 Live from the MGM Grand Hotel in Las Vegas. Extracting the signal from the noise. It's theCUBE, covering Splunk.com 2015. Brought to you by Splunk. Now, here are your hosts, John Furrier and George. Okay, welcome back everyone. We are here live in Las Vegas at Splunks.com for 2015. Hashtag Splunk.com. I'm John Furrier, the founder of SiliconANGLE. I'm George Gilbert. This is theCUBE's SiliconANGLE's flagship program. We go out to the event and extract the signal from the noise. Our next guest is Rick Fitz, SVP of IT Markets from Splunk. Welcome to theCUBE. Thank you. So, ITOps, DevOps, IT automation, IT service management. Yeah. A traditional walking and tackling market. Yeah. Getting some intelligence, some machine learning, some big data, game changing. We're trying to bring sexy back. You heard this morning from the keynote. Absolutely. I mean, it's an industry and a place that I've been for many, many years. And not a lot of innovation occurring in that space. In fact, I think a lot of vendors are actually walking away to some degree, moving on to other things. And we are absolutely focused on it, maniacally. IT transformation is a big topic, digital transformation, whatever buzzword people talk about it, certainly IT is becoming an integral part of the organization app development, top-lying growth, not just cost savings. Absolutely. Or provisioning iPhones for people. It really becomes very integral. And data's a key point of that. Right. It's also gotten more complex. Yeah, complexity keeps going up. So machine learning is a theme here. Yeah. So talk about the dynamic of data-driven, IT automation, IT intelligence, in this new normal of IT being a critical part of the infrastructure and business. Yeah, absolutely. I mean, as you continue, and look, the business has not changed. I mean, the drive for innovation just continues to outpace the ability for IT to keep up. That is a constant. It's been a constant for a long, long time. But it actually derives new approaches. I mean, you do things at the cost of the business above everything else. So forget about having to run it later on. Forget about having to take care of it as it falls over. And so IT sometimes is neglected in that regard. So what we're finding is a lot of customers actually using machine data as a way to get instant access to information, any information so that they can actually troubleshoot the environment or find out drive information or business intelligence from the data itself. So it's been just a wonderful journey as we continue to add more solution capabilities into this spunk enterprise platform. How has the cloud and internet of things in particular, in terms of megatrends, impacted the IT role? Because obviously cloud, we've been covering that here at SiliconANGLE for many, many years. Great disruptor, great economics. But behind me, you can see a car is being instrumented. But that highlights, kind of puts a sexy feel on internet of things. But data center guys have known about internet of things forever. It's in your DNA and squungs. I mean, log files are the result of those things called machines. Yeah, well first on the cloud side, what we're seeing is customers aren't making that journey overnight. They are absolutely taking, transforming business processes that they can as quickly as can to cloud-based services and then moving components of their data center into the cloud. So that requires a hybrid approach. I mean, you cannot make that transformation without paying attention to what you have, or it's connected to what is new. And so that's definitely an approach that we take. As far as the trend, or the mega trend of internet of things, it's really what you talk about. It's just simply a proliferation, as I like to say sometimes in a really geeky way, of IP addresses, right? And if there's more... Or I do the network. Yeah, there's more things out there, a lot more of them. And that's just has to do with the fact that technology continues to shrink its footprint, and therefore we as innovators can do a lot of interesting things with technologies. But what it does mean is in these small millions of things, they often send back small amounts of information about themselves, for that matter. But you get small amounts of information from millions of things, and you need to be able to absorb that. And our announcement today on 6.3, Enterprise 6.3, for example, was our HTTP collection feature and capability is a good example of capabilities where we're allowing us to consume messages, or if you will, receive messages from these devices directly into Splunk. And therefore we can actually monitor and manage those things. Yeah, I mean, in your example of the data, I'm not saying big data, it's actually data, small data, but happening very fast in some cases, or slow trickling in. That's right. Either way, these data points really render potentially insights. Yeah, it comes back down to the visualization and or exploring what would look like an anomaly on paper. That's right. But can double down into some sort of workflow change, business model change. Can you give an example of what you've seen in that area, like customer example, or some use case where you're like, hey, you know, before Splunk, after Splunk, these things have happened? Well, yeah, I mean, you saw today in a couple of the keynotes, I think that were really interesting. And they just, they continue to, we continue to see this, where technology, Coca-Cola, for example, deploying their dispensaries out into organizations. And, you know, it's technology that's actually dispensing food product. And therefore you have to deal with the fact that this is actually a piece of equipment that has to be taken care of. But much more, it's a digital piece of equipment. It's not a mechanical piece of equipment anymore. And therefore it puts off data, it puts off information that has to be managed. And, you know, when you think about that, that problem, nobody's set around and actually asked for, you know, how are we going to actually take care of and monitor this, or how are we going to make sure that it's serviceable and all that kind of stuff. That wasn't an engineering thought that went through the process. It was kind of an artifact after the thing was successful. And that then becomes a problem that is easy to solve from a big data perspective. Because if there's data available, bring it together and take a look at it. So these new digital devices, if you call it, whether it's a factory of digital devices, virtual factory, they're still instrumentable. Everything's now can be measured. So I got to ask you the big KPI questions, our key performance indicators, as it's called. People are usually behaved based on how they're measured. And compensation is tied to how they're measured. Traditionally KPI's has kind of been, in some cases, a great structural tool, but also an inhibitor to go out and to do new things. So how has this new systems of intelligence, services intelligence model, IT, SM, IT service intelligence, changing the KPI game? Because now you guys are proposing this notion that KPI's can be constructed almost on a daily basis that would have taken months and months to do. And that can ultimately change the outcome of a trajectory of a business. Yeah, well, two things. IT service intelligence, as your viewers will know, is basically a monitoring solution based on this big pool of data. So effectively, you structure the data into a series of services, and then you set various different thresholds or key performance indicators that determine if something's going to arrive. The big difference is that old way of doing this was to actually set some form of a static threshold or something that you would say, hey, we want to do 80% of this all the time. That world is gone. It doesn't exist anyway. The idea that things change normally as a course of business is just happenstance. It happens all the time. And those changes are critically important. So adding machine algorithms or machine learning algorithms that actually do dynamic variance in the performance of those systems is really important. Or even looking across the data and seeing anomalous behavior, that may be good behavior, or maybe bad behavior is certainly critically important. But applying those advanced analytics into the service context is what's really critically important. And I think it's a real big game changer for the industry. You know, if you take the Coke dispenser and you just think of it as a microservice. You know, the proliferation of these microservices that are kind of a femoral spin-up, spin-down, you could just think of this as, you know, far more complex application landscape. The folks who did the machine learning for security said very, very domain specific. You can't just generalize it. How do you take a look at all that incredibly new, you know, variety of information coming from service intelligence and make sense out of it without over sort of taxing the operator? There's a couple things. One is generally, in the security world, you're dealing with behavior. Behavior is something that you have to look at mathematically a lot different. And it is very domain specific. In addition to that, when you're looking at metric data, time series metric data, more specifically, it's reasonably structured once you put some structure to it. And you can actually, and it's mathematical, so you can actually do specific machine algorithms that apply broadly to those sets of problems. But it's very true that you actually have to look at these problems in different ways and continue to evolve. So while we've introduced new algorithms around anomaly detection and dynamic thresholding, we have a lot more to do and more progress that will be made over the next several years. In this regard, looking at patterns of machine data, determine how things are related to one another. There's something certainly we were looking at, but there's just a tremendous opportunity to continue. As the math gets better and we learn, our data scientists learn more ways of approaching things, we're just continuing to introduce that into product. Do you see a potential conflict with ISVs? Let's say a Kafka does now the message bus that everyone loves, their idea of their goal for building a business to help customers run that. Similarly with Redis and Redis Labs. Now you'll take a holistic view of first how to monitor and maybe do more things. And that's capturing value from the customer who's like, we can't put all those things together. But those companies are also trying to make money by helping customers do just their piece. Is there a conflict? No, I don't think so. Because I think what you'll find and you talk to the customers who are out here wandering around, they often use Splunk as a data fabric within their organization. And they'll pull data from all different sources, including log files, but those sources can be other vendors' technologies. And in fact, that's why you have a number of vendors and partners here in the auditorium. And that's a good source. Now the key value prop that you will continue to maintain over time is to correlate the various different behaviors that process various silos of data. There's no doubt about that. That is in fact what we do and do well. And that correlation capability or if you will advance analytics across those data sets is what our customers continue to value. So whether it's a Redis or some other data source that's doing some special processing, they're just a data source to us. And pulling that data into this is really important. On this correlating or cross-services, in the old world, we'd call that testing by a single vendor, right? And now it's sort of out in the wild, at least for the advanced customers who are piecing all these things together. Could this be pressuring, less sophisticated customers into more homogeneous solutions? I don't think so because here's the other trend that you just mentioned, which is microservices. The fact that things are ephemeral, they're moving, they're not there, they're there one day, gone the next. You know, it's an ever-changing, ever-moving environment. And I think that trend unto itself is just saying, look, here you just have to deal with complexity, fluidity, constant change. It's just the new norm. And being able to deal with that in real time is going to be critical. So the data that you see for every instant is going to be different in the future. It's not going to be the same patterns. And that's why we have to use this advanced map moving forward to discern through all these terabytes of information, terabytes in some cases, to give you the real-world view of what you need to pay attention to. So DevOps has been a really big trend in the cloud business. So we have developers, your customers, have guys out there now developing apps on infrastructure. Yeah. So I asked Pat Gelsinger at VMworld about DevOps, and he said, interesting survey, most of the people that go to these conferences and get a little survey, they're mostly from the ops side. Yeah. So IT ops really is in the wheelhouse. So DevOps, pure developer, okay, they got local hosts on their desk, they're programming the way. They have building apps. But the IT side is really becoming a key part of the DevOps conversation. Yeah. What have you noticed in that area that's changing, that's relevant to the overall cloud migration, cloud transformation? So we were just doing a couple of stories with customers just recently. And the number one driver for these two customers in general for moving to Splunk was the way that they received requests from the development teams was give me these logs. I need these logs to actually figure out the problem. And so they were literally manually collecting log information and handing it back to developers. They eventually ran into Splunk and started using Splunk to actually do that. And they found themselves all of a sudden becoming an information service provoker back to the provider, I should say, back to the dev team. And then they started working together, which was interesting, which is the formation of any kind of DevOps movement is where development operations work tandemly to one another. And I think if you move that even forward into the future, so that's kind of the current state where we see Splunk being very successful in this DevOps space. But if you move forward into the future, I think the other thing that you're going to see is that the DevOps teams are actually the tools that they're using to deliver their software to market. And there's a lot of open source technologies being used. There's various different forms of containers, containerizations, what have you. They're actually asking IT operations to actually monitor that delivery process as well. And in combination, we're seeing a lot more of operations being a service provider to the development organization. And I think that's a very good thing to see because. And their critical success factors are going to come down to having access to the data. Oh, absolutely, absolutely. And that data is changing every day. You know, the app log is still a great source of information if you're troubleshooting in a problem that happened while you weren't there. You don't have your debugger. It's a good thing to go back to the log and start there. Tell them about some of the top conversations that you're involved with customers. And honestly, let's shoot the arrow forward. Let's connect the dots. Next five years, IT is going to be a critical piece of the infrastructure business fabric because they're becoming closer to the IT technologies and the people are getting closer to the solutions. They used to be kind of like putting out fires all the time, managing service, tracking and stacking, all that stuff that us old guys were used to seeing and doing. But now, cutting edges, real-time service cataloging, you're seeing that rise of agile. Yeah. So what does that future look like? I think a couple of things. One, I think you'll see the, you know, continue to see the outsourcing of things that don't matter to IT, right? That, a few years ago, that was, everybody was worried about their jobs because it was like, okay, they're outsourcing us. But I think we've moved past that and through that now where IT is actually looking at how do we actually add value to the business? And we're starting to see some indications of that where they're using machine data to pull business-relevant information and applying that back to the business. And in supplying that back to the business, the business is changing its behavior in real-time. And I think that's having a dramatic impact. So I would anticipate a number of the customers are in this audience today to actually, over time, become much more connected to the business and the business outcomes than they ever have been in the past. So you're saying, basically, that the job security questions, that the jeans out of the box, no worries. I think we've repassed with that. So we moved past that. Now it's, okay, job, not only job security, job promotion comes into your ability to use the data, solve the problems, so it becomes much more important. So essentially a higher skilled, if you will, not the thing we're low skilled before, but more integral part. That's right. And technology is so interconnected with business now that you can't separate them any longer. And therefore IT has to be part of it. That's a great headline for our blog post. Job security, no more worries. No more worries. Your job's not going away. We're going to shift the value shifts. That's right. You've got to keep up with it. So don't fall asleep. Don't think you can't, but you have to keep up with it. It's like the mainframe. If you were going to hang on to the mainframe, your job security was limited. Well, there's still mainframes out there. But for us older guys, some of us still know what a mainframe is, for sure. Rick, thanks so much for coming on theCUBE. Congratulations, great show. Thanks for sharing your insight and the data here on theCUBE. Appreciate it. I appreciate having the opportunity. This is theCUBE bringing you the insight and the data. We're flunking all the guests here with great questions. We'll be back with more live, day one coverage of two days here in Las Vegas at Splunk.com. We'll be right back after this short break.