 So, the storage side of the big client piece, but still you've got that dynamic that I was talking about earlier. So, you know, if you have some kind of a company that's yours, the client is faster than you can pick up. So, you've got that dynamic going on. So, it's a matter of, okay, when does that split? When do you get that split? So, look, in the sense of this, and also not only in terms of the site, but in terms of the field, the passion, the voice-app. I feel I'm in a six-billion-dollar business. Everybody's acting like it's the start-up. And 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. 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've 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, and 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. Yep. 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 lost the line of critical mass when you've got the economist talking about a technology player, a big data definition. But, you know, we at AMA are seeing a lot of development in terms of big data. We've just recently finished up a study where 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, you know, whatever type of health you're talking about. Is it, you know, server performance? Is it, you know, 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. 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, you know, 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. You know, I come from a telecommunications background, and 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 green, 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. 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? 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 popular around? I think when you think about it, you know, 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, etc. 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 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. Of course you've got some organizations, when they look at the Googles and the Facebooks of the world, they come to the opposite conclusion 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 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 rates. You won't understand that, 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 and then maybe they won't be giving you a policy. Exactly. But you see that there's 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. That insurance company, I don't think any of us are really excited about, hey, let me go put this in my car. So, oh, my 16-year-old, I'm all over low-jack 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, from Boulder, Colorado, where we have a lot of government installations about NOAA and MCAR and things of that nature, do atmospheric research, required a Cray computer, a government grant, significant, you know, 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, you know, 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 more about Splunk, the company. Okay. 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. So what does your 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. You know, it's not putting roadblocks in 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, reined in going, oh, it's going to cost us this, it's going to 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 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 start talking big data and data warehousing from that perspective and you get people going, oh, okay, all right, yeah. You've got to realize that data warehouses were designed for telecommunications organizations and they did such a wonderful job at it that nobody uses the ones at 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 got to 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 want to get in and then start building up. We're seeing a lot of organizations with a kind of disruptive land and expand type of methodology. And then you're 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 are technologies like Splunk, other big data technologies, kind of disrupting the traditional BI and data warehousing world? We've seen, you mentioned it. 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 want to make to it, you've got to go through the two-year process 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 or all the data into the gates and then when we get closer to 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. Everyone's 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 2, 2 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 get the 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 that speed implementation and that time to value that gets the attention of the CFO the CFO then goes I think this project needs 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 it's an oracles of the world adapting in this new landscape. Do you see some doing better than others? I think that there's a lot to offer still in that traditional EDW. I've got good friends at Oracle, Teradata, 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 are 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. I agree, I think they are certainly complimentary now but some of the larger players in that old world are going to have to adapt because as we are seeing these big data platforms are adding more and more capabilities and getting easier and easier to use and before too long they are going to 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 going to leave some people, some vendors in a position where well if they don't adapt I'm not saying Oracle is going out of business anytime soon I don't think anyone is going to say that but I feel like especially Oracle they are really threatened I think by this movement because it kind of goes contrary to the way their whole business model the kind of scale up in 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 when they can start to pull that application of schema farther back that's when they are going to be successful if they maintain the wall at the edge people are going to move around them and go in different directions and 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