(upbeat music) >> From Washington, D.C., it's theCUBE, covering ScienceLogic Symposium 2019. Brought to you by ScienceLogic. >> Hi, I'm Stu Miniman, and you're watching theCUBE's exclusive coverage of ScienceLogic Symposium 2019 here at the Ritz Carlton in Washington, D.C. Happy to welcome to the program first-time guest but a long-time customer of ScienceLogic, Karl Fosburg, who's the senior director of systems integration at Hughes. Thanks so much for joining us. >> Thanks for having me. >> Alright, so we're here in D.C., and that's important 'cause first of all, you're based down here, and ScienceLogic is based down here. >> Yup. Bring us back a little bit. You said you'd been a customer a long time as to... maybe give us a little bit of the before picture, if you could. >> Sure, so yeah, we've been a customer for 12 years now, and we picked ScienceLogic for a big list of reasons, actually wrote the RFI itself, and probably 20 pages long. Lots of people came back and gave us responses. ScienceLogic was one of the short-listed candidates that we picked out. We did a bake-off with a couple other vendors, and ScienceLogic was the clear winner. >> All right. So Karl, lets zoom out for a second here >> Okay. and just give us a level set on Hughes, what Hughes is today. You know, I'm familiar with what Hughes was back in the day and there's certain pieces that are no longer there so give us a level set on the company in the business. >> Yeah, sure. So, Hughes is formally known as Hughes Network Systems, were owned by EchoStar Corporation and we're a managed service provider. We have a consumer business where we provide broadband internet to folks that live really out in the countryside and can't get cable, or DSL, or FIOS, things like that. We have about 1.4 million subscribers in our consumer business. We've also launched consumer services in South America, Brazil, Ecuador, Columbia, places like that. Really serving under-served areas for giving them broadband. We also have an enterprise business where we sell to credit card processing, gas and oil, pipelines, fast foods, places like that. >> Okay. So Karl, is it safe to say you use satellites but no longer put them into space? >> We use satellites, that's correct. We contract that out now. Yeah, we are the last remaining Hughes company. >> Yeah. So, service providers are always fascinating to me because we talk about enterprise IT and how fast things are changing. At least for my entire career, when I talk to service providers, change and growth is really just baked into the DNA. >> Yep. I need to move fast. When you talk about scale, it means something very different and living in that complex world, and just give us a little bit about what things are like in 2019 for you. >> Sure, yeah. The scale is always our challenge. Like I like to say, we have sales people too and they're out there selling new products and services constantly. So we needed to be able to grow with those sales. We started out with a couple thousand devices that needed monitor in applications. Now we're up to almost 30 thousand Nox systems that we monitor. Also, we're keeping track of nearly 2 million terminals and the status of them and things like that. So, yeah, scale is super important to us. >> Okay. So, bring us inside, where ScienceLogic fits into your equation. >> Sure. So when we put out our FI to industry years ago, we were trying to replace a whole bunch of different tools. We had other vendor products and things like that. We really wanted to consolidate tools as much as possible into a single platform. Traditional ICNP, SNMP monitoring is how we originally started. Now we have lots and lots of integration with other tools, APM products, different streaming media products. We're integrating more and more with streaming services now in terms of getting data into the platform. So, yeah ... >> Yeah. Karl, I'd love to get your viewpoint. Something that came through to me in the keynote is on the one hand the years like, oh, well AIOps is going to replace things like some of the traditional players here, but then you see onto the stage it's like, oh okay, we're actually going to have integrations with a number of these tools. So yes, there's overlap but it needs to be integrated. How do you look at that as, is this the primary product? Is this a piece of the product? How do data collection between all these various tools go together? Well, that's a great question 'cause that's exactly what we and lots of other folks are grappling with right now. We've got data producers all over the place now, and we're really focused on the data production and high quality data back at the source into a real pub-sub type of architecture of which we believe that ScienceLogic will be both a producer and consumer of that pub-sub architecture, and whether it's the one tool to rule them all or not? Probably not, no ones going to be that, and we've got lots of vendors that purport to be the one tool to rule them all. But really, we're focused on ScienceLogic at this point to be really the focus, especially for our operations folks. We've got 24/7 staff. They use ScienceLogic as their main tool that they go to. So that's really where we want the data to end. That's where we want as much intelligence to end as possible. >> So, I'd be curious... You've been using the tool for a dozen years now. 12 years ago the discussion of data wasn't no where near what it was today. >> Correct. Can you bring us through a little bit of that journey, and you mentioned data a bunch, but how important is that? Where are you in your journey for... There was that maturity model that was put up there, the role of data today, and where do you see it going? >> Well, data is everything today. 12 years ago we were grappling with things like naming conventions and simple firewall rules and whatnot. Those days are long, long past. Now, the data quality and the pipeline is what we're focused on right now 'cause like Dave said in the keynote, "Garbage in, garbage out". We're really really focused on trying to get good quality data by focusing on the source of the data. As opposed to fixing it after it's been moved into whatever platform it ends up in. So we're using proper scheme of management and trying to bake-day the governance into the actual engineered products, and if it's not governed data then you don't get to look at it. And that's really our focus. We're an engineering company at heart so we actually write most of our own software. So we're kind of in control of our own destiny there, and we're really focused on pushing that back because we think the benefits in the long run are going to be worth that investment to get clean data all the way back to the source. >> Yeah. So Karl, one of the big shifts I've seen in the last few years... When you talked about managing and monitoring, I used to as the administrator or controller, used to be able to go and touch all of those pieces. Today, there's more and more some of those pieces I need to manage not just the stuff that's in my environment or my hosted environment, but outside of my environment and doing public clouds. >> Yep. >> Bring us up to speed as to where does Cloud fit? What's your Cloud strategy? >> Sure. We're actually launching some of our first applications in GCP right now. So we're working with our Google partners in this particular case to integrate the data that they can collect natively in their systems, bring it back in as actionable events into ScienceLogic platform, while keeping the vast majority of the data native to their platform. No need to bring back application specific data unless we're actually going to do something with it, or if we need to cross-correlate it with other information. The data sources live in our data centers, not in GCP. So we need to combine it with information we know about, our on-prem equipment, plus the applications running there. So that's the data we'll bring back to cross correlate. >> How do you decide what lives where, and where does ScienceLogic fit in the whole discussion? >> Yeah, that's a good question. What lives where... We kind of go back to license models and cost models. We're pretty good sticklers about focus on doing proper upfront analysis to make sure we don't end up with some six or seven figure bill at the end of the year from a Cloud provider. We also tend to do a lot of stuff on-prem because a lot of our systems have to run in one of our data centers. If you've ever driven past our building you'll see these large large dish's antennas outside. A lot of our equipment has to be within milliseconds or microseconds even of those dishes. So we actually have a large data center presence kind of scattered around the country and around the world. So, we have the compute resources to do it ourselves. >> Yeah, and even I would think edge computing something that plays into what you're doing. What do you see as some of the main challenges as the kind of footprint for what you're doing and things to spread out more? >> Yeah. Keeping, let's say pet projects, and shadow IP projects, keeping them in check is a really big focus right now, and also with DevOps sort of the "I'll do everything, I'm going to be my own IP department" philosophy is a new challenge that we're facing. So integrating with what the DevOps guys are building into our overall monitoring strategy, that's when a new challenge has really creeped up or it last, lets say six months or a year. >> Okay. Is there an intersection between your use of the ScienceLogic in the DevOps team yet? >> Not a big one yet. I think we're still learning DevOps at this point. I consider it a lifestyle change, not really a thing that you go get. So, I think we're still kind of early adoption for DevOps, and really only greenfield projects at this point in time. >> Okay. How about the term of the show is AIOps, so what's your act in the AIOps? Where do things like machine learning and automation fit into your environment? >> Yeah. We actually have quite a few used cases where we really think that machine learning is going to help us a lot. Cross-correlation is a big area for us. We have lots of information, but figuring it out, feeding like the APMs and Cisco ACI software defined networking, and those bits of information all into one product, we've been challenging ScienceLogic on this for quite a while. It's like, okay, you guys know about everything now. Tell us something that we didn't know before, and that's kind of where we're at, and seeing the announcements from this morning was really encouraging that we're finally see the horizon at this point. >> Yeah. If you can, (mumble), but how has ScienceLogic been doing on the roadmap? What helps between ScienceLogic and your vendor ecosystem out there? What more could they be doing to make your life easier? >> Yeah, that's a good question. So, if you would ask me that a year ago I probably wouldn't have been as encouraged as I am today. It was a challenge and they're engineering company, we're an engineering company. Sometimes you have to focus on foundation, and it's not cool, it's not sexy, it's not shiny, but you have to do it. And I think they've been focused a lot on their foundational aspects of the product which will actually enable doing things like machine learning. There's no point in doing machine learning if you have bad data or if you have a platform that doesn't support very very fast queries, and the graph QL database. We think that we're going to use that extensively and through the API, not even through the UIs. So, I think foundation is important. I think they focused on it for the last couple of years. I think we're finally going to start to see the benefits of it. Both single factor sort of machine learning, anomaly detection, but we really want to see it on a cross domain. I want to be able to see in ScienceLogic impacted by in our full stack environment. >> Yeah. I'd expect you probably had some visibility into what was coming up in the Big Ben release. Is there anything that jumped out at you, or that you're ready to use day one? >> The automations, for sure. We'll use that definitely day one. The way they've gone through and really made it a lot easier to use. You don't have to be a python developer anymore to actually get a lot of benefits out of the product. So I can turn that over to some of our junior engineers to actually handle those things, and we get a lot more sophisticated with them now. Primarily we used to focus on, "oh, let's send an email" type of thing. Now we can actually execute back-end actions without having to have a programmer to do it. So that right away we're going to use out of box. >> Okay. And in that forward looking piece, without breaking any visibility you have into their roadmap, what would you like to see more? >> I'd like to see more getting performance data into their real scalable, laterally scalable back end. And that's certainly an area that I'd love to see as much progress as fast as possible on. Also the Pub-Sub subscribing to streams coming out of our Kafka cluster. We want that to be in the product as soon as possible 'cause we really believe that that's where the majority of our data of the future is going to come from. Also, new applications, they come and go. Docker containers spin up, spin down. So the state of something is no longer fixed and we need to be able to integrate with Kubernetes and our open shift platform to be able to know, "Well what should be running right now?" So, those are the things that are on our roadmap that we need out of the product as soon as possible. >> Yeah. So it definitely came to me that ScienceLogic's listening. Are they moving fast enough for you? >> No. No ones ever moved fast enough. So, yeah, they're moving so that's good, but yeah, I could use it today if they had it. >> All right. Karl, last thing, you've been to a few of the ScienceLogic events in the past. You've been to other industry shows, what's special about the show? What brings you and your team to ScienceLogic symposiums? >> One of the things that ScienceLogic does a really good job is they bring a lot of resources here, and actual resources that actually know stuff. It's not just telling me, "Oh, that shiny new object is going to be in the platform at some indeterminate time in the future." It's the actual engineers, people writing code, product managers, things like that. So having access directly to the people who actually do the platform updates and changes is super valuable. The new sensor where we can touch and feel, take attires on new things has been excellent this year. So I think that's probably the thing, just quick access to all the resources. We have a bit of an advantage, we're only 45 minutes up the road. We can come down here as need be to visit their headquarters but having everyone here at one time is great. >> All right. Well Karl Forsberb, really appreciate you sharing your history and experience in future direction as to where things are going on your end. >> All right. >> I'm Stu Miniman. We'll be back with lots more coverage here from ScienceLogic 2019. Thanks for watching theCube. (upbeat music)

(energetic music) >> 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, and 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 Minnix-Wolfe, who is the Senior Director of Product Marketing. And to her left, is Russ Elsner, who's the Senior Director of Product Strategy. Thank you so much for joining us. >> Thank you sir. >> Good, good to be here. >> All right, so Leslie let's start with you. Talk 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 injection in the keynote today. Tell us a little bit about your roll, 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, and Russ. >> I'm 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, the keynote this morning. The themes that I hear at many of the shows, you know we talk about things like digital transformation. But, we know the only constant in our environment is change. You know, 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, you know, we have arguments sometimes in analyst circles. And it's like are customers moving any faster. My peers that have been in the industry longer, they're like, Hogwash Stu. They never move faster they don't want change, we can't get them to move anything. I'm like, come on, if they don't the alternative is often, You're going to be... You know, you're competitors are going to take advantage of data and do things better. So, bring us a little bit of insight as what you're hearing from your customers both here and in your day to day. >> Sure, yeah, change is constant now and so one of the big challenges that our customers are facing is how do I keep up with it. The traditional manual processes that they've had in place for years are just not sufficient anymore. 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. >> All right, so Russ when I look at this space you know, tooling, monitoring has been something that in my career, has been a little bit messy. (laughter) Guess a little bit of an understatement even. It's an interesting... When I look at, kind of, that balance between what's happening in the infrastructure space and the application space. I went through, one of your partners over here is like "from legacy to server lists and how many weeks." (laughter) And I'm like okay that sounds good on a slide but, these things take awhile. >> Absolutely. Bring us inside a little bit, kind of the the application space an 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 server lists 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 that those applications don't live in isolation. It's that there part of broader business services and we're not rewriting everything and so the new shiny application and the new framework has to work with the old legacy application. 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 it's on PRIM it's on Bare Metal. Those things need to work together. So, how do you work with an environment that's like that? Enterprise, just by it's nature is incredibly heterogeneous, lot's of different technologies and that's not going to change. >> Yeah. It's going to be that way. >> You're preaching to the choir, here. You know, IT it always seems additive the answer is always and. And, unfortunately, nothing ever dies. By the way you want to run that wonderful Kubernetes Docker stuff and everything. I could do it on a mainframe with Z Linux. So, from that environment to the latest greatest hypercloud environment >> Right. Talk a little bit about your customers. Most of them probably have hundreds of applications. They're working through that portfolio. What goes where, how do I manage all of those various pieces, and not kill my staff? (laughter) One of the things we're spending 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 the heterogeneous nature and on top of that runs application. So they have their own data and there's APM space. So we're seeing a lot of interest in the work we're doing 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 root cause analysis, capacity planning and all the different things that 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. In the industry we talked about silos for so many time. And now it's like oh, we're going to get architects and generalists. And you know cloud changes everything, yes and no. (laughter) We understand where budgets sit for most CIO's today. So, bring us inside what you're seeing. >> Sure. Yeah, we're seeing a tremendous change. Where before we use to talk more to the infrastructure team, to the folks managing the servers, the storage the network. We're really seeing a broader audience. And a multiple constituent. We're looking at directors, VP's, CIO's, CEO's, architects. We're starting to see more people that are tools managers, folks that are involved in the application side of the house. So, it's really diverged. 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. To Russ's point in about being able to bring all this data together. As you bring it together, those different stakeholders have more visibility into each others areas. And they also have a better understanding of what the impact is when something goes down in the infrastructure, how it effects the app and vice versa. >> Leslie, the other thing I'm wondering if you can help me squint through, when I looked at the landscape, it's, you know, my ITSM's I've got my logging, I've got all my various tools and silos. When I hear something like, actually, your CEO Dave just said "Oh, we just had a customer that replaced 50 tools." with there it's like, How do you target that? How does a customer know that they have a solution that they have a challenge that you fit, Because, you understand, you can't be all things to all people. You've got certain partners that might claim that kind of thing. >> Right But, 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. 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 people are wanting to move away from them. 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, Microfocus acquisition. You're seeing that more organizations are looking to replace those tools in their entirety. And as a result of that they're looking at how can I minimize my tool set. 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 in 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 ScienceLogic is really the core to their monitoring and management environment, because we're bringing the data and we're feeding the data in to other systems as well as managing it within ScienceLogic. >> Russ, I actually heard, data was emphasized more that I expect. I know enough about the management and monitoring space. We understand data was important to that, 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 some about those relationships to data? We understand data's going to be everywhere and customers actually wrapping my arms around it make sure I can manage it, compliance and to 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 use 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 so how do we get this data, Do we need 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, because that's something a human can't do. So, you need machine help, you need to automate that. >> So, Leslie, there was in the keynote this morning that to start discussion of the AI Ops maturity model >> Right >> 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 of 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" (laughter) You know, 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 use 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 change, 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. >> What we're seeing is, I mean let's be real, people don't like to do the mundane tasks, right. So you think about, 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 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 your 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. >> Russ, anything else along the automation line. Great customer examples or any 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. A customer we worked with, they had an EPM tool, one of the great ones, you know, top of the magic quadrant kind of thing, and it kept on reporting code problems. The applications 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 APN data with the V center data and the network data what it turned out was, there was a packet loss in the hypervisor. So, it was actually network outage that was manifesting itself as a code problem, and as soon as they saw that, they said what's causing that network problem? They immediately found a 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 had been chasing after for months. That's a form of automation too is get the information to the human, so that they can make a smart decision. That's automation just as much as rebooting a >> Exactly server or cleaning a disk >> Well right, It's The Hitchhikers Guide to the Galaxy. Sometimes, the answers are easy if I know what question to ask. >> Exactly, yes. (laughter) >> And that's something we've seen from data scientists too. That's what their expertise is, is to help find that. All right, Leslie give us a little view forward. We heard a little bit, 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. 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 their not having to do a lot of the analysis themselves. There's, as you said earlier, there's the folks that are afraid 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 their going to have to get involved in as opposed to the things that are traditional and constant. So, Russ, I'd love for you to comment on 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 through out was dynamic. Because, if you talk about cloud you talk about containers. Down the road things like serverless. It's if it pulls every five minutes it's probably out of date. >> oh, Absolutely. I remember back when we talked big data, real time was one of those misnomers that got thrown out there. 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 >> Absolutely. >> Exactly. So, where do you guys see this directionally, and how do you get more dynamic? >> Well see, 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. So, we have to keep that 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. That's really where we're spending our energy is completing that loop. Closed loop system that can help humans do their jobs better and 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. >> And we'll be back with more coverage here from ScienceLogic Symposium 2019. I'm Stu Miniman, and thank you for watching theCUBE. (energetic music)

>> From Washington DC, it's theCUBE. Covering ScienceLogic Symposium 2019. Brought to you by ScienceLogic. >> I'm Stu Miniman and this is theCUBE's coverage of ScienceLogic Symposium 2019 here at The Ritz-Carlton in Washington DC. Really excited to welcome back to the program. It's the co-founder CEO and the Headmaster of Wizarding school, >> Wizarding school, yes. >> Dave Link, thank you so much for joining us. Great to be here Steve. >> All right, so Dave first of all congratulations, really been enjoying the event you know you you kicked it off in the keynote this morning great energy, really I think capturing you know where we are in you know IT in business today. We understand how things are changing so much and it's a complex world and ScienceLogic is trying to do Its part to help simplify and make it easier for IT to you know run at the speed of business and machines. >> That's exactly right. What's happening in the world right now is you've got a confluence of cloud apps, traditional legacy apps and they're colliding together and as they collide together you need new tools to manage that in a way that's different than what we've seen in the past. You're looking at lots of sources coming together to contextualize, not just seeing what's happening, understanding how systems relate to one another but acting upon them. Machine at machine speed means that automation is king and the wizard hat actually relates to a storyline we had earlier today when we think about how to educate the marketplace and the customers we realized that we needed a very new way of communicating. So videos E-Learning The Wizard of learning has been a theme of the show to help our customers to get up to speed and actually take full advantage of the application that we provide to help them deliver great service quality. >> Yeah well and we appreciate you bringing theCUBE to help with that video education of the community overall. >> That's right >> Yeah so you know look Dave you know wanted... let's step back for a second and you know we want to going to get to the business update but first you know the company is founded in 2003. You know cloud wasn't a term, some of the underlying foundations of what became cloud, you know existed back there. Those of us in the industry understand some of the waves that have happened there but you know to talk about cloud and micro services and all of these changes that have... So give us a little bit about that evolution about the original premise of the company, as we move now to you know the world of today and how you manage to keep the company moving and relevant >> I love telling this story Stu because it never gets old >> Yeah >> A lot of the original feces that we had about where service business service analysis was going, the application analysis connected to the infrastructure. Our belief was we were going to move to a world where it wasn't based on devices or nodes or systems. It was really based on this service and what we're seeing with cloud has accentuated that tenfold because services now are made up of compound things, technologies, service delivery mechanisms as a service platforms and they all have to work with one another The platform we built had an architecture that was very open that could take data streams from lots of different sources, create a common information model contextualize that and then act upon it. So now more than ever before, we really built the right platform with multi-tenancy, with role based access control with all the things that were really hard problems to solve code day one and now the thesis that we had that it was more about the service view is as important as it as it's ever been with ephemeral systems that are coming and going, with really containerized systems on top of virtual machines on top of their metal. All these abstraction layers require a different mindset but an open architecture is really at the heart of pulling lots of data streams together contextualizing it and then acting upon it >> Yeah, so I'm a sucker for Venn diagrams. so you heard that the analyst in the keynote this morning talked about AI ops and he said to the inner structure intersection of IT operations data science and machine learning >> Yes >> Data at the center of everything, it's something we've had a couple of waves of trying on intelligence and automation, are things we've been talking about for decades in IT. Give us a little bit as why some of those waves are coming together so that now and what you're doing is the right moment to really help accelerate. You've been having great growth for a number of years and project out some really strong growth for the next few. >> We have over the last five years the company has grown over five hundred and forty percent from a revenue perspective and I think that's the underpinnings of that relates to do we have the right market fit. Are we solving a problem that's material to customers that it's hard for them to solve without our product. But I really envision a future we've been working on this for a couple decades, right? The future is one I hope where from a artificial intelligence at machine speed where we're getting so predictive and understanding, through really smart scalable algorithms the future faults that may occur for you know we've both been at this for a long time. we've been talking about event correlation for many years. I envision a world where you're not doing event correlation when you've had an event, it's actually too late. Usually that's caused by a system telling you that there is a problem. So what we're really working on what we've talked a lot about here at the show is not just predictive analytics but really understanding what's abnormal and getting in front of a problem before there is a problem with the system with really super smart algorithms that help customers understand, many different data sets converge together and what they really mean so that you can get ahead of a service outage rather than have the fault that you're then working on correlating to infrastructure to application layers. >> You know the other thing that's been interesting for me to watch is, the core of where you started was really working with the service Fighters. I've had a chance to talk to a number of your service fighters >> Yes and Hughes has been with you since the early days up to you know one that just bought a couple of weeks ago and you know they're happy very. Talked about kind of the compare/contrast of the service riders and the enterprise because you know cloud is impacting you know the big hybrid hyper scale clouds are impacting both of those and the rate of change is affecting both of those in a lot of ways. So I'm curious as you see you know what what what's similar and what's different between going into those markets. >> When we thought about the problem for service providers there were two axes that we were looking at. Number one was from one instance of our platform you had to serve many customers that all had their own tenancy. But on top of that, you had to layer in a role based access control who could see what the customer had their view, the internal ops teams had their view. So building out a really complicated foundational model and an architecture that would support tenancy on steroids with one instance of our product was a really important linchpin of what's now, incredibly important to enterprises, because enterprises are getting into a moment where they're having to really act as service bureau's, service brokers and that means that all the different teams that support different technology silos, really have to work together as one and... but yet they still need their own views.So a lot of the foundational highly differentiated capabilities we built for service providers, for large scale globally distributed enterprises, actually meets a need profile that is very hard to find solutions that fit that profile and can give them that consolidated view but yet the deep dive view for the practitioner and we're finding that more and more enterprises, have follow-the-sun operations, follow-the-sun architecture teams, follow-the-sun engineering teams that need different views that is really hard to get most products that were built in this space were built for a single tenant enterprise view and that never gives you the granularity for each consumer and each persona to get the view that they need. So it's interesting that although we kind of over engineered those capabilities for the service provider needs it's becoming involved with the enterprises as they're looking at how do they need to do things as really a converged team, working as one team across many silo disciplines and that requires a very different way of thinking, a different tool space a different solution to the problem that we built kind of from the ground up. It's now really appropriate for the DevOps teams the teams that are really having to break down the silos and work as one team. >> Yeah the, the the the term that often gets misused and misunderstood is scale. But if you truly can build something that's distributed architecture for scale, It really opens up a lot of opportunities. One of the things you highlight it also is that, ScienceLogic puts a lot of investment into you know R&D and keep working on things big announcement of Big Ben, seemed I've had a chance to hear what everybody likes and the best. Talk a little bit about you know how you keep the development efforts going how you put that strong and effort on it and you know boy you know you said you worked on the UI for three years and now it sounds you know it's a bold statement to be like okay and everybody you're using this, you know you can't have the safety blanket of old away in new way for a while, >> You're constantly reinventing and refactoring code base to get to new outcomes for customers. we're spending between 35 and 40 percent of revenues on R&D. That's generally almost twice as much as many of our competitors and we're doing that because there is so much still to do. At times we have really thought carefully, could we scale back should we scale back our R&D spend but fortunately we've had a very supportive board of directors that believes in our vision. Believes in the vision that this is a unique moment in time the whole market is transitioning to a new tool set, because of all of the crosswinds of public cloud refactoring of applications containerization abstraction of the network, a storage, compute. All of these things combining together require a very different way of solving this problem We've, we've actually seen this play out in the past which again is why we're over investing in engineering. When you look at the mainframes and the compute architecture of mainframes and then we went to client-server, the tools that managed the mainframe really didn't manage the client-server. we've now gone from client-server to cloud the same things happening again. Because the needs are so different and we're going to see a very different generation of tools rule this next gen of requirements the customers have when they have a multitude of clouds that all work together to deliver an outcome to an application that you as a user are benefiting from. >> Alright so talked about the growth, talked about the investment, it's a strong industry validation today also. Gartner up on stage talked about the definition of AI ops they might not be fully in sync as to how mature the market is but it's still important that they are you know this is a trend and something to watch and it's on their hype cycle and Forrester released the wave which had congratulations ScienceLogic as the the top scorer up in the leaders category. So congratulations on that and what does that mean. >> Well we're thrilled about that because that external validation is what customers look at. It helps them with their analysis and that the talk tracks that everybody's on in our industry sometimes it's hard to discern who does what and how well each company does it to some degree from a marketing perspective many people use the same words so the good words are already used up. So sometimes it's hard to understand how each product is differentiated in the marketplace the Forrester wave report was so thorough so comprehensive, put us through over 30 use case scenarios where we had to demonstrate to get the qualifications for that ranking. So it wasn't just us responding in writing and waving our arms and throwing out a few powerpoints to get to that result we had to prove it and it feels the satisfaction of actually proving it for our team for our engineering team for everybody here at the company I'm so proud of everybody because that's really from a product perspective. We love those product recognition awards are actually sometimes more enjoyable than the growth recognition awards because that means you're really delivering a value to the customer where they're going to when they deploy the product they're going to have a good outcome. So that's what we're focused on and having Forrester put us at the top of the wave report is a special moment in the history of the company. >> Alright so Dave this is your user conference, so what I want to end on... Let's talk about the customers and here's here's my observation as you know, my first time coming to your event and I've talked to a number of seen some of the interactions there. There are certain products that customers love the relationship is an interesting and I would say a really good one the customers are really engaged and enjoying and liking it and it's almost like that friend that you can be like I really like you and your friends in their car I can be like this is how I want you to get better in ScienceLogic this is what you've done and I'm excited once on the roadmap and this is where I want you to go even more. So it's it's like you know that that friend that you can kind of hang out with and joke with and I've seen some of those relationships it's a good robust relationship and strong partnerships. It seems that you build with your customers am I getting the right vibe how do you look at your relationships with your customers. >> From a simple business perspective, I look at a couple things this is just as a run the business metric. On average our customers buy about twenty four, twenty five percent more capacity each year. On average our customers stay with us for 7-10 years. On average our customers pay us within 59 days. So I look at are we getting paid on time, do our customers buy more capacity each and every year and do we retain our customers. We retain about ninety five percent of our customers. So those metrics are really best-in-class, net subscription retention, DSO. All of those things are really good foundational indicators of we're doing a great job for our customers but what I love is this interaction that we have with them where they're they're never ending pressure on us to do better to strive for something that makes a day in their life a better day. I love that pressure it's uncomfortable many days of the week as I mentioned in my opening presentation but it makes us a better company and everybody in the company embodies this sense of how do we capture that synthesize it and then deliver against their needs and wants as quick as we can. So our innovation rates now are as high as they've ever been the throughput our of our development team this last quarter was the best we've ever seen in the history of the company, not just because we have more people but we're getting more done in the same amount of time. So all the KPIs that I look at are pointing in a really positive direction of great momentum for the business and really good alignment with customer needs and wants. We have probably the best market fit I've ever seen with the needs and wants of a net new customer and how our product fits against that. The Forrester wave report was yet another independent validation of how good our market fit in our strategy is right now to solve real problems that are very painful for customers to solve without our product >> Alright, Dave I can't let the head wizard gone without looking a little bit into the future. So as you look down the road what should we be looking as industry watchers to seem from ScienceLogic, seen from the industry you know I asked customers if they had a magic wand you know what would they do to make things better. You had a magic wand up on stage what will you be doing to make the industry better for all of us. >> There's so many things that when we think about making the industry better, it's a community and that means that among the key things that everybody's focused on right now for AI OPS is automation. So sharing those lessons learned cauterizing, validating the automation opportunities whether it's with provisioning systems, with end devices for capacity planning. All the things that we're doing we're starting to work with our customers to publish that broadly so that they can benefit from one another as quick as possible to take those best practices and throughout our community put them into production. If we do that each and every day and really focus on delivering that value across the customer base even for competitive customers. They compete with one another what we've seen is the spirit of cooperation and that to me is among the most satisfying parts of our customer and user community that it's a community that wants to help each other get better every day of the week and that's really hard mission as well. So from a trend line for the entire industry, I think we're all moving towards a moment in time where we have this autonomic capability where we know the applications are infrastructure, we're the tools that help us keep those applications running are getting smarter and smarter by the day and basically move us away from a fault and event correlation storyline to a predictive automation storyline >> Alright well Dave actually I said it on theCUBE a couple of years ago data holds the potential be that flywheel of growth for many years to come. Really appreciate you sharing the story and thanks again for having theCUBE at the event. >> Thanks too great to be here with you. Alright we'll be back with more coverage here from ScienceLogic Symposium 2019, I'm Stu Miniman and thank you for watching theCUBE.

>> from Washington, D. C. It's the queue covering science logic. Symposium twenty nineteen. Brought to you by Science Logic. >> Hi, I'm student men and this is the Cubes coverage of Science Logic. Symposium twenty nineteen here at the Ritz Carlton in Washington, D. C. Been four hundred sixty. People here just finished the afternoon Kino, and they've actually gone off to the evening event. It's thie yet to be finished. Spy Museum. They get a good three sixty view of Washington D. C. So the hallways are a little echoing in quiet but really excited to have on the final guest of the day. Eric Gordon, who's the vice president of business development and alliances as science logic. Erik, thanks so much for joining me, >> thanks to you. Great to be here. >> All right, so busy. Dev and Alliances. I've talked to a number of your partner's. I've gone through a lot of things, but you wear, I think, just like your CEO. A few different hats. Ah, and your old let's let's get into what your role is that the company? >> Yeah, it's actually changed over time, but for the most part I've to court responsibilities. One is I'm looking after our ecosystem of technology partners. And so we have from key strategic CE that we work with in the marketplace, in the cloud space on the data center, all across the ecosystem, a lot of different technologies. But we also have products that we resell input on our priceless that combined to create a solution for our customers in the second half of what my responsible is really focused on. What is our product strategy around integration? Automation? Because those Air Corps components to our platform and I look after that with several different teams. >> So let's talk about that the ecosystem pit person, the alliances. Because I got a lot of shows. I talked to a lot of companies, and it's all too easy for companies to be like, Oh, we're we're the best and we do so many different things. And when I first heard about the space in a ops, it's like, Oh, well, I I Ops is replacing a lot of waves and, you know, your average customer replaces fourteen tools. I heard there's one customer who replaces fifty tools, but at the same time, there was a strong focus about integrations in deeper even some of the products that you say, Yeah, there's overlap in that competitive, you know, you're working with those environments, so give us a little bit of the philosophy, how you balance that, you know, we want to do it all and help our customers to do lots of different things. And especially when you get to big customers and service providers, we understand that it's a big world and there never is that, you know, mythical single pane of glass. >> Yeah, no, totally agree. And we hear this a lot. You know, I've got a tool for this. I got a tool for that and or I had to Vendor come in and say that they could do it all. And you know, really, At the end of the day, if there's there's no one vendor on DH, you know the Venn diagrams of functionalities, air overlapping. That's the nature of the industry. And when we saw this on the early days of it with the big monopolies. But I think right now it's it's around. How do we saw the customer problem? Mohr effectively, From our perspective, we look at the combination of things. First is is what solutions out there give us good data data that we can use data that we can enrich, how we can leverage that to help drive better insights from other types of data that we collect so that theirs is where integration is a keep part of this on DH. What we know is that ultimately in our space, we're doing about monitoring a core collection. We're goingto have to click with everybody, so we're gonna have to integrate with any partner that might have some form of I. P are connected through an I p address to some sort of a p I. We need that data. So we have partnerships on that side. I think really, what's interesting is when we think about things like workflow or orchestration or types of mediation, we might integrate with other technologies to enrich that data further. So we look for partners that ultimately our customers air using things that we can do consolidation and drive better outcome with that enrich date experience. >> Yes, so let's drill down one little bit if you talk about like, you know a PM and SM tools out there some recent announcements and and you digging deeper on there. What what are some of the highlights? So one >> thing is, if you already have, like, agents are often come up, Our customs says, Well, I've got an A P M. Agent that's already doing some things. Well, that's great. We can leverage that, that there's some good insight that we can gather from either to apologies or other metrics or like in user experience. But we also go deeper on other aspects, like on the network side or on the infrastructure side, or on the the cloud service aside. So, you know, ultimately, it's a conversation of say, what? What can we leverage? What, what's accurate, what's in real time? And if there's things that we can, you know, gather, then that's our primary strategy. So I you know, I do think the ecosystem plays a key role in a i ops, but really, to do that, it's run automation because anything that we do, we have to do with scale and we have to do with security. We have to do it with the intent of driving some form of outcome. And so, you know, those are the key principles behind selecting technology partners. >> Okay, Let's talk some about that automation. It was a big discussion in the keynote this morning. Really talking about the maturity model. One of the analysts up there says you really want to make sure you separate things like, you know, the machine learning piece of it with the automation. The observation I've made a couple of times is, you know, yes. We all know you can automate a really bad process. And so I need toe, you know, make sure, you know, do I have good data And, you know, how am I making automation make me better Not just, you know, to change things. >> Yeah, well, I think it's Science Lodge that we look at. Automation is in every part of what we do within the product. From the from the collection of how we automate it scale how we consolidate that data. And then we're doing a lot of the data preparation using automation technologies. And then when we start to analyze and enrich that data, we're also using it Other algorithmic approaches, for example, topology and context. So if we know that some things connected weaken Dr An automation to make an inference and that data then feeds into the final step, which is around how we action on that. So we drive automation in the classic sense to say trigger workflow or, let's say, update another system of record or system of truth like a C M G B or a notification. And so one of things that we did hear from Garden this morning is engaging in an SM process. Is a core part of AI ai ops as muchas data collection and driving other forms of automation. >> All right, Do you have some examples of you know how automation you're helping your customers love any customer stories you've got along that line? >> Well, >> really. You know, there's so many stories we're hearing the halls of Symposium, and so it's it's it's hard to pick one, but, you know, I think all ten times what we say is, what what's driving your service desk time like you've got people you know, looking at all of these different dispirit systems, and we can look at it. Let's say a top end of your most sort of frequented events or alerts, or even look at your top service desk incidents and say, How could we automate that, you know. And some of that automation could be at the technology level, you know, simplest as restarting a service or prove you re provisioning of'Em. Or it could be clearing a log or even maybe shutting down an event because it's irrelevant. So there's There's several different examples in the cloud as well. Terms of how things air provisioning attached. And if we see something out of a policy, we can alarm that say, hey, maybe my storage costs are going to accelerate because someone made a bad change. So there's different ways that we can apply automation during the life cycle. But I think enhancing the service management component perhaps is one of the most impactful ones, >> you know. So, Eric, we azan industry automation been something we've been talking about for quite a while now, and they're they're sometimes pushback of, you know, from the end, users especially, you know, some of the practitioners out there as you know. Well, I could do it better. You know, the fear that you're going to lose your job. How are you seeing that progressing and you know, how were things different today? Both from a technology standpoint, as well as from your customers. Can't wait. >> I think if you asked any enterprise CIA already service provider, service delivery manager, they'd always say, I'd love to operate as much as I can when you get down on the practitioner level. You know, obviously I think there's some sort. Like I I do my job, Thank you very much. I have my favorite wit, my process. So I think there's a conversation depending on. You know, if we're saying hey from the practitioner side, is there set of data that you need or set of scripts? Or are things that you're doing manually that we can put into a workflow? And at the at the business layer, it's like, Do you feel like you're getting the value from some of the investments you've made? And is, how is automation? Help you realize that an example there is. We see oftentimes is around the quality of data that's going into the C, M. D. B and from AA AA. Lot of times we see that their investment in technology is like service now, and other platforms is fairly high expense, and they want to optimize that, and they want to realize the power of automation at the at the service level. So if we can, if we can convince, if you will, through a set of really concrete use cases that the data coming from science logic at the speed and the quality can actually improve the seemed to be to >> the level of >> really efficient automation. All of a sudden, people start to see that as a change as an opportunity. And that's where I think a I Ops is helping change the narrative, to say how automation Khun B really, really applied rather than just being this mystical concept that is hard to do. And, you know, people don't liketo think that a robot's taking their job. I think what's gonna happen is that machine learning algorithms are going to make jobs easier and, you know, ultimately were far, far from the point where a ized doing something and some sort of, you know, crazy automata way. But I think it's the deep learning, moving a machine learning to you. No good quality data sets that dr meaningful insights that's giving us a lot better view until where automation could play in the >> future. Yeah, absolutely. It's our belief that you know, automation. There's certain things that you probably don't want to do because repetitive, it's boring or mistake prone on DH. Therefore, you know automation can really help those environments move forward. You could move up the stack. You can manage those environment. There's definitely some retraining that that needs to happen often. But you know that the danger is if you're if you're doing now what you were doing five years ago, chances are your competition is moving along and, you know, finding a better way to do it. >> You know, just a point on this soup is really around the velocity of data that's coming in. So we're seeing, you know, we talked about the three bees. You know, the volume of data. You have to use automation to be able to manage that huge amount of different data sources, the variety. There's no human that can process the amount of machine information from the amount of technologies that you have on DH that you know. Obviously it's speed, right. The velocity and that is that is clearly not going to be something that any human could be capable of doing. And so there's a relationship here between technology and human processes and science logics and a really interesting position right now to really kind of help with that process. But more importantly, accelerate the value by being all to process it and make it intelligent. >> Wait, Erica, you're saying I'm not neo from the Matrix and I can't, you know, read through everything and be able to move faster than physics allows. Give >> yourself maybe fifteen, twenty years. We might be. You know that that you know, I don't think that that many people can really predict the impact of the you know, we'LL say machinery, evolving toe, artificial intelligence and there's it's going to be very used, case specific. But we do know one thing is that algorithms? Air helping. But algorithms are dependent on that clean data stack, right? And And if you can't handle the scale, then obviously there's going. It's going to be minimized in terms. Is total utility >> alright? Well, Eric, I get the good to let you give us that the final word from science logic from Symposium twenty nineteen on the Cube. >> So you know, the first thing is is this is there's two things that we learned from this event. The first thing is, is how our customers you're evolving in this dynamic space. And what we know is that if if you don't change, it's going to be a problem. Because the only consistent thing is change and change is happening faster on it. And we call that disruption. And so what we want to do is we want to understand how science AJ is a technology company. I can really help that customer go through that transition with confidence. And then, more importantly, is what could we do? Delivering better, more enrich solutions to our customers that actually are changing the way the game is played. And so we feel like we're a disrupter in the A ops market. We are. Certainly Forrester has helped us recognize that. But But we're not done work. We're continuing on this journey. >> All right, Well, Eric, routine. Thank you so much for sharing your insights and the journey towards Aye, Aye, Ops. Thanks so much to. All right. Well, that comes to an end of what we're doing here at science Logic. Symposium twenty nineteen. I know. I learned a lot. I hope you did too. I'm stew Minutemen. Thanks so much from our whole crew. Here it's Silicon Angle Media's The Cube. Check out the cube dot net for all the videos from this show, as well as where we'LL be in the future. Reach out if you have any questions and once again, thanks for joining us.

>> from Washington, D. C. It's the queue covering science logic. Symposium twenty nineteen. Brought to you by Science Logic >> Hi, I'm student men and this is the Cubes coverage of Science Logic. Symposium twenty nineteen here at the Ritz Carlton in Washington, D. C. First of all, want Welcome back to the program. Roger Putnam, Who's the vice president of Global Solutions? That science logic Thanks for coming back and what with programme A first time Rob Gruner listed is this loosened architect from Telstra. But >> Rob, I actually had >> a chance to talk to some of your co ords there, they said. Arav robs a wizard. He's an engineer that does everything. So you know, solutions. Architect. Of course, we know that they're out there. They do a lot of different things and asleep, leased. Your peers say you're somebody that does quite a lot of different >> things. Did Jack of All trades master of none unfortunate >> way? It's all right, don't you know it is in vogue now to be, you know, a generalist. It's, you know, we've gone from specialties to well, oh no, it's it's platforms and everything's going to be everything, so I have plenty of background with Telstra, but maybe talk a little bit about you know, your role in the organization and what what kind of things you're involved in. Since you know some of those trades that you >> are jack of all, >> probably our spies have come into Telstra's an acquisition. So, you know, working for small company, you tend to do everything on. For some reason, I've been allowed to continue to do that on developing expertise around science logic. And that means I've been involved across a lot of areas of the business as we've been adopting science logic more widely, and it's been quite interesting. Process means eye contact, that expertise and then see how it's applied across the organization. So it's been quite interesting, >> awesome. One of things that's been interested in me and in talking to service Friday is talking to the enterprise customers is two. You know how many tools they had, how many they replaced with science logic, but also what things it's integrating with and working with. It was a big focus on the keynote this morning is, you know, integrations with Sam and you know all these various pieces, so maybe give us a little bit of kind of the scope. You know how long's tells me you've been using science logic, How broads the deployment and you know what? What? What does it do in? What does it tie into >> a tte? The mammoth is more enterprise focused. So on. That's the area. Tell Stur I come from so it's really around delivering services to her customers. Quite recently, we've seen then looking in deploying science logic across their carriage spokes and managing services there. That's quite a large deployment. You know, we're quite happy with that in terms of what is going to be doing for the business on the integrations, their endless. So Telstra, like a lot of large organizations, has a lot of different systems to talk to. A lot of different service dis, depending on the operational areas. So in service now is one of those. But it's a hollow of other stuff on, so that's a very challenging process. And sounds objects being pretty good at, you know, spreading itself around. Those >> give us a little insight as to you know, how fast things are changing. You know, hear Kafka and Streams and, you know, constantly moving I've been looking at the, you know, communities and container stuff that's happening, which is which is fast moving. So >> are definitely say it. And Telstra's trying as hard as akin to move as quickly as the market can allowed. So definitely it's virtual izing. ITT's automating II ops is a big component of what we're doing. It is extremely important for the business. >> Okay, so Alps is something you're doing have to We're not as mature as we'd like to video. I'm not sure if you saw the keynote this morning, but they put out a maturity models So would love for you to, you know, where are you when you look at that? They kind of had the three criterias there is. There's kind of the the machine learning, there's the automation and I'm trying to remember the third piece that was there, but you know where where are you today? You know, how'd you get there? And you know what? What's what's a little bit of the road map going forward? >> I think it might be probably our ambitions to be in that the upper end of the spectrum and into remediation, But that's an ambition and I think we've got a while to go with that. So, uh, more than that, I can't coming off >> its interests. So they have that The keynote tomorrow they're going. Jean Kim speaking on the deaf ops. And, you know, I'm a big fan of the Phoenix project and they talked about, you know, the jack of all trades that does it all. He could sometimes be the bottleneck in the system. Absolutely. Because you can't be up. I need something fixed. Well, we'LL go to Rob Rob all fix it. That's great. That fire floating mode. I know I've done that in my career, and it's one of those things. Oh, jeez, you're never going to move at this job because you're replaceable. It's like that's a dangerous place to be. >> It is s >> o. You know, we talk a little bit about, you know, you said, you know, science logic. You know that they position themselves as this is going to help you move that, you know, machine speed and keep up with that. Give us a little bit the reality of what you're seeing. How what does that impact your job? Your organization? >> Look, I think sounds logic has done a wonderful job within the organization. It's it's the legacy infrastructure within any organization, particularly tells her scale. That's really holding you back on. There's a lot of Well, I think people level with Intel Street. Move as quickly as we can, but we have such a large number of legacy systems to deal with. You know, we're looking at one deployment of Sands object. We were looking at IDing systems to kill, So it's a big task >> the wonderful technical death that we've all inherited. So So you know, Roger, you know, this something we hear from all customers. It'd be lovely if I had the mythical, you know, unicorn that, you know, start from the ground up and you know, he can start afresh. But we always have to have that mix and give it a little bit about what you're seeing. You know, about the Telstra in a little bit broader, You know, >> I think what tell us she has done really well with taking advantage of our technology was they didn't come in with this attitude of would rip out everything that we have and just have a magic easy bun. Software doesn't work that way. I think we've all learned the lessons of tough deployments when you try to stay out of fix everything. So they came in with a really gradual, phased approach of Get a couple pieces done where they had gaps. You start to fill those gaps. What's happening during the last few years as we've seen the shift greater change and they've taken advantage of the platforms, nationalities a hole as they go through their digitization efforts. And so as they digitize, they taking this step by step by step approach to you know what you were saying earlier with Rob does. He doesn't answer the question of being the one man band, but they did was they build it all process wise, using software to drive the automation. So once it's done one time, you're not stuck on the person anymore. And so I think when we look at our most successful customers like Telstra, it's because they've had this gradual, phased approach where they're using software rather than single person bottlenecks. And rather than having these tiger teams to try to solve problems and moving towards a better process to take advantage of the world, we're in today. So how >> do you measure success? You know, what are some of the business outcomes or, you know, k p I's that you understand how you're moving from kind of where you were to where you want to be. >> Uh, that's a difficult one to answer because particularly sounds, logic was used in so many different context. So for a certain part of the business, we might say, Are we monitoring the full stack? I were giving customers real value invisibility through the whole dynamic of the business. And then, in another context, we using sound subject. We were just saying, We just need to deploy its scale. We need two one board as quickly as possible. We need to keep the cost down to a minimum. We need to keep events that's allow as possible. Okay, so it's more about the efficiency argument, so it's really depends and way we're trying to use it and how we're deploying it. So >> how do you have visibility across how everybody is doing and getting trained on the latest things and keeping up to date and sharing best practices? How do you manage that internally, and how do you how do you do you network with your peers on some of that? >> Well, we've tried Teo really within. Tell us we have a concept of centre of excellence. So it's really about, you know, being recognizes the business experts in particular area and allowing the business to understand. That's that. That's where the expertise sits on a certain we've done a very good job with that and then allowing and communicating that after the business as well. So it's a very tough asked. It's a big business. We have thirty thousand people so often one person doesn't know about another person, another floor on the buildings, you know, to try and spread it across the biz, since we have fifty officers worldwide. So it's a process, you >> know? I mean, Roger just want one of things that here is, you know, science logic. It's not a widget, and it's, you know, can fit in a lot of different environments and a lot of different uses. You know, I heard of, you know, strong emphasis in into training had your CEO on where in his wizard tat for for for the that the learning knowledge that was gonna happen. So you know you talk a little bit about how science logic is looking to address this, especially for some you know, large customers like Telstra. >> You know, I think there's a general skills gap in is a whole beyond our technology beyond what's taking place in the world today. And you know, I've been in the business for quite a while, and we've long focused on training the operator on how to utilize the technology to solve their specific problems. And while that those aspects really powerful, some of the things we've done recently to go a step further is when we hear similar questions. We started record all of those so our customers could watch videos of how to solve problems instead of just going onto some form and let me type some question and hope somebody responds to in the future. You have read it for that. So we've got a look at a better mechanism and video based training handheld handling the customers we can build out these use cases drives the platform value, and what Telstra does it's really unique is they use the platform less so from a perspective of can I manage X y Z technology. But what can I build on top of it? How can I break the platform to some extend? And Rob is a mad scientist for us here. I mean, could jump into this more. But they've broken the platform to solve those business needs by addressing them individually. And what we've done is we've taken his best practices, and we rolled them back out to the rest of our customers. So with Robin, tell Hsia and a couple of other really great customers were driving a better community and sense of community so less question, answer form, less traditional support, more video, more community, more share ability. And that's where you're going to get additional quality. Coming out from the products are being delivered. Makes sense to you, Robert. Absolutely. >> Yeah, Rob. I mean, I love any commentary on that. You know, the network effect of software especially would talk about Sasser as a service type things, you know, that's what sales force really came out. It was like a weight one customer. Ask for something and wake everybody. You can take advantage of that or something similar. So are you seeing that kind of dynamics today with science logic and with others >> well, perfectly within the Telstra business. Absolutely so by building a capital into one area, you can share it across. And we found that we've been able to then sell the system internally, your internal stakeholders, so they appreciate the value of it and we can build on that. And then our customers, whilst we don't necessarily lady with the product they can. They see what's going on, and they basically then take it on as a service as well. So it's very, very interesting process. >> So one thing we haven't talked about yet, but you talk about data, you know, what's the role of data in your environment is something that you know key to the platform from science logic. How you leveraging it? How's that changing in your environment? One of the opportunities there. >> It's interesting questions. So as the telco, we collect a lot of data on DA. Obviously we have federal agencies who make that a requirement as well. So we have an existing data like initiative on that's very full of moment, and science logic is where we're looking at how we can add to that the value, valuable information and provides, but like everyone else, is a lot of data to collect, and it's an interesting process to try and make sense out of it and react accordingly. I mean, as a business, we were responding to millions and millions events of a day. So it's, you know, it's a difficult thing. >> Yeah, one of things. When we look at things like you know, anything that requires training like machine learning or the like, There's the balance between I want to learn from everybody. But you know, you're in a competitive marketplace. I don't want my competitors necessarily to get things. So you know the software products usually Well, I can isolate, and it doesn't have specific information. But how do you look at that dynamic of making sure that you gain from what the industry is doing, but that, you know, you could still stay competitive in ahead of your competition? >> Uh, >> no. I don't have a necessary can answer that. I suppose my head's tied into really what I could do with a platform and how I can then bring new technologies into the company's. So that's really are spies remind spaces on, Really, it's what I'm focused on. So you know what we do with the daughter probably is. He's not necessarily big concerns. How >> about that? There was quite a lot of announcements this week. The number of integrations as well as you know, update to the product. Anything specifically that you've been waiting for or that has caught your eye, >> the service now integration. I think it is far more advanced than has been in the past. On we have aspect of the business used thinks over quite heavily. So the fact that that's now matured and much more robust and you know which sort of offering that'LL have a lot of impact on the business. So I definitely mean the machine learning is another great thing on the question of then how that develops over time. So we'LL see how that goes. You >> know, Roger loves you know what? When I've been digging into some is the feedback you've been getting from customers and what's been leading toe, you know, some of the enhancement. So I would love, love your take on what you're saying. >> You know, I think one of the things that tell Sharpe pushed us towards a few years back was we're going to build. We already have a data like we don't need you to function. Is there Data Lake? So its multiple different Veda lakes And this concept of how do I move later From one day to lake to a different data Lake lakes within lakes ponds. Whatever the terminology is today the data ocean, our family perfect. And I'm getting to that data ocean from our lake. We have to go get streaming data. So now I'm going to extremes against really geographic here. But, you know, Rob really pushed us to make sure we could go right to Kaka buses and pushed data out. So what do you do with the data? And so tell Strip has been a, you know, an early adopter of a lot of our technology. And by being an early adopter, they've pushed us in a number of directions. So I think when you see a lot of the functionality that we've released this week and we've announced, it's been because of our customer base because of our partners like Telstra, that need to drive the business for further and forward, especially the industry like Telco World, where everything is mobile everything's moving so fast and aggressively. They're really like a good sounding board for where we need to go and how do we get there and and that drive And that partnership is What I think I'm most excited about working with tell sure is they demand from us to be excellent, and that gets great product coming out. And we see the results this week with all of our customers excitingly looking at stream treating capability that Rob was pushing us for well in advance of anyone else. >> Yeah, Robin, I want to give you the final word. You know, I can't help but notice you actually co branded shirts you've got tell star on your arm wither with science logic there. So, obviously, more than just a vendor relationship there, maybe close us out with you know how important science logic is. Two to your business >> job, Critical part of the business. I mean, particularly where we're looking at the commodity aspect of many services, you know, we can't survive unless we can provide quality, invaluable information where customers and really sounds. Logic has been the key platform for that. So in some respects, we're resting, you know, an aspect of the business entirely and Scientology's hands and we're hoping they'LL deliver >> well, Robin Raj, Thank you so much for joining us. Just sharing all the progress that you've made in. You know where things were going? Thanks so much, thanks to all right. And I'm student men. This is the Cube at Science Logic Symposium twenty nineteen. Thanks for watching.

>> From Washington D.C. it's theCUBE covering ScienceLogic Symposium 2019. Brought to you by ScienceLogic. >> Hi, I'm Stu Miniman and this is theCUBE's coverage of ScienceLogic Symposium 2019 here at The Ritz-Carlton in Washington D.C. About 460 people here, the events' grown about 50%, been digging in with a lot of the practitioners, the technical people as well as some of the partners. And for this session I'm happy to welcome to the program for the first time guest, Surendra who is the vice president and CTO for automation in IBM's global technology services. And joining us also is Dave Link who is the co-founder and CEO of ScienceLogic. Gentlemen, thank you so much for joining us. >> Thank you for having us. >> Thanks for having us. >> Alright, so Surendra let's start with you. Anybody that knows IBM services at the core of your business, primary driver, large number of the presented to the employees at IBM are there. You've got automation in your title so, let's flush out a little bit for us, your part of the organization and your role there. >> Alright, so as you pointed out, IBM, a big part of IBM is services; it's a large component. And that two major parts of that and though we come together as one in terms of IBM services, one is much more focused on infrastructure services and the other one on business services. So, the automation I'm dealing with primarily is in the infrastructure services area which means all the way from resources you have in a persons data center going into now much more of course in a hybrid environment, hybrid multi-cloud, with different clouds out there including our own and providing the automation around that. And when we mean automation we mean the things that we have to do to keep our clients' environments healthy from a availability and performance standpoint; making sure that our environment then we respond to the changes that they need to the environment because it obviously evolves over time, we do that effectively and correctly and certainly another very important part is to make sure that they're secure and compliant. So, if you think of Maslow's hierarchy of the things that IT operations has to do that in a nutshell sums it up. That's what we do for our clients. >> Yeah, so Dave luckily we've got a one on one with you today to dig out lots of nuggets from the kino and talk a bit about the company but, you talk about IT operations and one of the pieces I've got infrastructure, I've got applications, ScienceLogic sits at an interesting place in this heterogeneous ever-changing world that we live in today. >> It does and the world's changing quickly because the clouds transforming the way people build applications. And that is causing a lot of applications to be refactored to take advantage of some of these technologies. The especially focused global scale we've seen them, we've used them, applications that we use on our phone. They require a different footprint and that requires then a different set of tools to manage an application that lives in the cloud and it also might live in a multi-cloud environment with some data coming from private clouds that populate information on public clouds. What we found is the tools industry is at a bit of a crossroads because the applications now need to be infrastructure aware, but the infrastructure could be served from a lot of different places, meaning they've got lots of data sources to sort together and contextualize to understand how they relate to one another real time. And that's the challenge that we've been focused on solving for our customers. >> Alright, Surendra I want to know if we can get a little bit more to automation and we talk automation, >> There's also IBM use for a number of years, the cognitive and there was the analyst that spoke in the kino this morning. He put cognitive as this overarching umbrella and underneath that you had the AI and underneath that you had that machine learning and deep learning pieces there. Can you help tease out a little bit for IBM global services in your customers? How do they think of the relationship between the MLAI cognitive piece in automation? >> So I think the way you laid it out, the way it was talked about this morning absolutely makes sense, so cognitive is a broad definition and then within that of course AI and the different techniques within AI, machine learning being one, natural language processing, national languages understanding which not as much statistically driven as being another type of AI. And we use all of these techniques to make our automation smarter. So, often times when we're trying to automate something, there can be very prescriptive type of automation, say a particular event comes in and then you take a response to it. But then often times you have situations where you have events especially what Dave was talking about; when an application is distributed not just a classic of distributed application, but now distributed of infrastructure you may have. Some of it may be running on the main frame, some of it actually running in different clouds. And all of this comes together, you have events and signals coming from all of this and trying to reason over where a problem may be originating from because now you have a slow performance. What's the reason for the slow performance? Trying to do some degree of root cause determination, problem determination; that's where some of the smarts comes in in terms of how we actually want to be able to diagnose a problem and then actually kick off maybe more diagnostics and eventually kick off actions to automatically fix that or give the practitioner the ability to fix that in a effective fashion. So that's one place. The other areas that one type of machine learning I shouldn't say one type, but deadly machine learning techniques lend themselves to that. There's another arena of causes a lot of knowledge and information buried in tickets and knowledge documents and things like that. And to be able to extract from that, the things that are most meaningful and that's where the natural language understanding comes in and now you marry that with the information that's coming from machines, which is far more contextualized. And to be able to reason over these two together and be able to make decisions, so that's where the automation. >> Wonder if we can actually, let's some of those terms I want to up level a little bit. I hear knowledge I hear information; the core of everything that people are doing these today, it's data. And what I heard, and was really illuminated to me listening to what I've seen of ScienceLogic is that data collection and leveraging and unlocking value of data is such an important piece of what they're doing. From an IBM standpoint and your customers, where does data fit into that whole discussion? How do things like ScienceLogic fit in the overall portfolio of solutions that you're helping customers through either manager, deploying and services? >> So definitely in the IT Ops arena, a big part of IT Ops, at the heart of it really is monitoring and keeping track of systems. So, all sets of systems throw off a lot of data whether it's log data, real time performance data, events that are happening, monitoring of the performance of the application and that's tons and tons of data. And that's where a platform like ScienceLogic comes in, as a monitoring system with capabilities to do what we call also event management. And in the old days, actually probably would have thought about monitoring event management and logs as somewhat different things; these worlds are collapsing together a bit more. And so this is where ScienceLogic has a platform that lends itself to a marriage of these faces in that sense. And then that would feed a downstream automation system of informing it what actions to take. Dave, thoughts on that? >> Dave, if you want to comment on that I've got some follow ups too, but. >> Yeah, there's many areas of automation. There's layers of automation and I think Surendra's worked with customers over a story career to help them through the different layered cakes of automation. You have automation related to provisioning systems, the provision and in some case provision based on capacity analytics. There's automation based on analysis of a root cause and then once you know it, conducting other layers of automation to augment the root cause with other insights so that when you send up a case or a ticket, it's not just the event but other information that somebody would have to go and do, after they get the event to figure out what's going on. So you do that at time of event that's another automation layer and then the final automation layer, is if you know predictively about how to solve the problem just going ahead if you have 99% confidence that you can solve it based on these use case conditions just solve it. So when you look at the different layers of automation, ScienceLogic is in some cases a data engine, to get accurate clean data to make the right decisions. In other cases, we'll kick off automations in other tools. In some cases we'll automate into ecosystem platforms whether it's a ticketing system, a service desk system, a notifications systems, that augment our platform. So, all those layers really have to work together real time to create service assurance that IBM's customers expect. They expect perfection they expect that excellence the brand that IBM presents means it just works. And so you got to have the right tooling in place and the right automation layers to deliver that kind of service quality. >> Yeah, Dave I actually been, one of the things that really impressed me is that the balance between on the one hand, we've talked to customers that take many many tools and replace it with ScienceLogic. But, we understand that there is no one single pane of glass or one tool to rule them all, the theme of the shows; you get the superheros together because it takes a team. You give a little bit of a history lesson which resonated me. I remember SNMP was going to solve everything for us, right? But, the lot of focus on all the integrations that works, so if you've got your APM tools, your ITSM tools or things you're doing in the cloud. It's the API economy today, so balancing that you want to provide the solutions for your customers, but you're going to work with many of the things that they have; it's been an interesting balance to watch. >> Yeah, I think that's the one thing we've realized over the years; you can't rip and replace years and years of work that's been done for good reason. I did hear today that one of our new customers is replacing a record 51 tools with our product. But a lot of these might be shadow IT tools that they've built on top of special instrumentation they might have for a specific use cases or applications or a reason that a subject matter expert would apply another tool, another automation. So, the thing that we've realized is that you've got to pull data from so many sources today to get machine learning, artificial intelligence is only as good as the data that it's making those decisions upon. >> Absolutely. >> So you've got to pull data from many different sources, understand how they relate to one another and then make the right recommendations so that you get that smooth service assurance that everybody's shooting for. And in a time where systems are ephemeral where they're coming and going and moving around a lot, that's compounding the challenge that operations has not just in all the different technologies that make up the service; where those technologies are being delivered from, but the data sources that need to be mashed together in a common format to make intelligent decisions and that's really the problem we've been tackling. >> Alright, Surendra I wonder if you can bring us inside your, you talked to a lot of enterprise customers and it helped share their voices to in this space, not sure if they're probably not calling it AI ops there, but some of the big challenges that they're facing where you're helping them to meet those challenges and where ScienceLogic fits in. >> So certainly the, yes, they probably don't want to talk about it that. They want to make sure that their applications are always up and performing the way they expect them to be and at the same time, being responsive to changes because they need to respond to their business demands where the applications and what they have out there continually has to evolve, but at the same time be very available. So, all the way from even if you think about something that is traditional and is batch jobs which they have large processing of batch jobs; sometimes those things slow down and because now they're running through multiple systems and trying to understand the precedence and actions you take when a batch job is not running properly; as just one example, right? Then what actions we want, first diagnosing why it's not working well. Is it because some upstream system is not providing it the data it needs? Is it clogged up because it's waiting on instructions from some downstream system? And then how do you recover from this? Do you stop the thing? Just kill it or do you have to then understand what downstream further subsequent batch jobs needs to or other jobs will be impacted because you killed this one? And all of that planning needs to be done in some fashion and the actions taken such that if we have to take an action because something has failed, we take the right kind of action. So that's one type of thing where it matters for clients. Certainly, performance is one that matters a lot and even on the most modern of applications because it may be an application that's entirely sitting on the cloud, but it's using five or 10 different SAS providers. Understanding which of those interactions may be causing a performance issue is a challenge because you need to be able to diagnose that and take some actions against that. Maybe it's a log in or the IDN management service that you getting from somewhere else and understanding if they have any issues and whether that provider is providing the right kind of monitoring or information about their system such that you can reason over it and understand; okay my service which is dependent on this other service is actually being impacted. And all these kind of things, it's a lot of data and these need to come together. That's where the platform something like ScienceLogic would come into play. And then taking actions on top of that is now where a platform also starts to matter because you start to develop different types of what we call content. So we distinguish the space between an automation platform or a framework plus and the content you need to have that. And ScienceLogic they talk about power packs and these things you need to have that essentially call out the work flows of the kind of actions you need to take when you have the falling signature of a certain bundle of events that have come together. Can you reason over it to say okay, this is what I need to do? And that's where a lot of our focus is to make sure that we have the right content to make sure that our clients applications stay healthy. Did that get to, I think build on what you were talking about a bit? >> Absolutely. Yes, you've got, it's this confluence of a know how an intelligence from working with customers, solving problems for them and being proactive against the applications that really run their business; and that means you're constantly adjusting. These networks I think Surendra's said it before, they're like living organisms. Based on load, based on so many factors; they're not stagnant, they're changing all the time, unless you need the right tools to understand not just anomaly's what's different, but the new technologies that come in to augmenting solutions and enhancing them and how that effects the whole service delivery cadence. >> Mr. Surendra, I want to give you the final word. One of the things I found heartening when I look at this big wave of AI that's been coming is, there's been good focus on what kind of business outcomes customers are having. >> Okay. >> Because back in the big data wave I remember we did the survey's and it was like what was the most common use case? And it was custom. And what you don't want to have is a science project, right? >> Right. >> Yes. >> You actually want to get things done. So any kinds you can give as to, I know you understand we're still early in a lot of these deployments and rollouts but what do you seeing out there? What are some of the lighthouse use cases? >> So, certainly for us, right? We've been at using data for a while now to improve the service assurance for our clients and I'll be talking about this tomorrow, but one of the things we have done is we found that now in terms of the events and incidents that we deal with, we can automatically respond with essentially no human interference or involvement I should say about 55% of them. And a lot of this is because we have an engine behind it where we get data from multiple different sources. So, monitoring event data, configuration data of the systems that matter, tickets; not just incident tickets but change tickets and all of these things and a lot of that's unstructured information and you essentially make decisions over this and say okay, I know I have seen this kind of event before in these other situations and I can identify an automation whether it's a power pack, an automotor, an Ansible module, playbook. that has worked in the situation before in another client and these two situations are similar enough such that I can now say with these kind of events coming in, or group events I can respond to it in this particular fashion; that's how we keep pushing the envelope in terms of driving more and more automation and automated response such that the I would say certainly the easy or the trivial kinds of I shouldn't say trivial, but the easy kinds of events and monitoring things we see in monitoring are being taken care of even the more somewhat moderate ones where file systems are filling out for some unknown reasons we know how to act on them. Some services are going down in some strange ways we know how to act on them to getting to even more complex things like the batch job type of thing. Example I gave you because those can be some really pernicious things can be happening in a broad network and we have to be able to diagnose that problem, hopefully with smarts to be able to fix that. And into this we bring in lots of different techniques. When you have the incident tickets, change tickets and all of that, that's unstructured information; we need to reason over that using natural language understanding to pick out the right I'm getting a bit technical here, verp no pas that matter that say okay this probably led to these kind of incidents downstream from typical changes. In another client in a similar environment. Can we see that? And can we then do something proactively in this case. So those are all the different places that we're bringing in AI, call it whatever you want, AIML into a very practical environment of improving certainly how we respond to the incidents that we have in our clients environments. Understanding when I talked about the next level changes when people are making changes to systems, understanding the risks associated with that change; based on all the learning that we have because we are very large service provider with essentially, approximately 1,000 clients. We get learning over a very diverse and heterogeneous experience and we reason over that to understand okay, how risky is this change? And all the way into the compliance arena, understanding how much risk there is in the environment that our clients facing because they're not keeping up with patches or configurations for security parameters that are not as optimal as they could be. >> Alright, well Surendra we really appreciate you sharing a glimpse into some of your customers and the opportunities that they're facing. >> Thank you. >> Thanks so much for joining us. Alright and Dave, we'll be talking to you a little bit more later. >> Great, thanks for having me. >> All right. >> Thank you. >> And thank you as always for watching. I'm Stu Miniman and thanks for watching theCUBE. >> Thank you Dave. >> Thank you. (upbeat techno music)

(upbeat music) >> From Washington D.C. it's theCUBE. Covering ScienceLogic Symposium 2019. Brought to you by ScienceLogic. >> I'm Stu Miniman and you're watching theCUBE's exclusive coverage of ScienceLogic Symposium 2019 here at the Ritz-Carlton in Washington D.C. Happy to welcome to the program a first time guest off the keynote stage this morning Bailey Szeto who is the Vice President of Customer and Seller Experience IT at Cisco. Thanks for coming and joining us. >> My pleasure. >> All right so Bailey, I've actually, you know I've watched, and partnered, and worked with Cisco my entire career but you actually changed my view of something about Cisco in your keynote this morning. And that's, you know, you said that 99% of Cisco's 50 billion dollars plus is transacted online so I should be thinking of you more as like Amazon.com you know, than as, you know the networking giant I've know my entire career. >> Well You know it's certainly true that most of our revenue comes through our online presence but it's perhaps in a different manner than what you're thinking right? So obviously we do do some business direct and we might have some stragglers selling, buying something with a credit card, but that's not the bulk of our business. The bulk of our business is through primarily partners, resellers and when I say online I meant B to B transactions. >> No no. I totally understand Bailey and what I love is you're in Cisco IT. >> That's right. >> And therefore we're not going to talk about a lot of the networking pieces. We're going to talk about what runs Cisco's business and you have the pieces and you know client success and support and all those run, and even, I didn't even realize the employee engagement all runs through you know Cisco.com >> That's right. >> And I love you did a nice little video. Gave all of those that have been in the industry. You kind of go through and look at the history of like oh okay there's the HTML stuff I used to code. >> That's right, that's right. >> Back in the 90s through all of the updates and yeah we definitely-- >> I was just expecting the little triangle with the guy like shoveling dirt under construction. You know the shovel right? >> Yeah the 404 not found. >> That's right, that's right. >> I know if I go to Cisco.com/go/product name that usually was a short cut to get me to some of the things I care about but for those people who weren't here for the key note or who might not know as much give us a little bit about you know your purview and kind of the scale and scope of what you do. >> Yeah so at Cisco I'm in Cisco IT. But I'm responsible for supporting all of the revenue generation portions of the company. So that's specifically marketing and what they do, sales and what sales does. Cisco services is a very big part of our company so I support the services organization. And most recently Cisco's been on a journey to really kind of move from a once and done hardware sales motion to a full reoccurring revenue type of stream. So we've stood up the whole customer success motion. And so I run the IT portions of that as well. And last but not least you heard me mention that 85% of our revenue actually comes through our partners. So I support all the systems that are partners interact with as well. >> Yeah it's interesting so we've done theCUBE at Cisco Live the last two years. >> Sure. >> And there's a observation I made a year ago when I started going to that show. And it was you know, if I'm a networking person but this applies to you know most people in IT, I used to manage stuff I could touch and go, I understand where it is and how I touch it and everything. Now a lot of what I have to deal with is outside of my purview and therefore I need to get into that environment kind of pair that with you know companies like yourself that are inquisitive. And so you have lots of change going on and lots of things that are in your environment there so we know change is the only constant in our industry. >> Without a doubt. >> So maybe give us a little bit of those dynamics and how that impact what's happening in your world. >> Yeah so I mean we talked a bit about my responsibilities and one of this is Cisco.com It's probably one of the more important platforms that I'm responsible for from an IT perspective. But I also mentioned that Cisco's a very, we grow through acquisitions a lot. It's one of our basic business strategies. And so every time we buy a company it's a big rush to kind of take that acquired company and integrate their online presence into Cisco.com right? So once a company is acquired we don't want people to think of it as a separate company both from a kind of marketing perspective but more importantly we're actually integrating that product into our Cisco ecosystem as well. So just having to move all that technology into Cisco.com is certainly a big job. But I think you are maybe asking this from a different perspective as well which is to say okay you know new technology is being introduced all the time and while it makes sense from a company portfolio perspective I think as a former IT person you're going to agree with me it makes our jobs a little bit more difficult. It's both a blessing and a curse right? From the perspective it's a blessing in that we get this great new technology to incorporate and use in our running of the business but it also adds a lot of complexity and so it's pretty important that we have both the systems and processes to be able to manage all that complexity in our infrastructure really. >> All right so infrastructure monitoring. >> Yes. >> Something you spent a lot of time talking about. I guess I'll set it up when I talked to my friends in the networking space these days or a lot of it, the joke is if you say single pane of glass they are going to spell it P-A-I-N because we understand that there's not one tool to rule them all. >> Right. >> Yes that I might have a primary piece but in the virtualization world I had to plug in to V Center and you know Cisco has you know you laid out a broad portfolio of various tools up and down and across the stack from you know security down to physical and upper layer and plus all the acquisitions. So can you lay out a little bit as to you know where ScienceLogic fits and there's a number of Cisco's tooling that that integrates in with. >> Yeah so when I talked about our journey with ScienceLogic you know Cisco of course has a number of tool and capabilities to take care of the pieces that we are known for. For example Application Dynamics is a great company that we bought and provides great insight into application health. But obviously in a network perspective right we have Cloud Management software, security software that type of thing and so I think what we realized in Cisco IT what my team realized is that it really isn't about a single system to rule them all it's about trying to find multiple platforms that can work together and really share data so as to drive richer insights. And so I think maybe the industry has been on a bit of a wrong path think it's you know it's not Lord of The Rings, one ring to rule them all or whatever right? It's about being able to use multiple applications but having the right data insides move around as needed so that depending on your lens or your role in IT whether you're a network guy or an application guy that you're going to use the tool that's more most natural to yourself but pulling in the right amount of data from those other parts to be able to get the right insight. >> Yeah I saw your closing slide mirrored the theme we've seen at the show of superheroes. So the super power everybody needs in IT today is how do I leverage my data and we understand that it probably takes more like the Avengers to be able to put those together because data is everywhere. >> Yeah the funny thing is that that wasn't actually a set theme. I think we must all have Avengers on our mind because everyone independently came up with the super hero concept. >> Yeah no spoilers on End Game either way though. >> That's right, that's right. >> Excellent so you know can you just bring us inside of some of that ScienceLogic journey? My understanding you're probably the largest enterprised employment of it so you know we always love to talk about scale and what that means and how it's been in your viewpoint. >> Yeah you know we actually before ScienceLogic we actually had our own system that Cisco IT wrote right and so you know as IT professionals we always think we can do it better than anyone else but we've reached a point where just so much technology and so much complexity came to the market that we really wanted to find a solution that would really kind of enable us to grow into the future with all the things that are happening right whether you're talking about Virtualization with Containers or you know Cloud native applications or Multicloud, these are all technology trends that have made our jobs in IT incredibly complex. And so we started to look for what could we replace our home grown monitoring platform with and ultimately we decided that ScienceLogic was the best fit for us. And since we've deployed it we as with most things we tend to stretch the scale especially with our vendors and so I think we are the largest ScienceLogic enterprise customer at this point. But we are seeing incredible benefits in terms of being able to connect ScienceLogic's Infrastructure Monitoring with our own Application Dynamics and really marry the two for those insightful bits that we get from both. >> All right so one of the big themed discussion here is that journey toward AI Ops. >> Yes. >> While we speak actually I've got a team in Mountainview that is at the DevNet Create Show which Cisco helped organize. >> Sure. >> We're doing two days of interviews there and DevSecOps is probably one of the key topics their going to be talking about. In your keynote this morning I heard IT Ops in a discussion there so bring us inside a little bit organizationally you know what you're seeing you know your viewpoint on these various trends that are you know helping to modernize you know transform operations. >> Yeah I think from a operations organization standpoint you're going to see the applications team and the infrastructure team work even closer together. Maybe one of the things that didn't really make super clear in my keynote this morning is I actually work on kind of the app side of the house right? I'm the direct interface to the business. And as such I actually don't interface with ScienceLogic directly but I'm a strong partner with my infrastructure team who are I think they are all sitting over there that do run ScienceLogic right and so in today's world you really can't just say oh this is infa problem they are going to deal with it. Because of that really big mix of well is it an infrastructure problem, is it an application health problem? And a lot of times it's both. And so organizationally it might be two separate organizations but the need to work together is you know even greater today than ever before. >> You're preaching to the choir. I mean when we launched Virtualization and then later when Containers came around there was the nirvana that oh I'm going to have some unit of infrastructure where the application people just don't need to worry about it. >> Right. >> You know serverless from it's name seems to imply that but we understand that eventually you know there's networking, there's storage, there's compute all underneath these kind of things. >> That's right. >> It's just repackaging so you know the applications important you know I'm long time infrastructure guy. >> That's right >> But, the number one rule is the reason we are here is to run that application and make sure your data you know gets where it needs to be otherwise you know we're not here just to power things. >> That's right. And I just realized I probably would get in trouble if I said it's actually the application, infrastructure, and of course the network all has to work together. >> Yeah well that's a given. Can you just we talked a little bit about App Dynamics you know when I think about Cisco you know broad portfolio, you know the SD-WAN, the ACI how do some of those fit into this discussion are there tie ins with what ScienceLogic is doing? >> It absolutely does. So as I talked about it when we talked about that collection of super heroes it's not a single super hero it's not a duo either it's really a big team. It's The Avengers right? And so when you think about Cisco's portfolio we have a lot of additional components needed to provide that modern operating IT operating platform right? So we talked about a lot about Application Dynamics we talked about ScienceLogic but what Cisco brings to the mix is things like ACI, Tetration, Policy Enforcement, Multicloud Management. So all those things again have to work together like The Avengers do to provide that modern platform. >> Yeah you mentioned multicloud and I know in your keynote you talked about AWS and GCP. >> That's right. >> How's Cloud changing things in your world? >> It absolutely is again it's I'll go back to the it's both a blessing and a curse right? The blessing is enormous capability that we get from the Cloud, enormous flexibility. As and example using Cisco.com as an example we host a lot of you know a lot of public information about our products and websites and data sheets and that type of thing on Cisco.com. And then a couple years ago we decided we're going to refresh the engagement of Cisco.com We wanted to make it much more personalized. We wanted to incorporate video. Those are all great things but the moment you try to throw video and guess what? Native video whether it be in English or French or Chinese or Japanese depending on where you are well that put an enormous strain on our infrastructure and if you had to travel if the packets had to travel from Japan to the United States to our data center that would slow things down. So we took advantage of Public Cloud to really kind of push out the content to the edges so that we could get localized content as close to the customer as possible. That's the great thing about it. But again the management of that increasing complexity right so both a blessing and a curse. AWS, GCP, we are using for doing a lot of video streaming work. And so again great capabilities from that platform as well. >> All right so we saw this week a lot of announcements of some of the integrations Service Now and App Dynamics were two of the ones that highlighted that I think impacted you. Anything from the announcements that is particularly excited you and I guess final on that is there anything roadmap wise that you know you'd be looking directionally for this phase to evolve towards? >> Yeah I think I was excited to see in fact that's one of the main reasons why we chose ScienceLogic in the first place was the quality and the amount of integrations that they have right? And so we're also a big Service Now customer and we see the benefits of automatically open cases in Service Now when ScienceLogic detects an issue as an example right? And I would say going forward we'll be looking to either have out of the box or if needed you know Cisco IT will build something even more integrations with the Cisco products. We already have App Dynamics but as I mentioned we have a lot of other components that are critical to the network and so we'll be looking for tighter integration and all this to drive really drive data together so that we can get to what I think what most people at this conference are hoping to achieve which is really driving towards automation and AI Ops right? So that's really the desire for I think for everyone attending this conference. It's certainly our desire in Cisco IT. And you know I'm looking forward to working with ScienceLogic to building out that roadmap. >> You know so I guess final question for you you talked about that automation, where are you when it comes to we look at you know things like machine learning and automation which if you listen to the analyst that spoke this morning is like you want to make sure you separate those things. >> That's right. >> We understand you know any of us that have done process and operations is you know you can automate a really bad process and it's not a good thing. >> That's right, that's right. >> So where are you on that journey? What do you see? You know what are the barriers that keep us from kind of the nirvana where you know oh geez I can actually just seal off the data center and let everything run? >> Right I think it's funny you mentioned Cisco Live so actually I present on a topic of AI at Cisco Live as well. So what this other speaker talked about really hit home with me understanding what is AI really. Because I think there's a general perception in the press that it's like this magical fairy dust you can just sprinkle on everything and it like makes everything perfect right? AI is really good at pattern recognition but you still need to put some check points and really have human beings kind of check the work of AI right? And so you know we actually have seen data center outages not Cisco but in the press when AI runs amok right? And so I think the first step of automation that's a given. We want to do that but that involves a lot of human beings kind of looking at the data and deciding okay these sequence of events can be cured by this set of automation. AI Ops is a something that's a whole different thing if you followed the definition of AI to say okay let the computer do it all on its own. I don't think we're there yet. I think we have a ways to go. And I certainly wouldn't trust want to trust our you know multi billion dollar business to AI Ops at this point in time. >> Well Bailey there's an event we did a couple years ago with a couple professors from MIT that are really forward looking on this and they say it's racing with the machines because people plus machines will always do better >> Yes. >> Than people alone or machines alone and hopefully that keeps some of us that are a little bit worried about the Skynets of the world taking over from getting a little bit too paranoid all of a sudden. >> I totally agree with that statement. In fact the quote that jumps in my head is "Better together". And I'll close with ScienceLogic App Dynamics better together. People AI better together. >> All right well Bailey since you ended on a perfect quote there thank you so much for joining and I hope to see you at Cisco Live San Diego. >> Fantastic, my pleasure. >> All right and thank you so much for watching theCUBE as always, I'm Stu Miniman here at ScienceLogic 2019 in Washington D.C. (upbeat music)

(upbeat music) >> From Washington, DC, it's theCUBE, covering ScienceLogic Symposium 2019. Brought to you by ScienceLogic. >> I'm Stu Miniman, and this is theCUBE's coverage of ScienceLogic Symposium 2019, here at the Ritz-Carlton, in Washington, DC. Have about 460 people, it's a good mix of enterprise users, of course there's government agencies, as well as a lot of service providers, which is really where ScienceLogic started and has, many of their customers are in that space. And happy to welcome to the program, coming to us from Europe, a first time guest on the program, Luc Horee, who's RCloud and innovation Manager at Realdolmen, who's, as I said, a service provider. Thanks so much for joining me. >> Thank you, no problem. >> So, you're based in Belgium, you're a service provider, tell us a little bit about Realdolmen, a little bit about the size, scope, number of users, and we'll take it from there. >> Realdolmen is an Belgium company, around 1,500 people in an country that is small compared to the U.S. So we have an total of 11 million people. One of the biggest service providers in Belgium, but we also do reselling, and we also do service integration. Our customers it's Belgium, it's what we call SMB market. But we have around, in total, I think three thousand customers in Belgium. Some only are buying products or licenses, others are in fact in full manage service operations. >> Okay great, yeah, the SMB market, as you call it, we understand, especially for service providers, really important market, help them, I don't want to have to manage my IT, I want to be able to go to experts that can do this. >> That's one of the reasons, yeah. >> RCloud and Innovation Manager, it's an interesting title, tell us a little bit about your role inside the company. >> I already worked for more than 36 years in the company, so I had a lot of jobs within the company. In the previous job I was a Operations Manager, and now I am RCloud and Innovation Manager. RCloud is our private cloud that we are using for hosting customers and serving customers. It's and active-active data center where we can do disaster recovery, set up, and so on. So for customers that are no longer interested in building their own data centers, that's what we doing. And it's for some of them also an in between on-premise data center and public clouds. So we have customers moving to Azure and AWS, and sometimes they just stay for specific reasons in our RCloud. Innovation Manger is about how do we set up, how do we improve our tooling, and how do we improve our processes in helping and unburdening our customers. >> You mentioned the public clouds like Azure and AWS, do you have relationships with them, do you have connections into some of those public clouds? >> We are Microsoft partner, so most of our customers are going to Azure, but it's also building up in Amazon, and we did receive some questions also about Google. >> Okay, great. So when we talk about operations, service providers, very rapid change environment, typically you have a lot of customers to be able to deal with, give us a little bit about what's changing in your business, the infrastructure management and the tooling space. >> In the tooling space, IT is moving, IT in motion is what we heard in the keynote this morning. Customers are expecting a lot, about dashboarding, they want to see how their business is behaving not about what is a device doing. We need to monitor more and more applications, and the business lines. So that's why we are implementing ScienceLogic, or had an implementation of ScienceLogic, and now we doing the second phase in building more runbook automations, more dashboarding, more experience levels instead of SLS or XLS. >> Great, I want to get to the automation, but first, you've only been using ScienceLogic for a couple of years now, bring us back to, was it a bunch of in-house tooling that you had created for managing before that drove us there, paint us a picture of kind of the before and how you ended up with ScienceLogic. >> We had Microsoft Systems Center Operations Manager, we had Nagios, we had some plugs-in on this tooling, so it was I think in total six or seven tools, and we did some interfacing about it. But yeah, seven tools interfacing, not easy to run, a lot of management, a lot of people involved, a lot of skills required, so the reason was simplify it. >> So did you completely eliminate those seven previous tools? >> Yup, as of the 1st of April this year they are gone. >> All right, so there was a little bit of a journey, can you walk us through a little bit about there, was it prying it away from certain people, was it maturity from your side or from a product standpoint, what were some of those points that took a little while to get there. >> It took a while just to convince everybody in the company, set up an organization, it's not only the tooling it's also the organization need to be involved, a lot of communication, there's a change process going on, and we implemented, the first customers were in November 2017 on the system, and since then every month we added sometimes two, sometimes five, sometimes seven, sometimes one, as customer, so the people internally and externally need to get used to the product, so that's step-by-step, keep it simple, do it slowly but fast, and with a deadline. >> So, you talk a little bit about your organization operationally, what's the impact then to your ultimate end user, do they see anything, has it changed how, has it improved cost? >> It's changed certainly for the customers, because the old tools, there was no multi tenancy, there was not easy logon, so they had no access to the dashboards, they were just waiting for the monthly reporting and say, okay, it was up, okay we were, now they can have access, we use single sign-on to do that, so the customers are happy that they can see, they can see line of business dashboarding, and so on. And certainly internally it did improve a lot of cost savings, because a lot of the things we are doing now is automation, and we started the integration with our ITSM tool, and that will go live, normally next week. >> Okay, what ITSM tool are you-- >> It's a German tool, it's from a company called OMNINET, and the tool is called OMNITRACKER. >> Great, talk now about that automation, where have you come so far, where do you see it progressing in the future? >> We started first with some task automation, we have an 24/7 operation team, first-line, and they were doing a lot of manual tasks, so where we can, and what we first did was automate some manual tasks. And now we are progressing with ITSM integration, bi-directional integration, and then we will start with removing from old mailboxes, where we can do some restart automatically, so we will take a look at the incidents, see what we can do, see if we can do some automation with that, and we will certainly progress very far, as far as possible to do more and more automation and less manual work. >> Great, tell us, you've attended this event before, what brings you back to the event? >> First of all I want to see a lot of the demos, what's coming, because we are today, 8.12 version was announced, we are on 8.9, we will move next week to 8.10, so what is coming, so I have to talk internally to people, okay, what's coming, I need to convince all program managers, service delivery managers, I can talk to customers what is coming, what they can expect, so that's one of the reasons. The other reason is to talk to other customers of ScienceLogic, what are you doing, what's helping you, what's not, and so on. >> Yeah, I noticed one of the things they talked about is making it easier to upgrade from versions, when you think about the cloud world, as we talk about it, is, if your customers are in Azure, you don't ask them what version of Azure they're running, you're running whatever version Microsoft has it, they patch it, they update it, if security fix happens it goes there, when you talk about moving from 8.9 to 10 to 12, that process of when do I do it, how do I do it, how's ScienceLogic doing it, keeping things easy to upgrade, were there things in the keynote that you were ready to jump on? >> We started with, the first version 8.3 or 4 I think, and we always try to be in good shape in the newer releases. So we already had some experience with upgrading, and it's going smooth. And whatever I heard from the system engineers, it's going better and better and better. So normally we have only a very small outage to do that, in fact it should be minimal, sometimes they switch over or something like that, when a database is changed, but normally operations is always running 24/7, and there is no interruption for operations. >> Has there been anything at the show that you've seen so far, either through the demos, talking to some of the experts, or in the keynote, that you want to highlight? >> One of the things that I have seen is the connection with the application, with the APM tools, that's what our customers also are requesting more and more, the integration of infrastructure and application, and the multi cloud of course. >> Yeah, that's definitely something we've heard. All right Luc, I want to give you the final word, things to take away, for people that haven't come to a ScienceLogic event, what you think that they should take away from an event like this. >> For me the greatest take-away is come here to learn. Come here to see what is possible, what the future is, what AIOps will mean in the future, prepare yourself for the next three to five years, that's the main reason. >> Great, well thank you so much, preparing for the next three to five years, we know the pace of change isn't slowing down at all, so it's great to be able to talk to a practitioner that's helping to manage and deal with so many of those environments, thanks so much for joining me. >> Thank you. >> All right, and we'll be back with more coverage here, be sure to check out thecube.net for all interviews, I'm Stu Miniman, and thanks so much for watching. (upbeat music)

>> From Washington DC, it's theCUBE. Covering the ScienceLogic Symposium 2019. Brought to you by ScienceLogic. >> Hi, I'm Stu Miniman, and this is theCUBE's special coverage of ScienceLogic symposium 2019 here at the Ritz-Carlton, in Washington DC, about 460 people here, I'm told over 50% growth, from last year's event, the first time we've had theCUBE here, really excited to be able to dig in, with a number of the executives, customers and partners, and no better way, to kick off than one of the users, here at the event, actually coming here from across the pond, here to the district, happy to welcome to the program first-time guest, Nigel Wilks who's the Head of Global Tooling at Computacenter, based in the UK, Nigel, thanks so much for joinin' us. >> Hey, a pleasure. >> And joining him from ScienceLogic we have Clive Spanswick, who's the Vice President of Sales from EMEA, Clive thanks so much for joinin' us. >> Pleasure to be here. >> All right, so Nigel, set the stage for us, coming to the event here, tell me what brings you here, and tell us a little bit about Computacenter. >> Yeah sure, so, we're a relatively new customer to ScienceLogic so, I think, what, we signed two, three weeks ago? So, not deployed yet, but got great expectations. So, there's a lot of background research in the sessions. Finding about more what the additional capabilities that we can unlock, which will help drive our business further forward. So, Computacenter is a large IT provider, global. Based in the UK, as headquarters. My area of the business is in the Managed Services sector. So realistically, we're looking to reduce our cost to serve. Be more proactive for our customers, and, we've got great expectations of what ScienceLogic can do around those areas. Unlocking more automation, and eventually leading down the AI path. >> So Nigel, what I heard in the keynote is, some of the same themes I've been hearing around the industry, we are unparalleled as to how fast things are changing in the industry, there's just more complexity, there's more heterogeneous environments, for companies like yours, usually agility is one of those things that's coming to the top of the environment and oh my gosh, when I became an analyst about nine years ago, it was the tooling and management options out there where usually some of the things that customers would say are weak in their environment, and something I think I've heard for my entire career, so, maybe give us a little bit as to some of what you're hearing from the business side and how it makes sure that you can run your services faster and ultimately serve your customers better and how your look at, I don't know whether you call it AI Ops, but this whole space, fits into that environment. >> Sure, so-- >> Yeah. >> We've with probably a lot of organic growth within Computacenter over quite a short period of time. Also through acquisitions, we've got quite a fragmented tooling landscape globally. So, nearly two years ago, we kind of set on the journey to become more of a global entity, and certainly from my perspective a tooling landscape. Looking to consolidate those down, simplify our services, again helping reduce our cost base, and then leverage the automation stuff I talked about earlier. So, just going to ScienceLogic, we're moving away from some of the big names. And consolidating over 50 tools into the one ScienceLogic solution. >> Wow. That's great, let's bring ya into the discussion Clive, yeah, I heard in the keynote this morning, it was, the typical customer, it's at least 14 tools. >> Yes. >> That get consolidated down. I think back about five years ago, frictionless and simplicity were the terms that I heard. I talked to a lot of companies, it's like "Oh okay, yes I've got integrations I need to do "if I'm doing acquisitions, whether I be in, "if I'm in services of course that's there" But, you know, financial industries, and, heck, Cisco IT who I'm going to be talking to later, does an acquisition a month, what are you seeing, give us a little bit of the EMEA flavor and-- >> Sure. >> How what Nigel's saying, how is that resonating with your customer base? >> Yeah, absolutely Stu. So we see this a lot with the leading service providers now that are really being challenged by their customers to really extend their portfolio of services, over an ever more diverse range of technologies, and this is one of the big challenges that has driven tool sprawl over the course of the last seven to ten years, so simplification of the toolset, is really one of the key drivers to really deliver outcomes for efficiency, so a lot of the way we see modern service providers operating today really is all about automation, to get to better automation at a lower cost you have to drive simplification into the tool chain. So, we see this a lot with our customers across the region and indeed worldwide, that taking the tools landscape and really collapsing that into a much more simplified model is an essential ingredient to drive efficiencies that then in turn can be delivered to the customer as lower-cost services, so that's the real driving force that we see for customers today. >> Alright. Nigel, we'd love to hear, I know you've just gone through the process of choosing but, what are you looking for, are there specific business drivers, how will success be measured in your environment? >> Part of the process was, to look at what our business requirements were. And map those on through an RFI process. Of which ScienceLogic were one of the vendors that took part. So I think at the benchmark of everything we did, at the heart of the whole process, was that business requirements. Just making sure that whichever toolsets we selected, would go down that route. We never expected to have a single-vendor solution, which, fortunately we've got ScienceLogic which covers the majority, but with the partner ecosystem, some of those guys are here today. It kind of rounds it up for us. But moving away from our current providers, some of those, they present challenges to us as well. Tryin' to unlock data that's within the platforms, some of those tools are through acquisitions. So as much as you've got a brand name as part of a whole stable of tools, they don't inter-operate very well. And the beauty of going to ScienceLogic was, everything comes in together, even the partner tools, which allows us to really look at what we can do in the future. >> Alright, so, Nigel I've got the tough question for you. When I came into the show, one of the things that really struck me, is how data's at the center of what's important here, you know, when we look at companies, digital transformation often is a buzzword, but, we've really defined the difference between the old way and the modern environment is, how is data something that can actually drive your business, are you data-driven in your decisions, can you monetize data, what I heard in the keynote discussion is, data's such an important, not just the collecting but leveraging, and that's driving the intelligence, the automation. How much did that focus on data play into your decision, and can you give us a little bit of insight as to how your company looks at the role of data in the IT world today? >> Well, it's very important, that's quite a simple solution to that one. So for me, an infrastructure tooling perspective, being able to bring all the data into one place but contextualize it as well, means that we can then do some good stuff. Again, driving us down that automation path but from an end-user point of view we've got end-user analytics, that can open up a lot of different worlds for us, predicting what issues users have, rather than calling a service desk, theoretically, going further down the future, we'll be calling them to say, "I can see you've got a problem, "I can fix it for you remotely." Those kind of decisions that we can make from that data. But in my kind of space, the infrastructure tooling side, we need to go onto that AI Ops journey, and as you heard this morning, now, or at least a few weeks ago, to get there, it's like getting the data into a good shape, knowing what we want to do with the AI Ops moving forward. So, automation's a good candidate, that helps up achieve some of our objectives, reduces customers' downtime as well but, we've also got to be careful that we're not tryin' to automate resolution to poor behavior. >> Yeah >> Yeah, so, rather than fixing the root cause, we need to actually look at things and say, "Is this an incident-worthy event, "is this something that "we need to actually do something with, "or is it just an automation candidate?" And it's going to drive some of those behaviors for us. >> Clive, I'd love to get your viewpoint as to what you're hearing from customers, when I listened to the analyst this morning, he's like "You need to really differentiate "between that machine learning piece, "and the automation." Because any of us that've worked in operations environment, you can automate a bad process. >> Yeah. >> And data doesn't necessarily mean good information, so we need to manage those things a little bit separately, and that maturation of where customers are for both automation and intelligence, is a tough one, when they did a poll when your CEO was up on stage, nobody's fully, turn things over to the computers. >> Yep, yep. >> So, where are your customers, how are they thinking through the AIML, the use of data, and those pieces? >> So see, I think to be fair, a lot of customers today, AI Ops as we know is a relatively new term to the market, so I think a lot of businesses are struggling to recognize their own maturity, and I think, what we learned from this morning from Dave Link, our CEO, about how you characterize yourself on the journey to AI Ops maturity I think is a very valuable thing, and I think as I look at a lot of the customers and we saw from the poll earlier in the main session, that a lot of businesses today are fairly in the middle of maturity, so they're really at about the point of consolidating all the data in one place, the next big step of that of course is to clean that data up and contextualize it, so that you can start to leverage that data for the meaningful outcomes, and that's really where the smarts of machine learning and early-stage AI really start to play. We still, to be fair, still a long way off from the realization of full AI, but there are many pragmatic things that you can do, to get you very well level set, to take full advantage when those opportunities start to present themselves. >> Alright. So, Nigel, you're goin' through this process to really modernize your toolset you said you're replacing a whole bunch of things with the new one, what ultimately will this mean to your end-user customers? >> I think a more proactive service. Just dialing it back down to the simple things. If we simplify our service, we can have, from a business point of view, we can be consistent in how we deliver service globally. But from an end-user point of view. At the end of the day, most of the stuff is event-driven. End users typically find those out before systems do. Just from whole new cycles, reducing false positives and things. But it also means that, again, automation is being at the heart of what we want to try and achieve. We can automatically fix these things, so it's less downtime. And then hopefully we can just kind of prevent. Automation's great, but prevention's better. >> Yeah. How do you see your journey going forward, when you look at that automation, I mean I can't imagine you at a day one, your desk, putting everything in and everything's there, do you have a roadmap out there as to how you look at your deployment and how you're going to change things internally? Yeah. This, realistically, is going to be a catalyst to how we do things. So what starts off as a tooling replacement project, becomes that overall, we can do things global process. Working a little bit smarter than we have been before, doing things on a larger scale, but using common processes. That's quite a big shift in how we work now. But also means from our sales forces perspective, they're selling the same thing, it doesn't matter which country they're in. It becomes more about delivery location, and a language. >> Great. Clive, give us a little bit as to, what are customers like Nigel, what should they expect once they've made the deployment, how long does that transformation take-- >> Sure. >> And what's the day one and then, three months, six months out? >> Sure, great questions. So the whole journey that we're exploring, with all of our customers, is this move to AI Ops and they've done really the support of the resilient digital experience for their customers. The journey itself is continuous. So, one of the big challenges that we know to be true in the space that we operate in, is the demand for constant change. So the idea and the process that we're going on with, with computer sensor is that, we will take you through a series of maturity stages, of crawl, walk and run. And then once we get them to run, it will be a case of continuous improvement and continuous development. We expect to get to the first break of that within the first quarter, we're going to be delivering instant value from the platform pretty much from the word go, but once we get into the process of business as usual, running the operation, it really becomes about the improvement of moving, from really the stages of helping them react better to incidents, and then moving into a much more proactive and predictive state, and then finally, the endgame of this of course, is to really get to the point of, automate to avoid the incidents happening altogether, and that really, I guess, is where we start to step towards the ultimate vision of AI Ops and the things that that can bring to bear. >> Alright, so, Nigel, I want you to take me inside your team, 'cause on the one hand we say, "I have a whole bunch of tools, "I'm going to simplify and I'm going to unify "and that's going to be great." And I'm sure there's many on your team they're like, "Ah, I hate this tool, and this one's a pain "and this and that. "But we kind of know how "to do everything that I'm doing today." So, one, give us a little insight as to, is there some of that clinging to the past, and, on the other hand, are there some things that, like, "Oh my gosh, I'm glad I will never have to do "one two or three ever again, "once I've gone through this process"? >> Great, great question, so, everyone has their favorite tool, or favorite bit of software. I think, internally, we've clearly got that challenge as well. But it's fair to say, the reverse is true, there's a lot of tools out there that the user base are more than happy to get rid of. But ultimately, I think as we've gone through the cycle with ScienceLogic, and certainly we've had some good workshops with the various user base, highlighting what's possible, we've had some really really positive feedback. I still expect challenges, change, change is a big thing, most people don't like change but, I think there's a great opportunity for people to, at the end of the day learn a new tool. Something different, something fresh. And also then, they can think about what the tool can do, how can we exploit it more, so, we're not locked into the model that we were in before, the tools that we'd use for years and we've worked in the same way. We've got an exciting journey to start looking at how we can derive better services, how we can simplify our services. How we can let customers self-serve, to a degree as well. So you know, I think it's an exciting journey that we're on. And I think it'll be good to come back next year and demonstrate where we are. >> I love that, I definitely want to talk about that, Clive, give you the final word on this. What final advice to you give him, he's made the decision, he's goin' onboard. Tell him, I'm sure, unicorns and rainbows and everything's going to be phenomenal, but, what are some of the things you hear from your customers as they roll things out, give him a little bit of the "Yay" and a little bit of the-- >> Sure >> Just "Hey make sure "we've educated everybody on this." >> Yeah, again, great question Stu. So, from working with our customer base, the big thing that we see is that this is a continuous journey. The journey doesn't stop. What we do is we make things progressively easier, and the opportunities to scale and standardize are almost limitless. I guess the one word of counsel I would give is that, one of the big things that we see, with any major transformation, we're talking about the automations we can deliver around monitoring but, with any transformation it is really how you start to shift the culture of the organization to work a way around the new ways of operating, and really winning the hearts and minds of the guys that this stuff is going to make the biggest difference to. So, we're talking in the first instance of course about the operational stakeholders and the key users, having them engaged, and really working that process to get the maximum benefit out of the platform. From there, really is about the improvements that they can achieve in customer experience and of course, as Nigel has already said, a lot of that is really centered around the opportunities it's going to present them to show real innovations, around their service portfolio and my guidance there would be, don't be shy to show the world of the possible, to your enterprise customers, because they are demanding more, and there is so much that they can do with the platform to really unleash super value to their customer base. >> Yeah I love that, the world of the possible, we understand all the stresses and strains put on business and IT today so, Clive, Nigel, thank you so much for joining us, Nigel we look forward to hearin' how things go, catch up with you in a year maybe. >> Pleasure. >> Of course, thank you. >> Alright, so we'll be here all day at the Ritz-Carlton in Washington DC, ScienceLogic Symposium 2019, I'm Stu Miniman and as always, thank you for watchin' theCUBE. 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