 From London, England, extracting the signal from the noise, it's theCUBE covered Discover 2015. Brought to you by Hewlett Packard Enterprise. Now your hosts, John Furrier and Dave Vellante. Hey, welcome back, everyone. We are here live in London, England for theCUBE special presentation of HP Discover, HP Enterprise, now called HPE Discover, because it's now officially different company and we are here on the ground getting all the action. This is theCUBE SiliconANGLE's flagship program where we go out to the events and extract the signal from the noise. I'm John Furrier, the founder of SiliconANGLE, my co-host Dave Vellante, co-founder of wikibon.com. Our next guest is Lewis Carr, Senior Director of Solutions Market Development at HP Enterprise. Welcome to theCUBE. Thank you, thank you very much, John. So your background. You owe us a quarter for not saying HPE at the first time. No, no, I was making a point in my fumbling of the, HPE guys are making a mistake, but they're wearing their pins. We make it a lot, yeah. They're wearing the pins to make up for it. HPE, obviously great new vibe here. People feel fresh, unshackled, and ready to go. The transformations now have some meat on the bone. They do. We saw and discover the unveiling of the four areas. Yup. Not necessarily product silos, the really solution-driven main thing. So you're involved in that. What era are you working on? Share with us some of the work that you're doing and how it relates to the overall impact of the transformation. Sure, John, so I have the good fortune of leading a small but mighty team that is there to manage the portfolio of solutions that we're going to begin to build underneath each of these transformation-area umbrellas. So for example, let's say, and empower the data-driven organization, we'll have solutions that'll be focused on supporting you through that journey of becoming data-driven. As you begin to discover the value of your data, we'll have solutions that will help you in doing that so that you can test out the value of the data before you invest a lot of money in building out infrastructure, because nobody wants to invest until they're sure that's actually going to turn into some insights that in turn will turn into actionable outcomes, right? So we'll have solutions for that. We'll have solutions that extend the existing infrastructure that you have so that you can build a data lake with Hadoop, or you can leverage SAP HANA if you currently have lots of SAP ERP or Sybase or other existing enterprise data warehouses. We'll also look at saying, okay, well, you've invested in figuring out what data is going to be of value to you. You're able to look at how to put together the infrastructure to support harnessing that data, and then project by project will help you to operationalize, turn that data associated with those projects into achievable superior business outcomes. So you're an accelerant in this Accelerate Next theme. Accelerate the insight, accelerate turning that insight into a business outcome that's going to mean something for you where you acquire new customers, where you innovate and find out a way to build better products and services. So yeah, absolutely it's accelerating. One of the key factors that we've seen when we talk to customers is their ability to achieve that outcome is continuous. So it's like, okay, we got it going, now we have to apply machine learning and make it better and make it better, and then stay ahead of the competition. Can you talk about how you're helping people operationalize? Right, right. So when we talk about operationalizing it, we have to talk about it from a standpoint of, what are your corporate goals? And that your goals are a function of who your customers are, what products you're building, what operations you need to streamline. So you have to think about that data in terms of how it's applied with respect to the people, the process, and increasingly technology at that outer edge, right? So for example, one of the key demos that we've been excited to bring to this discover versus prior discoverers is one around wind generating power, right? Wind mills, we've got three turbines out there and people just stop, they're taking pictures with the cameras and they can come over and they're like, what is this? And we explained to them, look, wind mills are capital equipment, they're expensive, they're the livelihood for a company that's in this sort of alternative energy area. They need to make sure that those wind mills are up all the time, right? And if one's going to go down, better that you foresee it's going to go down, there's some level of predictive analytics that tell you to send somebody out in advance to fix the thing, right? So this is how you look at achieving superior business outcomes at the point of the action where you're combining that human resource, which is expensive and you want to make sure you're using them in a proactive fashion if you're repairing that wind mill, but you also want to make sure that you're understanding how to instrument out this capital equipment in a way that you're pulling that data and making the right decisions that reduce your costs in terms of a supply chain management, in terms of when and how you fix that device. Well, it's a great example of digitizing a physical asset. Exactly. And creating a data pipeline from that, a feed from that. Right. Okay, so where does that data go? And where does HP fit? So HP fits into that data from a couple of different angles. The first one is the connectivity. If you've got all of these different pieces of capital equipment, let's say it's a hospital network, instead of the wind turbines, it's the MRI devices. I need to be able to network them up so that I can understand how well they're being utilized. I need to network them up also to determine when parts might fail, just like I would with the wind turbines. I need to be able to look at the people that are using these and see how I can support them better in making sure that they're utilizing them to the fullest extent properly. Particularly, let's say it is an MRI, then there's certain procedures and practices that the lab technicians would use in doing it. So there's the decision support aspect of how I work with them on that piece as well. So how do I provide them the right information at the right time? So there's that connectivity, making sure everything's connected up. There's security. Let's face it, we're always concerned with the security of our users, our applications, and our data from the standpoint of the devices we're using, such as these laptops, or in terms of the data center. But we forget the real frontier edge for security are things like this Internet of Things, right? I mean, that's an entire new attack surface. Big stakes. Big stakes and lots of opportunities for vulnerability. So we're taking our world-class enterprise security software and applying it to the Internet of Things and applying it in a big data fashion, right? Because there's all of these non-traditional sources of events and information from a SIM standpoint that I've got to worry about. And then there's the big data itself. First of all, we built our own big data platform to extend into petabytes of data for both the storage and aggregation and the analytics. But then we've also gone out. Haven, right, Haven, which the engines and they're being vertical and idle. But also we have to recognize that it's complement and extend. There's all these existing platforms out there that customers are still going to need to leverage. They're SAP platform. They're Terra data platforms. And they're going to extend those with things like SAP HANA. So we want to make sure that we can adjust and integrate in all these types of relevant data and all these different analytics engines so that we are able to effectively look at generating the analytics that will deliver these superior business outcomes. You bring up a good point that I want to drill down on. Both the mindset of this frontier, security frontier, sounds like an oxymoron because a lot of them are more surface area for attacks. But yet the business model opportunities are amazing. So this is to your point about not getting ahead of yourself or, as they say, get ahead of your skis where you could have a yard sale. That's the issue that people have right now is, hey, I want to have tooling available and a team that'll help me identify where the value is to double down on. This is kind of a DevOps ethos, right? So because there's so many opportunities to misfire. I mean, for instance, the turbine. It's not connected to the internet. You have battery issues, so cost could overruns. A lot of kind of weird cost to ownership hotspots, right? I mean, so you got to watch out. It's not as easy as it seems. Almost appears to be, oh, this is so easy. I'll just backhaul on the internet. Well, maybe, wait a minute, the turbine is very connectivity. Whoa, just backhaul wirelessly. Well, is there a battery? So again, a lot of challenges. So you guys help customers in that area, right? We do help them in that area, but when you talk about the challenges or that you want to try things as quick as possible, what we're finding with customers are that they don't want to just try one thing or multiple things serially. They're really interested in trying multiple things in parallel, trying them very quickly. And as you said, there is this element or possibility of failure. In most cases, particularly if you're doing very small iterations of things, small ideas, testing them very quickly, you want to fail fast. You want to fail fast and move on to the next idea, even better if you can do it with minimal investment and do several of them in parallel. And so that's really the way that people are really looking at leveraging all of these new sets of data, looking at these different ideas, looking at which ones generate the outcome and then scaling up after they find the right idea. What's the biggest conversation you have with customers because you're talking about experimentation, kind of an R&D exercise in real time, in market. So the hurdles have to be kind of short. Time frames have to be short. But also, they kind of don't know what they're looking for, right? And often you don't know the question until you start asking questions and looking at the answers and then those in turn generate new questions that generate new answers. And you sort of end up going down the rabbit hole and then back out with the right answer. What are some of the cool things that you've seen? Let's get into some examples that you can share. Some cool things and some kind of practical, not so cool but very meaty examples of some of the transformational experiences you've been involved in. Well, give you one example. There's a large Japanese airline and they had been working with one of our competitors that has a blue as their major color. And they'd been working with them for, I think, three years and had gotten absolutely nowhere because they were taking a very big bang or a waterfall approach to, let's test thoroughly this one large idea. We came in. A lot of consulting dollars in that too. There's lots of consulting dollars. It almost feels like dirty, doesn't it? It's like, well, come on, where's the meat? Right, and most of the customers we talk to now, it's not that they're unwilling to invest in consulting dollars, it's more that they're unwilling to wait a long period of time before they start to see some results from it. They're in a cul-de-sac with no solution. They can't take that anymore. Also, the market shifts fast too, right? The market shifts fast. And what you end up finding, take this Japanese airline, for example, they started testing different offers online to different demographic segments. And they found within three months that one of the key segments they could go after were Japanese housewives in their 40s and 50s that had a fair amount of disposable income and they liked to go together as groups shopping during this thing called Golden Week. I lived in Japan for four years. I remember it finally was like an extra holiday, hey. But they'll go shopping with their friends, not locally, but they'll fly to Tokyo or something like that. So they actually, from around the main island, they set up discounts and set up promotions and this turned out to increase their revenue significantly. Now, it was only one of many, many ideas that they had that this one stuck and they were able for very minimal investment to find this offer, change their bottom line. It's great. How about if I've asked a number of, John and I both, a number of guests about the HPE, the new company. And one of the big things that we talked about at the top of the segment today was the new balance sheet. HPE isn't carrying a lot of debt, very little debt actually. So that should have changed the games in terms of M&A. Sure. So talk about the balance between organic and M&A. You guys have made some M&A in this space in the past. Obviously Vertica, Home Run Acquisition. And Aruba, Aruba's awesome. Yeah, so, but specifically as it relates to the sort of data-driven organization, lot of innovation going on. There are a lot of open source stuff. So some of it, the IP is questionable. Sort of out there, people building on top of open source models. I presume you guys look at everything. I wonder if you could address sort of that balance, that organic and inorganic and how HPE, the new HPE affects that. You know, it's interesting because with open source, you absolutely need to embrace it. But you also need to understand that in general with open source, what it's good at is taking the lowest common denominator, taking something that's commoditized. Hadoop, for example. It's looking at commodity hardware and leveraging it to build the most low-cost data lake possible. But then if you actually look at the analytics modules that reside on top of this HDFS or Hadoop data file system data lake, they're not too bad, especially if you're just doing some very simple, low expectations in terms of performance types of batch mode analytics. If you're really talking about scaling up and doing enterprise-level types of analytics, you really have to look at some of the other products that are out there, whether it's SAP HANA, if you need to do analytics in real time, or it's, I would like to do it in batch, but I'd like that batch mode to take not three, four days, but a few seconds, right? So, basically we like to call it the speed of business. So, the speed of business can determine when and how you use open source and how you augment that with other products that are proprietary, if you will, but still open standard and off the shelf, but they're not open source, right? So, really you have to go back and look at the business requirements. And as we look at our customer's business requirements, it tells us when and where and how to embrace open source, but then how to augment around it both our partner's proprietary or for-profit products as well as our own. That's kind of the way we look at it in general. In the big data space, it's not just a matter of looking at, as you mentioned, Vertica, Polymer database, pure straight into the big data space. But there's other products, for example, we recently acquired a company called Voltage that does data security. One of the issues we found back to that liability versus is it an asset and how do you get value out of it? Many of our customers, when they first implemented Hadoop Lakes, they didn't bother to secure them. The data at rest was unsecured. So, if you get a hacker to come in and then leak out that information over time, it's a victory dance. It's not encrypted. It's like an open in the wild. Exactly. So, sometimes in the big data space, some of the acquisitions we've made aren't directly in the big data space, but they have a big impact on our overall offering. So, that's the other piece. So, we have to look at them all. I got to ask you to wrap up the segment, because you've been in the industry for a while, got a lot of experience. I want you to talk, share with the audience and talk about the dynamic around the new HP and how it relates to what's happening in this market right now. I mean, looking at the inflection points over the past few decades and generations of waves. What's happening right now? And what makes HP so relevant right now? I think what makes HP so relevant is we truly understand how to operationalize the underlying IT that has to be leveraged for business. If you're going to go into an ID economy and the new style of business is all about the underlying IT, it's not IT for the sake of IT. It's really about leveraging that IT to solve a business problem. And HP truly, HPE truly, I'm going to overquarter. HPE truly understands that. Really. And everyone's got a fresh legs right now. They feel good, the vibe internally. Absolutely. Everyone feels ready to guns blaring. Morale's way up. Okay, Lewis Carsonian director of Solutions Market Development at HPE, HPE Enterprise, the new HP. Been operating as a split entity for over almost a year, but now it's November 1st. Congratulations. And I do believe that we're living in a special time in this era. All this amazing stuff happening just in the past five to 10 years. Recently, even the past five has been a tsunami of change. It's a moving train. Hold on tight. Buckle your seat belts, rocket ship, HPE. Moving back more coverage from London. This is theCUBE. We'll be right back. Thanks for watching. This is theCUBE.