 Welcome back everybody. This is theCUBE, SiliconANGLE's premier flagship TV broadcast where we go out to the events and we basically extract the signal from the noise. We talk to the smartest people at the best technology events and bring that information to you. I'm Jeff Kelly with wikibon.org and I'm joined by my co-host Jeff Frick from SiliconANGLE. We're here of course at dot com 2012, Splunk's annual user conference. Well thanks Jeff. Yep, and we're having a great data journey as you know, that is the theme here. We hope you will join the conversation, hashtag data journey. We've had a great slate of folks on theCUBE today and we'll continue. But now we're going to go a slightly different twist. We're going to get an analyst. We're going to get somebody that's not paid or paid to be here. It's not using the technology specifically or maybe he'll tell us how it is. So we have John Myers, senior analyst from EMA which stands for Enterprise Management Associates if you want to look it up. John, welcome to theCUBE. Well thank you very much gentlemen. It's great to be here. Yeah, so it's great now. So we got kind of a different lens on this information. So really big data is a big term. It's thrown around a lot. I don't know if it's got quite to the hype where the taxi cab drivers in New York are asking about your big data investments. I guess there aren't that many places to play. But from your point of view, kind of what is, how do you kind of organize the big data space or where does Splunk fit in and how should people really think about this thing when I'm sure that there are CIOs sitting in offices right now going, oh my gosh, you know, I need to do something about this big data something. Where do I go? How do I organize my thoughts? I'm confused. No, no, I agree. And I think anytime that the economist does a special insert on a technology, you know it's kind of, I don't know if you want to call it jump the shark, but it's definitely a claustral line of critical mass when you've got the economist talking about a technology player, a big data definition. But you know, we at EMA are seeing a lot of development in terms of big data. We've just recently finished up a study, we're currently writing up the result sets. To your point about where do you see organizations going, we see big data being a little bit more about a set of requirements in terms of load, structure, response time, the economics associated with it. Then we do say, oh, it's really big data. And we see Splunk fitting in very nicely in terms of being able to acquire the information, analyze from an operational perspective, bring that into an area where you can take a look at it. We found one of our big use cases from our survey research is this concept of online archiving of data. Because a lot of organizations have tons and tons of data, but before the economics wouldn't allow them to store it for more than say three days or X amount of time. That's enough to give you that heartbeat, if you will, on your environment, but not enough to say, give me that historical context. I think that Splunk does a great job of being able to flip that over and saying, we're not just looking at the heartbeat of the organization, we're looking at the trends of whatever type of health you're talking about. Is it server performance? Is it data coming through the system? Is it what are these different pieces? And when you're able to give it that historical context, then you can start projecting out into the future and saying, this is where we can make our capital investment if we're talking strictly about our environment or we can go, what is that data in those operational systems say about our business and how do we utilize that as well? Yeah, for me, that's the interesting, really interesting part is when you're taking, when you're moving from strictly IT use case to actually affecting business outcomes. And we're seeing that come one of the trends, we're seeing here, of course, among the users, are they're often starting with that infrastructure monitoring and management and now they're moving off to new use cases where they're really solving business problems, not just IT problems. Again, not to downplay the importance of IT, but when you're extending it throughout the organization, it just increases the value of your investment. That's one of the great things when you're able to, an IT department that is able to go from heartbeat to strategic value to the organization, that's when they flip their value to the organization. Instead of being a cost center to be dragged back, their view is a strategic asset to be unleashed. I come from a telecommunications background maybe about three to five years ago, the folks at one of the New York City internet providers asked their network team, tell us about the health of the network. And they said, yeah, everything's screened, it's fine, you know, it's working okay. Then somebody said, take customer data and tell us the usage. And they found out that 5% of their user base had 50% of their usage. And they were like, okay, so either someone's running in legal ISP or we need to revisit our terms and conditions. That's something similar. That was a one-time use case with Splunk here. You can turn that into an ongoing thing. So you can judge your trends. So instead of it being special projects to go find out, you can track and trend and see, are we missing revenue opportunities? Or are we looking at too many costs for our service level agreements? For when you go into Mahogany Row and you talk to the C guys that aren't down in the data center, was there an event? Was there something loud that kind of got their attention that this data is not just IT data, that it is actually a lot of strategic value in there that we can do something with? I mean, was there a use case that's kind of popularly kicked around? Or you know, what kind of- When you think about it, a lot of these organizations have always talked about, our data is our strategic asset. And at that point they kind of walked away and said, okay, we need to do something else. But I think we're seeing organizations, I think the poster children are the Facebooks, the LinkedIn's, the et cetera. These are organizations that are built on using their own data as a strategic asset to move themselves forward. So I think that when you saw Amazon being able to do their recommendation engine and they're using not external data, internal data looking at behaviors, that was kind of the breakthrough. LinkedIn being able to say, no, it's not just a Rolodex, it's the relationships between the people in the Rolodex, being able to do that. So I think when you had those types of kind of standard bearers that are moving forward, now the key is now for those CIOs that are seeing, hey, how can we can act like Google? Now the question is, now how do we actually do that? And you need to be able to get the data, be able to have it wherever you need it to do the analysis, whether that be from an operational perspective for right now, again, that heartbeat or looking at it in terms of an analytical approach where we're trying to project forward into the future. And of course you've got some organizations when they look at the Googles and the Facebooks or the world, they come to the opposite and say, well, that's not us. And maybe that doesn't apply to us. That's starting to change. But we think certainly there really aren't, if you just look at the customers here, there's really no industry that is not impacted by data. So what are you seeing out there in the field among enterprises starting to get this, among the more traditional so-called enterprises? I think, I mean, I don't want to give a shout out to an organization, but if you watch TV, you'll see a business-to-consumer insurance organization that says, hey, let's put this little GPS in your car and we'll cut your insurance. You won't need to name it, but it's the cute redhead with the white outfit. Exactly. She doesn't tell you that if you don't pass when you stick the thing in, then maybe they won't be giving you a policy. But you see that there is an example of saying we move from the GPS, which is telling us which way to go to now having a recording device in our automobiles that allows us to do X, Y and Z. In terms of implementations, I think we're seeing business-to-business, like big fleet trucking, rental cars, things of that nature where you have a B to B relationship and you don't have all those, I don't want to call them, you don't have privacy concerns, but since you have that contractor, owner, employee, contractor, contractor relationship, you don't have them the way you do, say, whenever you talk about cell phone tracking and things of that nature. So I see B to B sensor-related data being that opening of the floodgate. And as you said, 10 years ago, we wouldn't have thought to use the data from the GPS to do anything, but now we're linking it to traffic, we're linking it to insurance, we're linking it to driver's behavior. And I think one of the big things was to get that adoption into B to C. We ought to, that insurance company, I don't think any of us are really excited about, hey, let me go put this in my car, but you go, oh, my 16-year-old, I'm all over low-jacket, my 16-year-old, and tracking them. So I think we're seeing a lot of that adoption is going to be from that perspective moving forward, as opposed to other things. But I definitely think B to B is one of the big, strong areas from collecting sensor data, from logistics, transportation, et cetera. And what about kind of from a competitive standpoint? So clearly, say like in the retail space, Walmart was way out ahead of everybody in terms of really being on top of their data and using it as a strategic advantage long before anyone else was talking about it. So does that continue to be a barrier to execute in terms of size and cost and expense, or in today's world with today's technologies, as it may be even flipped, because it's easier to implement as a small player, than it is a big, because you don't have the barriers to implementation? You know, I was just talking with someone here on the show, I'm from Boulder, Colorado, where we have a lot of government installations about NOAA and NCAR and things of that nature, to do an atmospheric research required a Cray computer, a government grant, significant, they basically built buildings to hold those computers. Walmart comes down the pike, they have both their size, their data set, and their strategic ability to analyze that data. You can't set the way back machine to today, there are grad students, actually undergrads at Stanford that have Hadoop clusters in their dorm rooms, they're analyzing data in much the same way that you might see a Walmart or another organization go. So I would say that barrier to entry in terms of technology, whether it be from a cost or a processing perspective, we've gone a long way from the days of Cray, the days of, you know, when Walmart was the only one who had all that POS data, now we're into an area where, if you can collect the data, you can put it into a particular place, then you can start to operate on it, and it becomes your imagination, limiting you not the size of your IT or the size of your budget. Very interesting. So I wonder if we could talk a little bit about, more about Splunk the company. Obviously they've had, they're on a great run, had a very successful IPO in April, just finished up a great quarter. I think they added about 400 new customers. What did you take on, why do you think Splunk is hitting kind of such a high note right now? Is there something about the, are they writing the wave of interest around big data, or is it something more in the product and technology that's really crossed a certain point and now it's even more. Well, when you're here on the event floor and you feel the buzz, the people here that are excited about, you know, this is a technology and they're like, oh, this is fantastic, there are people, you know, it's not putting roadblocks in what I'm trying to do, it's enabling what I'm trying to do. So I think that goes right into the quarter, the IPO, you know, all those things. And from the case studies that I've worked on, looking at Splunk, you're talking to the customer base, they've got, again, you get this enthusiasm coming back. It's not about the limitations, it's about this just opens the doors, and in fact, I think one guy said, it just kicks the doors down. And then again, you're into that area where it's like, this is cool, I can go and do things, I can be enabled not hold back, it rained in going, oh, it's gonna cost us this, it's gonna be like that, you know, everything I've heard from both here on the floor and the people I've talked with in terms of customer base, Splunk just enables them to do the things that they need to do as opposed to being that inhibiting force. Which is the complete opposite of what we've been, what you hear normally over the years from enterprise software vendors who I won't name. Yeah, again, I come from a telecom background and you know, you start talking big data and data warehousing from that perspective and you get people going, oh, okay, all right, yeah. And you gotta realize that data warehouses were designed for telecommunications organizations and they did such a wonderful job at it that nobody uses the ones that telecoms or they have very bad reputations, let me put it that way. But you know, when you've got a technology that says, let's not stop you, let's make you go faster. Let's not try to say you've gotta do it our way. They're saying, no, let's open the possibilities to see how it can work. And then I think the people at Splunk have done a great job with an adoption path that's not huge barrier to get in, you can get in the way you wanna get in and then start building up. We're seeing a lot of organizations with that kind of disruptive land and expand type of methodology. And then you definitely sensing that energy here where you can get in, start building up that critical mass and then really break out. So yeah, so how is, how are technologies like Splunk, other big data technologies kind of disrupting the traditional BI and data warehousing world? We've seen, you mentioned, I mean, the old data warehousing paradigm was you take 18 months, two years to model all your data, get it all in there, make this pristine data temple as my boss, Dave Vellante at Wikibon says. And then if you have any changes you wanna make to it, you gotta go through the two year processes again or you just don't do it, it's not worth the time and effort. And in the end, you've got this very expensive project that sometimes doesn't even get finished. Well, I think that's one of the things that links both big data to the disruptive technologies. We're looking at, in the example you just put out, you set the structure at the front gate and if you can't meet the structure, you can't get in the front gate. And that made a lot of sense back then. Now, we've got the ability to set that structure farther and farther back into the process because we now have the power to process through it faster that you could say, okay, let everybody in the gates and then, or all the data in the gates and then when we get closer to the decision time, then we can start to apply a structure to it or a schema or whatever you'd like to talk about it. And I think that's where big data really, again, that's where people are getting excited about this concept of big data. Everyone calls it unstructured. I once heard somebody say, well, unless it's a file with randomly generated bits, it's not unstructured. There's some structure to it. But when you have these multi-structured environments, you're bringing data in and you kind of decide, let me bring it all in and then decide what I'm going to do with it. The processing power, the cost, the economics, if you will, of the platforms really enable that so that you can make that decision closer and closer, that structure point closer and closer to the decision point. I think Splunk does a great job of enabling people to do that type of stuff where they're not forced to say, make that decision here and wait two years. We have a number we like to quote at EMA is that most data warehouses are two, two and 50. Two million dollars, two years to implement, 50% of them fail. So when you start flipping that around and saying how can we be up and running in four to six weeks or three months in terms of returning positive revenue or positive investment ROI numbers to the organization, that's where people are going to see it. And when you get to have that speed to implementation and that time to value, that gets the attention of the CFO. The CFO then goes, I think this project needs some more money as opposed to this one that's two years and doesn't need more money. So how do you see those data warehousing vendors, the terror data, it's an oracles of the world adapting in this new landscape? You see some doing better than others? I think that there's a lot to offer still in that traditional EDW. So I've got good friends at Oracle, terror data, IBM, the whole nine yards. But they offer a lot of great opportunities to do things. When you need to do counts, sums, et cetera, they offer a great opportunity because they're built upon a structured data environment, et cetera. When you go to an unstructured environment, when you have to do something like a semantic analytic or you don't know what you don't know. I always love the quote, the things you know, the things you know you don't know, then the things you don't know you don't know. Big data sits in that, the things you don't know, you don't know. Splunk is right there so that you can decide instead of having that three day window of data have a billing cycle, have a whole year worth of data at your availability. The data warehouses are still great about let's see what we know about the things we know and how do we churn through data associated with that. If you have a different type of problem then they don't do so great and that's where you get complimentary systems that allow you to have the best of both worlds. Yeah, I agree, I think they are certainly complimentary now but I think some of the larger players in that old world are gonna have to adapt because as we're seeing these big data platforms are adding more and more capabilities and getting easier and easier to use and before too long they're gonna be able to do a lot of the more traditional type of things that you think of in an enterprise data warehouse and that's gonna leave some people, some vendors in a position where well if they don't adapt, I'm not saying Oracle's going out of business anytime soon, I don't think anyone's gonna say that but I feel like especially Oracle they've got, they're really threatened I think by this movement because it kind of goes contrary to the way they, their whole business model, the kind of scale up and one big machine. You make an excellent point and I think a lot of the big traditional players as long as they stick to that apply the schema at the beginning is when they can start to pull that application of schema farther back. That's when they're gonna be successful. If they maintain the wall at the edge people are gonna move around them and go in different directions. I still think they provide great value in terms of the things that they do but when they can apply that schema later on and provide that value, rather than say no, say yes or maybe not, maybe not yes but not no now or no the now, that's gonna be allowed them to provide value going on into the future. Well thanks a lot, thanks a lot for coming on John. I appreciate it. Anybody that had to suffer through telco early on in this career is going to appreciate easier solutions and better ways to get things done. There's all kind of horror stories about trying to get the telco bill out to the right person with the right billing. But anyway, another great segment on theCUBE. Hopefully you learned a little bit. Hopefully you're joining the journey. Data journey is the hashtag. Thanks again John from EMA coming on board. We've got more guests lined up. We're gonna take a short break. We'll be right back to Splunk Comp 2012 at the Cosmopolitan Hotel in Las Vegas.