 Okay, we're back here live here at HP Discover in Frankfurt, Germany. I'm John Furrier, the founder of SiliconANGLE.com. And I'm joined here with Tom Norton, Global Director Client Solutions at HP Consulting. Welcome to theCUBE. Thank you, John. So, obviously HP has had a change a little bit with their services, peace, big part of their business, a lot of profitability involved with the enterprise group Dave Donatelli was on earlier, the leader of the group talking about the products and the solutions and talking really about the revenue contribution and also profitability. You guys continue to have a great presence with the customer in services. So, let's get started by just talking about what's going on with services right now and some of the news you have here and then we can have a conversation about some of the cool things you're doing. Well, that's great. So, one of the things our customers have been asking us a lot about lately is what they should do about big data. Their businesses are putting a lot of pressure on them to be able to support different initiatives. They're putting a lot of pressure on them to understand how they can build out a solution or a service to support the business requirements around information. So, what we've announced today is how they can develop strategies because really IT to remain relevant with cloud services that are being offered and the businesses doing a lot of work on their own is they really need to be proactive about how they can build out a flexible infrastructure that can support big data strategies because we know you've heard information all over the place about variety and volume and velocity but for IT that just means how can they handle the load and how can they handle the variance of the load and the different varieties of load that's going to be coming into them to support it in terms of performance but also in terms of being able to store it. One of the big trends that we've been covering on SiliconANGLE is big data, obviously, but at many, many different levels. Obviously, when you talk about IT, it's about, there's a lot of big data kind of going on within IT by itself but also now with applications. The game in the technology world kind of the same formula, computing and applications but now modernized with mobile and big data with analytics, for example. So I want to ask you, what are you seeing from your customer base around the kinds of things that they're looking for right now? Obviously, cloud and convergent infrastructure are enabling them to think differently. So what we're finding is there's a lot of demand for services. What are you seeing around those services? Around how big data and software-led infrastructure is changing some of the requirements for customers? Well, I think what they're challenged with is the integration points. They're getting a lot of information from partners and from different organizations about products. I've got a product that can do structured analytics very fast, I got a product that could do unstructured data very efficiently. But what they're challenged with is how to put all that together and make it efficient because really IT is all about efficiencies and cost-effectiveness and being able to respond quickly to demand. So what we see our customers asking about is how do I put those pieces together? They hear the word Hadoop and what does that mean and how does that fit? Or they're going to hear, I've got unstructured data, I hear autonomy, I hear Vertica, I hear others. How does that all fit together with the Vs? So really, for us, we're getting a lot of traction in helping them, first of all, set that strategy, set that expectation about how they can prepare for the volumes and varieties of data, but also how they can make it efficient, how they can introduce archiving that they can get rid of rather than throw away. They can store non-essential data today that may be available for analytics tomorrow. But also at the same time how they can speed up the analytic processing of data and be able to produce it faster and make it available faster to people so they can consume it. What are some of the projects you guys are working on? Can you give a little taste of the flavors of the products and projects you're working on? Oh, sure, so it's all over the place. So we spend some time now today looking at, for example, in the transportation industry, but specifically for airlines. If you think about airlines itself, there's data that supports the weather conditions around the airport. There's data about traffic in the airport. There's data from sensors coming off the aircraft themselves. And how does that data, all those data feeds, affect the performance of the airline, perfect the performance of the airport itself? So we've worked with organizations trying to understand that. Another example you can look in oil and gas where you have a lot of stores and there tends to be fraud. Same thing would happen in finance. How do you measure financial transactions and look for fraud? Finance is a little more interesting though. We're working with a bank today that wants to understand multiple different analytical pieces. They've got wealth management that wants to understand what's going on currently in the market. And how do they deal with if there's information about a product they want to buy or a stock they want to invest in? They're wealthiest customers. How do they know what's changing on a daily basis? But they also have information that they may be dealing with in terms of volumes coming from branch offices, for example, or maybe information in the market today about loans and how that's being affected in the global market. So all of these type of customers, whether you finance, healthcare, automotive, or transportation, all are very active in getting into big data in a very big way, both structured and unstructured. What are some of the hot verticals you're seeing? You mentioned obviously two, you just mentioned healthcare and finance. What are the hottest verticals if you can kind of roll on order, just kind of anecdotally, what are the hot verticals you're seeing the most activity in? Well, I think the last two are the two. So you can imagine healthcare. And you could take it from a regional healthcare provider who wants to know what's happening in the region and how it could be affected by things that are happening outside, even globally, about information trends, about health trends and things happening. So they may use information coming from Africa, for example, to understand how they can serve remote Minnesota. For example, where a doctor just is it, or they could serve Alaska. So healthcare is significant in terms of the volume. We also know that I talked about finances. It's critical just in accelerated decision points. But you could also look back and look at communications provider, for example. And in communication, it's really important to understand what their customers are doing in communication, what they're looking at, what they're saying, how they're communicating, so that you can improve that communication service and you can predict spikes in communication services. So IT has always been kind of there to serve the business units. And with analytics, one thing we've observed is that you have this agility concept. It's always been talked about agile infrastructure or whatnot. However you want to kind of put agile in there. Businesses seem to be driving a lot more demands into IT is saying, hey, we have to take the aerial example. Man, that's a really plausible, realistic way to improve the business. We've got data coming off the airplanes. We have sensors, we've got customer data, all this converging into one sensually system, right? Operating system, if you will, for lack of a better description. So that's changing IT. So how is that, or one, is that true? Do you believe that, see that same trend that businesses are leading IT? Is that happening? And what are some of the things that IT are doing to be more business-centric? Well, I think it's absolutely happening. Because a business, especially when it comes to big data, is going to dictate what they want IT to serve. So IT isn't really inventing anything here. The business is saying, I need information related to X. Now, that information may be a combination of different data sources, for example. So if I talk about wealth management, they're going to want to know market information, but they're also going to want to know trending information coming from social media, for example. If, is this stock being talked about in a negative way or a positive way? So with autonomy, we can have meaning-based communication come in in a very unstructured format and fed in and combined with a very structured market format that that wealth management group has. So that can be combined to be able to make some logical predictions on what may change in the overall marketplace. So IT has to be able to respond to that. They have to be able to handle the unstructured data and be able to manage it. They have to be able to speed up the processing of the analytics so it can be done in real time. And that still has to be interfaced with their legacy systems, their legacy mainframe systems, their legacy SAP systems, and so on. So all of that is really changing the scope of IT. It's challenging IT to a huge degree. So at HP, what we're trying to do is work with them to get them ahead of that. So that they can create services based on models of how data can be processed. Combination of unstructured and structured could do X. How would you, how would you, because I think that's a legitimate, everyone's feeling that same kind of pain point and looking at it that way, although maybe different for vertical, but like to solve that problem with HP, what do you guys think is available relative to the kind of the products? That's great, because that's how we approach it. So if you look at something like an app system, for example, what that allows people to do is to introduce the app system for Hadoop, which was announced today. We can introduce a system like that, which gives them full Hadoop functionality, but at a scale that they can adopt easily. But also with the app system and CMU that are part of the overall app system itself and app manager, they have the ability to predict trends, they can scale them, they can add cluster, they can add nodes within the cluster and scale the volume of information they can process easily from a centralized console. So the customer doesn't have to invest in a huge appliance that on a large scale is going to handle immediately petabytes of data. They can come in at a terabyte level and be able to expand to petabytes and multiple petabytes in an easily digestible form. It's not a big moment to kind of build around what you're saying. Yeah, that's what we're saying. Yeah, because one of the things they've asked us to do is what should their roadmap be? How much storage do they need today? How much compute power do they need today? And so what we've been able to do with consulting is to help them map that out, to build that plan. And that's helping them considerably because I've seen a number of my customers who have stopped, who have just jumped into Hadoop and stopped the development whatsoever after the pilot because they don't understand the overall strategy and what Hadoop plays versus what role Vertica may play or versus what role autonomy may play. So we've gotten into where we can make that combination of things happen in a serial point of view so they don't have to bite off everything all at once. They can invest, they can learn, they can expand, and they can add services on top of it. The insight has been challenging. Right. Do you agree with that? Oh, I think so. I think that that's the major issue in the market today. And I think a lot of people have talked about the analytics piece and data engineering or the science of data and data analytics. And I think that that is a growing trend. I think we're hearing it in the industry and even in academia. We're talking a lot about do we have enough people who really can understand the meaning that can come from data or can look at certain data patterns and can say this model is going to fit in this particular solution area. So for us it's very important to be able to work with our customers. And a lot of that comes out of our information management and analytics team. Valerie Logan leads that team. She's doing a lot of work right now to try and drive the science of it. And you'll hear that from Valerie a lot. And the science is important because it isn't the same. It isn't the same of what we used to do in terms of understanding data. You got to look at meaning, you got to look at trends and you got to have an understanding of what is potentially available in the data, not what's immediately available. Yeah, we saw that at Hadoop World, the understanding gap between data, say it is exploding, but the gap is even bigger. And, but that brings up the question around, which was another big discussion at Hadoop World that strata was around big data was the disruption. The customers have had business intelligence systems and data warehouses, basically old models. And so how do you talk to customers who would say, hey, I've invested millions in BI and data warehousing systems. What do I do now? That's a big question. It's a big question. And I think what we're advising is it's simply another part of the overall data environment, of the data ecosystem. We're not going to throw away transactional structure data from the data warehouse. It's still a critical compartment in manufacturing. There's no way you can deal to a way with supply chain, for example. You're always going to have that data that you're going to have to deal with. The art of big data is how you take that structure data and you combine it with unstructured data and information so that you can take a net result of both. That's where the science comes into it. It's where you look at both structured, unstructured. So we're not seeing the demise of more structured data warehousing. What we are seeing is a compliment to structured data warehousing that's extensible because there's no predictable end to how big unstructured data can be. But we do know that there is a function of structured data as well. So bringing those two together and you can do it either within Hadoop, unstructured, structured together to be able to manage it or you can introduce them both, manage it in Hadoop, create some structure, introduce the data warehouse into Vertica and make very aggressive, rapidly turn around data analytical decisions coming out of that data that you may not have been able to do years ago. Well, I mean, we're huge fans of what you're talking about because obviously we're seeing it every day when we cover the big data landscape. And one of the things that's interesting to us and why we're so excited is that you're at the beginning of an opportunity that's just so groundbreaking and so transformative because if we think about it and this is what we've said publicly is in the first time of the history of business, you can actually instrument your entire business. Absolutely. End to end, hiring to customer support and everything in between. So we think and we've been researching through our Wikibon, our research team that disruption will affect every value chain and value activity in all the businesses. It's so transformative that it's just almost mind-boggling at some level. So, one, do you agree with that statement? You must have to agree with that statement. I do agree with that. So, okay, that being true, it's so exciting. That's why big data is so hot. Every vertical is impacted. What are you guys seeing out there? What's your perspective? What is your personal perspective around that disruption, that instrumentation, the real-time nature of it? And how do you talk to customers when you see them for the first time saying, hey, I need help. What's the first couple conversations like? Oh, great. Well, the first conversation is always about the point solution first. Meaning, what do I do with this Hadoop thing? And how does that relate to big data overall? The second conversation becomes more constructive because then it is about, all right, I'm getting all these requests, but I have a lot of point solutions that are being presented. How do I fit them all together? So for us, it is very disruptive, but we also know that it's essential because studies are also going back to what you said are proving that businesses who are making decisions based on analytical data are actually growing and are more healthy than businesses that are making decisions based on past experience, for example. Of course, data concludes past experience and includes real-time activities, and it can project future models. The people that are only relying on past their own past experience are the people who are either stagnant or are not growing. Data analytics is allowing businesses to grow in very challenging times. Well, we'd love to talk more with you about this because this is an area we're actually doing a lot of active research on and also publishing around and I've talked with Dave Vellante privately around how we feel that this inflection, this explosion of big data, and we call software-led infrastructure or convergent infrastructure, is more powerful and bigger than the personal computer and the client-server revolution combined. And it's happening faster, right? At a much accelerated rate. So people are using predictive analytics. So if you think about client-server, like you mentioned SAP before you came on, I mean that transformed business, right? CRM and all that stuff. It was really amazing how that workflow kind of computing changed businesses. Now, okay, take that to the next level. How do you make real-time analytics a business model and a revenue driver? So that's kind of one area we were researching. So if you have anything you want to share, happy to further discuss that. The question I have asked for you to end on is, what is the next 12 months going to look like in your mind as you talk to customers? Are they moving into production faster? I mean last year seemed to be the proof of concept here. Now it's much more okay. Yeah, Hadoop is not so much a religious thing around Hadoop per se, it's really the value because no one talks about client-server anymore. They talk about the applications. So application integration is a big deal. So what's your perspective on that and we'll kind of end at that. Oh, sure. So I think what we see in the next 12 months is, well we use the word convergence a lot, but there is that integration piece which is part of convergence that says, how do these two complement each other? Because there are different point solutions and not one is all perfect for the other. Hadoop doesn't define big data and big data doesn't define Hadoop. So I think over the next 12 months people may have started with one but they know they've got to complement it with something else to make it really robust enough to answer the questions they have. So I think we're going to see a lot of integration efforts being done in the industry and simplifying that integration so it's predictable. Because right now people are just kind of piecing in things. But the more that we can create models of integration I think we're going to spend a lot of time in that because that's what customers need. They need to understand whether it's Vertica and Hadoop, Hadoop autonomy, autonomy and something else. How those pieces fit together, that's one piece. But I think we're going to see a lot more effort going to data modeling and analytical feature meaning that you don't have to rebuild an analytical query every time and between each different businesses. 60, 70, 80% of those queries may be very similar in how they look at data. It's how you extend that, those models and how you extend those templates is where the value is. So we're going to see efficiencies even on the analytical side that are coming as much out of necessity as anything else. So a quick question to follow up on that is quick one is do you see a correlation with mobile and all your big data discussions? Absolutely, it comes up every time. So if you think of the mobile users themselves they're the ones that are driving that immediate access. Almost like they want to have immediate access to information and they want it to be based on something they ask as opposed to just being able to pull up their dashboard. So it's taking that trend away from batch type processing so every morning I've got a dashboard to a mobile environment where people are asking for real time access to data and have it be in the format they request. Yeah, we said on theCUBE many times where I think it was SAP Sapphire that the iPhone and now the iPad really kind of changed the game because now CEOs are saying I want that report on my iPad. So mobile has seen to be the big kind of aha moment. Right, well we see that real combination when you have cloud and how data is exchanged between them. Mobility as far as mobile access to data and mobile access to applications and then data itself and how it's using the cloud and how it's using mobility as a presentation layer. So all three are kind of coming together and that's where IT is challenged. How do I balance that? And the real key though is how do you secure all of that? That's the question I get a lot. And how do I identify the users so they get access to apps or data? And how do I secure the data within the environment? As we say, it's the same wine and a new bottle. It's distributed computing. It's essentially connected devices on a network. And I think HP is going to have a really big opportunity with big data. I think autonomy, acquisition aside and I think that's going to really pay off for HP as they bring in other new elements to it because big data is the app. See, analytics is just an application. And I think that's going to be back to our original talk, computing. Power and all the things that go around it and apps. So big data is an instrumentation capability in real time. So exciting, congratulations. Tom, thanks for coming on theCUBE. Okay, we'll be right back with our next guest after this short break.