 Okay, we're back live at Stratoconference. I'm John Furrier with SiliconANGLE.com. This is theCUBE, our flagship telecast, and Stratoconference has evolved into the intersection between the growth markets of technology and the innovation, and we're here covering it, SiliconANGLE.tv. Tons of different things this year. I'm here with my co-host. I'm Dave Vellante of Wikibon.org, and we're here with Bill Schmarzo of EMC, part of EMC's consulting services division. Bill, welcome back. Thanks. Last time I saw you, we were at the EMC CIO conference, which was kind of an interesting event, and we had you hosted a little breakout session with the CIOs, and it was an interesting discussion, wasn't it? Oh my gosh, yeah, and I think what's been exciting is how, from that event, how many things have started to just explode around us? You know, just getting the message out to the CIOs that we have contact with, and then they're starting to ask more and more questions, how do I use this stuff? Where is it more applicable? It's a good time to be in the data business. It was interesting. I felt like the audience that you were facilitating was sort of mixed, right? Some people were like jumping on board and really excited about it. Others were sort of a little scared, I think. Yeah. Do you see that broadly? Oh my gosh, yes, in fact, what I've noticed is there's at least a stratus, two segments of the marketplace out there. There's the companies out here, like in the Bay Area, you know, the Facebooks, the Yahoo, the Twitters, all those sort of folks who sort of are all in on big data, but then you go to like Omaha and St. Louis and, you know, Des Moines, Iowa and Columbus and they don't even know where and how to start yet. So it's just, it's a real separated marketplace. There's some that really get it and are in a half spot and there's some who just don't get it who are just really struggling. Bill, you know, I've been on theCUBE multiple times. You're a CUBE alumni, Bill Schmarzo, legend in theCUBE. So the folks out there don't know, you are the CTO, you're involved in a lot of the top level serious tech conversations, the whiteboards, the architecture of big data for EMC corporation, but you live in, we live together in Palo Alto, we've known each other and but you've also been around the block, you've been in this business for a long time. You've seen the data warehouse, you know the business intelligence market. Dave and I were talking on the intro about the changing of the guard, how the data models in the past were protected, company owned, now they're opening up. We just heard from Marquis Smith from the nonprofit talking about, you know, new tools and the democratization of data. What's happening? How are you seeing the landscape for this data business as this new emerging, as you climb the big data mountain, new vistas, new opportunities are emerging and the whole business intelligence data warehouse market is changing. What's your view, looking at from your personal perspective and then now that you're at EMC, what's the landscape look like and what's around the corner? So John, I think that's a great question and in fact, because I come from that BI data warehouse background, a lot of my former clients and friends and contacts asked me lots of questions about, you know, hey Bill, I know you're in the big data space, you know, you've been in the BI data warehouse space, what does this mean to us? I mean, what are the things I need to do to be prepared for sort of stuff? And my guidance to them, for example, is that, you know, your point John about, you know, you can't build architectural lock-in through data models anymore. You've got to open them up, you've got to have flexibility and agility and the first time you come to a client in a big data discussion and start talking about an architectural lock-up based on data structures, they're going to kick you out the door. But you know, 10 years ago, that's how we competed, right? My data model is better than your data model. I'll give you my data model if you spend, you know, 10 million dollars in software for me. But you know, that world has changed and so, you know, we've got technologies out there like Hadoop, what basically are schemaless when you throw data in and so the idea that you're going to put a structure around that and lock people in is ludicrous. So tell me the state of the market right now. So, would you just, I mean, so, first of all, that sounds like oral and oracle does, but, but that's, but oracle's a big player and a big partner of EMC, whatever. Part of EMC. Exactly, but seriously, but what's the like out there? I mean, for customers who buy, spends millions of dollars buying BI stuff, just a few years ago, their payback is a little bit out on the horizon and they have to live through this. So the questions are, do they throw it away? Do they build around it? What is the current, are they dear in the headlights? What's going on? Well, some are dear in the headlights, but they can't throw it away. Obviously, politically, there's many reasons why they just can't toss that stuff away. And so the opportunity out there is to help them figure out how do I take advantage of these new, more high velocity, more rich data sources and these predictive analytic capabilities? How do I basically transform these dashboards that are retrospective, are hindsight looking into more predictive real-time dashboards? So there's actually a big opportunity out there for people who are already in the BI space, but to basically adopt and learn some new skills, take some new approaches and to take those backward-looking BI environments and make them much more productive or predictive in real-time. So basically what you're saying is, they know that they're screwed and they have to retool and so they have to take their existing BI and make it tailorable or modular, fix it, to intersect in with Hadoop so they have to build essentially connectors and other things to embrace it and integrate it in, right? Yeah, but I wouldn't say they're screwed. I mean, the thing is that most of BI implementations, they really understand the business questions customers are trying to ask and answer. That's a great starting point. I mean, they've already got a really good feel for the business drivers, the KPIs, the data structures themselves, I mean, the data contents themselves. Maybe the structures aren't important anymore, but they understand what data is available and those are all the building blocks whether you're going to do BI or big data. So some people think that enterprise data warehousing is a do-over. I'm hearing from you, it's not necessarily a do-over, it's maybe a do-better. Yeah, yeah. I'm kind of liking that and I think that, so I recently had a conversation with a customer and observation in one does not make a trend, though we supposedly could try to make it one, that they're really smart customers. They're really smart. And what they're saying is that they're seeing in their environment that a key separation between the BI environment and the analytics environment and that they know today that for operation reasons they have to provide reports, they have to provide dashboards for compliance for regulation reasons, I mean, they just have to provide those kind of reports. But there's also this analytic environment that provides an opportunity for them to provide more predictive real-time stuff and they want to separate those environments and they're seeing things like Hadoop, for example, as being this place where all the data gets dumped into and then it gets scrounged off and parsed and pushed into the data warehouse environment for this compliance and regulatory reporting and then it gets segmented off into the analytics environment for the more predictive real-time sort of analysis. So it's allowing them to do better. Like they're not throwing away the data warehouse, they're definitely not throwing away their BI tools but it's a way to take the stuff that sits underneath that and make it more useful, more rich and more actionable. Let's talk a little bit about the objectives of the traditional objectives of the enterprise data warehouse was the single version of the truth. And I think it feels like we're further away from that than we've ever been now, especially with all this, you know, the Hadoop and unstructured data coming in. Are the objectives now of big data different? I don't think so. You still think it's a single version of the truth. I think if, you know, if Bill Schmarzo walks into US Bank and the clerk there should know as much as possible about Bill Schmarzo, right? Should know all the accounts he's got, all the credit card he has, should know about what his family holds and you want to augment that with, you know, what are his interests, socially? He's a baseball fan. He should connect to these sort of people here. So what are things, how do I take that single version of truth in my CRM system, for example, that I've spent millions and millions of dollars to build out and how do I augment that? And so I don't see in many places where companies who are dealing with customers, that's still the gold for them and so I don't see that becoming a throwaway. And do you feel like the use cases may be somewhat different in terms of maybe not so much an emphasis on reporting? That'll stay, but it's more on, okay, how do we actually sell this person something? Amen, it's all about actionability. It's all about how do I take what I know about this person and their cohorts, for example. So I make sure that I prescribe the right products, the right services that make the most sense to them to get them to buy more. You'll have a couple of questions. One question I have is how is big data changing some of the key verticals that you guys sell into and how is big data changing the EMC vertical as a business? So two pronged question there. One, out in the customer base, good financial, health care and et cetera, et cetera. We're seeing all kinds of conversations here at Strata and all in the SiliconANGLE communities all about health care from politics to journalism. Data is changing everything. So in the verticals, what specifically is data, is data a one size fits all, it's the same change everywhere, is it changed by vertical? It's definitely a change by vertical, but it definitely is impacting each of the different verticals. Let's give me an example, health care, for example. So I'm working with a health care provider and the challenge they have isn't necessarily the advanced analytics. Their first issue is how do I take advantage of all these disparate data sources? How do I virtualize data that's both inside of my organization and outside of my organization? So their first challenge is really about how do I get this full view of my patients and the kind of treatments I give to those patients? How do I understand more about their lifestyles, their DNA, their entity and things like this so that I can make sure I prescribe the right medications and lifestyle changes that are the most effective to them? So that industry is very much focused on data. I mean, they can't even do analysis today because they're stuck in the data area. However, there are companies like obviously the online retailers, for example, who are way ahead in the data game and they've been bringing data in already, they've got a good handle on how to bring in social data and so they're already in the point now where they're trying to figure out how do I drive business insights? How do I cross sell, upsell? How do I target more effectively? How do I identify market trends and get products in those market trends more quickly? So basically, if I can deconstruct what you just said is retail is interested in big data because there's a direct sales impact where healthcare, ah yes, HIPAA, it's like IT, old IT. I mean, most healthcare have a ton of IT spend but they're not refreshing a lot. I mean, they're refreshing just for the sake of growing their business. Is that true? Is that the case? Or is it more of now everyone's top line driven? Well, I think healthcare and a couple of the people I talked to in that space, our firm believer, they're between 60 to 70% of the money they spend in healthcare is wasted. So there's a huge operational efficiency as they think are out there. Challenge number one. That's worse than IT. Yeah, it is. There's a lot of over-treatment or under-treatment, mistreatment, fraud, all kinds of things that go on in that space and it all relates back to the starting point to being able to bring this data together. How do I know, for example, that there is a collection of doctors down in Florida who are artificially charging me a bunch of different, for a bunch of services, never rendered? If that data is buried in eight different systems, it's hard to spot that. If I ran it together, that stuff surfaces right away. And so, I guess my point is there are some industries that are really wrestling with some data issues today and some industries have already kind of solved that or have gotten a good handle on that and are now really trying to go to the next level as far as the analytics aspect. Yeah, it's kind of like when you get to it. It's a fireman when he gets to the house. It's like, you know, the house has dead smoke. And which room do you start? You start with the one with the most flames. You know, what is your view on the current customer landscape that you guys work with and you consult with? What room is on fire? What's, you know, you start with, you got to take care of that first and you get to the other rooms later. First of all, get everyone out of the house, right? Yeah. Get all the things out of the room. No, but seriously, you know, what is the one area first that you go to and you say, okay, we got to knock this out if I'm dealing with a customer who's in the B2C space, right? Who's dealing with individual consumers like us. The first, the low hanging fruit for them is integration of social media data into the current customer insight app operations, right? They've already spent a lot of money in their CRM systems and looking at creating this ECRM sort of concept. They're looking to bring in all the social media data in so they can target better. They understand their interests better. They can, they can start building social graphs with their customers. And so in the B2C space, the thing that I see first and foremost is integration of social media data to have better customer insights. On the other side is companies that are really concerned about operational efficiencies. This is where the healthcare companies would fall in. This is where, you know, utilities fall in. Anybody who's got a network of devices, whether they're ATMs or kiosks or servers or wind turbines or such, they're trying to figure out how to drive operational effectiveness across their network. So whether it be predictive maintenance, being able to figure out forecasting capacity and things like that. So there's kind of two, I'm seeing two distinct markets today. Again, the B2C space is really understanding more about their customers. The operational space is, how do I leverage this machine-generated data to really operationalize and make more effective my operations. All right, well my final question then, Dave, you can take it away, but this is more of a philosophical one. So how, from the Bill Schmarzo perspective, how is big data changing the world? Oh, man, John. Or how will it change the world? Is it changing the world and how will it change the world? How will our world be different and what does it enable, what kind of change are we going to see? You know, it could go a couple of ways, right? It could go evil, right, where people know more about you and everything you thought was private about your life is now public domain and available, and there's that big brother aspect of it. I actually don't think that's a likely opportunity. I think what's going to happen is that we're going to see a renaissance in the sense that companies are going to know more about us and give to us that which is relevant, and we'll see things like personalized pricing and personalized products and personalized service. We'll get a point where the vision of this one-to-one marketing, right, personalization and things becomes a reality, not just in marketing, but across all kinds of different things. Well, I know you work for EMC and one of their quote pillars is trust, but seriously, I mean, I'm seeing identity and trust as a thread going forward, and will society be better, nicer and cleaner with the transparency around it? I mean, you're talking about essentially connecting at a level, knowing someone's interests. You said go evil, but also could go in a positive way. Oh, I think it's going to go positive more than evil because I think evil has a way of being punished, right? There might be a little bit of evil genius going on, and that is squashed by good genius. Oh, by the way, I just got a tweet from Aleister Kroll, who's the event MC and organizer in head honcho here at Strada. He says, Schmarzo was excellent at Jumpstart, which is the program he just did for the MBA school, data MBA. He could be the dean of a data MBA school. Oh, man. I like that. I like it. You got some props on Twitter from Aleister? Yeah. Yeah, it's from Aleister, and he's got some serious bread, you know? You know, that cost me 40 bucks, so I hope that came out well. That was expensive. That's Drake. He's smooth, Alice, and we appreciate it. Now, John, Bill's going to be back here with us on Thursday. You're going to kick off the morning with us on Thursday. We're going to talk about techniques for a big data analytics. We're going to go deeper in here, so I don't want to steal your thunder there, but it's going to get a little preview. Yeah, we're going to go into the Jumpstart things that you, the top case, a little drill down into use cases. That's more of a practical view of it. Here's just more, just shooting the breeze about tech. Yeah, I'm just walking by, and you guys drag me in here and trying to abuse me. We take care of CUBE alumni. See, you know, you're right. It's like the Stanley Cup, the names engraved at, you know, CUBE year three. I'm honored. I'm honored. Well, thanks for coming by.