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Splunk Inc. (NASDAQ:SPLK)

February 11, 2013 3:00 pm ET

Executives

Godfrey R. Sullivan - Chairman, Chief Executive Officer and President

Analysts

Raimo Lenschow - Barclays Capital, Research Division

Raimo Lenschow - Barclays Capital, Research Division

I'm really happy to introduce Godfrey Sullivan, the CEO of Splunk, to our keynote speech here at the Big Data event. I mean, I have to say if I look at the -- if I look at my comsheets and the companies I classify on the big data, there's one that stands out from the growth profile and from the market opportunity and that is Splunk. Seamlessly [ph], I was asked by my salesforce and a few of you sitting in the audience [indiscernible], and I was up on the mic saying, "I don't know." So maybe Godfrey can help me a little bit here today. But with that, we have a presentation and then we have time for Q&A. Just to remind everyone, we are in a quiet period so, I'll try to -- I did try ask -- to ask already how the quarter was and I didn't get an answer, but if you can rephrase the question, maybe, good luck. Godfrey, thanks for being here. Thank you.

Godfrey R. Sullivan

Well, hello, everyone. It's nice to see you, and thanks for the opportunity to spend the lunch hour with you. I have to tell you, I'm just coming from the Splunk Global Sales Kickoff Meeting at a hotel across town and you're a lot better behaved than they are. You're also a lot better dressed. Let me tell you this is -- I have a couple of my colleagues here in case. I'm going to stay really light given I was up here for the last couple of presentations. I think I'll stay really light on the technology side and talk more and more about the customers and how they use us and maybe how this all fits together but I do want to, in case you have any technical questions, identify 2 of my colleagues that are here, Eddie Satterly. Eddie, can you stand up, and Clint Sharp? Eddie, both come our -- from our customer base and have managed very large Splunk and open-source combined environment. So if you want to know how this stuff actually works, asked these guys because they know it intimately.

Okay. Yes, we're in our quiet period so you guys, you know that. I've heard too many definitions of big data and this is really the one I like the best which is when the size of the data itself becomes a part of the problem. And so I think you've had enough on big data so far this morning that I won't spend too much time explaining it except maybe how we view it, like what's the difference between big data as the market defines it and how it affects our business. So you know it's growing. Okay.

So my background coming out of the world of BI was sort of to the left, that business application environment. And the very mature marketplace, relational databases, OLAP cubes, relational report writers, query tools. You know all those companies. They're very good at what they do. And that data was big, too, so why did data suddenly become big? In fact, Google had done an equivalently good job of indexing a lot of the documents around the world, FIDs, GPS systems, web logs, all of a sudden, there was this new generation of data. And it's really the machine-generated data that's one of the fastest-growing and most complex, but also most valuable segments of this marketplace, and that's where Splunk comes in. So what does big data look like? It's pretty sexy. I mean I don't know how of you have actually seen -- have any of you actually seen any big data because it's big but, like, what does it look like? This is what it looks like. Now that, you have to admit, is sexy. In fact, I can see you guys heating up right there in your chairs, wishing you could take a book of this stuff home tonight and pore through it. But that's what it looks like. And it doesn't matter whether it's coming from a Twitter feed or a phone, a call detail record off of a cell phone, some sort middleware, Apache log, something, an identification system. But that's what it looks like and it comes in massive volumes and it comes in wide varieties. And by the way, those logs can change on a frequent basis. So the ability to deal with variability, with the change, is a big piece of what success is in this marketplace. But what can you actually do with it? Well, I want to talk about some customers in just a moment because we have the coolest customers you've ever seen and lots of them, about 5,000 of them around the world in 80 countries. So what are they doing with Splunk, with all these machine-generated data? They're improving their security posture, they're improving their application performance, they're looking at user behavior through website or online transactions, they're looking at customer satisfaction by making sure that SLAs per delivery of closed transactions is completed and, of course, they're improving their business outcomes. So we have a lot of customers around the world who are successful in delivering those types of benefits. So what happens? I mean, what does this whole machine data, the sexy stuff I talked about, what actually happens here? So this is sort of like the beginning and end of my technical conversation. But inside of all of these events, these machine events are fields and inside of those fields are things like a user session, your IP address, a message like login failed or an error, or a product ID code if you're shopping on the Internet. So this explosion of data came from the fact that if you and I went to the store in years past and we just walked up and down in all the aisles, we looked at all the products, we bought a belt and walked out, the only

[Audio Gap]

thing in the old days of structured data that got captured was the SKU and the belt, the price, your name and address. All the customer information that's already in their system and you bought a belt. This sort of an example of what happens every time you click on a URL on a website, every time you look at a product, every time -- not whether you bought it or not. Every step you take through a website generates a stack of logs like that. You guys all know this stuff. Okay. So that's what happens, is that this whole stack of logs gets created and it could be the authentication system, it could be the Apache server looking at the web logs, it could be a rewrite operation to the database. There's a lot of activity going on, but every time you make a click, you're generating a massive amount of information. So instead of just you put the belt -- you bought, put the belt in your bag and the POS system recorded that one sale, the amount of information available exploded. So it's really Internet commerce, mobile, some of these other things that cause data to become big. And Splunk's ability to strip those logs, to ingest them at high speeds, strip them down to their smallest parts and then let you ask any questions is the essence of why Splunk is growing so rapidly. So you get into this wide range of new technology. So if you think about the old days of RDBs, they were really good at read-write operations, highly structured data and all that. That's my old world. But guess what comes with that? It comes about a year-long project where you have to design the questions upfront. You have to know what questions you intend to ask in order to get the rows and columns to intersect to give you the answer. So there's a lot of planning that goes into these systems before you ever can deploy. And not surprisingly, there is oftentimes $5 to $10 worth of services for every $1 of software because it takes so long to make those technologies operational. You find some of those same attributes as you go across the right. So some of the MapReduce technologies, really, really good at doing a distributed query because you map the data across lots of disk drives, you can then reduce back the answers without having to wait for the hardware compute to happen, so really good. Some of the other NoSQL technologies, sort of like the next generation of OLTP, really good at fast read-write stuff. And then you go to Splunk. You say, "Well, what's the -- why is Splunk different?" What's -- what are we doing? And we loved time-series data. We'll take anything and ask you your tax or what have you. We can do binaries a little hard, but if it has a timestamp, we love it. And because of that, we're really, really good at that. So it's almost like any technology that you apply to these problems, they will be optimized to do some things really well and then you have to give up on some other stuff. So we're not really a database, if you will, but we're really good at indexing and analyzing time-series information, and there's a lot of it. Okay. So now we say, "Well, how does that correlate to customer spend? How does that correlate to how CIOs think about their business?" And the answer is, I never -- almost never talk to a CIO who says, "Boy, I'd really like to spend some time talking about your time-based machine events." They never -- I wish they would, but they never said that. What they is, like this, "We're focused on growth, on expanding use of information and analytics, we're trying to deliver better operational results." This is the language that comes out of a CIO's mouth and all they want to know is that what you are doing for them can deliver an ROI, an improved customer service, a better transaction environment, more security, that sort of thing. So now we're starting to see a little bit of a disconnect between the pure technology conversation and what customers actually care about. So I thought it was fascinating that Svetlana just recently released on her blog last month, the first of the Gartner Hype Cycles. And she's saying, "Where is Big Data now?" And her premise is you see it there, "My most advanced Hadoop clients are also getting disillusioned." The only consistent success reported by my clients is with Splunk. Now that's not to just the Hadoop gang. That's one more really good file store. The question is, what's it being purposed for? Let's go back to the CIO's objectives and priorities and say, what is it you're trying to do? And then any of these technologies might be applied with greater or less success, depending on the jobs. So everything will go through a peak and a valley and it happens in every market and it'll happen in this one. So now, I get to the quick Splunk advertisement. So here's Splunk, this is what we do, all those sexy machine logs that I showed you. You guys are calming down now. Everybody okay? That's what we do. We ingest that data. Customers don't have to do very much about it. We take it down from that paragraph of junk. We take it down to its smallest piece parts without you having to do anything and we put it into our file system. Proprietary, yes. Patented time-series file system. Very high-speed, massive scale. Easy in. Once it's in there, Splunk indexes it, and arrange it. It puts the IP addresses where they belong and all the sales, all the passwords where they belong, your names, the product ID. It puts all those stuff where it belongs without you having to do anything. And then you can index it. And once it's indexed, you can ask it basically any question. So there's one huge difference between Splunk and all that traditional technologies of -- like, the RDB world and the like. And that is, we only apply schema at query time. Okay? We don't apply schema at index -- at digestion time or at index time. And what does that mean? So instead of that year-long design cycle I talked about earlier, you skip all that. You can download Splunk. A lot of our customers, free customers, download Splunk. They put it on a server, they wire it up, they're indexing and searching in 10 minutes or an hour. So the time to productivity is massively fast. Once we have that data in the index, you can do ad hoc search, you can do monitoring, you can build alerts, you can run reports, you can also save it off into either the Splunk storage engine or Hadoop or one of our favorites, Cassandra, put a variety of other storage mechanisms that are great for cheap batch storage. Great for acute batch storage. Okay. Splunk platform, basically two parts. One is the core engine, which does all that collecting and indexing and searching. And then the user interfaces. So that you have the search language itself as well as APIs and SDKs and the like. So that's -- it's basically -- it's -- I have to say, it's one of the most amazing things I've seen in my life in technology, which is you can actually download a product that scales to hundreds of servers in a data center, you can download it and install it in 10 minutes on your Mac and start searching your Mac. It's a pretty, pretty amazing piece of code. So when we go to market, how does that manifest itself? Where do we go? We go into these markets primarily. And all the ones on the tree on the left, that's our bread and butter. That's probably 90% of our day-to-day revenues. We go in to solve application, problems. We go in to solve IT infrastructure. We do root cause analysis on failures or peaks or bad performance, that sort of thing. We improve security posture. SIMs were really hot a few years ago. They're all but dead now because there's a new thing called advanced persistent threat that your SIM didn't think about. You need to be able to have a mass engine you apply to all those logs. And then you can detect anomalies and bad actors, where it came from and all that. So security, a big part of our business. The ones on the right are what I would call the more emerging markets for Splunk because customers are starting to use us more and more for things like web intelligence or business analytics and industrial data. So the example before about the hospital that came up in the previous session, there's a great, big hospital corporation in America that uses Splunk for exactly that use case. They gather all the data from those portable devices that the nurses and doctors carry around and they were afraid they were going to have to spend $3 million to replace all those systems because they appear to be failing. How do they know that they weren't being recharged? Splunk. Okay? Okay. Developed package and solutions and apps. On top of the indexer, we have all kinds of templates, all kinds of templates. And they're roughly synonymous with our core use cases like applications, monitoring or infrastructure or security that sort of thing. About half of that one -- there is 300 or 400 apps upon Splunkbase, this content, and a big chunk of them are written by Splunkers. That's what a lot of people at Splunk do for fun, and a lot of them are contributed by our customers. So you see community examples there. And we're just delighted to welcome -- it's kind of how Splunk behaves as an open-source company, is to be able welcome and encourage contributions to the Splunk index and its performance by the community. And what's pretty cool about this is these are just examples of the one from the infrastructure area, so this is just one of the segments, but there's -- we offer templates. So you take a core of Splunk system, you stand it up on your server in a day or less, you drop the Microsoft Exchange template on top of it and it looks like it was born to monitor an Exchange environment. You can drop the VMware template on top of it and it looks like it was born to monitor a VMware environment. It's pretty cool. So a very flexible engine and lots of contents on top of that engine that enables us to pursue more markets than we probably deserved to pursue, but it's because of that energy that we get from the community and the number of content use cases that they contribute back.

Okay. We just released some SDKs. So this is fairly new for us. But we have, of course, want to get to the developer marketplace, especially the corporate developers. So there's so many of our customers who say, "Gee, Splunk, I want to build an app that our customer support organization uses." Today, they're using the Splunk search language to go find out when a customer has a bad -- problems that manifest itself in IT. I couldn't get my cellphone activated or something like that. What they really want is they want a form-based app with workflow that goes to their ticket system and all of that, and they'll build that in Java or more likely Python or something like that. So they want so that the person in the call center doesn't see a Splunk screen necessarily. They just see the support screens with the workflow. Great. Here's your Python SDK, go write that, you can drive the Splunk engine. Voilà! You get all your workflows. So a lot of our customers want to be able to build in their standard language environments and so we're now shipping SDKs for all that.

One slide and one slide only on our cloud business. So a lot of our customers run Splunk in the cloud. Some of our biggest customers run Splunk Enterprise, the product that we deploy on print, in the cloud because they have their apps running in the cloud. So we have one, there's a big game company that runs a 10-terabyte indexing system per day, 10 terabytes of data per day through Splunk that just looks at their games -- their online games and the underlying physical infrastructure on which it runs. That's what they do with it. So that's -- we operate in the cloud. We kind of go where the machines are. So Storm is intended to help developers because developers all go to the cloud first these days. They don't ask IT to stand up a rack of stuff. They say, "Let's just go up an Amazon or Heroku, start up some -- start up our project and then if it's successful, we'll deploy it." So we want them to use Splunk while they're developing. Why? Because all that sexy data, remember that sexy data? All that stuff that I showed you, you can tell your app what to spit out. Most people do error codes and failures. But you could also tell it, make sure you include the product IDs or the customer names in the log and then we can easily analyze that data in realtime. So that's what we want out of the cloud offering, is to be able to help developers learn how to instrument their applications so that you can get the intelligence as easily as that. Okay? 5,000 customers around the world, more than that, all industry verticals. The use cases I've been describing are pretty horizontal application, performance, infrastructure, web, security, industrial data. It's kind of shared across all industry segments. So we're not overweight in one.

One of the things I love most about Splunk, having been here almost 5 years, is how our customers grow with us. So a lot of our initial users started with a free download. We start with a free download. We have some -- I don't know how many hundreds of thousands of customers we have in over 200 countries that are using our free product. But they start -- a lot of them start there and then they start to use more and more data. We have a data limit on the free one. So then they buy something from us. An initial use case, it's probably $10,000 to $20,000 ASP. Pretty inexpensive for enterprise software. And guess what? They're already live in production and happy before they ever spent that money. So that's kind of enterprise software turned upside down, isn't it? Then, they grow and the Splunk use cases start to spread. And I know you hear this from your own guys. Pretty soon, multiple departments are using it and we start to grow into sort of the workgroup area, multi-department and ultimately what -- where we aspire to go with our customers is enterprise, where they have a great, big bit bucket and all the data is coming into Splunk, all the machine data, event data. And we can analyze that and spit it back out to all the different departments. We have a lot of customers now who are moving to that life cycle. So I have permission to speak just momentarily about Barclays. So when Barclays' exec came to our SplunkLive!, I met him at SplunkLive! about 3 years ago in London. And they were in the midst of a security and compliance use case and they were about to make a decision to stand up a security and compliance use case on top of another company's technology. Unfortunately, it was built on a relational database with a rules engine on top, which so many are, and it didn't scale. So he came to the SplunkLive! and learned about it and I here's -- I have a 2-page sort of report card. So you can see that their technology had ceased to scale and other limitations they were unable to ask ad hoc were complex questions of the data. So this was in 2011. We went through a full year, POC cycle with Barclays. This was not an overnight thing. This was a very carefully thought out because they'd already been down this path with someone else. Originally rolled out, significant deployment retail bank, now underpins significant compliance regulatory and security processes through the bank. Okay?

Multi-departmental, on the way to enterprise. Teams outside of security also looking at Splunk. Now, today the GIS Services Group, which is an IT services organization, is handling a system that's -- we would call it moderate by our size, over 1 billion security events processed and stored per day, 430 different data sources. Okay? That ingestion piece, that ability to strip all those things down and make sense out of them is very important. It's really good IP. 300,000 end points hardware and 12 data centers, 60 core infrastructure servers. You see the numbers. Okay? Splunk can scale, no problem. And it gets the job done, but as you put more data in, you get more value back which is how those other departments keep coming saying, "Hey, can we solve this problem with Splunk because it stands up and deploy so quickly." Okay. A couple of others. I'm sorry this stuff is running off the screen. Why does Cricket use Splunk? So the thing I love about this example is that they have a monthly cell phone service and you pay -- there's no 2-year contract, so churn is a big deal for them. So how do they reduce churn? They give you a music service. As long as you're on their cell phone, you can download their music service. It's kind of like a free iPhone or free iTunes. But they needed to be able to analyze that information. So they went from using Splunk for IT infrastructure at monitoring just saying, "Let's ingest all the mobile data. Let's ingest all the downloads data so that we can understand what are the top 10 popular songs being downloaded today? Who are the most popular artists? How many times is a song being downloaded? Is it being completed or not?" They only want to pay royalties on completed downloads, not partial downloads or failed downloads. So it's really important to understand it's all in the logs. I'm telling you, these things are sexy. You're going to get -- you'll take one thing out of this talk, right? You guys are all going to buy a magazine or something, logs.

Salesforce. What is Salesforce? They started using us for IT monitoring, typical infrastructure monitoring. The thing I love about the Salesforce, they probably have about 800 daily users of Splunk. So all kinds of departments are sort of logging in now and looking at dashboards and reports and getting alerts. The most recent one that I thought was one kind of fun was that one of their VPs of Product Management came to me and said, "As we're rolling out chatter -- rolling out chat, we're watching the user behavior in the logs because if a group of people go somewhere through the chat software and get stuck and then shell out, they log out of the system or whatever. We know we have a user interface problem." So they're actually using Splunk as a part of the product design process because the user flow is pretty easy to track. Okay? A really good customer of ours.

Why doesn't Intuit use Splunk? You heard about wire transfer earlier. That's pretty easy. It's all in the logs. All you have to do is say, "Splunk, go find any large attempted wire transfer that's coming from an IP address that looks a little suspicious or someone who has multiple failed logins and then a successful login and an attempted wire transfer." It's pretty easy to correlate the fields that are in a log and come up with an example that would be bad. You guys know, if you failed a login 2 or 3 times, you probably forgot your password. If you have a situation where there's 10 or 15 failed logins followed by a successful login and a quick attempt to complete a transaction, that's probably a bad thing. You don't need a highly sophisticated piece of software to figure that out. All you have to have is a few guys who can look at that data, learn it and then start to put up the queries in place to say, "Hey, Splunk, if these conditions happen, send me an alert." And Intuit has saved multiple 7-figure attempted wire transfer, fraud attempts with Splunk. It's pretty basic stuff.

Okay, Tesco. Love Tesco. So they're one of the largest online retailers in the U.K. They do something like 400,000 transactions per day and they're actually combining weather data with their online data that Splunk is analyzing so they'll know how that would impact shipments and what kinds of fresh goods they need to do. Because they actually deliver groceries and that sort of thing. Again, started with infrastructure monitoring, moved into app, moved into web, moved into customer analytics. It's just fantastic. They love Splunk so much that the Tesco champion flew over to Tokyo to present at our SplunkLive! in Tokyo. They're phenomenal.

Okay, MetroPCS. Call detail records. So here's another data source, call data records. It's all in the logs. So we came in to Metro, we were only in the IT infrastructure department. We had like 100 gig-license. I'm almost done. So like a little 100-gig license and they were overdoing a great, big data warehouse project over in the business side and our champion walks through and says, "Why don't you guys try that with Splunk?" They've been at it for a year and they're having trouble with the scale of cell phone logs and all that. So in about a month, we pulled up a multi-terabyte index and we ingest all their cell phone logs and all those servers and all that stuff that goes with it. And now they can use it to calculate cost per call, track the call through their tower systems, understand which area distributors in foreign countries are the most efficient distributors, call routing. You have to see what the cell phone providers see. Then you'd be more careful who to call.

So in summary, what we're doing is creating a new category of software. And it's -- we call it operational intelligence. It's kind of like a combination of lots of new types of data with a software solution that you can stand up very quickly and use almost immediately, applies to lots of different folks and we kind of call it operational intelligence. That's what you get out of it. And I don't know what it will ultimately be called. Maybe someday Gartner will come up with a quadrant for it. But we're very proud of our growing global customer base, now north of 5,000 customers. The results we have achieved are because of customer enthusiasm for what we do and that's kind of what inspires us. So thank you for your attention and we'll take questions.

Question-and-Answer Session

Raimo Lenschow - Barclays Capital, Research Division

Okay. Let me start with a couple and I'm sure, the audience will have some as well. Godfrey, as you lead Splunk and as you think about the opportunity, how do you balance that whole need to scale with kind of -- I mean, eventually, there's some profits that need to be [indiscernible], but how do you scale a business like that? Because the opportunities are everywhere, basically. What are the criteria for you?

Godfrey R. Sullivan

Well, we always think about people as the #1 objective. That's what makes Splunk what it is. And so hiring great people is going to be a basics and fundamentals answer that may not fit, but it really is. Last year, we went from just over 400 employees. Now, we have over 700. So hiring that many people in a year and having them sort of get it, you have to break down all the old habits and behaviors that if you work in an enterprise software, from all the stuff that takes years to deploy and customers are unhappy, so no, that's not how we do things here. We do things very differently at Splunk. So you have to do the blood transfusion with all the people you hire and make sure they know what makes -- our customers only value technical truth. They don't value marketing stuff. They love our T-shirts. They don't value fluff. So it's all about technical truth and it's all about that. So as we scale, we have a tailwind. I think of it as enterprise software with a tailwind where our customer evangelism and enthusiasm is so strong that what we really do is try to empower that. So we have -- in fact, you are all invited in your local neighborhoods, if we have a SplunkLive! event going on, we have usually 2 a week all around the world and we just invite the customers to come. So we can't know all these markets. It's just too much for us to know. So we really ask our customers to help us and they come to our SplunkLive! and present. So we say good morning, there's a coffee, here are the customers. And we let the customers talk to each other and it's amazing how much more quickly you can scale if you have a successful customer describing what they've done to another customer. And then it just grows like wildfire and we get out of the way. So one of the best ways to scale is not the try so hard.

Raimo Lenschow - Barclays Capital, Research Division

Yes. I mean, I have to say I went to the recent New York one and the scary thing was not the T-shirt but the capes that people had on with Splunk. Anyway just...

Godfrey R. Sullivan

We just a disclosed -- we did disclose one metric that's -- and I'll tell you about here, which is even although it's in our quiet period, we just disclosed it to a roomful of salespeople over there. So I assume it's no longer in the private domain and that is, 100,000. I'm going tell you, the number is 100,000. That's how many T-shirts we distributed last year. So you can take and write that down.

Raimo Lenschow - Barclays Capital, Research Division

Just briefly, on the -- as Dave is on the audience. I mean, one of the questions that I get asked from the investor base, and it's more -- nothing a -- it's on a current question, it's more a fundamental question about your businesses. At the moment, it's all about this data volume that you're injecting. And that's how you kind of think about the monetization, but if you're getting big customers like Barclays, I'm sure they are -- the debate about look look [ph] and do you really want to go -- I mean, I'm indexing probably 1% of the data I can. And do I really want to pay for the volume that goes up there? Can you kind of frame the kind of discussion you have with clients about it and how you're predicting it?

Godfrey R. Sullivan

Sure. So one of the beautiful things about Splunk is how cheap it is. So we don't charge for the number of users, we don't charge based on how much you store, we don't charge how much -- how many servers you have it running it on. We don't charge by all those traditional ways. What we charge base is how much you're actively indexing on a daily average. And so that's how our customers can download the free software off the web, use it to their heart's content. It's like driving your Ferrari in first gear. If you want to shift to second, you have to give me some money. But by then, you've already had a pretty good time. So that's how it works. And then when you want to get to fourth gear or fifth gear, now you're saying, "Jesus, Ferrari is pretty expensive." So what we do is we typically drive the volume per gig down as they get bigger. And then we're also doing a lot of enterprise-level agreements. So as customers go from that departmental to multi-departmental enterprise, then we typically change the conversations and say, "Okay, well, over the next 3 years, do any LA-type thing and whatever." We do that and you see that show up in our deferred. So it's -- a lot of the customers who grow to a level where they are ready to go enterprise scale, then we just change the contract and do some multi-year thing and we do that all the time.

Raimo Lenschow - Barclays Capital, Research Division

Yes. Before I'd open up -- like one last question for me here, it's like -- I mean, you've managed software companies for quite a while. With Hyperion you had a very good experience. Splunk is a different beast because of what it does and -- but also the growth rates that you're seeing out of there and that can potentially actually continue for growth for quite a long time. What are the big learning lessons -- the big learning experience that you had out of that as a CEO?

Godfrey R. Sullivan

Well, I don't know. I learn at Splunk everyday. I mean, that's -- it's -- I wasn't a founder. I give our founders just enormous credit for having thought through this very carefully and then we kind got of lucky. We kind of caught the wave of this data explosion. I guess if I've learned anything over all those years, it's really still about the team that you build. So I pay a lot of attention to filling out both my directs, the next level and next level. I really pay a lot of attention the organizational design and development because they're doing all the work. I'm here talking to you and somebody else is out building the product or marketing or selling it. So it's building the team that builds this thing that so exciting. On the wire this morning at 9:00, Fast Company just published their list of innovative companies and to our great surprise and delight, Splunk was listed #4 as the Fourth Most Innovative Company in the World and #1 Most Innovative in Big Data. And we don't even think about it that way. We just think about we have a job to do and we want to take these products to the market and make customers successful. So I'd say the one learning experience is you -- over the years, you try to do less yourself and you try to empower others more so that they can make better decisions, faster decisions and the like. So we run a very flat, open, decision-oriented team. I would rather make a mistake and correct it than not to make a decision. So...

Raimo Lenschow - Barclays Capital, Research Division

Yes, okay. Perfect. Questions from the audience? We have, like, time for probably one or two. All right, shy audience. I know now that I've warned everyone not to ask financial questions --

Godfrey R. Sullivan

They want to ask numbers.

Raimo Lenschow - Barclays Capital, Research Division

Yes. So...

Godfrey R. Sullivan

Get over it. Let go.

Raimo Lenschow - Barclays Capital, Research Division

Just maybe just one quick one for me. If you think about the areas you can go into, there's operational intelligence, but if I look -- think back to the Splunk event, there is kind of people looking at application management and infrastructure management and security, backlogs, is there anything that you would stop your team saying, "I don't want to go there?" Or how do you kind of manage that growth into different areas?

Godfrey R. Sullivan

Right. So this is the hardest thing about running Splunk, is deciding what not to do. When you have a very flexible technology that can be deployed very quickly, you find that customers come up with all kinds of great ideas about what to do with Splunk. But it's not repeatable for us. So we empower the customers to go forward with that and we support them, but we don't actually make a market of it. One of the ways we sort of test drive markets is with the content. So these apps and these templates that are up on Splunkbase, we'll throw a piece of content out there, almost all of our software or all of our content are apps and so far they're free. I think we charge for 2 or 3 of them and all the other hundreds of them are free. So we watch that. And as you watch customer traction with the template, you get a pretty good idea if that's a market that you actually want to pay attention to and put some more muscle behind. And we can actually follow a template and then decide, do we want that to become a full-blown app with a full life cycle management around product and go-to-market stuff and all that? So that's what we sort of do, is we -- we use -- it's a goat rotter, you throw the templates out there and let the customers improve on them and then you follow and get traction. It is hard but one of the ways of security is one way -- over time, we've evolved and we actually sell an app or 2 there in that segment. We have a virtual leader of that market. We have a virtual leader in the field of security. We, like, we call them the purple people, and you can see those all over the world in the Splunk org charts wherever they are, in development, in the field, product management, marketing. We have a virtual security team that is able to perform at a very high level in that way, towards that segment. So we kind of start with content and then follow its traction and then we ultimately, organizationally optimize for it.

Raimo Lenschow - Barclays Capital, Research Division

How do you -- can you just talk about how do you think about competition? Because it's like, it's a big exciting area. There's some -- have been some news that some startups got a lot of money of late. But what are you most worried about as a CEO for in terms, like, establishment that's trying to do something more in there, new guys coming out?

Godfrey R. Sullivan

Sure. There's so many good ways to do anything that I think about competition back to what is the customer trying to achieve? If we can help the customer achieve it in record time with great results, we'll probably get rewarded for that business. And we've learned over time what we really do well, which are those segments that I showed you on that block chart. So that's kind of how we think about it. We know who our competitors are in each of those segments. Generally, what we run into in competition is someone who's really good at that one block. So they're a security vendor and they do that thing really well, but they don't have an indexing engine that can -- the more data you put into it, the more value you get and it can scale out to where you can ask operational questions, business questions, analytic, whatever. So we tend to -- I mean, this is how it works with every customer. Your CIO friends will validate this. When we go out to compete, the guy who's up in the block will be arguing that they have more functionality than we do at that thing, and they're probably right. And we argue, you don't want to do that thing. You want to do this big thing, you want to get this big indexing engine where you can put lots of data into it so that you can ask more questions. So where they win, it's because the customer made a decision for the narrow-purpose, high-functionality point solution and where we win is when we are able to paint a broader vision about what you want to do with your data. That's how it works everyday.

Raimo Lenschow - Barclays Capital, Research Division

Yes, and if you think about the -- who would be, I mean, potentially coming up, if you think about could an IBM be that kind of develop something that looks like Splunk and beat. I mean, the thing for is more like can you just kind of highlight again for me, for the audience the importance of the platform because I think that seems to be like the platform and the applications are sitting on top of that, as kind of the sticky part.

Godfrey R. Sullivan

Right. Gosh, on a day-to-day basis, we think about sort of legacy vendors and we don't consider them competition because this is a new way to do it and customers seem to be validating that. As I look ahead, I think it will be more of a choice about how -- do customers want to spend years trying to build something or do they want to buy it? So if you look at enterprise software spend globally and over time, one of interesting things you'll see is that about half of all revenue spent on enterprise software is spent on custom built and about half is on purchased software. That's about how the data shakes out. And our job is to move people from wanting to manage their logs the old-fashioned way, the manual way or the -- something. And have them -- have an experience where Splunk can do it for them immediately and easy and fast and no headaches and also improve every other part of their systems. So I really look at long-term competition as the money spent on all the kits, all the tools, all the things you have to bolt together to get something to work and the amount of time it takes. And we have a lot of customers who start there. The little ones don't. They just -- they love the fact that they can just use Splunk and -- out-of-the-box and off they go. A lot of the bigger ones start and say, "Well, we can do this, we're -- we have thousands of developers and we're smart people and they go off and spend a couple of years trying to design something that does this and then we come back in and we replace that system. So there's still a lot of opportunity for us and the competition in my mind is more about market awareness and it's about customer success. The more customers we get in every category, it starts to become its own rolling thunder. So now I'm bullish about our aspirations in the future because we have so many customers who encourage us to that place so...

Raimo Lenschow - Barclays Capital, Research Division

Perfect. Hey, with that, Godfrey, thank you very much for joining us. I know it's a busy day for you. Thank you.

Godfrey R. Sullivan

Thanks, everyone. Appreciate the invitation.

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Source: Splunk, Inc. Presents at Barclays Second Annual Big Data Conference, Feb-11-2013 12:00 PM
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