 Okay, we're back here live at HP Discover in Las Vegas. This is Silicon Angles, the Cube, our flagship program. We go out to the events, extract the signal from the noise. This is day two of three days of coverage. This is the Cube, and we're here with Andrew Joyner, the GM of Emerging Technologies and Marketing at HP Autonomy. It's the official title. Autonomy. So we talked multiple cubes about autonomy. What's the update? So a lot of news that we're doing here at autonomy, a lot of announcements that we've got going on. We were just part of the Haven announcement that was announced today with George Kedifa and HP software. So pretty excited about it. And then autonomy's got our own roadmap strategies that we've been announcing. Yesterday we did an announcement on how we're really furthering our information governance business, a lot of excitement around some of the tools and delivering new applications out and information governance. And of course today, we're going to talk about our marketing cloud. You know, what's interesting is when you're at HP you discover many people think of it as a technology conference. But as we know, what's the big segment or it's big change in the industry is all around information, the I and IT. And so the business side of getting excited about information, really that whole transformation is do I have access, do I have software that I can manage and take advantage of that information? And so we're moving autonomy to try to talk to those chief legal officers, those chief compliance officers, those chief marketing officers. Typically you wouldn't reach in a technology conference. A lot of announcements, a lot of new innovations to reach out to them. I want to bring in my co-host of the segment, Jeff Kelly, analyst at Wikimont who covers big data to get his perspective, but before that I want to ask you that the trend in the industry is to break down the silos. You're seeing the collapse of this box works here to much more integrated solution. You guys have been integrating autonomy for the past year within HP. And obviously big data, Meg Whitman on the keynote yesterday, very consistent. Look at, we're marching down to big data paths. Very clear mobility, clouds, security and big data. So what has been some of the things that you guys have done internally within HP shared with the audience out there? Just some of the integration influence you've had on the product side software because you guys have been, I don't want to say infecting but cross pollinating within HP. Could you share some specifics? Yeah, the autonomy technology while it was traditionally sold through a direct sales force now it's showing up in a lot of different ways inside of HP and the go to market. Obviously HP with over 300,000 employees generates a tremendous amount of human information and much like other large organizations can benefit from the autonomy technology it's used right now in hundreds of projects literally across HP. One of the ones I'm excited about here at Discover and talking about drinking our own champagne is we have set up in front of the blogger's lounge a social analytics essentially command center. And what we're doing is we're using idle to process all the social media chatter, all the video, all the images being used around this event discovering insights, helping us engage with the blogging community, out of the box something that's pretty exciting. Yeah, of course we're excited, the cube is trending. There we go. That's exactly right. So that's on the social side, it ties to the story that NASCAR was using but how they're using it. I mean the Kevin Bacon's keynote for the folks that didn't see the keynote Kevin Bacon came out kind of rift on the whole six degrees of separations with Kevin Bacon but what he was really talking about was the shift, the mindset and the actual working shift to social collaboration where ultimately there's one or no degrees of separation, everything's addressable, big data is about measuring and addressing things and obviously the scandal with Prism and the NSA has been ongoing now for a week but that highlights the mainstream America that look at everything's measurable and technology on the database side and in memory, flash, et cetera is outcapable. So for the folks that are just kind of catching up to that what do you say to them about big data? How real is it from your perspective? So I think big data is absolutely real and what's interesting about big data is I think it goes back to your silo conversation which is if you can flow the information, the information can flow across the enterprise, you can start realizing that opportunity. I think the trouble with big data is the characterizations of it are all over the map. If we've looked around and interviewed people here and discover and you do a lot of interviewing I'm sure you get widely different definitions. So we've tried to help the market with our own characterizations of when you look at the growth in big data. We tend to think of it almost like a you and what you have is just think of the spectrum on the far left-hand side you have sort of the data that's being generated from the internet of things. You have the sensors, the technology, these are things that are popping off of different technologies that are out there whether they're cell phone towers, whether we're walking around on cell phones. It's the things generated from these devices that's one of the fastest growing areas. In the middle you sort of have business data. These are the things that are coming out of the outputs of your CRM systems, your ERP system but on the right-hand side you have your human information. We know that we're generating and I just saw a stat yesterday, Google, eight years old. We're now uploading an hour of video a second. Instagram, 100 million users, 40 million photos a day. So tremendous amount of growth of this human information. What we've got is HP's got these two unique assets that are really attacking the highest gross spectrums of big data. And so as we drive those down to the Coruscita there's a lot of noise in the middle but really those are the systems that aren't growing as fast as on the outside. And I think what people are realizing with our announcement at Haven is if we can write applications that hit these spectrums there's a lot of opportunity to tap into that information. So Andrew you mentioned earlier that you're trying to kind of talk to CMOs and it's about lines of growth. And I completely agree with that strategy. I think big data is much more, this is more of a business story to me than it is a technology story. The technology is pretty cool, it's an enabler but really it's about business value. But I'm curious. Obviously HP, a huge hardware company selling mainly to IT traditionally. So you're selling more I assume to the business. So you're having conversations with CMOs and other lines of business about helping them understand the business value of big data and what autonomy brings to the table and now Haven and larger platforms. So we've got probably some CMOs watching right now where it might be struggling with how do I tell that story to hire up execs? I want to invest in big data technology. How do you tell that story? What is the business value? So I think the business value especially to marketers is I think there's an expectation that the data is now available. I don't think a marketer should necessarily have to care what the platform is underneath. What we want to say to them is however the data is being presented to you whether it's being segments of customers that exist in a database whether it's their actual conversations they're having with you when they call in your call center to visit your website or they talk about you in social media. We don't want you to have to worry about the underlying technologies not being able to support your ambitions to understand whatever information that you want to actually make a personalized offer for. A great example of that is a application that our ES services teams put together sort of around the Haven platform. It takes structured insight it's taking frequent flyer numbers and mapping them to obviously to the Twitter handles that their airline flyers provide to them. And then what it's looking at is it's looking at the tweets and the social traffic. What it's looking at for its flyer base is are there things that they're sharing that potentially we can give them a personalized offer around. So let's say the person shares they're a great tennis fan they love watching Wimbledon. What they might want to do is they might want to run a fair offer immediately from that chatter to that cluster of people that talks about from where that person lives to where they could fly to to see the next U.S. open tennis tournament. Trying to take what used to be social media for a marketer which was all about customer service they like my brand and I like my brand tying it to revenue which is make a personalized offer and see if we can get uplift from our engagement. I think that sort of trend which is don't worry about whether it was in a structured database whether it was in a tweet whether you have the right tools it's what do you need to do for your business which is I want to find my customers I want to understand them better and then I want to make them have a more personalized dynamic experience with my brand that I can tie to revenue. Once you do that you can start opening up a lot of possibilities for them. Well so interesting the example you gave me was the insight and then taking the action. So I think a lot of people get the insight what they struggle with. Okay now what's the action to take? You exactly how do you help people do that? You know that's the one thing that autonomy brings to this equation that many people don't realize and I think it's the biggest limitations of BI technologies. Especially when you look at big data the problem with big data in BI technologies is I think you're going to generate too many insights. Looking at a huge amount of data they don't know what they're looking for they start asking a lot of questions you end up with too many insights then what do you do with too many insights which one should you act on? The reality is with autonomy we've got a optimization solution that actually takes the insight and allows you to test various hypotheses. So what you could do is is one of our customers Avis has spoken for us live on stage and talked about a number of different things to do with our insight. We helped them segment their customers came to their website. They have different types of travelers have casual travelers have business travelers as they looked at overall insight from the BI technologies and the economy they saw that the actual or at least the insight would suggest that people were going to be tighter with their wallets. They thought that they may have to lower the price of their various units GPS units and so forth. It turns out that if you test the hypothesis their business travelers were actually less price sensitive than the casual travelers. They could actually raise the price of the GPS units. They tested that hypothesis on the back end of course everyone had the same price but they tested that hypothesis what they found out was they could actually generate more money. So in a down economy which you thought traditional insight you'd have to lower your prices. You test the hypothesis which is potentially this cluster of people is insensitive and they made money. So I think the ability to test these hypotheses whether it's pricing strategies whether it's call scripts that your people are outbound or receiving whether it's different support strategies creatives on the websites that's how you actually act on the insights you have. That's a great example. Especially in a tight economy and as you said the conventional wisdom is we'll pay the lower prices to appeal to our customers. And in fact, the kind of insight you just provided in the action it took to really provide a competitive differentiation in this kind of data market. Well it did. We had a great one with a mortgage provider who was trying to look back and try to get a better signal of risk to credit default swaps. Big data was the opportunity for them to realize that they took all the phone calls and took as much data as they could gather. And it was interesting as there was one insight that traditionally would have been thrown away. And it's when people went into a website to apply for a mortgage rate. You typically go through a wizard. You click one, two, three, four and you get your rate. You're filling out your income and so forth. Typically that's boring click stream data that will be discarded. In the age of big data you can collect them. What they were able to do is they found a pattern when they ran this through the autonomy software. And what they could see is when the person would go one, two, three, four get their rate and would go backwards they would boost their income. They classified this person as an income booster. What ended up happening is that was an early signal potential credit default risk. So it's data that you typically could have discarded. Now you can track it. Now you can apply pattern technologies. And now earlier in the process you have those signals of risk to look for. That's the opportunity that big data provides. Well, so again another great example but I guess how did they come about even thinking to test that? So like where do you get started? Where do you say well where could this be insights be? There's so many data sources so many things you could potentially test for and analyze. How do you actually start to focus as you're getting started? Well, so you have to have two things. And I think it's one of the things that with autonomy in Vertica is unique. So with the autonomy software the data lets you tell the story. With other types of structured you have to write the queries and ask the data. So sometimes when you put the worlds together you can have a pattern emerge. I tell a story very early time when you look at the different patterns and the information in our autonomy software and tell the story that's very interesting in DreamWorks, Jeffrey Katzenberg's talks about. We did a social media analysis for DreamWorks. And what we did is we looked at chatter that was coming out of other upcoming movie around Madagascar. You would think you'd see the typical things that they would see in social media because they were promoting a mad wig out application. They were promoting different things about the movie that thought they just see that type of chatter. Turns out a pattern, different pattern emerged. One of the patterns that clustered was around children running and screaming from theaters. Okay, not a lot of chatter but it was different. The ideas were completely different than the excitement around the movie. The reason why is one of the theater chains that inserted the improper trailer reel. So they put in one about Ghost Rider and Nicholas Cage and he has fire and he's wearing a black loader mask and it's scary and he's a ghost and he's riding on a motorcycle. So it's something that emerged and expressed from the data because the ideas contained in that were so distinct from the happiness that people had in the movie that it emerged. So it's about the distance of ideas about the distance of these patterns that can emerge. You can get different answers when you run the software through. But that's how when you apply big data, sometimes the expression of what's happening in there will bubble up. So one of the key things that the technology provides is it allows the insights, as you said, to bubble up to the surface without having to necessarily know what you're looking for going in. That's exactly right. We've worked sometimes with fashion designers and it's very interesting that they like to work with the autonomy software from the standpoint of if they're trying to learn what their customer base or potential prospects think about fashion, the colors, the textures, the things they want to feel or touch, they don't necessarily want the common answer. They want potentially other things that are rounding that are being expressed and emerged. And so sometimes it's those fringe insights that are most important for brands to go and test. And then essentially what we do is you get them the ability to go and test that hypothesis. Let's take those insights you may have, let's run a campaign on your website, let's try to promote those clothes or those different types of shoes, and let's see if customers are resonating. And so it's sometimes finding those fringe insights that are more interesting than with the collective, some of all that is. Andrew, final question, because we're up in the time limit here. Obviously operational analytics is a theme that comes in with you guys with Big Data and Haven. You've announced Haven. Share with the folks, final question. What should they know about the autonomy situation within HP? What's the sound bite? What's the update? Well, I think the excitement in autonomy is being felt right now. Meg talked about it in our last earnings conference about autonomy, stabilizing. We have much bigger ambitions than that. We want to realize the potential. If you look at the growth of human friendly information, 67%, we've got a great class of applications, whether it's from governance, where we have some of the top Wall Street firms, where there's information analytics, where we have some of the most complicated use cases that we're solving. And then we also have digital marketing, we have some of the top brands. We think there's tremendous amount of growth rate. Haven is going to be another one of those vehicles that we can drive as a strategy, as a community, to help customers realize 100% of your information, regardless of the challenge. You've got someone who's behind you, who's going to help you maximize that return on your information assets. And so we want to be a part of that community. Autonomy's going to be a part of the Haven community. We're going to be a part of bringing our own solutions to a variety of markets. I'm pretty excited about that. Okay, his autonomy update here inside the Cube, HP Discover Day 2. I'm John Furrier with Jeff Kelly. Be right back after this short break.