 Live from Orlando, Florida, it's theCUBE. Covering.conf18, brought to you by Splunk. Welcome back to Orlando, everybody. I'm Dave Vellante with my co-host Stu Miniman. You're watching theCUBE, the leader in live tech coverage. We're brought here by Splunk. This is SplunkConf18, hashtag SplunkConf18. Susan St. Ledger is here. She's the president of World Wide Field Operations at Splunk. Susan, thanks for coming on theCUBE. Thanks so much for having me, Dave. So you're welcome. So we've been reporting, actually this is our seventh year. We've been watching the evolution of Splunk going from sort of hardcore IT ops, sec ops, now really evolving. In doing some of the things that, when everybody talked about big data back in the day, and Splunk really didn't, they talked about doing all these things that actually they're using Splunk for now. So it's interesting to see that this has been a big tailwind for you guys. But anyway, big week for you guys. How do you feel? I feel incredible. We've announced more innovations today, just today, than we have probably in the last three years combined. We have another big set of innovations to announce tomorrow. And just as an indicator of that, I think you heard Tim today, our CTO say on stage, we to date have 282 patents, and we are one of the world leaders in terms of the number of patents that we have. And we have 500 pending. So if you think about 282 since the inception of the company and 500 pending, it's a pretty exciting time for Splunk. Yeah, people talk about that flywheel. We were talking, Stu and I were talking earlier about some of the financial metrics. And you know, you have a lot of large deals, seven figure deals, which you guys pointed out on your call, that's the outcome of having happy customers. It's not like you're trying to engineer that. You're just serving customers, and that's what they do. But talk about how Splunk Next is really bringing you into new areas. Yeah, so Splunk Next is so exciting. There's really three major pillars, if you will, design principles to Splunk Next. One is to help our customers access data wherever it lives. Another one is to get actionable outcomes from the data. And the third one is to allow, unleash the power of Splunk to more users. So those are really the three pillars. And if you think about maybe how we got there, we have all of these people within IT and security that are the experts on Splunk, the Splunk Ninjas, if you will. And they see the power of Splunk and how it can help all these other departments. And so they're being pulled in to help those other departments. And they're basically saying, Splunk, help us help our business partners, make it easier to get there, to help them. Unleash the power of Splunk for them so they don't necessarily knit us for all of their needs. And so that's really what Splunk Next is all about. It's about making, again, access data easier, actionable outcomes, and then more users. And so we're really excited about it. So talk about those new users. I mean, obviously the IT ops, they're your peeps. Yeah. So are they sort of advocating to you into the line of business? Or are you being dragged into the line of business? What's that dynamic like? Yeah, it's definitely, we're customer success first. And we're listening to our customers and they're asking us to take them, to go there with them, right? They're being pulled, they know that what we say with our customers, what our deepest customers understand about us is everybody needs Splunk. It's just not everyone knows it yet. And so they're teaching their business why they need it. And so it's really a powerful thing. And so we're partnering with them to say, how do we help them create business applications, more which you'll see tomorrow in our announcements, to help their business users. You know, one of the things that strikes us, we were talking, it was the DevOps gentlemen. When you look at companies that are successful with so-called digital transformation, they have data at the core. And they have sort of like a, I don't want to say a single data model, but it's not a data model of stovepipes. And that's what he described. And essentially, if I understand the power of Splunk, just in talking to some of your customers, it's really that singular data model that everybody can collaborate on with, get advice from each other across the organization. So not this sort of stovepipe model. It seems like a fundamental linchpin of digital transformation, even though you guys haven't been using that, overusing that term, thank you for that. That's sort of a sign of Splunk. You didn't use the big data term when big data was all hot, now you use it. Same thing with digital transformation. You're fundamental, it would seem to me, to a lot of companies' digital transformation. That's exactly what we are, Dave. If you think about, we started at 19 security, but the reason for that is they were the first ones to truly do digital transformation, right? Those are just the two organizations that started. But exactly the way that they did it, now all the other business units are trying to do it. And that same exact platform, that same exact platform that we use, there's no reason we can't use it for those other areas, those other functions. But if we want to go there faster, we have to make it easier to use Splunk, and that's what you're seeing with Splunknext. I look at my career, the last couple of decades, we've been talking about, oh well, we're going to leverage data, and we want to be predictive on the models, but the latest wave of AI, ML, and deep learning, what I heard and what you're talking about in the Splunknext, maybe you could talk a little bit about why it's real now, and why we're actually going to be able to do more with our data to be able to extract the value out of it and really enable businesses. Sure, so I think machine learning is at the heart of it, and we actually do two things from a machine learning perspective. Number one is within each of our market groups, so IT security, IT operations, we have data scientists that work to build models within our applications, so we build our own models, and then we're hugely transparent with our customers about what those models are so they can tweak them if they like, but we pre-build those so that they have them in each of those applications, so that's number one, and that's part of the actionable outcomes, right? ML helps drive actionable outcomes so much faster. The second aspect is the MLTK, right, which is we give our customers an MLTK so they can build their own algorithms and leverage all of the models that are out there as well. So I think that two-fold approach really helps us accelerate the insights that we give to our customers. Susan, how are you evolving your go-to-market model as you think about Splunknext and just think about more line-of-business interactions? What are you doing on the go-to-market side? Yeah, so the go-to-market, when you think about reaching all of those other verticals, if you will, right, it's very much going to be about the ecosystem, right? So it's going to be about the solution provider ecosystem, about the ISV ecosystem, about the big SIs, both Boutique and the global SIs, to help us really drive Splunknext into all of the verticals and meet their needs, and so that will be one of the big things that you see. We will obviously still have our horizontal focus across IT and security, but we are really understanding what are the use cases within financial services? What are the use cases within healthcare that can be repeated thousands of times? And if you saw some of the announcements today, in particular, the data stream processor, which allows you to act on data in motion with millisecond response, that now puts you as close to real time as anything we've ever seen in the data landscape, and that's going to open up just a series of use cases that nobody ever thought of using Splunknext for. Yeah, so I wonder what you're hearing from customers when they talk about how do they manage that pace of change out there. I really like, I walked around the show floor stuff, been hearing lots of people talking about containers, and we had one of your customers talking about how Kubernetes fits into what they're doing. Seems like it really is a sweet spot for Splunknext that you can deal with all of these different types of information, and it makes it even more important for customers to come to you. Yeah, as you heard from Doug today in our keynote, our CEO in the keynote, it is a messy world, right? And part of the mess is just because it's a digital explosion, and it's not going to get any slower, it's just going to continue that faster. And I know you met with some of our customers earlier today, NIF and Carnival. If you think about the landscape of NIF, right? I mean, their mission is to protect the arsenal of nuclear weapons for the country, right? To make them more efficient, to make them safer. And if you think about all of that, they not only have traditional IT operations and security they have to worry about, but they have this landscape of lasers and all these sensors everywhere. And when you look at that, that's the messy data landscape, and I think that's where Splunknext is so uniquely positioned because of our approach. You can operate on data in motion or at rest, and because there is no structuring up front. I want to come back to what you said about real time. Because I've said this now for a couple of years, Splunknext never used to use the term big data. When big data was at its peak of what is a gardener call it, the hype cycle. You guys didn't use that term. And so when you think about the use cases, and in the big data world, you've been hearing about real time forever. Now you're talking about it. Enterprise data warehouse, cheaper EDWs, fraud detection, better analytics for the line of business, obviously security and IT ops. These are some of the use cases that we used to hear about in big data. You're doing like all of these now, and sort of your platform can be used in all of these sort of traditional big data use cases. Am I understanding that properly? You're 100% understanding it properly. Splunknext has, again, really evolved. And as you think about, again, some of the announcements today, think about data fabric search, right? Rather than saying you have to put everything into one instance or everything into one place, right? We're saying we will let you operate across your entire landscape and do your searches at scale. And Splunknext was already the fastest at searching across your global enterprise to start with. And we were two to three times faster than anybody who competed with us. And now we improve that today by 1400%. I don't even know where. Like you just look at, again, it ties back to the innovations and what's being done in our developer community within our engineering team. And those traditional use cases that I talked about in Big It, it was kind of an open source mess. Really complex, zookeeper is the big joke, right? And always, you know, hive and pig and, you know, H base and blah, blah, blah. And we're practitioners of a lot of that stuff. It's very complex. Essentially, you've got a platform that now can be used the same platform that you're using in your traditional base that you're bringing to the line of business. Correct? Exactly right. It's the same exact platform. We are definitely putting the power of Splunk in the user's hands, though, by doing things like mobile. You saw mobile in AR today. And again, I wish I could talk about what's coming tomorrow, but let's just say our business users are going to be pretty blown away by what they're going to see tomorrow in our announcements. Yeah, so I mean, I'm presuming these are modern. It's modern software, microservices, API base. So if I want to bring in those open source tools, tools I can. In fact, what you'll actually see when you understand more about the architecture is, we're actually leveraging a lot of open source in what we do. So capabilities of Spark and Flink. But what we're doing is we're masking the complexity of those from the user. So instead of you having to do your own Spark environment and your own Flink environment and having to figure out Kafka on your own and how you subscribe to it, we're giving you all that. We're masking all that for you and giving you the power of leveraging those tools. So this becomes increasingly important, in my opinion, especially as you start bringing in things like AI and machine learning and deep learning because that's going to be adopted both within a platform like yours, but outside as well. So you have to be able to bring in innovations from others, but at the same time to simplify it and reduce that complexity, you've got to infuse AI into your own platform and that's exactly what you're doing. That's exactly what we're doing. It's in our platform, it's in our applications, and then we provide the toolkit, the SDK, if you will, so the user's going to take it to another level. All right, so you've got 16,000 customers today. If I understand the vision of Spark Next, you're looking to get an order of magnitude more customers than you have as a dressed on market. Talk to us about the changes that need to happen in the field. Is it just you're hitting an inflection point and you've got those evangelists out there and I see the capes and the pheasers all over the show, so how does your field get ready to reach that broader audience? Yeah, I think that's a great question. Again, once again, it will, I'll tell you what we're doing internally, but it's also about the ecosystem, right? In order to go broader, it has to be about this Splunk ecosystem. And on the technology side, we're opening the aperture, right? It's microservices, it's APIs, it's cloud, there's so much available for that ecosystem. And then from a go-to-market perspective, it's really about understanding where the use cases are that can be repeated thousands of times, right? The big problems that each of those verticals are trying to solve as opposed to the one-corny use case that you could solve for one customer. And that was actually one of the things we found is when we did analysis, we used to do case studies on big data. Number one use case that always came back was custom, because nothing was repeatable. And now we're seeing a little bit more industry-specific issues. I was at Microsoft at night last week, and Microsoft is going deep on verticals to get specific as to, for IoT and AI, how they can get specific in those environments. I agree. I think again, one of the things that's so unique about Splunk platform is because it is the same platform that's at the underlying aspect that serves all of those use cases. We have the ability, in my opinion, to do it in a way that's far less custom than anybody else. So we've seen the ecosystem evolve as well. Again, six, seven years ago, it was kind of a tiny technology ecosystem. And last year in DC, we saw it really starting to expand. Now you walk around here, you see some big booths from some of the SI partners. That's critical, because that's global scale, deep, deep industry expertise, but also board level relationships. So that's another part of the go-to-markets. Splunk becomes more strategic. There's a massive TAM expansion that potentially that we're witnessing with Splunk. How do you see those conversations changing? Are you personally involved in more of those boardroom discussions? Definitely personally involved in your spot on to say that that's what's happening. And I think a perfect example is you talked to Carnival today, right? We didn't typically have a lot of CEOs at the Splunk conference, right? Now we have CEOs coming to the Splunk conference, right? Because it is at that level of strategic to our customers. And so when you think about Carnival, and yes, they're using it for the traditional IT ops and security use cases, but they're also using it for their customer experience. And who would ever think, you know, 10 years ago or even five years ago of Splunk as a customer experience platform, but really what's at the heart of customer experience, it's data. So speaking of the CEO of Carnival, Arnold Donald, it's kind of an interesting name. Yes. And so he stood up on the stage today talking about diversity and doubling down on diversity. He's an African-American. You know, frankly in our industry, you don't see a lot of African-Americans CEOs. You don't see a ton of women CEOs. You don't see a ton of women with president in their title. So he made a really kind of interesting statement where he said something to the effect of 40 years ago when I started in the business, I didn't work with a lot of people like me. And I thought that was a very powerful statement. And he also said, essentially, look it, if we're diverse, we're going to beat you every time. Your thoughts as an executive in tech and a woman in tech? So first of all, I 100% agree with him and I can actually go back to my start. I was a computer scientist at NSA, so I didn't see a lot of people who looked like me. And so from that perspective, I know exactly where he's coming from. And I'll tell you at Splunk, we have a huge investment in diversity and not because it's a checkbox, but because we believe in exactly what he says. It's a competitive edge. When you get people who think differently, because you came from a different background, because you're a different ethnicity, because you were educated differently, whatever it is, whether it's gender, whether it's ethnicity, whether it's just a different approach to thinking, all differentiation puts a different lens and that way you don't have stovepipe thinking. And what I love about our culture at Splunk is that we call it a high growth mindset and if you're not intellectually curious and you don't want to think beyond the boundaries, then it's probably not a good fit for you. And a big part of that is having a diverse environment. We do a lot at Splunk to drive that. We actually posted our gender diversity statistics last year because we believe if you don't measure it, you're never going to improve it. And it was a big step, right? To say we want to publish it, we want to hold ourself accountable and we've done a really nice job of moving it a little over 1% in one year, which for our population is pretty big. But we're doing really unique things. Like we have, all job descriptions are now analyzed. There's actually scientific analysis that can be done to make sure that the job description does not bias whether men or women, whether men alone or whether it's gender neutral. So that's exciting. Obviously, we have a big women in technology program and we have a high potential focus on our top women as well. What's interesting about your story, Susan, and we spent a lot of time on theCUBE talking about diversity generally and women in tech specifically. We support a lot of WIT events. And we always talk and frequently we're talking about women in engineering roles or computer science roles and how they oftentimes, even when they graduate with that degree, they don't come into tech. And what strikes me about your path is you're technical and yet now you've become this business executive. So, and I would imagine that having that background, that technical background only helped in terms of, especially in this industry. So there are paths beyond just the technical role. 100%. First of all, it's a huge advantage. I believe it's the core reason why I am where I am today because I have the technical aptitude. And while I enjoy the business side of it as much and I love the sales side and the marketing side and all of the above, the truth of the matter is at my core, I think it's that intellectual curiosity that came out of my technical background that kept me going and really made me very, I took risks, right? And if you look at my career, it's much more of a jungle gym than a ladder. And the way, you know, I always give advice to young people who, generally it's young women who ask, but sometimes it's the young men as well, which is like, how did you get to where you are? How do I plan that? How do I get? The other part of the matter is you can't, if you try and plan it, it's probably not going to work out the exact way that you plan. And so my advice is to make sure that you, every time you're going to make a move, you ask yourself, what am I going to learn? Who am I going to learn from? And what is it going to add to my experience that I can materially, you know, say is going to help me on a path to where I ultimately want to be. But I think if you try and figure it out and plan a perfect ladder, I also think that when you try and do a ladder, you don't have what I call pivots, which is looking at things from different lenses, right? So me having been on the engineering side, on the sales side, on the services side of things, it gives me a different lens in understanding the entire experience of our customers, as well as the internals of an organization. And I think that people who pivot, generally are people who are intellectually curious and have intellectual capacity to learn new things. And that's what I look for when I hire people. I love that, you took a non-linear progression to the path that you're in now. And speaking of the technical depth, I think if you're in this business, you better like tech, or what are you doing in this business. But the more you understand technology, the more you can connect the dots between how technology is impacting business and then how it can be applied in new ways. So, well, congratulations on your career. You got a long way to go. And thanks so much for coming on theCUBE. Thank you so much, David Steele, really appreciate it. It was a pleasure. Thank you. Okay, keep it right there, but Stu and I will be back with our next guest. We're live from Splunk.COP.COP 18. You're watching theCUBE.