 Okay, we're back. This is Dave Vellante of Wikibon.org and this is theCUBE, Silicon Angles, continuous live wall-to-wall coverage from HP Discover in Frankfurt. This is day two, and we've been covering all the events here, all the action. We had, you know, Meg Whitman's keynote. We were covering yesterday and then a breaking analysis today and we're just going, as they say, all day, all night. We're here with Valerie Logan, who's the vice president's strategy and portfolio for the information and analytics business at HP. Within enterprise services, is that right? Within enterprise services, exactly. Yeah, good job. All right, so welcome to theCUBE. Thank you so much, Dave. So we heard Meg yesterday talk about some of the key strategies, a few pillars, cloud, and one of them was information optimization. That's right. You know, I think of it as big data, but it seems like you guys look at it as a superset of big data. Is that fair? You know, I think it's a good observation. I mean, everybody's talking about big data and analytics, but actually, you know, the size of the data is part of it, the speed of the data is part of it, but man, there's a lot of sharing of data going on, there's a lot of securing of data going on, so I think big data's just the beginning of this whole information explosion that's occurring. So what's your angle on this whole big data information optimization trend? Yeah, well, there's a few angles. I think the biggest angle is, you know, I think it's very easy to get caught up in the terms and the taxonomies and all the crazy technology, which is amazing and needed, but at the end of the day, there's really two basic problems that our customers are having. One is, how do you manage information and how do you exploit it and use it, right? And so we're really excited about, you know, leveraging the technology's HP to manage it, but then leveraging our information scientists to get the value out. So I look at that, you know, I look at it like a balance sheet. You got information liabilities and assets. Yeah. Up until recently, information really was looked at as a liability. I mean, certainly with the Enron Debacle and the whole compliance issue and Sarbanes-Oxley, it was, uh-oh, email archiving, we've got to plug the holes and really was, how do I delete information? Can I do that? Just keep everything. And when you keep everything, it's hard to manage and it increases risk. And then all of a sudden, this big data trend comes along, it's like, wow, how do I monetize that? So it's this yin and yang thing going on. So first of all, do you agree with that assessment and how are your customers getting through that balance? Good observation. I think it's really, with any asset, I think there's always kind of that ebb and flow of, you know, how do you make the most of it and how do you mitigate the risk? I mean, if you think about other kinds of assets in a company, you know, people, employees are assets. You know, assets can be tremendous value to the company or they can be a risk if not managed correctly, right? You think about financial assets. You know, they can take either end too. So with information assets, I think you really hit kind of the two dimensions of what we're helping clients with. One is, how do you protect those assets and really adhere to compliance standards, a lot of the security standards, but then how do you really get the value out to better engage with customers, right? So the structured and unstructured information that's available, we like to call it human-friendly information, which is, I love how our autonomy team talks about it. They say, it's anything that we as humans read, see, or listen to, right? So it's anything that's in a text message, anything on a video or in an audio and how do you mine all of that data and basically use that to better engage with your customers. And I love to like make this personal, right Dave? I mean, it's like you and I, I mean, we go into a store and we swipe a shopper's club card or we have an insurance policy or we drive a car, right? These are all creating a data exhaust that is an opportunity for our companies that we deal with to better engage with us. Yeah, so data exhaust and the key, of course, is how do you find the signal through all that noise. Let me ask you a question about organization. Because I've always said general counsel should not be driving the strategy of the company, but again, I go back a few years, sort of mid 2000s federal rules of civil procedure. All of a sudden the general counsel tail was wagging the corporate strategy dog and saying we have to do this and putting handcuffs on people. And so has that changed? Is the general counsel still in the driver's seat or is the CMO really now the one getting the budget? Well, that is an interesting tension, right? I'll tell you that we're seeing more and more regulations, you know, we were just meeting a few weeks ago with a bank who the requirement on the bank now, this is a bank in the US, the requirement is that they have to be able to provide a report back on all complaints. And this isn't just complaints that come into the call center, these are complaints that are out in the social sphere. So they need to be able to take an integrated view of all of the complaints that are coming against that bank. And of course, the bank is saying, well, we got to comply, but then they're also saying, man, that's a goldmine of information on relationships. So how do we use that, right? To maybe create more of a complaint management and customer engagement dashboard. That's a great use case. So let's take that situation. So you're talking about an area where you don't have just a single pipe into the company. Okay, call center grade, I can record the calls, I can document it, done, boom, in the vault. Getting to it, searching it is somewhat of a challenge, but still now multiply that by all the touch points, chat, email, online, and Twitter. And social, and blog. So okay, so how did this customer deal with that problem, the regulatory compliance issue, and then we'll talk about the exploitation piece. Yeah, I mean, again, back to those two basic dimensions of how do you manage all that information, get your arms around it, and gather, and cleanse a lot of that data, because a lot of it's coming in, it's in really different sources. And then how do you actually turn that into the reports that are required, as well as using it for other kind of analytics that you need. So the whole management of that information is really key, because you've got to be able to, in some cases, align it to a specific customer like you as Dave. And sometimes it's blinded, right? You don't want to actually identify it down to a specific customer. It's more of a demographic. So it's getting that information management piece, right? So what was the, so take us through that example. What was the, so we got the use case. What was the solution? How did they go about approaching it, and how did HP help? Yeah, so it's really a blending of taking their traditional data warehouse environment, where they've already integrated to a single version of the customer, right? Single version of the customer, the truth. So they'd already created that EDW with the customer data integration, because they had a lot of those different touch points with Dave's name, different ways. And then blending that with the social monitoring, where they're listening to what's occurring in the social sphere, they bring that in, and then they, in some cases, can match it by person, and in some cases, it's more demographic. Yeah, so okay, so it's Dave who's got a savings account and a checking account, might have a mortgage. Yeah. You know, might have an investment account. Yeah. So the banks had to go through, remember the period of time where they didn't know that that Dave and that Dave and that Dave and that Dave were the same Dave. Yeah, well, exactly, and you know what they're trying to do. I mean, I thought this was a really interesting, even more finer point, is they're trying to make sure that in interactions in a call center, or interactions in a branch, when you go in and maybe you're filling out a mortgage for your house, they want to make sure there's no strong arming of you in the mortgage process, so they're looking for any kind of words that are going to indicate any kind of improprieties in the branch. They've been the regulators. Yeah. Okay, so they consolidate the account information, got that, and then, how did they deal with all the social data? What were the data sources, and how did they actually, you know, get through the exhausts of the pieces that mattered? Some of it's social data, but some of it actually is coming in through their own call centers, right? So it's the text records that the call center agent is actually putting into the logs. Some of it is the audio files. So this is actually a project that we're just starting to do with them using the autonomy software, because autonomy is able to extract meaning without having keywords to search for. And this is, I think, a really important kind of nuance, and, you know, I grew up as a business intelligence professional and mainly looking at structured data, but I'll tell you some, in the unstructured data, you don't know how people are going to refer to certain things. So we're working with a big automotive company who's looking at warranty, right? Warranty analytics and how they, how people report issues with the car. And what happens is, people will call up a call center or interact with someone and they'll describe what's happening with the car with very different terms. Like you might say, yeah, I was screaming around the corner turning left and I heard a screech. And I might say, yeah, I was making a left turn at the signal and I heard a bump, right? And those two, you know, if you're doing a Google search and looking at keywords, you're not going to find that those two are the same thing. Yeah, so keywords don't translate necessarily into the linguistics of how customers communicate. Yeah, it's a piece of it, right? I mean, keywords are important, but it's not necessarily something that learns on its own. So how about this notion of, to mention BI, BI background that triggered something. In the world of BI, you have a transactional database and then you feed maybe ETL data into a data warehouse and then you do your analysis and then some time later you get reports. So, people have put forth this premise that increasingly you're going to see the transactional database feed the analytic database in real time and allow people and or machines to make decisions in real time. Is that a pipe dream or are we actually on the verge of that? I think we're already seeing that today. I mean, the automation of decision making, when you have people who can really nail it down to a specific decision and a specific moment and if you can automate a business rule and you have the data sources coming in, you absolutely have the opportunity to do that. But I think there's also still always going to be needs for periodic historical data to be able to monitor and manage historical performance. So, I think there's a balance of those that I think everybody loves the real time term and I think there's great opportunities to make decisions in moments. But man, there's a lot of reasons to look back at historical performance and looking at stuff periodically. So, you're in the services organization, right? So, what role does your organization play in this whole IM and analytic space? Yeah, being at HP, it's funny because my friends and buddies from other companies are like, wait a minute, you're a consultant at HP? What's up with that? I thought they do technology and I'm like, actually, I grew up as a mathematician. I came out of grad school doing operations research so I'm like a math girl. I love to solve problems with math. And then, for 20 years, I've been trying to solve problems with business intelligence, but the technology never really has caught up. And so, now, people ask me why I'm at HP. I'm like, I'm a scientist, right? I'm an applied scientist. I'm someone who's out there like understanding insurance companies, understanding telephone companies, understanding my kids' providers and going, man, how does this relate? So, enterprise services, we're consultants, we're advisors, and we help run these environments, these mission-critical environments that are keeping all this stuff up and running. So, you're the life cycle planning, design, implementation, and management. Yeah, the whole thing. The whole thing. And here's where the cloud and security, the other two areas that Meg describes. I mean, I don't know that the understanding of those three areas is coming out clear enough. Cloud security and information, if you think about sharing information, sharing information across companies, like in healthcare, we work with a state in the U.S. who wanted to look at the outbreak of epidemics, right? And they wanted to go, they want to pull data from a few different sources, like hospitals and pharmacies. Well, that has to be served up in an exchange somewhere, and that better be secure. But that's right in that trinity of what HP's all about. Pretty cool, huh, Dave? That is great. I really appreciate you coming by and spending some time with our audience. And first time on theCUBE, I hope you can come back. I sure hope so. Thanks, Dave. All right, everybody, thanks for watching. Keep it right there. We're right back with our next guest. This is Dave Vellante. We're live from HP Discover in Frankfurt. This is theCUBE.