 Live from the MGM Grand Convention Center in Las Vegas, Nevada. It's theCUBE at splunk.conf 2014. Brought to you by headline sponsor, Splunk. Here are your hosts, Jeff Kelly and Jeff Frick. Hey, welcome back. I'm Jeff Frick. You're watching theCUBE. We're at splunk.conf 2014, the fifth annual Splunk user conference here at the MGM Grand in Las Vegas, Nevada. I'm Jeff Frick. I'm here with my co-host, Jeff Kelly. Thanks, Jeff. When we're joined by John Rukowski, who's with Forester and Analyst Covering Infrastructure and Operations Professionals. That's correct. John, thanks for joining us on theCUBE. No problem. It's great to be finally on theCUBE. I've seen the channel online and stuff and it's finally great to be here. Excellent, welcome. That's great to hear. We're glad to have you. So we were just chatting a little bit before we went on air that you've been covering or you introduced to Splunk in any event many years ago, 2007. If you can retell that story a little bit. How you first came upon Splunk. It seems like such a long time ago now. When people say 2007, it's like, wow, that does seem quite a long time ago. So actually, I was first introduced to Splunk. It was actually a Microsoft Management Summit. And they introduced their technology to me as being a bit like a Google search engine for Windows servers. You could aggregate log files, do searches for important bits of information. And I thought, well, that's definitely interesting. And I can see some of the use cases for this. But is it really disruptive? And I looked at it and look at where we are today. Look at the size of this conference. I mean, today, Splunk are in the MGM Grand. This is where IBM hold IBM pulse. So the growth of this company has been absolutely phenomenal. Yeah, so let's walk through that a little bit. How did they achieve this kind of growth? I mean, you cover, among other things, application performance management and infrastructure monitoring, those kind of areas. And certainly that's where Splunk plays, but they do more than that as well. And if they've kind of, we've talked a little bit on theCUBE earlier about, they've confounded to Wall Street a little bit because Wall Street doesn't quite understand exactly what they are. Are they a tools company, a platform company? How do you account for this growth? Really, what they're doing very well is that they're able to record, analyze data. And I think in today's kind of modern world, in a kind of digital economy, then data is really the new gold. If you can mine that data, turn that into information very, very rapidly, present it in the right context to an audience, that's when you really generate business insight. So Splunk's development, its platform, has enabled for this rapid recording of data, kind of rapid interrogation of data. It's just had the right platform. Plus, as the solution is developed, it's provided great features in order to interrogate that data. And I think with Splunk Enterprise 6 and moving onwards, it's made it very easy even for non-technical people or technical professionals to interrogate that data, so the use of pivot has been a great addition to the products. So a lot of it, I mean, we have to remember, we live in a world in which technology fuels our lives, it fuels our businesses. And where there's technology, there's data. The more you can understand that data, mind that data, turn it into insight, then the more potential competitive advances you're going to get. Well, contrast that to, or put Splunk into context in terms of the APM market. So what, the things you just described, were the traditional APM vendors not able to do that in a way, were they more focused on reporting versus analytics? I mean, how has Splunk been able to disrupt that particular market? Yeah, well, Geoff, you're kind of hitting the nail on the head, as we'd say in the UK. Basically, you know, a lot of them, kind of monitoring or management approaches when it comes to application performance management, when it comes to infrastructure workload management. I would classify a lot of those approaches as being kind of a rear view mirror look. So monitoring the performance, the availability of applications of infrastructure workloads, reporting, alerting, you know, those are very kind of IT centric terms. It's not very kind of forward looking. Now granted, the APM market, application performance management market, is shifting very, very quickly. If you look at the number of kind of the key players there, they are moving into this area of analytics. I think everyone understands the value of data and so in data and information. But historically, monitoring management has been about rear view analysis and not forward looking analysis, but that's starting to change. So whereas Splunk has been, you know, kind of sitting on top of a pedal still, for a number of years in regards to analyzing machine data, I see that changing very, very quickly. There's going to be a number of more players in here. We're going to see APM players coming in. We're going to see those more established players. So like, sort of, you know, IBM and maybe even CA come in here and try and disrupt that market. Everyone wants a piece of understanding data. And talk about why that's so important, specifically in the application development world of having forward looking, well, both real time and also to some extent predictive looking, predictive analytics, when you're talking about supporting applications in the so-called DevOps mindset. So the reality is, I mean, the world in terms of delivering applications, in terms of delivering business services, we're having to move faster and faster. Customers, employees are demanding this, you know. Internally, business services applications are there to kind of make the workforce more productive. In terms of externally, mobile apps, web-based applications are making sure that you can engage with your customers as and when they want to engage and delight them at the same time. And the reality is, you know, when it comes to predictive analytics, it's really about understanding that customer's behavior. And I think this is where Splunk's kind of evolution is starting to take them. It's not so much about understanding the performance of an application or infrastructure. At the end of the day, you can have a highly performing application, a always available kind of infrastructure workload. It's about understanding that customer, that user, kind of what they want, what delights them. Because if you can understand that, you can serve them in a better way. And that's what kind of understanding data and big data analytics or business intelligence and as a whole host of words you can apply to that. This is what it's really about. It's been about understanding your customer, understanding your customer personas, their journeys, how they engage with your organization in order to better serve them, in order to delight them and in order to stay one step ahead of your competition. Because if you don't do that, if you don't delight them via your applications and business services, then there's one thing which can be guaranteed. They will switch to another provider. So clearly, yeah, I mean, I'd like the way you frame that. They are moving from this idea of keeping your application performing at top speeds and reaction time versus what are exactly, what value is it delivering to customers and how can we improve on that? The value which is delivering to customers today is that as the engagement channel starts to change to being kind of digital engagement channels, so customers are engaging with organizations through digital means, whether that's mobile apps today, maybe tomorrow that's going to be wearable technology, that's just around the corner. All of these consumer devices rely on software, rely on the performance and availability of applications. So for the reality is for kind of IT operations professionals who are here today, and that's kind of Splunk's kind of bread and butter, that's kind of where they come from. They're really helping safeguard and optimize commercial success of their organizations. So the reality is, you know, why this is important is that because it's not just about performance and availability, anymore technical performance and availability, it's about commercial performance and availability. So that's where the use case is starting to shift. And now are you seeing the underlying, so the application developers, do they have to develop a new skill essentially to understand the shift? You know that if they're used to looking at, oh, I want to keep my application running if I do that, I'll check the box versus adapting to what the customer actually wants or how you can serve the customer better. I think ultimately the world of IT, so the world of the IT professional, whether you're talking about the IT operations professional, whether you're talking about the developer, the architect, I think we're going through an evolution, you know, brought about by changes such as kind of DevOps, changes such as cloud, but the reality is organizations are having to move faster. And any IT professional really has to take an outside in approach to the services which they're developing and the kind of services which they're supporting. First and foremost, they need to understand what does my industry do? Why do my customers buy from my organization? Why do they engage with my organization? Because they have to understand that the services which they're developing, applications which they're developing really are linked to the commercial success and more importantly, the brand of their business. What strikes me is this kind of DevOps move into the way things are delivered, not so much from a software development methodology, but really from a perspective. It's not the day where you go into the room with the whiteboard and do an MRD, which is a big thick thing, and then you do the PRD and that's a big thing. That's really not the way anymore, right? It's deliver it, listen, make change. Deliver, listen, make change. And making the changes based on real customers and what they want. It's a very different kind of methodology of going to market and trying to deliver that customer value as opposed to trying to design the perfect solution or what you think people want or a better speed and fee than my competitor. Really that outward focus. Yeah, the reality is, or we really see the shift here is that DevOps or sometimes DevOps is really a marketing term. I hear that DevOps far too often. It's really about modern service deliveries, about the evolution of IT. We're delivering faster, but we still need to maintain quality. So those feedback loops become very, very important. The feedback loops between operations and development. But more importantly, what you need to do is understand that there's a full value chain here, which you need to kind of understand all the way from the external customer, all the way through to the backend kind of server teams and kind of database teams. The reality really is that in order to move faster, you need to have information and data at your fingertips. And this is where a good analytics solution enables you to do that. It enables you to record that data and turn that data into information very, very rapidly. And this information and insight can be used to optimize these feedback loops and provide information to the right audience. And not only rapidly, but democratically. I think that's the other big thing. I think that feels like traditional BI fell down in that you had to have really smart people coming up with really smart hypothesis that could manage a whole bunch of data. Where now it's really switched in this post-Google world where the data's all there. Now your job is to kind of query it and take a little feedback, query it again, take a little feedback and to pump that all the way down into lots of people doing their jobs as opposed to the hallowed ones with all the PhDs in the back office behind Mahogany Row. Exactly, we live in the world at the moment which is focused on what's called the data scientist. They're the new saviours of the organization. To me, if you're in the role of being a data scientist, that's a limited role. It's a tactical response to not having the solutions which we require today in order to kind of mind and understand that data and turn it into information and insight. But you know, a good analytics solution is looking to understand the questions which have been asked of the information in order to remove that need of the expensive data scientist. And I honestly believe that Splunk are kind of moving in that direction with the easy kind of pivot tables, being able to easily interrogate that data, making it so that you don't have to be a data expert in order to query the data. Well, that's where then we start to remove that kind of need for the specialist data scientist skills. Yeah, and the other piece that strikes me is their focus on bringing in lots of different data. I don't care if it's structured or it's unstructured. I don't care if it's coming off a mobile phone or if it's coming off a Twitter stream. I don't care if it's between two machines in the data center or a connection to the mainframe. There's value in that and pulling it together, aggregating it together. Certainly they're benefiting from reduced prices in hardware and lots of faster computers. But to bring all that together and then let people query it in almost a more human way. I just think of when you go into an agile software development shop, right? They've got the big boards all over the place and the burn downs and this power of a feedback loop in a software development environment. And now we just had Aaron on talking about his customer agents also having big boards all over the place, looking for changes in application performance or changes in people hitting a particular place and then responding with that real time feedback loop. Exactly. You emphasized the point about structured and unstructured data. And again, I don't care about that definition. This whole notion of structured, unstructured data in 2014, it really is not kind of, it doesn't hold weight anymore. For me, any analytics company is doing business intelligence because data is so and technology based data is so intrinsic to commercial success to the applications which have been run. Then it doesn't matter whether you're looking at structured and unstructured data. And it's actually, I've just finished writing a report which talks about kind of monitoring and how we need to elevate the role of monitoring to be really being about situational awareness. And when I talk about monitoring, I also refer to analytics as well. And the reality is this notion of situational awareness comes from the US Marines. So when a US Marine is on the battlefield, they need to have the right information at the right moment in order to make the right next decision because it's critical. You know, in the world of IT, in the world of business, well, our battlefields may be different, but it's still the same concept. It's about making sure you're getting the right data and information at the right time in order to make the right decision. Now, more and more, as we kind of see the analytics market moving forward, it's not about the human-making decisions. It's about automation then kicking in to make automated kind of actions. That's the kind of next iteration as well. Now, do you see Splunk playing a role in that? Can they move to, right now, most of the Splunk solutions or people are looking at dashboards and then they're making decisions. Can Splunk move into that automation role? And I agree with you. I think when you can automate real-time intelligent decision-making, that's when you can make a significant impact in your organization. Because if you're dealing with applications or a consumer doing something on a mobile app, you don't have time or something to sit back and say, hmm, here's the offer we should make to that person. It's got to be automated in an intelligent way. Can Splunk play a role in that? I think they will play a role because they're setting themselves up as being this operational intelligence platform. You know, any good enterprise solution today has got open APIs, which means that you can easily integrate the solution with other solutions. The automation market is still very much emerging in that sense. But, you know, before we can automate something, there's a fundamental aspect which needs to be addressed. Any automation means that we must trust the data which is being recorded. And that is not a kind of step in which, you know, Splunk can help people trust the data, but that's really an evolution of we as human beings. We need to be able to trust machines. I know for my history, we've been brought up in the 80s, when I was brought up on Terminator. I don't want that to happen. But, you know, fundamentally, this is the way which we're going. For organizations to move faster, for them to serve services to you as a consumer and applications to you, to delight you, to deliver services in context, then automation plays a big field in this. But the analytics engine behind this, the algorithms which turn data for information is just key for automation to be successful. How are we going to get to that point where we can trust the machine? Is that a, you know, you referenced, you know, what do you grow of? Is it a generational thing? Is it going to take the next generation? Or is there something that can happen in the meantime to move us along and people like you and I and help us trust the machines? Some of this is definitely, you know, a generation thing. As we see, you know, the so-called millennials coming into the workplace, I think there's more of an acceptance about, you know, being comfortable with the use of technology, with the use of automation. I mean, my two-year-old already kind of understands how to use, you know, a modern smartphone. So she's already kind of saying the name of a smartphone. I'm not going to give that smartphone any more credit or that company any more credit. But she knows how to use it, how to access videos. She's literally knowing which one it is. Yeah. And so some of this is a generation thing. But, you know, some of this is that if you really want to stay or remain competitive, then, you know, every organization today who wants to remain competitive is trying to understand their data, trying to turn data into information, trying to use analytic solutions. But what's the next stage then? Well, you need to automate. You need to do it faster than your competition. So automation just becomes part of being competitive. So we will see that shift coming along. But we have to become more comfortable and trust the data which is being kind of recorded and which has been analyzed. Yeah, and I think, you know, one of the other things that's going to move it along is when those early adopters are seeing success and their competitors are seeing themselves being passed by. Yeah. You know, fear can be a great motivator for the adoption of technology and new approaches. This is more of an approach, not necessarily any given technology. I mean, there's any number of technologies that might help us get to that point where you can automate some of these real-time decision-makings. But it's more about the approach and, as you say, understanding and trusting that the decisions are going to be accurate and are going to provide the value that you expect. Exactly. I mean, you mentioned this is not a kind of solution, but it's an approach. And what we do in IT today, we're very good at productizing different kind of trends. We talk about cloud. We talk about big data, business intelligence. We talk about DevOps. And we productize and segment each of these areas. And in reality, all these areas are interlinked together. This is about an evolution in the way we deliver technologies, but an evolution in our relationship with technology. Well, how do you think this is going to impact the so-called mega vendors out there? This is kind of getting back to the products, but can they, you know, the vendors that brought us the more traditional data management, data warehouse, in the eye world, a rear-view mirror look, the very structured approach where you've got to know the question ahead of time, wait three months to get the data warehouse modeled. Three months is probably being generous. Can they pivot and, well, what stops an IBM or an Oracle for coming in and doing what Splunk does? Can they make that transition? What are the barriers for them? There's many barriers. Some of this, you have to remember these kind of big organizations, you know, they're public companies and they have to kind of address the stock market. They have to address their shareholders. They have to make profit. And so when you're in a cycle of selling solutions, which is still selling, because, you know, IT's still fragmented to the selling, one monitoring solution to the database team, another one to the server team, they're selling, you know, different solutions everywhere, then that makes it easy to generate kind of profit and you get locks into a very kind of short-term cycle. So all you're doing is trying to satisfy, you know, the stock market, Wall Street in that sense. And so in the last year, we've seen actually organizations transition to being private again. So they can start to become innovative but they don't have to answer to the stock market. And so this could be a challenge for Splunk actually. You know, Splunk is a public organization now. So it'd be interesting to see how quickly or how innovative they still are in the next couple of years because they have to maintain and generate profit. Yeah, well, and, you know, we're seeing they're still losing money. They're close to break even, but I guess last quarter, but generally they're still losing money because they're investing it all in the, back in the company and, you know, trying to lay the foundation for even further growth. But that's a critical question, I think you identified, is when do they take their foot off the pedal a little bit? When do they start trying to turn a profit? And how does that impact their ability to innovate and stay ahead of all the kind of next-generation competitors that are nipping at their heels? Yeah, I suppose that for that question, you can say, well, when is Amazon going to make a profit? Well, yeah, I guess they're going to get a lot of pressure but it's not stopping them from just plowing all the revenue back into the company. So we'll see. Exactly. At the end of the day for Splunk, if they really want to be disruptive and they're moving into the business intelligence kind of market and disrupt that market, which I think they're on track to do, then they've got to maintain that innovation. They've got to maintain, they've got to spread their brand beyond IT and into the business. You know, I want to be at a conference in the next couple of years of Splunk conference and rather than the CIO of G Capital coming on the stage, I want the CMO to come on stage and show how they've used Splunk to generate insight, to generate competitive advantage. And I think that'll be the time when you know that Splunk has been really, really successful. Well, John, thanks for coming on. John Rakowski from Forrester, great insight. I always love getting analysts on. Great point of views and some really good insights as the focus and the locus of competitive forces is changing as everyone's becoming a software company. It's terrific. So again, you're watching theCUBE. Jeff Kelly here with me, Jeff Frick. Our third year here at Splunk.conf. We'll see what's happening in a couple of years, how many CMOs we have on the keynote. We'll be right back with our next guest after this short break.