 And welcome back, we are here at the DuckConf 2012 in beautiful Las Vegas at the Cosmopolitan Hotel. This of course is Splunk's annual user conference, I'm Jeff Kelly with wikibon.org, I'm joined here by my co-host Jeff Frick from SiliconANGLE. Hello everyone, welcome back, we've had a full day here at the conference with a lot of great speakers, we've had customers, we've had the CEO with the CMO, but now we're going to cut right to the chase, we've got the crazy founders, we're happy to have them on board. I think everybody's a little punchy at the end of the day, but that's okay, that's the way we like it. So we've got Eric Swan and Rob Doss joining us, it's quite a run, quite a run you guys have sat through, right, seven and a half years since the beginning of this adventure? Probably closer to ten. Closer to ten? Yeah, I mean seven and a half, ten because once we kind of came off our other job, we didn't know what we wanted to do, but we're sitting around trying to figure out what it was we were going to do for a long time before we even kind of honed in on anything. So I think it was closer to ten years. Closer to ten. Funded almost eight years. Funded eight years ago. Funded eight years ago. Yep. And how did you hone in on, well backup, how did you pick the word Splunk? Well, there's two parts to this, there's why Splunk and then how do we get it, the difference between the ten years and eight years that Rob's sort of alluding to is there's a two year period of time when we tried to figure it out. This isn't our first rodeo, we've done startups before and we thought this time around we'd spend a little more time up front doing the homework and I think I just honestly, one of the reasons that I think we're sitting here today with a healthy company that's doing well is because we did a bunch of homework up front and we can go into lengthy detail what that was. One of the things we did is we talked to lots of customers and we came apart upon the problem by talking to people and one of the things that we heard people talk about was they had this massive amount of data and that when they went looking through it, it was a lot like cave diving. Literally it was this big, ugly muck of data and you put on your hip waders and your headlamp and you sort of went into the cave looking blindly for stuff and they would, it's sort of like election they actually used the word Splunking my data. I don't know if that's true and we're post-rationalizing or whether we making that up but a couple of interesting things is Splunk then as a, we're very brand sensitive that Splunk is a little like Google. It's a misspelling of a slightly geekier weird term like Google and Google are misspellings and they can use it as an adjective or a verb or a noun. I guess you can't use it as a noun but you wouldn't want to look that up and figure out what a Splunk noun is. I'm not the guy you want to look at. By the way, our English grammar question spot hated the name and it took a lot of convincing to, I mean they're like enterprise software company called Splunk are you guys nuts? But when you think about it, it was kind of like, almost like the consumerization of enterprise software at this point in time. So it was entirely on purpose that we picked a name that was catchy. Slightly inappropriate. Yeah, I mean all, it's slightly inappropriate and everything just so that it was unforgettable and it didn't sound like other enterprise software companies because we really wanted to focus at end users, not C level folks and kind of a turning around of the enterprise sales model at that point in time and I think the name fit really well with that. Whether you like it or hate it, you're going to remember it. And when you're a two person company, one of the things you struggle with is just a brand that people remember and you know, you'll remember it. Yeah, absolutely. Picking a name is one of the worst things you can do. It's very hateful process. It sucks. I think you guys hit on a good one. So what's interesting is this is like almost 10 years ago. So this is before the term big data was popular or even coined probably. So, but nevertheless, you're still dealing with, I mean, big data is relative. So even then you were dealing with pretty large data volumes. And in a way, the industry is now kind of caught up with you. So tell us a little bit about kind of that big data evolution from the time you started and how that's kind of changed over the last 10 years and how Splunk's mission is likewise had to kind of adapt into this new world that we're calling, you know, the big data era. Well, I mean, so literally from the day that we wrote the first part of the code, it was really, really slow. So it couldn't consume big data if it tried. But it was obvious that you could see if you were using it what the potential was if you could stick a lot of data into it. And our fabulous engineering staff of people a lot smarter than Eric and I spent a lot of time just honing it like squeezing water out of a rock, release after release after release after release. And we never thought about big data. We never thought at first we didn't even think we had anything to do with analytics. And, you know, we came up with the reporting stuff, but it was, you know, I don't even think that I was interested in doing reporting early on. It didn't make sense to me. I was like, come on, everything's about search. It's all about search, not the reporting. It turns out the reporting was a very critical piece. But now it's big data. And I think we were very lucky that somebody came up with the term big data right at the time. Somebody put a name about this around this. And we had never even thought that it didn't even make sense to us. All of a sudden somebody put it into a category and we just fell right into it. I think that's kind of the way that it happened. It never really was a thought. Success is often luck. Right time, right place. We've done companies in minutes just off the wrong place at the wrong time. Also, you know, I don't think we ever thought of ourselves, and maybe today, even big data, we're a machine data company. We started with machine data. And it just sort of happens to be that machine data is often big data. But we started out as machine data. We've always been interested in, I mean, one of the reasons why we now have to sort of the Google search model was is early on we did to machine data what Google did to human-generated data. So the analogy has been that human-generated content's growing. I guess your cameras are like this, right? It's growing this fast. Machine-generated data, we had these graphs from early on. And now they're big data graphs, but early on they were machine data graphs. And you had tools like Google for dealing with human-generated content to find something. But you didn't have the analogous tool for the machine data. And what you're finding is this sort of maybe 40-year revolution going on where machine data is becoming extremely valuable just like human-generated content. And over the next period of years, you're going to see everything's growing up data, whether it's airplanes and cars and buses or whether it's medical devices, everything's going to be throwing off data. Yet there's no tool effectively like a Google or any kind of tool to deal with that massive amount of information. So we're sort of like Google looks up there. We look down here. We index it all and allow you to dig through it. And I think that's where the revolution's occurring, right? I think there's a slower, slightly less visible revolution going on with the fact that all those machines, the only way it's going to scale is if machines get a little bit more intelligent, whether you're remote controlling thermostats, or whether you're dealing with cars that are getting more intelligent or buildings that are smarter, that's our place in the world. Now, what's interesting, and you said you've done some startups. And I think a lot of us haven't. Probably a lot of our viewers have been at various startups. And you always hope it's your last startup and it works. But oftentimes it doesn't work out that way. So now that it worked out and sounds like you haven't really had a pivot point, you've basically been to grow off kind of the original idea, which is spectacular. But now you've got the new challenges of growing the company, maintaining a culture that you guys obviously, you've got some personality. You're excited about who you are and what companies are. But you've got to grow it with new leadership or extra leadership and just a lot more people. And now you kind of, by de facto, getting out are the leader in the space, right? There is a category now, big data. And you are kind of the leader. How has that kind of changed things? Or how does that kind of, do you feel in that responsibility to kind of lead the charge and take it places where no one's really thought of it going before? Or you just keep doing your thing and having fun and coming up with some of the greatest titles I've seen at any company that I've loved it. That's it. That was the answer to a very long question. Yeah. Now we, you know, I mean, as far as the culture is concerned, Eric and I and we, in previous companies that we have founded or been very early members of have created a very similar culture and it was really nice to see that culture sort of grow to a much larger. I think it's really difficult to keep culture like that because as you, when we first started, we were hiring people that were just like us, crazy, listening to the same kind of music, doing the same kind of thing and enjoying the same kind of stuff. And after a while you can't do that anymore because there's not a lot of those people around. And you hired them all. You're hiring different people, different cultures. Some of them are quieter, some of them aren't. And how do you maintain a company with all these diverse people? You have to find diversity in people. You can't get everybody that's all the same. And so how do you maintain the culture? It's hard. I mean, there are certain, as long as we keep people around from the old days that remember the way that the company was, I think that they can continue to hold that, to hold that as they keep hiring people. It's totally important to keep that culture around. And I think that's one of the, one of probably the largest reasons that Splunk is successful is because of that culture. We have people that are working here longer than they've ever worked anywhere in their lives. We have people that came out of school. This is, it was their first job and now they've been here six, seven years. I think that's a testament to the culture. And I think that's, you know, like I said, probably the most important thing about building a company that's like this. Yeah, absolutely. And you can tell that the employees here really feel connection. Absolutely. Really invested in this company. And I wonder, you know, we often hear about, it's very easy to understand what a company does, but not always why they do it. What is your mission? Why do you do what you do? Why is it so important? You know, we know it's machine generated data, getting insights from that data, but why is it important for you? And how do you keep everyone so engaged? Well, I think personally I think hiring people like Godfrey Sullivan and the rest of the staff is totally critical. It's not about, it's not me keeping people engaged. It's not Eric keeping people engaged. It's hiring the best of the best and then enjoying the success of that, right? So how many of these people that have been here seven years in the stick around if the company wasn't doing well? I don't know, that's a really good question, right? I mean, you can drink beer and have cocktails all day long and other companies too. A lot of people on the south of the market these days. Yeah. I think the initial idea still has a lot of legs. The customers that have not adopted this technology, there's so many of them still out there that don't even understand it yet. So even with what we have today, I think that we can grow this to a really large company. But that doesn't mean that you stand still, so. And then we used to have up on the walls that we're only at mile three of the marathon. It really feels like you're in the middle of the game, right? We're not done. It's not done. No, we're not done. You're not maybe even a quarter of the way done yet. And I think a lot of people feel that. Like we're just, you're not done. There's so much more we have to do. In fact, I was talking to one of our earliest clinical engineers. He's like, Eric, the roadmap has never been thicker, richer, deeper, more options, you know, and the team's executing well. And I think as long as there's customers who like the product that you're delivering new functionality, that the roadmap is thick and people want it and people are liking it, you're not done, right? I think the problem would be if the company ever started to, you know what I mean on the financial side, that users started losing enthusiasm or there was that lack of that really positive, our roadmap is almost extensively driven by customer input that just love the product. They love it and they say, damn, if it just, please Eric, can you make it do this? And we have so much to do. I think people just react to that, right? They come to work in the morning because there's just, there's so much stuff that people need done. And that's not always true in the companies I've been in. You sort of hit that stall point where you don't exactly know what to do. Maybe you need to pivot or maybe you're running out of stuff. In our case, it's just getting, the pipeline's getting bigger and thicker. I think also like, even from the days that we were doing research when it was just a few of us doing cold calls, I mean, just to figure out whether we even had an idea that made sense that we were starting to build, we wanted to build this company to last, not to be a flash in the pan, not to be something which was two years and then got acquired by Google, but really a company that was built to last, a new kind of company that had legs. We've been acquired before, it sucks. Yeah, and we're very fortunate that the way we built it turned out. I mean, it turned out the way that we wanted it to. So they're giving us the signal over there on the other side of the control desk. So just to kind of close, you've been living it for 10 years. What are some of your favorite customer success story? Your favorite customer uses of Splunk that you just would have no idea that either somebody would think they use it that way or that the relationships that they were able to query were just so fantastic that, wow, what a fantastic tool of discovery as we are on the data discovery. Yeah, I was doing a homebrew kit yesterday. That's cool. I haven't heard that one. I haven't heard that one. I'm monitoring a homebrew. That's monitoring a homebrew kit with Splunk. I like that. Anything you're doing with yeast is good. Rob makes wine, so. How many data sources does he have on the boiling keg back in the kitchen, hopefully? There was one person that we talked to who was using it to monitor and create a feedback loop inside of a greenhouse environment, monitoring things like moisture and humidity and then controlling the louvers that let the air in based on what it saw. So having the software actually make decisions on how to control the environment based on the feedback it was getting from the sensors. I thought that was really cool. I think what Nest Labs is doing with the thermostat data is incredibly cool. I think that the idea of taking, of putting sensors on everything, everything that you touch in 10 years from now will have sensors on it. We're working with car companies, airplane companies, power generation companies. You know, we started out in the data center and that was cool. But it's really, that was the first mountain. The next mountain is there's this whole world beyond servers that is just about everything else you touch, right? It's just, it's everything else on the planet that's somehow physically a device. And we're seeing everyday new use cases of people, whether they're people rolling tractors and they're doing, as Rob said, as the tractors rolling, it's collecting data, whether it's delivery trucks that have mounted sensor devices and they're capturing weather data, climate information, travel time, and they're just saving it. They don't even know why yet. They're just like, okay. I think the use case that Garfrey mentioned in the keynote today with the Japanese people that are doing the elevator is probably the most stunning use case I've heard of in the last year. It may turn out that that's a great economic indicator and no one ever looked at it. Lots of hospital beds that are now throwing off data that we're working with. So there's some really cool, it's not just servers and data centers, it's all the other stuff that we've got going on. Yeah, I mean this big data really touches every industry. It's obviously a great business to be in. As you mentioned, you've got more things to do and as more and more devices, devices you never thought before might be, you know, equipable sensors, that's another data source, just increases the possibilities of things you can do. Absolutely, absolutely, absolutely cool. Well guys, thank you so much for coming. We really appreciate you making time. I know you guys are probably very busy here at dot com 2012. Or not. Or not. Nevertheless, we appreciate it anyway. So this is Jeff Kelly with wikibon.org. We are at dot com 2012, Splunk's user conference. We want to thank Splunk of course for having us here. Really appreciate it. Bringing you two full days of live coverage of the event. Again, I'm Jeff Kelly with wikibon.org. My co-host Jeff Frick from Silicon Angle. And we're gonna wrap up the day shortly. We'll be back with one more segment, kind of giving you our final thoughts on the day and then previewing tomorrow and the interviews we'll have for tomorrow. So thanks a lot and please stick around.