 From Washington D.C., it's theCUBE, covering ScienceLogic Symposium 2019. Brought to you by ScienceLogic. Welcome back to theCUBE's coverage of ScienceLogic Symposium 2019. I'm Stu Miniman, we're here at the Ritz Carlton in Washington D.C., happy to welcome to the program two first-time guests from ScienceLogic. To my left is Leslie Minix-Wolf, who's the senior director of product marketing. And to her left is Russ Ellsner, who's the senior director of product strategy. Thank you so much for joining us. Thank you, Stu. Great to be here. All right, so Leslie, let's start with you. Talking a lot about the product, a whole lot of announcements. Big Ben, on the keynote this morning, everybody's in getting a little bit more of an injection in the keynote today. Tell us a little bit about your role, what you work on inside of ScienceLogic. Okay, so I am basically responsible for enterprise product marketing. So my job is to spin the story and help our sales guys successfully sell the product. All right, Russ? Part of the product strategy team. So I have product management responsibilities. I work a lot with the analytics and applications, and I spend a lot of time in the field with our customers. All right, so Leslie, let's start with enterprise. And the keynote this morning, the themes that I hear at many of the shows. We talk about things like digital transformation, but we know the only constant in our environment is change. And it's good. I've actually talked to a couple of your customers, and one of them this morning, he's like, look, most people don't like change. I do, I'm embracing it, I'm digging in. It's good, but we have arguments sometimes in analyst circles, and it's like, are customers moving any faster? And my peers that have been in the astronaut longer, they're like, hogwash stew. They never move faster and they don't want change, and we can't get them to move any things. I'm like, come on, if they don't, the alternative is often you're going to be, your competitors are going to take advantage of data and do things better. So bring us into a little bit of insight is what you're hearing from your customers, both here and in your day to day. Sure, yeah, I mean, change is constant now. And so one of the big challenges that our customers are facing is, how do I keep up with it? And the traditional manual processes that they've had in place for years are just not sufficient anymore. And so they're looking for ways to move faster, to automate some of the processes that they've been doing manually, to find ways to free up resources, to focus on things that do require a human to be involved, but they really need to have more automation in their day to day operations. So. All right, so Russ, when I look at this space, tooling, monitoring has been something that in my career, it's been a little bit messy. Yeah. I guess a little bit of an understatement even. It's an interesting, when I look at kind of that balance between what's having the infrastructure space and the application space. I went through one of your partners over here is from legacy to serverless and how many weeks and I'm like, oh, okay, that sounds good on a slide, but these things take a while. So bring us inside a little bit kind of the application space and how that marries with the underlying pieces and monitoring. Yeah, you have a lot of transformations happening. There's a lot of new technologies and trends happening. You hear about serverless or containers or microservices, and that does represent a part of the application world. There are applications being written with those technologies, but one of the things is those applications don't live in isolation. It's that they're part of broader business services and we're not rewriting everything, right? And so the new shiny application and the new framework has to work with the old legacy application. And so a big piece of what we see is how do we collapse those different silos of information? How do we merge that data into something meaningful? You can have the greatest Kubernetes based microservice application, but if it requires a SAP instance that's on-prem, on bare metal, those things need to work together. So how do you deal with an environment that's like that? An enterprise, just by its nature, is incredibly heterogeneous, lots of different technologies, and that's not going to change. It's going to be that way. You're preaching the choir here. IT, it always seems additive. The answer is always and, and unfortunately nothing ever dies. It's like, by the way, you want to run that wonderful Kubernetes, Docker stuff and everything? I can do it on a mainframe with Zlinux. So, you know, from that environment all to the latest, greatest hyper cloud environment. So talk a little bit about, you know, your customers, you know, most of them probably have, you know, hundreds of applications. They're working through that portfolio. What goes where? How do I manage all of those various pieces and, you know, not kill my staff? Yeah. Yeah. And so one of the things, you know, we spend a lot of time with this is that obviously we come from a background of infrastructure management. So we understand the different technologies, different layers and, you know, the heterogeneous nature. On top of that runs applications. And so they have their own data and there's a APM space. So we're seeing a lot of interest in the work we're doing right now with taking our view of the infrastructure and marrying it to the application view that we're getting from tools like AppDynamics or Dynatrace or New Relic. And so we're able to take that data and leverage it on top of the infrastructure to give you a single view, which aids in cause analysis and capacity planning and all the different things people want to do which lead us to automation. So this idea of merging data from lots of sources is a big theme for us. All right. So Leslie, who are some of the key constituents that you're talking to, to messaging to, you know, we, you know, in the industry we talked about silos for so many time and now it's like, oh, we're going to get to architects and generalists and, you know, cloud changes everything, yes and no. And, you know, we understand where budgets sit for most CIOs today. So bring us inside what you're seeing. Sure. Yeah. We're seeing a tremendous change. So where before we used to talk more to the infrastructure team, to the, you know, the folks managing the servers, the storage, the network, we're really seeing a broader audience and a multiple constituents. So we're looking at, you know, directors, VP, CIOs, CEOs, architects. We're starting to see more people. There are tools managers, folks that are involved in the application side of the house. So it's really diverged. And so you're not going in and talking to one person. You're talking to lots of different teams, lots of different organizations that need to work together. And to Russ's point about, you know, being able to bring all this data together as you bring it together, those different stakeholders have more visibility into each other's areas and they also have a better understanding of what the impact is when something goes out in the infrastructure, how it affects the app and vice versa. Yeah, Leslie, the other thing, I'm wondering if you can help me squint through. You know, when I looked at the landscape it's, you know, my ITSMs and I've got, you know, my logging, I've got all my various tools and silos and when I hear something like, you know, actually, you know, your CEO Dave said, oh, we just had a customer that replaced 50 tools with there. It's like, how do you target that? How does the customer know that they have a solution that you have a challenge that you fit? Because you understand, it can't be all things to all people, you know, you've got certain partners that might claim that kind of thing. But, you know, where you fit in the marketplace, how do you balance that? Well, so I think what we're seeing now is that there have been some big players for a long time, you know, what we refer to fondly as the big four and those companies really haven't evolved to the extent that they can support the latest technology certainly at the speed with which organizations are adopting them. So they might be able to support some of the legacy but they've really become so cumbersome, so complicated and difficult to maintain that people are wanting to move away from them. And I would say five years ago, most organizations weren't willing to move down that path but with some of the recent acquisitions, the Broadcom acquisition, micro-focus acquisition, you're seeing that more organizations are looking to replace those tools in and their entirety. And as a result of that, they're looking at how can I minimize my tool set? How do I, I'm not going to get rid of everything and only have one vendor, but how do I pick the right tools and bring them together? And this is one of the areas where we do extremely well in that we can bring in data, we can integrate with other tools, we can give you the full picture but we're kind of that hub, that central and I think we heard that earlier today from Bailey at Cisco where he talked about science logic is really the core to their monitoring and management environment because we're bringing the data and we're feeding the data into other systems as well as managing it within science logic. Yeah, Russi, I actually heard data was emphasized more than I expect. I know enough about the management and monitoring space. We understand data was important to that. I mean, I'm a networking guy by background. We've been talking about leveraging the data for network and using some automation and things like that but it's a little bit different. Can you talk to them about those relationships to data and we understand data's going to be everywhere and customers actually wrapping my arms around it, make sure I can manage it, compliance and hopefully get value out of that is one of the most important things in today. Absolutely, so one of the things we stress a lot when we talk about data, it used to be that data was hard to come by. We were data poor and so how do we get, we don't have a probe there, how do we get this data to an agent? That's different now. Data is we are drowning in data, we have so much data so really the key is to give that data context and so for us that means a lot of structure and topology and dependencies across the layers of abstraction, across the application and we think that's really the key to taking this just vast unstructured mess of data that isn't useful to the business and actually be able to take, apply analytics and actually take action and ultimately drive automation by learning and maintaining that structure in real time automatically. Is it something a human can't do so you need machine help, you need to automate that. All right, so Leslie, there was in the keynote this morning that discussion of the AIOps maturity model. And one of the things struck me is, there was not a single person in the poll that said, yes, I've gone fully automated. And first there's the maturity, the technology, the term and where we are. But there's also that, let's put it on the table. That fear sometimes is to, oh my gosh, the machines are taking our jobs, we laugh but it is something that needs to be addressed. How are you addressing that? Where are your customers with, at least that willingness? Because I used to run operations for a number of years and I told my team, look, you're going to have more work next year and you're going to have more things changed. So if you can't simplify, automate, get rid of things, I've got to have somebody helping me and boy those robots would be a good help there, so yeah. Yeah, well what we're seeing is, I mean, let's be real, like people don't like to do the mundane tasks, right? So you think about like, do you really, when you report an issue to the service desk, do you really want to open that ticket? Do you want to enter in all that information yourself? Do you want to provide all the details that they need in order to help you? No, people don't do it. They put in the bare minimum and then what ends up happening is there's this back and forth as they try to collect more information. It's things like that that you want to automate. You want to be able to take that burden off of the individuals and do the things or at least allow them to do the things that they really need to do, the things that require their intelligence. So we can do things like clean up storage disk space when you're starting to run out of disk space or we can restart a service or we might apply a configuration change that we know that is inconsistent in environment. So there's lots of things like that that you can automate without actually replacing the individual. You're just freeing them up to do more high-level thinking. You know, Russ, anything else along the automation lines? Great customer examples or successes that you've seen that are worth sharing? Yeah, automation also comes in the form of connecting the breadcrumbs. So we have a great example that what customer we worked with where they had an APM tool, one of the great ones, top of the magic quadrant kind of thing and it kept on reporting code problems. The application is going down, affecting revenue, huge visibility and it's saying, code problem, code problem, code problem but the problem is jumping around. Sometimes it's here, sometimes it's there so it seemed like a ghost. So when we connected that data, the APM data with the vCenter data and the network data what it turned out was there was a packet loss in the hypervisor and so it was actually network outage that was manifesting itself as a code problem and as soon as they said that they said, oh, what's causing that network problem? They immediately found the big spike of traffic and were able to solve it. They always had the data. They had the network data, they had the VMware data, they had the JVM data, they didn't know to connect the dots and so by us putting it right next to each other, we connected the dots and it was a human ultimately that said, I know what's wrong, I can fix that but it took them 30 seconds to solve a problem that they'd been chasing after for months and that's a form of automation too is get the data to the human so that they can make a smart decision. That's automation just as much as rebooting a server or cleaning a desk. Well yeah, right, if the Hitchhackers guide to the galaxy sometimes the answers are easy if I know what question to ask, right? And that's something we've seen from data scientists too that that's what their expertise is to help find that. All right, Leslie, it gives a little view for we heard a little bit, you know, so many integrations, the AI ops journey, what should customers be looking for forward? What are they asking you to help bring them along that journey? Oh gosh, they're asking us to make it easier on all counts, like whether it's easier to collect the data, easier to add the context to the data, easier to analyze the data. So we're putting more and more analytics into our platform so that they're not having to do a lot of the analysis themselves. And you know, there's, as you said earlier, there's the folks that are afraid that they're going to lose their job because the robots or the machines are taking over. That's not really where I see it. It's just that we're bringing the automation in ways and the analytics in ways that they don't want to have to do so that they can look at it and solve the really gnarly problems and start focusing on areas that are not necessarily going to be automatable or predictable. It's the things that are unusual that they're going to have to get involved in as opposed to the things that are traditional and constant. Yeah, so Russ, I'd love for you to comment kind of the same question and just a little bit of feedback I got talking to some of the customers is they like directionally where it's going, but the term they threw out was dynamic because if you talk about cloud, talk about containers, down the road things like serverless, it's if it pulls every five minutes, it's probably out of date. So I remember back when we talked big data, real time was one of those misnomers that got thrown out there and really what we always said is what real time needs to mean is the data in the right place to the right people to solve the issue. So where do you guys see this directionally and how do you get more dynamic? Well, yeah, dynamic exists in a bunch of different ways. How immediate is the data? How accurate is the dependency map? And that's changing and shifting all the time. And so we have to keep at up to date automatically in our product. It's also the analytics that get applied, the recommendations you make. And one of the things, you can talk to data scientists and they can build a model, train a model, test a model and find something. But if they find something that was true three weeks ago, it's irrelevant. So we need to build systems that can do this in real time that they can in real time, meaning gather data in real time, understand the context in real time, recognize the behavior and make a recommendation or take an action. There's a lot of stuff that we have to do to get there. We have a lot of the pieces in place. It's a really cool time in the industry right now because we have the tools, we have the technology and it's a need that needs to be filled and that's really where we're spending our energy is completing that loop, closed loop system that can help humans do their job better in a more automated way. Awesome. Well, Leslie and Russ, thanks so much for sharing your visibility into what customers are doing and the progress with your platforms. All right, thank you, Stu. Great, great talking. And we'll be back with more coverage here from ScienceLogic Symposium 2019. I'm Stu Miniman and thank you for watching theCUBE.