from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hello everyone welcome to this cube conversation here in Palo Alto California I'm John Fourier host of the cube we are in the cube studios we're here with Ranga Rao's Senior Director of Product Management Cisco Networking group and David link the CEO of science logic tell what their part is guys great to see you thanks I come in it glad to be here so you guys had a great event by the way symposium in DC thanks a lot of momentum yeah it was fun to watch he was there eight videos up on YouTube but you guys are classic partnership here with Cisco talk about the relationship you guys are meeting in the channel got a lot of joint customers a lot of innovation talk about the relationship between science logic and Cisco Cisco and science logic have been working together because our customers demand us to work together right so across more for more than 10 years science logic has been a strong part of for Cisco working across various different business units when we started working on ACI which is application centric infrastructure which is the product that my group works on we thought science logic was a perfect partner to work with and in fact some of our joint customers including Cisco IT which is a customer of science logic two came to the table and said we needed an integration with science logic so customers are a huge part of the genesis of the partnership and that's what keeps us going together and the fact that we have such strong synergies from a technology perspective makes it really easy for us to collaborate and the fact that we have both open platforms with strong api's makes it really easy for us to collaborate and this is the real haveá-- we're here to the chalk tracks around you know these API so this abstraction software layer zci has kind of gone the next level we covered that at Cisco live Dave we talked before about your value proposition you're on the front lines Cisco obviously has this new programmability model where their goal is to leverage the value the network abstract away the complexities and allow people to get more value out of it how is that working in what and how do you guys tie into Cisco it's really an ecosystem of technologies and partnerships that deliver outcomes for customers Cisco's advancing technology so fast we've seen an innovation sprint from Cisco it's actually causing us to sprint with them on behalf of their customers but ultimately we've had to introduce deep integrated monitoring across all the fabrics that they support for software-defined and that includes visibility into the ACCAC eye fabrics but it also goes through into the virtual machines the storage layers the operating systems the application layer so when you pull all of that together that's a day to challenge for the enterprise to make sure they're delivering outcomes to the customer that are above expectations recently we supported support for multi-site and multi pod ACI fabric we're working on a CI anywhere in the cloud we're really Cisco's extending the datacenter hyper-converged solutions to deliver value propositions no matter where the applications live so that's a huge step forward and then it causes operational initiatives to say how are we going to solve that problem for our customers no matter where the application lives so that's really where we're focused helping solve problems on that day to side of make all these technologies come to collect together to deliver a great outcome for the end-user and so they're enabling you with the with the ACI if a hyper converges to go out and do your thing yes so we instrument all those different really abstracted components because what we have with container management with Software Defined it's abstraction on top of core routes which and server and hypervisor technologies that bring it together in an intuitive way ultimately what we've seen from the enterprise and service providers is they really want infrastructure delivered as a service and that's really where Cisco's headed helping make that a reality with these products we just helped bring them together with the instrumentation analytics to operate them as one system you know ranking this is a great example of what we're seeing in this modern era with the data center on premises modernization growth of the cloud the advent of you know real hybrid cloud and private cloud as well as public cloud you guys are in a good position so I want to kind of dig into some talk tracks one you mentioned day two operates I've heard that term kicked around before cuz this is kind of speaks to this modernization in IT operations you know enabling an environment for you know Network compute storage to work seamlessly this is this is the real deal what is day to operations can you like define what that is yeah so our customers essentially go through the journey of building and network for some purpose to deliver an application or a service to their end users so the we think of the process of them building the network is day zero configuring the network for the particular operator a particular service as day one and everything that they deal with which is a lot of complexity which is they where they spend 80% of their time 90% of their budget has data operations which is a very complex domain so this is the area that we have been focusing within our business unit to make our customers lives easier with products that essentially solve some of these problems and collaborating with partners like science logic to make the operations of our customers much easier a important part of data operations is making sure that we provide the light right kind of abstractions for our customers initially customers used to configure switches on a switch by switch basis using command line interfaces with obscure commands what we have done within the data center as of like 2014 is brought in the application centric abstraction so customers can configure the network in the language of the application which is the intent interesting you know I'm old enough to remember those days of standing those networks up day one day zero on day one but I think day two has become really the new environment because day 2 operates was simply you know make sure the lights are on provision the switches top of racks all that stuff that went on and then you managed it you had your storage administrator all these things we're kind of static perimeter based security all that now is kind of thrown away so I think day two is almost like the reality of the situation because you now have micro services you've got apps and DevOps manding to have the agility and programmability the network so I got to ask is if that's true and you got cloud over the top happening this means that the software has to be really rock-solid because it's not getting less complex it's getting more complex so it's what is science logic fit in today too we've been focused on all these different technologies you bring together so from an intent based perspective Cisco's been really focused on intent based solutions but that lines up to a business service the business service is made up of a lot of different technologies that can come from many different locations to deliver you an application to you where you're super satisfied with an outcome its delivering productivity to you all the great things that you're hoping to experience when you interact with an application but behind the scenes there's a whole myriad of technologies that we instrument from a fault configuration performance analytics and really an analysis perspective to see all these multivariate data streams coming together in a hub where we can analyze them and understand the relationships the context of how all of those data feeds come together to enable a service so if we know that service view again no matter where the service is coming from in Cisco is now supporting ECI anywhere so that service could be sitting in a lot of different places today and we're seeing more and more hybrid applications and I think that's around for a long time to stay for good reasons security compliance and other reasons you've got to bring all that together and understand real time the real time operational viewpoint of how is it now and more so that proactive insight to know if you have an anomaly across any one of these performance variants how that may impact the service so is it going to be impacting the service and really help operations stay proactive I think that's where the DevOps and focuses right now look at evolution of DevOps gene King was on the cube said 3% of enterprises have adopted Ewa certainly there's the early adopters we all know who they are they're there DevOps cloud native from day one but really the adoption of DevOps is not yet there on mainstream is getting there but you're speaking to day two operations as kind of like operations you mentioned developers the apps that needs to be built to require all this infrastructure program ability this is where I think a CIS can I guess you need intrument ation so you need IT ops and you got to have program ability of the network but everyone's talking about automation so to me it sounds like there's an automation story in here if you got an instrument everything you got to have move beyond command line and configuring is that how does that fit it how's the automation finish yeah absolutely first of all within the ACI fabric we have we have a controller based approach so there's a single place of managing the entire infrastructure today we have customers who use two hundred-plus physicals which is being managed by a single point that's a huge amount of automation for provisioning the network from the perspective of managing the network the controller continuously looks at what's going on and essentially we have a product core network assurance engine which look which are which is a second pair of eyes which will tell you proactively if there are problems in the network right but a broader automation is needed where you can actually look at information from various different silos because network as important as we as cisco think is one part of the whole puzzle rate information comes from many different places so there's a platform that's needed where people can funnel in pieces of information from various different places and analyze that pieces of information figure out trends find the things that are of interest to them and operate in a data-driven fashion they want to get your thoughts on this next talk track around the impact of the cloud because if this happens the automations is pretty much agreed upon the industry therefore we've gotta automate things that are repetitive mundane tasks and certainly the network's a lot of command line stuff that can be automated away value will shift to other places but with the impact of cloud operation the operational side of the data center is looking more and more cloud like so in a way whether the debate of moving to the private cloud versus on-premises goes away and it becomes more of a cloud operation story on-premises multi-cloud on public clouds is kind of a new system this is the operational shift this is where all the action is talk about your perspective on this because this is kind of like you know it's not a simple saying lift and shift and moving into the cloud it's I want cloud like economics I want cloud like elasticity I want all that benefit on premises that's day two in my opinion would you agree I do and I think that's sometimes lost on the industry that we have a lot of clouds that we have to serve and for good purpose they're gonna live in different places but back to the earlier comment you've got to then pull information into a data hub I'll just call it in an architecture of data where you've got it from multiple sources whether it's clouds or private hyper-converged the wireless to the end-user all these different layers often that are being abstracted we've got to really understand how that relates to a user experience so when we think about what are the end results we're trying to achieve we're trying to be proactive so among the things that we're working on from a vision perspective instead of thinking about waiting for a system across any one of these tiers to have a fault where it tells you I've got a problem here's a trap here's a log I've experienced this problem we really want to do a lot more on the front end that performance analytics the anomaly detection to get across multivariate all these very 'it's mean this kind of performance health and in a performance score cisco has been investing heavily here we have as well jointly for some of our customers what I'd like to see in the future our vision is that you rely less on fault management and more on the proactive analytics side so that you understand anomalous behavior and how that could impact your experience as an end-user and fix it through automation before there is a problem so that's a very different thought I'd love to say our industry should in the future worry less about event correlation and more about predictive behavior so that's where we're spending a lot I don't like so wherever the false look for the goodness to I mean where's the zag lesion but you have to have all these data streams and you have to understand how they can textually relate to one another to make those important decisions and recommendations well you know I've always said this on the cube you can you know in this world of digital you can instrument everything so you soon it's going to be a matter of time for seeing what everything's happening but knowing what to look at it's kind of like what you're getting at yes hey Rach I talked about your your perspective because again and one of the things that Dave Volante and I just do many we talk about all the time on the cube is we debate this cloud conversation because I think my opinion is it's one big distributed architecture second operating system the cliff it's all the cloud they're all edges nodes and arcs on a distributed a dissenter certainly isn't going away but if everything is a network connection well that's the edge your data center you got this is you're in the business of networking right what's your take on all this because you know if it's a cloud operations it's a shift from the old IT to the new IT what's your perspective on this so the moniker that we have been using this year is that there's nothing centered about the data centers like you said there are workloads that reside in many different environments including the cloud so customers are demanding consistent operations and consistent management capabilities across this many different environments right so you're right the data center itself is turning out more to be like a cloud and we have even seen large cloud providers like offer solutions that sit within a customer's data center right so that's one area in which the words evolving another area is in terms of all the tools that are coming together to solve some of the operational problems to be more predictive and more proactive yeah you know I like to draw horns sometimes too many minute we keep on coin the term private cloud years ago and everyone was throwing hate at them you know on the way I don't know what's this private cloud nonsense if that's what's happening there's a private cloud it's a hybrid cloud multiple clouds you have public cloud and again you're gonna have multi purpose pick the right cloud for the workload kind of environment going on kind of like the way the tools business would but it's still platform so so guys thanks for coming in and sharing your insights I really appreciate that before we go take a minute to get the plug in for what you guys are working on give the company update what's going on you're hiring revenues up what's happening give us a state of the science logic what's going on so we had a great first quarter the best first quarter in the history of the company the health of the business is good I think the underlying theme is the transform of infrastructure is causing a lot of people to rethink the monitoring tooling as to how do we need to manage in this new operating environment you mentioned DevOps I think the real key there is the developer really wants to have the application be infrastructure aware and he needs good information coming from not 50 places but from a trusted place where he can make sure the application knows about how all the infrastructure that's supporting it no matter where it is is behaving and that's really the wind behind the sales driving our business we grew quarter-over-quarter sequentially with our subscription over a hundred percent in q1 so we're really thrilled with where the business is headed excited about the momentum and this is a really important partnership for us because everybody uses Cisco all of our enterprise customer service provider government customers Cisco is embedded in virtually every customer that we work with so we have to have the best support kind of that thought leadership of support for our customers for them to entrust management of those core applications through our platform right it gives a quick plug for the data center networking group what's happening there what's the the hot items what's the with give the plug quickly so very quickly I think the journey that we have been on is a CI any where to take a CI and its management and operations paradigm to many different environments we introduced support for AWS earlier in this year we are working on support for Azure and soon we'll have support for Google public clouds in all these environments we want our customers to have consistent experience and the way we get that is through solutions working with partners where we offer consistent solutions across all these environments for our customers and working with science logic as a very important partner to solve problems for our joint customers and you guys have always had a great Channel great ecosystem now you have not new for you to partner yeah we have like open API is open platform 65 plus partners that we work with so all customer focus well let me put you on the spot one last question got you here because your guru and networking and you know you've been around the block you've seen the different waves what's the biggest wake-up call that customers are having with respect to the old way of doing networking and the new way cuz clearly everyone has come to the realization that the perimeter based security model and static networking has to be more dynamic what's the big wake-up call that you think customers are seeing now with this new modern era I think customers are realizing more and more how important technologies part of it as part of their business sometimes it even drives the techni drives the business and helps customers make ditions on what's the right path to take for their business so what this applications become really important and the nerve center which is the network that supports the application becomes really important so customers are demanding us to build the best network possible to support this modern world that's continuously evolving so did you think a stab at that customer wake-up call what's your perspective on this what's the big R from your experience over the years you can't use tools that were built 20 years ago to continue operating global networks so we see a lot of the industry it's about a ten billion dollar total addressable market changing over because the market fit of the old tools that people have relied upon for many years aren't solving modern problems Oh guys thanks for the insight appreciating and good to see the partnership doing well thanks for coming into the cube studio we have Ranga Rao senior director of product management Cisco Networking group and David Lynch CEO of science logic here for cube conversation I'm Sean Fourier thanks for watching you [Music] you

>> 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 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 inspirational music) >> Hello everyone, I'm John Furrier in the Palo Alto Studios for Cube Conversation. I'm here with David Link who's the CEO of ScienceLogic. David, thanks for coming in. Good to see you. >> Great to be here, John. >> So, thanks for coming in. You came in from D.C., that's where your headquarters and ScienceLogic, you guys are having good business run right now. You're self-funded early on, now you get to venture back. Take a minute to explain how you guys got started, what does the company do? >> So, this is the classic story of entrepreneurship. We started in the garage. Myself and a couple of co-founders believed that IT management operations was broken and it was broken because a lot of the industry had really focused on having silos of data, the silos of data, the network, the application, the security, the storage, now cloud, containers and every technology had its own data silo of manageability. We believe that that was intrinsically wrong to understand how the service that combined all these different applications and technologies was behaving. We wanted a service view, so we brought it all together, kicked off, really the first seven years we boot strapped the company, the first year and a half we coded, got the product to market, it grew very quickly, got to the Inc. 500 a couple times, and then we attracted a lot of financing options. We had about 250 companies approach us. We never made one outbound call and fortunately, we had some really great and strong investors in EA, then Intel Capital, and three and a half years ago, our last round of financing was with Goldman Sachs and they've really been a great catalyst to help us continue our growth over the last five years. I think we've grown about 540% on the revenue side, so it's been an exciting time. >> Well congratulations. It's always a good success story to be a hot deal when you don't have to make any calls, they come to you. >> Yes. >> And that's good, that's part of growth, but I got to ask you what year did you start the company up? >> 2003. >> So, it's not obvious then, it's obvious to you as a visionary, but now people now know IT operations is broken. Cloud highlights it in a big way. The lights get turned on, the cockroaches are running around, but web services were still booming at that time. You start to see the beginning of the whole web services movement, you guys saw this early. Now, it's well recognized that IT operations can be automated away and Cloud certainly has an automations vibe to it. AI has been a big part of the AI operations. Is this kind of where you guys started with that vision? Was the original vision kind of where it is today? Take us through kind of what you saw and what's happening today. >> So, thematically we have this next wave of the computer architecture, Cloud computer architecture, edge computing where the way you manage that kind of infrastructure is different than the classic client server. There are different needs, different requirements, and that thematically has led with the change of infrastructure. Applications are changing and applications are now more infrastructure-aware. When we started the company, usually applications sat on one system or a cluster of systems and they weren't widely distributed. So now that the applications profile is changing, the architects are changing to microservices, that really puts huge strain on our industry. The industry, the total adjustable market, is about 25 billion dollars a year annual spent on tools. John, if you can imagine that. 25 billion a year is spent. It's going through an amazing, I would say, tectonic shift because why? Infrastructure's shifting and as more people move workloads to the Cloud into what I would call ephemeral workloads where they're moving around, that causes all kinds of pressure on the systems and record to manage that so that you understand what is happening at this moment in time. Where is it? What Cloud is it running on? How's the application performing? And you really need to tie the application to the infrastructure real-time. >> I want to get your thoughts on this. I interviewed a CIO this past week for a big company. I won't say the name 'cause we haven't published the video yet, but he told me candidly, he said that, look it, we outsourced everything and we outsourced our way into oblivion and what he meant by that was is that the core competency of IT, and he reference the book, Nick Carr, IT Doesn't Matter, which kind of was true, but wasn't true. Now, IT has a competitive advantage and essentially, they had this anemic IT department that was outsourced and they lost their competitive advantage, so he's like the reinvestment in IT is more than ever now because of Cloud, because of these new environments. So, I kind of believe that to be true. I'm sure you do too, but the reaction really is is you've got a lot of Legacy vendors that were dictating how to do things. >> Yes. >> I'm IBM, I'm Oracle, you got to do it this way and you were kind of constrained, IT was constrained by that. Now, you got to be much more agile, you have workloads that are dynamic, provisioning, orchestration, this is a whole new dynamic. What's the impact to the IT buyer, the IT environment with this new model, this new modern dynamic, new modern era? >> When you think about CIOs and CEOs, the pressure that they have to be Cloud first. Cloud first is such a strong... At the Board level, there's pressure. The adoption of Cloud now is happening faster and more rapidly than the adoption of virtualization, maybe it's doubling in the speed in the time warp, but what that means is that most CIOs are dealing with as many as nine to 11 Clouds, not one. You have a federation of Clouds: Private Clouds, public Clouds, software as a service Clouds, and that's your IT landscape, so it's changing so quickly that you have to think of it in a more federated approach. That means that the way you used to manage your private systems, and now your public systems, are really different and you've got to look at them more holistically because often they're communicating with one another in hybrid architectures. So, that's really the heart at our mission, to provide the context of how all the services you're trying to deliver as a CIO are behaving. What's their availability? What's the risk of the service having a problem? And knowing that real-time is ultimately what you want to do with your Cloud first strategy, but you need the right tooling operationally to affect that kind of outcome for your team. >> So, what's the core problem that you guys are solving? 'Cause obviously, there's a lot of complexity now, it's a new environment, so I still got the baggage of some Legacy environments. Is it monitoring you're solving? I guess, what's the core problem is my question that you guys are solving? If you had to kind of finish that, the core problem is blank. >> The core problem is visibility. The Holy Grail is application to infrastructure and the problem is that's becoming so complicated because everything is moving around. The more abstraction layers where it's a container, which is abstracted on top of a virtual machine, which is on top of bare-metal server. SD-WAN is an abstraction on top of an MPLS network. So, you have all of these layers that get from a software-defined perspective, they get abstracted away from the actual equipment that it's running on. Well, when that happens, where is the problem? Because it's moving around. The problem isn't in one place. So, that application to infrastructure awareness, it's almost like one of the things that we've looked at in the world of Facebook. You've got a lot of relationships, you've got videos, you've got friends, you've got all these different connections that are constantly moving around with data streams. What we do as a company is pull all these different data streams from the technologies themselves, from the Cloud providers, from the application layer, pull it together in a data hub that we can then understand how they all relate to one another so you can really, truly understand service impact and that is the crux of the problem most companies are dealing with now. You've got to fight with your Legacy, 'cause you still have that and it's not going away tomorrow, so you've got to make sure you're good at that, you've also got Cloud, the Cloud first initiative, and then you've got in between systems that are using both. That's really where we play. We're really good at the Legacy, we're good at Cloud, and connecting the two together and that is a really tough space because most Legacy providers really didn't get good with managing hyperactive ephemeral Cloud estates. The guys who started over the last five years building tools to manage the Cloud are really good at Cloud, but they don't cover Legacy. They're not going to cover a net app or hyper-converge, typically. So, we combine the both, Legacy and Cloud together in one management system, monitoring management paradigm, and then there's an automation engine where we actually proactively remediate problems real-time. So, the three together is where algorithmic operations, AI Ops, comes together. >> David, I want to dig into the offering, but before we get there, I want to get your thoughts on two trends: one is multi-Cloud. Recently, we've seen a lot of hybrid Cloud discussion, but now the big hubbub is multi-Cloud and the other one is AI Operations. So, I've been saying on The Cube, everyone who's in IT Operations is screwed, going to get automated away by AI. It's kind of tongue in cheek, but it's kind of a reality is that those old business models that were based upon certain service levels are going to be done in software. Now, you've got multi-Cloud. So, first question is what is multi-Cloud definition that you have for that? What does it mean? What is multi-Cloud? >> In our world, multi-Cloud is... Most large organizations use more than one Cloud and half of that is driven by what Cloud is best to operate a particular application profile? Amazon's really good at a lot of application profiles, but Azure might be better at certain Microsoft profiles, and then Google has profiles, and IBM Watson has profiles. Depending upon what you're trying to do with the application, where it was born, how it's living, how it's been re-factored, you're going to use one Cloud or the other, but most customers that we see have many Clouds. There really isn't one Cloud management scape when you're using... Vendors are still reasonably proprietary in the public hyper-scales. >> Some are better than others. >> And some are better. It depends on the use case. So, we try to bring all that together so that you're not looking at four panels, you're looking at one. >> So, you make it easy with one dash port. Okay, AI Operations. This is a hot trend, a lot of venture capitals are funding companies that have AI Ops in it, machine-learning obviously booming, no doubt software automation is coming. I'm seeing it everywhere. What does that mean? What is the definition of AI Operations? I mean, I'm bombastic at saying the industry sectors is going to crumble. I kind of think it will, but it will shift, but what is the impact to IT Operations with AI and what is AI Ops? >> We like to think of it as a life cycle. So, when you look at the life cycle of operations you have at the beginning of the life cycle, provisioning, so when we think about algorithmic, there's many different layers of automation: machine learning, cognitive learning, and you're going to use different parts of algorithmic operations for different parts of the life cycle. So at the very beginning, you're going to connect generally to a provisioning system so you know what's been provisioned or de-provisioned so we can automatically align a manageability template because nobody can be on a keyboard now, John. This has to be all machine to machine. So, once then it gets provisioned, then there's the run operate part and how do you learn from the normal operating conditions that you're looking for? The anomalies that you would look for to detect things aren't behaving appropriately? And then, once you understand those anomalies and the patterns, you can remediate them proactively, adding resources, decreasing resources, changing configurations, those are the things that kind of that last tier, and then that final tier, when there is a problem, if there is a problem, you've got to then raise a ticket, you've got to then work through the incident management of that ticket so there's another multi-step layers of automation to the incident management orchestration layer of solving problems, closing out a ticket. So, we have so many different layers across that life cycle that we plug into, most of which are native to our core platform. >> And your secret sauce is managing all the workloads that are moving around really fast, so to complicate that even further, you've got a lot of stuff moving around to track it all. I love what you said about not typing on the keyboard anymore, but essentially I'll translate that from what I heard was command line interface of CLIs has been the primary mechanism for dealing with either network and or storage, which is moving packets from here to there and moving storage from now to then, storing stuff. So, CLI is moving to a programmable model? This is the big takeaway. So, I totally think this is the mega trend. The command line interface mode of operation is moving to programmable, which hits your run and operate. >> Correct. >> This is the mega trend. Your thoughts? >> It is and that's one of the layers of complication because instead of a CLI, it's an API, and it's usually a restful API or a graph API. Those APIs are very different in construct and instead of talking to one device, that one device is virtualized into a hundred or a thousand and so with one API call, you actually create a thousand devices versus one device and understanding how one system is behaving, like a CLI would be to one system, right? So, that is a layer of complication where when we make an API call, we break it up into hundreds of things that then we track and understand the tenancy of what is a multi-tenant nature of that? What is the organization? What is the service view for all these little components that are part of one API call? And that abstraction layer makes it really difficult for the enterprise because the one thing about our API economy right now, there is no standard. Every vendor chooses their own formats for their products and in some cases, many formats for products in a product family. So, that layer of complexity, John, is what we're really solving for. The customer doesn't have to worry about that. We take care of that for them, but you're right, the API has become the CLI and it's just a level of complexity beyond what most enterprises are wanting to deal with themselves. That's why they bring us in to help. >> That is so important too that the data's in the API. >> That's right. >> That's key and Cloud's got orchestration challenges, state and state-less applications. All right, let's get into ScienceLogic's offering. So, what do you guys provide to customers? Talk about the product. How do you guys deliver it? Is it software, is it Cloud, is it service, is it appliance? Take us through the offering. What's the key secret sauce? How do people buy and use your product? >> So, our product's delivered as a service. You can use it in the Cloud. We deliver it as a service in our Cloud, but we also provide it if customers are using Amazon or IBM or Google or Microsoft. They can put our product, same code-base, same product, they subscribe to it, it's a subscription license model, so it's a pay-as-you-go and you pay for the number of devices that are under management. Typically, there are some customers, whether it's in the government, financial services, or international locations where they might want to deploy our product on premise, so we offer the same mode, either in the Cloud or on premise, but most customers now are choosing to deploy the product in the Cloud and that is a really easy... It's easy to get >> That's good for you guys. >> It's great for us because there's consistency of operations, we can keep everything up to date, and most customers want technology delivered as a service. They just want it to work. They want it to solve the business problem and do it easily, efficiently, even better, solve complex problems in an easy format. >> Give some customer examples or benefits or anecdotal stories around customers that have used your service that extracted benefits and value out of it, and second part of that question is when does someone know they need your product? What are the smoke signals? Is something breaking or is it just pain? When do they know to call you guys? So first one is customer examples or stories and then how does someone know who's watching this, hey I might need these guys? >> There are four segments that we cover. We have customers all over the world. There's enterprise customers. This is really a product for large enterprise, Fortune 1000 companies, so Clorox would be a customer, Hughes Satellite would be a customer, Cisco Systems out here in the valley is a customer, Dell, EMC, so it depends on what problem we're trying to solve for the customer. >> So large IT deployments basically? >> Very large, multinational, big networks, hundreds of thousands of devices, tens of thousands of devices is where those companies have immense complexity, lots of heterogeneous technology that comes together to deliver a service. They need a really robust solution to manage that proactively. So, enterprise customers, service providers, so a lot of managed service providers, infrastructure service providers, Telcos, they all use it, so I think we have about 60% of the infrastructure as a service providers use our product to deliver managed services to their customers and then the federal government all over the world, we have government customers around the world. I think right now about 70,000 organizations use our product every day and it's fairly evenly split, AMIA and AsiaPac, and then the US is our biggest market. >> You know, it's interesting you mention heterogeneous. I always kind of smile because you mentioned client server earlier. Every wave has their reflection point and I think what's going on with Cloud and I'd love to get your reaction is that Cloud, where it's winning, is it's a scale out, large scale, pool of resources. We look at what's going on with Amazon, all this, is that you don't need to know what service they have, just get more servers, so you're scaling out. >> Yes. >> But now, you need to have heterogeneous components. It's not just X-86. You could have a GPU, you have other stuff, AI going on, so heterogeneous is different now, but it's still the same came, it's still complex, it needs to be abstracted away. Is this kind of the key area that you're riding on? Is that right? What's your thoughts about that concept? >> Well to a large degree, John, the Cloud providers have really provided a layer for you to not have to worry about that, but we've seen customers actually with hyper-converged environments that they build in-house and or systems that they built because of geo-fencing in different countries that need the data kept in the country. There are requirements that drive people to build their own system, so the real thing that we're seeing a tremendous struggle with right now is that context, understanding what connects to what. All the different technologies that come together, all the heterogeneity that comes together to deliver a service, and whether you buy best in class technologies to solve one part of the stack, the landscape of whether it's your load balancer or a caching server or the database or the server, the network, all those different components, the security layer, those components that come together, often people have chosen specific technologies to solve those problems. The Cloud kind of abstracts that away with they hyper-scalers, but often you're putting infrastructure that you have on prem combined with infrastructure in the Cloud to deliver an aggregate solution so that multi-tiered architecture, just like back in the day, a three-tiered architecture, we're seeing those emerging again with public Cloud because you might want the data that actually generates the information on the web client's side to be in your data center, but you still have to understand how the service is behaving. So, we really look at all layers of the stack to solve the problem and that's really hard to do. >> Well David, great to have this conversation. Before we end, I want you to get a quick plug in for the company. How many employees, offices? What's the revenue like? What's your goals? You don't have to share the revenue if you don't want to, but if you want to, you can. Give a plug for the company. What's happening? >> Well, I'm really proud of what the team's done. We've got a great team of employees, about 370 employees today, full-time, they're spread all over the world, probably 80% are here in the Americas and the vision for the company, we think that this is a big opportunity. We are far from done. We really started the company to disrupt the industry 'cause the industry, as I said, was a silo industry and it really is, 20 years later, it's still that way. It's not really converged into a unified solution. We have great aspirations. Every year we've been growing the business 40, 50% a year for the last several years, and this year, we'll round over 100 million within the next 12 months of our run rate, so it's an exciting time for the company. >> Well, you've got a great model, SAS, in a massively growing and changing market, complex market, heterogeneous networks, apps are all being abstracted away and automation's driving this, so I think it's a perfect storm of innovation. Congratulations and thanks for chatting on The Cube here in Palo Alto. >> Love to be here, John. Thanks for having me. >> John Ferrier here, Cube Conversation, and we're here with David Link, CEO of ScienceLogic, and also the founder. Self-funded, big venture rounds, growing like a weed, based in D.C. This is the Cube Conversation. I'm John Furrier. Thanks for watching. (dramatic inspirational 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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