to getting faster diagnoses from your doctors. Right. Such peace of mind being able to actually hear that answer faster know how to go tackle something. >> Savannah: Great point, yeah. >> You know, and, and you know, what's even more interesting is from a business perspective, you know the projections are over the next five years about 90% of customers are going to use AI applications in in some fashion, right. >> Savannah: Wow. >> And the reason why that's interesting is because if you look at it today, only about 15% of of them are doing so. Right. So we're early. So when we're talking growth and the opportunity, it's, it's amazing. >> Yeah. I can, I can imagine. So when you're talking to customers, what are are they excited? Are they nervous? Are you educating them on how to apply Dell technology to advance their AI? Where are they off at because we're so early? >> Yeah well, I think they're figuring it out what it means to them, right? >> Yeah. Because there's so many different customer applications of it, right? You have those in which, you know, are on on the highest end in which that our new XE products are targeting that when they think of it. You know, I I, I like to break it down in this fashion in which artificial intelligence can actually save human lives, right? And this is those extreme workloads that I'm talking about. We actually can develop a Covid vaccine faster, right. Pandemic tracking, you know with global warming that's going on. And we have these extreme weather events with hurricanes and tsunamis and all these things to be able to get advanced notice to people to evacuate, to move. I mean, that's a pretty profound thing. And it is, you know so it could be used in that way to save lives, right? >> Absolutely. >> Which is it's the natural outgrowth of the speeds and feeds discussions that we might have internally. It's, it's like, oh, oh, speed doubled. Okay. Didn't it double last year? Yeah. Doubled last year too. So it's four x now. What does that mean to your point? >> Andrea: Yeah, yeah. >> Savannah: Yeah. >> Being able to deliver faster insight insights that are meaningful within a timeframe when otherwise they wouldn't be meaningful. >> Andrea: Yeah. >> If I tell you, within a two month window whether it's going to rain this weekend, that doesn't help you. In hindsight, we did the calculation and we figured out it's going to be 40 degrees at night last Thursday >> Knowing it was going to completely freeze here in Dallas to our definition in Texas but we prepare better to back to bring clothes. >> We were talking to NASA about that yesterday too. I mean, I think it's, it's must be fascinating for you to see your technology deployed in so many of these different use cases as well. >> Andrea: Absolutely, absolutely. >> It's got to be a part of one of the more >> Andrea: Not all of them are extreme, right? >> Savannah: Yeah. >> There's also examples of, you know natural language processing and what it does for us you know, the fact that it can break down communication barriers because we're global, right? We're all in a global environment. So if you think about conference calls in which we can actually clearly understand each other and what the intent is, and the messaging brings us closer in different ways as well. Which, which is huge, right? You don't want things lost in translation, right? So it, it helps on so many fronts. >> You're familiar with the touring test idea of, of, you know whether or not, you know, the test is if you can't discern within a certain number of questions that you're interacting with an AI versus a real human, then it passes the touring test. I think there should be a natural language processing test where basically I say, fine >> Andrea: You see if people was mad or not. >> You tell me, you tell me. >> I love this idea, David. >> You know? >> Yeah. This is great. >> Okay. AI lady, >> You tell me what I meant. >> Yeah, am I actually okay? >> How far from, that's silly example but how far do you think we are from that? I mean, what, what do you seeing out there in terms of things where you're kind of like, whoa, they did this with technology I'm responsible for, that was impressive. Or have you heard of things that are on the horizon that, you know, again, you, you know they're the big, they're the big issues. >> Yeah. >> But any, anything kind of interesting and little >> I think we're seeing it perfected and tweaked, right? >> Yeah. >> You know, I think going back to my daughter it goes from her screaming at Alexa 'cause she did hear her right the first time to now, oh she understands and modifies, right? Because we're constantly tweaking that technology to have a better experience with it. And it's a continuum, right? The voice to text capabilities, right. You know, I I'd say early on it got most of those words, right Right now it's, it's getting pretty dialed in. Right. >> Savannah: That's a great example. >> So, you know, little things, little things. >> Yeah. I think I, I love the, the this thought of your daughter as the example of training AI. What, what sort of, you get to look into the future quite a bit, I'm sure with your role. >> Andrea: Absolutely. >> Where, what is she going to be controlling next? >> The world. >> The world. >> No, I mean if you think about it just from a generational front, you know technology when I was her age versus what she's experiencing, she lives and breathes it. I mean, that's the generational change. So as these are coming out, you have new folks growing with it that it's so natural that they are so open to adopting it in their common everyday behaviors. Right? >> Savannah: Yeah. >> But they'd they never, over time they learn, oh well how it got there is 'cause of everything we're doing now, right. >> Savannah: Yeah. >> You know, one, one fun example, you know as my dad was like machines are taking over the world is not, not quite right. Even if when you look at manufacturing, there's a difference in using AI to go build a digital simulation of a factory to be able to optimize it and design it right before you're laying the foundation that saves cost, time and money. That's not taking people's jobs in that extreme event. >> Right. >> It's really optimizing for faster outcomes and, and and helping our customers get there which is better for everyone. >> Savannah: Yeah and safer too. I mean, using the factory example, >> Totally safer. >> You're able to model out what a workplace injury might be or what could happen. Or even the ergonomics of how people are using. >> Andrea: Yeah, should it be higher so they don't have to bend over? Right. >> Exactly. >> There's so many fantastic positive ways. >> Yeah so, so for your dad, you know, I mean it's going to help us, it's going to make, it's going to take away when I. Well I'm curious what you think, David when I think about AI, I think it's going to take out a lot of the boring things in life that, that we don't like >> Andrea: Absolutely. Doing. The monotony and the repetitive and let us optimize our creative selves maybe. >> However, some of the boring things are people's jobs. So, so it is, it it it will, it will it will push a transition in our economy in the global economy, in my opinion. That would be painful for some, for some period of time. But overall beneficial, >> Savannah: Yes. But definitely as you know, definitely there will be there will be people who will be disrupted and, you know. >> Savannah: Tech's always kind of done that. >> We No, but we need, I, I think we need to make sure that the digital divide doesn't get so wide that you know that, that people might not be negative, negatively affected. And, but, but I know that like organizations like Dell I believe what you actually see is, >> Andrea: Yeah. >> No, it's, it's elevating people. It's actually taking away >> Andrea: Easier. >> Yeah. It's, it's, it's allowing people to spend their focus on things that are higher level, more interesting tasks. >> Absolutely. >> David: So a net, A net good. But definitely some people disrupted. >> Yes. >> I feel, I feel disrupted. >> I was going to say, are, are we speaking for a friend or for ourselves here today on stage? >> I'm tired of software updates. So maybe if you could, if you could just standardize. So AI and ML. >> Andrea: Yeah. >> People talk about machine learning and, and, and and artificial intelligence. How would you differentiate the two? >> Savannah: Good question. >> It it, it's, it's just the different applications and the different workloads of it, right? Because you actually have artificial intelligence you have machine learning in which the learn it's learning from itself. And then you have like the deep learning in which it's diving deeper in in its execution and, and modeling. And it really depends on the workload applications as long as well as how large the data set is that's feeding into it for those applications. Right. And that really leads into the, we have to make sure we have the versatility in our offerings to be able to meet every dimension of that. Right. You know our XE products that we announced are really targeted for that, those extreme AI HPC workloads. Right. Versus we also have our entire portfolio products that we make sure we have GPU diversity throughout for the other applications that may be more edge centric or telco centric, right? Because AI isn't just these extreme situations it's also at the edge. It's in the cloud, it's in the data center, right? So we want to make sure we have, you know versatility in our offerings and we're really meeting customers where they're at in regards to the implementation and and the AI workloads that they have. >> Savannah: Let's dig in a little bit there. So what should customers expect with the next generation acceleration trends that Dell's addressing in your team? You had three exciting product announcements here >> Andrea: We did, we did. >> Which is very exciting. So you can talk about that a little bit and give us a little peek. >> Sure. So, you know, for, for the most extreme applications we have the XE portfolio that we built upon, right? We already had the XC 85 45 and we've expanded that out in a couple ways. The first of which is our very first XC 96 88 way offering in which we have Nvidia's H 100 as well as 8 100. 'Cause we want choice, right? A choice between performance, power, what really are your needs? >> Savannah: Is that the first time you've combined? >> Andrea: It's the first time we've had an eight way offering. >> Yeah. >> Andrea: But we did so mindful that the technology is emerging so much from a thermal perspective as well as a price and and other influencers that we wanted that choice baked into our next generation of product as we entered the space. >> Savannah: Yeah, yeah. >> The other two products we have were both in the four way SXM and OAM implementation and we really focus on diversifying and not only from vendor partnerships, right. The XC 96 40 is based off Intel Status Center max. We have the XE 86 40 that is going to be in or Nvidia's NB length, their latest H 100. But the key differentiator is we have air cold and we have liquid cold, right? So depending on where you are from that data center journey, I mean, I think one of the common themes you've heard is thermals are going up, performance is going up, TBPs are going up power, right? >> Savannah: Yeah. >> So how do we kind of meet in the middle to be able to accommodate for that? >> Savannah: I think it's incredible how many different types of customers you're able to accommodate. I mean, it's really impressive. I feel lucky we've gotten to see these products you're describing. They're here on the show floor. There's millions of dollars of hardware literally sitting in your booth. >> Andrea: Oh yes. >> Which is casual only >> Pies for you. Yeah. >> Yeah. We were, we were chatting over there yesterday and, and oh, which, which, you know which one of these is more expensive? And the response was, they're both expensive. It was like, okay perfect >> But assume the big one is more. >> David: You mentioned, you mentioned thermals. One of the things I've been fascinated by walking around is all of the different liquid cooling solutions. >> Andrea: Yeah. >> And it's almost hysterical. You look, you look inside, it looks like something from it's like, what is, what is this a radiator system for a 19th century building? >> Savannah: Super industrial? >> Because it looks like Yeah, yeah, exactly. Exactly, exactly. It's exactly the way to describe it. But just the idea that you're pumping all of this liquid over this, over this very, very valuable circuitry. A lot of the pitches have to do with, you know this is how we prevent disasters from happening based on the cooling methods. >> Savannah: Quite literally >> How, I mean, you look at the power requirements of a single rack in a data center, and it's staggering. We've talked about this a lot. >> Savannah: Yeah. >> People who aren't kind of EV you know electric vehicle nerds don't appreciate just how much power 90 kilowatts of power is for an individual rack and how much heat that can generate. >> Andrea: Absolutely. >> So Dell's, Dell's view on this is air cooled water cooled figure it out fit for for function. >> Andrea: Optionality, optionality, right? Because our customers are a complete diverse set, right? You have those in which they're in a data center 10 to 15 kilowatt racks, right? You're not going to plum a liquid cool power hungry or air power hungry thing in there, right? You might get one of these systems in, in that kind of rack you know, architecture, but then you have the middle ground the 50 to 60 is a little bit of choice. And then the super extreme, that's where liquid cooling makes sense to really get optimized and have the best density and, and the most servers in that solution. So that's why it really depends, and that's why we're taking that approach of diversity, of not only vendors and, and choice but also implementation and ways to be able to address that. >> So I think, again, again, I'm, you know electric vehicle nerd. >> Yeah. >> It's hysterical when you, when you mention a 15 kilowatt rack at kind of flippantly, people don't realize that's way more power than the average house is consuming. >> Andrea: Yeah, yeah >> So it's like your entire house is likely more like five kilowatts on a given day, you know, air conditioning. >> Andrea: Maybe you have still have solar panel. >> In Austin, I'm sorry >> California, Austin >> But, but, but yeah, it's, it's staggering amounts of power staggering amounts of heat. There are very real problems that you guys are are solving for to drive all of these top line value >> Andrea: Yeah. >> Propositions. It's super interesting. >> Savannah: It is super interesting. All right, Andrea, last question. >> Yes. Yes. >> Dell has been lucky to have you for the last decade. What is the most exciting part about you for the next decade of your Dell career given the exciting stuff that you get to work on. >> I think, you know, really working on what's coming our way and working with my team on that is is just amazing. You know, I can't say it enough from a Dell perspective I have the best team. I work with the most, the smartest people which creates such a fun environment, right? So then when we're looking at all this optionality and and the different technologies and, and, and you know partners we work with, you know, it's that coming together and figuring out what's that best solution and then bringing our customers along that journey. That kind of makes it fun dynamic that over the next 10 years, I think you're going to see fantastic things. >> David: So I, before, before we close, I have to say that's awesome because this event is also a recruiting event where some of these really really smarts students that are surrounding us. There were some sirens going off. They're having competitions back here. >> Savannah: Yeah, yeah, yeah. >> So, so when they hear that. >> Andrea: Where you want to be. >> David: That's exactly right. That's exactly right. >> Savannah: Well played. >> David: That's exactly right. >> Savannah: Well played. >> Have fun. Come on over. >> Well, you've certainly proven that to us. Andrea, thank you so much for being with us This was such a treat. David Nicholson, thank you for being here with me and thank you for tuning in to theCUBE a lot from Dallas, Texas. We are all things HPC and super computing this week. My name's Savannah Peterson and we'll see you soon. >> Andrea: Awesome.

(upbeat jingle) >> Cameraman: Just look, Mike. >> Good afternoon everyone, and welcome back to Supercomputing. We're live here with theCUBE in Dallas. I'm joined by my cohost, David. Wonderful to be sharing the afternoon with you. And we are going to be kicking things off with a very thrilling discussion from two important thought leaders at Dell. Bhavesh and Shreya, thank you so much for being on the show. Welcome. How you doing? How does it feel to be at Supercomputing? >> Pretty good. We really enjoying the show and enjoying a lot of customer conversations ongoing. >> Yeah. Are most of your customers here? >> Yes. Most of the customers are, mostly in the Hyatt over there and a lot of discussions ongoing. >> Yeah. Must be nice to see everybody show off. Are you enjoying the show so far, Shreya? >> Yeah, I missed this for two years and so it's nice to be back and meeting people in person. >> Yeah, definitely. We all missed it. So, it's been a very exciting week for Dell. Do you want to talk about what you're most excited about in the announcement portfolio that we saw yesterday? >> Absolutely. >> Go for it, Shreya. >> Yeah, so, you know, before we get into the portfolio side of the house, you know, we really wanted to, kind of, share our thoughts, in terms of, you know, what is it that's, kind of, moving HPC and supercomputing, you know, for a long time- >> Stock trends >> For a long time HPC and supercomputing has been driven by packing the racks, you know, maximizing the performance. And as the work that Bhavesh and I have been doing over the last, you know, couple of generations, we're seeing an emerging trend and that is the thermal dissipated power is actually exploding. And so the idea of packing the racks is now turning into, how do you maximize your performance, but are able to deliver the infrastructure in that limited kilowatts per rack that you have in your data center. >> So I, it's been interesting walking around the show seeing how many businesses associated with cooling- >> Savannah: So many. >> are here. And it's funny to see, they open up the cabinet, and it's almost 19th-century-looking technology. It's pipes and pumps and- >> Savannah: And very industrial-like. >> Yeah, very, very industrial-looking. Yeah, and I think, so that's where the, the trends are more in the power and cooling. That is what everybody is trying to solve from an industry perspective. And what we did when we looked at our portfolio, what we want to bring up in this timeframe for targeting more the HPC and AI space. There are a couple of vectors we had to look at. We had to look at cooling, we had to look at power where the trends are happening. We had to look at, what are the data center needs showing up, be it in the cooler space, be it in the HPC space, be it in the large install happening out there. So, looking at those trends and then factoring in, how do you build a node out? We said, okay, we need to diversify and build out an infrastructure. And that's what me and Shreya looked into, not only looking at the silicon diversity showing up, but more looking at, okay, there is this power, there is this cooling, there is silicon diversity. Now, how do you start packing it up and bringing it to the marketplace? So, kind of, those are some of the trends that we captured. And that's what you see, kind of, in the exhibit floor today, even. >> And Dell technology supports both, liquid cooling, air cooling. Do you have a preference? Is it more just a customer-based? >> It is going to be, and Shreya can allude to it, it's more workload and application-focused. That is what we want to be thinking about. And it's not going to be siloed into, okay, is we going to be just targeting air-cooling, we wanted to target a breadth between air to liquid. And that's how we built into our portfolio when we looked at our GPUs. >> To add to that, if we look at our customer landscape, we see that there's a peak between 35 to 45 kilowatts per rack. We see another peak at 60, we see another peak at 80, and we've got selects, you know, very specialized customers above hundred kilowatts per rack. And so, if we take that 35 to 45 kilowatts per rack, you know, you can pack maybe three or four of these chassis, right? And so, to what Bhavesh is saying, we're really trying to provide the flexibility for what our customers can deliver in their data centers. Whether it be at the 35 end where air cooling may make complete sense. As you get above 45 and above, maybe that's the time to pivot to a liquid-cool solution. >> So, you said that there, so there are situations where you could have 90 kilowatts being consumed by a rack of equipment. So, I live in California where we are very, very closely attuned to things like the price for a kilowatt hour of electricity. >> Seriously. >> And I'm kind of an electric car nerd, so, for the folks who really aren't as attuned, 90 kilowatts, that's like over a hundred horsepower. So, think about a hundred horsepower worth of energy being used for compute in one of these racks. It's insane. So, we, you can kind of imagine a layperson can kind of imagine the variables that go into this equation of, you know, how do we, how do we bring the power and get the maximum bang for, per kilowatt hour. But, are there any, are there any kind of interesting odd twists in your equations that you find when you're trying to figure out. Do you have a- >> Yeah, and we, a lot of these trends when we look at it, okay, it's not, we think about it more from a power density that we want to try to go and solve. We are mindful about all the, from an energy perspective where the energy prices are moving. So, what we do is we try to be optimizing right at the node level and how we going to do our liquid-cooling and air cooled infrastructure. So, it's trying to, how do you keep a balance with it? That's what we are thinking about. And thinking about it is not just delivering or consuming the power that is maybe not needed for that particular node itself. So, that's what we are thinking about. The other way we optimize when we built this infrastructure out is we are thinking about, okay, how are we go going to deliver it at the rack level and more keeping in mind as to how this liquid-cooling plumbing will happen. Where is it coming into the data center? Is it coming in the bottom of the floor? Are we going to do it on the left hand side of your rack or the right hand side? It's a big thing. It's like it becomes, okay, yeah, it doesn't matter which side you put it on, but there is a piece of it going into our decision as to how we are going to build that, no doubt. So, there are multiple factors coming in and besides the power and cooling, which we all touched upon, But, Shreya and me also look at is where this whole GPU and accelerators are moving into. So, we're not just looking at the current set of GPUs and where they're moving from a power perspective. We are looking at this whole silicon diversity that is happening out there. So, we've been looking at multiple accelerators. There are multiple companies out there and we can tell you there'll be over three 30 to 50 silicon companies out there that we are actively engaged and looking into. So, our decision in building this particular portfolio out was being mindful about what the maturity curve is from a software point of view. From a hardware point of view and what can we deliver, what the customer really needs in it, yeah. >> It's a balancing act, yeah. >> Bhavesh: It is a balancing act. >> Let's, let's stay in that zone a little bit. What other trends, Shreya, let's go to you on this one. What other trends are you seeing in the acceleration landscape? >> Yeah, I think you know, to your point, the balancing act is actually a very interesting paradigm. One of the things that Bhavesh and I constantly think about, and we call it the Goldilocks syndrome, which is, you know, at that 90 and and a hundred, right? Density matters. >> Savannah: A lot. >> But, what we've done is we have really figured out what that optimal point is, 'cause we don't want to be the thinnest most possible. You lose a lot of power redundancy, you lose a lot of I/O capability, you lose a lot of storage capability. And so, from our portfolio perspective, we've really tried to think about the Goldilocks syndrome and where that sweet spot is. >> I love that. I love the thought of you all just standing around server racks, having a little bit of porridge and determining >> the porridge. Exactly the thickness that you want in terms of the density trade off there. Yeah, that's, I love that, though. I mean it's very digestible. Are you seeing anything else? >> No, I think that's pretty much, Shreya summed it up and we think about what we are thinking about, where the technology features are moving and what we are thinking, in terms of our portfolio, so it is, yeah. >> So, just a lesson, you know, Shreya, a lesson for us, a rudimentary lesson. You put power into a CPU or a GPU and you're getting something out and a lot of what we get out is heat. Is there a measure, is there an objective measure of efficiency in these devices that we look at? Because you could think of a 100 watt light bulb, an incandescent light bulb is going to give out a certain amount of light and a certain amount of heat. A 100 watt equivalent led, in terms of the lumens that it's putting out, in terms of light, a lot more light for the power going in, a lot less heat. We have led lights around us, thankfully, instead of incandescent lights. >> Savannah: Otherwise we would be melting. >> But, what is, when you put power into a CPU or a GPU, how do you measure that efficiency? 'Cause it's sort of funny, 'cause it's like, it's not moving, so it's not like measuring, putting power into a vehicle and measuring forward motion and heat. You're measuring this, sort of, esoteric thing, this processing thing that you can't see or touch. But, I mean, how much per watt of power, how do you, how do you measure it I guess? Help us out, from the base up understanding, 'cause people generally, most people have never been in a data center before. Maybe they've put their hand behind the fan in a personal computer or they've had a laptop feel warm on their lap. But, we're talking about massive amounts of heat being generated. Can you, kind of, explain the fundamentals of that? >> So, the way we think about it is, you know, there's a performance per dollar metric. There's a performance per dollar per watt metric and that's where the power kind of comes in. But, on the flip side, we have something called PUE, power utilization efficiency from a data center aspect. And so, we try to marry up those concepts together and really try to find that sweet spot. >> Is there anything in the way of harvesting that heat to do other worthwhile work, I mean? >> Yes. >> You know, it's like, hey, everybody that works in the data center, you all have your own personal shower now, water heated. >> Recirculating, too. >> Courtesy of Intel AMD. >> Or a heated swimming pool. >> Right, a heated swimming pool. >> I like the pool. >> So, that's the circulation of, or recycling of that thermal heat that you're talking about, absolutely. And we see that our customers in the, you know, in the Europe region, actually a lot more advanced in terms of taking that power and doing something that's valuable with it, right? >> Cooking croissant and, and making lattes, probably right? >> (laughing) Or heating your home. >> Makes me want to go on >> vacation, a pool, croissants. >> That would be a good use. But, do you, it's more on the PUE aspect of it. It's more thinking about how are we more energy efficient in our design, even, so we are more thinking about what's the best efficiency we can get, but what's the amount of heat capture we can get? Are we just kind of wasting any heat out there? So, that's always the goal when designing these particular platforms, so that's something that we had kept in mind with a lot of our power and cooling experts within Dell. When thinking about, okay, is it, how much can we get, can we capture? If we are not capturing anything, then what are we, kind of, recirculating it back in order to get much better efficiency when we think about it at a rack level and for the other equipment which is going to be purely air-cooled out there and what can we do about it, so. >> Do you think both of these technologies are going to continue to work in tandem, air cooling and liquid cooling? Yeah, so we're not going to see- >> Yeah, we don't, kind of, when we think about our portfolio and what we see the trends moving in the future, I think so, air-cooling is definitely going to be there. There'll be a huge amount of usage for customers looking into air-cooling. Air-cooling is not going to go away. Liquid-cooling is definitely something that a lot of customers are looking into adopting. PUE become the bigger factor for it. How much can I heat capture with it? That's a bigger equation that is coming into the picture. And that's where we said, okay, we have a transition happening. And that's what you see in our portfolio now. >> Yeah, Intel is, Intel, excuse me, Dell is agnostic when it comes to things like Intel, AMD, Broadcom, Nvidia. So, you can look at this landscape and I think make a, you know, make a fair judgment. When we talk about GPU versus CPU, in terms of efficiency, do you see that as something that will live on into the future for some applications? Meaning look, GPU is the answer or is it simply a question of leveraging what we think of as CPU cores differently? Is this going to be, is this going to ebb and flow back and forth? Shreya, are things going to change? 'Cause right now, a lot of what's announced recently, in the high performance computer area, leverages GPUs. But, we're right in the season of AMD and Intel coming out with NextGen processor architectures. >> Savannah: Great point. >> Shreya: Yeah >> Any thoughts? >> Yeah, so what I'll tell you is that it is all application dependent. If you rewind, you know, a couple of generations you'll see that the journey for GPU just started, right? And so there is an ROI, a minimum threshold ROI that customers have to realize in order to move their workloads from CPU-based to GPU-based. As the technology evolves and matures, you'll have more and more applications that will fit within that bucket. Does that mean that everything will fit in that bucket? I don't believe so, but as, you know, the technology will continue to mature on the CPU side, but also on the GPU side. And so, depending on where the customer is in their journey, it's the same for air versus liquid. Liquid is not an if, but it's a when. And when the environment, the data center environment is ready to support that, and when you have that ROI that goes with it is when it makes sense to transition to one way or the other. >> That's awesome. All right, last question for you both in a succinct phrase, if possible, I won't character count. What do you hope that we get to talk about next year when we have you back on theCUBE? Shreya, we'll start with you. >> Ooh, that's a good one. I'm going to let Bhavesh go first. >> Savannah: Go for it. >> (laughs) >> What do you think, Bhavesh? Next year, I think so, what you'll see more, because I'm in the CTI group, more talking about where cache coherency is moving. So, that's what, I'll just leave it at that and we'll talk about it more. >> Savannah: All right. >> Dave: Tantalizing. >> I was going to say, a little window in there, yeah. And I think, to kind of add to that, I'm excited to see what the future holds with CPUs, GPUs, smart NICs and the integration of these technologies and where that all is headed and how that helps ultimately, you know, our customers being able to solve these really, really large and complex problems. >> The problems our globe faces. Wow, well it was absolutely fantastic to have you both on the show. Time just flew. David, wonderful questions, as always. Thank you all for tuning in to theCUBE. Here live from Dallas where we are broadcasting all about supercomputing, high-performance computing, and everything that a hardware nerd, like I, loves. My name is Savannah Peterson. We'll see you again soon. (upbeat jingle)

>>Welcome to the customer. Obsession begins with data session. Uh, thank you for, for attending. Um, at Cloudera, we believe that a custom session begins with, uh, with, with data. Um, and, uh, you know, financial services is Cloudera is largest industry vertical. We have approximately 425 global financial services customers, uh, which consists of 82 out of a hundred of the largest global banks of which we have 27 that are globally systemic banks, uh, four out of the five, uh, top stock exchanges, eight out of the 10 top wealth management firms and all four of the top credit card networks. Uh, so as you can see most financial services institutions utilize Cloudera for data analytics and machine learning. Uh, we also have over 20 central banks and it doesn't or so financial regulators. So it's an incredible footprint, which glimpse Cloudera, lots of insight into the many innovations that our customers are coming in up >>With >>Customers have grown more independent and demanding. Uh, they want the ability to perform many functions on their own and, uh, be able to do it. Uh, he do them on their mobile devices, uh, in a recent Accenture study, more than 50% of customers, uh, are focused on, uh, improving their customer experience through more personalized, uh, offers in advice. The study found that 75% of people are actually willing to share their data for better personalized offers and more efficient and intuitive of services >>Together. And >>A better understanding of your customers use all the data available to develop a complete view of your customer and, uh, and better serve them. Uh, this also breaks down, uh, costly silos, uh, shares data in, in accordance with privacy laws and assists with regulatory adherence. So different and organizations are going to be at different points in their data analytics and AI journey. Uh, there are several degrees of streaming and batch data, both structured and unstructured. Uh, you need a platform that can handle both, uh, with common, with a common governance layer, um, near real time and real real-time sources help make data more relevant. So if you look at this graphic, looking at it from left to right, uh, normal streaming and batch data comes from core banking and, uh, and lending operations data in pretty much a structured format as financial institutions start to evolve. >>Uh, they start to ingest near real-time streaming that comes not only from customers, but also from, from newsfeeds for example, and they start to capture more behavioral data that they can use to evolve their models, uh, and customer experience. Uh, ultimately they start to ingest more real-time streaming data, not only, um, standard, uh, sources like market and transaction data, but also alternative sources such as social media and connected sources, such as wearable devices, uh, giving them more, more data, better data, uh, to extract intelligence and drive personalized actions based on data in real time at the right time, um, and use machine learning and AI, uh, to drive anomaly detection and protect and predict, uh, present potential outcomes. >>So this >>Is another way to look at it. Um, this slide shows the progression of the big data journey as it relates to a customer experience example, um, the dark blue represents, um, visibility or understanding your customer. So we have a data warehouse and are starting to develop some analytics, uh, to know your customer and start to provide a better customer 360 experience. Uh, the medium blue area, uh, is, uh, customer centric or where we learn, uh, the customer's behavior. Uh, at this point we're improving our analytics, uh, gathering more customer centric information to perform, uh, some more exploratory, uh, data sciences. And we can start to do things like cross sell or upsell based on the customer's behavior, which should improve, uh, customer retention. The light blue area is, uh, is proactive customer inter interactions or where we now have the ability, uh, to predict customers needs and wants and improve our interaction with the customer, uh, using applied machine learning and, and AI, uh, clap the Cloudera data platform. >>Um, you know, business use cases require enabling, uh, the end-to-end journey, which we referred to as the data life cycle, uh, what the data life cycle, what is the data life cycle that our customers want to take their data through to enable the end-to-end data journey. If you ask our customers, they want different types of analytics, uh, for their diverse user bases to, to help them implement their, their, their use cases while managed by a centralized security and governance later layer. Uh, in other words, um, the data life cycle to them provides multifunction analytics, uh, at each stage within the data journey, uh, that, uh, integrated and centralized, uh, security, uh, and governance, for example, uh, enterprise data consists of real-time and transactional type type data. Examples include, uh, clickstream data, web logs, um, machine generated, data chatbots, um, call center interactions, uh, transactions, uh, within legacy applications, market data, et cetera. >>We need to manage, uh, that data life cycle, uh, to provide real enterprise data insights, uh, for use cases around enhance them personalized customer experience, um, customer journey analytics, next best action, uh, sentiment and churn analytics market, uh, campaign optimization, uh, mortgage, uh, processing optimization and so on. Um, we bring a diverse set of data then, um, and then enrich it with other data about our customers and products, uh, provide reports and dashboards such as customer 360 and use predictions from machine learning models to provide, uh, business decisions and, and offers of, uh, different products and services to customers and maintain customer satisfaction, um, by using, um, sentiment and turn analytics. These examples show that, um, the whole data life cycle is involved, um, and, uh, is in continuous fashion in order to meet these types of use cases, uh, using a single cohesive platform that can be, uh, that can be served by CDP, uh, the data, the Cloudera data platform. >>Okay. Let's, uh, let's talk about, uh, some of the experiences, uh, from our customers. Uh, first we'll talk about Bunco, something there. Um, Banco Santander is a major global bank headquartered in Spain, uh, with, uh, major operations and subsidiaries all over Europe and north and, and south America. Uh, one of its subsidiary, something there UK wanted to revolutionize the customer experience with the use of real-time data and, uh, in app analytics, uh, for mobile users, however, like many financial institutions send them there had a, he had a, had a large number of legacy data warehouses spread across many business use, and it's within consistent data and different ways of calculating the same metrics, uh, leading to different results. As a result, the company couldn't get the comprehensive customer insights it needed. And, uh, and business staff often worked on multiple versions of the truth. Sometimes there worked with Cloudera to improve a single data platform that could support all its workloads, including self-service analytics, uh, operational analytics and data science processes in processing 10 million transactions, daily or 30,000 transactions per second at peak times. >>And, uh, bringing together really, uh, nearly two to two petabytes of data. The platform provides unprecedented, uh, customer insight and business value across the organization, uh, over 80 cents. And Dera has realized impressive, uh, benefits spanning, uh, new revenues, cost savings and risk reductions, including creating analytics for, for corporate customers with near real-time shopping behavior, um, and, and helping identify 7,000 new corporate, uh, customer prospects, uh, reducing capital expenditures by, uh, 3.2 million annually and decreasing operating expenses by, uh, 650,000, um, enabling marketing to realize, uh, 2.4 million in annual savings on, on cash back on commercial transactions, um, and protecting 3.7 million customers from financial crime impacts through 95, new proactive control alerts, improving risk and capital calculations to reduce the amount of money. It must set aside, uh, as part of a, as part of risk mandates. Uh, for example, in one instance, the risk team was able to release a $5.2 million that it had withheld for non-performing credit card loans by properly identifying healthy accounts miscategorized as high risk next, uh, let's uh, talk about, uh, Rabo bank. >>Um, Rabobank is one of the largest banks in the Netherlands, uh, with approximately 8.3 million customers. Uh, it was founded by farmers in the late 19th century and specializes in agricultural financing and sustainability oriented banking, uh, in order to help its customers become more self-sufficient and, uh, improve their financial situations such as debt settlement, uh, rebel bank needed to access, uh, to a varied mix of high quality, accurate, and timely customer data, the talent, uh, to provide this insight, however, was the ability to execute sophisticated and timely data analytics at scale Rabobank was also faced with the challenge of, uh, shortening time to market. Uh, it needed easier access to customer data sets to ensure that they were using and receiving the right financial support at the right time with, with, uh, data quality and speed of processing. Um, highlighted as two vital areas of improvement. Robert bank was looking to incorporate, um, or create new data in an environment that would not only allow the organization to create a centralized repository of high quality data, but also allow them to stream and, uh, conduct data analytics on the fly, uh, to create actionable insights and deliver a strong customer service experience. >>Rabobank >>Leverage Cloudera due to its ability to cope with heavy pressures on data processing and its capability of ingesting large quantities of real-time streaming data. They were able to quickly create a new data lake that allowed for faster queries of both historical and real-time data to analyze customer loan repayment patterns, uh, to up to the minute transaction records, um, Robert bank and, and its customers could now immediately access, uh, the valuable data needed to help them understand, um, the status of their financial situation, this enabled, uh, rebel bank to spot financial disasters before they happened, enabling them to gain deep and timely insights into which customers were at risk of defaulting on loans. Um, having established the foundation of a modern data architecture Rabobank is now able to run sophisticated machine learning algorithms and, uh, financial models, uh, to help customers manage, um, financial, uh, obligations, um, including, uh, loan repayments, and are able to generate accurate, uh, current liquidity overviews, uh, no next, uh, let's, uh, speak about, um, uh, OVO. >>Uh, so OVO is the leading digital payment rewards and financial services platform in Indonesia, and is present in 115 million devices across the company across the country. Excuse me. Um, as the volume of, of products, uh, within Obos ecosystem increases, the ability to ensure marketing effectiveness is critical to avoid unnecessary waste of time and resources, unlike competitors, uh, banks, w which use traditional mass marketing, uh, to reach customers over, oh, decided to embark on a, on a bold new approach to connect with customers via a ultra personalized marketing, uh, using the stack, the team at OVO were able to implement a change point detection algorithm, uh, to discover customer life stage changes. This allowed OVO, uh, to, uh, build a segmentation model of one, uh, the contextual offer engine Bill's recommendation algorithms on top of the product, uh, including collaborative and context-based filters, uh, to detect changes in consumer consumption >>Patterns. >>As a result, OVO has achieved a 15% increase in revenue, thanks to this, to this project, um, significant time savings through automation and eliminating the chance of human error and have reduced engineers workloads by, by 30%. Uh, next let's talk about, uh, bank Bri, uh, bank Bri is one of the largest and oldest, uh, banks in Indonesia, um, engaging in, in general banking services, uh, for its customers. Uh, they are headquartered in, in Jakarta Indonesia, uh, BR is a well-known, uh, for its, uh, focused on financing initiative initiatives and serves over 75 million customers through its more than 11,000 offices and rural outposts, >>Um, Bri >>Needed to gain better understanding of their customers and market, uh, to improve the efficiency of its operations, uh, reduce losses from non-performing loans and address the rising concern around data security from regulators and consumers, uh, through enhanced fraud detection. This would require the ability to analyze vast amounts of, uh, historical financial data and use those insights, uh, to enhance operations and, uh, deliver better service. Um, Bri used Cloudera's enterprise data platform to build an agile and reliable, uh, predictive augmented intelligence solution. Uh, Bri was now able to analyze 124 years worth of historical financial data and use those insights to enhance its operations and deliver better services. Um, they were able to, uh, enhance their credit scoring system, um, the solution analyzes customer transaction data, and predicts the probability of a customer defaulting on, on payments. Um, the following month, it also alerts Bri's loan officers, um, to at-risk customers, prompting them to take the necessary action to reduce the likelihood of a Vanette profit lost. Uh, this resulted in improved credits in, in improved, uh, credit scoring system, uh, that cut down the approval of micro financing loans, uh, from two weeks to two days to two minutes and, uh, enhanced, uh, fraud detector. >>All right. Uh, this example shows a tabular representation, uh, the evolution of a customer retention use case, um, the evolution of data and analytics, uh, journey that, uh, that for that use case, uh, from aware, uh, text flirtation, uh, to optimization, to being transformative, uh, with every level, uh, data sources increase. And, uh, for the most part, uh, are, are less, less standard, more dynamic and less structured, but always adding more value, more insights into the customer, uh, allowing us to continuously improve our analytics, increase the velocity of the data we ingest, uh, from, from batch, uh, to, uh, near real time, uh, to real-time streaming, uh, the volume of data we ingest continually increases and we progress, uh, the value of the data on our customers, uh, is continuously improving, allowing us to interact more proactively and more efficiently. And, and with that, um, I would, uh, you know, ask you to consider an assess if you are using all the, uh, the data available to understand, uh, and service your customers, and to learn more about, about this, um, you know, visit cloudera.com and schedule a meeting with Cloudera to learn more. And with that, thank you for your time. And thank you for listening.

>>Uh, hi, I'm Joe Rodriguez, managing director of financial services at Cloudera. Uh, welcome to the fight fraud with a data session, uh, at Cloudera, we believe that fighting fraud with, uh, uh, begins with data. Um, so financial services is Cloudera's largest industry vertical. We have approximately 425 global financial services customers, uh, which consists of 82 out of a hundred of the largest global banks of which we have 27 that are globally systemic banks, uh, four out of the five top, uh, stock exchanges, uh, eight out of the top 10 wealth management firms and all four of the top credit card networks. So as you can see most financial services institutions, uh, utilize Cloudera for data analytics and machine learning, uh, we also have over 20 central banks and a dozen or so financial regulators. So it's an incredible footprint which gives Cloudera lots of insight into the many innovations, uh, that our customers are coming up with. Uh, criminals can steal thousands of dollars before a fraudulent transaction is detected. So the cost of, uh, to purchase a, your account data is well worth the price to fraudsters. Uh, according to Experian credit and debit card account information sells on the dark web for a mere $5 with the CVV number and up to $110. If it comes with all the bank information, including your name, social security number, date of birth, uh, complete account numbers and, and other personal data. >>Um, our customers have several key data and analytics challenges when it comes to fighting financial crime. The volume of data that they need to deal with is, is huge and growing exponentially. Uh, all this data needs to be evaluated in real time. Uh, there is, uh, there are new sources of, of streaming data that need to be integrated with existing, uh, legacy data sources. This includes, um, biometrics data and enhanced, uh, authentication, uh, video surveillance call center data. And of course all that needs to be integrated with existing legacy data sources. Um, there is an analytics arms race between the banks and the criminals and the criminal networks never stop innovating. They also we'll have to deal with, uh, disjointed security and governance, security and governance policies are often set per data source, uh, or application requiring redundant work, work across workloads. And, and they have to deal with siloed environments, um, the specialized nature of platforms and people results in disparate data sources and data management processes, uh, this duplicates efforts and, uh, divides the, the business risk and crime teams, limiting collaboration opportunities between CDP enhances financial crime solutions, uh, to be holistic by eliminating data gaps between siloed solutions with, uh, an enterprise data approach, uh, advanced, uh, data analytics and machine learning, uh, by deploying an enterprise wide data platform, you reduce siloed divisions between business risk and crime teams and enable better collaboration through industrialized machine learning. >>Uh, you tighten up the loop between, uh, detection and new fraud patterns. Cloudera provides the data platform on which a best of breed applications can run and leverage integrated machine learning cloud Derrick stands rather than replaces your existing fraud modeling applications. So Oracle SAS Actimize to, to name a few, uh, integrate with an enterprise data hub to scale the data increased speed and flexibility and improve efficacy of your entire fraud system. It also centralizes the fraud workload on data that can be used for other use cases in applications like enhanced KYC and a customer 360 4 example. >>I just, I wanted to highlight a couple of our partners in financial crime prevention, uh, semi dine, and Quintex, uh, uh, so send me nine provides fraud simulation using agent-based modeling, uh, machine learning techniques, uh, to generate synthetic transaction data. This data simulates potential fraud scenarios in a cost-effective, uh, GDPR compliant, virtual environment, significantly improved financial crime detection systems, semi dine identifies future fraud topologies, uh, from millions of, of simulations that can be used to dynamically train, uh, new machine learning algorithms for enhanced fraud identification and context, um, uh, connects the dots within your data, using dynamic entity resolution, and advanced network analytics to create context around your customers. Um, this enables you to see the bigger picture and automatically assesses potential criminal beads behavior. >>Now let's go some of our, uh, customers, uh, and how they're using cloud caldera. Uh, first we'll talk about, uh, United overseas bank, or you will be, um, you'll be, is a leading full service bank in, uh, in Asia. It, uh, with, uh, a network of more than 500 offices in, in 19 countries and territories in Asia, Pacific, Western Europe and north America UA, um, UOB built a modern data platform on Cloudera that gives it the flexibility and speed to develop new AI and machine learning solutions and to create a data-driven enterprise. Um, you'll be set up, uh, set up it's big data analytics center in 2017. Uh, it was Singapore's first centralized big data unit, uh, within a bank to deepen the bank's data analytic capabilities and to use data insights to enhance, uh, the banks, uh, uh, performance essential to this work was implementing a platform that could cost efficiently, bring together data from dozens of separate systems and incorporate a range of unstructured data, including, uh, voice and text, um, using Cloudera CDP and machine learning. >>UOB gained a richer understanding of its customer preferences, uh, to help make their, their banking experience simpler, safer, and more reliable. Working with Cloudera UOB has a big data platform that gives business staff and data scientists faster access to relevant and quality data for, for self-service analytics, machine learning and, uh, emerging artificial intelligence solutions. Um, with new self-service analytics and machine learning driven insights, you'll be, uh, has realized improvements in, in digital banking, asset management, compliance, AML, and more, uh, advanced AML detection capabilities, help analysts detect suspicious transactions either based on hidden relationships of shell companies and, uh, high risk individuals, uh, with, uh, Cloudera and machine learning, uh, technologies. You you'll be, uh, was able to enhance AML detection and reduce the time to identify new links from months 2, 3, 3 weeks. >>Excellent mass let's speak about MasterCard. So MasterCard's principle businesses to process payments between banks and merchants and the credit issuing banks and credit unions of the purchasers who use the MasterCard brand debit and credit cards to make purchases MasterCard chose Cloudera enterprise for fraud detection, and to optimize their DW infrastructure, delivering deepens insights and best practices in big data security and compliance. Uh, next let's speak about, uh, bank Rakka yet, uh, in Indonesia or Bri. Um, it, VRI is one of the largest and oldest banks in Indonesia and engages in the provision of general banking services. Uh, it's headquartered in Jakarta Indonesia. Uh, Bri is well known for its focus on financing initiatives and serves over 75 million customers through it's more than 11,000 offices and rural service outposts. Uh, Bri required better insight to understand customer activity and identify fraudulent transactions. Uh, the bank needed a solid foundation that allowed it to leverage the power of advanced analytics, artificial intelligence, and machine learning to gain better understanding of customers and the market. >>Uh, Bri used, uh, Cloudera enterprise data platform to build an agile and reliable, predictive augmented intelligence solution, uh, to enhance its credit scoring system and to address the rising concern around data security from regulators, uh, and customers, uh, Bri developed a real-time fraud detection service, uh, powered by Cloudera and Kafka. Uh, Bri's data scientists developed a machine learning model for fraud detection by creating a behavioral scoring model based on customer savings, uh, loan transactions, deposits, payroll and other financial, um, uh, real-time time data. Uh, this led to improvements in its fraud detection and credit scoring capabilities, as well as the development of a, of a new digital microfinancing product, uh, with the enablement of real-time fraud detection, VRI was able to reduce the rate of fraud by 40%. Uh, it improved, uh, relationship manager productivity by two and a half fold. Uh, it improved the credit score scoring system to cut down on micro-financing loan processing times from two weeks to two days to now two minutes. So fraud prevention is a good area to start with a data focus. If you haven't already, it offers a quick return on investment, uh, and it's a focused area. That's not too entrenched across the company, uh, to learn more about fraud prevention, uh, go to kroger.com and to schedule, and you should schedule a meeting with Cloudera, uh, to learn even more. Uh, and with that, thank you for listening and thank you for your time. >>Welcome to the customer. Obsession begins with data session. Uh, thank you for, for attending. Um, at Cloudera, we believe that a custom session begins with, uh, with, with data, um, and, uh, you know, financial services is Cloudera is largest industry vertical. We have approximately 425 global financial services customers, uh, which consists of 82 out of a hundred of the largest global banks of which we have 27 that are globally systemic banks, uh, four out of the five top stock exchanges, eight out of the 10 top wealth management firms and all four of the top credit card networks. Uh, so as you can see most financial services institutions utilize Cloudera for data analytics and machine learning. Uh, we also have over 20 central banks and it doesn't or so financial regulators. So it's an incredible footprint, which glimpse Cloudera, lots of insight into the many innovations that our customers are coming up with. >>Customers have grown more independent and demanding. Uh, they want the ability to perform many functions on their own and, uh, be able to do it. Uh, he do them on their mobile devices, uh, in a recent Accenture study, more than 50% of customers, uh, are focused on, uh, improving their customer experience through more personalized offers and advice. The study found that 75% of people are actually willing to share their data for better personalized offers and more efficient and intuitive services to get it better, better understanding of your customers, use all the data available to develop a complete view of your customer and, uh, and better serve them. Uh, this also breaks down, uh, costly silos, uh, shares data in, in accordance with privacy laws and assists with regulatory advice. It's so different organizations are going to be at different points in their data analytics and AI journey. >>Uh, there are several degrees of streaming and batch data, both structured and unstructured. Uh, you need a platform that can handle both, uh, with common, with a common governance layer, um, near real time. And, uh, real-time sources help make data more relevant. So if you look at this graphic, looking at it from left to right, uh, normal streaming and batch data comes from core banking and, uh, and lending operations data in pretty much a structured format as financial institutions start to evolve. Uh, they start to ingest near real-time streaming data that comes not only from customers, but also from, from newsfeeds for example, and they start to capture more behavioral data that they can use to evolve their models, uh, and customer experience. Uh, ultimately they start to ingest more real time streaming data, not only, um, standard, uh, sources like market and transaction data, but also alternative sources such as social media and connected sources, such as wearable devices, uh, giving them more, more data, better data, uh, to extract intelligence and drive personalized actions based on data in real time at the right time, um, and use machine learning and AI, uh, to drive anomaly detection and protect and predict, uh, present potential outcomes. >>So this is another way to look at it. Um, this slide shows the progression of the big data journey as it relates to a customer experience example, um, the dark blue represents, um, visibility or understanding your customer. So we have a data warehouse and are starting to develop some analytics, uh, to know your customer and start to provide a better customer 360 experience. Uh, the medium blue area, uh, is a customer centric or where we learn, uh, the customer's behavior. Uh, at this point we're improving our analytics, uh, gathering more customer centric information to perform, uh, some more exploratory, uh, data sciences. And we can start to do things like cross sell or upsell based on the customer's behavior, which should improve, uh, customer retention. The light blue area is, uh, is proactive customer inter interactions, or where we now have the ability, uh, to predict customers needs and wants and improve our interaction with the customer, uh, using applied machine learning and, and AI, uh, the Cloudera data platform, um, you know, business use cases require enabling, uh, the end-to-end journey, which we referred to as the data life cycle, uh, what the data life cycle, what is the data life cycle that our customers want, uh, to take their data through, to enable the end to end data journey. >>If you ask our customers, they want different types of analytics, uh, for their diverse user bases to help them implement their, their, their use cases while managed by a centralized security and governance later layer. Uh, in other words, um, the data life cycle to them provides multifunction analytics, uh, at each stage, uh, within the data journey, uh, that, uh, integrated and centralized, uh, security, uh, and governance, for example, uh, enterprise data consists of real time and transactional type type data. Examples include, uh, click stream data, web logs, um, machine generated, data chat bots, um, call center interactions, uh, transactions, uh, within legacy applications, market data, et cetera. We need to manage, uh, that data life cycle, uh, to provide real enterprise data insights, uh, for use cases around enhanced them, personalized customer experience, um, customer journey analytics next best action, uh, sentiment and churn analytics market, uh, campaign optimization, uh, mortgage, uh, processing optimization and so on. >>Um, we bring a diverse set of data then, um, and then enrich it with other data about our customers and products, uh, provide reports and dashboards such as customer 360 and use predictions from machine models to provide, uh, business decisions and, and offers of, uh, different products and services to customers and maintain customer satisfaction, um, by using, um, sentiment and churn analytics. These examples show that, um, the whole data life cycle is involved, um, and, uh, is in continuous fashion in order to meet these types of use cases, uh, using a single cohesive platform that can be, uh, that can be served by CDP, uh, the data, the Cloudera data platform. >>Okay. Uh, let's talk about, uh, some of the experiences, uh, from our customers. Uh, first we'll talk about Bunco suntan there. Um, is a major global bank headquartered in Spain, uh, with, uh, major operations and subsidiaries all over Europe and north and, and south America. Uh, one of its subsidiaries, something there UK wanted to revolutionize the customer experience with the use of real time data and, uh, in app analytics, uh, for mobile users, however, like many financial institutions send them there had a, he had a, had a large number of legacy data warehouses spread across many business use, and it's within consistent data and different ways of calculating the same metrics, uh, leading to different results. As a result, the company couldn't get the comprehensive customer insights it needed. And, uh, and business staff often worked on multiple versions of the truth. Sometime there worked with Cloudera to improve a single data platform that could support all its workloads, including self-service analytics, uh, operational analytics and data science processes, processing processing, 10 million transactions daily or 30,000 transactions per second at peak times. >>And, uh, bringing together really, uh, nearly two to two petabytes of data. The platform provides unprecedented, uh, customer insight and business value across the organization, uh, over 80 cents. And there has realized impressive, uh, benefits spanning, uh, new revenues, cost savings and risk reductions, including creating analytics for, for corporate customers with near real-time shopping behavior, um, and, and helping identify 7,000 new corporate, uh, customer prospects, uh, reducing capital expenditures by, uh, 3.2 million annually and decreasing operating expenses by, uh, 650,000, um, enabling marketing to realize, uh, 2.4 million in annual savings on, on cash, on commercial transactions, um, and protecting 3.7 million customers from financial crime impacts through 95, new proactive control alerts, improving risk and capital calculations to reduce the amount of money. It must set aside, uh, as part of a, as part of risk mandates. Uh, for example, in one instance, the risk team was able to release a $5.2 million that it had withheld for non-performing credit card loans by properly identifying healthy accounts miscategorized as high risk next, uh, let's uh, talk about, uh, Rabobank. >>Um, Rabobank is one of the largest banks in the Netherlands, uh, with approximately 8.3 million customers. Uh, it was founded by farmers in the late 19th century and specializes in agricultural financing and sustainability oriented banking, uh, in order to help its customers become more self-sufficient and, uh, improve their financial situations such as debt settlement, uh, rebel bank needed to access, uh, to a varied mix of high quality, accurate, and timely customer data, the talent, uh, to provide this insight, however, was the ability to execute sophisticated and timely data analytics at scale Rabobank was also faced with the challenge of, uh, shortening time to market. Uh, it needed easier access to customer data sets to ensure that they were using and receiving the right financial support at the right time with, with, uh, data quality and speed of processing. Um, highlighted as two vital areas of improvement, Rabobank was looking to incorporate, um, or create new data in an environment that would not only allow the organization to create a centralized repository of high quality data, but also allow them to stream and, uh, conduct data analytics on the fly, uh, to create actionable insights and deliver a strong customer experience bank level Cloudera due to its ability to cope with heavy pressures on data processing and its capability of ingesting large quantities of real time streaming data. >>They were able to quickly create a new data lake that allowed for faster queries of both historical and real time data to analyze customer loan repayment patterns, uh, to up to the minute transaction records, um, Robert bank and, and its customers could now immediately access, uh, the valuable data needed to help them understand, um, the status of their financial situation in this enabled, uh, rebel bank to spot financial disasters before they happened, enabling them to gain deep and timely insights into which customers were at risk of defaulting on loans. Um, having established the foundation of a modern data architecture Rabobank is now able to run sophisticated machine learning algorithms and, uh, financial models, uh, to help customers manage, um, financial, uh, obligations, um, including, uh, long repayments and are able to generate accurate, uh, current real liquidity. I refuse, uh, next, uh, let's uh, speak about, um, uh, OVO. >>Uh, so OVO is the leading digital payment rewards and financial services platform in Indonesia, and is present in 115 million devices across the company across the country. Excuse me. Um, as the volume of, of products within Obos ecosystem increases, the ability to ensure marketing effectiveness is critical to avoid unnecessary waste of time and resources, unlike competitors, uh, banks, w which use traditional mass marketing, uh, to reach customers over, oh, decided to embark on a, on a bold new approach to connect with customers via, uh, ultra personalized marketing, uh, using the Cloudera stack. The team at OVO were able to implement a change point detection algorithm, uh, to discover customer life stage changes. This allowed OVO, uh, to, uh, build a segmentation model of one, uh, the contextual offer engine Bill's recommendation algorithms on top of the product, uh, including collaborative and context-based filters, uh, to detect changes in consumer consumption patterns. >>As a result, OVO has achieved a 15% increase in revenue, thanks to this, to this project, um, significant time savings through automation and eliminating the chance of human error and have reduced engineers workloads by, by 30%. Uh, next let's talk about, uh, bank Bri, uh, bank Bri is one of the largest and oldest, uh, banks in Indonesia, um, engaging in, in general banking services, uh, for its customers. Uh, they are headquartered in, in Jakarta Indonesia, uh, PR is a well-known, uh, for its, uh, focused on micro-financing initiative initiatives and serves over 75 million customers through more than 11,000 offices and rural outposts, um, Bri needed to gain better understanding of their customers and market, uh, to improve the efficiency of its operations, uh, reduce losses from non-performing loans and address the rising concern around data security from regulators and consumers, uh, through enhanced fraud detection. This would require the ability to analyze the vast amounts of, uh, historical financial data and use those insights, uh, to enhance operations and, uh, deliver better service. >>Um, Bri used Cloudera's enterprise data platform to build an agile and reliable, uh, predictive augmented intelligence solution. Uh, Bri was now able to analyze 124 years worth of historical financial data and use those insights to enhance its operations and deliver better services. Um, they were able to, uh, enhance their credit scoring system, um, the solution analyzes customer transaction data, and predicts the probability of a customer defaulting on, on payments. Um, the following month, it also alerts Bri's loan officers, um, to at-risk customers, prompting them to take the necessary action to reduce the likelihood of the net profit lost, uh, this resulted in improved credit, improved credit scoring system, uh, that cut down the approval of micro financing loans, uh, from two weeks to two days to, to two minutes and, uh, enhanced fraud detection. >>All right. Uh, this example shows a tabular representation, uh, the evolution of a customer retention use case, um, the evolution of data and analytics, uh, journey that, uh, that for that use case, uh, from aware, uh, text flirtation, uh, to optimization, to being transformative, uh, with every level, uh, data sources increase. And, uh, for the most part, uh, are, are less, less standard, more dynamic and less structured, but always adding more value, more insights into the customer, uh, allowing us to continuously improve our analytics, increase the velocity of the data we ingest, uh, from, from batch, uh, to, uh, near real time, uh, to real-time streaming, uh, the volume of data we ingest continually increases and we progress, uh, the value of the data on our customers, uh, is continuously improving, allowing us to interact more proactively and more efficiently. And, and with that, um, I would, uh, you know, ask you to consider and assess if you are using all the, uh, the data available to understand, uh, and service your customers, and to learn more about, about this, um, you know, visit cloudera.com and schedule a meeting with Cloudera to learn more. And with that, thank you for your time. And thank you for listening.

>> From Boston, Massachusetts, it's The Cube, covering LiveWorx '18. Brought to you by PTC. >> Welcome back to Boston, everybody. This is the LiveWorx show, hosted by PTC, and you're watching The Cube, the leader in live tech coverage. I'm Dave Vellante with my co-host, Stu Miniman, covering IoT, Blockchain, AI, the Edge, the Cloud, all kinds of crazy stuff going on. Kevin Ashton is here. He's the inventor of the term, IoT, and the creator of the Wemo Home Automation platform. You may be familiar with that, the Smart Plugs. He's also the co-founder and CEO of Zensi, which is a clean tech startup. Kevin, thank you for coming on The Cube. >> Thank you for having me. >> You're very welcome. So, impressions of LiveWorx so far? >> Oh wow! I've been to a few of these and this is the biggest one so far, I think. I mean, it's day one and the place is hopping. It's like, it's really good energy here. It's hard to believe it's a Monday. >> Well, it's interesting right? You mean, you bring a ton of stayed manufacturing world together with this, sort of, technology world and gives us this interesting cocktail. >> I think the manufacturing world was stayed in the 1900s but in the 21st century, it's kind of the thing to be doing. Yeah, and this... I guess this is, you're right. This is not what people think of when they think of manufacturing, but this is really what it looks like now. It's a digital, energetic, young, exciting, innovative space. >> Very hip. And a lot of virtual reality, augmented reality. Okay, so this term IoT, you're accredited, you're the Wikipedia. Look up Kevin, you'll see that you're accredited with inventing, creating that term. Where did it come from? >> Oh! So, IoT is the Internet of Things. And back in 1990s, I was a Junior Manager at Proctor & Gamble, consumer goods company. And we were having trouble keeping some products on the shelves, in the store, and I had this idea of putting this new technology called RFID tags. Little microchips, into all Proctor products. Gamble makes like two billion products a year or something and putting it into all of them and connecting it to this other new thing called the internet, so we'd know where our stuff was. And, yeah the challenge I faced as a young executive with a crazy idea was how to explain that to senior management. And these were guys who, in those days, they didn't even do email. You send them an email, they'd like have their secretary print it out and then hand write a reply. It would come back to you in the internal mail. I'm really not kidding. And I want to put chips in everything. Well the good news was, about 1998, they'd heard of the internet, and they'd heard that the internet was a thing you were supposed to be doing. They didn't know what it was. So I literally retitled my PowerPoint presentation, which was previously called Smart Packaging, to find a way to get the word Internet in. And the way I did it was I wrote, Internet of Things. And I got my money and I founded a research center with Proctor & Gamble's money at MIT, just up the road here. And basically took the PowerPoint presentation with me, all over the world, to convince other people to get on board. And somehow, the name stuck. So that's the story. >> Yeah, it's fascinating. I remember back. I mean, RFID was a big deal. We've been through, you know-- I studied Mechanical Engineering. So manufacturing, you saw the promise of it, but like the internet, back in the 90s, it was like, "This seems really cool. "What are you going to do with it?" >> Exactly, and it kind of worked. Now it's everywhere. But, yeah, you're exactly right. >> When you think back to those times and where we are in IoT, which I think, most of us still say, we're still relatively early in IoT, industrial internet. What you hear when people talk about it, does it still harken back to some of the things you thought? What's different, what's the same? >> So some of the big picture stuff is very much the same, I think. We had this, the fundamental idea behind the MIT research, behind the Internet of Things was, get computers to gather the relevant information. If we can do that, now we have this whole, powerful new paradigm in computing. Coz it's not about keyboards anymore, and in places like manufacturing, I mean Proctor & Gamble is a manufacturing company, they make things and they sell them. The problem in manufacturing is keyboards just don't scale as an information capture technology. You can't sit in a warehouse and type everything you have. And something goes out the door and type it again. And so, you know, in the 90s, barcodes came and then we realized that we could do much better. And that was the Internet of Things. So that big picture, wouldn't it be great if we knew wherever things was, automatically? That's come true and at times, a million, right? Some of the technologies that are doing it are very unexpected. Like in the 1990s, we were very excited about RFID, partly because vision technology, you know, cameras connected to computers, was not working at all. It looked very unpromising, with people been trying for decades to do machine vision. And it didn't work. And now it does, and so a lot of things, we thought we needed RFID for, we can now do with vision, as an example. Now, the reason vision works, by the way, is an interesting one, and I think is important for the future of Internet of Things, vision works because suddenly we had digital cameras connected to networks, mainly in smartphones, that we're enable to create this vast dataset, that could then be used to train their algorithms, right? So what is was, I've scanned in a 100 images in my lab at MIT and I'm trying to write an algorithm, machine vision was very hard to do. When you've got hundreds of, millions of images available to you easily because phones and digital cameras are uploading all the time, then suddenly you can make the software sing and dance. So, a lot of the analytical stuff we've already seen in machine vision, we'll start to see in manufacturing, supply chain, for example, as the data accumulates. >> If you go back to that time, when you were doing that PowerPoint, which was probably less than a megabyte, when you saved it, did you have any inkling of the data explosion and were you even able to envision how data models would change to accommodate, did you realize at the time that the data model, the data pipeline, the ability to store all this distributed data would have to change? Were you not thinking that way? >> It's interesting because I was the craziest guy in the room. When I came to internet bandwidth and storage ability, I was thinking in, maybe I was thinking in gigabytes, when everyone else was thinking in kilobytes, right? But I was wrong. I wasn't too crazy, I was not crazy enough. I wouldn't, quick to quote, quite go so far as to call it a regret, but my lesson for life, the next generation of innovators coming up, is you actually can't let, kind of, the average opinion in the room limit how extreme your views are. Because if it seems to make sense to you, that's all that matters, right? So, I didn't envision it, is the answer to your question, even though, I was envisioning stuff, that seemed crazy to a lot of other people. I wasn't the only crazy one, but I was one of the few. And so, we underestimated, even in our wildest dreams, we underestimated the bandwidth and memory innovation, and so we've seen in the last 25 years. >> And, I don't know. Stu, you're a technologist, I'm not, but based on what you see today, do you feel like, the technology infrastructure is there to support these great visions, or do we have to completely add quantum computing or blockchain? Are we at the doorstep, or are we decades away? >> Oh, were at the doorstep. I mean, I think the interesting thing is, a lot of Internet of Things stuff, in particular, is invisible for number of reasons, right? It's invisible because, you know, the sensors and chips are embedded in things and you don't see them, that's one. I mean, there is a billion more RFID tags made in the world, than smartphones every year. But you don't see them. You see the smartphone, someone's always looking at their smartphone. So you don't realize that's there. So that's one reason, but, I mean, the other reason is, the Internet of Things is happening places and in companies that don't have open doors and windows, they're not on the high street, right? They are, it's warehouses, it's factories, it's behind the scenes. These companies, they have no reason to talk about what they are doing because it's a trade secret or it's you know, just not something people want to write about or read about, right? So, I just gave a talk here, and one of the examples I gave was a company who'd, Heidelberger. Heidelberger makes 60% of the offset printing presses in the world. They're one of the first Internet of Things pioneers. Most people haven't heard of them, most people don't see offset printers everyday. So the hundreds of sensors they have in their hundreds of printing presses, completely invisible to most of us, right? So, it's definitely here, now. You know, will the infrastructure continue to improve? Yes. Will we see things that are unimaginable today, 20 years from today? Yes. But I don't see any massive limitations now in what the Internet of Things can become. >> We just have a quick question, your use case for that offset printing, is it predictive maintenance, or is it optimization (crosstalk). >> It is initially like, it was in 1990s, when the customer calls and says, "My printing press isn't working, help", instead of sending the guide and look at the diagnostics, have the diagnostics get sent to the guide, that was the first thing, but then gradually, that evolves to realtime monitoring, predictive maintenance, your machine seems to be less efficient than the average of all the machines. May be we can help you optimize. Now that's the other thing about all Internet of Things applications. You start with one sensor telling you one thing for one reason, and it works, you add two, and you find four things you can do and you add three, and you find nine things you can do, and the next thing you know, you're an Internet of Things company. You never meant to be. But yeah, that's how it goes. It's a little bit like viral or addictive. >> Well, it's interesting to see the reemergence, new ascendancy of PTC. I mean, heres a company in 2003, who was, you know, bouncing along the ocean's floor, and then the confluence of all this trends, some acquisitions and all of a sudden, they're like, the hot new kid on the block. >> Some of that's smart management, by the way. >> Yeah, no doubt. >> And, I don't work for PTC but navigating the change is important and I want to say, all of the other things I just talked about in my talk, but, you know, we think about these tools that companies like PTC make as design tools. But they're very quickly transitioning to mass production tools, right? So it used be, you imagined a thing on your screen and you made a blueprint of it. Somebody made it in the shop. And then it was, you didn't make it in a shop, you had a 3D printer. And you could make a little model of it and show management. Everyone was very excited about that. Well, you know, what's happening now, what will happen more is that design on the screen will be plugged right in to the production line and you push a button and you make a million. Or your customer will go to a website, tweak it a little bit, make it a different color or different shape or something, and you'll make one, on your production line that makes a million. So, there's this seamless transition happening from imagining things using software, to actually manufacturing them using software, which is very much the core of what Internet of Things is about and it's a really exciting part of the current wave of the industrial revolution. >> Yeah, so Kevin, you wrote a book which follows some of those themes, I believe, it's How to Fly A Horse. I've read plenty of books where it talks about people think that innovation is, you know, some guy sitting under a tree, it hits him in the head and he does things. But we know that, first of all, almost everybody is building on you know, the shoulders of those before us. Talk a little bit about creativity, innovation. >> Okay. Sure. >> Your thoughts on that. >> So, I have an undergraduate degree in Scandinavian studies, okay? I studied Ibsen in 19th century Norwegian, at university. And then I went to Proctor & Gamble and I did marketing for color cosmetics. And then the next thing that happened to me was I'm at MIT, right? I'm an Executive Director of this prestigious lab at MIT. And I did this at the same time that the Harry Potter books were becoming popular, right? So I already felt like, oh my God! I've gone to wizard school but nobody realizes that I'm not a wizard. I was scared of getting found out, right? I didn't feel like a wizard because anything I managed to create was like the 1000th thing I did after 999 mistakes. You know, I was like banging my head against the wall. And I didn't know what I was doing. And occasionally, I got lucky, and I was like, oh they're going to figure out, that I'm not like them, right? I don't have the magic. And actually what happened to me at MIT over four years, I figured out nobody had the magic. There is no magic, right? There were those of us who believed this story about geniuses and magic, and there were other people who were just getting on with creating and the people at MIT were the second group. So, that was my revelation that I wasn't an imposter, I was doing things the way everybody I'd ever heard of, did them. And so, I did some startups and then I wanted to write a book, like kind of correcting the record, I guess. Because it's frustrating to me, like now, I'm called the inventor of the Internet of Things. I'm not the inventor of the Internet of Things. I wrote three words on a PowerPoint slide, I'm one of a hundred thousand people that all chipped away at this problem. And probably my chips were not as big as a lot of other people's, right? So, it was really important to me to talk about that, coz I meet so many people who want to create something, but if it doesn't happen instantly, or they don't have the brilliant idea in the shower, you know, they think they must be bad at it. And the reality is all creating is a series of steps. And as I was writing the book, I researched, you know, famous stories like Newton, and then less famous stories like the African slave kid who discovered how to farm vanilla, right? And found that everybody was doing it the same way, and in every discipline. It doesn't matter if it's Kandinsky painting a painting, or some scientist curing cancer. Everybody is struggling. They're struggling to be heard, they're struggling to be understood, they're struggling to figure out what to do next. But the ones who succeed, just keep going. I mean, and the title, How To Fly A Horse is because of the Wright brothers. Coz that's how they characterized the problem they were trying to solve and there are classic example of, I mean, literally, everybody else was jumping off mountains wit wings on their back, and dying, and the Wright brothers took this gradual, step by step approach, and they were the ones who solved the problem, how to fly. >> There was no money, and no resources, and Samuel Pierpont Langley gave up. >> Yeah, exactly. The Wright brothers were bicycle guys and they just figured out how to convert what they knew into something else. So that's how you create. I mean, we're surrounded by people who know how to do that. That's the story of How To Fly A Horse. >> So what do we make of, like a Steve Jobs. Is he an anomaly, or is he just surrounded by people who, was he just surrounded by people who knew how to create? >> I talk about Steve Jobs in the book, actually, and yeah, I think the interesting thing about Jobs is defining characteristic, as I see it. And yeah, I followed the story of Apple since I was a kid, one of the first news I ever saw was an Apple. Jobs was never satisfied. He always believed things could be made better. And he was laser focused on trying to make them better, sometimes to the detriment of the people around him, but that focus on making things better, enabled him, yes, to surround himself with people who were good at doing what they did, but also then driving them to achieve things. I mean, interesting about Apple now is, Apple are sadly becoming, kind of, just another computer company now, without somebody there, who is not-- I mean, he's stand up on stage and say I've made this great thing, but what was going on in his head often was, but I wish that curve was slightly different or I wish, on the next one, I'm going to fix this problem, right? And so the minute you get satisfied with, oh, we're making billions of dollars, everything's great, that's when your innovation starts to plummet, right? So that was, I think to me, Jobs was a classic example of an innovator, because he just kept going. He kept wanting to make things better. >> Persistence. Alright, we got to go. Thank you so much. >> Thank you guys. >> For coming on The Cube. >> Great to see you. >> Great to meet you, Kevin. Alright, keep it right there buddy. Stu and I will be back with our next guest. This is The Cube. We're live from LiveWorx at Boston and we'll be right back.

>> From the SiliconANGLE media office in Boston, Massachusetts, it's theCUBE! Now, here's your host, Dave Vellante. >> Hi, everybody, welcome to this special CUBE Conversation. You know, big data workloads have evolved, and the infrastructure that runs big data workloads is also evolving. Big data, AI, other emerging workloads need infrastructure that can keep up. Welcome to this special CUBE Conversation with Patrick Osborne, who's the vice president and GM of big data and secondary storage at Hewlett Packard Enterprise, @patrick_osborne. Great to see you again, thanks for coming on. >> Great, love to be back here. >> As I said up front, big data's changing. It's evolving, and the infrastructure has to also evolve. What are you seeing, Patrick, and what's HPE seeing in terms of the market forces right now driving big data and analytics? >> Well, some of the things that we see in the data center, there is a continuous move to move from bare metal to virtualized. Everyone's on that train. To containerization of existing apps, your apps of record, business, mission-critical apps. But really, what a lot of folks are doing right now is adding additional services to those applications, those data sets, so, new ways to interact, new apps. A lot of those are being developed with a lot of techniques that revolve around big data and analytics. We're definitely seeing the pressure to modernize what you have on-prem today, but you know, you can't sit there and be static. You gotta provide new services around what you're doing for your customers. A lot of those are coming in the form of this Mode 2 type of application development. >> One of the things that we're seeing, everybody talks about digital transformation. It's the hot buzzword of the day. To us, digital means data first. Presumably, you're seeing that. Are organizations organizing around their data, and what does that mean for infrastructure? >> Yeah, absolutely. We see a lot of folks employing not only technology to do that. They're doing organizational techniques, so, peak teams. You know, bringing together a lot of different functions. Also, too, organizing around the data has become very different right now, that you've got data out on the edge, right? It's coming into the core. A lot of folks are moving some of their edge to the cloud, or even their core to the cloud. You gotta make a lot of decisions and be able to organize around a pretty complex set of places, physical and virtual, where your data's gonna lie. >> There's a lot of talk, too, about the data pipeline. The data pipeline used to be, you had an enterprise data warehouse, and the pipeline was, you'd go through a few people that would build some cubes and then they'd hand off a bunch of reports. The data pipeline, it's getting much more complex. You've got the edge coming in, you've got, you know, core. You've got the cloud, which can be on-prem or public cloud. Talk about the evolution of the data pipeline and what that means for infrastructure and big data workloads. >> For a lot of our customers, and we've got a pretty interesting business here at HPE. We do a lot with the Intelligent Edge, so, our Edgeline servers in Aruba, where a a lot of the data is sitting outside of the traditional data center. Then we have what's going on in the core, which, for a lot of customers, they are moving from either traditional EDW, right, or even Hadoop 1.0 if they started that transformation five to seven years ago, to, a lot of things are happening now in real time, or a combination thereof. The data types are pretty dynamic. Some of that is always getting processed out on the edge. Results are getting sent back to the core. We're also seeing a lot of folks move to real-time data analytics, or some people call it fast data. That sits in your core data center, so utilizing things like Kafka and Spark. A lot of the techniques for persistent storage are brand new. What it boils down to is, it's an opportunity, but it's also very complex for our customers. >> What about some of the technical trends behind what's going on with big data? I mean, you've got sprawl, with both data sprawl, you've got workload sprawl. You got developers that are dealing with a lot of complex tooling. What are you guys seeing there, in terms of the big mega-trends? >> We have, as you know, HPE has quite a few customers in the mid-range in enterprise segments. We have some customers that are very tech-forward. A lot of those customers are moving from this, you know, Hadoop 1.0, Hadoop 2.0 system to a set of essentially mixed workloads that are very multi-tenant. We see customers that have, essentially, a mix of batch-oriented workloads. Now they're introducing these streaming type of workloads to folks who are bringing in things like TensorFlow and GPGPUs, and they're trying to apply some of the techniques of AI and ML into those clusters. What we're seeing right now is that that is causing a lot of complexity, not only in the way you do your apps, but the number of applications and the number of tenants who use that data. It's getting used all day long for various different, so now what we're seeing is it's grown up. It started as an opportunity, a science project, the POC. Now it's business-critical. Becoming, now, it's very mission-critical for a lot of the services that drives. >> Am I correct that those diverse workloads used to require a bespoke set of infrastructure that was very siloed? I'm inferring that technology today will allow you to bring those workloads together on a single platform. Is that correct? >> A couple of things that we offer, and we've been helping customers to get off the complexity train, but provide them flexibility and elasticity is, a lot of the workloads that we did in the past were either very vertically-focused and integrated. One app server, networking, storage, to, you know, the beginning of the analytics phase was really around symmetrical clusters and scaling them out. Now we've got a very rich and diverse set of components and infrastructure that can essentially allow a customer to make a data lake that's very scalable. Compute, storage-oriented nodes, GPU-oriented nodes, so it's very flexible and helps us, helps the customers take complexity out of their environment. >> In thinking about, when you talk to customers, what are they struggling with, specifically as it relates to infrastructure? Again, we talked about tooling. I mean, Hadoop is well-known for the complexity of the tooling. But specifically from an infrastructure standpoint, what are the big complaints that you hear? >> A couple things that we hear is that my budget's flat for the next year or couple years, right? We talked earlier in the conversation about, I have to modernize, virtualize, containerizing my existing apps, that means I have to introduce new services as well with a very different type of DevOps, you know, mode of operations. That's all with the existing staff, right? That's the number one issue that we hear from the customers. Anything that we can do to help increase the velocity of deployment through automation. We hear now, frankly, the battle is for whether I'm gonna run these type of workloads on-prem versus off-prem. We have a set of technology as well as services, enabling services with Pointnext. You remember the acquisition we made around cloud technology partners to right-place where those workloads are gonna go and become like a broker in that conversation and assist customers to make that transition and then, ultimately, give them an elastic platform that's gonna scale for the diverse set of workloads that's well-known, sized, easy to deploy. >> As you get all this data, and the data's, you know, Hadoop, it sorta blew up the data model. Said, "Okay, we'll leave the data where it is, "we'll bring the compute there." You had a lot of skunk works projects growing. What about governance, security, compliance? As you have data sprawl, how are customers handling that challenge? Is it a challenge? >> Yeah, it certainly is a challenge. I mean, we've gone through it just recently with, you know, GDPR is implemented. You gotta think about how that's gonna fit into your workflow, and certainly security. The big thing that we see, certainly, is around if the data's residing outside of your traditional data center, that's a big issue. For us, when we have Edgeline servers, certainly a lot of things are coming in over wireless, there's a big buildout in advent of 5G coming out. That certainly is an area that customers are very concerned about in terms of who has their data, who has access to it, how can you tag it, how can you make sure it's secure. That's a big part of what we're trying to provide here at HPE. >> What specifically is HPE doing to address these problems? Products, services, partnerships, maybe you could talk about that a little bit. Maybe even start with, you know, what's your philosophy on infrastructure for big data and AI workloads? >> I mean, for us, we've over the last two years have really concentrated on essentially two areas. We have the Intelligent Edge, which is, certainly, it's been enabled by fantastic growth with our Aruba products in the networks in space and our Edgeline systems, so, being able to take that type of compute and get it as far out to the edge as possible. The other piece of it is around making hybrid IT simple, right? In that area, we wanna provide a very flexible, yet easy-to-deploy set of infrastructure for big data and AI workloads. We have this concept of the Elastic Platform for Analytics. It helps customers deploy that for a whole myriad of requirements. Very compute-oriented, storage-oriented, GPUs, cold and warm data lakes, for that matter. And the third area, what we've really focused on is the ecosystem that we bring to our customers as a portfolio company is evolving rapidly. As you know, in this big data and analytics workload space, the software development portion of it is super dynamic. If we can bring a vetted, well-known ecosystem to our customers as part of a solution with advisory services, that's definitely one of the key pieces that our customers love to come to HP for. >> What about partnerships around things like containers and simplifying the developer experience? >> I mean, we've been pretty public about some of our efforts in this area around OneSphere, and some of these, the models around, certainly, advisory services in this area with some recent acquisitions. For us, it's all about automation, and then we wanna be able to provide that experience to the customers, whether they want to develop those apps and deploy on-prem. You know, we love that. I think you guys tag it as true private cloud. But we know that the reality is, most people are embracing very quickly a hybrid cloud model. Given the ability to take those apps, develop them, put them on-prem, run them off-prem is pretty key for OneSphere. >> I remember Antonio Neri, when you guys announced Apollo, and you had the astronaut there. Antonio was just a lowly GM and VP at the time, and now he's, of course, CEO. Who knows what's in the future? But Apollo, generally at the time, it was like, okay, this is a high-performance computing system. We've talked about those worlds, HPC and big data coming together. Where does a system like Apollo fit in this world of big data workloads? >> Yeah, so we have a very wide product line for Apollo that helps, you know, some of them are very tailored to specific workloads. If you take a look at the way that people are deploying these infrastructures now, multi-tenant with many different workloads. We allow for some compute-focused systems, like the Apollo 2000. We have very balanced systems, the Apollo 4200, that allow a very good mix of CPU, memory, and now customers are certainly moving to flash and storage-class memory for these type of workloads. And then, Apollo 6500 were some of the newer systems that we have. Big memory footprint, NVIDIA GPUs allowing you to do very high calculations rates for AI and ML workloads. We take that and we aggregate that together. We've made some recent acquisitions, like Plexxi, for example. A big part of this is around simplification of the networking experience. You can probably see into the future of automation of the networking level, automation of the compute and storage level, and then having a very large and scalable data lake for customers' data repositories. Object, file, HTFS, some pretty interesting trends in that space. >> Yeah, I'm actually really super excited about the Plexxi acquisition. I think it's because flash, it used to be the bottleneck was the spinning disk, flash pushes the bottleneck largely to the network. Plexxi gonna allow you guys to scale, and I think actually leapfrog some of the other hyperconverged players that are out there. So, super excited to see what you guys do with that acquisition. It sounds like your focus is on optimizing the design for I/O. I'm sure flash fits in there as well. >> And that's a huge accelerator for, even when you take a look at our storage business, right? So, 3PAR, Nimble, All-Flash, certainly moving to NVMe and storage-class memory for acceleration of other types of big data databases. Even though we're talking about Hadoop today, right now, certainly SAP HANA, scale-out databases, Oracle, SQL, all these things play a part in the customer's infrastructure. >> Okay, so you were talking before about, a little bit about GPUs. What is this HPE Elastic Platform for big data analytics? What's that all about? >> I mean, we have a lot of the sizing and scalability falls on the shoulders of our customers in this space, especially in some of these new areas. What we've done is, we have, it's a product/a concept, and what we do is we have this, it's called the Elastic Platform for Analytics. It allows, with all those different components that I rattled off, all great systems in of their own, but when it comes to very complex multi-tenant workloads, what we do is try to take the mystery out of that for our customers, to be able to deploy that cookie-cutter module. We're even gonna get to a place pretty soon where we're able to offer that as a consumption-based service so you don't have to choose for an elastic type of acquisition experience between on-prem and off-prem. We're gonna provide that as well. It's not only a set of products. It's reference architectures. We do a lot of sizing with our partners. The Hortonworks, CloudEra's, MapR's, and a lot of the things that are out in the open source world. It's pretty good. >> We've been covering big data, as you know, for a long, long time. The early days of big data was like, "Oh, this is great, "we're just gonna put white boxes out there "and off the shelf storage!" Well, that changed as big data got, workloads became more enterprise, mainstream, they needed to be enterprise-ready. But my question to you is, okay, I hear you. You got products, you got services, you got perspectives, a philosophy. Obviously, you wanna sell some stuff. What has HPE done internally with regard to big data? How have you transformed your own business? >> For us, we wanna provide a really rich experience, not just products. To do that, you need to provide a set of services and automation, and what we've done is, with products and solutions like InfoSight, we've been able to, we call it AI for the Data Center, or certainly, the tagline of predictive analytics is something that Nimble's brought to the table for a long time. To provide that level of services, InfoSight, predictive analytics, AI for the Data Center, we're running our own big data infrastructure. It started a number of years ago even on our 3PAR platforms and other products, where we had scale-up databases. We moved and transitioned to batch-oriented Hadoop. Now we're fully embedded with real-time streaming analytics that come in every day, all day long, from our customers and telemetry. We're using AI and ML techniques to not only improve on what we've done that's certainly automating for the support experience, and making it easy to manage the platforms, but now introducing things like learning, automation engines, the recommendation engines for various things for our customers to take, essentially, the hands-on approach of managing the products and automate it and put into the products. So, for us, we've gone through a multi-phase, multi-year transition that's brought in things like Kafka and Spark and Elasticsearch. We're using all these techniques in our system to provide new services for our customers as well. >> Okay, great. You're practitioners, you got some street cred. >> Absolutely. >> Can I come back on InfoSight for a minute? It came through an acquisition of Nimble. It seems to us that you're a little bit ahead, and maybe you say a lot a bit ahead of the competition with regard to that capability. How do you see it? Where do you see InfoSight being applied across the portfolio, and how much of a lead do you think you have on competitors? >> I'm paranoid, so I don't think we ever have a good enough lead, right? You always gotta stay grinding on that front. But we think we have a really good product. You know, it speaks for itself. A lot of the customers love it. We've applied it to 3PAR, for example, so we came out with some, we have VMVision for a 3PAR that's based on InfoSight. We've got some things in the works for other product lines that are imminent pretty soon. You can think about what we've done for Nimble and 3PAR, we can apply similar type of logic to Elastic Platform for Analytics, like running at that type of cluster scale to automate a number of items that are pretty pedantic for the customers to manage. There's a lot of work going on within HPE to scale that as a service that we provide with most of our products. >> Okay, so where can I get more information on your big data offerings and what you guys are doing in that space? >> Yeah, so, we have, you can always go to hp.com/bigdata. We've got some really great information out there. We're in our run-up to our big end user event that we do every June in Las Vegas. It's HPE Discover. We have about 15,000 of our customers and trusted partners there, and we'll be doing a number of talks. I'm doing some work there with a British telecom. We'll give some great talks. Those'll be available online virtually, so you'll hear about not only what we're doing with our own InfoSight and big data services, but how other customers like BTE and 21st Century Fox and other folks are applying some of these techniques and making a big difference for their business as well. >> That's June 19th to the 21st. It's at the Sands Convention Center in between the Palazzo and the Venetian, so it's a good conference. Definitely check that out live if you can, or if not, you can all watch online. Excellent, Patrick, thanks so much for coming on and sharing with us this big data evolution. We'll be watching. >> Yeah, absolutely. >> And thank you for watcihing, everybody. We'll see you next time. This is Dave Vellante for theCUBE. (fast techno music)

>> Narrator: From the VMware Campus in Palo Alto California, it's The Cube covering Women Transforming Technology. (upbeat music) >> I'm Lisa Martin with the Cube and we are on the ground in Palo Alto with VMware for the third annual, Women Transforming Technology event. Excited to welcome back to the Cube, Lily Chang, VP of strategic transformation here at VMware. Lily it's great to have you back. >> Thank you, it's fantastic to have this event again, for the third time in the history. >> Yes, in fact, I read online that it was sold out within hours and the keynote this morning was... >> Lily: Fantastic >> Fantastic >> And very inspiring. >> Very inspiring. For those of you who don't know, Laila Ali was the keynote this morning. What a great analogy, not just being a sports star, but being someone, a woman, in a very male dominated industry who just had this sort of natural confidence that she just knew what her purpose was. I thought that was a very inspiring message for those of us in tech as well. >> Yeah, and it's also very key that women leaders, such as herself, is willing to come out and share the story, and be the role model and set a path and show the example for the younger generation to follow and to look up to. That is incredible. >> I love for one of the things she said, Lily, when she said she still sometimes kind of loses sight and has to reignite that inner warrior. I thought that was a really important and empowering message too that even really strong women who are naturally confident still have times where they have to kind of remind themselves of what their purpose is. I just thought that was a very impactful statement and I think regardless of any industry you're in. >> That is absolutely true. I mean, we're only human, right? So every one of us experiences challenges in life so there are times even all genders, you're going to bump into road blocks, you're going to bump into challenges and then you need to self motivating and lift yourself up and rise to the ocassions of the challenge. A lot of times these changes, and I'm sure it's true for her as well, that actually make her a better leader. >> Definitely. So you are one of the board members of Women Who Code. This is something that's very near and dear to VMwear's heart. VMwear got involved in 2016 when it was about a 10,000 person organization. >> Actually, a little bit less than that. >> A little less than 10,000? And now it's? >> We were very young. >> And now how large is it? >> It's 137,000 members globally, 20 counties, 60 cities. >> So what's the mission of Women Who Code? >> The mission is very simple. Basically we want to basically help all women that inspire and excel in their technical career journey and in their career development. So that's basically the simple mission statement and for that a very critical thrust that Women Who Code has and kind of coincide with VMware's community vision, is basically technical woman community. So they were very young but we saw the passion, we saw the commitment, and we believed that this is a great mutual opportunity because we want to be a global company. We want to not only view leadership within U.S., we wanted it to be in NIA, to be in APJ, We have R & D research offices everywhere and so we basically collaborated with Women Who Code and that has been a very successful leadership program which only work with them. And they basically blossomed under the collaboration and we're not the only company but we are the one of two founding partner in sponsor for Women Who Code. >> It's grown dramatically as you said. >> Lily: Dramatically. >> Yeah, just a couple of years since you've been involved with VMware. What are a some of things that have surprised you about, not just the growth, but about some of the lesson that maybe you've learned by watching some of these other women come into this organization and be inspired and impact their careers? >> So I see the story, both in VMware woman leadership, and also in outside community woman leadership. Right? So what I see is all these woman basically have the passion but they were a little bit worried about let it come out but when you're actually in a community you're supporting one and other and you have that platform where they feel very comfortable to communicate, network, share, and learn, and so basically that is a very powerful thing and I see the growth and the booster of the potential, it's kind of like we lift them up all of a sudden. Right? One of the stories recently is that, for example, on the external side, We have basically a Canada city director is all volunteer positions. Right. And within a year, she actually moved from a line management position to basically to a director position because the city director role basically expose you to basically get the community view out and that encourage you and challenge you to basically has hands on soft leadership skill and so a lot of the technical woman have a lot of technology and a lot of the technologist mentality but you need to accompany that with a lot of the soft skill. And then the combination of the two that makes a perfect combination. And we see a lot of that in our VMware women as well. So we set out to do basically cities in China, we actually opened China for Women Who Code. It was zero member, and now it has like 3,000-4,000 members. It's actually in China. It's a little bit of a difficult mysterious place. Right? But we made it happen in Beijing. We made it happen in Shanghai. And it's basically participate by a lot of the local company, not just multi-national company. And in India we actually open it up, and in India now is blossomed like crazy so there are like since VMware's opening up in Bangalore basically there are three other cities that joined in. India is like basically a rose in blossoming peak point right now. And we also opened up a Sophia, so basically we work with women who go to do a corporate leadership program. And within the first year, where we appointed some of the city directors from our women, basically we have experience about a 50% promotion rate and pretty much 100% retention rate. >> Lisa: Wow. >> Yeah. >> 50% promotion and 100% retention is incredible. >> It is incredible, so I see that miracle happening and then I become very convinced after year one and then I've also learned that I'm not the only leader in the world that believes in this. That's the reason why they blossom like crazy. >> I imagine growing up in China, I was reading a little bit about your story, that the expansion in China must mean something a bit personal for you as well. It sounds like you were a bit fortunate though, with your parents saying "hey," you had two choices when you graduated from college, flight attendant, or secretary and your parents thought "she should have more options that that." So maybe kind of full circle, how was that for you when those two in Shanghai and Beijing opened? >> To me, I feel like, that is what is 21st century supposed to be. I wish it were true in the 19th century and but bottom line is, minor correction, actually I did interview for those two positions. I was rejected. I was not qualified. >> Lisa: Lucky VMware. >> Yeah. (laughing) Actually lucky United State. >> There you go. >> So basically my dad and my mom, they basically raised me up very differently in that era. They basically feel that they give me kind of almost a virtual space where I do not feel there is any difference between genders. They always made me feel like I'm a equal citizen in the family. I have the same speaking right, my dad, my mom both foster me that so when they learned that I could not get those two possible jobs and I was very well educated, graduated from the best university in the island, quoting my dad, he basically "invested on me," right? So he basically said "well" what he needs to do is "continue to invest in me." So that's the reason why he exported me to United States and then basically I went to the graduate school here and then since then I been very blessed. So this is almost like the Beijing and Shanghai success of the Women Who Code. It's almost like I'm giving it back to my origin. Right? And I'm bringing a lot of the blend between the western and eastern culture together. Right? To open that up which is fantastic and basically in the global environment to make it very diverse and inclusive at the same time. >> So you had really strong parents who instilled this belief in you that you could do anything. When we look at some of the statistics that show that less than 25% of technical roles are held by women and then we also look at the retention, the attrition is so high in tech. What were some of the things that kept you kind of focused on your dreams? How did you kind of foster that persistence? And I'm wondering what your advice is for women who are in tech and might be thinking of leaving. >> Well, very interesting, so first advice I have is, basically believe in yourself and dream very big. Because that, and the second this is never afraid of change. Change is always a good thing and that has been throughout my growth in a foreign country as well as here. Right? And I remember when I was in the university, even thought it was the best university, and I actually changed department and major twice and the third time I attempted to do it, because at that time I told my dad, say "hey, I heard there's this cool computer science thing I really want to go do" he did some calculation and said "look, if you transfer again, the third time, it will take you five to six years to graduate" so he said "no, just stick with it and then later on you want to move, go ahead." Right? So in grad school I changed again and I was very blessed that there are a lot of sponsors and mentors. Not just my parents. Throughout my growth and throughout my journey in the career basically really foster and help me, supported me, give me a lot of advice, so I'm a big believer in mentorship and sponsorship and that's what I believe the technical woman community will offer. It's kind of a genetically built it within that philosophy in the community. Right? It doesn't matter which forum. It is basically bringing in the common belief and the vision together and it's basically peer to peer mentorship and because there are different walks and different levels of women and technologist in that community then you actually could do the tiering and peering and basically help people to either inspire, basically move into new career journey, or elevating themselves. So I'm a very big believer in mentorship and sponsorship. >> Speaking of change, we talked about the changes you've made previously. You've made a big change from R & D to financier. >> Lily: That's correct. >> The very first at VMware to do that? >> Lily: Yes, very first... >> Tell us about kind of the impetus and what excited you and what you are benefiting from. >> Well, I'd been in the R & D career for a couple decades and so every ten years I look at my resume and then I kind of try to have an out of body experience to basically advise myself and say, what would you do differently, so that you actually are setup for the growth for the next ten years. Right? So when I look at my career about a year ago I basically said to myself and said "well, you've got enough R & D experience, you made enough investment. For you to be in the next journey you really need to have the business experience." And even though I have basically with VMware's support and sponsorship I did go back to the business school and got kind of the Berkeley business certificate and I got lots of great executives supporting me. But the reality is if you don't do that role, day in and day out, and really experience it blended into your DNA, it's not going to come natural. Right? And I don't want to be an imposter, so essentially I made a fairly major determination that I want to basically switch into business world. So I'm kind of a unique case in the sense that I'm both over-qualified and under-qualified at the same time. I'm very lucky that I have a lot of the executive sponsorship that I was able to find a perfect role that allowed me to learn and excel and basically be inspired basically in my role today and that is something fantastic. Only after I transfer that's where I learn that I'm actually the first employee in VMware's history that moved from R & D to finance and I still remain as the only one so far and I hope that my success can actually inspire more R & D people because I truly believe that a lot of times when you can actually can look at from the other lens it would just simply make you be able to do your original job better. Like right now, I would tell my old R & D self that some of the decision I made I would have debated and petitioned and argued and thought about it in a completely different way because my thinking has shift which I think is a very healthy shift. >> I agree, and you know, one of the things that Laila Ali said this morning was basically encouraging people to get uncomfortable, to be comfortable and that's, you talked about change, absolutely there's so many opportunities and we know that on one level but it can be pretty intimidating to change something. But I love also what you said. I think there's a parallel with saying now that you have this business experience looking through that other lens at R & D, you would have made decisions differently and I think that is very reflective and an opportunity for organizations to invest in creating a more diverse executive team. When you bring in that though diversity. >> Lily: Exactly. >> And it just opens the door, not just seeing things through different lenses and perspectives whether we're talking about gender or what not, but the profitability that can come from that alone is tremendous. >> Yeah, so for example one of the things that there is a statistics actually based on McKinsey for company that basically has reasonable percentage blend of woman leadership actually grows better and makes much sounder decision and so the experience I have moving from R & D to business and then now I work still very closely with R & D community and the product business unit, basically that's kind of a testemonial for that because the decision making all of a sudden is multi facet. And you always will be able to make a better decision and a sound decision. Now, you will be able to see a different risk at a different level, and we will be communicating in a more common language, like I used to not be able to speak the business tone and the business language, now I actually can be that effective communication bridge, which I find it very powerful and very exciting and very illuminating in terms of just the whole shift, make it a very worth while actually. It's just a very fantastic personal and professional experiences so far. >> You studied that Mckinsey report and that was actually mentioned this morning that the press release that VMwear did with the Stanford Institute investing 15 million in building a womens innovation lab to study the barriers, identify how to remove those barriers, but in that press release McKinsey report found that, and this is shocking, that companies that have more diversity at the executive level, are 21% more profitable. >> Lily: Exactly. >> That's a huge number. >> That's because you actually, for business, right? The technology moves so fast and there are so many different factors will be coming in hitting the business, giving business decision, you just go down a unique lane and not basically bringing all the different facets of perspective, you tend to basically gradually work yourself into a corner or you may just believe what you want to believe. Right? So that's where the other genders perspective or even the inclusive culture will bring you, basically. So this is my firm belief. Right? It's just in a different dimension basically. >> And I think that's great advice for all walks of life Lily. Thank you so much for stopping by The Cube and sharing with us what you're doing with Women Who Code and congratulations on being the first VMware to successfully transition from R & D to finance. >> Yeah, I actually hit my one year anniversary. >> Oh congratulations and thanks so much for your time. >> Thank you. >> We want to thank you for watching the cube. I'm Lisa Martin, on the ground at Women Transforming Technology VMware. Thanks for watching. (digital music)

>> Announcer: Live from Las Vegas, it's theCUBE, covering Dell Technologies World 2018, brought to you buy Dell EMC and its ecosystem partners. >> Welcome back to SiliconANGLE's Media Production of theCUBE, live here from Dell Technologies World 2018. I'm Stu Miniman, and I have the distinct pleasure of welcoming Walter Isaacson to our program. Author, podcaster, I read every biography that you publish. I listen to every podcast, so thank you. So, Walter, this is a conference of geeks, you know? And I say that lovingly, 14 thousand people. They love technology; they love ideas. You have the chance to study and research some of the, you know, most brilliant minds, that we've had the last couple hundred years. Where do you get your inspiration from? >> You know, I love the fact that the most creative of people, from Leonardo Da Vinci to Einstein, Ben Franklin, Steve Jobs, Ada Lovelace, whomever they may be, all love the humanities and the science. They stand at that intersection of sort of liberal acts technology, and that's so important in today's world. We can have enormous amounts of data, and the question is, how do you connect humans to it? How do you add the human factor? And so, that's where I get my inspiration, from people who stand at that interaction of humanities and technology. >> Yeah, one of my favorite books of yours is the Innovators. You talked about history, and there's things that we've been looking at or trying. When you talk about forecasting or predicting something, sometimes we have great ideas, but if I take us, you know, decades or longer to get there, any kind of, you know, big inspirations? What do you say to people that work in the tech world, just how they should think about things like that? >> Well, first of all, things happen sometimes slower than you expect, until that inflection point, when they happen faster than you expect. >> It's like going broke, you know? It happens really slow, and then it happens fast. >> I guess we shouldn't say that in Vegas, here where we are for this conference, but I think that the main thing to do is to be one of those people that has an intuitive feel for how humans are going to find a product or service to be transformative to them. And, you know, we didn't know we needed a thousand songs in our pocket till the iPod came along. You know, likewise, we didn't know we needed transistors until somebody invented the transistor radio, and we could take it along with us. So, what turns us on? What makes us human? >> Yeah, so many things out there. You've been not only writing; you're doing podcasts now. What do you think of kind of the state of content? People say sometimes nobody reads anymore. You do hard research, a team of people. What's your thoughts about content these days? >> Well, I think the business model for journalism and production of content has been decimated at times, partly because it's all ad-driven in terms of journalism and, you know, video, and we need to get back to a time when people valued content and are willing to have a direct relationship with the content provider. About 80% of the revenue now for, say, reported or journalistic content does either the Google, Facebook, Instagram, some aggregator, so I think we have to look at the next way of finding micro-payment subscription models that work in addition to the advertising-driven model. >> Yeah, there's so many people sometimes, they look at all of this change, and they get kind of pessimistic. You know, we're going to have the AI apocalypse, or the robots are going to take over. Shows like here we're, that technology is, I say, by definition, are positive about technology. When I read your writings, you seem to have a very positive outcome. >> Oh, I'm definitely optimistic about where technology takes us. You know, I write in the Innovators, begin with Ada Lovelace, who was Lord Byron's daughter. Her father was a lud eyed, you know, defended the followers of Ned Lot, who was smashing the looms of England, thinking that technology would put people out of work. But Ada was somebody who said, "I get it. The punch card's telling those looms how to do patterns could make a calculating machine be able to do numbers, as well as words, as well as pictures." She envisioned the computer, and the notion of technology increases the number of people in the textile industry in England in the 19th century. And the computer has led to so many more jobs than its destroyed, so I think technology will always augment human creativity, not destroy it. >> So, last thing I wanted to ask you, Walter, is, we're here at Dell Technologies World. 34 years ago, Michael Dell started this. And he's a special individual. We've had the opportunity to talk to him, get to know him. I've told people that, you know, inside the company, if you reach out to him, he actually will respond. He seems very special in today's day in age. You've got background with Michael. Tell me, how do you-? >> I think it practically begins with his parents, his late mother and his father, you know, his father's still alive. Care a lot about education; care a lot about creativity. Deeply humane in the sense that they love all of society, human civil discourse, and that's why there's a humanity I see that Michael Dell is able to embed in his products, whether it's a Dell laptop I always use or the new servers, and Dell EMC, which enables people across platforms to say, "How do we collaborate; how do we be creative?" >> All right, well, Walter, I just say thank you so much. A pleasure having you on the program. And you've been watching theCUBE. I'm Stu Miniman. Always check out thecube.net for all of our broadcasts, and we also, like Walter, have a podcast. Check it out on iTunes. >> Walter: Thank you, Stu. >> Thank you. (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)

>> Narrator: From Nice, France, it's theCUBE, covering .NEXT Conference 2017 Europe. Brought to you by Nutanix. Welcome back, I'm Stu Miniman, and this is theCUBE's live coverage from Nutanix .NEXT in Nice, France. Always love when we get to dig in of some of the practitioners, the users at this conference 'cause a lot of 'em have shown up for this show. Happen to welcome to the program first time guest Mohammed Ibrahim, who's the head of IT with Securities and Investment Company or SICO headquartered out of Bahrain. Great to see you Hello, how are you? Good to see you, too. It's really my pleasure to participate and to be here and joining the .NEXT Conference. I'm very lucky to be here. Thank you so much. And you've been at both of the European shows? Exactly, I have attended last year in Vienna, and that was really good as well. And this year I really see a very big development and enhancement and the difference between this year and last year which is a very good progress. So, Mohammed, first tell us about SICO. How long's the company been around? Kind of the breadth of coverage and locations and the like. Yeah, SICO, it's actually, it's a wholesale bank headquartered in Bahrain, and we are a premiere wholesale bank in the region, Middle East and North Africa. We do business in two different lines like asset management because we manage more than one billion US dollar as asset management and portfolio managers, and we are also a custodian house. And the brokerage business, this is one of our main business lines as well because we are brokers and we started as brokers, and now we are a wholesale bank. Our coverage as I said is MENA, Middle East and North Africa, and we have our also subsidiary brokerage arm in United Arab Emirates. It's SICO UAE. It's our brokerage arm there, and they are also working under SICO. You were telling me SICO's been around since 1995. Give us a little bit of your background. How long have you been on there? Actually, yeah, SICO established in 1995, and I joined SICO in 2007, and since that time I'm at SICO, I started as the head of infrastructure, and now I am the head of IT looking after the whole IT and services in SICO. Maybe explain to us those roles, infrastructure and IT, and kind of how many people, how many data centers, that kind of stuff. We have actually one data center which is on the main side, and we have another data center in our DR side, that's the recovery side. And yeah, it's very, very sophisticated because we are operating as a bank and we have a core banking system. We have a trading platform. We are serving more than 1,900 customers, and our customers are government, pension fund, high-worth net individuals, corporates, so we have fund managers. This all our customers, and this is actually very critical customers for us. We are in IT of course. We have couple of units. We have the infrastructure and application. I was actually entitled for the infrastructure, looking after the platform and security network, and recently from couple of years back, I have been promoted to be the head of IT and looking after the applications as well as the infrastructure. Great, and you've got security under your preview which I have to imagine is taking a lot of time and budget these days. Exactly, it was a very, again, critical task and a critical position because handling the security, it was really important for the business and for our data and our confidentiality. So, it was really a good practice and a good experience. And now we are actually enhancing more in our information security policy because as you know, cyber security is one of the important topics, especially when you go to the digitalization. And this is our main purpose and our main target, is to do digitalization automation and enhancing this domain, plus ensuring the security is very standard, very high level, matching the whole expectations, the fund regulator, as well as the worldwide standards. Brought up a great point, Mohammed, there. I want you to explain to our audience what is digitization mean to SICO? For us, digitalization is actually, it's more than giving online services. Automation for our services as well. Make it easy because the wholesale bank actually have different line of services, and getting into access to your data, to your portfolio managing your orders, placing your orders, getting your positions, guaranteeing your cash statements, this is all actually, transferring your cash, this is all something that it's very important for the customer. And in many places, even in the Gulf, even in the area, it happens manually, so we are trying to be more automated, more smart, and this is for us, is the digitalization in the time being. Okay, so let's dig into the part of your job but your whole job too, the infrastructure itself. What's the role of infrastructure when you're doing the digitalization? You've probably gone through some transformations there. If you can tell us a little bit about kind of what it was like, and kind of what led to where you are and where you're going? Mainly, this is a very important question actually, and I love to answer it because when I joined SICO, it was a traditional infrastructure. As many people did, it's a physical implementation. You have servers, you have network switches, so it was a very traditional. And this was actually the challenge is to move SICO from the traditional way of the infrastructure into very simple way and very standard way, allowing you to grow, allowing you to add more applications, allowing you to really develop and focus more on the functionalities other than infrastructure. Since you also have limited resources in terms of IT resources, so you need really to think about simple infrastructure giving you the functionalities you expect, giving you the stability, that resiliency, and as well as giving you the opportunity to add more sort of critical applications on top of that. So I have to imagine in your time virtualization played a role in this, and when did Nutanix come into the picture? Actually, Nutanix came into the picture when we decided to go with our online and trading platform, SICO Life. SICO Life, it's actually a very important and critical product for us because it allows our customers to get the direct market access, and we are currently online with seven markets, and we are going for the globe as well because we are planning to go for Europe and US markets. So to build this kind of critical system you have to have a cloud. You have to think about virtualization because again, following the traditional implementation of infrastructure, it will not help you. And it will take a long, long time, and it will be very complex in terms of administration and support. For this reason, we have to had actually our private cloud, because again, you will stuck with the regulator if you go with the public cloud. If you tell him I'm going for a public cloud, he will tell you it's against confidentiality. You cannot take the customer information and put it somewhere. So we said, okay, we will go for our private cloud, and this was a challenge. We need a hyper-converged infrastructure. We need infrastructure that is smart enough to be hosting all our VMs with a central monitoring, central sort of administration, and easy as well. So we have converted more than couple of solutions around the world, and Nutanix was one of the proposed solutions coming to us. And we have done a very sophisticated vendor selection, and I think we have taken the right decision when we have selected Nutanix to be our infrastructure for a trading platform. Before I get into the Nutanix a little bit more, some people when they hear I built a private cloud, they say, well, you virtualized some environment, you did some things. What were your internal requirement? What makes it a cloud versus just okay, I've automated some things, or I've done some things? Did you have certain criteria that you went through or what did you do? Did you benchmark yourselves against the public cloud from kind of the usage and the agility? How do you sort that out? Again, it's very important, the question, because this was the strategy when I joined SICO from the beginning. As I said, we have or we had actually, a traditional infrastructure and the market, and the standards was ahead actually. So you have to bring SICO infrastructure into the standards. Traditional infrastructure, it doesn't give you that facility to grow and to add more sort of systems. It's very difficult, so this was actually the criteria and the requirements from our side. We need to have a simple infrastructure where we can add additional servers seamlessly. We can grow, we can expand, we can add more resources without rebuilding the whole infrastructure because the physical implementation, if you're stuck with the capacity, you have to shut down the server, bring a new server, do another implementation, bring everybody involved to do the new implementation. But with the virtualization, it's easy. It's a virtual server. It's a data, you take it somewhere. Just only you need to provide the infrastructure that can host it. And with the Nutanix or with the hyper-converged infrastructure, you can whenever you need additional resources, you can add resources, and you can keep your application running as is. You can keep your data as is, and without interrupting the business, without interrupting the operation, and without interrupting customers as well. And this was actually the criteria when we selected and when we decided to go with our hybrid-converged infrastructure. Okay, that's great. Do you have any metrics as to kind of operations or how many headcount you have working on things? What's been the impact of the planned Nutanix? This what we have done actually. I told you we did like a vendor selection, and we compared two different vendors. And actually, to be more honest, four vendors actually. Monitoring and developing and comparing different technologies. So if you go with the traditional infrastructure and implementation, how it will go in terms of support, in terms of implementation, timelines, the cost, post-implementation, support, even with the converged infrastructure because I remember in that time we had a converged infrastructure where some people like well-known companies were talking about converged infrastructure. And we had the hyper-converged infrastructure solution, which was a very a new into the market. For this reason, we had taken, I think, a decision where everybody said, Mohammed and SICO, I think you are taking a very what you can say? It's a very new decision. It's something that is-- Say it's risky? Exactly, some people consider it a little bit risky because you are doing something, it's still not yet many people did it, especially in the financial services and the banking sector. But as I told you, it was a challenge that you had to take and you had to go through because you have to have your own private cloud. Why, because you have to host whatever VMs you need. Whenever you need to add a VM, it will be very easy for you. Whenever you want to expand, it will be also easy for you. And with your resources, current IT resources, you can still handle this sophisticated systems and the critical systems. And this was a challenge because again every time you implement a new system, you add to payroll additional resources and you hire more resources. The business will be killed. You said something I've heard lots from financial markets these days is, the business is changing, so you need to have the agility to be able to respond and deliver what you need. And to be honest, again, I will tell you frankly speaking, now the management and the business decision makers, they look to the IT that they have a buttons. When I tell you something, you should press the button and bring it to me. They don't actually think about how much sophisticated that your system have already in the background. So they don't care about the technicality. They care more about the functionality and the deliverables. IT, it's very now challenging and the decision makers and the IT management and the technical resources we have a very challenge, a very high challenge, that whenever they get the requirements and a lot of priorities are coming from the business, they have to be always ready. So if you don't have a simple and a proper infrastructure that can really flexible, help you achieve all of these kind of deliverables, then you will stuck. And people will look at you like you are in 19th century. So we are now in 91, we are growing. We have to grow. We have to be very fast like others. It sounds like your saying Nutanix provides the easy button for the infrastructure. From my experience and from the implementation we have done, I think Nutanix, with our systems, it could really achieve our target. And we could really implement the trading platform in a very good time as we expected, even less. And we could really do this kind of performance. We could really achieve the deliverables as we expected. We have more than expected performance. We have the right choice in terms of expansion. We have also good support from Nutanix, which is really helping a lot in terms of critical systems because it's a 24 by 7. I cannot actually afford a couple minutes even downtime. It's the markets. I'm accessing the markets, so I'm placing orders, and these orders are money. And if the customer while placing the order his order did not reach the market because of the system, he will kill us. (laughs) Exactly, this is how much, and actually it's seconds because the price in the markets is changing, and the customer is placing the order. So if I did not give him the very stable platform that he can really place the order into the market with this moment and then it got delayed, then he will lose money. And I will lose the customer, and I will lose the business. For this reason, it's very critical and it's very important to have such a simple, flexible, reliable solution for your system. Mohammed Ibrahim, really appreciate the updates on what SICO has been doing. Thank you so much and best of luck. We'll be back with lots more programming here from theCUBE's coverage of Nutanix .NEXT, I'm Stu Miniman. You're watching theCUBE.